The ARRIVE guidelines 2.0

This section of the website provides detailed explanations about each item of the guidelines. Use the left-hand side menu to navigate to each item.

To facilitate a step-wise approach to improving reporting, the guidelines are organised into two prioritised sets: 

ARRIVE Essential 10

These ten items are the basic minimum that must be included in any manuscript describing animal research. Without this information readers and reviewers cannot assess the reliability of the findings.

Recommended Set

These items complement the Essential 10 set and add important context to the study described. Reporting the items in both sets represents best practice.

Each item of the guidelines includes examples of good reporting from the published literature, extracted from different types of studies, in model organisms ranging from mammals to invertebrates. This battery of examples will be regularly expanded.

Consulting this information during the planning of an animal study ensures that researchers can benefit from the explanations and advice on experimental design, minimisation of bias, sample size and statistical analyses, helping the design of rigorous and reliable in vivo experiments.

The Explanation and Elaboration for the ARRIVE guidelines 2.0 were originally published in PLOS Biology doi:10.1371/journal.pbio.3000411 under a CC-BY license.
 

Essential 10

1. Study design

For each experiment, provide brief details of study design including:

Explanation

The choice of control or comparator group is dependent on the experimental objective. Negative controls are used to determine if a difference between groups is caused by the intervention (e.g. wild-type animals vs genetically modified animals, placebo vs active treatment, sham surgery vs. surgical intervention). Positive controls can be used to support the interpretation of negative results or determine if an expected effect is detectable.  It may not be necessary to include a separate control with no active treatment if, for example, the experiment aims to compare a treatment administered by different methods (e.g. intraperitoneal administration vs. oral gavage), or animals that are used as their own control in a longitudinal study. A pilot study, such as one designed to test the feasibility of a procedure might also not require a control group.

For complex study designs, a visual representation is more easily interpreted than a text description, so a timeline diagram or flow chart is recommended. Diagrams facilitate the identification of which treatments and procedures were applied to specific animals or groups of animals, and at what point in the study these were performed. They also help to communicate complex design features such as whether factors are crossed or nested (hierarchical/multi-level designs), blocking (to reduce unwanted variation, see item 4 – Randomisation), or repeated measurements over time on the same experimental unit (repeated measures designs), see [1-3] for more information on different design types. The Experimental Design Assistant (EDA) is a platform to support researchers in the design of in vivo experiments, it can be used to generate diagrams to represent any type of experimental design [4].

For each experiment performed, clearly report all groups used. Selectively excluding some experimental groups (for example because the data are inconsistent, or conflict with the narrative of the paper) is misleading and should be avoided [5]. Ensure that test groups, comparators and controls (negative or positive) can be identified easily. State clearly if the same control group was used for multiple experiments, or if no control group was used. 

 

References

  1. Festing MF and Altman DG (2002). Guidelines for the design and statistical analysis of experiments using laboratory animals. ILAR journal. http://www.ncbi.nlm.nih.gov/pubmed/12391400
  2. Bate ST and Clark RA (2014). The design and statistical analysis of animal experiments. Cambridge University Press. Cover image http://assets.cambridge.org/97811070/30787/cover/9781107030787.jpg
  3. Ruxton G and Colegrave N (2017). Experimental design for the life sciences. Fourth Edition. Oxford University Press. doi: 10.1017/CBO9781139344319
  4. Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, Fry D, Karp NA, Macleod M, Moon L, Stanford SC and Lings B (2017). The Experimental Design Assistant. PLoS Biol. doi: 10.1371/journal.pbio.2003779
  5. The BMJ Scientific misconduct. (Access Date: 10 january 2020). Available at: https://www.bmj.com/about-bmj/resources-authors/forms-policies-and-checklists/scientific-misconduct

Examples

Example 1 

“The DAV1 study is a one-way, two-period crossover trial with 16 piglets receiving amoxicillin and placebo at period 1 and only amoxicillin at period 2. Amoxicillin was administered orally with a single dose of 30 mg.kg-1. Plasma amoxicillin concentrations were collected at same sampling times at each period: 0.5, 1, 1.5, 2, 4, 6, 8, 10 and 12 h..” [1]

Example 2 

“Example of a study plan created using the Experimental Design Assistant showing a simple comparative study for the effect of two drugs on the metastatic spread of two different cancer cell lines. Block randomisation has been used to create 3 groups containing an equal number of zebrafish embryos injected with either cell line, and each group will be treated with a different drug treatment (including vehicle control). Each measurement outcome will be analysed by 2-way ANOVA to determine the effect of drug treatment on growth, survival and invasion of each cancer cell line.” [2]

References

  1. Nguyen TT, Bazzoli C and Mentre F (2012). Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models. Statistics in medicine. doi: 10.1002/sim.4390
  2. Hill D, Chen L, Snaar-Jagalska E and Chaudhry B (2018). Embryonic zebrafish xenograft assay of human cancer metastasis [version 2; referees: 2 approved]. F1000Research. doi: 10.12688/f1000research.16659.2

 

Explanation

Within a design, biological and technical factors will often be organised hierarchically, such as cells within animals and mitochondria within cells, or cages within rooms and animals within cages. Such hierarchies can make determining the sample size difficult (is it the number of animals, cells or mitochondria?). The sample size is the number of experimental units per group. The experimental unit is defined as the biological entity subjected to an intervention independently of all other units, such that it is possible to assign any two experimental units to different treatment groups. It is also sometimes called the unit of randomisation. In addition, the experimental units should not influence each other on the outcomes that are measured.

Commonly, the experimental unit is the individual animal, each independently allocated to a treatment group (e.g. a drug administered by injection). However, the experimental unit may be the cage or the litter (e.g. a diet administered to a whole cage, or a treatment administered to a dam and investigated in her pups), or it could be part of the animal (e.g. different drug treatments applied topically to distinct body regions of the same animal). Animals may also serve as their own controls receiving different treatments separated by washout periods; here the experimental unit is an animal for a period of time. There may also be multiple experimental units in a single experiment, such as when a treatment is given to a pregnant dam and then the weaned pups are allocated to different diets [1]. See [2-4] for further guidance on identifying experimental units.  

Conflating experimental units with subsamples or repeated measurements can lead to artificial inflation of the sample size. For example, measurements from 50 individual cells from a single mouse represent n = 1 when the experimental unit is the mouse. The 50 measurements are subsamples and provide an estimate of measurement error so should be averaged or used in a nested analysis. Reporting n = 50 in this case is an example of pseudoreplication [5]. It underestimates the true variability in a study, which can lead to false positives and invalidate the analysis and resulting conclusions [5,6]. If, however, each cell taken from the mouse is then randomly allocated to different treatments and assessed individually, the cell might be regarded as the experimental unit.

Clearly indicate the experimental unit for each experiment so that the sample sizes and statistical analyses can be properly evaluated.

 

References

  1. Burdge GC, Lillycrop KA, Jackson AA, Gluckman PD and Hanson MA (2008). The nature of the growth pattern and of the metabolic response to fasting in the rat are dependent upon the dietary protein and folic acid intakes of their pregnant dams and post-weaning fat consumption. Br J Nutr. doi: 10.1017/S0007114507815819
  2. Bate ST and Clark RA (2014). The design and statistical analysis of animal experiments. Cambridge University Press. Cover image http://assets.cambridge.org/97811070/30787/cover/9781107030787.jpg
  3. Lazic SE, Clarke-Williams CJ and Munafò MR (2018). What exactly is ‘N’ in cell culture and animal experiments? PLOS Biology. doi: 10.1371/journal.pbio.2005282
  4. NC3Rs Experimental unit. (Access Date: 21/03/2019). Available at: https://eda.nc3rs.org.uk/experimental-design-unit
  5.  Lazic SE (2010). The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC Neuroscience. doi: 10.1186/1471-2202-11-5
  6. Hurlbert SH (1984). Pseudoreplication and the design of ecological field experiments. Ecological Monographs. doi: 10.2307/1942661

Examples

Example 1 

 “The present study used the tissues collected at E15.5 from dams fed the 1X choline and 4X choline diets (n = 3 dams per group, per fetal sex; total n = 12 dams). To ensure statistical independence, only one placenta (either male or female) from each dam was used for each experiment. Each placenta, therefore, was considered to be an experimental unit.” [1] 

Example 2 

“We have used data collected from high-throughput phenotyping, which is based on a pipeline concept where a mouse is characterized by a series of standardized and validated tests underpinned by standard operating procedures (SOPs)…The individual mouse was considered the experimental unit within the studies.” [2] 

Example 3 

“Fish were divided in two groups according to weight (0.7-1.2 g and 1.3-1.7 g) and randomly stocked (at a density of 15 fish per experimental unit) in 24 plastic tanks holding 60 L of water.” [3] 

Example 4 

“In the study, n refers to number of animals, with five acquisitions from each [corticostriatal] slice, with a maximum of three slices obtained from each experimental animal used for each protocol (six animals each group).” [4]

 

References

  1. Kwan S, King J, Grenier J, Yan J, Jiang X, Roberson M and Caudill M (2018). Maternal Choline Supplementation during Normal Murine Pregnancy Alters the Placental Epigenome: Results of an Exploratory Study. Nutrients. doi: 10.3390/nu10040417
  2. Karp NA, Mason J, Beaudet AL, Benjamini Y, Bower L, Braun RE, Brown SDM, Chesler EJ, Dickinson ME, Flenniken AM, Fuchs H, Angelis MHd, Gao X, Guo S, Greenaway S, Heller R, Herault Y, Justice MJ, Kurbatova N, Lelliott CJ, Lloyd KCK, Mallon A-M, Mank JE, Masuya H, McKerlie C, Meehan TF, Mott RF, Murray SA, Parkinson H, Ramirez-Solis R, et al. (2017). Prevalence of sexual dimorphism in mammalian phenotypic traits. Nature communications. doi: 10.1038/ncomms15475
  3. Ribeiro FdAS, Vasquez LA, Fernandes JBK and Sakomura NK (2012). Feeding level and frequency for freshwater angelfish. Revista Brasileira de Zootecnia. doi: 10.1590/S1516-35982012000600033 
  4. Grasselli G, Rossi S, Musella A, Gentile A, Loizzo S, Muzio L, Di Sanza C, Errico F, Musumeci G, Haji N, Fresegna D, Sepman H, De Chiara V, Furlan R, Martino G, Usiello A, Mandolesi G and Centonze D (2013). Abnormal NMDA receptor function exacerbates experimental autoimmune encephalomyelitis. Br J Pharmacol. doi: 10.1111/j.1476-5381.2012.02178.x
Essential 10

2. Sample size

Explanation

The sample size relates to the number of experimental units in each group at the start of the study, and is usually represented by n (see item 1 – Study design for further guidance on identifying and reporting experimental units). This information is crucial to assess the validity of the statistical model and the robustness of the experimental results.

The sample size in each group at the start of the study may be different from the n numbers in the analysis (see item 3 – Inclusion and exclusion criteria), this information helps readers identify attrition or if there have been exclusions, and in which group they occurred. Reporting the total number of animals used in the study is also useful to identify if any were re-used between experiments.

Report the exact value of n per group and the total number in each experiment (including any independent replications). If the experimental unit is not the animal, also report the total number of animals to help readers understand the study design. For example, in a study investigating diet using cages of animals housed in pairs, the number of animals is double the number of experimental units.

Examples

Example 1 

[1]

“Treatment and transplantation received by the animals for each group. The group were named after the treatment they received in treatment phase 1 and 2: S = saline, L = L-DOPA thus, SS group received saline in treatment phase 1 and 2, SL group received saline in treatment phase 1 and L-DOPA in treatment phase 2, LS received L-DOPA in Treatment phase 1 and saline in treatment phase 2 and LL received L-DOPA in treatment phase 1 and 2. *These groups originally numbered 12 animals but some developed tumors unrelated to the experiment, so had be removed from the study.” [1]

 

References

  1. Breger LS, Kienle K, Smith GA, Dunnett SB and Lane EL (2017). Influence of chronic L-DOPA treatment on immune response following allogeneic and xenogeneic graft in a rat model of Parkinson's disease. Brain Behav Immun. doi: 10.1016/j.bbi.2016.11.014

Explanation

For any type of experiment, it is crucial to explain how the sample size was determined. For hypothesis-testing experiments, where inferential statistics are used to estimate the size of the effect and to determine the weight of evidence against the null hypothesis, the sample size needs to be justified to ensure experiments are of an optimal size to test the research question [1,2] (see item 13 – Objectives). Sample sizes that are too small (i.e. underpowered studies) produce inconclusive results, whereas sample sizes that are too large (i.e. overpowered studies) raise ethical issues over unnecessary use of animals and may produce trivial findings that are statistically significant but not biologically relevant [3]. Low power has three effects: first, within the experiment, real effects are more likely to be missed; second, where an effect is detected, this will often be an over-estimation of the true effect size [4]; and finally, when low power is combined with publication bias, there is an increase in the false positive rate in the published literature [5]. Consequently, low powered studies contribute to the poor internal validity of research and risk wasting animals used in inconclusive research [6].

Study design can influence the statistical power of an experiment and the power calculation used needs to be appropriate for the design implemented. Statistical programs to help perform a priori sample size calculations exist for a variety of experimental designs and statistical analyses, both freeware (web based applets and functions in R) and commercial software [7-9]. Choosing the appropriate calculator or algorithm to use depends on the type of outcome measures and independent variables, and the number of groups. Consultation with a statistician is recommended, especially when the experimental design is complex or unusual.

Where the experiment tests the effect of an intervention on the mean of a continuous outcome measure, the sample size can be calculated a priori, based on a mathematical relationship between the predefined, biologically relevant effect size, variability estimated from prior data, chosen significance level, power and sample size (See "Information used in a power calculation", below, and [10,11] for practical advice). If you have used an a priori sample size calculation, report: 

  • the analysis method (e.g. two-tailed student’s t-test with a 0.05 significance threshold) 
  • the effect size of interest and a justification explaining why an effect size of that magnitude is relevant 
  • the estimate of variability used (e.g. standard deviation) and how it was estimated 
  • the power selected 

Information used in power calculation

Sample size calculation is based on a mathematical relationship between the following parameters: effect size, variability, significance level, power and sample size. Questions to consider are:  

The primary objective of the experiment – what is the main outcome measure? 

The primary outcome measure should be identified in the planning stage of the experiment; it is the outcome of greatest importance, which will answer the main experimental question. 

The predefined effect size – what is a biologically relevant effect size? 

The effect size is estimated as a biologically relevant change in the primary outcome measure between the groups under study. This can be informed by similar studies and involves scientists exploring what magnitude of effect would generate interest and would be worth taking forward into further work. In preclinical studies, the clinical relevance of the effect should also be taken into consideration.  

What is the estimate of variability? 

Estimates of variability can be obtained: 

  • From data collected from a preliminary experiment conducted under identical conditions to the planned experiment, e.g. a previous experiment in the same lab, testing the same treatment under similar conditions on animals with the same characteristics.  
  • From the control group in a previous experiment testing a different treatment. 
  • From a similar experiment reported in the literature. 

Significance threshold – what risk of a false positive is acceptable? 

The significance level or threshold (α) is the probability of obtaining a false positive. If it is set at 0.05 then the risk of obtaining a false positive is 1 in 20 for a single statistical test. However, the threshold or the p values will need to be adjusted in scenarios of multiple testing (e.g. by using a Bonferroni correction). 

Power - what risk of a false negative is acceptable? 

For a predefined, biologically meaningful effect size, the power (1-β) is the probability that the statistical test will detect the effect if it genuinely exists (i.e. true positive result). A target power between 80-95% is normally deemed acceptable, which entails a risk of false negative between 5-20%.  

Directionality - will you use a one or two-sided test? 

The directionality of a test depends on the distribution of the test statistics for a given analysis. For tests based on t or z distributions (such as t-tests), whether the data will be analysed using a one or two-sided test relates to whether the alternative hypothesis (H1) is directional or not. An experiment with a directional (one-sided) H1 can be powered and analysed with a one-sided test with the goal of maximising the sensitivity to detect this directional effect. Controversy exists within the statistics community on when it is appropriate to use a one-sided test [12]. The use of a one-sided test requires justification of why a treatment effect is only of interest when it is in a defined direction and why they would treat a large effect in the unexpected direction no differently from a non-significant difference [13]. Following the use of a one-sided test, the investigator cannot then test for the possibility of missing an effect in the untested direction. Choosing a one-tailed test for the sole purpose of attaining statistical significance is not appropriate. 

Two-sided tests with a non-directional H1 are much more common and allow researchers to detect the effect of a treatment regardless of its direction.  

Note that analyses such as ANOVA and chi-square are based on asymmetrical distributions (F- distribution and chi-square distribution) with only one tail. Therefore, these tests do not have a directionality option. 

There are several types of studies where a priori sample size calculations are not appropriate. For example, the number of animals needed for antibody or tissue production is determined by the amount required and the production ability of an individual animal. For studies where the outcome is the successful generation of a sample or a condition (e.g. the production of transgenic animals), the number of animals is determined by the probability of success of the experimental procedure.

In early feasibility or pilot studies, the number of animals required depends on the purpose of the study. Where the objective of the preliminary study is primarily logistic or operational (e.g. to improve procedures and equipment), the number of animals needed is generally small. In such cases power calculations are not appropriate and sample sizes can be estimated based on operational capacity and constraints [14]. Pilot studies alone are unlikely to provide adequate data on variability for a power calculation for future experiments. Systematic reviews and previous studies are more appropriate sources of information on variability [15].

If no power calculation was used to determine the sample size, state this explicitly and provide the reasoning that was used to decide on the sample size per group. Regardless of whether a power calculation was used or not, when explaining how the sample size was determined take into consideration any anticipated loss of animals or data, for example due to exclusion criteria established upfront or expected attrition (see item 3 – Inclusion and exclusion criteria).

 

References

  1. Vahidy F, Schäbitz W-R, Fisher M and Aronowski J (2016). Reporting standards for preclinical studies of stroke therapy. Stroke. doi: 10.1161/STROKEAHA.116.013643
  2. Muhlhausler BS, Bloomfield FH and Gillman MW (2013). Whole animal experiments should be more like human randomized controlled trials. PLoS Biol. doi: 10.1371/journal.pbio.1001481
  3. Jennions MD and Møller AP (2003). A survey of the statistical power of research in behavioral ecology and animal behavior. Behavioral Ecology. doi: 10.1093/beheco/14.3.438
  4. Lazic SE, Clarke-Williams CJ and Munafò MR (2018). What exactly is ‘N’ in cell culture and animal experiments? PLOS Biology. doi: 10.1371/journal.pbio.2005282
  5. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ and Munafo MR (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. doi: 10.1038/nrn3475
  6. Würbel H (2017). More than 3Rs: The importance of scientific validity for harm-benefit analysis of animal research. Lab animal. doi: 10.1038/laban.1220
  7. R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing,. Available at: https://www.R-project.org/
  8. Peng C-YJ, Long H and Abaci S (2012). Power Analysis Software for Educational Researchers. The Journal of Experimental Education. doi: 10.1080/00220973.2011.647115
  9. Charan J and Kantharia ND (2013). How to calculate sample size in animal studies? J Pharmacol Pharmacother. doi: 10.4103/0976-500X.119726
  10. Bate ST and Clark RA (2014). The design and statistical analysis of animal experiments. Cambridge University Press. Cover image http://assets.cambridge.org/97811070/30787/cover/9781107030787.jpg
  11. Festing MFW (2018). On determining sample size in experiments involving laboratory animals. Laboratory Animals. doi: 10.1177/0023677217738268
  12. Freedman LS (2008). An analysis of the controversy over classical one-sided tests. Clin Trials. doi: 10.1177/1740774508098590
  13. Ruxton GD and Neuhäuser M (2010). When should we use one-tailed hypothesis testing? Methods in Ecology and Evolution. doi: 10.1111/j.2041-210X.2010.00014.x
  14. Reynolds PS (2019). When power calculations won’t do: Fermi approximation of animal numbers. Lab Animal. doi: 10.1038/s41684-019-0370-2
  15. Bate S. How to decide your sample size when the power calculation is not straightforward. (Access Date: 02/08/2018). Available at: https://www.nc3rs.org.uk/news/how-decide-your-sample-size-when-power-calculation-not-straightforward

Examples

Example 1

 “The sample size calculation was based on postoperative pain numerical rating scale (NRS) scores after administration of buprenorphine (NRS AUC mean = 2.70; noninferiority limit = 0.54; standard deviation = 0.66) as the reference treatment…and also Glasgow Composite Pain Scale (GCPS) scores…using online software (Experimental design assistant; https://eda.nc3rs.org.uk/eda/login/auth). The power of the experiment was set to 80%. A total of 20 dogs per group were considered necessary.” [1] 

Example 2 

“We selected a small sample size because the bioglass prototype was evaluated in vivo for the first time in the present study, and therefore, the initial intention was to gather basic evidence regarding the use of this biomaterial in more complex experimental designs.” [2]

 

References

  1. Bustamante R, Daza MA, Canfrán S, García P, Suárez M, Trobo I and Gómez de Segura IA (2018). Comparison of the postoperative analgesic effects of cimicoxib, buprenorphine and their combination in healthy dogs undergoing ovariohysterectomy. Veterinary Anaesthesia and Analgesia. doi: 10.1016/j.vaa.2018.01.003
  2. Spin JR, Oliveira GJPLd, Spin-Neto R, Pires JR, Tavares HS, Ykeda F and Marcantonio RAC (2015). Avaliação histomorfométrica da associação entre biovidro e osso bovino liofilizado no tratamento de defeitos ósseos críticos criados em calvárias de ratos. Estudo piloto. Revista de Odontologia da UNESP. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-25772015000100037&nrm=iso

 

Essential 10

3. Inclusion and exclusion criteria

Explanation

Inclusion and exclusion criteria define the eligibility or disqualification of animals and data once the study has commenced. To ensure scientific rigour, the criteria should be defined before the experiment starts and data are collected [1-4]. Inclusion criteria should not be confused with animal characteristics (see item 8 – Experimental animals) but can be related to these (e.g. body weights must be within a certain range for a particular procedure) or related to other study parameters (e.g. task performance has to exceed a given threshold). In studies where selected data are re-analysed for a different purpose, inclusion and exclusion criteria should describe how data were selected.

