<i>Ziziphus spina-christi</i> leaves methanolic extract alleviates diethylnitrosamine-induced hepatocellular carcinoma in rats
Bibliographic record
Abstract
This study evaluated the antitumor activity of a methanolic extract from the leaves of Ziziphus spina-christi (ZSCL) against diethylnitrosamine (DENA)-induced hepatocarcinoma in rats. The phytochemical constituents, in vitro antioxidant and cytotoxic activities of ZSCL extract were investigated. Male Wistar rats were distributed among 6 groups: (i) normal control; (ii) ZSCL1-treated rats (100 mg/kg body mass; “b.m.”); (iii) ZSCL2-treated rats (300 mg/kg b.m.); (iv) rats with DENA-induced hepatocarcinoma; (v and vi) rats with hepatocarcinoma that were treated with either (v) ZSCL1 or (vi) ZSCL2. Serum liver function and levels of oxidative stress were assayed. The expression of hepatocyte growth factor, insulin-like growth factor-1 receptor, B cell lymphoma-2, and matrix metalloproteinase-9 oncogenes were quantified in liver samples. Histological examination of the liver tissues was performed. The ZSCL was rich in essential fatty acids, phytol, and polyphenolic flavones (luteolin and quercetin) with strong free-radical and peroxide scavenging activities and cytotoxic activity. Administration of ZSCL1 and ZSCL2 to the rats produced no toxic effects. DENA induced hepatocellular carcinoma and cholangioma by producing oxidative stress and upregulating the expression of hepatic oncogenes. Treatment of DENA-induced hepatocarcinoma with ZSCL2 ameliorated all of the abnormalities induced by DENA except for cholangioma. In conclusion, the ZSCL (300 mg/kg b.m.) displayed strong therapeutic activity against DENA-induced hepatocellular carcinoma via targeting oxidative stress and oncogenes.
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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".