Combined but not single administration of vitamin C and <scp>l</scp>-carnitine ameliorates cisplatin-induced gastric mucosa damage in male rats
Bibliographic record
Abstract
Although cisplatin is a potent anticancer drug, it instigates oxidative and pro-inflammatory reactions that pose significant and distressing clinical symptoms. Therefore, this study investigated the effects of vitamin C and (or) l-carnitine on cisplatin-induced gastric mucosa damage in rat. The rats were allocated into 6 groups (n = 5). The control group received distilled water, while the treatment groups received cisplatin alone (CIP), or cisplatin with vitamin C, l-carnitine, or their combination. Cisplatin caused disruption of the gastric mucosa histoarchitecture and altered the mucus barrier function. Moreover, the stomach tissue of the CIP-treated group showed increased levels of oxidative stress markers (malondialdehyde and H2O2) and decreased activities of antioxidant (superoxide dismutase, glutathione peroxidase, catalase, glutathione S-transferase) and non-antioxidant (reduced glutathione) enzymes. These deleterious events were accompanied with significant increases in pro-inflammatory cytokines and inflammatory infiltration markers, myeloperoxidase and inducible nitric oxide synthase. However, the administration of both vitamin C and l-carnitine, and not either of the two showed additive effects in attenuating the adverse effects of cisplatin. The histological results agreed with the biochemical assays. The study concluded that the combined administration of vitamin C and l-carnitine, but not the single therapy, could prevent the adverse effects of cisplatin on gastric tissue.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".