Influence of the prodrugs 5‐fluorocytosine and CPT‐11 on ovarian cancer cells using genetically engineered stem cells: tumor‐tropic potential and inhibition of ovarian cancer cell growth
Bibliographic record
Abstract
Recent studies have shown that genetically engineered stem cells (GESTECs) to produce suicide enzymes that convert non-toxic prodrugs to toxic metabolites selectively migrate toward tumor sites and reduce tumor growth. In the present study, we evaluated whether these GESTECs were capable of migrating to human ovarian cancer cells and examined the potential therapeutic efficacy of the gene-directed enzyme prodrug therapy against ovarian cancer cells in vitro. The expression of cytosine deaminase (CD) or carboxyl esterase (CE) mRNA of GESTECs was confirmed by RT-PCR. A modified transwell migration assay was performed to determine the migratory capacity of GESTECs to ovarian cancer cells. GESTECs (HB1.F3.CD or HB1.F3.CE cells) engineered to express a suicide gene (CD or CE) selectively migrated toward ovarian cancer cells. A [(3)H] thymidine incorporation assay was conducted to measure the proliferative index. Treatment of human epithelial ovarian cancer cell line (SKOV-3, an ovarian adenocarcinoma derived from the ascites of an ovarian cancer patient) with the prodrugs 5-fluorocytosine (5-FC) or camptothecin-11 (CPT-11) in the presence of HB1.F3.CD or HB1.F3.CE cells resulted in the inhibition of ovarian cancer cell growth. Based on the data presented herein, we suggest that GESTECs expressing CD/CE may have a potent advantage to selectively treat ovarian cancers.
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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".