Nanoemulsion of Coleus Scutellarioides Leaf Extract Ameliorates Diabetes-Induced Hyperglycemia and Organ Damage in Rats
Bibliographic record
Abstract
This experimental study evaluated the effectiveness of nanoemulsion of ethanol extract of miana leaf (Coleus scutellarioides (L.) Benth.) in controlling type 1 diabetes mellitus in male rats. The optimal formulation (F2), obtained from a ratio of VCO: Tween 80: Propylene glycol (2:10:5), yielded a particle-sized nanoemulsion of 59.2 nm, a polydispersity index of 0.353, and a zeta potential of -32.26 mV. Male Wistar rats were induced with streptozotocin (100 mg/kg BW, intraperitoneally), and diabetes was confirmed 72 hours post-induction when blood glucose levels exceeded 250 mg/dL. The nanoemulsion was administered intraperitoneally once daily for 28 days at doses of 50, 75, and 100 mg/kg BW. The normal control group (non-diabetic) exhibited baseline urea and creatinine levels of 20.59 mg/dL and 0.26 mg/dL, respectively. By day 28, the group receiving 100 mg/kg BW showed urea and creatinine levels of 22.88 mg/dL and 0.28 mg/dL, respectively, which were not significantly different from the normal controls (p > 0.05). Different doses showed optimal effects on different parameters: 50 mg/kg BW was most effective for blood glucose reduction (84.3% reduction by day 28), 75 mg/kg BW provided optimal pancreatic protection (mean damage score of 2.0), while 100 mg/kg BW showed superior renal protective effects. Phytochemical screening of the ethanol extract confirmed the presence of secondary metabolites, including alkaloids, flavonoids, saponins, tannins, and steroids. Statistical analyses were performed using one-way ANOVA followed by post hoc tests for parametric data and the Kruskal–Wallis test for non-parametric data, revealing significant differences among treatment groups (p < 0.05).
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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 | 0.001 |
| 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".