Exclusion criteria may result from technical or welfare issues such as complications anticipated during surgery, or circumstances where test procedures might be compromised (e.g. development of motor impairments that could affect behavioural measurements). Criteria for excluding samples or data include failure to meet quality control standards, such as insufficient sample volumes, unacceptable levels of contaminants, poor histological quality, etc. Similarly, how the researcher will define and handle data outliers during the analysis should also be decided before the experiment starts (see item 3b for guidance on responsible data cleaning).

Exclusion criteria may also reflect the ethical principles of a study in line with its humane endpoints (see item 16 – Animal care and monitoring). For example, in cancer studies an animal might be dropped from the study and euthanised before the predetermined time point if the size of a subcutaneous tumour exceeds a specific volume [5]. If losses are anticipated, these should be considered when determining the number of animals to include in the study (see item 2 – Sample size). While exclusion criteria and humane endpoints are typically included in the ethical review application, reporting the criteria used to exclude animals or data points in the manuscript helps readers with the interpretation of the data and provides crucial information to other researchers wanting to adopt the model.

Best practice is to include all a priori inclusion and exclusion/outlier criteria in a pre-registered protocol (see item 19 – Protocol registration). At the very least these criteria should be documented in a lab notebook and reported in manuscripts, explicitly stating that the criteria were defined before any data was collected.

 

References

  1. Avey MT, Moher D, Sullivan KJ, Fergusson D, Griffin G, Grimshaw JM, Hutton B, Lalu MM, Macleod M, Marshall J, Mei SHJ, Rudnicki M, Stewart DJ, Turgeon AF, McIntyre L and Canadian Critical Care Translational Biology G (2016). The devil is in the details: incomplete reporting in preclinical animal research. PLoS ONE. doi: 10.1371/journal.pone.0166733
  2. Vahidy F, Schäbitz W-R, Fisher M and Aronowski J (2016). Reporting standards for preclinical studies of stroke therapy. Stroke. doi: 10.1161/STROKEAHA.116.013643
  3. Rice ASC, Morland R, Huang W, Currie GL, Sena ES and Macleod MR (2013). Transparency in the reporting of in vivo pre-clinical pain research: The relevance and implications of the ARRIVE (Animal Research: Reporting In Vivo Experiments) guidelines. Scandinavian Journal of Pain. doi: 10.1016/j.sjpain.2013.02.002
  4. Salkind NJ (2010). Encyclopedia of research design. Sage. doi: 10.4135/9781412961288
  5. Workman P, Aboagye EO, Balkwill F, Balmain A, Bruder G, Chaplin DJ, Double JA, Everitt J, Farningham DAH, Glennie MJ, Kelland LR, Robinson V, Stratford IJ, Tozer GM, Watson S, Wedge SR, Eccles SA and An ad hoc committee of the National Cancer Research I (2010). Guidelines for the welfare and use of animals in cancer research. British Journal Of Cancer. doi: 10.1038/sj.bjc.660564

Examples

Example 1 

“The animals were included in the study if they underwent successful MCA occlusion (MCAo), defined by a 60% or greater drop in cerebral blood flow seen with laser Doppler flowmetry. The animals were excluded if insertion of the thread resulted in perforation of the vessel wall (determined by the presence of sub-arachnoid blood at the time of sacrifice), if the silicon tip of the thread became dislodged during withdrawal, or if the animal died prematurely, preventing the collection of behavioral and histological data.” [1]

 

References

  1. Sena ES, Jeffreys AL, Cox SF, Sastra SA, Churilov L, Rewell S, Batchelor PE, van der Worp HB, Macleod MR and Howells DW (2013). The benefit of hypothermia in experimental ischemic stroke is not affected by pethidine. Int J Stroke. doi: 10.1111/j.1747-4949.2012.00834.x

Explanation

Animals, experimental units, or data points that are unaccounted for can lead to instances where conclusions cannot be supported by the raw data [1]. Reporting exclusions and attritions provides valuable information to other investigators evaluating the results, or who intend to repeat the experiment or test the intervention in other species. It may also provide important safety information for human trials (e.g. exclusions related to adverse effects).

There are many legitimate reasons for experimental attrition, some of which are anticipated and controlled for in advance (see Item 3a) but some data loss might not be anticipated. For example, data points may be excluded from analyses due to an animal receiving the wrong treatment, unexpected drug toxicity, infections or diseases unrelated to the experiment, sampling errors (e.g. a malfunctioning assay that produced a spurious result, inadequate calibration of equipment), or other human error (e.g. forgetting to switch on equipment for a recording).

Most statistical analysis methods are extremely sensitive to outliers and missing data. In some instances, it may be scientifically justifiable to remove outlying data points from an analysis, such as obvious errors in data entry or measurement with readings that are outside a plausible range. Inappropriate data cleaning has the potential to bias study outcomes [2]; providing the reasoning for removing data points enables the distinction to be made between responsible data cleaning and data manipulation. Missing data, common in all areas of research, can impact the sensitivity of the study and also lead to biased estimates, distorted power and loss of information if the missing values are not random [3]. Analysis plans should include methods to explore why data are missing. It is also important to consider and justify analysis methods that account for missing data [4,5].

There is a movement towards greater data sharing (see item 20 – Data access), along with an increase in strategies such as code sharing to enable analysis replication. These practices, however transparent, still need to be accompanied by a disclosure on the reasoning for data cleaning, and whether methods were defined before any data were collected.

Report all animal exclusions and loss of data points, along with the rationale for their exclusion. For example, this information can be summarised as a table or a flowchart describing attrition in each treatment group. Accompanying this information should be an explicit description of whether researchers were blinded to the group allocations when data or animals were excluded (see item 5 – Blinding and [6]). Explicitly state where built-in models in statistics packages have been used to remove outliers (e.g. GraphPad Prism’s outlier test). 

 

References

  1. Kafkafi N, Agassi J, Chesler EJ, Crabbe JC, Crusio WE, Eilam D, Gerlai R, Golani I, Gomez-Marin A, Heller R, Iraqi F, Jaljuli I, Karp NA, Morgan H, Nicholson G, Pfaff DW, Richter SH, Stark PB, Stiedl O, Stodden V, Tarantino LM, Tucci V, Valdar W, Williams RW, Würbel H and Benjamini Y (2018). Reproducibility and replicability of rodent phenotyping in preclinical studies. Neuroscience & Biobehavioral Reviews. doi: 10.1016/j.neubiorev.2018.01.003
  2. Scott S, Kranz JE, Cole J, Lincecum JM, Thompson K, Kelly N, Bostrom A, Theodoss J, Al‐Nakhala BM, Vieira FG, Ramasubbu J and Heywood JA (2008). Design, power, and interpretation of studies in the standard murine model of ALS. Amyotrophic Lateral Sclerosis. doi: 10.1080/17482960701856300
  3. Kang H (2013). The prevention and handling of the missing data. Korean J Anesthesiol. doi: 10.4097/kjae.2013.64.5.402
  4. Allison PD (2001). Missing data. SAGE Publications. doi: 10.4135/9781412985079
  5. Jakobsen JC, Gluud C, Wetterslev J and Winkel P (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol. doi: 10.1186/s12874-017-0442-1
  6. Holman C, Piper SK, Grittner U, Diamantaras AA, Kimmelman J, Siegerink B and Dirnagl U (2016). Where have all the rodents gone? The effects of attrition in experimental research on cancer and stroke. PLoS Biol. doi: 10.1371/journal.pbio.1002331

Examples

Example 1 

“Pen was the experimental unit for all data. One entire pen (ZnAA90) was removed as an outlier from both Pre-RAC and RAC periods for poor performance caused by illness unrelated to treatment...Outliers were determined using Cook’s D statistic and removed if Cook’s D > 0.5. One steer was determined to be an outlier for day 48 liver biopsy TM and data were removed.” [1] 

Example 2 

“Seventy-two SHRs were randomized into the study, of which 13 did not meet our inclusion and exclusion criteria because the drop in cerebral blood flow at occlusion did not reach 60% (seven animals), postoperative death (one animal: autopsy unable to identify the cause of death), haemorrhage during thread insertion (one animal), and disconnection of the silicon tip of the thread during withdrawal, making the permanence of reperfusion uncertain (four animals). A total of 59 animals were therefore included in the analysis of infarct volume in this study. In error, three animals were sacrificed before their final assessment of neurobehavioral score: one from the normothermia/water group and two from the hypothermia/pethidine group. These errors occurred blinded to treatment group allocation. A total of 56 animals were therefore included in the analysis of neurobehavioral score.” [2]  

Example 3 

[3]

“Flow chart showing the experimental protocol with the number of animals used, died and included in the study…After baseline CMR and echocardiography, MI was induced by left anterior descending (LAD) coronary artery ligation (n = 48), as previously described. As control of surgery procedure, sham operated mice underwent thoracotomy and pericardiotomy without coronary artery ligation (n = 12).” [3]

 

References

  1. Genther-Schroeder ON, Branine ME and Hansen SL (2018). Effects of increasing supplemental dietary Zn concentration on growth performance and carcass characteristics in finishing steers fed ractopamine hydrochloride. Journal of animal science. http://dx.doi.org/10.1093/jas/sky094
  2. Sena ES, Jeffreys AL, Cox SF, Sastra SA, Churilov L, Rewell S, Batchelor PE, van der Worp HB, Macleod MR and Howells DW (2013). The benefit of hypothermia in experimental ischemic stroke is not affected by pethidine. Int J Stroke. http://dx.doi.org/10.1111/j.1747-4949.2012.00834.x
  3. Castiglioni L, Colazzo F, Fontana L, Colombo GI, Piacentini L, Bono E, Milano G, Paleari S, Palermo A, Guerrini U, Tremoli E and Sironi L (2015). Evaluation of Left Ventricle Function by Regional Fractional Area Change (RFAC) in a Mouse Model of Myocardial Infarction Secondary to Valsartan Treatment. PLOS ONE. http://dx.doi.org/10.1371/journal.pone.0135778

 

Explanation

The exact number of experimental units analysed in each group (i.e. the n number) is essential information for the reader to interpret the analysis, it should be reported unambiguously. All animals and data used in the experiment should be accounted for in the data presented. Sometimes, for good reasons, animals may need to be excluded from a study (e.g. illness or mortality), or data points excluded from analyses (e.g. biologically implausible values). Reporting losses will help the reader to understand the experimental design process, replicate methods, and provide adequate tracking of animal numbers in a study, especially when sample size numbers in the analyses do not match the original group numbers.

For each outcome measure, indicate numbers clearly within the text or on figures, and provide absolute numbers (e.g. 10/20, not 50%). For studies where animals are measured at different time points, explicitly report the full description of which animals undergo measurement, and when [1].

 

References

  1. Vahidy F, Schäbitz W-R, Fisher M and Aronowski J (2016). Reporting standards for preclinical studies of stroke therapy. Stroke. doi: 10.1161/STROKEAHA.116.013643

Examples

Example 1 

“Group F contained 29 adult males and 58 adult females in 2010 (n = 87), and 32 adult males and 66 adult females in 2011 (n = 98). The increase in female numbers was due to maturation of juveniles to adults. Females belonged to three matrilines, and there were no major shifts in rank in the male hierarchy. Six mid to low ranking individuals died and were excluded from analyses, as were five mid-ranking males who emigrated from the group at the beginning of 2011.” [1] 

Example 2 

“The proportion of test time that animals spent interacting with the handler (sniffed the gloved hand or tunnel, made paw contact, climbed on, or entered the handling tunnel) was measured from DVD recordings. This was then averaged across the two mice in each cage as they were tested together and their behaviour was not independent…Mice handled with the home cage tunnel spent a much greater proportion of the test interacting with the handler (mean ± s.e.m., 39.8 ± 5.2 percent time of 60 s test, n = 8 cages) than those handled by tail (6.4 ± 2.0 percent time, n = 8 cages), while those handled by cupping showed intermediate levels of voluntary interaction (27.6 ± 7.1 percent time, n = 8 cages).” [2] 

 

References

  1. Brent LJ, Heilbronner SR, Horvath JE, Gonzalez-Martinez J, Ruiz-Lambides A, Robinson AG, Skene JH and Platt ML (2013). Genetic origins of social networks in rhesus macaques. Scientific reports. doi: 10.1038/srep01042
  2. Gouveia K and Hurst JL (2017). Optimising reliability of mouse performance in behavioural testing: the major role of non-aversive handling. Scientific reports. doi: 10.1038/srep44999

 

Essential 10

4. Randomisation

Explanation

Using appropriate randomisation methods during the allocation to groups ensures that each experimental unit has an equal probability of receiving a particular treatment and provides balanced numbers in each treatment group. Selecting an animal ‘at random’ (i.e. haphazardly or arbitrarily) from a cage is not statistically random as the process involves human judgement. It can introduce bias that influences the results, as a researcher may (consciously or subconsciously) make judgements in allocating an animal to a particular group, or because of unknown and uncontrolled differences in the experimental conditions or animals in different groups. Using a validated method of randomisation helps minimise selection bias and reduce systematic differences in the characteristics of animals allocated to different groups [1-3]. Inferential statistics based on non-randomised group allocation are not valid [4,5]. Thus, the use of randomisation is a prerequisite for any experiment designed to test a hypothesis. Examples of appropriate randomisation methods include online random number generators (e.g. https://www.graphpad.com/quickcalcs/randomize1/), or a function like Rand() in spreadsheet software such as Excel, Google Sheets, or LibreOffice. The EDA has a dedicated feature for randomisation and allocation concealment [6].

Systematic reviews have shown that animal experiments that do not report randomisation or other bias-reducing measures such as blinding, are more likely to report exaggerated effects that meet conventional measures of statistical significance [7-9]. It is especially important to use randomisation in situations where it is not possible to blind all or parts of the experiment but even with randomisation, researcher bias can pervert the allocation. This can be avoided by using allocation concealment (see item 5 – Blinding). In studies where sample sizes are small, simple randomisation may result in unbalanced groups; here randomisation strategies to balance groups such as randomising in matched pairs [10-12] and blocking are encouraged [13]. Reporting the precise method used to allocate animals or experimental units to groups enables readers to assess the reliability of the results and identify potential limitations.

Report the type of randomisation used (simple, stratified, randomised complete blocks, etc.; see “Considerations for the randomisation strategy” below), the method used to generate the randomisation sequence (e.g. computer-generated randomisation sequence, with details of the algorithm or programme used), and what was randomised (e.g. treatment to experimental unit, order of treatment for each animal). If this varies between experiments, report this information specifically for each experiment. If randomisation was not the method used to allocate experimental units to groups state this explicitly and explain how the groups being compared were formed. 

Considerations for the randomisation strategy

Simple randomisation 

All animals/samples are simultaneously randomised to the treatment groups without considering any other variable. This strategy is rarely appropriate as it cannot ensure that comparison groups are balanced for other variables that might influence the result of an experiment.

Randomisation within blocks  

Blocking is a method of controlling natural variation among experimental units. This splits up the experiment into smaller sub-experiments (blocks), and treatments are randomised to experimental units within each block [5,13,14]. This takes into account nuisance variables that could potentially bias the results (e.g. cage location, day or week of procedure).

Stratified randomisation uses the same principle as randomisation within blocks, only the strata tend to be traits of the animal that are likely to be associated with the response (e.g. weight class or tumour size class). This can lead to differences in the practical implementation of stratified randomisation as compared to block randomisation (e.g. there may not be equal numbers of experimental units in each weight class).

Other randomisation strategies 

Minimisation is an alternative strategy to allocate animals/samples to treatment group to balance variables that might influence the result of an experiment. With minimisation the treatment allocated to the next animal/sample depends on the characteristics of those animals/samples already assigned. The aim is that each allocation should minimise the imbalance across multiple factors [15]. This approach works well for a continuous nuisance variable such as body weight or starting tumour volume.

Examples of nuisance variables that can be accounted for in the randomisation strategy 

  • Time or day of the experiment  
  • Litter, cage or fish tank 
  • Investigator or surgeon – different level of experience in the people administering the treatments, performing the surgeries, or assessing the results may result in varying stress levels in the animals or duration of anaesthesia 
  • Equipment (e.g. PCR machine, spectrophotometer) – calibration may vary 
  • Measurement of a study parameter (e.g. initial tumour volume) 
  • Animal characteristics (e.g. sex, age bracket, weight bracket) 
  • Location – exposure to light, ventilation and disturbances may vary in cages located at different height or on different racks, which may affect important physiological processes

Implication for the analysis  

If blocking factors are used in the randomisation, they should also be included in the analysis. Nuisance variables increase variability in the sample, which reduces statistical power. Including a nuisance variable as a blocking factor in the analysis accounts for that variability and can increase the power, thus increasing the ability to detect a real effect with fewer experimental units. However, blocking uses up degrees of freedom and thus reduces the power if the nuisance variable does not have a substantial impact on variability. 

 

References

  1. Schulz KF, Chalmers I, Hayes RJ and Altman DG (1995). Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. Jama. doi: 10.1001/jama.273.5.408
  2. Schulz KF and Grimes DA (2002). Allocation concealment in randomised trials: defending against deciphering. Lancet (London, England). doi: 10.1016/s0140-6736(02)07750-4
  3. Chalmers TC, Celano P, Sacks HS and Smith Jr H (1983). Bias in treatment assignment in controlled clinical trials. New England Journal of Medicine. doi: 10.1056/NEJM198312013092204
  4. Greenberg BG (1951). Why randomize? Biometrics. doi: 10.2307/3001653
  5. Altman DG and Bland JM (1999). Statistics notes. Treatment allocation in controlled trials: why randomise? BMJ. doi: 10.1136/bmj.318.7192.1209
  6. Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, Fry D, Karp NA, Macleod M, Moon L, Stanford SC and Lings B (2017). The Experimental Design Assistant. PLoS Biol. doi: 10.1371/journal.pbio.2003779
  7. Hirst JA, Howick J, Aronson JK, Roberts N, Perera R, Koshiaris C and Heneghan C (2014). The need for randomization in animal trials: an overview of systematic reviews. PLoS ONE. doi: 10.1371/journal.pone.0098856
  8. Vesterinen HM, Sena ES, ffrench-Constant C, Williams A, Chandran S and Macleod MR (2010). Improving the translational hit of experimental treatments in multiple sclerosis. Multiple Sclerosis Journal. doi: 10.1177/1352458510379612
  9. Bebarta V, Luyten D and Heard K (2003). Emergency medicine animal research: does use of randomization and blinding affect the results? Acad Emerg Med. doi: 10.1111/j.1553-2712.2003.tb00056.x.
  10. Taves DR (1974). Minimization: a new method of assigning patients to treatment and control groups. Clinical pharmacology and therapeutics. doi: 10.1002/cpt1974155443
  11. Saint-Mont U (2015). Randomization does not help much, comparability does. PLOS ONE. doi: 10.1371/journal.pone.0132102
  12. Laajala TD, Jumppanen M, Huhtaniemi R, Fey V, Kaur A, Knuuttila M, Aho E, Oksala R, Westermarck J, Mäkelä S, Poutanen M and Aittokallio T (2016). Optimized design and analysis of preclinical intervention studies in vivo. Scientific reports. doi: 10.1038/srep30723
  13. Bate ST and Clark RA (2014). The design and statistical analysis of animal experiments. Cambridge University Press. Cover image http://assets.cambridge.org/97811070/30787/cover/9781107030787.jpg
  14. Kang M, Ragan BG and Park JH (2008). Issues in outcomes research: an overview of randomization techniques for clinical trials. J Athl Train. doi: 10.4085/1062-6050-43.2.215
  15. Altman DG and Bland JM (2005). Treatment allocation by minimisation. BMJ. doi: 10.1136/bmj.330.7495.843

Examples

Example 1 

“Fifty 12-week-old male Sprague-Dawley rats, weighing 320–360g, were obtained from Guangdong Medical Laboratory Animal Center (Guangzhou, China) and randomly divided into two groups (25 rats/group): the intact group and the castration group. Random numbers were generated using the standard = RAND() function in Microsoft Excel.” [1]    

Example 2 

“Animals were randomized after surviving the initial I/R, using a computer based random order generator.” [2] 

Example 3 

“At each institute, phenotyping data from both sexes is collected at regular intervals on age-matched wildtype mice of equivalent genetic backgrounds. Cohorts of at least seven homozygote mice of each sex per pipeline were generated…The random allocation of mice to experimental group (wildtype versus knockout) was driven by Mendelian Inheritance.” [3] 

 

References

  1. Zhao S, Kang R, Deng T, Luo L, Wang J, Li E, Luo J, Liu L, Wan S and Zhao Z (2018). Comparison of two cannulation methods for assessment of intracavernosal pressure in a rat model. PLoS One. doi: 10.1371/journal.pone.0193543
  2. Jansen of Lorkeers SJ, Gho JMIH, Koudstaal S, van Hout GPJ, Zwetsloot PPM, van Oorschot JWM, van Eeuwijk ECM, Leiner T, Hoefer IE, Goumans M-J, Doevendans PA, Sluijter JPG and Chamuleau SAJ (2015). Xenotransplantation of Human Cardiomyocyte Progenitor Cells Does Not Improve Cardiac Function in a Porcine Model of Chronic Ischemic Heart Failure. Results from a Randomized, Blinded, Placebo Controlled Trial. PLOS ONE. doi: 10.1371/journal.pone.0143953
  3. Karp NA, Mason J, Beaudet AL, Benjamini Y, Bower L, Braun RE, Brown SDM, Chesler EJ, Dickinson ME, Flenniken AM, Fuchs H, Angelis MHd, Gao X, Guo S, Greenaway S, Heller R, Herault Y, Justice MJ, Kurbatova N, Lelliott CJ, Lloyd KCK, Mallon A-M, Mank JE, Masuya H, McKerlie C, Meehan TF, Mott RF, Murray SA, Parkinson H, Ramirez-Solis R, et al. (2017). Prevalence of sexual dimorphism in mammalian phenotypic traits. Nature communications. doi: 10.1038/ncomms15475

 

 

Explanation

Ensuring there is no systematic difference between animals in different groups apart from the experimental exposure is an important principle throughout the conduct of the experiment. Identifying nuisance variables (sources of variability or conditions that could potentially bias results) and managing them in the design and nalysis increases the sensitivity of the experiment. For example, rodents in cages at the top of the rack may be exposed to higher light levels, which can affect stress [1].

