Anticancer Gene-engineered MSC-mediated Cancer Cell Death: An Imaging Demonstration
Bibliographic record
Abstract
This study was performed to demonstrate the transportation of an engineered MSC-produced intracellular anticancer gene product between mesenchymal stem cell (MSC) and cancer cells. MSC-mediated anticancer strategy has held great promise owing to MSCs’ capacity of tumor-directed migration and the availability of specific anticancer genes. All anticancer genes that have been used in previous MSC-mediated anticancer studies were limited in functioning via extracellular mechanisms, mainly because of the restriction by cell membrane to macromolecules including proteins. In order to apply the majority of potent anticancer genes to the MSC-mediated anticancer system, a specifically designed expression vector which bears an intracellular anticancer gene, PTEN, is utilized to demonstrate the feasibility of the system in cancer therapies. A transacting activator of transcription (TAT) was introduced into an expression vector followed by a segment for PTEN-RFP fusion protein. A direct demonstration of PTEN-RFP transportation between MSC and cancer cells was obtained from direct co-cultures. A marked cancer cell death was observed in indirect co-cultures with conditioned media from PTEN-transfected MSCs. The demonstration of PTEN-engineered MSC-produced PTEN transportation indicates the feasibility of applying intracellular anticancer gene expression system in MSC-mediated strategies for cancer therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".