Beyond Leptin and Adiponectin: The Diverse Roles of Adipokines in the Myocardial Hypertrophic Process and Heart Failure and Their Potential Contribution in Obesity
Bibliographic record
Abstract
It is now widely recognized that adipocytes have the ability to produce a myriad of bioactive compounds released into the circulation and affecting distal organs, including the heart. These factors, termed adipokines, are also produced by various tissues in addition to adipocytes, including cardiac tissue, and have the ability to modulate cardiac function and the response to pathology. Among the processes greatly affected by adipokines is myocardial remodelling due to hypertrophy and fibrosis, two processes that contribute to the development of heart failure. This is particularly relevant under conditions of obesity, and the accompanying increased adiposity, in general, results in increased adipokine production. The effects of adipokines on cardiac remodelling can be both beneficial or adverse, depending on the adipokine type, such as adiponectin and leptin, respectively. The molecular bases underlying the effects of adipokines on myocardial remodelling have been extensively studied and likely involve a multiplicity of cell signalling processes, thus demonstrating substantial complexity. Emerging evidence suggests that these proteins play an important role in cardiac pathology. Their precise contribution is yet to be determined with certainty, as this likely reflects a balance between pro-remodelling and anti-remodelling factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".