<scp>MicroRNAs</scp> in oligodendrocyte development and remyelination
Bibliographic record
Abstract
Oligodendrocytes are the glial cells responsible for the formation of myelin around axons of the central nervous system (CNS). Myelin is an insulating layer that allows electrical impulses to transmit quickly and efficiently along neurons. If myelin is damaged, as in chronic demyelinating disorders such as multiple sclerosis (MS), these impulses slow down. Remyelination by oligodendrocytes is often ineffective in MS, in part because of the failure of oligodendrocyte precursor cells (OPCs) to differentiate into mature, myelinating oligodendrocytes. The process of oligodendrocyte differentiation is tightly controlled by several regulatory networks involving transcription factors, intracellular signaling pathways, and extrinsic cues. Understanding the factors that regulate oligodendrocyte development is essential for the discovery of new therapeutic strategies capable of enhancing remyelination. Over the past decade, microRNAs (miRNAs) have emerged as key regulators of oligodendrocyte development, exerting effects on cell specification, proliferation, differentiation, and myelination. This article will review the role of miRNAs on oligodendrocyte biology and discuss their potential as promising therapeutic tools for remyelination.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.003 |
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".