An aqueous extract from <i>Moringa oleifera</i> leaves ameliorates hepatotoxicity in alloxan-induced diabetic rats
Bibliographic record
Abstract
This study was carried out to evaluate the possible mechanisms through which an aqueous extract from MO leaves demonstrates hepatoprotective effects in alloxan-induced diabetic rats. Eighty albino rats were assigned to 4 groups. The control group was orally administered sterile saline. The second group was injected with alloxan (150 mg/kg body mass (b.m.)) by intraperitoneal injection (i.p.). The third group was given MO (250 mg/kg b.m.) orally, daily. The fourth group was injected with alloxan, as for the second group, and administrated an aqueous extract of MO leaves, as for the third group. Alloxan induced degenerative changes in hepatic and pancreatic tissues, increased hepatic lipid peroxidation, and increased gene expression of PC and caspase 3. However, it decreased the activities of hepatic SOD and CAT, and gene expression of GS. In contrast, the MO extract prevented changes to the histoarchitecture of hepatic and pancreatic tissues and normalized the reduced hepatic levels of glutathione, as well as the activities of SOD and CAT, and the gene expression of GS, while reducing blood glucose levels, hepatic lipid peroxidation, and the gene expression of PC and caspase 3. This study indicated that an aqueous extract of MO leaves can be a potent antioxidant and used as an hepatoprotective agent.
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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".