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Record W3157539676 · doi:10.1016/j.smhs.2021.04.002

Harnessing the cardiovascular benefits of exercise: Are Nrf2 activators useful?

2021· review· en· W3157539676 on OpenAlexaff
Babatunde Fasipe, Shunchang Li, Ismail Laher

Bibliographic record

VenueSports Medicine and Health Science · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics, phytochemicals, and oxidative stress
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOxidative stressTranscription factorReactive oxygen speciesAntioxidantDiseaseChemistryCell biologyBioinformaticsMedicineBiologyGeneInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

The ability of physical activity to ameliorate cardiovascular disease and improve cardiovascular health is well accepted, but many aspects of the molecular mechanisms underlying these benefits are incompletely understood. Exercise increases the levels of reactive oxygen species (ROS) through various mechanisms. This triggers the activation of Nrf2, a redox-sensitive transcription factor activated by increases in oxidative stress. Activation of Nrf2 mitigates oxidative stress by increasing the nuclear transcription of many antioxidant genes while also mediating additional beneficial effects through the cytoprotective nature of Nrf2 signaling. Understanding the transcriptional patterns of Nrf2 caused by exercise can help in the design of pharmacological mimicry of the process in patients who are unable to exercise for various reasons.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.004

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 head0.077
GPT teacher head0.359
Teacher spread0.282 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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