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Record W4233970060 · doi:10.22215/etd/2014-10260

Stressor re-exposure effects on behaviour and expression of pro-inflammatory cytokines in male and female mice

2014· dissertation· en· W4233970060 on OpenAlexaff
Stephanie Hudson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsCarleton University
Fundersnot available
KeywordsStressorNeurochemicalImmune systemCytokineAnxietyProinflammatory cytokinePsychologyInternal medicineEndocrinologyMedicinePhysiologyInflammationClinical psychologyImmunologyPsychiatry

Abstract

fetched live from OpenAlex

The neurochemical effects of stressful events involve a range of adaptive responses to environmental challenges. However, repeated stressors may result in the development of psychopathologies such as anxiety and depression. The immune signaling molecule, pro-inflammatory cytokines, has been linked to the development of such illnesses. Interestingly, distinct differences in stressor responsiveness exist between the sexes, alongside a much higher rate of affective disorders in females. In the present experiments we examined if repeated exposure to stressors would impact male and female CD-1 mice differently with respect to behaviour as well as cytokine expression. Repeatedly stressed males demonstrated impulsive behaviour along with sensitized IL-1β expression. In follow-up experiments the role of 17β-estradiol was examined, with stressed males that were exposed to estrogen showing reduced TNF-α expression while similarly treated females had a drastic elevation in IL-6. The current results demonstrate the differences in the stressor response system between the sexes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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 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
Published2014
Admission routes1
Has abstractyes

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