Antioxidant Phytochemicals as Potential Therapy for Diabetic Complications
Bibliographic record
Abstract
The global prevalence of diabetes continues to increase partly due to rapid urbanization and an increase in the aging population. Consequently, this is associated with a parallel increase in the prevalence of diabetic vascular complications which significantly worsen the burden of diabetes. For these diabetic vascular complications, there is still an unmet need for safe and effective alternative/adjuvant therapeutic interventions. There is also an increasing urge for therapeutic options to come from natural products such as plants. Hyperglycemia-induced oxidative stress is central to the development of diabetes and diabetic complications. Furthermore, oxidative stress-induced inflammation and insulin resistance are central to endothelial damage and the progression of diabetic complications. Human and animal studies have shown that polyphenols could reduce oxidative stress, hyperglycemia, and prevent diabetic complications including diabetic retinopathy, diabetic nephropathy, and diabetic peripheral neuropathy. Part of the therapeutic effects of polyphenols is attributed to their modulatory effect on endogenous antioxidant systems. This review attempts to summarize the established effects of polyphenols on endogenous antioxidant systems from the literature. Moreover, potential therapeutic strategies for harnessing the potential benefits of polyphenols for diabetic vascular complications are also discussed.
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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".