Antitumor effect of sarcnu in a 06-methylguanine-DNA methyltransferase positive human glioma xenograft model
Bibliographic record
Abstract
Objective: To assess whether novel analogue of nitrosoureas, 2-chloroethyl-3-sarcosinamide-1-nitrosourea (SarCNU), has antitumor effect to 0 6 -methylguanine-DNA methyltransferase (MGMT) positive tumors in vivo . Methods: MGMT positive human glioma cell line SF-767 xenografts in nude mice were treated with SarCNU. The antitumor efficacy of SarCNU was compared with the results of 1, 3-bis(2-chloroethyl)-1-nitrosourea (BCNU) treatment with or without 0 6 -benzylguanine (0 6 -BG) preadministration. Results: Since the SF-767 is MGMT strongly positive, BCNU treatment alone did not result in a satisfactory anticancer effect. As expected, 0 6 -BG by depleting MGMT activity, significantly enhanced BCNU antitumor efficacy ( P <0.001). More interestingly, SarCNU treatment alone had a better antitumor effect than O 6 -BG plus BCNU treatment (F=51.7, P =0.00036). Conclusion: Since SarCNU enters cells via extraneuronal monoamine transporter (EMT), the enhanced antitumor activity of SarCNU in this MGMT positive human tumor xenograft model may be due to the presence of EMT in SF-767. SarCNU may be used as an alternative treatment for MGMT positive tumors, specifically for tumors expressing EMT.
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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".