Tranilast ameliorates cyclophosphamide-induced lung injury and nephrotoxicity
Bibliographic record
Abstract
The world-wide increase in cancer incidence imposes a corresponding significant increase in the use of chemotherapeutic agents. Nephrotoxicity is a side effect frequently encountered with cyclophosphamide (CP), which is also well-known to cause acute and chronic lung toxicities. The current study focuses on the evaluation of the potential protective efficacy of tranilast against acute and subacute CP-induced lung and kidney injuries in male Swiss Albino mice. Intraperitoneal CP significantly impaired oxidant/anti-oxidant balance and increased inflammatory cell count in bronchoalveolar lavage fluid, serum creatinine, blood urea nitrogen (BUN), tumor necrosis factor-α (TNF-α) and lactate dehydrogenase (LDH) levels, with significant impairment of lung and kidney architectures. Tranilast taken orally for 8 and 14 days significantly enhanced mice anti-oxidant defense mechanisms; it increased lung and kidney SOD activity, GSH content and reduced lipid peroxidation. Tranilast significantly reduced serum creatinine and BUN. Furthermore, it decreased accumulation of inflammatory cells in the lungs. Serum TNF-α, LDH, total lung and kidney protein contents significantly declined as well. Histopathological examination revealed concomitant significant tissue recovery. Such results show a significant protective potential of tranilast against deleterious lung and kidney damage induced by CP, probably by enhancing host antioxidant defense mechanism, decreasing cytotoxicity, and decreasing expression of inflammatory cytokines.
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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".