Hepatoprotective effect of nanoniosome loaded <i>Myristica fragrans</i> phenolic compounds in mice‐induced hepatotoxicity
Bibliographic record
Abstract
In this study, nanoniosome-loaded Myristica fragrans' (MF) phenolic compounds (NLMP) were synthesized and characterized for their physical properties, and hepatoprotective effects on mice with liver toxicity induced by L-asparaginase (LA) injection. According to the results, NLMP has a spherical shape with a 263 nm diameter, a zeta potential of -26.55 mV and a polydispersity index (PDI) of 0.192. The weight and feed intake of mice induced with hepatotoxicity were significantly (p ≤ 0.05) increased after they were treated with NLMP (2.5 mg/kg body weight of mice). In addition, the blood levels of triglyceride (TG), cholesterol (Chol), liver enzymes (aspartate aminotransferase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP)) and total bilirubin were significantly (p ≤ 0.05) decreased. A significant increase (p ≤ 0.05) in the blood levels of the antioxidant defence system (glutathione peroxidase (GPX), superoxide dismutase (SOD) and catalase (CAT)) were also reported after NLMP treatment. NLMP was also led to a significant decrease (p ≤ 0.05) in inflammatory-related gene expression of inducible nitric oxide synthase (iNOS) and Interferon-gamma (IFN-γ) in the liver, as well as a meaningful (p ≤ 0.05) increase in the expression of SOD as an antioxidant status biomarker. Consequently, the NLMP is recommended as a potential dietary supplement to alleviate the symptoms of LA-induced hepatotoxicity.
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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".