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Record W4205788840 · doi:10.4103/mgmj.mgmj_67_21

Hypothesized biological mechanisms by which exercise-induced irisin mitigates tumor proliferation and improves cancer treatment outcomes

2021· article· en· W4205788840 on OpenAlexaff
Chidiebere Emmanuel Okechukwu, Chidubem Ekpereamaka Okechukwu, Ayman Agag, Naufal Naushad, Abdalla Ali Deb

Bibliographic record

VenueMGM Journal of Medical Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEndocrinologyInternal medicineMyokineSignal transductionDownregulation and upregulationProtein kinase ACoactivatorKinaseSkeletal muscleChemistryCancer researchBiologyCell biologyMedicineTranscription factorBiochemistry

Abstract

fetched live from OpenAlex

Exercise has been linked to a significant decrease in cancer pathogenesis. Irisin is an exercise-induced myokine that is released from the skeletal muscle upon cleavage of the membrane of fibronectin type III domain-containing protein 5. Exercise has been revealed to raise irisin concentration in the blood and muscle cells via the upregulation of peroxisome proliferator receptor γ coactivator-1α expression. Exercise-induced irisin reduces the risk of numerous cancers by burning excess body fat. We hypothesized that exercise-induced irisin may mitigate tumor proliferation by inducing apoptosis and improving cancer treatment outcomes via modulating several signaling and metabolic pathways, mainly by increasing the phosphorylation of adenosine monophosphate-activated protein kinase and acetyl-CoA-carboxylase, via deactivating the phosphatidylinositol 3-kinase/protein kinase B Snail signaling pathway, by upregulating the apoptosis pathway through the inhibition of epithelial–mesenchymal transition and via stimulating caspase activity.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 integrity0.0000.001
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 head0.055
GPT teacher head0.344
Teacher spread0.289 · 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 designTheoretical or conceptual
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

Citations1
Published2021
Admission routes1
Has abstractyes

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