Meningeal inflammation and cortical demyelination in acute multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Cortical gray matter (GM) pathology, involving demyelination and neurodegeneration, associated with meningeal inflammation, could be important in determining disability progression in multiple sclerosis (MS). However, we need to know more about how cortical demyelination, neurodegeneration, and meningeal inflammation contribute to pathology at early stages of MS to better predict long-term outcome. METHODS: Tissue blocks from short disease duration MS (n = 12, median disease duration = 2 years), progressive MS (n = 21, disease duration = 25 years), non-diseased controls (n = 11), and other neurological inflammatory disease controls (n = 6) were quantitatively analyzed by immunohistochemistry, immunofluorescence, and in situ hybridization. RESULTS: ) microglia/macrophages were increased in cases with subpial lesions, and the density of neurons was significantly reduced in acute MS normal appearing and lesion GM, compared to controls (p < 0.005). Significant meningeal inflammation and lymphoid-like structures were seen in 4 of 12 acute MS cases. The extent of meningeal inflammation correlated with microglial/macrophage activation (p < 0.05), but not the area of cortical demyelination, reflecting the finding that lymphoid-like structures were seen adjacent to GM lesions as well as areas of partially demyelinated/remyelinated, cortical GM. INTERPRETATION: Our findings demonstrate that cortical demyelination, neuronal loss, and meningeal inflammation are notable pathological hallmarks of acute MS and support the need to identify early biomarkers of this pathology to better predict outcome. Ann Neurol 2018;84:829-842.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".