Methylseleninic acid sensitizes Notch3 activated ovarian cancer cells to carboplatin
Bibliographic record
Abstract
Ovarian cancer is the deadliest of gynecologic cancers and is usually diagnosed at advanced stage due to invalidated screening test. Although carboplatin has been used for treating ovarian cancer for years, ovarian cancer eventually develops resistance to this platinum‐containing drug. Animal studies have demonstrated efficacies of methylseleninic acid (MSeA) in counteracting tumorigenesis. Expression of Notch3 is associated with chemoresistance and correlated with poor overall survival of human ovarian cancer patients. Here we tested the hypothesis that MSeA can sensitize OVCA429/NICD3 cells to carboplatin treatment. Overexpression of NICD3 (the constitutively active form of Notch3) in OVCA429 ovarian cancer cells (OVCA429/NICD3) rendered them resistance to carboplatin compared to OVCA429 cells transduced with an empty vector (OVCA429/pCEG). Co‐treatment with MSeA sensitized OVCA429/NICD3, but not OVCA429/pCEG cells to carboplatin treatment. These two chemicals appear to synergistically kill OVCA429/NICD3 cells, which can be suppressed by the presence of N ‐acetyl cysteine or DNA damage kinase inhibitors of ATM and DNA‐PK cs . In summary, MSeA sensitizes OVCA429/NICD3 cells to carboplatin treatment in a pathway involves oxidative stress, ATM and DNA‐PK cs , suggesting a new strategy to improve the efficacy of carboplatin treatment against ovarian cancer. Grant Funding Source : UMCP
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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".