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Exercise induces Hsp70 in smooth muscle cells of the cerebral vasculature

2009· article· en· W3175353001 on OpenAlexafffund
Marcus J Van Aarsen, Earl G. Noble

Bibliographic record

VenueThe FASEB Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHsp70Ex vivoNeuroprotectionCerebral blood flowHeat shock proteinVascular smooth muscleCerebral circulationInternal medicineEndotheliumMedicineEndocrinologyAnatomyBiologySmooth muscle

Abstract

fetched live from OpenAlex

Exercise (EX) is a complex physiological stressor of the cardiovascular system. EX is known to increase expression of the cytoprotective inducible isoform of the 70 kDa heat shock protein (Hsp70) in blood vessels of working muscle. The objective of this study was to determine whether EX induces a similar increase in Hsp70 content in the cerebral vasculature. Sprague‐Dawley rats (n=10) were randomly assigned to either an EX (60 min treadmill running, 30 m/min, 5 consecutive days) or control group. Brain was harvested 24 hours following the final EX bout and sectioned for immunohistological localization of Hsp70, vascular endothelium and smooth muscle. Results showed increased Hsp70 expression in the cerebral vasculature post‐EX versus controls. Post‐EX, Hsp70 was always observed in the vascular smooth muscle of large cerebral vessels adjacent to the midbrain, and was also infrequently present in the vascular endothelium of these vessels. No Hsp70 was detected in neural regions of the brain. Although unclear, increased expression of Hsp70 may be a response to the change in characteristics of blood and blood flow that occur due to EX, or due to increased autonomic outflow to the vascular smooth muscle. Regardless, the neuroprotective nature of EX may be in part due to the protection of cerebral blood vessels by Hsp70. Supported by NSERC #8170‐05 Grant Funding Source Internal

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.265

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 integrity0.0000.001
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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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