Laboratory Animals最新文献

Random treatment assignment is essential in demonstrating a causal relationship between a treatment and the outcome of interest. Randomisation ensures that animals assigned to different treatment groups do not differ from each other systematically, except for the randomly assigned treatment. The randomisation pattern should also dictate the statistical analysis.

Heterogeneity of study samples is ubiquitous in animal experiments. Here, we discuss the different options of how to deal with heterogeneity in the statistical analysis of a single experiment. Specifically, data from different sub-groups (e.g. sex, strain, age cohorts) may be analysed separately, heterogenization factors may be ignored and data pooled for analysis, or heterogenization factors may be included as additional variables in the statistical model. The cost of ignoring a heterogenization factor is an inflated estimate of the variance and a consequent loss of statistical power. Therefore, it is usually preferable to include the heterogenization factor in the statistical model, especially if the heterogenization factor has been introduced intentionally (e.g. using both sexes). If heterogenization factors are included, they can be treated either as fixed factors in an analysis of variance design or sometimes as random effects in mixed effects regression models. Finally, for an appropriate sample size estimation, it is necessary to decide whether to treat heterogenization factors as nuisance variables, or whether the experiment should be powered to be able to detect not only the main effect of the treatment but also interactions between heterogenization factors and the treatment variable.

Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample t-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and p-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.

The purpose of many preclinical studies is to determine whether an experimental intervention affects an outcome through a particular mechanism, but the analytical methods and inferential logic typically used cannot answer this question, leading to erroneous conclusions about causal relationships, which can be highly reproducible. A causal mediation analysis can directly test whether a hypothesised mechanism is partly or completely responsible for a treatment's effect on an outcome. Such an analysis can be easily implemented with modern statistical software. We show how a mediation analysis can distinguish between three different causal relationships that are indistinguishable when using a standard analysis.

Null hypothesis significance testing is a statistical tool commonly employed throughout laboratory animal research. When experimental results are reported, the reproducibility of the results is of utmost importance. Establishing standard, robust, and adequately powered statistical methodology in the analysis of laboratory animal data is critical to ensure reproducible and valid results. Simulation studies are a reliable method for assessing the power of statistical tests, however, biologists may not be familiar with simulation studies for power despite their efficacy and accessibility. Through an example of simulated Harlan Sprague-Dawley (HSD) rat organ weight data, we highlight the importance of conducting power analyses in laboratory animal research. Using simulations to determine statistical power prior to an experiment is a financially and ethically sound way to validate statistical tests and to help ensure reproducibility of findings in line with the 4R principles of animal welfare.

The development of alternative methods for monitoring cardiorespiratory function without restraint or surgical implantation is attracting growing interest for both ethical and scientific reasons. For this purpose, a new non-invasive jacketed telemetry tool consisting in a radio device maintained in a jacket worn by the animal was previously developed to improve cardiorespiratory monitoring. It allows simultaneous monitoring of cardiac activity by surface electrocardiagram, respiratory function by respiratory inductive plethysmography, and locomotor activity by accelerometry. However, this tool has only been validated under conditions of low/intermediate activity levels or in anesthetized animals. This study aimed to evaluate the feasibility of using this system in the challenging conditions of an exertion protocol. Male Wistar rats (n = 10, 8-9 weeks old) were subjected to an incremental treadmill exercise protocol including speed levels from 5 to 40 cm s^-1 separated by 30-s breaks. Heart rate (HR) and minute ventilation (assessed by minute volume; MV) were continuously monitored. At the end of each running level and during the 30-s breaks, HR and MV showed a significant increase compared to resting values. They returned to the baseline within 60 min of post-exercise recovery. Overall, our results demonstrated (i) the ability of the animal to run while wearing the device and (ii) the ability of the device to reliably monitor cardiorespiratory adaptation to treadmill exercise despite significant mechanical disturbances. In conclusion, this study highlights the possibility of non-invasively monitoring cardiorespiratory functional variables that were previously unattainable under conditions of high activity in freely moving animals.

UK Health Security Agency is required to investigate the pathogenesis of emerging or re-emerging infections and to test novel interventions, such as vaccines and therapeutics against these and other diseases, such as tuberculosis and Ebola, that have a significant impact on human health world-wide. Research into the causative agents (mainly BSL 3 and 4) using a wide range of animal species as pre-clinical models brings a number of challenges in terms of effective biocontainment to address human safety whilst optimising delivery of scientific objectives and the welfare of the animals. Here we describe the strategies used for high containment of species that include mice, ferrets, hamsters, rabbits and macaques that have been infected with high consequence pathogens. To ensure relevance of these models we frequently challenge by the aerosol route and monitor the development of disease and protective or therapeutic efficacy by methodologies similar to those used in the clinic. We have devised methods of sampling that can inform on pathogenesis and immune function that include lung lavage and medical imaging such as computed tomography and positron emission tomography-computed tomography. Imaging assists our assessment of progression to disease whilst providing refinement in application of early humane endpoints. We have developed directional flow containment systems that provide quantifiable operator protection whilst allowing group housing and a wide range of enrichment strategies appropriate for each species. Furthermore, we have demonstrated our improvements in animal welfare through use of a software-based Animal Welfare Assessment Grid that was developed with help of NC3Rs funding and enables us to quantify the lifetime experience of animals.