Reporting the strategies implemented to minimise potential differences that arise between treatment groups during the course of the experiment, enables others to assess the internal validity. Strategies to report include: standardising (keeping conditions the same, e.g. all surgeries done by the same surgeon), randomising (e.g. the sampling or measurement order), blocking or counterbalancing (e.g. position of animal cages or tanks on the rack), to ensure groups are similarly affected by a source of variability. In some cases, practical constraints prevent some nuisance variables from being randomised, but they can still be accounted for in the analysis (see item 7 – Statistical methods).

Report the methods used to minimise confounding factors alongside the methods used to allocate animals to groups. If no measures were used to minimise confounders (e.g. treatment order, measurement order, cage or tank position on a rack), explicitly state this and explain why.

 

References

  1. Ishida A, Mutoh T, Ueyama T, Bando H, Masubuchi S, Nakahara D, Tsujimoto G and Okamura H (2005). Light activates the adrenal gland: timing of gene expression and glucocorticoid release. Cell Metab. doi:10.1016/j.cmet.2005.09.009

Examples

Example 1 

“Randomisation was carried out as follows. On arrival from El-Nile Company, animals were assigned a group designation and weighed. A total number of 32 animals were divided into four different weight groups (eight animals per group). Each animal was assigned a temporary random number within the weight range group. On the basis of their position on the rack, cages were given a numerical designation. For each group, a cage was selected randomly from the pool of all cages. Two animals were removed from each weight range group and given their permanent numerical designation in the cages. Then, the cages were randomized within the exposure group.” [1] 

Example 2 

“...test time was between 08.30am to 12.30pm and testing order was randomized daily, with each animal tested at a different time each test day.” [2] 

Example 3 

“Bulls were blocked by BW into four blocks of 905 animals with similar BW and then within each block, bulls were randomly assigned to one of four experimental treatments in a completely randomized block design resulting in 905 animals per treatment. Animals were allocated to 20 pens (181 animals per pen and five pens per treatment).” [3] 

 

References

  1. El-Agroudy NN, El-Naga RN, El-Razeq RA and El-Demerdash E (2016). Forskolin, a hedgehog signalling inhibitor, attenuates carbon tetrachloride-induced liver fibrosis in rats. Br J Pharmacol. doi: 10.1111/bph.13611
  2. Carrillo M, Migliorati F, Bruls R, Han Y, Heinemans M, Pruis I, Gazzola V and Keysers C (2015). Repeated Witnessing of Conspecifics in Pain: Effects on Emotional Contagion. PLOS ONE. doi: 10.1371/journal.pone.0136979
  3. Del Bianco Benedeti P, Paulino PVR, Marcondes MI, Maciel IFS, da Silva MC and Faciola AP (2016). Partial Replacement of Ground Corn with Glycerol in Beef Cattle Diets: Intake, Digestibility, Performance, and Carcass Characteristics. PLOS ONE. doi: 10.1371/journal.pone.0148224
Essential 10

5. Blinding

Explanation

Researchers often expect a particular outcome, and can unintentionally influence the experiment or interpret the data in such a way as to support their preferred hypothesis [1]. Blinding is a strategy used to minimise these subjective biases.

Whilst there is primary evidence of the impact of blinding in the clinical literature that directly compares blinded vs unblinded assessment of outcomes [2], there is limited empirical evidence in animal research [3,4]. There are, however, compelling data from systematic reviews showing that non-blinded outcome assessment leads to the treatment effects being overestimated, and the lack of bias-reducing measures such as randomisation and blinding can contribute to as much as 30-45% inflation of effect sizes [5-7].

Ideally, investigators should be unaware of the treatment(s) animals have received or will be receiving, from the start of the experiment until the data have been analysed. If this is not possible for every stage of an experiment (see "Blinding during different stages of an experiment" below), it should always be possible to conduct at least some of the stages blind. This has implications for the organisation of the experiment and may require help from additional personnel, for example a surgeon to perform interventions, a technician to code the treatment syringes for each animal, or a colleague to code the treatment groups for the analysis. Online resources are available to facilitate allocation concealment and blinding [8].

Blinding during different stages of an experiment

During allocation 

Allocation concealment refers to concealing the treatment to be allocated to each individual animal from those assigning the animals to groups, until the time of assignment. Together with randomisation, allocation concealment helps minimise selection bias, which can introduce systematic differences between treatment groups.

During the conduct of the experiment

Where possible, animal care staff and those who administer treatments should be unaware of allocation groups to ensure that all animals in the experiment are handled, monitored and treated in the same way. Treating different groups differently based on the treatment they have received could alter animal behaviour and physiology, and produce confounds.

Welfare or safety reasons may prevent blinding of animal care staff but in most cases, blinding is possible. For example, if hazardous microorganisms are used, control animals can be considered as dangerous as infected animals. If a welfare issue would only be tolerated for a short time in treated but not control animals, a harm-benefit analysis is needed to decide whether blinding should be used.

During the outcome assessment

The person collecting experimental measurements or conducting assessments should not know which treatment each sample/animal received, and which samples/animals are grouped together. Blinding is especially important during outcome assessment, particularly if there is a subjective element (e.g. when assessing behavioural changes or reading histological slides) [3]. Randomising the order of examination can also reduce bias.

If the person assessing the outcome cannot be blinded to the group allocation (e.g. obvious phenotypic or behavioural differences between groups) some, but not all, of the sources of bias could be mitigated by sending data for analysis to a third party, who has no vested interest in the experiment and does not know whether a treatment is expected to improve or worsen the outcome.

During the data analysis

The person analysing the data should know which data are grouped together to enable group comparisons, but should not be aware of which specific treatment each group received. This type of blinding is often neglected, but is important as the analyst makes many semi-subjective decisions such as applying data transformation to outcome measures, choosing methods for handling missing data and handling outliers. How these decisions will be made should also be decided a priori.

Data can be coded prior to analysis so that the treatment group cannot be identified before analysis is completed. 

Specify whether blinding was used or not for each step of the experimental process (see table above) and indicate what particular treatment or condition the investigators were blinded to, or aware of.

If blinding was not used at any of the steps outlined in the table above, explicitly state this and provide the reason why blinding was not possible, or not considered. 

 

References

  1. Nuzzo R (2015). How scientists fool themselves–and how they can stop. Nature News. doi: 10.1038/526182a
  2. Hróbjartsson A, Thomsen ASS, Emanuelsson F, Tendal B, Hilden J, Boutron I, Ravaud P and Brorson S (2012). Observer bias in randomised clinical trials with binary outcomes: systematic review of trials with both blinded and non-blinded outcome assessors. BMJ. doi: 10.1136/bmj.e1119
  3. Rosenthal R and Fode KL (1963). The effect of experimenter bias on the performance of the albino rat. Behavioral Science. doi: 10.1002/bs.3830080302
  4. Rosenthal R and Lawson R (1964). A longitudinal study of the effects of experimenter bias on the operant learning of laboratory rats. Journal of Psychiatric Research. doi: 10.1016/0022-3956(64)90003-2
  5. Hirst JA, Howick J, Aronson JK, Roberts N, Perera R, Koshiaris C and Heneghan C (2014). The need for randomization in animal trials: an overview of systematic reviews. PLoS ONE. doi: 10.1371/journal.pone.0098856
  6. Vesterinen HM, Sena ES, ffrench-Constant C, Williams A, Chandran S and Macleod MR (2010). Improving the translational hit of experimental treatments in multiple sclerosis. Multiple Sclerosis Journal. doi: doi:10.1177/1352458510379612
  7. Macleod MR, van der Worp HB, Sena ES, Howells DW, Dirnagl U and Donnan GA (2008). Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality. Stroke. doi: 10.1161/strokeaha.108.515957
  8. Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, Fry D, Karp NA, Macleod M, Moon L, Stanford SC and Lings B (2017). The Experimental Design Assistant. PLoS Biol. doi: 10.1371/journal.pbio.2003779

Examples

Example 1 

“For each animal, four different investigators were involved as follows: a first investigator (RB) administered the treatment based on the randomization table. This investigator was the only person aware of the treatment group allocation. A second investigator (SC) was responsible for the anaesthetic procedure, whereas a third investigator (MS, PG, IT) performed the surgical procedure. Finally, a fourth investigator (MAD) (also unaware of treatment) assessed GCPS and NRS, mechanical nociceptive threshold (MNT), and sedation NRS scores.” [1]

Example 2 

“…due to overt behavioral seizure activity the experimenter could not be blinded to whether the animal was injected with pilocarpine or with saline.” [2] 

Example 3 

“Investigators could not be blinded to the mouse strain due to the difference in coat colors, but the three-chamber sociability test was performed with ANY-maze video tracking software (Stoelting, Wood Dale, IL, USA) using an overhead video camera system to automate behavioral testing and provide unbiased data analyses. The one-chamber social interaction test requires manual scoring and was analyzed by an individual with no knowledge of the questions.” [3]

 

References

  1. Bustamante R, Daza MA, Canfrán S, García P, Suárez M, Trobo I and Gómez de Segura IA (2018). Comparison of the postoperative analgesic effects of cimicoxib, buprenorphine and their combination in healthy dogs undergoing ovariohysterectomy. Veterinary Anaesthesia and Analgesia. doi: 10.1016/j.vaa.2018.01.003
  2. Neumann A-M, Abele J, Kirschstein T, Engelmann R, Sellmann T, Köhling R and Müller-Hilke B (2017). Mycophenolate mofetil prevents the delayed T cell response after pilocarpine-induced status epilepticus in mice. PLOS ONE. doi: 10.1371/journal.pone.0187330
  3. Hsieh LS, Wen JH, Miyares L, Lombroso PJ and Bordey A (2017). Outbred CD1 mice are as suitable as inbred C57BL/6J mice in performing social tasks. Neuroscience Letters. doi: 10.1016/j.neulet.2016.11.035
Essential 10

6. Outcome measures

Explanation

An outcome measure (also known as a dependent variable or a response variable) is any variable recorded during a study (e.g. volume of damaged tissue, number of dead cells, specific molecular marker) to assess the effects of a treatment or experimental intervention. Outcome measures may be important for characterising a sample (e.g. baseline data) or for describing complex responses (e.g. ‘haemodynamic’ outcome measures including heart rate, blood pressure, central venous pressure, and cardiac output). Failure to disclose all the outcomes that were measured introduces bias in the literature as positive outcomes (e.g. those statistically significant) are reported more often [1-4].

Explicitly describe what was measured, especially when measures can be operationalised in different ways. For example, activity could be recorded as time spent moving or distance travelled. Where possible, the recording of outcome measures should be made in an unbiased manner (e.g. blinded to the treatment allocation of each experimental group; see item 5 – Blinding). Specify how the outcome measure(s) assessed are relevant to the objectives of the study. 

 

References

  1. John LK, Loewenstein G and Prelec D (2012). Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling. Psychological Science. doi: 10.1177/0956797611430953
  2. Dwan K, Altman DG, Arnaiz JA, Bloom J, Chan AW, Cronin E, Decullier E, Easterbrook PJ, Von Elm E, Gamble C, Ghersi D, Ioannidis JP, Simes J and Williamson PR (2008). Systematic review of the empirical evidence of study publication bias and outcome reporting bias. PLoS One. doi: 10.1371/journal.pone.0003081
  3. Tsilidis KK, Panagiotou OA, Sena ES, Aretouli E, Evangelou E, Howells DW, Al-Shahi Salman R, Macleod MR and Ioannidis JP (2013). Evaluation of excess significance bias in animal studies of neurological diseases. PLoS Biol. doi: 10.1371/journal.pbio.1001609
  4. Sena ES, van der Worp HB, Bath PM, Howells DW and Macleod MR (2010). Publication bias in reports of animal stroke studies leads to major overstatement of efficacy. PLoS Biol. doi: 10.1371/journal.pbio.1000344

Examples

Example 1 

“The following parameters were assessed: threshold pressure (TP; intravesical pressure immediately before micturition); post-void pressure (PVP; intravesical pressure immediately after micturition); peak pressure (PP; highest intravesical pressure during micturition); capacity (CP; volume of saline needed to induce the first micturition); compliance (CO; CP to TP ratio); frequency of voiding contractions (VC) and frequency of non-voiding contractions (NVCs).” [1] 

 

References

  1. Claudino MA, Leiria LOS, da Silva FH, Alexandre EC, Renno A, Mónica FZ, de Nucci G, Fertrin KY, Antunes E, Costa FF and Franco-Penteado CF (2015). Urinary Bladder Dysfunction in Transgenic Sickle Cell Disease Mice. PLOS ONE. doi: 10.1371/journal.pone.0133996

Explanation

In a hypothesis-testing experiment, the primary outcome measure answers the main biological question. It is the outcome of greatest importance, identified in the planning stages of the experiment and used as the basis for the sample size calculation (see item 2 - Sample size). For exploratory studies it is not necessary to identify a single primary outcome and often multiple outcomes are assessed (see item 13 – Objectives).

In a hypothesis-testing study powered to detect an effect on the primary outcome measure, data on secondary outcomes are used to evaluate additional effects of the intervention but subsequent statistical analysis of secondary outcome measures may be underpowered, making results and interpretation less reliable [1,2]. Studies that claim to test a hypothesis but do not specify a pre-defined primary outcome measure, or those that change the primary outcome measure after data were collected (also known as primary outcome switching) are liable to selectively report only statistically significant results, favouring more positive findings [3].

Registering a protocol in advance protects the researcher against concerns about selective outcome reporting (also known as data dredging or p-hacking) and provides evidence that the primary outcome reported in the manuscript accurately reflects what was planned [4] (see item 19 – Protocol registration).

In studies using inferential statistics to test a hypothesis (e.g. t-test, ANOVA), if more than one outcome was assessed, explicitly identify the primary outcome measure and state whether it was defined as such prior to data collection and whether it was used in the sample size calculation. If there was no primary outcome measure, explicitly state so. 

 

References

  1. John LK, Loewenstein G and Prelec D (2012). Measuring the Prevalence of Questionable Research Practices With Incentives for Truth Telling. Psychological Science. doi: 10.1177/0956797611430953
  2. Landis SC, Amara SG, Asadullah K, Austin CP, Blumenstein R, Bradley EW, Crystal RG, Darnell RB, Ferrante RJ, Fillit H, Finkelstein R, Fisher M, Gendelman HE, Golub RM, Goudreau JL, Gross RA, Gubitz AK, Hesterlee SE, Howells DW, Huguenard J, Kelner K, Koroshetz W, Krainc D, Lazic SE, Levine MS, Macleod MR, McCall JM, Moxley RT, 3rd, Narasimhan K, Noble LJ, et al. (2012). A call for transparent reporting to optimize the predictive value of preclinical research. Nature. doi: 10.1038/nature11556
  3. Head ML, Holman L, Lanfear R, Kahn AT and Jennions MD (2015). The Extent and Consequences of P-Hacking in Science. PLOS Biology. doi: 10.1371/journal.pbio.1002106
  4. Munafò MR, Nosek BA, Bishop DVM, Button KS, Chambers CD, Percie du Sert N, Simonsohn U, Wagenmakers E-J, Ware JJ and Ioannidis JPA (2017). A manifesto for reproducible science. Nature Human Behaviour. doi: 10.1038/s41562-016-0021

Examples

Example 1 

“The primary outcome of this study will be forelimb function assessed with the staircase test. Secondary outcomes constitute Rotarod performance, stroke volume (quantified on MR imaging or brain sections, respectively), diffusion tensor imaging (DTI) connectome mapping, and histological analyses to measure neuronal and microglial densities, and phagocytic activity…The study is designed with 80% power to detect a relative 25% difference in pellet-reaching performance in the Staircase test.” [1] 

Example 2 

“The primary endpoint of this study was defined as left ventricular ejection fraction (EF) at the end of follow-up, measured by magnetic resonance imaging (MRI). Secondary endpoints were left ventricular end diastolic volume and left ventricular end systolic volume (EDV and ESV) measured by MRI, infarct size measured by ex vivo gross macroscopy after incubation with triphenyltetrazolium chloride (TTC) and late gadolinium enhancement (LGE) MRI, functional parameters serially measured by pressure volume (PV-)loop and echocardiography, coronary microvascular function by intracoronary pressure- and flow measurements and vascular density and fibrosis on histology. Based on a power calculation (estimated effect 7.5% [6], standard deviation of 5%, a power of 0.9 and alpha of 0.05) 8 pigs per group were needed.” [2] 

 

References

  1. Emmrich J, Neher J, Boehm-Sturm P, Endres M, Dirnagl U and Harms C (2018). Stage 1 Registered Report: Effect of deficient phagocytosis on neuronal survival and neurological outcome after temporary middle cerebral artery occlusion (tMCAo) [version 3; referees: 2 approved]. F1000Research. doi: 10.12688/f1000research.12537.3
  2. Jansen of Lorkeers SJ, Gho JMIH, Koudstaal S, van Hout GPJ, Zwetsloot PPM, van Oorschot JWM, van Eeuwijk ECM, Leiner T, Hoefer IE, Goumans M-J, Doevendans PA, Sluijter JPG and Chamuleau SAJ (2015). Xenotransplantation of Human Cardiomyocyte Progenitor Cells Does Not Improve Cardiac Function in a Porcine Model of Chronic Ischemic Heart Failure. Results from a Randomized, Blinded, Placebo Controlled Trial. PLOS ONE. doi: 10.1371/journal.pone.0143953
Essential 10

7. Statistical methods

Explanation

The statistical analysis methods implemented will reflect the goals and the design of the experiment, they should be decided in advance before data are collected (see item 19 – Protocol registration). Both exploratory and hypothesis-testing studies might use descriptive statistics to summarise the data (e.g. mean and SD, or median and range). In exploratory studies where no specific hypothesis was tested, reporting descriptive statistics is important for generating new hypotheses that may be tested in subsequent experiments but it does not allow conclusions beyond the data. In addition to descriptive statistics, hypothesis-testing studies might use inferential statistics to test a specific hypothesis.

Reporting the analysis methods in detail is essential to ensure readers and peer-reviewers can assess the appropriateness of the methods selected and judge the validity of the output. The description of the statistical analysis should provide enough detail so that another researcher could re-analyse the raw data using the same method and obtain the same results. Make it clear which method was used for which analysis.

Analysing the data using different methods and selectively reporting those with statistically significant results constitutes p-hacking and introduces bias in the literature [1,2]. Report all analyses performed in full. Relevant information to describe the statistical methods include: 

  • the outcome measures 
  • the independent variables of interest 
  • the nuisance variables taken into account in each statistical test (e.g. as blocking factors or covariates),  
  • what statistical analyses were performed and references for the methods used  
  • how missing values were handled 
  • adjustment for multiple comparisons 
  • the software package and version used, including computer code if available [3] 

The outcome measure is potentially affected by the treatments or interventions being tested, but also by other factors, such as the properties of the biological samples (sex, litter, age, weight, etc.), and technical considerations (cage, time of day, batch, experimenter, etc.). To reduce the risk of bias, some of these factors can be taken into account in the design of the experiment, for example by using blocking factors in the randomisation (see item 4 – Randomisation). Factors deemed to affect the variability of the outcome measure should also be handled in the analysis, for example as a blocking factor (e.g. batch of reagent or experimenter), or as a covariate (e.g. starting tumour size at point of randomisation).

Furthermore, to conduct the analysis appropriately, it is important to recognise the hierarchy that can exist in an experiment. The hierarchy can induce a clustering effect; for example, cage, litter or animal effects can occur where the outcomes measured for animals from the same cage/litter, or for cells from the same animal, are more similar to each other. This relationship has to be managed in the statistical analysis by including cage/litter/animal effects in the model or by aggregating the outcome measure to the cage/litter/animal level. Thus, describing the reality of the experiment and the hierarchy of the data, along with the measures taken in the design and the analysis to account for this hierarchy, is crucial to assessing whether the statistical methods used are appropriate.

For bespoke analysis, for example regression analysis with many terms, it is essential to describe the analysis pipeline in detail. This could include detailing the starting model and any model simplification steps.

