Quercetin nanoparticle ameliorates 5-fluorouracil-induced testicular damage in mice: A biochemical and histological study
Bibliographic record
Abstract
Background 5-Fluorouracil (5-FU) is one of the most broadly chemotherapeutic compounds employed in treating solid tumors. Quercetin (QU) as a flavonoid has diverse positive clinical applications including antioxidative, antiproliferative, and anti-inflammatory. The current study aims to assess the histopathological changes and anti-oxidative effects of QU loaded with chitosan nanoparticles (QU-NPs) on the testicular injury triggered by 5-FU in mice. Methods Twenty male albino mice were randomly divided into 4 groups as follows: the control, 5-FU, 5-FU + QU (5 mg/kg), and 5-FU + QU-NPs (5 mg/kg) for 14 days. Oxidative marker [malondialdehyde (MDA)] and antioxidant markers [superoxide dismutase (SOD) and catalase (CAT)] were examined in serum. Testicular damage stemmed from a single dose injection of 5-FU intraperitoneally (i.p.). The control group received normal saline and the treatment groups received QU with i.p. injection for 14 days. Results Serum MDA level was lower in the QU and QU-NPs treated groups than in the 5-FU group. Moreover, serum CAT levels were remarkably increased in both the QU- and QU-NPs treated groups compared to the 5-FU group. In the case of SOD, the most considerable difference was found between the 5-FU group and the QU-NPs, but not the QU group. Further, both QU and QU-NPs, particularly the QU-NPs, ameliorated 5-FU-induced testicular tissue damage, as evidenced by a decrease in hyperemia, edema, and vacuolation, and an improvement in the area of seminiferous tubules, and spermatocyte and seminiferous tubule counts. Conclusion Nanotechnology and fabrication of QU-NPs can ameliorate the testicular damage caused by 5-FU.
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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 | 0.001 | 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".