The protective effects of silymarin nanoemulsion on 5-fluorouracil-induced gastrointestinal toxicity in rats
Bibliographic record
Abstract
5-Fluorouracil (5FUra) is the third most popular chemotherapeutic component employed to treat solid tumors. In the present study, we aimed to appraise the silymarin (SM) and silymarin nanoemulsion (SMN) effect on 5FUra-induced gastrointestinal toxicity in adult male rats. A total of 30 male Wistar rats were divided into 6 groups including the control (Crl) group, and groups treated with SMN (5 mg.kg-1), SM (5 mg.kg-1), 5FUra+SMN (5 mg.kg-1), and 5FUra+SM (5 mg.kg-1) by IP injection for 14 days. And gastrointestinal toxicity was induced by a single intraperitoneal (IP) injection of 5FUra (100 mg.kg-1) for the last group in the study. Treating rats with SM and SMN diminished elevating malondialdehyde (MDA) levels, and improved total antioxidant capacity (TAC) levels. Also, the intensity of mRNA expression of interleukin-2 (IL-2) and tumor necrosis factor-alpha (TNF-α) caused by 5FUra in the gastrointestinal tissue tract, and macroscopic oral ulcerations decreased, ass well as weight loss was prevented, particularly in the SMN group. Moreover, in the microscopic scope, there were significant improvements in the levels of hyperemia, hyaline, and inflammatory cell infiltration in the tongue, esophagus, and intestinal tissues in the FUra+SMN and FUra+SM groups compared to 5FUra. Hence, treatment with SM and SMN reduced oxidative stress, histopathological degeneration, and gene expression of inflammatory markers in the gastrointestinal tract. According to the results, treatment with SM and SMN markedly decreases the gastrointestinal toxicity caused by 5FUra.
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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) | 0.000 | 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) | 0.001 | 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".