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Record W1511259711

Regulatory effect of heat shock protein 70 in stress-induced rat intestinal epithelial barrier dysfunction.

2009· article· en· W1511259711 on OpenAlexaff
Ping–Chang Yang, Yahong Tu, Mary H. Perdue, Christine Oluwole, Stevie Struiksma

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsHsp70Intestinal permeabilityUssing chamberHeat shock proteinMedicineBarrier functionIntestinal mucosaIntestinal epitheliumEpitheliumShock (circulatory)ImmunologyInternal medicineCell biologyPathologyBiologyBiochemistryGene
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Psychological stress is one of the factors associated with many human diseases; the mechanisms need to be further understood. METHODS: Rats were subjected to chronic water avoid stress. Intestinal epithelial heat shock protein (HSP) 70 was evaluated. The intestinal epithelial permeability was examined with Ussing chamber technique. RESULTS: HSP70 was detected in normal intestinal epithelial cells. Psychological stress decreased HSP70 in the intestinal epithelial cells that correlated with the stress-induced intestinal epithelial hyperpermeability. Pretreatment with HSP70 abrogated stress-induced intestinal barrier dysfunction. CONCLUSIONS: Chronic stress inhibits HSP70 activity in rat intestinal epithelial layer that is associated with intestinal epithelial barrier dysfunction, which can be prevented by pretreatment with HSP70 protein. (Yang PC, Tu YH, Perdue MH, Oluwole C, Struiksma S. Regulatory effect of heat shock protein 70 in stress-induced rat intestinal epithelial barrier dysfunction.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.248
Teacher spread0.236 · 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

Citations11
Published2009
Admission routes1
Has abstractyes

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