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Record W2064382910 · doi:10.4061/2011/972807

Molecular Mechanisms in Exercise-Induced Cardioprotection

2011· article· en· W2064382910 on OpenAlexaff
Saeid Golbidi, Ismail Laher

Bibliographic record

VenueCardiology Research and Practice · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCardioprotectionMedicineEndoplasmic reticulumAutophagyHeat shock proteinMitochondrionNitric oxidePotassium channelBioinformaticsNeurosciencePharmacologyCell biologyCardiologyInternal medicineBiologyIschemiaApoptosisBiochemistry

Abstract

fetched live from OpenAlex

Physical inactivity is increasingly recognized as modifiable behavioral risk factor for cardiovascular diseases. A partial list of proposed mechanisms for exercise-induced cardioprotection include induction of heat shock proteins, increase in cardiac antioxidant capacity, expression of endoplasmic reticulum stress proteins, anatomical and physiological changes in the coronary arteries, changes in nitric oxide production, adaptational changes in cardiac mitochondria, increased autophagy, and improved function of sarcolemmal and/or mitochondrial ATP-sensitive potassium channels. It is currently unclear which of these protective mechanisms are essential for exercise-induced cardioprotection. However, most investigations focus on sarcolemmal KATP channels, NO production, and mitochondrial changes although it is very likely that other mechanisms may also exist. This paper discusses current information about these aforementioned topics and does not consider potentially important adaptations within blood or the autonomic nervous system. A better understanding of the molecular basis of exercise-induced cardioprotection will help to develop better therapeutic strategies.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.135
GPT teacher head0.385
Teacher spread0.250 · 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

Citations87
Published2011
Admission routes1
Has abstractyes

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