Protective Effects of a Polyphenol‐Rich Extract from <i>Syzygium cumini</i> (L.) Skeels Leaf on Oxidative Stress‐Induced Diabetic Rats
Bibliographic record
Abstract
Syzygium cumini (L.) Skeels has been reported to exert anti‐inflammatory and cardiometabolic activities due to its high content of polyphenols. We characterized the chemical composition and assessed the antidiabetic effects of a novel polyphenol‐rich extract (PESc) obtained from S. cumini leaf. Rats were injected with alloxan (150 mg/kg, ip, ALX group) and followed up for 7 days. Some were orally treated with PESc (50 mg/kg/day) for 7 days before and after diabetes induction (ALX‐PP) or only for 7 days after alloxan injection (ALX‐P). ALX‐P and ALX‐PP decreased fasting glycemia in 37 and 43%, respectively, as compared to ALX. Triglycerides and total cholesterol serum levels were also significantly reduced in comparison to ALX. PESc presented high polyphenol concentration (71.78 ± 8.57 GAE/100 g), with flavonoid content of 8.21 ± 0.42 QE/100 g. Upon HPLC‐MS/MS and MS/MS studies, five main polyphenols—gallic acid, quercetin, myricetin, and its derivatives—were identified. Myricetin was predominant (192.70 ± 16.50 μg/mg PESc), followed by measurable amounts of gallic acid (11.15 ± 0.90 μg/mg PESc) and quercetin (4.72 ± 0.06 μg/mg PESc). Kinetic assessment of total antioxidant capacity revealed PESc high potency, since maximum response was reached within 5 min reaction time in a concentration‐dependent manner. Specific antioxidant activity of PESc was assessed against both DPPH• and ABTS•+, showing strong activity (IC50: 3.88 ± 1.09 and 5.98 ± 1.19 μg/mL, resp.). PESc also inhibited lipoxygenase activity (IC50: 27.63 ± 8.47), confirming its antioxidant activity also on biologically relevant radicals. Finally, PESc induced insulin secretion by directly stimulating INS‐1E β cells in the absence of any cytotoxic effect. Overall, our results support that PESc is a potent antioxidant phytocomplex with potential pharmacological use as a preventive antidiabetic natural product.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".