Laboratory mice are predominantly used for one experiment only, i.e., new mice are ordered or bred for every new experiment. Moreover, most experiments use relatively young mice in the range of late adolescence to early adulthood. As a consequence, little is known about the day-to-day life of adult and aged laboratory mice. Here we present a long-term data set with three consecutive phases conducted with the same male mice over their lifetime in order to shed light on possible long-term effects of repeated cognitive stimulation. One third of the animals was trained by a variety of learning tasks conducted up to an age of 606 days. The mice were housed in four cages with 12 animals per cage; only four mice per cage had to repeatedly solve cognitive tasks for getting access to water using the IntelliCage system. In addition, these learner mice were tested in standard cognitive tests outside their home-cage. The other eight mice served as two control groups living in the same environment but without having to solve tasks for getting access to water. One control group was additionally placed on the test set-ups without having to learn the tasks. Next to the cognitive tasks, we took physiological measures (body mass, resting metabolic rate) and tested for dominance behavior, and attractivity in a female choice experiment. Overall, the mice were under surveillance until they died a natural death, providing a unique data set over the course of virtually their entire lives. Our data showed treatment differences during the first phase of our lifetime data set. Young learner mice showed a higher activity, less growth and resting metabolic rate, and were less attractive for female mice. These effects, however, were not preserved over the long-term. We also did not find differences in dominance or effects on longevity. However, we generated a unique and valuable set of long-term behavioral and physiological data from a single group of male mice and note that our long-term data contribute to a better understanding of the behavioral and physiological processes in male C57Bl/6J mice.

It has become mandatory for the application for allowance of animal experimentation to rate the severity of the experimental procedures. In order to minimize suffering related to animal experimentation it is therefore crucial to develop appropriate methods for the assessment of animal suffering. Physiological parameters such as hormones or body weight are used to assess stress in laboratory animals. However, such physiological parameters alone are often difficult to interpret and leave a wide scope for interpretation. More recently, behavior, feelings and emotions have come increasingly into the focus of welfare research. Tests like preference tests or cognitive bias tests give insight on how animals evaluate certain situations or objects, how they feel and what their emotional state is. These methods should be combined in order to obtain a comprehensive understanding of the well-being of laboratory animals.

Current animal protection laws require replacement of animal experiments with alternative methods, whenever such methods are suitable to reach the intended scientific objective. However, searching for alternative methods in the scientific literature is a time-consuming task that requires careful screening of an enormously large number of experimental biomedical publications. The identification of potentially relevant methods, e.g. organ or cell culture models, or computer simulations, can be supported with text mining tools specifically built for this purpose. Such tools are trained (or fine tuned) on relevant data sets labeled by human experts. We developed the GoldHamster corpus, composed of 1,600 PubMed (Medline) articles (titles and abstracts), in which we manually identified the used experimental model according to a set of eight labels, namely: “in vivo”, “organs”, “primary cells”, “immortal cell lines”, “invertebrates”, “humans”, “in silico” and “other” (models). We recruited 13 annotators with expertise in the biomedical domain and assigned each article to two individuals. Four additional rounds of annotation aimed at improving the quality of the annotations with disagreements in the first round. Furthermore, we conducted various machine learning experiments based on supervised learning to evaluate the corpus for our classification task. We obtained more than 7,000 document-level annotations for the above labels. After the first round of annotation, the inter-annotator agreement (kappa coefficient) varied among labels, and ranged from 0.42 (for “others”) to 0.82 (for “invertebrates”), with an overall score of 0.62. All disagreements were resolved in the subsequent rounds of annotation. The best-performing machine learning experiment used the PubMedBERT pre-trained model with fine-tuning to our corpus, which gained an overall f-score of 0.83. We obtained a corpus with high agreement for all labels, and our evaluation demonstrated that our corpus is suitable for training reliable predictive models for automatic classification of biomedical literature according to the used experimental models. Our SMAFIRA - “Smart feature-based interactive” - search tool (https://smafira.bf3r.de) will employ this classifier for supporting the retrieval of alternative methods to animal experiments. The corpus is available for download (https://doi.org/10.5281/zenodo.7152295), as well as the source code (https://github.com/mariananeves/goldhamster) and the model (https://huggingface.co/SMAFIRA/goldhamster).

