Efficacy and Safety of Resveratrol in Type 1 Diabetes Patients: A Two-Month Preliminary Exploratory Trial
Bibliographic record
Abstract
Resveratrol has been reported to be beneficial against diabetes complications. The objective of this study was to evaluate the efficacy of resveratrol in decreasing hyperglycemia in patients with type 1 diabetes (T1D) by a preliminary investigation designed as an exploratory clinical trial. Thirteen patients with T1D from both the sexes participated in this trial. All patients received resveratrol in 500 mg capsules, twice daily for 60 days. Bodyweight, fasting blood sugar (FBS), hemoglobin A1c (HbA1c), insulin, homeostasis model of assessment for insulin resistance (HOMA-IR), homeostasis model of assessment for β-cell function (HOMA-β), and markers of liver and kidney damage, inflammation, and oxidative stress were measured before the intervention, at 30 days and at 60 days. Resveratrol supplementation for 60 days significantly decreased FBS and HbA1c in comparison with the baseline values. Resveratrol treatment also resulted in a decrease in the level of a marker for oxidative stress, malondialdehyde, and an increase in total antioxidant capacity in T1D patients. Insulin, HOMA-IR, HOMA-β, and markers of liver and kidney function and inflammation were not significantly affected by resveratrol treatment. Overall, the results showed that 60 days of resveratrol supplementation exerted strong antidiabetic and antioxidant effects in patients with T1D.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".