Multiple Sclerosis: Unraveling the Neuropathology and Mechanisms of Neurodegeneration
Bibliographic record
Abstract
Multiple Sclerosis (MS) is a debilitating autoimmune disease affecting the central nervous system, and has been the focus of intense research for the past 20 years. A better understanding of immune-related pathogenic mechanisms is necessary for the development of: (1) novel methods to monitor disease progression, (2) earlier diagnoses through unveiling new biomarkers, and (3) the invention of more effective and personalized MS treatments therapies. Several disease modifying treatments (e.g., natalizumab, fingolimod and beta interferons) have been approved for MS; however with no cure, the current treatment paradigm has shifted to the notion of ‘no evident disease activity’. While many of these Food and Drug Administration approved MS treatments have been shown to reduce the number of relapses and lesions, the paradox concerning MS treatments, namely the overuse of T cell activation as a target, necessitates the need for identifying entirely new contributors to disease pathology. MS pathogenesis has been associated with many theories, ranging from the release of proinflammatory cytokines from macrophages and microglial cells to B cell-derived demyelinating antibodies. As such, these hypotheses should be considered in addition to the role of T cells when developing novel MS treatment plans and identifying aligning biomarkers. Recently, clinical trials (ClinicalTrials.gov Identifier: NCT00040482, NCT00342134, and NCT00342134) investigating autologous hematopoietic stem cell transplantation have demonstrated success in MS. Future research should focus on identifying non-invasive biomarkers, such as blood concentrations of miRNAs, as an indication of underlying pathology and to aid in early diagnosis, tracking disease progression and identifying more effective and personalized MS treatments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".