From the preference of one good over another, the strength of the preference cannot automatically be inferred. While money is the common denominator to assess the value of goods in humans, it appears difficult at first glance to put a price tag on the decisions of laboratory animals. Here we used consumer demand tests to measure how much work female mice expend to obtain access to different liquids. The mice could each choose between two liquids, one of which was free. The amount of work required to access the other liquid, by contrast, increased daily. In this way, the value of the liquid can be determined from a mouse's microeconomic perspective. The unique feature is that our test was carried out in a home-cage based setup. The mice lived in a group but could individually access the test-cage, which was connected to the home-cage via a gate. Thereby the mice were able to perform their task undisturbed by group members and on a self-chosen schedule with minimal influence by the experimenter. Our results show that the maximum number of nosepokes depends on the liquids presented. Mice worked incredibly hard for access to water while a bitter-tasting solution was offered for free whereas they made less nosepokes for sweetened liquids while water was offered for free. The results demonstrate that it is possible to perform automated and home-cage based consumer demand tests in order to ask the mice not only what they like best but also how strong their preference is.

Background Due to the lack of complexity and variety of stimuli, conventional housing conditions of laboratory mice do not allow these animals to fully express their behavioral repertoire, including manipulative and cognitive activities. Therefore, we designed mechanical puzzles, so-called lockboxes, for mice that can be provided in their home cages. We investigated the impact of the lockbox enrichment on their phenotype and affective state when compared to conventional housing (CH) and super-environmental enrichment (SEE). Methods Young adult female C57BL/6JCrl mice were examined before and after 2-month exposure to the different types of enrichment in a phenotyping test battery, including tests for trait and state anxiety-related behavior, calorimetric measurements, body weight measurements, the analysis of stress hormone metabolite concentrations, and sequential problem-solving abilities with a novel lockbox. At the end of the study, adrenal gland weights were determined and pathohistological evaluation was performed. For all continuous variables, the relative variability was calculated. Results While the different types of enrichment affected trait anxiety-related behavior, neither state anxiety-related behavior nor physiological variables (i.e., bodyweight, resting metabolic rate, stress hormone metabolite concentrations, adrenal gland weights) were influenced. LE improved sequential problem-solving (i.e., solving novel lockboxes) when compared to SEE. Regardless of the housing condition, the relative variability increased in most variables over time, although the coefficient of variation decreased for some variables, especially in animals with access to LE. There was no evidence of toxicopathological effects associated with the material from which the lockboxes were made. Conclusions All lockboxes are available as open-source tool. LE revealed beneficial effects on the affective state of laboratory mice and their performance in solving novel lockboxes. Neither relevant phenotype of the mice nor reproducibility of the data were compromised by LE, similar to SEE. The lockboxes may also be used as novel approach for assessing cognition in mice.

Preference tests help to determine how highly individuals value different options to choose from. During preference testing, two or more options are presented simultaneously, and options are ranked based on the choices made. Presented options, however, influence each other, where the amount of influence increases with the number of options. Multiple binary choice tests can reduce this degree of influence, but conventional analysis methods do not reveal the relative strengths of preference, i.e., the preference difference between options. Here, we demonstrate that multiple binary comparisons can be used not only to rank but also to scale preferences among many options (i.e., their worth value). We analyzed human image preference data with known valence scores to develop and validate our approach to determine how known valence ranges (high vs. low) converge on a scaled representation of preference data. Our approach allowed us to assess the valence of ranked options in mice and rhesus macaques. By conducting simulations, we developed an approach to incorporate additional option choices into existing rank orders without the need to conduct binary choice tests with all original options, thus reducing the number of animal experiments needed. Two quality measures, consensus error and intransitivity ratio, allow for assessing the achieved confidence of the scaled ranking and better tailoring of measurements required to improve it further. The software is available as an R package (“simsalRbim”). Our approach optimizes preference testing, e.g., in welfare assessment, and allows us to efficiently and quantitatively assess the relative value of options presented to animals.