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Record W6910587816 · doi:10.48448/9e9x-8158

The importance of calm mice: why should early life stress studies be conducted in an isolated quiet room

2021· other· en· W6910587816 on OpenAlexaff

Bibliographic record

VenueUnderline Science Inc. · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of OttawaCarleton University
Fundersnot available
KeywordsQUIETGlutamatergicStressorAnimal modelAffect (linguistics)AMPA receptorFight-or-flight responseStress (linguistics)

Abstract

fetched live from OpenAlex

Various models of early life stress (ELS) emphasize housing animals in a quiet and isolated room, undisturbed by or shared with other investigators. While increased human activity in animal housing facilities is known to affect an array of animal behavioural, metabolic, and physiological functions, whether it creates a stressful environment and affects the developmental trajectory of the immature brain is unknown. Here, we present data outlining the implication for housing immature mice in regular housing (RH) as compared to a separate quiet room (SQR) on α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) function during a critical period of brain development. The SQR had low traffic, was accessed by a single investigator, and housed a single rack, while the RH room was located in a high traffic area, shared by 16 investigators and staff, and housed 6 full racks. Using whole-cell patch-clamp recordings, we demonstrate that pups housed in RH show significant increases in AMPAR function in pyramidal neurons in both CA1 (p10-11) and layer IV auditory cortex (p12-15). Therefore, for ELS studies, considerations of traffic and noise levels should be taken when choosing animal housing. A quiet and separate room is required as additional stressors can permanently alter the maturation of glutamatergic synapses in the developing brain, potentially impacting research outcomes and subsequent interpretation.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.381
Teacher spread0.272 · 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.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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