Current status of neuroprotective and neuroregenerative strategies in multiple sclerosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Immune-mediated demyelination and consequent degeneration of oligodendrocytes and axons are hallmark features of multiple sclerosis (MS). Remyelination declines in progressive MS, causing permanent axonal loss and irreversible disabilities. Strategies aimed at enhancing remyelination are critical to attenuate disease progression. OBJECTIVE: We systematically reviewed recent advances in neuroprotective and regenerative therapies for MS, covering preclinical and clinical studies. METHODS: We searched three biomedical databases using defined keywords. Two authors independently reviewed articles for inclusion based on pre-specified criteria. The data were extracted from each study and assessed for risk of bias. RESULTS: Our search identified 7351 studies from 2014 to 2020, of which 221 met the defined criteria. These studies reported 262 interventions, wherein 92% were evaluated in animal models. These interventions comprised protein, RNA, lipid and cellular biologics, small molecules, inorganic compounds, and dietary and physiological interventions. Small molecules were the most highly represented strategy, followed by antibody therapies and stem cell transplantation. CONCLUSION: While significant strides have been made to develop regenerative treatments for MS, the current evidence illustrates a skewed representation of the types of strategies that advance to clinical trials. Further examination is thus required to address current barriers to implementing experimental treatments in clinical settings.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".