MitoNEET Provides Cardioprotection via Reducing Oxidative Damage and Conserving Mitochondrial Function
Bibliographic record
Abstract
Cardiometabolic diseases exert a significant health impact, leading to a considerable economic burden globally. The metabolic syndrome, characterized by a well-defined cluster of clinical parameters, is closely linked to an elevated risk of cardiovascular disease. Current treatment strategies often focus on addressing individual aspects of metabolic syndrome. We propose that exploring novel therapeutic approaches that simultaneously target multiple facets may prove more effective in alleviating the burden of cardiometabolic disease. There is a growing body of evidence suggesting that mitochondria can serve as a pivotal target for the development of therapeutics aimed at resolving both metabolic and vascular dysfunction. MitoNEET was identified as a binding target for the thiazolidinedione (TZD) class of antidiabetic drugs and is now recognized for its role in regulating various crucial cellular processes. Indeed, mitoNEET has demonstrated promising potential as a therapeutic target in various chronic diseases, encompassing cardiovascular and metabolic diseases. In this review, we present a thorough overview of the molecular mechanisms of mitoNEET, with an emphasis on their implications for cardiometabolic diseases in more recent years. Furthermore, we explore the potential impact of these findings on the development of novel therapeutic strategies and discuss potential directions for future research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".