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Record W2117470117 · doi:10.2174/187152807780832274

Heat Shock Paradox and a New Role of Heat Shock Proteins and their Receptors as Anti-Inflammation Targets

2007· review· en· W2117470117 on OpenAlexaff
Yu Chen, Tracy S. Voegeli, Peter P. Liu, Earl G. Noble, R. William Currie

Bibliographic record

VenueInflammation & Allergy - Drug Targets · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsWestern University
Fundersnot available
KeywordsHeat shock proteinInflammationCell biologyIntracellularReceptorProinflammatory cytokineBiologyExtracellularInnate immune systemImmunologyBiochemistry

Abstract

fetched live from OpenAlex

This article discusses the role of heat shock proteins (Hsps) and their receptors as anti-inflammation targets. Hsps are highly conserved proteins that protect cells against noxious or deleterious stimulus. Intracellular Hsps function as molecular chaperones governing protein assembly, folding, or transport and as anti-apoptotic regulators of cell signalling pathways leading to cell death. In addition, intracellular Hsps have recently been shown to have an anti-inflammatory role in various inflammatory conditions such as infection, ischemia/reperfusion injury, and cardiovascular diseases. However, the heat shock response and the induction of Hsps have paradoxical effects against cell injury. Hsp induction before a pro-inflammatory stimulus is clearly beneficial but Hsp induction after a pro-inflammatory stimulus is cytotoxic. These paradoxical and contradictory effects may result from the different functions of intracellular versus extracellular Hsps. Extracellular Hsps released from cells with compromised integrity may function as danger signals activating innate immunity by interacting with their receptors. Therefore, modulating the levels of intracellular Hsps or the activities of Hsp receptors will be potential drug targets in inflammation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.296
Teacher spread0.275 · 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 designOther design
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

Citations94
Published2007
Admission routes1
Has abstractyes

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