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Record W2277179412

Sex differences in the induced expression of Hsp70 and Hsp27 in the brain and heart of rats

2013· article· en· W2277179412 on OpenAlexvenueno aff
Danielle Rioux

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsnot available
Fundersnot available
KeywordsHsp70Hsp27Expression (computer science)Internal medicineNeuroscienceBiologyChemistryCell biologyHeat shock proteinMedicineGeneticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

There are sex differences in degenerative disease prevalence in humans. Most models of degenerative disease use male animals. Examining female and male responses to stress may give insight into disease prevalence. Heat shock proteins are chaperones linked to damaged proteins in degenerative diseases and may be expressed differentially in females and males. My goal was to characterize the induced expression of Hsp70 and Hsp27 in the brain and heart of female and male rats. Rats were heat shocked, brains and hearts were removed 24 hours after, and western analyses were done to quantify the expression of these proteins. Immunofluorescence was used to localize Hsp70 and Hsp27 in the hippocampus. Overall, male rats have significantly greater induced expression of both Hsp70 and Hsp27 in the brain. In the hippocampus, Hsp70 was localized in blood vessels and microglia, and Hsp27 was localized in astrocytes, following heat shock.

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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