The evolution of mesenchymal stem cell-derived neural progenitor therapy for Multiple Sclerosis: from concept to clinic
Bibliographic record
Abstract
This review delves into the generation and therapeutic applications of mesenchymal stem cell-derived neural progenitors (MSC-NPs) in Multiple Sclerosis (MS), a chronic autoimmune disease characterized by demyelination, neuroinflammation, and progressive neurological dysfunction. Most current treatment paradigms primarily aimed at regulating the immune response show little success against the neurodegenerative aspect of MS. This calls for new therapies that would play a role in neurodegeneration and functional recovery of the central nervous system (CNS). While utilizing MSC was found to be a promising approach in MS therapy, the initiation of MSC-NPs therapy is an innovation that introduces a new perspective, a dual-action plan, that targets both the immune and neurodegenerative mechanisms of MS. The first preclinical studies using animal models of the disease showed that MSC-NPs could migrate to damaged sites, support remyelination, and possess immunomodulatory properties, thus, providing a solid basis for their human application. Based on pilot feasibility studies and phase I clinical trials, this review covers the transition from preclinical to clinical phases, where intrathecally administered autologous MSC-NPs has shown great hope in treating patients with progressive MS by providing safety, tolerability, and preliminary efficacy. This review, after addressing the role of MSCs in MS and its animal model of experimental autoimmune encephalomyelitis (EAE), highlights the significance of the MSC-NP therapy by organizing its advancement processes from experimental models to clinical translation in MS treatment. It points out the continuing obstacles, which require more studies to improve therapeutic protocols, uncovers the mechanisms of action, and establishes long-term efficacy and safety in larger controlled trials.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".