Modulating inflammation and neuroprotection in multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a neurological disorder of the central nervous system with a presentation and disease course that is largely unpredictable. MS can cause loss of balance, impaired vision or speech, weakness and paralysis, fatigue, depression, and cognitive impairment. Immunomodulation is a major target given the appearance of focal demyelinating lesions in myelin-rich white matter, yet progression and an increasing appreciation for gray matter involvement, even during the earliest phases of the disease, highlights the need to afford neuroprotection and limit neurodegenerative processes that correlate with disability. This review summarizes key aspects of MS pathophysiology and histopathology with a focus on neuroimmune interactions in MS, which may facilitate neurodegeneration through both direct and indirect mechanisms. There is a focus on processes thought to influence disease progression and the role of oxidative stress and mitochondrial dysfunction in MS. The goals and efficacy of current disease-modifying therapies and those in the pipeline are discussed, highlighting recent advances in our understanding of pathways mediating disease progression to identify and translate both immunomodulatory and neuroprotective therapeutics from the bench to the clinic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".