The therapeutic potential of mesenchymal stem cell transplantation as a treatment for multiple sclerosis: consensus report of the International MSCT Study Group
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it