Astrocytes in the Pathogenesis of Multiple Sclerosis: An In Situ MicroRNA Study
Bibliographic record
Abstract
Astrocytes are increasingly recognized as active contributors to the disease process in multiple sclerosis (MS), rather than being merely reactive. We investigated the expression of a selected microRNA (miRNA) panel that could contribute both to the injury and to the recovery phases of the disease. Individual astrocytes were laser microdissected from brain sections. We then compared the miRNAs' expressions in MS and control brain samples at different lesional stages in white versus grey matter regions. In active MS lesions, we found upregulation of ischemia-related miRNAs in white but not grey matter, often with reversion to the normal state in inactive lesions. In contrast to our previous findings on MS macrophages, expression of 2 classical inflammatory-related miRNAs, miRNA-155 and miRNA-146a, was reduced in astrocytes from active and chronic active MS lesions in white and grey matter, suggesting a lesser direct pathogenetic role for these miRNAs in astrocytes. miRNAs within the categories regulating aquaporin4 (-100, -145, -320) and glutamate transport/apoptosis/neuroprotection (-124a, -181a, and -29a) showed some contrasting responses. The regional and lesion-stage differences of expression of these miRNAs indicate the remarkable ability of astrocytes to show a wide range of selective responses in the face of differing insults and phases of resolution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".