Anti-proliferative Effect of Engineered Neural Stem Cells Expressing Cytosine Deaminase and Interferon-β against Lymph Node–Derived Metastatic Colorectal Adenocarcinoma in Cellular and Xenograft Mouse Models
Bibliographic record
Abstract
PURPOSE: cytosine deaminase (CD) (HB1.F3.CD) and human interferon-β (IFN-β) (HB1.F3.CD.IFN-β) were employed against lymph node-derived metastatic colorectal adenocarcinoma. MATERIALS AND METHODS: CD can convert a prodrug, 5-fluorocytosine (5-FC), to active 5-fluorouracil, which inhibits tumor growth through the inhibition of DNA synthesis,while IFN-β also strongly inhibits tumor growth by inducing the apoptotic process. In reverse transcription polymerase chain reaction analysis, we confirmed that HB1.F3.CD cells expressed the CD gene and HB1.F3.CD.IFN-β cells expressed both CD and IFN-β genes. RESULTS: In results of a modified trans-well migration assay, HB1.F3.CD and HB1.F3.CD.IFN-β cells selectively migrated toward SW-620, human lymph node-derived metastatic colorectal adenocarcinoma cells. The viability of SW-620 cells was significantly reduced when co-cultured with HB1.F3.CD or HB1.F3.CD.IFN-β cells in the presence of 5-FC. In addition, it was found that the tumor-tropic properties of these engineered human neural stem cells (hNSCs) were attributed to chemoattractant molecules including stromal cell-derived factor 1, c-Kit, urokinase receptor, urokinase-type plasminogen activator, and C-C chemokine receptor type 2 secreted by SW-620 cells. In a xenograft mouse model, treatment with hNSC resulted in significantly inhibited growth of the tumor mass without virulent effects on the animals. CONCLUSION: The current results indicate that engineered hNSCs and a prodrug treatment inhibited the growth of SW-620 cells. Therefore, hNSC therapy may be a clinically effective tool for the treatment of lymph node metastatic colorectal cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".