When reporting descriptive statistics, explicitly state which measure of central tendency is reported (e.g. mean or median) and which measure of variability is reported (e.g. standard deviation, range, quartiles or interquartile range). Also describe any modification made to the raw data before analysis (e.g. relative quantification of gene expression against a house-keeping gene). For further guidance on statistical reporting, refer to the SAMPL (Statistical Analyses and Methods in the Published Literature) guidelines [4]

 

References

  1. Tsilidis KK, Panagiotou OA, Sena ES, Aretouli E, Evangelou E, Howells DW, Al-Shahi Salman R, Macleod MR and Ioannidis JP (2013). Evaluation of excess significance bias in animal studies of neurological diseases. PLoS Biol. doi: 10.1371/journal.pbio.1001609
  2. Head ML, Holman L, Lanfear R, Kahn AT and Jennions MD (2015). The Extent and Consequences of P-Hacking in Science. PLOS Biology. doi: 10.1371/journal.pbio.1002106
  3. British Ecological Society (2017). A guide to reproducible code in ecology and evolution. Available at: https://www.britishecologicalsociety.org/wp-content/uploads/2017/12/guide-to-reproducible-code.pdf
  4. Lang TA and Altman DG (2015). Basic statistical reporting for articles published in biomedical journals: the "Statistical Analyses and Methods in the Published Literature" or the SAMPL Guidelines. Int J Nurs Stud. doi: 10.1016/j.ijnurstu.2014.09.006

Examples

Example 1 

“Analysis of variance was performed using the GLM procedure of SAS (SAS Inst., Cary, NC). Average pen values were used as the experimental unit for the performance parameters. The model considered the effects of block and dietary treatment (5 diets). Data were adjusted by the covariant of initial body weight. Orthogonal contrasts were used to test the effects of SDPP processing (UV vs no UV) and dietary SDPP level (3% vs 6%). Results are presented as least squares means. The level of significance was set at P < 0.05.” [1] 

Example 2 

“All risk factors of interest were investigated in a single model. Logistic regression allows blocking factors and explicitly investigates the effect of each independent variable controlling for the effects of all others...As we were interested in husbandry and environmental effects, we blocked the analysis by important biological variables (age; backstrain; inbreeding; sex; breeding status) to control for their effect. (The role of these biological variables in barbering behavior, particularly with reference to barbering as a model for the human disorder trichotillomania, is described elsewhere…). We also blocked by room to control for the effect of unknown environmental variables associated with this design variable. We tested for the effect of the following husbandry and environmental risk factors: cage mate relationships (i.e. siblings, non-siblings, or mixed); cage type (i.e. plastic or steel); cage height from floor; cage horizontal position (whether the cage was on the side or the middle of a rack); stocking density; and the number of adults in the cage. Cage material by cage height from floor; and cage material by cage horizontal position interactions were examined, and then removed from the model as they were nonsignificant. N = 1959 mice were included in this analysis.” [2] 

 

References

  1. Polo J, Rodríguez C, Ródenas J, Russell LE, Campbell JM, Crenshaw JD, Torrallardona D and Pujols J (2015). Ultraviolet Light (UV) Inactivation of Porcine Parvovirus in Liquid Plasma and Effect of UV Irradiated Spray Dried Porcine Plasma on Performance of Weaned Pigs. PLOS ONE. doi: 10.1371/journal.pone.0133008
  2. Garner JP, Dufour B, Gregg LE, Weisker SM and Mench JA (2004). Social and husbandry factors affecting the prevalence and severity of barbering ('whisker trimming') by laboratory mice. Applied Animal Behaviour Science. doi: 10.1016/j.applanim.2004.07.004

Explanation

Hypothesis tests are based on assumptions about the underlying data. Describing how assumptions were assessed, and whether these assumptions are met by the data, enables readers to assess the suitability of the statistical approach used. If the assumptions are incorrect, the conclusions may not be valid. For example, the assumptions for data used in parametric tests (such as a t-test, Z-test, ANOVA, etc.) are that the data are continuous, the residuals from the analysis are normally distributed, the responses are independent, and that different groups have similar variances.

There are various tests for normality, for example the Shapiro-Wilk and Kolmogorov-Smirnov tests. However, these tests have to be used cautiously. If the sample size is small, they will struggle to detect non-normality, if the sample size is large, the tests will detect unimportant deviations. An alternative approach is to evaluate data with visual plots e.g. normal probability plots, box plots, scatterplots. If the residuals of the analysis are not normally distributed, the assumption may be satisfied using a data transformation where the same mathematical function is applied to all data points to produce normally distributed data (e.g. loge, log10, square root).

Other types of outcome measures (binary, categorical, or ordinal) will require different methods of analysis, and each will have different sets of assumptions. For example, categorical data are summarised by counts and percentages or proportions, and are analysed by tests of proportions; these analysis methods assume that data are binary, ordinal or nominal, and independent [1].

For each statistical test used (parametric or non-parametric), report the type of outcome measure and the methods used to test the assumptions of the statistical approach. If data were transformed, identify precisely the transformation used and which outcome measures it was applied to. Report any changes to the analysis if the assumptions were not met and an alternative approach was used (e.g. a non-parametric test was used which does not require the assumption of normality). If the relevant assumptions about the data were not tested, state this explicitly.

 

References

  1. Ruxton G and Colegrave N (2017). Experimental design for the life sciences. Fourth Edition. Oxford University Press. doi: 10.1017/CBO9781139344319

Examples

Example 1 

“Model assumptions were checked using the Shapiro-Wilk normality test and Levene’s Test for homogeneity of variance and by visual inspection of residual and fitted value plots. Some of the response variables had to be transformed by applying the natural logarithm or the second or third root, but were back-transformed for visualization of significant effects.” [1] 

Example 2 

The effects of housing (treatment) and day of euthanasia on cortisol levels were assessed by using fixed-effects 2-way ANOVA. An initial exploratory analysis indicated that groups with higher average cortisol levels also had greater variation in this response variable. To make the variation more uniform, we used a logarithmic transform of each fish's cortisol per unit weight as the dependent variable in our analyses. This action made the assumptions of normality and homoscedasticity (standard deviations were equal) of our analyses reasonable.” [2]  

 

References

  1. Nemeth M, Millesi E, Wagner K-H and Wallner B (2015). Sex-specific effects of diets high in unsaturated fatty acids on spatial learning and memory in guinea pigs. PLOS ONE. doi: 10.1371/journal.pone.0140485
  2. Keck VA, Edgerton DS, Hajizadeh S, Swift LL, Dupont WD, Lawrence C and Boyd KL (2015). Effects of habitat complexity on pair-housed zebrafish. J Am Assoc Lab Anim Sci. https://www.ncbi.nlm.nih.gov/pubmed/26224437
Essential 10

8. Experimental animals

Explanation

The species, strain, substrain, sex, weight, and age of animals are critical factors that can influence most experimental results [1-5]. Reporting the characteristics of all animals used is equivalent to standardised human patient demographic data; these data support both the internal and external validity of the study results. It enables other researchers to repeat the experiment and generalise the findings. It also enables readers to assess whether the animal characteristics chosen for the experiment are relevant to the research objectives.

When reporting age and weight, include summary statistics for each experimental group (e.g. mean and standard deviation) and, if possible, baseline values for individual animals (e.g. as supplementary information or a link to a publicly accessible data repository). As body weight might vary during the course of the study, indicate when the measurements were taken. For most species, precise reporting of age is more informative than a description of the developmental status (e.g. a mouse referred to as an adult can vary in age from six to twenty weeks [6]). In some cases, however, reporting the developmental stage is more informative than chronological age, for example in juvenile Xenopus, where rate of development can be manipulated by incubation temperature [7].

Reporting the weight or the sex of the animals used may not feasible for all studies. For example, sex may be unknown for embryos or juveniles, or weight measurement may be particularly stressful for some aquatic species. If reporting these characteristics can be reasonably expected for the species used and the experimental setting but are not reported, provide a justification. 

 

References

  1. Clayton JA and Collins FS (2014). Policy: NIH to balance sex in cell and animal studies. Nature News. doi: 10.1038/509282a
  2. Shapira S, Sapir M, Wengier A, Grauer E and Kadar T (2002). Aging has a complex effect on a rat model of ischemic stroke. Brain Res. doi: 10.1016/s0006-8993(01)03270-x
  3. Vital M, Harkema JR, Rizzo M, Tiedje J and Brandenberger C (2015). Alterations of the murine gut microbiome with age and allergic airway disease. J Immunol Res. doi: 10.1155/2015/892568
  4. Bouwknecht JA and Paylor R (2002). Behavioral and physiological mouse assays for anxiety: a survey in nine mouse strains. Behavioural brain research. doi: 10.1016/s0166-4328(02)00200-0
  5. Simon MM, Greenaway S, White JK, Fuchs H, Gailus-Durner V, Wells S, Sorg T, Wong K, Bedu E, Cartwright EJ, Dacquin R, Djebali S, Estabel J, Graw J, Ingham NJ, Jackson IJ, Lengeling A, Mandillo S, Marvel J, Meziane H, Preitner F, Puk O, Roux M, Adams DJ, Atkins S, Ayadi A, Becker L, Blake A, Brooker D, Cater H, et al. (2013). A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol. doi: 10.1186/gb-2013-14-7-r82
  6. Jackson SJ, Andrews N, Ball D, Bellantuono I, Gray J, Hachoumi L, Holmes A, Latcham J, Petrie A, Potter P, Rice A, Ritchie A, Stewart M, Strepka C, Yeoman M and Chapman K (2017). Does age matter? The impact of rodent age on study outcomes. Laboratory Animals. doi: 10.1177/0023677216653984
  7. Khokha MK, Chung C, Bustamante EL, Gaw LW, Trott KA, Yeh J, Lim N, Lin JC, Taverner N, Amaya E, Papalopulu N, Smith JC, Zorn AM, Harland RM and Grammer TC (2002). Techniques and probes for the study of Xenopus tropicalis development. Dev Dyn. doi: 10.1002/dvdy.1018 

Examples

Example 1 

“One hundred and nineteen male mice were used: C57BL/6OlaHsd mice (n = 59), and BALB/c OlaHsd mice (n = 60) (both from Harlan, Horst, The Netherlands). At the time of the EPM test the mice were 13 weeks old and had body weights of 27.4 ± 0.4 g and 27.8 ± 0.3 g, respectively (mean ± SEM).” [1]

Example 2 

“Histone Methylation Profiles and the Transcriptome of X. tropicalis Gastrula Embryos. To generate epigenetic profiles, ChIP was performed using specific antibodies against trimethylated H3K4 and H3K27 in Xenopus gastrula-stage embryos (Nieuwkoop-Faber stage 11–12), followed by deep sequencing (ChIP-seq). In addition, polyA-selected RNA (stages 10–13) was reverse transcribed and sequenced (RNA-seq).” [2] 

 

References

  1. Okva K, Nevalainen T and Pokk P (2013). The effect of cage shelf on the behaviour of male C57BL/6 and BALB/c mice in the elevated plus maze test. Lab Anim. doi: 10.1177/0023677213489280
  2. Akkers RC, van Heeringen SJ, Jacobi UG, Janssen-Megens EM, Francoijs KJ, Stunnenberg HG and Veenstra GJ (2009). A hierarchy of H3K4me3 and H3K27me3 acquisition in spatial gene regulation in Xenopus embryos. Dev Cell. doi: 10.1016/j.devcel.2009.08.005

Explanation

The animals’ provenance, their health or immune status and their history of previous testing or procedures, can influence their physiology and behaviour as well as their response to treatments, and thus impact on study outcomes. For example, animals of the same strain, but from different sources, or animals obtained from the same source but at different times, may be genetically different [1]. The immune or microbiological status of the animals can also influence welfare, experimental variability and scientific outcomes [2-4].

Report the health status of all animals used in the study, and any previous procedures the animals have undergone. For example, if animals are specific pathogen free (SPF), list the pathogens that they were declared free of. If health status is unknown or was not tested, explicitly state this.

For genetically modified animals, describe the genetic modification status (e.g. knockout, overexpression), genotype (e.g. homozygous, heterozygous), manipulated gene(s), genetic methods and technologies used to generate the animals, how the genetic modification was confirmed, and details of animals used as controls (e.g. littermate controls [5]).

Reporting the correct nomenclature is crucial to understanding the data and ensuring that the research is discoverable and replicable [6-8]. Useful resources for reporting nomenclature for different species include:

 

References

  1. Festing MF and Altman DG (2002). Guidelines for the design and statistical analysis of experiments using laboratory animals. ILAR journal. http://www.ncbi.nlm.nih.gov/pubmed/12391400
  2. Mahler Convenor M, Berard M, Feinstein R, Gallagher A, Illgen-Wilcke B, Pritchett-Corning K and Raspa M (2014). FELASA recommendations for the health monitoring of mouse, rat, hamster, guinea pig and rabbit colonies in breeding and experimental units. Lab Anim. doi: 10.1177/0023677213516312
  3. Baker DG (1998). Natural Pathogens of Laboratory Mice, Rats, and Rabbits and Their Effects on Research. Clinical Microbiology Reviews. doi: 10.1128/cmr.11.2.231
  4. Velazquez EM, Nguyen H, Heasley KT, Saechao CH, Gil LM, Rogers AWL, Miller BM, Rolston MR, Lopez CA, Litvak Y, Liou MJ, Faber F, Bronner DN, Tiffany CR, Byndloss MX, Byndloss AJ and Baumler AJ (2019). Endogenous Enterobacteriaceae underlie variation in susceptibility to Salmonella infection. Nat Microbiol. doi: 10.1038/s41564-019-0407-8
  5. Holmdahl R and Malissen B (2012). The need for littermate controls. European journal of immunology. doi: 10.1002/eji.201142048
  6. Mallapaty S (2018). In the name of reproducibility. Lab Animal. doi: 10.1038/s41684-018-0095-7
  7. Sundberg JP and Schofield PN (2010). Commentary: Mouse Genetic Nomenclature:Standardization of Strain, Gene, and Protein Symbols. Veterinary Pathology. doi: 10.1177/0300985810374837
  8. Montoliu L and Whitelaw CBA (2011). Using standard nomenclature to adequately name transgenes, knockout gene alleles and any mutation associated to a genetically modified mouse strain. Transgenic Research. doi: 10.1007/s11248-010-9428-z

Examples

Example 1

“A construct was engineered for knock-in of the miR-128 (miR-128-3p) gene into the Rosa26 locus. Rosa26 genomic DNA fragments (~1.1 kb and ~4.3 kb 5′ and 3′ homology arms, respectively) were amplified from C57BL/6 BAC DNA, cloned into the pBasicLNeoL vector sequentially by in-fusion cloning, and confirmed by sequencing. The miR-128 gene, under the control of tetO-minimum promoter, was also cloned into the vector between the two homology arms. In addition, the targeting construct also contained a loxP sites flanking the neomycin resistance gene cassette for positive selection and a diphtheria toxin A (DTA) cassette for negative selection. The construct was linearized with ClaI and electroporated into C57BL/6N ES cells. After G418 selection, seven-positive clones were identified from 121 G418-resistant clones by PCR screening. Six-positive clones were expanded and further analyzed by Southern blot analysis, among which four clones were confirmed with correct targeting with single-copy integration. Correctly targeted ES cell clones were injected into blastocysts, and the blastocysts were implanted into pseudo-pregnant mice to generate chimeras by Cyagen Biosciences Inc. Chimeric males were bred with Cre deleted mice from Jackson Laboratories to generate neomycin-free knockin mice. The correct insertion of the miR-128 cassette and successful removal of the neomycin cassette were confirmed by PCR analysis with the primers listed in Supplementary Table 1.” [1]

Example 2

“The C57BL/6J (Jackson) mice were supplied by Charles River Laboratories. The C57BL/6JOlaHsd (Harlan) mice were supplied by Harlan. The α-synuclein knockout mice were kindly supplied by Prof. [X] (Cardiff University, Cardiff, United Kingdom.) and were congenic C57BL/6JCrl (backcrossed for 12 generations). TNFα−/− mice were kindly supplied by Dr. [Y] (Queens University, Belfast, Northern Ireland) and were inbred on a homozygous C57BL/6J strain originally sourced from Bantin & Kingman and generated by targeting C57BL/6 ES cells. T286A mice were obtained from Prof. [Z] (University of California, Los Angeles, CA). These mice were originally congenic C57BL/6J (backcrossed for five generations) and were then inbred (cousin matings) over 14 y, during which time they were outbred with C57BL/6JOlaHsd mice on three separate occasions.” [2]

 

References

  1. Huang W, Feng Y, Liang J, Yu H, Wang C, Wang B, Wang M, Jiang L, Meng W, Cai W, Medvedovic M, Chen J, Paul C, Davidson WS, Sadayappan S, Stambrook PJ, Yu XY and Wang Y (2018). Loss of microRNA-128 promotes cardiomyocyte proliferation and heart regeneration. Nature communications. doi: 10.1038/s41467-018-03019-z
  2. Ranson A, Cheetham CEJ, Fox K and Sengpiel F (2012). Homeostatic plasticity mechanisms are required for juvenile, but not adult, ocular dominance plasticity. Proceedings of the National Academy of Sciences. doi: 10.1073/pnas.1112204109
Essential 10

9. Experimental procedures

For each experimental group, including controls, describe the procedures in enough detail to allow others to replicate them, including:

Explanation

Essential information to describe in the manuscript includes the procedures used to develop the model (e.g. induction of the pathology), the procedures used to measure the outcomes, and pre- and post-experimental procedures, including animal handling, welfare monitoring and euthanasia. Animal handling can be a source of stress and the specific method used (e.g. mice picked up by tail or in cupped hands) can affect research outcomes [1-3]. Details about animal care and monitoring intrinsic to the procedure are discussed in further detail in item 15 – Animal care and monitoring. Provide enough detail to enable others to replicate the methods and highlight any quality assurance and quality control used [4,5]. A schematic of the experimental procedures with a timeline can give a clear overview of how the study was conducted. Information relevant to distinct types of interventions and resources are described below.

Examples of information to include when reporting specific types of experimental procedures and resources

Procedures

Resources

Pharmacological procedures (intervention and control)

  • Drug formulation
  • Dose
  • Volume
  • Concentration
  • Site and route of administration
  • Frequency of administration
  • Vehicle or carrier solution formulation and volume
  • Any evidence that the pharmacological agent used reaches the target tissue

Surgical procedures (including sham surgery)

  • Description of the surgical procedure
  • Anaesthetic used (including dose and other information listed in pharmacological procedures section above)
  • Pre and post analgesia regimen
  • Pre-surgery procedures (e.g. fasting)
  • Aseptic techniques
  • Monitoring (e.g. assessment of surgical anaesthetic plane)
  • Whether the procedure is terminal or not
  • Post-surgery procedures
  • Duration of the procedure and duration of anaesthesia
  • Physical variables measured

Pathogen infection (intervention and control)

  • Infectious agent
  • Dose load
  • Vehicle or carrier solution formulation and volume
  • Site and route of infection
  • Timing or frequency of infection

Euthanasia

  • Method of euthanasia, including the humane standards the method complies with, such as the American Veterinary Medical Association (AVMA) [6]
  • Pharmacological agent, if used (including dose and information listed in pharmacological procedures section above)
  • Any measures taken to reduce pain and distress before or during euthanasia
  • Timing of euthanasia
  • Tissues collected post-euthanasia and timing of collection

Cell lines

  • Identification
  • Provenance
  • Verification and authentication
  • RRID [7,8]

Reagents (e.g. antibodies, chemicals)

  • Manufacturer
  • Supplier
  • Catalogue number
  • Lot number (if applicable)
  • Purity of the drug (if applicable)
  • RRID

Equipment and software

  • Manufacturer
  • Supplier
  • Model/version number
  • Calibration procedures (if applicable)
  • RRID

Where available, cite the Research Resource Identifier (RRID) for reagents and tools used [7,8]. RRIDs are unique and stable, allowing unambiguous identification of reagents or tools used in a study, aiding other researchers to replicate the methods.

Detailed step-by-step procedures can also be saved and shared online, for example using Protocols.io [9], which assigns a Digital Object Identifier (DOI) to the protocol and allows cross-referencing between protocols and publications.

