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Record W2000490904 · doi:10.1139/h08-069

Heat shock proteins and exercise: a primer

2008· review· en· W2000490904 on OpenAlexaffvenue
Earl G. Noble, Kevin Milne, C.W. James Melling

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsHeat shock proteinInflammationBiologySkeletal muscleCell biologyHeat shockBioinformaticsNeuroscienceImmunologyGeneticsEndocrinologyGene

Abstract

fetched live from OpenAlex

Heat shock proteins (HSPs) are, in general, prosurvival molecules within the cellular environment, and the overexpression of even just 1 family of HSPs can lead to protection against and improvements after a variety of stressors. Not surprisingly, a fertile area of study has grown out of efforts to exploit the innate biologic behaviour of HSPs. Exercise, because of the inherent physiologic stresses associated with it, is but 1 stimulus that can result in a robust increase in various HSPs in several tissues, not the least of which happen to be the heart and skeletal muscle. The purpose of this review is to introduce the reader to the major HSP families, the control of their expression, and some of their biologic functions, specifically with respect to the influence of exercise. Moreover, as the first in a series of reviews from a common symposium, we will briefly introduce the concepts presented by the other authors, which include the effects of different exercise paradigms on skeletal muscle HSPs in the adult and aged systems, HSPs as regulators of inflammation, and the ion channel stabilizing effects of HSPs.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.004

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 head0.024
GPT teacher head0.306
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations162
Published2008
Admission routes2
Has abstractyes

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