miRNAs As Emerging Regulators of Oligodendrocyte Development and Differentiation
Bibliographic record
Abstract
Chronic demyelination is a hallmark of neurological disorders such as multiple sclerosis (MS) and several leukodystrophies. In the central nervous system (CNS), remyelination is a regenerative process that is often inadequate during these pathological states. In the MS context, in situ evidence suggests that remyelination is mediated by populations of oligodendrocyte progenitor cells (OPCs) that proliferate, migrate, and differentiate into mature, myelin-producing oligodendrocytes at sites of demyelinated lesions. The molecular programming of OPCs into mature oligodendrocytes is governed by a myriad of complex intracellular signaling pathways that modulate this process. Recent research has demonstrated the importance of specific and short non-coding RNAs, known as microRNAs (miRNAs), in regulating OPC differentiation and remyelination. Fortunately, it may be possible to take advantage of numerous developmental studies (both human and rodent) that have previously characterized miRNA expression profiles from the early neural progenitor cell to the late myelin-producing oligodendrocyte. Here we review much of the work to date and discuss the impact of miRNAs on OPC and oligodendrocyte biology. Additionally, we consider the potential for miRNA-mediated therapy in the context of remyelination and brain repair.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".