 

References

  1. larkson JM, Dwyer DM, Flecknell PA, Leach MC and Rowe C (2018). Handling method alters the hedonic value of reward in laboratory mice. Scientific reports. doi: 10.1038/s41598-018-20716-3
  2. Gouveia K and Hurst JL (2017). Optimising reliability of mouse performance in behavioural testing: the major role of non-aversive handling. Scientific reports. doi: 10.1038/srep44999
  3. Hurst JL and West RS (2010). Taming anxiety in laboratory mice. Nat Methods. doi: 10.1038/nmeth.1500
  4. Hewitt JA, Brown LL, Murphy SJ, Grieder F and Silberberg SD (2017). Accelerating Biomedical Discoveries through Rigor and Transparency. ILAR journal. doi: 10.1093/ilar/ilx011
  5. Almeida JL, Cole KD and Plant AL (2016). Standards for Cell Line Authentication and Beyond. PLoS Biol. doi: 10.1371/journal.pbio.1002476
  6. Leary SL, Underwood W, Anthony R, Cartner S, Corey D, Grandin T, Greenacre C, Gwaltney-Bran S, McCrackin M, Meyer R, Miller D, Shearer J, Yanong R, Golab G and Patterson-Kane E (2013). AVMA guidelines for the euthanasia of animals: 2013 edition. https://www.avma.org/KB/Policies/Pages/Euthanasia-Guidelines.aspx
  7. Bandrowski AE and Martone ME (2016). RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods. Neuron. doi: 10.1016/j.neuron.2016.04.030
  8. Bandrowski A, Brush M, Grethe JS, Haendel MA, Kennedy DN, Hill S, Hof PR, Martone ME, Pols M, Tan SC, Washington N, Zudilova-Seinstra E and Vasilevsky N (2016). The Resource Identification Initiative: A Cultural Shift in Publishing. J Comp Neurol. doi: 10.1002/cne.23913
  9. Teytelman L and Stoliartchouk A (2016). Protocols.io: Reducing the knowledge that perishes because we do not publish it. Information Services & Use. doi: 10.1371/journal.pbio.100253

Examples

Example 1

[1]

“Fig … shows the timeline for instrumentation, stabilization, shock/injury, and resuscitation…Animals were food-deprived for 18 hours before surgery, but allowed free access to water. On the morning of surgery, swine were sedated with tiletamine-zolazepam (Telazol®; 5-8 mg/kg IM; Zoetis Inc., Kalamazoo MI) in the holding pen, weighed, and masked with isoflurane (3%, balance 100% O2) for transport to the lab. The marginal ear vein was catheterized for administration of atropine (0.02 mg/kg IV; Sparhawk Laboratories, Lenexa KS), and buprenorphine for pre-emptive analgesia (3 mg/ml IV; ZooPharm, Laramie WY). Ophthalmic ointment (Puralube®; Fera Pharmaceuticals) was applied to prevent corneal drying. Animals were intubated in dorsal recumbency with a cuffed 6 or 7 Fr endotracheal tube. Anesthetic plane was maintained by isoflurane (1-1.5%; 21-23 % O2, balance N2). Oxygen saturation (sPO2, %) and heart rate (HR) were monitored with a veterinary pulse oximeter placed in the buccal cavity (Masimo SET Radical-7; Irvine CA). Core temperature was monitored with a rectal probe and maintained at 36.5-38oC with a microprocessor-controlled feedback water blanket (Blanketrol® II, Cincinnati Sub-Zero (CSZ) Cincinnati, OH) placed under the animal. Anesthetic depth was assessed every 5 min for the duration of the experiment by reflexes (corneal touch, pedal flexion, coronary band pinch) and vital signs (sPO2, HR, core temperature).” [1]

Example 2

“For the diet-induced obesity (DIO) model, eight-week-old male mice had ad libitum access to drinking water and were kept on standard chow (SFD, 10.9 kJ/g) or on western high-fat diet (HFD; 22 kJ/g; kcal from 42% fat, 43% from carbohydrates and 15% from protein; E15721-34, Ssniff, Soest, Germany) for 15 weeks (https://dx.doi.org/10.17504/protocols.io.kbacsie).” [2]

Example 3

“The frozen kidney tissues were lysed. The protein concentration was determined with the Pierce BCA assay kit (catalogue number 23225; Thermo Fisher Scientific, Rockford, IL, USA). A total of 100–150 μg total proteins were resolved on a 6–12% SDS-PAGE gel. The proteins were then transferred to a nitrocellulose membrane, blocked with 5% skimmed milk for 1 h at room temperature and incubated overnight at 4°C with primary antibodies against the following proteins: proliferating cell nuclear antigen (PCNA; Cat# 2586, RRID: AB_2160343), phospho-AMPK (Cat# 2531, RRID: AB_330330), phospho-mTOR (Cat# 2971, RRID: AB_330970)…The β-actin (Cat# A5441, RRID: AB_476744) antibody was obtained fromSigma. The blots were subsequently probed with HRP-conjugated anti-mouse (Cat# A0216) or anti-rabbit IgG (Cat# A0208; Beyotime Biotechnology, Beijing, China) at 1:1000. Immunoreactive bands were visualized by enhanced chemiluminescence, and densitometry was performed using ImageJ software (RRID: SCR_003070, Bio-Rad Laboratories).” [3]

 

References

1. Reynolds PS, Fisher BJ, McCarter J, Sweeney C, Martin EJ, Middleton P, Ellenberg M, Fowler E, Brophy DF, Fowler AA, 3rd, Spiess BD and Natarajan R (2018). Interventional Vitamin C: A Strategy for Attenuation of Coagulopathy and Inflammation in a Swine Polytrauma Model. The journal of trauma and acute care surgery. doi: 10.1097/ta.0000000000001844

2. Bauters D, Bedossa P, Lijnen HR and Hemmeryckx B (2018). Functional role of ADAMTS5 in adiposity and metabolic health. PLoS One. doi: 10.1371/journal.pone.0190595

3. Lian X, Wu X, Li Z, Zhang Y, Song K, Cai G, Li Q, Lin S, Chen X and Bai XY (2019). The combination of metformin and 2-deoxyglucose significantly inhibits cyst formation in miniature pigs with polycystic kidney disease. Br. J. Pharmacol. doi: 10.1111/bph.14558

 

Explanation

Clearly report the frequency and timing of experimental procedures and measurements, including the light and dark cycle (e.g. 12L:12D), circadian time cues (e.g. lights on at 08:00), and experimental time sequence (e.g. interval between baseline and comparator measurements or interval between procedures and measurements). Along with innate circadian rhythms, these can affect research outcomes such as behavioural, physiological, and immunological parameters [1,2]. Also report the timing and frequency of welfare assessments, taking into consideration the normal activity patterns (see item 15 – Animal care and monitoring). For example, nocturnal animals may not show behavioural signs of discomfort during the day [3].

If the timing of procedures or measurements varies between animals, this information can be provided as a supplementary table listing each animal.

 

References

  1. Bartlang MS, Neumann ID, Slattery DA, Uschold-Schmidt N, Kraus D, Helfrich-Förster C and Reber SO (2012). Time matters: pathological effects of repeated psychosocial stress during the active, but not inactive, phase of male mice. Journal of Endocrinology. doi: 10.1530/joe-12-0267
  2. Paul AK, Gueven N and Dietis N (2017). Morphine dosing strategy plays a key role in the generation and duration of the produced antinociceptive tolerance. Neuropharmacology. doi: 10.1016/j.neuropharm.2017.04.034
  3. Hawkins P, Morton DB, Burman O, Dennison N, Honess P, Jennings M, Lane S, Middleton V, Roughan JV, Wells S and Westwood K (2011). A guide to defining and implementing protocols for the welfare assessment of laboratory animals: eleventh report of the BVAAWF/FRAME/RSPCA/UFAW Joint Working Group on Refinement. Laboratory Animals. doi: 10.1258/la.2010.010031

Examples

Example 1

“Blood pressure, heart rate, oxygen saturation and amount of blood extracted were recorded every 5 minutes. Blood samples were drawn at baseline (pre injury), 0 minutes (immediately after injury), and after 30 and 60 minutes.” [1]

Example 2

“After a 5-h fast (7:30–12:30am), awake and freely moving mice were randomized and subjected to three consecutive clamps performed in the same mice as described above, with a 2 days recovery after each hyperinsulinemic/hypoglycemic (mHypo, n = 6) or hyperinsulinemic/euglycemic (mEugly, n = 4) clamps.” [2]

 

References

  1. Hagemo JS, Jørgensen JJ, Ostrowski SR, Holtan A, Gundersen Y, Johansson PI, Næss PA and Gaarder C (2013). Changes in fibrinogen availability and utilization in an animal model of traumatic coagulopathy. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. doi: 10.1186/1757-7241-21-56
  2. Emery M, Nanchen N, Preitner F, Ibberson M and Roduit R (2016). Biological Characterization of Gene Response to Insulin-Induced Hypoglycemia in Mouse Retina. PLOS ONE. doi: 10.1371/journal.pone.0150266

Explanation

Physiological acclimatisation after a stressful event, such as transport (e.g. between supplier, animal facility, operating theatre and laboratory), but before the experiment begins allows stabilisation of physiological responses of the animal [1,2]. Protocols vary depending on species, strain, and outcome; for example physiological acclimatisation following transportation of different animals can take anywhere from 24 hours to more than one week [3]. Procedural acclimatisation, immediately before a procedure, allows stabilisation of the animals’ responses after unaccustomed handling, novel environments, and previous procedures, which otherwise can induce behavioural and physiological changes [4,5]. Standard acclimatisation periods may vary between research labs and this information cannot be inferred by readers.

Indicate where studies were performed (e.g. dedicated laboratory space or animal facility, home cage, open field arena, water maze) and whether periods of physiological or procedural acclimatisation were included in the study protocol, including type and duration. If the study involved multiple sites, explicitly state where each experiment and sample analysis was performed. Include any accreditation of laboratories if appropriate (e.g. if samples were sent to a commercial laboratory for analysis).

 

References

  1. Holmes AM, Emmans CJ, Coleman R, Smith TE and Hosie CA (2018). Effects of transportation, transport medium and re-housing on Xenopus laevis (Daudin). Gen Comp Endocrinol. doi: 10.1016/j.ygcen.2018.03.015
  2. Conour LA, Murray KA and Brown MJ (2006). Preparation of animals for research--issues to consider for rodents and rabbits. ILAR J. doi: 10.1093/ilar.47.4.283
  3. Obernier JA and Baldwin RL (2006). Establishing an appropriate period of acclimatization following transportation of laboratory animals. ILAR Journal. doi: 10.1093/ilar.47.4.364
  4. Krahn DD, Gosnell BA and Majchrzak MJ (1990). The anorectic effects of CRH and restraint stress decrease with repeated exposures. Biological psychiatry. doi: 10.1016/0006-3223(90)90046-5
  5. Pitman DL, Ottenweller JE and Natelson BH (1988). Plasma corticosterone levels during repeated presentation of two intensities of restraint stress: chronic stress and habituation. Physiology & behavior. doi: 10.1016/0031-9384(88)90097-2

Examples

Example 1

“Fish were singly housed for 1 week before being habituated to the conditioning tank over 2 consecutive days. The conditioning tank consisted of an opaque tank measuring 20 cm (w) 15 cm (h) 30 cm (l) containing 2.5 l of aquarium water with distinct visual cues (spots or stripes) on walls at each end of the tank…During habituation, each individual fish was placed in the conditioning apparatus for 20 minutes with free access to both compartments and then returned to its home tank.” [1]

 

References

  1. Brock AJ, Goody SMG, Mead AN, Sudwarts A, Parker MO and Brennan CH (2017). Assessing the Value of the Zebrafish Conditioned Place Preference Model for Predicting Human Abuse Potential. The Journal of pharmacology and experimental therapeutics. doi: 10.1124/jpet.117.242628

Explanation

There may be numerous approaches to investigate any given research problem, therefore it is important to explain why a particular procedure or technique was chosen. This is especially relevant when procedures are novel or specific to a research laboratory, or constrained by the animal model or experimental equipment (e.g. route of administration determined by animal size [1]).

 

References

  1. Turner PV, Brabb T, Pekow C and Vasbinder MA (2011). Administration of substances to laboratory animals: routes of administration and factors to consider. Journal of the American Association for Laboratory Animal Science : JAALAS. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3189662/

Examples

Example 1

“Because of the very small caliber of the murine tail veins, partial paravenous injection is common if 18F-FDG is administered by tail vein injection (intravenous). This could have significantly biased our comparison of the biodistribution of 18F-FDG under various conditions. Therefore, we used intraperitoneal injection of 18F-FDG for our experiments evaluating the influence of animal handling on 18F-FDG biodistribution.” [1]

Example 2

“Since Xenopus oocytes have a higher potential for homologous recombination than fertilized embryos…we next tested whether the host transfer method could be used for efficient HDR-mediated knock-in.  We targeted the C-terminus of X. laevis Ctnnb1 (β-catenin), a key cytoskeletal protein and effector of the canonical Wnt pathway, because previous studies have shown that addition of epitope tags to the C-terminus do not affect the function of the resulting fusion protein (Fig…)…CRISPR components were injected into X. laevis oocytes followed by host transfer or into embryos.” [2]

 

References

  1. Fueger BJ, Czernin J, Hildebrandt I, Tran C, Halpern BS, Stout D, Phelps ME and Weber WA (2006). Impact of animal handling on the results of 18F-FDG PET studies in mice. Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
  2. Aslan Y, Tadjuidje E, Zorn AM and Cha SW (2017). High-efficiency non-mosaic CRISPR-mediated knock-in and indel mutation in F0 Xenopus. Development. doi: 10.1242/dev.152967

 

Essential 10

10. Results

For each experiment conducted, including independent replications, report:

Explanation

Summary/descriptive statistics provide a quick and simple description of the data, they communicate quantitative results easily and facilitate visual presentation. For continuous data, these descriptors include a measure of central tendency (e.g. mean, median) and a measure of variability (e.g. quartiles, range, standard deviation) to help readers assess the precision of the data collected. Categorical data can be expressed as counts, frequencies, or proportions.

Report data for all experiments conducted. If a complete experiment is repeated on a different day, or under different conditions, report the results of all repeats, rather than selecting data from representative experiments. Report the exact number of experimental units per group so readers can gauge the reliability of the results (see item 2 – Sample size, and item 3 – Inclusion and exclusion criteria). Present data clearly as text, in tables, or in graphs, to enable information to be evaluated, or extracted for future meta-analyses [1]. Report descriptive statistics with a clearly identified measure of variability for each group. Example 1 shows data summarised as means and standard deviations and, in brackets, ranges. Boxplots are a convenient way to summarise continuous data, plotted as median and interquartile range, as shown in Example 2.

 

References

  1. Michel MC, Murphy TJ and Motulsky HJ (2020). New author guidelines for displaying data and reporting data analysis and statistical methods in experimental biology. Mol. Pharmacol. doi: 10.1124/mol.119.118927

Examples

Example 1

[1]

“Bioacoustic parameters of new species of miniaturised cophyline microhylids...Values are presented as mean ± standard deviation, with range in brackets. na = not applicable. *In all species except R. proportionalis calls consist of a single note according to the definition herein, and in these species call duration is therefore synonymous with note duration.” [1]

Example 2

[2]

“Fractions of the unperturbed elements of calcium release in cardiac myocytes. MORPHOL: fractions of compact dyads estimated by morphometry from electron microscopic images…ELPHYS: fractions of the early CRF components estimated by fitting records of integral fluorescence signals …. CTR - control myocardium; IMY - injured myocardium. All collected data are shown. Box plots show the 25%, 50% and 75% percentiles; whiskers show 10% and 90% percentile. Solid squares denote the means.” [2]

 

References

  1. Scherz MD, Hutter CR, Rakotoarison A, Riemann JC, Rodel MO, Ndriantsoa SH, Glos J, Hyde Roberts S, Crottini A, Vences M and Glaw F (2019). Morphological and ecological convergence at the lower size limit for vertebrates highlighted by five new miniaturised microhylid frog species from three different Madagascan genera. PLoS One. doi: 10.1371/journal.pone.0213314
  2. Novotová M, Zahradníková A, Nichtová Z, Kováč R, Kráľová E, Stankovičová T, Zahradníková A and Zahradník I (2020). Structural variability of dyads relates to calcium release in rat ventricular myocytes. Sci. Rep. doi: 10.1038/s41598-020-64840-5

Explanation

In hypothesis-testing studies using inferential statistics, investigators frequently confuse statistical significance and small p-values, with biological or clinical importance [1]. Statistical significance is usually quantified and evaluated against a preassigned threshold, with p < 0.05 often used as a convention. However, statistical significance is heavily influenced by sample size and variation in the data (see item 2 – Sample size). Investigators must consider the size of the effect that was observed and whether this is a biologically relevant change.

Effect sizes are often not reported in animal research, but they are relevant to both exploratory and hypothesis-testing studies. An effect size is a quantitative measure that estimates the magnitude of differences between groups, or strength of relationships between variables. It can be used to assess the patterns in the data collected and make inferences about the wider population from which the sample came. The confidence interval for the effect indicates how precisely the effect has been estimated, and tells the reader about the strength of the effect [2]. In studies where statistical power is low, and/or hypothesis-testing is inappropriate, providing the effect size and confidence interval indicates how small or large an effect might really be, so a reader can judge the biological significance of the data [3,4]. Reporting effect sizes with confidence intervals also facilitates extraction of useful data for systematic review and meta-analysis. Where multiple independent studies included in a meta-analysis show quantitatively similar effects, even if each is statistically non-significant, this provides powerful evidence that a relationship is ‘real’, although small.

Report all analyses performed, even those providing non-statistically significant results. Report the effect size to indicate the size of the difference between groups in the study, with a confidence interval to indicate the precision of the effect size estimate.

 

References

  1. Wasserstein RL, Schirm AL and Lazar NA (2019). Moving to a World Beyond “p < 0.05”. The American Statistician. doi: 10.1080/00031305.2019.1583913
  2. Altman DG (2005). Why we need confidence intervals. World J Surg. doi: 10.1007/s00268-005-7911-0
  3. Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, Elbourne D, Egger M and Altman DG (2010). CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. doi: 10.1136/bmj.c869
  4. Nakagawa S and Cuthill IC (2007). Effect size, confidence interval and statistical significance: a practical guide for biologists. Biological reviews of the Cambridge Philosophical Society. doi: 10.1111/j.1469-185X.2007.00027.x

Examples

Example 1

[1]

“For all traits identified as having a significant genotype effect for the Usp47tm1b(EUCOMM)Wtsi line (MGI:5605792), a comparison is presented of the standardized genotype effect with 95% confidence interval for each sex with no multiple comparisons correction. Standardization, to allow comparison across variables, was achieved by dividing the genotype estimate by the signal seen in the wildtype population. Shown in red are statistically significant estimates. RBC: red blood cells; BMC: bone mineral content; BMD: bone mineral density; WBC: white blood cells.” [1]

 

References

  1. Karp NA, Mason J, Beaudet AL, Benjamini Y, Bower L, Braun RE, Brown SDM, Chesler EJ, Dickinson ME, Flenniken AM, Fuchs H, Angelis MHd, Gao X, Guo S, Greenaway S, Heller R, Herault Y, Justice MJ, Kurbatova N, Lelliott CJ, Lloyd KCK, Mallon A-M, Mank JE, Masuya H, McKerlie C, Meehan TF, Mott RF, Murray SA, Parkinson H, Ramirez-Solis R, et al. (2017). Prevalence of sexual dimorphism in mammalian phenotypic traits. Nature communications. doi: 10.1038/ncomms15475
Recommended Set

11. Abstract

Explanation

A transparent and accurate abstract increases the utility and impact of the manuscript, and allows readers to assess the reliability of the study [1]. The abstract is often used as a screening tool by readers to decide whether to read the full article or whether to select an article for inclusion in a systematic review. However, abstracts often either do not contain enough information for this purpose [2], or contain information that is inconsistent with the results in the rest of the manuscript [3,4]. In systematic reviews, initial screens to identify papers are based on titles, abstracts and keywords [5]. Leaving out of the abstract information such as the species of animal used or the drugs being tested, limits the value of preclinical systematic reviews as relevant studies cannot be identified and included. For example, in a systematic review of the effect of the MVA85A vaccine on tuberculosis challenge in animals, the largest preclinical trial did not include the vaccine name in the abstract or keywords of the publication, the paper was only included in the systematic review following discussions with experts in the field [6].

To maximise utility, include details of the species, sex and strain of animals used, and accurately report the methods, results and conclusions of the study.  Also describe the objectives of the study, including whether it was designed to either test a specific hypothesis or to generate a new hypothesis (see item 13 – Objectives). Incorporating this information will enable readers to interpret the strength of evidence, and judge how the study fits within the wider knowledge base.

 

References

  1. Haynes RB, Mulrow CD, Huth EJ, Altman DG and Gardner MJ (1990). More informative abstracts revisited. Ann Intern Med. doi: 10.7326/0003-4819-113-1-69
  2. Hair K, Macleod MR, Sena ES, Sena ES, Hair K, Macleod MR, Howells D, Bath P, Irvine C, MacCallum C, Morrison G, Clark A, Alvino G, Dohm M, Liao J, Sena C, Moreland R, Cramond F, Currie GL, Bahor Z, Grill P, Bannach-Brown A, Marcu D-C, Antar S, Blazek K, Konold T, Dingwall M, Hohendorf V, Hosh M, Gerlei KZ, et al. (2019). A randomised controlled trial of an intervention to improve compliance with the ARRIVE guidelines (IICARus). Research Integrity and Peer Review. doi: 10.1186/s41073-019-0069-3
  3. Pitkin RM, Branagan MA and Burmeister LF (1999). Accuracy of data in abstracts of published research articles. Jama. doi: 10.1001/jama.281.12.1110
  4. Boutron I, Altman DG, Hopewell S, Vera-Badillo F, Tannock I and Ravaud P (2014). Impact of spin in the abstracts of articles reporting results of randomized controlled trials in the field of cancer: the SPIIN randomized controlled trial. J Clin Oncol. doi: 10.1200/JCO.2014.56.7503
  5. Bannach-Brown A, Przybyla P, Thomas J, Rice ASC, Ananiadou S, Liao J and Macleod MR (2019). Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error. Syst Rev. doi: 10.1186/s13643-019-0942-7
  6. Kashangura R, Sena ES, Young T and Garner P (2015). Effects of MVA85A vaccine on tuberculosis challenge in animals: systematic review. Int J Epidemiol. doi: 10.1093/ije/dyv142

Examples

Example 1

“BACKGROUND AND PURPOSE: Asthma is an inflammatory disease that involves airway hyperresponsiveness and remodelling. Flavonoids have been associated to anti-inflammatory and antioxidant activities and may represent a potential therapeutic treatment of asthma. Our aim was to evaluate the effects of the sakuranetin treatment in several aspects of experimental asthma model in mice.

EXPERIMENTAL APPROACH: Male BALB/c mice received ovalbumin (i.p.) on days 0 and 14, and were challenged with aerolized ovalbumin 1% on days 24, 26 and 28. Ovalbumin-sensitized animals received vehicle (saline and dimethyl sulfoxide, DMSO), sakuranetin (20 mg kg–1per mice) or dexamethasone (5 mg kg–1 per mice) daily beginning from 24th to29th day. Control group received saline inhalation and nasal drop vehicle. On day 29, we determined the airway hyperresponsiveness, inflammation and remodelling as well as specific IgE antibody. RANTES, IL-5, IL-4, Eotaxin, IL-10, TNF-a, IFN-g and GMC-SF content in lung homogenate was performed by Bioplex assay, and 8-isoprostane and NF-kB activations were visualized in inflammatory cells by immunohistochemistry.

KEY RESULTS: We have demonstrated that sakuranetin treatment attenuated airway hyperresponsiveness, inflammation and remodelling; and these effects could be attributed to Th2 pro-inflammatory cytokines and oxidative stress reduction as well as control of NF-kB activation.

