Obtaining sufficient blood volume from mice significantly facilitates experimental research. This study explored the inferior vena cava puncture under continuous cardiac perfusion (IVCP-UCCP) technique and evaluated its efficiency in comparison with conventional cardiac puncture (CP). In an initial dose-finding study, 50 mice were randomly assigned to one of 10 groups with escalating perfusion volume from 0.5 to 4.5 ml in 0.5-ml increments. The minimum perfusion volume was determined to be 2 ml in collecting whole circulating blood. In the next comparison using the conventional method, 40 mice were randomly assigned to one of two groups denoting different blood collection methods: Group 1: CP, Group 2: IVCP-UCCP. The results showed 1) that the cells and undiluted blood volume collected via IVCP-UCCP was over twofold higher than that by CP (p < 0.001), confirmed by the cell counts and hematoxylin-eosin staining of different tissues slides (p < 0.001); 2) the new technique did not alter the cellular composition or viability, which was verified by routine blood tests and flow cytometry (p > 0.05); 3) the blood collected via the novel technique was diluted 2.1 times: the hemato-biochemical indicator results multiplied by 2.1 were identical with the test results of blood from CP (p > 0.05). Together, the refined blood collection method of IVCP-UCCP completely extracted the limited blood resources in mice, significantly enhanced the utilization of each mouse, and thus offered scientific and ethical benefits. This technique may be also applicable for other small animal models.

The strategy of centralizing equipment sanitation and processing was developed by a top-tier public university to address the growing physical infrastructure and human resource challenges of its expanding in-vivo research enterprise. Subsequently, a center for automated equipment processing physically separate from all animal research facilities was developed and has operated consistently since 2016. The facility incorporates systems such as process automation to sanitize and sterilize equipment as efficiently as possible. Analysis of the differences between the new centralized and old distributed research enterprise configurations shows a total estimated fiscal benefit of almost US$54 m to date projected out to US$124 m through 2028. Utility consumption of operations over nine years in the new configuration was estimated to be decreased by over 125 million gallons of water, 14 million gallons of chilled water, 121 million pounds of steam, and almost 3.6 million kilowatts of electricity, with consumption savings significantly increased projected out to 2028. Additional operational and organizational benefits as well as direct research benefits were identified. The new configuration has functioned for nine years without detectable cross contamination within the research enterprise thus providing evidence that the location of equipment processing can be less of a "microbial" risk factor than previously attributed.

Facilities involved in laboratory animal research often face ethical challenges such as: what should I do with the animals that are no longer suitable for experimental purposes? One of the common answers to this question is to kill them. And while numerous scientifically justifiable reasons exist for killing laboratory animals, we must not overlook our ethical responsibility towards these sentient beings. Animal facility managers and scientists frequently find themselves in a moral dilemma, torn between furthering their research and addressing the well-being of experimental animals required for their studies. We elaborated a concept consisting of six decision trees and recommendations for making informed decisions about the need to kill laboratory animals in research facilities, considering legal and ethical considerations. The concept is based on the German regulatory perspective. However, the measures and decisions for animal welfare can be implemented in all laboratory animal facilities. These recommendations suggest several courses of action, including implementing consistent breeding plans, exploring alternative uses, reassigning surplus animals and their organs, and establishing appropriate housing capacity limits that ensure species-appropriate care. We encourage scientists and animal facility managers to develop and implement decision-making frameworks and procedures tailored to their specific facilities, in the hope that this work will promote a thoughtful and responsible approach to the complex challenges associated with the killing of laboratory animals, advancing scientific progress and the humane treatment of these animals.

