A Potential Therapy Using Engineered Stem Cells Prevented Malignant Melanoma in Cellular and Xenograft Mouse Models
Bibliographic record
Abstract
PURPOSE: In the present study, human neural stem cells (hNSCs) with tumor-tropic behavior were used as drug delivery vehicle to selectively target melanoma. A hNSC line (HB1.F3) was transduced into two types: one expressed only the cytosine deaminase (CD) gene (HB1.F3. CD) and the other expressed both CD and human interferon-β (IFN-β) genes (HB1.F3.CD. IFN-β). MATERIALS AND METHODS: This study verified the tumor-tropic migratory competence of engineered hNSCs on melanoma (A375SM) using a modified Boyden chamber assay in vitro and CM-DiI staining in vivo. The antitumor effect of HB1.F3.CD and HB1.F3.CD.IFN-β on melanoma was also confirmed using an MTT assay in vitro and xenograft mouse models. RESULTS: A secreted form of IFN-β from the HB1.F3.CD.IFN-β cells modified the epithelial-mesenchymal transition (EMT) process and metastasis of melanoma. 5-Fluorouracil treatment also accelerated the expression of the pro-apoptotic protein BAX and decelerated the expression of the anti-apoptotic protein Bcl-xL on melanoma cell line. CONCLUSION: Our results illustrate that engineered hNSCs prevented malignant melanoma cells from proliferating in the presence of the prodrug, and the form that secreted IFN-β intervened in the EMT process and melanoma metastasis. Hence, neural stem cell-directed enzyme/prodrug therapy is a plausible treatment for malignant melanoma.
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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".