CONCLUSIONS AND IMPLICATIONS: These results highlighted the importance of counteracting oxidative stress by flavonoids in this asthma model and suggest sakuranetin as a potential candidate for studies of treatment of asthma.” [1]

Example 2

 “In some parts of the world, the laboratory pig (Sus scrofa) is often housed in individual, sterile housing which may impose stress. Our objectives were to determine the effects of isolation and enrichment on pigs housed within the PigTurn® — a novel penning system with automated blood sampling — and to investigate tear staining as a novel welfare indicator. Twenty Yorkshire × Landrace weaner pigs were randomly assigned to one of four treatments in a 2 × 2 factorial combination of enrichment (non-enriched [NE] or enriched [E]) and isolation (visually isolated [I] or able to see another pig [NI]). Pigs were catheterised and placed into the PigTurns® 48 h post recovery. Blood was collected automatically twice daily to determine white blood cell (WBC) differential counts and assayed for cortisol. Photographs of the eyes were taken daily and tear staining was quantified using a 0–5 scoring scale and Image-J software to measure stain area and perimeter. Behaviour was video recorded and scan sampled to determine time budgets. Data were analysed as an REML using the MIXED procedure of SAS. Enrichment tended to increase proportion of time standing and lying laterally and decrease plasma cortisol, tear-stain area and perimeter. There was a significant isolation by enrichment interaction. Enrichment given to pigs housed in isolation had no effect on plasma cortisol, but greatly reduced it in non-isolated pigs. Tear-staining area and perimeter were highest in the NE-I treatment compared to the other three treatments. Eosinophil count was highest in the E-NI treatment and lowest in the NE-I treatment. The results suggest that in the absence of enrichment, being able to see another animal but not interact may be frustrating. The combination of no enrichment and isolation maximally impacted tear staining and eosinophil numbers. However, appropriate enrichment coupled with proximity of another pig would appear to improve welfare.” [2]

 

References

  1. Toledo AC, Sakoda CPP, Perini A, Pinheiro NM, Magalhães RM, Grecco S, Tibério IFLC, Câmara NO, Martins MA, Lago JHG and Prado CM (2013). Flavonone treatment reverses airway inflammation and remodelling in an asthma murine model. Br J Pharmacol. doi: 10.1111/bph.12062
  2. DeBoer SP, Garner JP, McCain RR, Lay Jr DC, Eicher SD and Marchant-Forde JN (2015). An initial investigation into the effects of isolation and enrichment on the welfare of laboratory pigs housed in the PigTurn® system, assessed using tear staining, behaviour, physiology and haematology. Animal Welfare. doi: 10.7120/09627286.24.1.015
Recommended Set

12. Background

Explanation

Scientific background information for an animal study should demonstrate a clear evidence gap and explain why an in vivo approach was warranted. Systematic reviews of the animal literature provide the most convincing evidence that a research question has not been conclusively addressed, by showing the extent of current evidence within a field of research. They can also inform the choice of animal model by providing a comprehensive overview of the models used along with their benefits and limitations [1-3].

Describe the rationale and context of the study and how it relates to other research, including relevant references to previous work. Outline evidence underpinning the hypothesis or objectives and explain why the experimental approach is best suited to answer the research question.

 

References

  1. Avey MT, Fenwick N and Griffin G (2015). The use of systematic reviews and reporting guidelines to advance the implementation of the 3Rs. Journal of the American Association for Laboratory Animal Science : JAALAS. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4382619/
  2. Hooijmans CR and Ritskes-Hoitinga M (2013). Progress in using systematic reviews of animal studies to improve translational research. PLOS Medicine. doi: 10.1371/journal.pmed.1001482
  3. Sena ES, Currie GL, McCann SK, Macleod MR and Howells DW (2014). Systematic reviews and meta-analysis of preclinical studies: why perform them and how to appraise them critically. J. Cereb. Blood Flow Metab. doi: 10.1038/jcbfm.2014.28

 

Examples

Example 1

“For decades, cardiovascular disease has remained the leading cause of mortality worldwide...[and] cardiovascular research has been performed using healthy and young, non-diseased animal models. Recent failures of cardioprotective therapies in obese insulin-resistant, diabetic, metabolic syndrome-affected and aged animals that were otherwise successful in healthy animal models has highlighted the need for the development of animal models of disease that are representative of human clinical conditions…In the clinical setting, elderly male patients often present with both testosterone deficiency (TD) and the metabolic syndrome (MetS). A strong and compounding association exists between MetS and TD which may have significant impact on cardiovascular disease and its outcomes which is not addressed by current models…their mutual presentation in the clinical setting warrants the development of appropriate animal models of the MetS with hypogonadism, especially in the context of cardiovascular disease research.” [1]

 

References

  1. Donner DG, Elliott GE, Beck BR, Bulmer AC and Du Toit EF (2015). Impact of Diet-Induced Obesity and Testosterone Deficiency on the Cardiovascular System: A Novel Rodent Model Representative of Males with Testosterone-Deficient Metabolic Syndrome (TDMetS). PLOS ONE. doi: 10.1371/journal.pone.0138019

Explanation

Provide enough detail for the reader to assess the suitability of the animal model used to address the research question. Include information on the rationale for choosing a particular species, explain how the outcome measures assessed are relevant to the condition under study, and how the model was validated. Stating that an animal model is commonly used in the field is not appropriate, and a well-considered, detailed rationale should be provided.

When the study models an aspect of a human disease, indicate how the model is appropriate for addressing the specific objectives of the study [1]. This can include a description of how the induction of the disease, disorder, or injury is sufficiently analogous to the human condition, how the model responds to known clinically-effective treatments, how similar symptoms are to the clinical disease and how animal characteristics were selected to represent the age, sex, and health status of the clinical population [2].

 

References

  1. Willner P (1986). Validation criteria for animal models of human mental disorders: Learned helplessness as a paradigm case. Progress in Neuro-Psychopharmacology and Biological Psychiatry. doi: 10.1016/0278-5846(86)90051-5
  2. van der Worp HB, Howells DW, Sena ES, Porritt MJ, Rewell S, O'Collins V and Macleod MR (2010). Can Animal Models of Disease Reliably Inform Human Studies? PLOS Medicine. doi: 10.1371/journal.pmed.1000245

Examples

Example 1

“…we selected a pilocarpine model of epilepsy that is characterized by robust, frequent spontaneous seizures acquired after a brain insult, well-described behavioral abnormalities, and poor responses to antiepileptic drugs. These animals recapitulate several key features of human temporal lobe epilepsy, the most common type of epilepsy in adults.” [1]

Example 2

“Transplantation of healthy haematopoietic stem cells (HSCs) is a critical therapy for a wide range of malignant haematological and non-malignant disorders and immune dysfunction…Zebrafish are already established as a successful model to study the haematopoietic system, with significant homology with mammals…Imaging of zebrafish transparent embryos remains a powerful tool and has been critical to confirm that the zebrafish Caudal Haematopoietic Tissue (CHT) is comparable to the mammalian foetal haematopoietic niche...Xenotransplantation in zebrafish embryos has revealed highly conserved mechanisms between zebrafish and mammals. Recently, murine bone marrow cells were successfully transplanted into zebrafish embryos, revealing highly conserved mechanism of haematopoiesis between zebrafish and mammals…Additionally, CD34 enriched human cells transplanted into zebrafish were shown to home to the CHT and respond to zebrafish stromal-cell derived factors…” [2]

 

References

  1. Hunt RF, Girskis KM, Rubenstein JL, Alvarez–Buylla A and Baraban SC (2013). GABA progenitors grafted into the adult epileptic brain control seizures and abnormal behavior. Nature neuroscience. doi:10.1038/nn.3392
  2. Hamilton N, Sabroe I and Renshaw SA (2018). A method for transplantation of human HSCs into zebrafish, to replace humanised murine transplantation models. F1000Res. doi:10.12688/f1000research.14507.2

 

Recommended Set

13. Objectives

Explanation

Explaining the purpose of the study by describing the question(s) that the research addresses, allows readers to determine if the study is relevant to them. Readers can also assess the relevance of the model organism, procedures, outcomes measured, and analysis used.

Knowing if a study is exploratory or hypothesis-testing is critical to its interpretation. A typical exploratory study may measure multiple outcomes and look for patterns in the data, or relationships that can be used to generate hypotheses. It may also be a pilot study which aims to inform the design or feasibility of larger subsequent experiments. Exploratory research helps researchers to design hypothesis-testing experiments, by choosing what variables or outcome measures to focus on in subsequent studies.

Testing a specific hypothesis has implications for both the study design and the data analysis [1,2]. For example, an experiment designed to detect a hypothesised effect will likely need to be analysed with inferential statistics, and a statistical estimation of the sample size will need to be performed a priori (see item 2 – Sample size). Hypothesis-testing studies also have a pre-defined primary outcome measure, which is used to assess the evidence in support of the specific research question (see item 6 – Outcome measures).

In contrast, exploratory research investigates many possible effects, and is likely to yield more false positive results because some will be positive by chance. Thus results from well-designed hypothesis-testing studies provide stronger evidence than those from exploratory or descriptive studies. Independent replication and meta-analysis can further increase the confidence in conclusions.

Clearly outline the objective(s) of the study, including whether it is hypothesis-testing or exploratory, or if it includes research of both types. Hypothesis-testing studies may collect additional information for exploratory purposes, it is important to distinguish which hypotheses were prespecified and which originated after data inspection, especially when reporting unanticipated effects or outcomes that were not part of the original study design.

 

References

  1. Festing MF and Altman DG (2002). Guidelines for the design and statistical analysis of experiments using laboratory animals. ILAR journal. http://www.ncbi.nlm.nih.gov/pubmed/12391400
  2. Kimmelman J, Mogil JS and Dirnagl U (2014). Distinguishing between exploratory and confirmatory preclinical research will improve translation. PLoS Biol. doi: 10.1371/journal.pbio.1001863

Examples

Example 1

“The primary objective of this study was to investigate the cellular immune response to MSC injected into the striatum of allogeneic recipients (6-hydroxydopamine [6-OHDA]-hemilesioned rats, an animal model of Parkinson's disease [PD]), and the secondary objective was to determine the ability of these cells to prevent nigrostriatal dopamine depletion and associated motor deficits in these animals.” [1]

Example 2

“In this exploratory study, we aimed to investigate whether calcium electroporation could initiate an anticancer immune response similar to electrochemotherapy. To this end, we treated immunocompetent balb/c mice with CT26 colon tumors with calcium electroporation, electrochemotherapy, or ultrasound-based delivery of calcium or bleomycin.” [2]

Example 3

“While characterizing a rab-6.2-null C. elegans strain for another study, we observed that rab-6.2(ok2254) animals were fragile. We set out to analyze the fragile-skin phenotype in rab-6.2(ok2254) animals genetically…We observed several ruptured animals on our rab-6.2(ok2254) culture plates during normal maintenance, a phenotype very rarely observed in wild-type cultures…We hypothesized that RAB-6.2 is required for skin integrity.” [3]

 

References

  1. Camp DM, Loeffler DA, Farrah DM, Borneman JN and LeWitt PA (2009). Cellular immune response to intrastriatally implanted allogeneic bone marrow stromal cells in a rat model of Parkinson's disease. Journal of neuroinflammation. doi: 10.1186/1742-2094-6-17
  2. Falk H, Forde PF, Bay ML, Mangalanathan UM, Hojman P, Soden DM and Gehl J (2017). Calcium electroporation induces tumor eradication, long-lasting immunity and cytokine responses in the CT26 colon cancer mouse model. OncoImmunology. doi: 10.1080/2162402X.2017.1301332
  3. Kim JD, Chun AY, Mangan RJ, Brown G, Mourao Pacheco B, Doyle H, Leonard A and El Bejjani R (2019). A conserved retromer-independent function for RAB-6.2 in C. elegans epidermis integrity. J. Cell Sci. doi: 10.1242/jcs.223586
Recommended Set

14. Ethical statement

Explanation

Authors are responsible for complying with regulations and guidelines relating to the use of animals for scientific purposes. This includes ensuring that they have the relevant approval for their study from an appropriate ethics committee and/or regulatory body before the work starts. The ethical statement provides editors, reviewers and readers with assurance that studies have received this ethical oversight [1]. This also promotes transparency and understanding about the use of animals in research and fosters public trust.

Provide a clear statement explaining how the study conforms to appropriate regulations and guidelines. Include the name of the institution where the research was approved and the ethics committee who reviewed it (e.g. Institutional Animal Care and Use Committee [IACUC] in the US or Animal Welfare and Ethical Review Body [AWERB] in the UK) and indicate protocol or project licence numbers so that the study can be identified. Also add any relevant accreditation e.g. AAALAC (American Association for Accreditation of Laboratory Animal Care) [2] or GLP (Good Laboratory Practice).

If the research is not covered by any regulation and formal ethical approval is not required (e.g. a study using animal species not protected by regulations or law), demonstrate that international standards were complied with and cite the appropriate reference. In such cases, provide a clear statement explaining why the research is exempt from regulatory approval.

 

References

  1. McGrath JC and Lilley E (2015). Implementing guidelines on reporting research using animals (ARRIVE etc.): new requirements for publication in BJP. Br J Pharmacol. doi: 10.1111/bph.12955
  2. Bayne K and Turner PV (2019). Animal Welfare Standards and International Collaborations. ILAR journal. doi: 10.1093/ilar/ily024

Examples

Example 1

“All procedures were conducted in accordance with the United Kingdom Animal (Scientific Procedures) Act 1986, approved by institutional ethical review committees (Alderley Park Animal Welfare and Ethical Review Board and Babraham Institute Animal Welfare and Ethical Review Board) and conducted under the authority of the Project Licence (40/3729 and 70/8307, respectively).” [1]

Example 2

“All protocols in this study were approved by the Committee on the Ethics of Animal Experiments of Fuwai Hospital, Peking Union Medical College and the Beijing Council on Animal Care, Beijing, China (IACUC permit number: FW2010-101523), in compliance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH publication no.85-23, revised 1996).” [2]

Example 3

“Samples and data were collected according to Institut de Sélection d’Animale (ISA) protocols, under the supervision of ISA employees. Samples and data were collected as part of routine animal data collection in a commercial breeding program for layer chickens in The Netherlands. Samples and data were collected on a breeding nucleus of ISA for breeding purposes only, and is a non-experimental, agricultural practice, regulated by the Act Animals, and the Royal Decree on Procedures. The Dutch Experiments on Animals Act does not apply to non-experimental, agricultural practices. An ethical review by the Statement Animal Experiment Committee was therefore not required. No extra animal discomfort was caused for sample collection for the purpose of this study.” [3]

 

References

  1. Redfern WS, Tse K, Grant C, Keerie A, Simpson DJ, Pedersen JC, Rimmer V, Leslie L, Klein SK, Karp NA, Sillito R, Chartsias A, Lukins T, Heward J, Vickers C, Chapman K and Armstrong JD (2017). Automated recording of home cage activity and temperature of individual rats housed in social groups: The Rodent Big Brother project. PLoS ONE. doi: 10.1371/journal.pone.0181068
  2. Wang X, Xue Q, Yan F, Liu J, Li S and Hu S (2015). Ulinastatin Protects against Acute Kidney Injury in Infant Piglets Model Undergoing Surgery on Hypothermic Low-Flow Cardiopulmonary Bypass. PLOS ONE. doi: 10.1371/journal.pone.0144516
  3. Berghof TV, van der Klein SA, Arts JA, Parmentier HK, van der Poel JJ and Bovenhuis H (2015). Genetic and Non-Genetic Inheritance of Natural Antibodies Binding Keyhole Limpet Hemocyanin in a Purebred Layer Chicken Line. PLoS One. doi: 10.1371/journal.pone.0131088
Recommended Set

15. Housing and husbandry

Explanation

The environment determines the health and wellbeing of the animals and every aspect of it can potentially affect their behavioural and physiological responses, thereby affecting research outcomes [1]. Different studies may be sensitive to different environmental factors, and particular aspects of the environment necessary to report may depend on the type of study [2]. Examples of housing and husbandry conditions known to affect animal welfare and research outcomes are listed in the table below; consider reporting these elements and any other housing and husbandry conditions likely to influence the study outcomes.

Examples of information to consider when reporting housing and husbandry, and their effects on laboratory animals

Information to report

Examples of effects on laboratory animals

Cage/tank/housing system (type and dimensions)

Affects behaviour[3] and fear learning [4]. Tank colour affects stress in aquatic species [5,6].

Food and water (type, composition, supplier and access)

Affects body weight, tumour development, nephropathy severity [7], and the threshold for developing Parkinsonian symptoms [8]. Maternal diet affects offspring body weight [9].

Bedding and nesting material

Affects behavioural responses to stress [10] and pain [11].

Temperature and humidity

Modifies tumour progression [12]. Regulates sexual differentiation in zebrafish [13].

Sanitation (frequency of cage/tank water changes, material transferred, water quality)

Affects blood pressure, heart rate, behaviour [14]. Adds an additional source of variation [15,16].

Social environment (group size and composition/stocking density)

Compromises animal welfare [17]. Induces aggressive behaviour [18,19] and stress [6].

Biosecurity (level)

The microbiological status of animals causes variation in systemic disease parameters [20].

Lighting (type, schedule and intensity)

Modifies immune and stress responses [21].

Environmental enrichment

 

Reduces anxiety [22,23], stress [22,23] and abnormal repetitive behaviour [24-26]. Reduces susceptibility to epilepsy [27] and osteoarthritis [28] and modifies the pathology of neurological disorders [29]. Increases foraging behaviour in fish [30].

Sex of the experimenter

Affects physiological stress and pain behaviour [31].

Environment, either deprived or enriched, can affect a wide range of physiological and behavioural responses [32]. Specific details to report include, but are not limited to, structural enrichment (e.g. elevated surfaces, dividers), resources for species-typical activities (e.g. nesting material, shelters or gnawing sticks for rodents; plants or gravel for aquatic species), toys or other tools used to stimulate exploration, exercise (e.g. running wheel), and novelty. If no environmental enrichment was provided, this should be clearly stated with justification. Similarly, scientific justification needs to be reported for withholding food and water [33], and for singly housing animals [34,35].

If space is an issue, relevant housing and husbandry details can be provided in the form of a link to the information in a public repository, or as supplementary information.

 

References

  1. Nevalainen T (2014). Animal husbandry and experimental design. ILAR J. doi: 10.1093/ilar/ilu035
  2. (2014). Guidance for the description of animal research in scientific publications. ILAR J. doi: 10.1093/ilar/ilu070
  3. Bailoo JD, Murphy E, Varholick JA, Novak J, Palme R and Würbel H (2018). Evaluation of the effects of space allowance on measures of animal welfare in laboratory mice. Scientific reports. doi: 10.1038/s41598-017-18493-6
  4. Kallnik M, Elvert R, Ehrhardt N, Kissling D, Mahabir E, Welzl G, Faus-Kessler T, de Angelis MH, Wurst W, Schmidt J and Holter SM (2007). Impact of IVC housing on emotionality and fear learning in male C3HeB/FeJ and C57BL/6J mice. Mamm Genome. doi: 10.1007/s00335-007-9002-z
  5. Holmes AM, Emmans CJ, Jones N, Coleman R, Smith TE and Hosie CA (2016). Impact of tank background on the welfare of the African clawed frog, Xenopus laevis (Daudin). Applied Animal Behaviour Science. doi: 10.1016/j.applanim.2016.09.005
  6. Pavlidis M, Digka N, Theodoridi A, Campo A, Barsakis K, Skouradakis G, Samaras A and Tsalafouta A (2013). Husbandry of zebrafish, Danio rerio, and the cortisol stress response. Zebrafish. doi: 10.1089/zeb.2012.0819
  7. Haseman JK, Ney E, Nyska A and Rao GN (2003). Effect of diet and animal care/housing protocols on body weight, survival, tumor incidences, and nephropathy severity of F344 rats in chronic studies. Toxicol Pathol. doi: 10.1080/01926230390241927
  8. Morris JK, Bomhoff GL, Stanford JA and Geiger PC (2010). Neurodegeneration in an animal model of Parkinson's disease is exacerbated by a high-fat diet. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. doi: 10.1152/ajpregu.00449.2010
  9. Bayol SA, Farrington SJ and Stickland NC (2007). A maternal 'junk food' diet in pregnancy and lactation promotes an exacerbated taste for 'junk food' and a greater propensity for obesity in rat offspring. Br J Nutr. doi: 10.1017/S0007114507812037
  10. Gaskill BN and Garner JP (2017). Stressed out: providing laboratory animals with behavioral control to reduce the physiological effects of stress. Lab Animal. doi: 10.1038/laban.1218
  11. Robinson I, Dowdall T and Meert TF (2004). Development of neuropathic pain is affected by bedding texture in two models of peripheral nerve injury in rats. Neurosci Lett. doi: 10.1016/j.neulet.2004.06.078
  12. Kokolus KM, Capitano ML, Lee CT, Eng JW, Waight JD, Hylander BL, Sexton S, Hong CC, Gordon CJ, Abrams SI and Repasky EA (2013). Baseline tumor growth and immune control in laboratory mice are significantly influenced by subthermoneutral housing temperature. Proceedings of the National Academy of Sciences of the United States of America. doi: 10.1073/pnas.1304291110
  13. Lawrence C (2007). The husbandry of zebrafish (Danio rerio): A review. Aquaculture. doi: https://doi.org/10.1016/j.aquaculture.2007.04.077
  14. Duke JL, Zammit TG and Lawson DM (2001). The effects of routine cage-changing on cardiovascular and behavioral parameters in male Sprague-Dawley rats. Contemp Top Lab Anim Sci. https://www.ncbi.nlm.nih.gov/pubmed/11300670
  15. Prager E, Bergstrom H, Grunberg N and Johnson L (2011). The Importance of Reporting Housing and Husbandry in Rat Research. Front Behav Neurosci. doi: 10.3389/fnbeh.2011.00038
  16. Rosenbaum MD, VandeWoude S and Johnson TE (2009). Effects of Cage-Change Frequency and Bedding Volume on Mice and Their Microenvironment. Journal of the American Association for Laboratory Animal Science : JAALAS. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2786931/
  17. Kappel S, Hawkins P and Mendl MT (2017). To Group or Not to Group? Good Practice for Housing Male Laboratory Mice. Animals : an Open Access Journal from MDPI. doi: 10.3390/ani7120088
  18. Van Loo PLP, Mol JA, Koolhaas JM, Van Zutphen BFM and Baumans V (2001). Modulation of aggression in male mice: influence of group size and cage size. Physiology & behavior. doi: 10.1016/S0031-9384(01)00425-5
  19. Adams CE, Turnbull JF, Bell A, Bron JE and Huntingford FA (2007). Multiple determinants of welfare in farmed fish: stocking density, disturbance, and aggression in Atlantic salmon (Salmo salar). Can J Fish Aquat Sci. doi: 10.1139/F07-018
  20. Bleich A and Hansen AK (2012). Time to include the gut microbiota in the hygienic standardisation of laboratory rodents. Comparative immunology, microbiology and infectious diseases. doi: 10.1016/j.cimid.2011.12.006
  21. Dauchy RT, Dupepe LM, Ooms TG, Dauchy EM, Hill CR, Mao L, Belancio VP, Slakey LM, Hill SM and Blask DE (2011). Eliminating animal facility light-at-night contamination and its effect on circadian regulation of rodent physiology, tumor growth, and metabolism: a challenge in the relocation of a cancer research laboratory. J Am Assoc Lab Anim Sci. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103282/
  22. Chapillon P, Manneché C, Belzung C and Caston J (1999). Rearing Environmental Enrichment in Two Inbred Strains of Mice: 1. Effects on Emotional Reactivity. Behavior Genetics. doi: 10.1023/a:1021437905913
  23. Hendershott TR, Cronin ME, Langella S, McGuinness PS and Basu AC (2016). Effects of environmental enrichment on anxiety-like behavior, sociability, sensory gating, and spatial learning in male and female C57BL/6J mice. Behavioural brain research. doi: 10.1016/j.bbr.2016.08.004
  24. Garner JP (2005). Stereotypies and other abnormal repetitive behaviors: potential impact on validity, reliability, and replicability of scientific outcomes. ILAR journal. doi: 10.1093/ilar.46.2.106
  25. Gross AN-M, Engel AKJ and Würbel H (2011). Simply a nest? Effects of different enrichments on stereotypic and anxiety-related behaviour in mice. Applied Animal Behaviour Science. doi: 10.1016/j.applanim.2011.06.020
  26. Wurbel H (2001). Ideal homes? Housing effects on rodent brain and behaviour. Trends in neurosciences. doi: 10.1016/s0166-2236(00)01718-5
  27. Auvergne R, Déan C, El Bahh B, Arthaud S, lespinet-najib v, Rougier A and Le Gal La Salle G (2002). Delayed kindling epileptogenesis and increased neurogenesis in adult rats housed in an enriched environment. doi: 10.1016/S0006-8993(02)03355-3
  28. Salvarrey-Strati A, Watson L, Blanchet T, Lu N and Glasson SS (2008). The influence of enrichment devices on development of osteoarthritis in a surgically induced murine model. ILAR journal.
  29. Hannan AJ (2014). Environmental enrichment and brain repair: harnessing the therapeutic effects of cognitive stimulation and physical activity to enhance experience-dependent plasticity. Neuropathology and applied neurobiology. doi: 10.1111/nan.12102
  30. Braithwaite VA and Salvanes AGV (2005). Environmental variability in the early rearing environment generates behaviourally flexible cod: implications for rehabilitating wild populations. P Roy Soc B-Biol Sci. doi: 10.1098/rspb.2005.3062
  31. Sorge RE, Martin LJ, Isbester KA, Sotocinal SG, Rosen S, Tuttle AH, Wieskopf JS, Acland EL, Dokova A, Kadoura B, Leger P, Mapplebeck JCS, McPhail M, Delaney A, Wigerblad G, Schumann AP, Quinn T, Frasnelli J, Svensson CI, Sternberg WF and Mogil JS (2014). Olfactory exposure to males, including men, causes stress and related analgesia in rodents. Nature Methods. doi: 10.1038/nmeth.2935
  32. Kotloski RJ and Sutula TP (2015). Environmental enrichment: evidence for an unexpected therapeutic influence. Experimental neurology. doi: 10.1016/j.expneurol.2014.11.012
  33. Jensen TL, Kiersgaard MK, Sorensen DB and Mikkelsen LF (2013). Fasting of mice: a review. Lab Anim. doi: 10.1177/0023677213501659
  34. National Research Council (2011). Guide for the Care and Use of Laboratory Animals. 8th Edition. The National Academies Press. doi: 10.17226/12910
  35. European Commission (2010). Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes. Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32010L0063

