Tropisetron attenuates tumor growth and progression in an experimental model of mouse lung cancer
Bibliographic record
Abstract
Abstract The antineoplastic effects of 5‐hydroxytryptamine (5‐HT) receptor antagonists have been shown in previous studies. However, the exact underlying mechanisms mediating these antineoplastic effects are unclear. In the present study, we assessed the antineoplastic effects of tropisetron, a 5‐HT receptor antagonist, in an experimental model of lung cancer in BALB/c mouse. Lewis lung carcinoma cell line was used to induce lung cancer. Mice were divided into four groups (n = 6) as follows: tumor‐bearing mice + tropisetron (5 mg/kg intraperitoneally [IP]), tumor‐bearing mice + tropisetron (10 mg/kg IP), tumor‐bearing mice + saline, healthy mice + tropisetron (10 mg/kg). Tumor burden, interferon‐γ (IFN‐γ), interleukin (IL)‐4, pathological response, Ki‐67, and E‐cadherin were assessed using enzyme‐linked immunosorbent assay, and real‐time polymerase chain reaction. Comet assay was used to assess DNA toxicity. Tropisetrone‐treated animals (either 5 or 10 mg/kg) showed significantly lower tumor sizes at the day 24th after tumor induction. Tropisetron received animals also showed significantly higher levels of IFN‐γ, E‐cadherin, pathologic response, and necrotic cells compared to the saline‐treated counterparts. In addition, the levels of IL‐4, and Ki‐67 were significantly lower in tropisetrone treated mice in comparison with control. Furthermore, tropisteron coadministration signifcantly reduced H 2 O 2 ‐induced DNA toxicity while treatment with tropisteron alone showed no adverse effect on DNA. Tropisetrone can be used as a potential antineoplastic drug in lung cancer. This agent can promote its antineoplastic effects in part through modulating inflammatory and proliferating markers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".