The Effect of Polyphenols on Kidney Disease: Targeting Mitochondria
Bibliographic record
Abstract
Mitochondrial function, including oxidative phosphorylation (OXPHOS), mitochondrial biogenesis, and mitochondria dynamics, are essential for the maintenance of renal health. Through modulation of mitochondrial function, the kidneys are able to sustain or recover acute kidney injury (AKI), chronic kidney disease (CKD), nephrotoxicity, nephropathy, and ischemia perfusion. Therapeutic improvement in mitochondrial function in the kidneys is related to the regulation of adenosine triphosphate (ATP) production, free radicals scavenging, decline in apoptosis, and inflammation. Dietary antioxidants, notably polyphenols present in fruits, vegetables, and plants, have attracted attention as effective dietary and pharmacological interventions. Considerable evidence shows that polyphenols protect against mitochondrial damage in different experimental models of kidney disease. Mechanistically, polyphenols regulate the mitochondrial redox status, apoptosis, and multiple intercellular signaling pathways. Therefore, this review attempts to focus on the role of polyphenols in the prevention or treatment of kidney disease and explore the molecular mechanisms associated with their pharmacological activity.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".