Chamomile and oregano extracts synergistically exhibit antihyperglycemic, antihyperlipidemic, and renal protective effects in alloxan-induced diabetic rats
Bibliographic record
Abstract
The bio-activities of separate Matricaria chamomilla (chamomile) and Origanum vulgare (oregano) are well studied; however, the combined effects of both natural products in animal diabetic models are not well characterized. In this study, alloxan-induced male albino rats were treated with single dose aqueous suspension of chamomile or oregano at dose level of either 150 or 300 mg/kg body mass or as equal parts as combination by stomach tube for 6 weeks. After treatment, blood samples were assessed for diabetic, renal, and lipid profiles. Insulin, amylase activity, and diabetic renal apoptosis were further evaluated. Treatment with higher dose of the extracts (300 mg/kg) as individual or as mixture of low doses (150 mg/kg of both the extracts) had significant mass gain, hypoglycemic effect (p ≤ 0.05) with decreased amylase activity and increased serum insulin levels. Restoration of renal profile, lipid profile with increase in HDL-c (p ≤ 0.05) along with reversal of pro-apoptotic Bax and anti-apoptotic Bcl-2 were well observed with 300 mg/kg mixture, showing synergistic activity of the extracts compared with individual low dose of 150 mg/kg. Collectively, our results indicate that combination of chamomile and oregano extracts will form a new class of drugs to treat diabetic complications.
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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 | 0.001 | 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".