Pilot trial of intravenous autologous culture-expanded mesenchymal stem cell transplantation in multiple sclerosis
Bibliographic record
Abstract
Background: Mesenchymal stem cells (MSCs) exhibit immunomodulatory, tissue-protective, and repair-promoting properties in vitro and in animals. Clinical trials in several human conditions support the safety and efficacy of MSC transplantation. Published experience in multiple sclerosis (MS) is modest. Objective: To assess feasibility, safety, and tolerability and explore efficacy of autologous MSC transplantation in MS. Methods: Participants with relapsing-remitting multiple sclerosis (RRMS) or secondary progressive multiple sclerosis (SPMS), Expanded Disability Status Scale score 3.0–6.5, disease activity or progression in the prior 2 years, and optic nerve involvement were enrolled. Bone-marrow-derived MSCs were culture-expanded and then cryopreserved. After confirming fulfillment of release criteria, 1–2 × 10 6 MSCs/kg were thawed and administered IV. Results: In all, 24 of 26 screened patients were infused: 16 women and 8 men, 10 RRMS and 14 SPMS, mean age 46.5, mean Expanded Disability Status Scale score 5.2, 25% with gadolinium-enhancing magnetic resonance imaging (MRI) lesions. Mean cell dosage (requiring 1–3 passages) was 1.9 × 10 6 MSCs/kg (range, 1.5–2.0) with post-thaw viability uniformly ⩾95%. Cell infusion was tolerated well without treatment-related severe or serious adverse events, or evidence of disease activation. Conclusion: Autologous MSC transplantation in MS appears feasible, safe, and well tolerated. Future trials to assess efficacy more definitively are warranted.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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".