Isoflurane anesthesia prior to carbon dioxide euthanasia is recognized as a refinement by many guidelines. Facilities lacking access to a vaporizer can use the "drop" method, whereby liquid anesthetic is introduced into an induction chamber. Knowing the least aversive concentration of isoflurane is critical. Previous work has demonstrated that isoflurane administered with the drop method at a concentration of 5% is aversive to mice. Other work has shown that lower concentrations (1.7% to 3.7%) of isoflurane can be used to anesthetize mice with the drop method, but aversion to these concentrations has not been tested. We assessed aversion to these lower isoflurane concentrations administered with the drop method, using a conditioned place aversion (CPA) paradigm. Female C57BL/6J (OT-1) mice (n = 28) were randomly allocated to one of three isoflurane concentrations: 1.7%, 2.7%, and 3.7%. Mice were acclimated to a light-dark apparatus. Prior to and following dark (+ isoflurane) and light chamber conditioning sessions, mice underwent an initial and final preference assessment; the change in the duration spent within the dark chamber between the initial and final preference tests was used to calculate a CPA score. Aversion increased with increasing isoflurane concentration: from 1.7% to 2.7% to 3.7% isoflurane, mean ± SE CPA score decreased from 19.6 ± 20.1 s to -25.6 ± 23.2 s, to -116.9 ± 30.6 s (F1,54 = 15.4, p < 0.001). Our results suggest that, when using the drop method to administer isoflurane, concentrations between 1.7% and 2.7% can be used to minimize female mouse aversion to induction.

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 theory and practice of statistics comprises two main schools of thought: frequentist statistics and Bayesian statistics. Frequentist methods are most commonly used to analyze animal-based laboratory data, while Bayesian statistical methods have been implemented less widely and may be relatively unfamiliar to practitioners in experimental science. This paper provides a high-level overview of Bayesian statistics and how they compare with frequentist methods. Using examples in rodent toxicity research, we argue that Bayesian methods have much to offer laboratory animal researchers. We advocate for increased attention to and adoption of Bayesian methods in laboratory animal research. Bayesian statistical theory, methods, software, and education have advanced significantly in the last 30 years, making these tools more accessible than ever.

Blinding and randomisation are important methods for increasing the robustness of pre-clinical studies, as incomplete or improper implementation thereof is recognised as a source of bias. Randomisation ensures that any known and unknown covariates introducing bias are randomly distributed over the experimental groups. Thereby, differences between the experimental groups that might otherwise have contributed to false positive or -negative results are diminished. Methods for randomisation range from simple randomisation (e.g. rolling a dice) to advanced randomisation strategies involving the use of specialised software. Blinding on the other hand ensures that researchers are unaware of group allocation during the preparation, execution and acquisition and/or the analysis of the data. This minimises the risk of unintentional influences resulting in bias. Methods for blinding require strong protocols and a team approach. In this review, we outline methods for randomisation and blinding and give practical tips on how to implement them, with a focus on animal studies.

The normality assumption postulates that empirical data derives from a normal (Gaussian) population. It is a pillar of inferential statistics that enables the theorization of probability functions and the computation of p-values thereof. The breach of this assumption may not impose a formal mathematical constraint on the computation of inferential outputs (e.g., p-values) but may make them inoperable and possibly lead to unethical waste of laboratory animals. Various methods, including statistical tests and qualitative visual examination, can reveal incompatibility with normality and the choice of a procedure should not be trivialized. The following minireview will provide a brief overview of diagrammatical methods and statistical tests commonly employed to evaluate congruence with normality. Special attention will be given to the potential pitfalls associated with their application. Normality is an unachievable ideal that practically never accurately describes natural variables, and detrimental consequences of non-normality may be safeguarded by using large samples. Therefore, the very concept of preliminary normality testing is also, arguably provocatively, questioned.

Statistically based experimental designs have been available for over a century. However, many preclinical researchers are completely unaware of these methods, and the success of experiments is usually equated only with 'p < 0.05'. By contrast, a well-thought-out experimental design strategy provides data with evidentiary and scientific value. A value-based strategy requires implementation of statistical design principles coupled with basic project management techniques. This article outlines the three phases of a value-based design strategy: proper framing of the research question, statistically based operationalisation through careful selection and structuring of appropriate inputs, and incorporation of methods that minimise bias and process variation. Appropriate study design increases study validity and the evidentiary strength of the results, reduces animal numbers, and reduces waste from noninformative experiments. Statistically based experimental design is thus a key component of the 'Reduction' pillar of the 3R (Replacement, Reduction, Refinement) principles for ethical animal research.