Bibliographic record
Abstract
Multiple sclerosis (MS) is a complex disease characterized by autoimmune demyelination of the myelin sheath of the central nervous system. Varying degrees of disability result from disruptions in neural, physical, and psychological processes. No clear cause of MS has been identified, but specific genetic mutations and environmental effects have been established as factors in disease development. However, the low disease concordance rates and transmission disequilibrium observed in MS prevent genetic factors alone from explaining increased susceptibility. As a result, the involvement of epigenetics, or heritable changes in the genome that are not caused by alterations in the underlying DNA sequence, has been proposed to resolve these apparent discrepancies. This article focuses on recent research into three primary epigenetic mechanisms and their relation to MS susceptibility: DNA methylation, histone modifications, and post-transcriptional regulation of target genes by microRNA. These epigenetic phenomena interact with each other and environmental stimuli to produce disease. Promising experimental research has revealed that inhibitors of such epigenetic activity reduce disease phenotypes in animal models. In this article, ways in which epigenetic changes can induce MS development are described and prospective research avenues are proposed. Keywords: epigenetics; multiple sclerosis (MS); DNA methylation; histones; microRNA (miRNA)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".