The role of CYP-sEH derived lipid mediators in regulating mitochondrial biology and cellular senescence: implications for the aging heart
Bibliographic record
Abstract
Cellular senescence is a condition characterized by stable, irreversible cell cycle arrest linked to the aging process. The accumulation of senescent cells in the cardiac muscle can contribute to various cardiovascular diseases (CVD). Telomere shortening, epigenetic modifications, DNA damage, mitochondrial dysfunction, and oxidative stress are known contributors to the onset of cellular senescence in the heart. The link between mitochondrial processes and cellular senescence contributed to the age-related decline in cardiac function. These include changes in mitochondrial functions and behaviours that arise from various factors, including impaired dynamics, dysregulated biogenesis, mitophagy, mitochondrial DNA (mtDNA), reduced respiratory capacity, and mitochondrial structural changes. Thus, regulation of mitochondrial biology has a role in cellular senescence and cardiac function in aging hearts. Targeting senescent cells may provide a novel therapeutic approach for treating and preventing CVD associated with aging. CYP epoxygenases metabolize N-3 and N-6 polyunsaturated fatty acids (PUFA) into epoxylipids that are readily hydrolyzed to diol products by soluble epoxide hydrolase (sEH). Increasing epoxylipids levels or inhibition of sEH has demonstrated protective effects in the aging heart. Evidence suggests they may play a role in cellular senescence by regulating mitochondria, thus reducing adverse effects of aging in the heart. In this review, we discuss how mitochondria induce cellular senescence and how epoxylipids affect the senescence process in the aged heart.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".