Additional effects of engineered stem cells expressing a therapeutic gene and interferon-β in a xenograft mouse model of endometrial cancer
Bibliographic record
Abstract
Endometrial cancer is the most common gynecologic malignancy in women worldwide. In the present study, we evaluated the effects of neural stem cell-directed enzyme/prodrug therapy (NDEPT) designed to more selectively target endometrial cancer. For this, we employed two different types of neural stem cells (NSCs), HB1.F3.CD and HB1.F3.CD.IFN-β cells. Cytosine deaminase (CD) can convert the non-toxic prodrug, 5-fluorocytosine (5-FC), into a toxic agent, 5-fluorouracil (5-FU), which inhibits DNA synthesis. IFN-β is a powerful cytotoxic cytokine that is released by activated immune cells or lymphocytes. In an animal model xenografted with endometrial Ishikawa cancer cells, the stem cells stained with CM-DiI were injected into nearby tumor masses and 5-FC was delivered by intraperitoneal injection. Co-expression of CD and IFN-β significantly inhibited the growth of cancer (~50-60%) in the presence of 5-FC. Among migration-induced factors, VEGF gene was highly expressed in endometrial cancer cells. Histological analysis showed that the aggressive nature of cancer was inhibited by 5-FC in the mice treated with the therapeutic stem cells. Furthermore, PCNA expression was more decreased in HB1.F3.CD.IFN-β treated mice rather than HB1.F3.CD treated mice. To confirm the in vitro combined effects of 5-FU and IFN-β, 5-FU was treated in Ishikawa cells. 5-FU increased the IFN-β/receptor 2 (IFNAR2) and BXA levels, indicating that 5-FU increased sensitivity of endometrial cancer cells to IFN-β, leading to apoptosis of cancer cells. Taken together, these results provide evidence for the efficacy of therapeutic stem cell-based immune therapy involving the targeted expression of CD and IFN-β genes at endometrial cancer sites.
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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) | 0.000 | 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 | 0.001 |
| 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".