Anti-Tumor Effect and Neurotoxicity of Ethanol Adjuvant Therapy after Surgery of a Soft Tissue Sarcoma
Bibliographic record
Abstract
Wide resection is the main treatment for sarcomas; however, when they are located near major nerves, their sacrifices might affect limb function. The efficacy of ethanol adjuvant therapy for sarcomas has not been established. In this study, the anti-tumor effect of ethanol, as well as its neurotoxicity, were assessed. In vitro anti-tumor effect of ethanol as evaluated using MTT, wound healing, and invasion assays on a synovial sarcoma cell line (HS-SY-II). In vivo, an assessment was conducted in nude mice (implanted with subcutaneous HS-SY-II) treated with different ethanol concentrations after surgery with a close margin. Sciatic nerve neurotoxicity was assessed with electrophysiological and histological examination. In vitro, ethanol concentrations at 30% and higher showed cytotoxic effects in MTT assay and markedly reduced migration and invasive ability of HS-SY-II. In vivo, both 30% and 99.5% ethanol concentrations, compared to 0% concentration, significantly reduced the local recurrence. However, in the group treated with 99.5% ethanol, nerve conduction tests showed prolonged latency and decreased amplitude, and morphological changes suggestive of nerve degeneration were observed in the sciatic nerve, while the 30% ethanol did not cause neurological damage. In conclusion, 30% is the optimal concentration for ethanol adjuvant therapy after close-margin surgery for sarcoma.
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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 | 0.000 |
| 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".