The therapeutic potential of mesenchymal stem cell transplantation as a treatment for multiple sclerosis: consensus report of the International MSCT Study Group
Bibliographic record
Abstract
Current therapies for multiple sclerosis effectively reduce inflammation, but do little in terms of repair to the damaged central nervous system. Cell-based therapies may provide a new strategy for bolstering regeneration and repair through neuro-axonal protection or remyelination. Mesenchymal stem cells modulate pathological responses in experimental autoimmune encephalitis, alleviating disease, but also stimulate repair of the central nervous system through the release of soluble factors. Autologous and allogeneic mesenchymal stem cells have been safely administered to individuals with hemato-oncological diseases and in a limited number of patients with multiple sclerosis. It is therefore reasonable to move mesenchymal stem cells transplantation into properly controlled human studies to explore their potential as a treatment for multiple sclerosis. Since it is likely that the first such studies will probably involve only small numbers of patients in a few centers, we formed an international panel comprising multiple sclerosis neurology and stem cell experts, as well as immunologists. The aims were to derive a consensus on the utilization of mesenchymal stem cells for the treatment of multiple sclerosis, along with protocols for the culture of the cells and the treatment of patients. This article reviews the consensus derived from our group on the rationale for mesenchymal stem cell transplantation, the methodology for generating mesenchymal stem cells and the first treatment protocol for multiple sclerosis patients.
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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 | 0.032 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".