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Record W4410327634 · doi:10.1016/j.physbeh.2025.114947

Circadian responses to non-photic treatments in BTBR mice

2025· article· en· W4410327634 on OpenAlexafffund
Jhenkruthi Vijaya Shankara, Katelyn G. Horsley, Naila F. Jamani, Zhi A. Robinson, Michael C. Antle

Bibliographic record

VenuePhysiology & Behavior · 2025
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCircadian rhythmPhotic zonePhotic StimulationPsychologyNeuroscienceBiologyVisual perceptionPerception

Abstract

fetched live from OpenAlex

The BTBR T+ Itpr3tf/J mouse (BTBR) differs from C57BL/6 mice in various circadian parameters, including freerunning period (FRP), circadian responses to light, and prominent circadian responses to scheduled feeding. The circadian system is also sensitive to a host of non-photic cues, which can modify and reset freerunning rhythms as well as modulate responses to other zeitgebers such as light. Here we examine how the BTBR mouse responds to various non-photic treatments. Because activity levels can modulate the FRP, we first examined if the shorter FRP of BTBR mice resulted from their higher activity levels. While overall activity was lower when housed without a running wheel, this did not significantly alter their FRP. When housed in constant light, exposure to a 6 h dark pulse improved the quality of the locomotor rhythms for both BTBR and C57 mice. BTBR mice had significantly smaller phase shifts to midday treatments with either a 3 h sleep deprivation procedure or an injection of the serotonin 1A/7 receptor agonist (±) 8-OH-DPAT than did the comparison C57BL/6J strain. However, BTBR mice did exhibit larger responses to midday refeeding pulses following 18 h food deprivation. Their unique circadian phenotype, particularly their short FRP, makes them a useful model for examining circadian responses in mice.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.318
Teacher spread0.291 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2025
Admission routes2
Has abstractyes

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