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Record W4406802862 · doi:10.12997/jla.2025.14.1.40

Cardioprotective Effects of Adiponectin-Stimulated Autophagy

2025· review· en· W4406802862 on OpenAlexafffund
Eddie Tam, Mireille Ouimet, Gary Sweeney

Bibliographic record

VenueJournal of Lipid and Atherosclerosis · 2025
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of OttawaYork University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsAutophagyAdiponectinCell biologyMedicineInternal medicineChemistryBiologyApoptosisBiochemistryObesityInsulin resistance

Abstract

fetched live from OpenAlex

Cardiovascular diseases (CVDs), including heart failure, pose a significant economic and health burden worldwide. Current treatment strategies for heart failure are greatly limited, in that they mainly mitigate symptoms or delay further progression. In contrast, therapies aimed at proactively preventing the onset of heart failure could greatly improve outcomes. Adiponectin is an adipocyte-derived hormone that confers an array of cardioprotective effects. It exerts anti-inflammatory effects, improves metabolic function, mitigates endothelial cell dysfunction, and reduce cardiomyocyte cell death. Furthermore, it has gained increasing attention for its ability to activate autophagy, a conserved cellular pathway that facilitates the degradation and recycling of cell components. The disruption of autophagy has been linked to CVDs including heart failure. Additionally, growing evidence also points to specific forms of autophagy, namely mitophagy and lipophagy, as crucial adaptive responses in protection against CVDs. The protective effects of adiponectin, autophagy, mitophagy, and lipophagy against CVDs along with potential therapeutic implications will be discussed.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

Explore more

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