Examples

Example 1

“Breeding colonies were kept in individually ventilated cages (IVCs; Tecniplast, Italy) at a temperature of 20°C to 24°C, humidity of 50% to 60%, 60 air exchanges per hour in the cages, and a 12/12-hour light/dark cycle with the lights on at 5:30 AM. The maximum caging density was five mice from the same litter and sex starting from weaning. As bedding, spruce wood shavings (Lignocel FS-14; J. Rettenmaier und Soehne GmbH, Rosenberg, Germany) were provided. Mice were fed a standardized mouse diet (1314, Altromin, Germany) and provided drinking water ad libitum. All materials, including IVCs, lids, feeders, bottles, bedding, and water were autoclaved before use. Sentinel mice were negative for at least all Federation of laboratory animal science associations (FELASA)-relevant murine infectious agents…as diagnosed by our health monitoring laboratory, mfd Diagnostics GmbH, Wendelsheim, Germany.” [1]

Example 2

“Same sex litter mates were housed together in individually ventilated cages with two or four mice per cage. All mice were maintained on a regular diurnal lighting cycle (12:12 light:dark) with ad libitum access to food (7012 Harlan Teklad LM-485 Mouse/Rat Sterilizable Diet) and water. Chopped corn cob was used as bedding. Environmental enrichment included nesting material (Nestlets, Ancare, Bellmore, NY, USA), PVC pipe, and shelter (Refuge XKA-2450-087, Ketchum Manufacturing Inc., Brockville, Ontario, Canada). Mice were housed under broken barrier-specific pathogen-free conditions in the Transgenic Mouse Core Facility of Cornell University, accredited by AAALAC (The Association for Assessment and Accreditation of Laboratory Animal Care International).” [2]

 

References

  1. Heykants M and Mahabir E (2016). Estrous cycle staging before mating led to increased efficiency in the production of pseudopregnant recipients without negatively affecting embryo transfer in mice. Theriogenology. doi: 10.1016/j.theriogenology.2015.10.027
  2. Gallastegui A, Cheung J, Southard T and Hume KR (2018). Volumetric and linear measurements of lung tumor burden from non-gated micro-CT imaging correlate with histological analysis in a genetically engineered mouse model of non-small cell lung cancer. Lab Anim. doi: 10.1177/0023677218756457
Recommended Set

16. Animal care and monitoring

Explanation

A safe and effective analgesic plan is critical to relieve pain, suffering and distress. Untreated pain can affect the animals’ biology and add variability to the experiment; however specific pain management procedures can also introduce variability, affecting experimental data [1,2]. Under-reporting of welfare management procedures contributes to the perpetuation of non-compliant methodologies and insufficient or inappropriate use of analgesia [2] or other welfare measures. A thorough description of the procedures used to alleviate pain, suffering and distress provides practical information for researchers to replicate the method.

Clearly describe pain management strategies, including:

  • specific analgesic
  • administration method (e.g. formulation, route, dose, concentration, volume, frequency, timing, and equipment used)
  • rationale for the choice (e.g. animal model, disease/pathology, procedure, mechanism of action, pharmacokinetics, personnel safety)
  • protocol modifications to reduce pain, suffering and distress (e.g. changes to the anaesthetic protocol, increased frequency of monitoring, procedural modifications, habituation, etc.)

If analgesics or other welfare measures, reasonably expected for the procedure performed, are not performed for experimental reasons, report the scientific justification [3].

 

References

  1. Jirkof P (2017). Side effects of pain and analgesia in animal experimentation. Lab Anim (NY). doi: 10.1038/laban.1216
  2. Carbone L and Austin J (2016). Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare. PLoS One. doi: 10.1371/journal.pone.0155001
  3. Gaspani L, Bianchi M, Limiroli E, Panerai AE and Sacerdote P (2002). The analgesic drug tramadol prevents the effect of surgery on natural killer cell activity and metastatic colonization in rats. Journal of neuroimmunology. doi: 10.1016/s0165-5728(02)00165-0

 

 

Examples

Example 1

“If piglets developed diarrhea, they were placed on an electrolyte solution and provided supplemental water, and if the diarrhea did not resolve within 48 h, piglets received a single dose of ceftiofur (5.0 mg ceftiofur equivalent/kg of body weight i.m [Excede, Zoetis, Florham Park, NJ]). If fluid loss continued after treatment, piglets then received a single dose of sulfamethoxazole and trimethoprim oral suspension (50 mg/8 mg per mL, Hi-Tech Pharmacal, Amityville, NY) for 3 consecutive days.” [1]

Example 2

“One hour before surgery, we administered analgesia to the mice by offering them nut paste (Nutella; Ferrero, Pino Torinese, Italy) containing 1 mg per kg body weight buprenorphine (Temgesic; Schering-Plough Europe, Brussels, Belgium) for voluntary ingestion, as described previously. The mice had been habituated to pure nut paste for 2 d prior to surgery.” [2]

Example 3

“If a GCPS score equal or greater than 6 (out of 24) was assigned postoperatively, additional analgesia was provided with methadone 0.1 mg kg−1 IM (or IV if required)…and pain reassessed 30 minutes later. The number of methadone doses was recorded.” [3]

 

References

  1. Getty CM and Dilger RN (2015). Moderate Perinatal Choline Deficiency Elicits Altered Physiology and Metabolomic Profiles in the Piglet. PLoS One. doi: 10.1371/journal.pone.0133500
  2. Teilmann AC, Falkenberg MK, Hau J and Abelson KS (2014). Comparison of silicone and polyurethane catheters for the catheterization of small vessels in mice. Lab Anim (NY). doi: 10.1038/laban.570
  3. Bustamante R, Daza MA, Canfrán S, García P, Suárez M, Trobo I and Gómez de Segura IA (2018). Comparison of the postoperative analgesic effects of cimicoxib, buprenorphine and their combination in healthy dogs undergoing ovariohysterectomy. Veterinary Anaesthesia and Analgesia. doi: 10.1016/j.vaa.2018.01.003

 

Explanation

Reporting adverse events allows other researchers to plan appropriate welfare assessments and minimise the risk of these events occurring in their own studies. If the experiment is testing the efficacy of a treatment, the occurrence of adverse events may alter the balance between treatment benefit and risk [1].

Report any adverse events that had a negative impact on the welfare of the animals in the study (e.g. cardiovascular and respiratory depression, CNS disturbance, hypothermia, reduction of food intake). Indicate whether they were expected or unexpected. If adverse events were not observed, or not recorded during the study, explicitly state this.

 

References

  1. Muhlhausler BS, Bloomfield FH and Gillman MW (2013). Whole Animal Experiments Should Be More Like Human Randomized Controlled Trials. PLoS Biol. doi: 10.1371/journal.pbio.1001481

Examples

Example 1

“Murine lymph node tumors arose in 11 of 12 mice that received N2-transduced human cells. The neo gene could be detected in murine cells as well as in human cells. Significant lymphoproliferation could be seen only in the murine pre-T cells. It took 5 months for murine leukemia to arise; the affected mice displayed symptoms of extreme sickness rapidly, with 5 of the 12 mice becoming moribund on exactly the same day (Figure…), and 6 others becoming moribund within a 1-month period…Of the 12 mice that had received N2-transduced human cells, 11 had to be killed because they developed visibly enlarged lymph nodes and spleen, hunching, and decrease in body weight, as shown in Figure…The 12th mouse was observed carefully for 14 months; it did not show any signs of leukemia or other adverse events, and had no abnormal tissues when it was autopsied…The mice were observed at least once daily for signs of illness, which were defined as any one or more of the following: weight loss, hunching, lethargy, rapid breathing, skin discoloration or irregularities, bloating, hemi-paresis, visibly enlarged lymph nodes, and visible solid tumors under the skin. Any signs of illness were logged as “adverse events” in the experiment, the mouse was immediately killed, and an autopsy was performed to establish the cause of illness.” [1]

Example 2

“Although procedures were based on those reported in the literature, dogs under Protocol 1 displayed high levels of stress and many experienced vomiting. This led us to significantly alter procedures in order to optimize the protocol for the purposes of our own fasting and postprandial metabolic studies.” [2]

 

References

  1. Bauer G, Dao MA, Case SS, Meyerrose T, Wirthlin L, Zhou P, Wang X, Herrbrich P, Arevalo J, Csik S, Skelton DC, Walker J, Pepper K, Kohn DB and Nolta JA (2008). In vivo biosafety model to assess the risk of adverse events from retroviral and lentiviral vectors. Mol Ther. doi: 10.1038/mt.2008.93
  2. Bellanger S, Benrezzak O, Battista MC, Naimi F, Labbe SM, Frisch F, Normand-Lauziere F, Gallo-Payet N, Carpentier AC and Baillargeon JP (2015). Experimental dog model for assessment of fasting and postprandial fatty acid metabolism: pitfalls and feasibility. Lab Anim. doi: 10.1177/0023677214566021

 

Explanation

Humane endpoints are predetermined morphological, physiological and/or behavioural signs that define the circumstances under which an animal will be removed from an experimental study. The use of humane endpoints can help minimise harm while allowing the scientific objectives to be achieved [1]. Report the humane endpoints that were established for the specific study, species and strain. Include clear criteria of the clinical signs monitored [2], and clinical signs that led to euthanasia or other defined actions. Include details such as general welfare indicators (e.g. weight loss, reduced food intake, abnormal posture) and procedure-specific welfare indicators (e.g. tumour size in cancer studies [3], sensory motor deficits in stroke studies [4]).

Report the timing and frequency of monitoring, taking into consideration the normal circadian rhythm of the animal and timing of scientific procedures, as well as any increase in the frequency of monitoring (e.g. post-surgery recovery, critical times during disease studies, or following the observation of an adverse event). Publishing score sheets of the clinical signs that were monitored [5] can help guide other researchers to develop clinically relevant welfare assessments, particularly for studies reporting novel procedures.

This information should be reported even if no animal reached any of the humane endpoints. If no humane endpoints were established for the study, explicitly state this.

 

References

  1. Hendriksen C, Morton D and Cussler K (2011). Use of humane endpoints to minimise suffering. From: The COST manual of animal care and use. CRC Press, Florida, USA. https://www.cost.eu/publications/the-cost-manual-of-laboratory-animal-care-and-use-refinement-reduction-and-research/   
  2. Hawkins P, Morton DB, Burman O, Dennison N, Honess P, Jennings M, Lane S, Middleton V, Roughan JV, Wells S and Westwood K (2011). A guide to defining and implementing protocols for the welfare assessment of laboratory animals: eleventh report of the BVAAWF/FRAME/RSPCA/UFAW Joint Working Group on Refinement. Laboratory Animals. doi: 10.1258/la.2010.010031
  3. Workman P, Aboagye EO, Balkwill F, Balmain A, Bruder G, Chaplin DJ, Double JA, Everitt J, Farningham DAH, Glennie MJ, Kelland LR, Robinson V, Stratford IJ, Tozer GM, Watson S, Wedge SR, Eccles SA and An ad hoc committee of the National Cancer Research I (2010). Guidelines for the welfare and use of animals in cancer research. British Journal Of Cancer. doi: 10.1038/sj.bjc.6605642
  4. Percie du Sert N, Alfieri A, Allan SM, Carswell HV, Deuchar GA, Farr TD, Flecknell P, Gallagher L, Gibson CL, Haley MJ, Macleod MR, McColl BW, McCabe C, Morancho A, Moon LD, O'Neill MJ, Perez de Puig I, Planas A, Ragan CI, Rosell A, Roy LA, Ryder KO, Simats A, Sena ES, Sutherland BA, Tricklebank MD, Trueman RC, Whitfield L, Wong R and Macrae IM (2017). The IMPROVE Guidelines (Ischaemia Models: Procedural Refinements Of in Vivo Experiments). J Cereb Blood Flow Metab. doi: 10.1177/0271678X17709185
  5. Morton DB (2000). A systematic approach for establishing humane endpoints. ILAR Journal. doi: 10.1093/ilar.41.2.80

 

Examples

Example 1

“Both the research team and the veterinary staff monitored animals twice daily. Health was monitored by weight (twice weekly), food and water intake, and general assessment of animal activity, panting, and fur condition…The maximum size the tumors allowed to grow in the mice before euthanasia was 2000 mm3.” [1]

 

References

  1. Muscella A, Vetrugno C, Cossa LG, Antonaci G, De Nuccio F, De Pascali SA, Fanizzi FP and Marsigliante S (2016). In Vitro and In Vivo Antitumor Activity of [Pt(O,O'-acac)(gamma-acac)(DMS)] in Malignant Pleural Mesothelioma. PLoS One. doi: 10.1371/journal.pone.0165154
Recommended Set

17. Interpretation/ scientific implications

Explanation

It is important to interpret the results of the study in the context of the study objectives (see item 13 – Objectives). For hypothesis-testing studies, interpretations should be restricted to the primary outcome (see item 6 – Outcome measures). Exploratory results derived from additional outcomes should not be described as conclusive, as they may be underpowered and less reliable.

Discuss the findings in the context of current theory, ideally with reference to a relevant systematic review, as individual studies do not provide a complete picture. If a systematic review is not available, take care to avoid selectively citing studies that corroborate the results, or only those that report statistically significant findings [1].

Where appropriate, describe any implications of the experimental methods or research findings for improving welfare standards or reducing the number of animals used in future studies (e.g. the use of a novel approach reduced the results’ variability, thus enabling the use of smaller group sizes without losing statistical power). This may not be the primary focus of the research but reporting this information enables wider dissemination and uptake of refined techniques within the scientific community.

 

References

  1. Glasziou P, Altman DG, Bossuyt P, Boutron I, Clarke M, Julious S, Michie S, Moher D and Wager E (2014). Reducing waste from incomplete or unusable reports of biomedical research. Lancet (London, England). doi: 10.1016/s0140-6736(13)62228-x

Examples

Example 1

“This is in contrast to data provided by an ‘intra-renal IL-18 overexpression’ model (43), and may reflect an IL-18 concentration exceeding the physiologic range in the latter study.” [1]

Example 2

“The new apparatus shows potential for considerably reducing the number of animals used in memory tasks designed to detect potential amnesic properties of new drugs...approximately 43,000 animals have been used in these tasks in the past 5 years but with the application of the continual trials apparatus we estimate that this could have been reduced to 26,000…with the new paradigm the number of animals needed to obtain reliable results and maintain the statistical power of the tasks is greatly reduced.” [2]

Example 3

“In summary, our results show that IL-1Ra protects against brain injury and reduces neuroinflammation when administered peripherally to aged and comorbid animals at reperfusion or 3 hours later. These findings address concerns raised in a recent systematic review on IL-1Ra in stroke…and provide further supporting evidence for IL-1Ra as a lead candidate for the treatment of ischemic stroke.” [3]

 

References

  1. Schirmer B, Wedekind D, Glage S and Neumann D (2015). Deletion of IL-18 Expression Ameliorates Spontaneous Kidney Failure in MRLlpr Mice. PLOS ONE. doi: 10.1371/journal.pone.0140173
  2. Ameen-Ali KE, Eacott MJ and Easton A (2012). A new behavioural apparatus to reduce animal numbers in multiple types of spontaneous object recognition paradigms in rats. J Neurosci Methods. doi: 10.1016/j.jneumeth.2012.08.006
  3. Pradillo JM, Denes A, Greenhalgh AD, Boutin H, Drake C, McColl BW, Barton E, Proctor SD, Russell JC, Rothwell NJ and Allan SM (2012). Delayed Administration of Interleukin-1 Receptor Antagonist Reduces Ischemic Brain Damage and Inflammation in Comorbid Rats. Journal of Cerebral Blood Flow & Metabolism. doi: 10.1038/jcbfm.2012.101

Explanation

Discussing the limitations of the work is important to place the findings in context, interpret the validity of the results, and ascribe a credibility level to its conclusions [1]. Limitations are unavoidable in scientific research, and describing them is essential to share experience, guide best practice, and aid the design of future experiments [2].

Discuss the quality of evidence presented in the study, and consider how appropriate the animal model is to the specific research question. A discussion on the rigour of the study design to isolate cause and effect (also known as internal validity [3]) should include whether potential risks of bias have been addressed [4] (see item 2 – Sample size, item 3 – Inclusion and exclusion criteria, item 4 – Randomisation and item 5 – blinding).

 

References

  1. Ioannidis JP (2007). Limitations are not properly acknowledged in the scientific literature. J. Clin. Epidemiol. doi: 10.1016/j.jclinepi.2006.09.011
  2. Puhan MA, Akl EA, Bryant D, Xie F, Apolone G and ter Riet G (2012). Discussing study limitations in reports of biomedical studies- the need for more transparency. Health Qual Life Outcomes. doi: 10.1186/1477-7525-10-23
  3. Wieschowski S, Chin WWL, Federico C, Sievers S, Kimmelman J and Strech D (2018). Preclinical efficacy studies in investigator brochures: Do they enable risk–benefit assessment? PLOS Biology. doi: 10.1371/journal.pbio.2004879
  4. Reichlin TS, Vogt L and Würbel H (2016). The Researchers’ View of Scientific Rigor—Survey on the Conduct and Reporting of In Vivo Research. PLoS ONE. doi: 10.1371/journal.pone.0165999

 

Examples

Example 1

“Although in this study we did not sample the source herds, the likelihood of these herds to be Influenza A virus (IAV) positive is high given the commonality of IAV infections in the Midwest. However, we cannot fully rule out the possibility that new gilts became infected with resident viruses after arrival to the herd. Although new gilts were placed into isolated designated areas and procedures were in place to minimize disease transmission (eg. isolation, vaccination), these areas or procedures might not have been able to fully contain infections within the designated areas.” [1]

Example 2

“Even though our data demonstrates that sustained systemic TLR9 stimulation aggravates diastolic HF in our model of gene-targeted diastolic HF, there are several limitations as to mechanistic explanations of causality, as well as extrapolations to clinical inflammatory disease states and other HF conditions. First, our pharmacological inflammatory model does not allow discrimination between effects caused by direct cardiac TLR9 stimulation to that of indirect effects mediated by systemic inflammation. Second, although several systemic inflammatory conditions have disturbances in the innate immune system as important features, and some of these again specifically encompassing distorted TLR9 signalling…sustained TLR9 stimulation does not necessarily represent a clinically relevant inflammatory condition. Finally, the cardiac myocyte SERCA2a KO model does not adequately represent the molecular basis for, or the clinical features of, diastolic HF.” [2]

 

References

  1. Diaz A, Perez A, Sreevatsan S, Davies P, Culhane M and Torremorell M (2015). Association between Influenza A Virus Infection and Pigs Subpopulations in Endemically Infected Breeding Herds. PLOS ONE. doi: 10.1371/journal.pone.0129213
  2. Dhondup Y, Sjaastad I, Scott H, Sandanger Ø, Zhang L, Haugstad SB, Aronsen JM, Ranheim T, Holmen SD, Alfsnes K, Ahmed MS, Attramadal H, Gullestad L, Aukrust P, Christensen G, Yndestad A and Vinge LE (2015). Sustained Toll-Like Receptor 9 Activation Promotes Systemic and Cardiac Inflammation, and Aggravates Diastolic Heart Failure in SERCA2a KO Mice. PLOS ONE. doi: 10.1371/journal.pone.0139715
Recommended Set

18. Generalisability/ translation

Explanation

An important purpose of publishing research findings is to inform future research. In the context of animal studies, this might take the form of further in vivo research or another research domain (e.g. human clinical trial). Thoughtful consideration is warranted, as additional unnecessary animal studies are wasteful and unethical. Similarly, human clinical trials initiated based on insufficient or misleading animal research evidence increase research waste and negatively influence the risk-benefit balance for research participants [1,2].

