MétaCan
Menu
Back to cohort
Record W2118299296 · doi:10.2174/187152811796117726

Heat Shock Proteins Protect Against Ischemia and Inflammation Through Multiple Mechanisms

2011· review· en· W2118299296 on OpenAlexafffund
Q Jones, Tracy S. Voegeli, Gefeng Li, Yu Chen, R. William Currie

Bibliographic record

VenueInflammation & Allergy - Drug Targets · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsDalhousie University
FundersHeart and Stroke Foundation of Canada
KeywordsHeat shock proteinHsp27Hsp70InflammationIschemiaShock (circulatory)HSP60Chaperone (clinical)MedicineCell biologyBiologyInternal medicinePathologyBiochemistryGene

Abstract

fetched live from OpenAlex

After heat shock or other metabolic stress, heat shock proteins (Hsps) are expressed at high levels in all tissues and cells. The highly inducible 70 kDa heat shock protein (Hsp70) is associated with improved post-ischemic myocardial contractile recovery. Similarly, the small 27 kDa heat shock protein (Hsp27), that is abundant in muscle, is also linked with improved myocardial function after ischemic injury. Various Hsps have pro-survival functions that include chaperone, antiapoptotic and/or anti-inflammatory activity. In this review we will summarize our understanding of myocardial protection and present evidence for protection having time dependent aspects that appear to be stimulus dependent. Keywords: Heat shock proteins, heart, ischemia, inflammation, myocardial protection, Hsp70, Hsps, Insulin-Induced Myocardial Protection, cytokines

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.028
GPT teacher head0.289
Teacher spread0.261 · 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

Citations55
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueInflammation & Allergy - Drug TargetsSame topicHeat shock proteins researchFrench-language works237,207