Association of α-Synuclein Immunoreactivity With Inflammatory Activity in Multiple Sclerosis Lesions
Bibliographic record
Abstract
Multiple sclerosis (MS) has neurodegenerative features including neuronal and axonal loss and widespread atrophy of the brain and spinal cord. The cause of this neurodegeneration has been largely attributed to inflammation, but other mechanisms, including those associated with classic neurodegenerative diseases such as the alpha-synucleinopathies, might also be involved in MS pathogenesis. In this study, 96 brain lesions containing varying degrees of inflammatory activity from 12 autopsied MS cases were compared with corresponding regions from 6 neuropathologically normal controls; 2 cerebral biopsy lesions from an MS patient were also studied. We found alpha-synuclein immunoreactivity in the cytoplasm of cells in MS lesions with inflammatory activity but not in control samples. alpha-Synuclein-immunoreactive cells were identified in active (15/15 lesions in the brainstem, 9/13 in cerebral hemispheres) and chronic active (14/15 in the brainstem, 12/22 in cerebral hemispheres) lesions but were absent in chronic inactive lesions (0/31); the greater immunoreactivity in brainstem compared with cerebral hemisphere lesions was significant (p < 0.05). Double-immunofluorescence staining revealed localization of alpha-synuclein immunoreactivity mostly in neurons, microglia/macrophages, and oligodendrocytes, and only rarely in astrocytes. The results suggest that alpha-synuclein expression regulated by inflammatory signals may contribute to neurodegenerative processes in MS lesions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".