Consider the type of study conducted to assess the implication of the findings. Well-designed hypothesis-testing studies provide more robust evidence than exploratory studies (see item 13 – Objectives). Findings from a novel, exploratory study may be used to inform future research in a broadly similar context. Alternatively, enough evidence may have accumulated in the literature to justify further research in another species or in humans. Discuss what (if any) further research may be required to allow generalisation or translation. Discuss and interpret the results in relation to current evidence, and in particular whether similar [3] or otherwise supportive [4] findings have been reported by other groups. Discuss the range of circumstances in which the effect is observed, and factors which may moderate that effect. Such factors could include for example the population (e.g. age, sex, strain, species), the intervention (e.g. different drugs of the same class), and the outcome measured (e.g. different approaches to assessing memory). 

 

References

  1. Chalmers I, Bracken MB, Djulbegovic B, Garattini S, Grant J, Gülmezoglu AM, Howells DW, Ioannidis JPA and Oliver S (2014). How to increase value and reduce waste when research priorities are set. The Lancet. doi: 10.1016/S0140-6736(13)62229-1
  2. Wieschowski S, Chin WWL, Federico C, Sievers S, Kimmelman J and Strech D (2018). Preclinical efficacy studies in investigator brochures: Do they enable risk–benefit assessment? PLOS Biology. doi: 10.1371/journal.pbio.2004879
  3. Voelkl B, Vogt L, Sena ES and Würbel H (2018). Reproducibility of preclinical animal research improves with heterogeneity of study samples. PLOS Biology. doi: 10.1371/journal.pbio.2003693
  4. Munafò MR and Davey Smith G (2018). Robust research needs many lines of evidence. Nature. doi: 10.1038/d41586-018-01023-3

 

Examples

Example 1

“Our results demonstrate that hDBS robustly modulates the mesolimbic network. This finding may hold clinical relevance for hippocampal DBS therapy in epilepsy cases, as connectivity in this network has previously been shown to be suppressed in mTLE. Further research is necessary to investigate potential DBS-induced restoration of MTLE-induced loss of functional connectivity in mesolimbic brain structures.” [1]

Example 2

“The tumor suppressor effects of GAS1 had been previously reported in cell cultures or in xenograft models, this is the first work in which the suppressor activity of murine Gas1 is reported for primary tumors in vivo. Recent advances in the design of safe vectors for transgene delivery may result in extrapolating our results to humans and so a promising field of research emerges in the area of hepatic, neoplastic diseases.” [2]

 

References

  1. Van Den Berge N, Vanhove C, Descamps B, Dauwe I, van Mierlo P, Vonck K, Keereman V, Raedt R, Boon P and Van Holen R (2015). Functional MRI during Hippocampal Deep Brain Stimulation in the Healthy Rat Brain. PLOS ONE. doi: 10.1371/journal.pone.0133245
  2. Sacilotto N, Castillo J, Riffo-Campos ÁL, Flores JM, Hibbitt O, Wade-Martins R, López C, Rodrigo MI, Franco L and López-Rodas G (2015). Growth Arrest Specific 1 (Gas1) Gene Overexpression in Liver Reduces the In Vivo Progression of Murine Hepatocellular Carcinoma and Partially Restores Gene Expression Levels. PLOS ONE. doi: 10.1371/journal.pone.0132477

 

Recommended Set

19. Protocol registration

Explanation

Akin to the approach taken for clinical trials, protocol registration has emerged as a mechanism that is likely to improve the transparency of animal research [1-3]. Registering a protocol before the start of the experiment enables researchers to demonstrate that the hypothesis, approach and analysis were planned in advance and not shaped by data as they emerged; it enhances scientific rigour and protects the researcher against concerns about selective reporting of results [4,5]. A protocol should consist of a) the question being addressed and the key features of the research that is proposed, such as the hypothesis being tested and the primary outcome measure (if applicable), the statistical analysis plan; and b) the laboratory procedures to be used to perform the planned experiment.

Protocols may be registered with different levels of completeness. For example, in the Registered Report format offered by an increasing number of journals, protocols undergo peer review and if accepted, the journal commits to publishing the completed research regardless of the results obtained [1].

Other online resources include the Open Science Framework [6], which is suitable to deposit PHISPS (Population; Hypothesis; Intervention; Statistical Analysis Plan; Primary; Outcome Measure; Sample Size Calculation) protocols [7] and provide researchers with the flexibility to embargo the preregistration and keep it from public view until the research is published, and selectively share it with reviewers and editors. The EDA can also be used to generate a time-stamped PDF, which sets out key elements of the experimental design [8]. This can be used to demonstrate that the study conduct, analysis and reporting were not unduly driven by emerging data. As a minimum we recommend registering protocols containing all PHISPS components as outlined above.

Provide a statement indicating whether or not any protocol was prepared before the study, and if applicable, provide the time-stamped protocol or the location of its registration. Where there have been deviations from the protocol, describe the rationale for these changes in the publication so that readers can take this into account when assessing the findings.

 

References

  1. Chambers CD, Feredoes E, Muthukumaraswamy SD and Etchells PJ (2014). Instead of "playing the game" it is time to change the rules: Registered Reports at AIMS Neuroscience and beyond. AIMS Neuroscience. doi: 10.3934/Neuroscience2014.1.4
  2. Chalmers I, Bracken MB, Djulbegovic B, Garattini S, Grant J, Gülmezoglu AM, Howells DW, Ioannidis JPA and Oliver S (2014). How to increase value and reduce waste when research priorities are set. The Lancet. doi: 10.1016/S0140-6736(13)62229-1
  3. Nosek BA and Lakens D (2014). Registered Reports a method to increase the credibility of published results. Soc Psychol-Germany. doi: 10.1027/1864-9335/a000192
  4. Kaplan RM and Irvin VL (2015). Likelihood of Null Effects of Large NHLBI Clinical Trials Has Increased over Time. Plos One. doi: 10.1371/journal.pone.0132382 
  5. Allen C and Mehler D (2018). Open Science challenges, benefits and tips in early career and beyond. PsyArXiv. doi: 10.31234/osf.io/3czyt
  6. Nosek BA, Ebersole CR, DeHaven AC and Mellor DT (2018). The preregistration revolution. Proceedings of the National Academy of Sciences of the United States of America. doi: 10.1073/pnas.1708274114
  7. Macleod M and Howells D (2016). Protocols for laboratory research. Evidence-based Preclinical Medicine. doi: doi:10.1002/ebm2.21
  8. Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, Fry D, Karp NA, Macleod M, Moon L, Stanford SC and Lings B (2017). The Experimental Design Assistant. PLoS Biol. doi: 10.1371/journal.pbio.2003779

Examples

Example 1

“A detailed description of all protocols can be found in the Registered Report (Kandela et al., 2015). Additional detailed experimental notes, data, and analysis are available on the Open Science Framework (OSF) (RRID: SCR_003238) (https://osf.io/xu1g2/…).” [1]

Example 2

“To maximise the objectivity of the presented analyses, we preregistered this study with its two hypotheses, its planned methods, and its complete plan of data analysis before the start of data collection (https://osf.io/fh8eq/), and we closely adhered to our plan…All statistical analyses closely followed our preregistered analysis plan (https://osf.io/fh8eq/).” [2]

Example 3

“We preregistered our analyses with the Open Science Framework which facilitates reproducibility and open collaboration in science research…Our preregistration: Sheldon and Griffith (2017), was carried out to limit the number of analyses conducted and to validate our commitment to testing a limited number of a priori hypotheses. Our methods are consistent with this preregistration…” [3]

 

References

  1. Mantis C, Kandela I, Aird F and Reproducibility Project: Cancer B (2017). Replication Study: Coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs. eLife. doi: 10.7554/eLife.17584
  2. Jeronimo S, Khadraoui M, Wang DP, Martin K, Lesku JA, Robert KA, Schlicht E, Forstmeier W and Kempenaers B (2018). Plumage color manipulation has no effect on social dominance or fitness in zebra finches. Behavioral Ecology. doi: 10.1093/beheco/arx195
  3. Sheldon EL and Griffith SC (2018). Embryonic heart rate predicts prenatal development rate, but is not related to post-natal growth rate or activity level in the zebra finch (Taeniopygia guttata). Ethology. doi: 10.1111/eth.12817
Recommended Set

20. Data access

Explanation

A data sharing statement describes how others can access the data on which the paper is based. Sharing adequately annotated data allows others to replicate data analyses, so that results can be independently tested and verified. Data sharing allows the data to be repurposed and new datasets to be created by combining data from multiple studies (e.g. to be used in secondary analyses). This allows others to explore new topics and increases the impact of the study, potentially preventing unnecessary use of animals and providing more value for money. Access to raw data also facilitates text and automated data mining [1].

An increasing number of publishers and funding bodies require authors or grant holders to make their data publicly available [2]. Journal articles with accompanying data may be cited more frequently [3,4]. Datasets can also be independently cited in their own right, which provides additional credit for authors. This practice is gaining increasing recognition and acceptance [5]. 

Where possible, make available all data that contribute to summary estimates or claims presented in the paper. Data should follow the FAIR guiding principles [6], that is data are findable, accessible (i.e. do not use outdated file types), interoperable (can be used on multiple platforms and with multiple software packages) and re-usable (i.e. have adequate data descriptors).

Data can be made publicly available via a structured, specialised (domain-specific), open access repository such as those maintained by NCBI (National Center for Biotechnology Information, https://www.ncbi.nlm.nih.gov/) or EBI (European Bioinformatics Institute, https://www.ebi.ac.uk/). If such a repository is not available, data can be deposited in unstructured but publicly available repositories (e.g. Figshare (https://figshare.com/), Dryad (https://datadryad.org/), Zenodo (https://zenodo.org/) or Open Science Framework (https://osf.io/)). There are also search platforms to identify relevant repositories with rigorous standards, e.g. FairSharing (https://fairsharing.org/) and re3data (https://www.re3data.org/).

 

References

  1. Kafkafi N, Mayo CL and Elmer GI (2014). Mining mouse behavior for patterns predicting psychiatric drug classification. Psychopharmacology (Berl). doi: 10.1007/s00213-013-3230-6
  2. Stodden V, Guo P and Ma Z (2013). Toward reproducible computational research: an empirical analysis of data and code policy adoption by journals. PLOS ONE. doi: 10.1371/journal.pone.0067111
  3. Piwowar HA, Day RS and Fridsma DB (2007). Sharing detailed research data is associated with increased citation rate. PLoS ONE. doi: 10.1371/journal.pone.0000308
  4. DataCitationSynthesisGroup Joint declaration of data citation principles. (Access Date: 22 May). Available at: https://doi.org/10.25490/a97f-egyk
  5. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, ’t Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. doi: 10.1038/sdata.2016.18

 

Examples

Example 1

“Data Availability Statement: All data are available from Figshare at doi: 10.6084/m9.figshare.1288935.” [1]

Example 2

“A fundamental goal in generating this dataset is to facilitate access to spiny mouse transcript sequence information for external collaborators and researchers. The sequence reads and metadata are available from the NCBI (PRJNA342864) and assembled transcriptomes (Trinity_v2.3.2 and tr2aacds_v2) are available from the Zenodo repository (https://doi.org/10.5281/zenodo.808870), however accessing and utilizing this data can be challenging for researchers lacking bioinformatics expertise. To address this problem we are hosting a SequenceServer32 BLAST-search website (http://spinymouse.erc.monash.edu/sequenceserver/http://spinymouse.erc.monash.edu/sequenceserver/). This resource provides a user-friendly interface to access sequence information from the tr2aacds_v2 assembly (to explore annotated protein-coding transcripts) and/or the Trinity_v2.3.2 assembly (to explore non-coding transcripts).” [2]

 

References

  1. Federer LM, Lu Y-L, Joubert DJ, Welsh J and Brandys B (2015). Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff. PLoS ONE. doi: 10.1371/journal.pone.0129506
  2. Mamrot J, Legaie R, Ellery SJ, Wilson T, Seemann T, Powell DR, Gardner DK, Walker DW, Temple-Smith P, Papenfuss AT and Dickinson H (2017). De novo transcriptome assembly for the spiny mouse (Acomys cahirinus). Scientific reports. doi: 10.1038/s41598-017-09334-7

 

Recommended Set

21. Declaration of interests

Explanation

A competing or conflict of interest is anything that interferes with (or could be perceived as interfering with) the full and objective presentation, analysis, and interpretation of the research. Competing or conflicts of interest can be financial or non-financial, professional or personal. They can exist in institutions, in teams, or with individuals. Potential competing interests are considered in peer review, editorial and publication decisions; the aim is to ensure transparency, and in most cases, a declaration of a conflict of interest does not obstruct the publication or review process.

Examples are provided below. If unsure, declare all potential conflicts, including both perceived and real conflicts of interest [1].

Examples of competing or conflicts of interest

Financial:

Funding and other payments received or expected by the authors directly arising from the publication of the study, or funding or other payments from an organisation with an interest in the outcome of the work.

Non-financial:

Research that may benefit the individual or institution in terms of goods in kind. This includes unpaid advisory position in a government, non-government organisation or commercial organisations.

Affiliations:

Employed by, on the advisory board or a member of an organisation with an interest in the outcome of the work.

Intellectual property:

Patents or trademarks owned by someone or their organisation. This also includes the potential exploitation of the scientific advance being reported for the institution, the authors, or the research funders.

Personal:

Friends, family, relationships, and other close personal connections to people who may potentially benefit financially or in other ways from the research.

Ideology:

Beliefs or activism (e.g. political or religious) relevant to the work. Membership of a relevant advocacy or lobbying organisation.

References

  1. Bero L, Anglemyer A, Vesterinen H and Krauth D (2016). The relationship between study sponsorship, risks of bias, and research outcomes in atrazine exposure studies conducted in non-human animals: Systematic review and meta-analysis. Environment International. doi: 10.1016/j.envint.2015.10.011

 

Examples

Example 1

“The study was funded by Gubra ApS. LSD; PJP ; GH ; KF and HBH are employed by Gubra ApS. JJ and NV are the owners of Gubra ApS. Gubra ApS provided support in the form of materials and salaries for authors LSD ; PJP ; GH ; KF ; HBH ; JJ and NV.” [1]

Example 2

“The authors have declared that no competing interests exist.” [2]

 

References

  1. Dalbøge LS, Pedersen PJ, Hansen G, Fabricius K, Hansen HB, Jelsing J and Vrang N (2015). A Hamster Model of Diet-Induced Obesity for Preclinical Evaluation of Anti-Obesity, Anti-Diabetic and Lipid Modulating Agents. PLOS ONE. doi: 10.1371/journal.pone.0135634
  2. Garcia de la serrana D, Vieira VLA, Andree KB, Darias M, Estévez A, Gisbert E and Johnston IA (2012). Development Temperature Has Persistent Effects on Muscle Growth Responses in Gilthead Sea Bream. PLOS ONE. doi: 10.1371/journal.pone.0051884

Explanation

The identification of funding sources allows the reader to assess any competing interests, and any potential sources of bias. For example, bias, as indicated by a prevalence of more favourable outcomes, has been demonstrated for clinical research funded by industry compared to studies funded by other sources [1-3]. Evidence for preclinical research also indicates that funding sources may influence the interpretation of study outcomes [4,5].

Report the funding information including the financial supporting body(s) and any grant identifier(s). If the study was supported by several sources of funding, list them all, including internal grants. Specify the role of the funder in the design, analysis, reporting and/or or decision to publish. If the research did not receive specific funding but was performed as part of the employment of the authors, name the employer.

 

References

  1. Lundh A, Sismondo S, Lexchin J, Busuioc OA and Bero L (2012). Industry sponsorship and research outcome. Cochrane Database of Systematic Reviews. doi: 10.1002/14651858.MR000033.pub2
  2. Popelut A, Valet F, Fromentin O, Thomas A and Bouchard P (2010). Relationship between Sponsorship and Failure Rate of Dental Implants: A Systematic Approach. PLOS ONE. doi: 10.1371/journal.pone.0010274
  3. Lexchin J, Bero LA, Djulbegovic B and Clark O (2003). Pharmaceutical industry sponsorship and research outcome and quality: systematic review. Bmj. doi: 10.1136/bmj.326.7400.1167
  4. Krauth D, Anglemyer A, Philipps R and Bero L (2014). Nonindustry-Sponsored Preclinical Studies on Statins Yield Greater Efficacy Estimates Than Industry-Sponsored Studies: A Meta-Analysis. PLoS Biology. doi: 10.1371/journal.pbio.1001770
  5. Bero L, Anglemyer A, Vesterinen H and Krauth D (2016). The relationship between study sponsorship, risks of bias, and research outcomes in atrazine exposure studies conducted in non-human animals: Systematic review and meta-analysis. Environment International. doi: 10.1016/j.envint.2015.10.011

Examples

Example 1

“Support was provided by the Italian Ministry of Health: Current research funds PRC 2010/001 [http://www.salute.gov.it/] to MG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” [1]

Example 2

“This study was financially supported by the Tuberculosis and Lung Research Center of Tabriz University of Medical Sciences and the Research Council of University of Tabriz. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” [2]

Example 3

“This work was supported by the salary paid to AEW. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” [3]

 

References

  1. Genchi M, Prati P, Vicari N, Manfredini A, Sacchi L, Clementi E, Bandi C, Epis S and Fabbi M (2015). Francisella tularensis: No Evidence for Transovarial Transmission in the Tularemia Tick Vectors Dermacentor reticulatus and Ixodes ricinus. PLOS ONE. doi: 10.1371/journal.pone.0133593
  2. Kolahian S, Sadri H, Shahbazfar AA, Amani M, Mazadeh A and Mirani M (2015). The Effects of Leucine, Zinc, and Chromium Supplements on Inflammatory Events of the Respiratory System in Type 2 Diabetic Rats. PLOS ONE. doi: 10.1371/journal.pone.0133374
  3. Eyre-Walker A and Stoletzki N (2013). The Assessment of Science: The Relative Merits of Post-Publication Review, the Impact Factor, and the Number of Citations. PLOS Biology. doi: 10.1371/journal.pbio.1001675

Glossary

Bias: The over- or under-estimation of the true effect of an intervention. Bias is caused by inadequacies in the design, conduct, or analysis of an experiment, resulting in the introduction of error. 

Descriptive and inferential statistics: Descriptive statistics are used to summarise the data. They generally include a measure of central tendency (e.g. mean or median) and a measure of spread (e.g. standard deviation or range). Inferential statistics are used to make generalisations about the population from which the samples are drawn. Hypothesis tests such as ANOVA, Mann-Whitney or t-tests are examples of inferential statistics. 

Effect size: Quantitative measure of differences between groups, or strength of relationships between variables. 

Experimental unit: Biological entity subjected to an intervention independently of all other units, such that it is possible to assign any two experimental units to different treatment groups. Sometimes known as: unit of randomisation. 

External validity: Extent to which the results of a given study enable application or generalisation to other studies, study conditions, animal strains/species, or humans. 

False negative: Statistically non-significant result obtained when the alternative hypothesis is true. In statistics, it is known as the type II error. 

False positive: Statistically significant result obtained when the null hypothesis is true. In statistics, it is known as the type I error. 

Independent variable: Variable that the researcher either manipulates (treatment, condition, time), or is a property of the sample (sex) or a technical feature (batch, cage, sample collection) that can potentially affect the outcome measure. Independent variables can be scientifically interesting, or nuisance variables. Also known as: predictor variable. 

Internal validity: Extent to which the results of a given study can be attributed to the effects of the experimental intervention, rather than some other, unknown factor(s) (e.g. inadequacies in the design, conduct, or analysis of the study introducing bias). 

Nuisance variable: Variables that are not of primary interest but should be considered in the experimental design or the analysis because they may affect the outcome measure and add variability. They become confounders if, in addition, they are correlated with an independent variable of interest, as this introduces bias. Nuisance variables should be considered in the design of the experiment (to prevent them from becoming confounders) and in the analysis (to account for the variability and sometimes to reduce bias). For example, nuisance variables can be used as blocking factors or covariates. 

Null and alternative hypotheses: The null hypothesis (H0) is that there is no effect, such as a difference between groups or an association between variables. The alternative hypothesis (H1) postulates that an effect exists. 

Outcome measure: Any variable recorded during a study to assess the effects of a treatment or experimental intervention. Also known as: dependent variable, response variable. 

Power: For a predefined, biologically meaningful effect size, the probability that the statistical test will detect the effect if it exists (i.e. the null hypothesis is rejected correctly). 

Sample size: Number of experimental units per group, also referred to as n.