Molecular Prospective on Malignant Transformation of Mesenchymal Stem Cells: An Issue in Cell Therapy
Bibliographic record
Abstract
Mesenchymal stem cell (MSCs) therapy, as a rapidly developing area of medicine, holds great promise for the treatment of a variety of medical conditions. MSCs are multipotent stem cells that can be isolated from various tissues and could self-renew and differentiate. They secrete cytokines and trophic factors that create a regenerative microenvironment and have immunomodulatory properties. Although clinical trials have been conducted with MSCs in various diseases, concerns regarding the possibility of malignant transformation of these cells have been raised. The studies showed a higher rate of hematological malignancy and carcinogenesis in experimental models after MSC transplantation. The mechanisms underlying malignant transformation of MSCs are complex and not fully understood, but they are believed to involve the presence of special signaling molecules and alterations in cell behavior regulation pathways. Possible pathways that lead to MSCs' oncogenic transformation occur through two mechanisms: spontaneous and stimulated malignant transformation, including cell fusion, fusion proteins, and the tumor microenvironment. MSC-based therapies have the potential to revolutionize medicine, and addressing the issue of malignancy is crucial to ensure their safety and efficacy. Therefore, the purpose of the present review is to summarize the potential mechanisms of the malignant transformation of MSCs. [Figure: see text].
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".