Antiviral Activity of Extract of Neem (Azadirachta Indica) leaves: An in vivo study
Bibliographic record
Abstract
Background: HCV in mainly replicate in liver cell and cause liver damage. The replication of HCV may be inhibited by leaves extract of neem (Azadirachta Indica). Aim: To find the antiviral activity of extract of neem (Azadirachta Indica) leaves. Methods: Thirty five hepatitis C positive and disappointment of conservative treatment were studied. Effect of neem (Azadirachta indica) leaves was seen in serum of patient. Extract of Azadirachta indica leaves was prepared and given orally to patients. The seropositivity of hepatitis C was estimated prior and after the leave extract of neem by HCV- RNA quantitative analysis by Polymerase chain reaction. Level of ALT, AST and total protein were estimated by standard kit methods. Results: A high HCV seropositivity was observed in patients before taking neem extract. We observed that after using the leave extract of Azadirachta indica or neem, the HCV seropositivity was significantly decreased. Levels of enzymes ALT and AST were also significantly decreased after taking neem extract. However, the level of serum protein was not changed. Conclusion: Extracted leaves of Azadirachta indica may serve as valuable regimen against hepatitis C virus as it has an ability to inhibit the protease responsible for replication of virus. Keywords: HCV seropositivity, Azadirachta indica leaves, transaminases
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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) | 0.002 | 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".