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Opportunistically using a Chronic Unpredictable Stress study to investigate ‘inactive-but-awake’ behaviour as a potential welfare indicator in laboratory rats

2024· article· en· W4394580508 on OpenAlexaff
LE Young, RT McCallum, ML Perreault, G.J. Mason

Bibliographic record

VenueApplied Animal Behaviour Science · 2024
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAnimal welfareChronic stressPsychologyWelfareStress (linguistics)Pet therapyHUBzeroVeterinary medicineMedicineBiologyEcologyNeurosciencePolitical science

Abstract

fetched live from OpenAlex

Being awake but motionless, inactive-but-awake (IBA), has been suggested to reflect low arousal negative affect in several species. This preliminary study investigated IBA in rats exposed to Chronic Unpredictable Stress (CUS), opportunistically (to comply with the 3Rs) during biomedical research modelling depression. Twenty individually-housed Wistar females underwent a 6-week CUS protocol involving daily stressors (e.g. cold, food deprivation). Half, housed on high shelves and dosed with potential antidepressant, were assumed relatively less stressed by the CUS (sub-group ‘RLS’); half, on more anxiogenic low shelves and dosed with vehicle, were assumed relatively more stressed (‘RMS’). If IBA reflects negative affect, then IBA should be increased by CUS duration, and by being RMS rather than RLS: predictions tested by observing rats in their homecages during CUS Weeks 0, 2 and 4 (blind to treatment, sub-group and hypothesis). Assumptions that negative affect increased with sustained CUS, and being RMS compared to RLS, were checked via the rat-specific stress indicator, chromodacryorrhea (ocular porphyrin). Facial/postural changes were also assessed, in exploratory attempts to identify particularly welfare-sensitive sub-types of IBA. Additionally, we evaluated stereotypic behaviour: a hyper-active response to negative states that mouse data suggest may be an alternative to waking inactivity. We found that chromodacryorrhea was an easy-to-score cageside welfare indicator. Chromodacryorrhea was also highly sensitive to CUS (increasing with very large effect sizes). IBA increased with CUS duration, alongside the chromodacryorrhea (the two significantly covarying in two of three observation weeks). Thus as predicted, IBA increased, paralleled by physiological stress, over weeks of aversive experience. RMS rats also showed more chromodacryorrhea than RLS, supporting the designation of these sub-groups as respectively relatively more versus less stressed. This group difference was not mirrored by IBA, however: a false negative result for this potential welfare indicator. Turning to sub-types of IBA, forms involving ocular squinting increased with CUS, while those involving hunched postures or backward-pointing ears did not. Furthermore, IBA involving squinting seemed higher in RMS than RLS rats. Stereotypic behaviour, in contrast, decreased with CUS, and negatively covaried with IBA, suggesting that hypo- and hyper-activity are alternative responses. Overall, these preliminary results thus suggest that IBA, especially sub-types involving ocular squinting, may indicate poor welfare in rats. This needs replicating with negative controls, male subjects, and other types of challenge, but cautiously adds to growing evidence that particular forms of inactivity can indicate negative affect. Welfare science/biomedical collaborations can also advance welfare understanding without involving additional animal-use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.311
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractno

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