Astrocytes in multiple sclerosis
Bibliographic record
Abstract
Recent experimental and clinical studies on astrocytes are unraveling the capabilities of these multi-functional cells in normal homeostasis, and in central nervous system (CNS) disease. This review focuses on understanding their behavior in all aspects of the initiation, evolution, and resolution of the multiple sclerosis (MS) lesion. Astrocytes display remarkable flexibility and variability of their physical structure and biochemical output, each aspect finely tuned to the specific stage and location of the disease, participating in both pathogenic and beneficial changes seen in acute and progressive forms. As examples, chemo-attractive or repulsive molecules may facilitate the entry of destructive immune cells but may also aid in the recruitment of oligodendrocyte precursors, essential for repair. Pro-inflammatory cytokines may attack pathogenic cells and also destroy normal oligodendrocytes, myelin, and axons. Protective trophic factors may also open the blood-brain barrier and modulate the extracellular matrix to favor recruitment and persistence of CNS-specific immune cells. A chronic glial scar may confer structural support following tissue loss and inhibit ingress of further noxious insults and also inhibit migration of reparative cells and molecules into the damaged tissue. Continual study into these processes offers the therapeutic opportunities to enhance the beneficial capabilities of these cells while limiting their destructive effects.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".