Hepatoprotective activity of the neem-based constituent azadirachtin-A in carbon tetrachloride intoxicated Wistar rats
Bibliographic record
Abstract
The aim of this study was to investigate the hepatoprotective role of azadirachtin-A in carbon tetrachloride (CCl4) induced hepatotoxicity in rats. The group allotment for the animals used in the hepatoprotective study included a vehicle treatment group, CCl4 (1 mL · (kg body mass)(-1)) treatment group, silymarin (100 μg · (kg body mass)(-1) · day(-1)) + CCl4 treatment group, and groups treated with different doses of azadirachtin-A (100 or 200 μg · (kg body mass)(-1) · day(-1)) + CCl4. On the 9th day, blood was obtained for measuring the biochemical parameters, and liver tissue was obtained for pathological examination. The acute toxicity test with azadirachtin-A (500, 1000, or 2000 μg · (kg body mass)(-1)) indicated no mortality after 14 days of treatment; further, there was no change in behavior, food consumption, or organ mass. However with the higher dose, some hematological parameters showed changes. Hepatoprotective studies revealed that the CCl4 treatment group exhibited a decrease in total protein and albumin levels, whereas a significant increase in BUN, AST, ALT, and ALP levels were noticed compared with the vehicle-treated control, indicating that there was liver damage caused by CCl4. Histology and ultrastructure study confirmed that pretreatment with azadirachtin-A dose-dependently reduced hepatocellular necrosis and, therefore, protected the liver against toxicity caused by CCl4. The results from this study indicate that pretreatment with azadirachtin-A at the higher dose levels, moderately restores the rat liver to normal. This study confirms that azadirachtin-A possesses greater hepatoprotective action; however, the effective concentration needs to be determined.
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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 | 0.001 |
| 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".