Dicer deficiency affects microglial function during demyelination and impairs remyelination
Bibliographic record
Abstract
Microglia are essential regulators of central nervous system (CNS) homeostasis, playing key roles in demyelination and remyelination. Dysregulated microglial activity contributes to pathological inflammation and impaired repair processes in demyelinating diseases. Here, we investigate the role of Dicer1, a critical enzyme in microRNA biogenesis, in affecting microglial function, demyelination, and remyelination. Loss of Dicer1 in microglia resulted in amplified inflammatory responses, defective myelin debris clearance, and disruption of metabolic homeostasis, leading to exacerbated demyelination and delayed remyelination. Transcriptomic analysis revealed significant upregulation of inflammatory pathways, including interferon signaling and JAK/STAT activation, alongside a loss of homeostatic microglial gene expression. Protein-level validation confirmed sustained secretion of pro-inflammatory cytokines such as IFN-γ, IL-16, and CXCL12, creating a chronic inflammatory environment that impaired remyelination. Furthermore, Dicer1-deficient microglia failed to support oligodendrocyte progenitor cells (OPCs) differentiation/maturation, with increased apoptosis of mature oligodendrocytes (OLs), contributing to remyelination failure. These findings identify Dicer1 as a critical regulator of microglial homeostasis and inflammation resolution, highlighting its potential as a therapeutic target to mitigate inflammation and promote repair in demyelinating diseases.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".