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Record W2160613769 · doi:10.1161/01.cir.102.14.1703

Confocal Microscopic Localization of Constitutive and Heat Shock–Induced Proteins HSP70 and HSP27 in the Rat Heart

2000· article· en· W2160613769 on OpenAlexafffund
J. Leger, Frank M. Smith, R. William Currie

Bibliographic record

VenueCirculation · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsDalhousie University
FundersHeart and Stroke Foundation of Canada
KeywordsHeat shock proteinMedicineHsp70Hsp27Shock (circulatory)ConfocalConfocal microscopyCell biologyInternal medicineBiochemistryBiologyOpticsPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Heat-shock treatment of rats elevates expression of heat-shock proteins, which play a role in improving the contractile recovery and reducing infarct size in hearts after ischemic injury. However, the location of these proteins in the heart is unknown. METHODS AND RESULTS: Anesthetized rats were heat-shocked by elevation of body temperature to 42 degrees C to 42.5 degrees C for 15 minutes, followed by 24 hours of recovery. Control and heat-shocked hearts were extirpated and perfused briefly with saline followed by 2% paraformaldehyde in PBS. Confocal immunofluorescence microscopy of control hearts revealed that HSP27 was localized in cardiomyocytes in a pattern reminiscent of Z bands and was colocalized with neuronal markers in somata and axons. No obvious change in HSP27 content or distribution occurred after heat shock. Confocal microscopy revealed little or no HSP70 in control hearts. After heat shock, HSP70 was detected neither in cardiomyocytes nor in neuronal elements within the heart, but HSP70 was abundant in small blood vessels found between the ventricular cardiomyocytes. CONCLUSIONS: Heat shock induces a cell type-specific expression of HSP70 in blood vessels but not myocytes or intrinsic cardiac neurons, suggesting that blood vessels play a primary role in myocardial protection.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.105
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.017
GPT teacher head0.286
Teacher spread0.270 · 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.

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

Citations63
Published2000
Admission routes2
Has abstractyes

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