Harnessing the therapeutic potential of mesenchymal stem cells in multiple sclerosis
Bibliographic record
Abstract
Phase I clinical trials exploring the use of autologous mesenchymal stem cell (MSC) therapy for the treatment of multiple sclerosis (MS) have begun in a number of centers across the world. MS is a complex and chronic immune-mediated and neurodegenerative disease influenced by genetic susceptibility and environmental risk factors. The ideal treatment for MS would involve both attenuation of detrimental inflammatory responses, and induction of a degree of tissue protection/regeneration within the CNS. Preclinical studies have demonstrated that both human-derived and murine-derived MSCs are able to improve outcomes in the animal model of MS, experimental autoimmune encephalomyelitis. How MSCs ameliorate experimental autoimmune encephalomyelitis is being intensely investigated. One of the major mechanisms of action of MSC therapy is to inhibit various components of the immune system that contribute to tissue destruction. Emerging evidence now supports the idea that MSCs can access the CNS where they can provide protection against tissue damage, and may facilitate tissue regeneration through the production of growth factors. The prospect of cell-based therapy using MSCs has several advantages, including the relative ease with which they can be extracted from autologous bone marrow or adipose tissue and expanded in vitro to reach the purity and numbers required for transplantation, and the fact that MSC therapy has already been used in other human disease settings, such as graft-versus-host and cardiac disease, with initial reports indicating a good safety profile. This article will focus on the theoretical and practical issues relevant to considerations of MSC therapy in the context of MS.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".