Handling Techniques to Reduce Stress in Mice
Bibliographic record
Abstract
Laboratory animals are subjected to multiple manipulations by scientists or animal care providers. The stress this causes can have profound effects on animal well-being and can also be a confounding factor for experimental variables such as anxiety measures. Over the years, handling techniques that minimize handling-related stress have been developed with a particular focus on rats, and little attention to mice. However, it has been shown that mice can be habituated to manipulations using handling techniques. Habituating mice to handling reduces stress, facilitates routine handling, improves animal wellbeing, decreases data variability, and improves experimental reliability. Despite beneficial effects of handling, the tail-pick up approach, which is particularly stressful, is still widely used. This paper provides a detailed description and demonstration of a newly developed mouse-handling technique intended to minimize the stress experienced by the animal during human interaction. This manual technique is performed over 3 days (3D-handling technique) and focuses on the animal's capacity to habituate to the experimenter. This study also shows the effect of previously established tunnel handling techniques (using a polycarbonate tunnel) and the tail-pick up technique. Specifically studied are their effects on anxiety-like behaviors, using behavioral tests (Elevated-Plus Maze and Novelty Suppressed Feeding), voluntary interaction with experimenters and physiological measurement (corticosterone levels). The 3D-handling technique and the tunnel handling technique reduced anxiety-like phenotypes. In the first experiment, using 6-month-old male mice, the 3D-handling technique significantly improved experimenter interaction. In the second experiment, using 2.5-month-old female, it reduced corticosterone levels. As such, the 3D-handling is a useful approach in scenarios where interaction with the experimenter is required or preferred, or where tunnel handling may not be possible during the experiment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".