Adenosine potentiates the therapeutic effects of neural stem cells expressing cytosine deaminase against metastatic brain tumors
Bibliographic record
Abstract
Tumor-tropic properties of neural stem cells (NSCs) provide a novel approach with which to deliver targeting therapeutic genes to brain tumors. Previously, we developed a therapeutic strategy against metastatic brain tumors using a human NSC line (F3) expressing cytosine deaminase (F3.CD). F3.CD converts systemically administered 5-fluorocytosine (5-FC), a blood-brain barrier permeable nontoxic prodrug, into the anticancer agent 5-fluorouracil (5-FU). In this study, we potentiated a therapeutic strategy of treatment with nucleosides in order to chemically facilitate the endogenous conversion of 5-FU to its toxic metabolite 5-FU ribonucleoside (5-FUR). In vitro, 5-FUR showed superior cytotoxic activity against MDA-MB-435 cancer cells when compared to 5-FU. Although adenosine had little cytotoxic activity, the addition of adenosine significantly potentiated the in vitro cytotoxicity of 5-FU. When MDA-MB‑435 cells were co-cultured with F3.CD cells, F3.CD cells and 5-FC inhibited the growth of MDA-MB-435 cells more significantly in the presence of adenosine. Facilitated 5-FUR production by F3.CD was confirmed by an HPLC analysis of the conditioned media derived from F3.CD cells treated with 5-FC and adenosine. In vivo systemic adenosine treatment also significantly potentiated the therapeutic effects of F3.CD cells and 5-FC in an MDA-MB-435 metastatic brain tumor model. Simple adenosine addition improved the antitumor activity of the NSCs carrying the therapeutic gene. Our results demonstrated an increased therapeutic potential, and thereby, clinical applicability of NSC-based gene therapy.
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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".