Multiple Sclerosis and Neuroinflammation: The Overview of Current and Prospective Therapies
Bibliographic record
Abstract
Persistent neuroinflammation is now recognized as a chief pathological component of practically all neurodegenerative diseases. Neuroinflammation in the central nervous system (CNS), is accompanied with immune responses of glial cells. Glial cells respond to pathological stimuli through antigen presentation, and cytokine and chemokine signaling. Therefore, limiting CNS inflammation represents prospective therapeutic approach in diseases like Alzheimer's, amyotrophic lateral sclerosis, Parkinson's, ischemia, various psychiatric disorders and Multiple sclerosis (MS). As a complex disease, MS is characterized by neuroinflamation, demyelination and sequential axonal loss. Due to unknown etiology and the heterogeneous presentation of the disease, MS is hard to treat and the search for potential therapeutics is wide and meticulous. However, finding a proper antineuroinflammatory drug may bring an advance in selecting novel treatment regimens of ample of neurodegenerative diseases and neurological disorders. The present review gives the overview of the existing and potential therapies in MS, aimed to modulate neuroinflammation and ensure neuroprotection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".