Hibiscus-cisplatin combination treatment decreases liver toxicity in rats while increasing toxicity in lung cancer cells via oxidative stress- apoptosis pathway
Bibliographic record
Abstract
Cisplatin (CIS) is a broad-spectrum anti-carcinogen that causes cytotoxic effects both in normal and cancer cells. The purpose of this study was to test whether Hibiscus sabdariffa (HS) extract can reduce CIS-induced hepatotoxicity in rodents and to assess its anticancer activity in vitro. Treatment with HS extract at daily doses of 500 mg/kg before and after a single dose of CIS (10 mg/kg) reduced hepatotoxicity in Wistar male albino rats. HS extract reduced activity of hepatic damage marker enzymes ( i.e. alanine and aspartate aminotransferases), necrosis, and apoptosis in liver tissues of CIS-treated rats. This hepatic protection was associated with reduced oxidative stress in liver tissues. The antioxidant effects of HS were manifested as a normalization of malondialdehyde levels and glutathione levels which were all raised after CIS-induction. In addition, HS treatment resulted in a decrease of catalase, and superoxide dismutase activity. The combined effects of CIS and HS were also studied in two human lung cancer cell lines (A549 and H460). Treatment with HS (20 μg /mL) enhanced the cytotoxic activity of CIS both in A549 and H460 cell lines. Interestingly, HS increased CIS-induced apoptosis and oxidative stress more clearly in A549 cells indicating that HS extract in combination with CIS could increase the efficacy of CIS in the treatment of cancer.
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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 | 0.001 |
| 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".