Involvement of α‐Synuclein in Neurodegeneration in Multiple Sclerosis?
Bibliographic record
Abstract
Multiple sclerosis (MS) is associated with neurodegenerative features including widespread brain atrophy, neuronal/axonal loss in the deep gray matter nuclei, as well as demyelination in the cortex and in normal appearing white matter. The cause of neurodegeneration in MS is unclear. We aimed to test the hypothesis that the neurodegeneration in MS may be contributed by the mechanisms underlying classic neurodegenerative diseases such as α‐synucleinopathies. Seventy‐nine MS lesions from 9 patients were examined, compared with the corresponding regions of 6 neuropathologically normal controls. α‐Synuclein immunoreactivity was found in the cytoplasm of neurons, activated microglia, and oligodendrocytes within and surrounding the MS lesions. The immunoreactive cells were identified in the active [15/15 within the brainstem; 5/8 within the cerebral hemispheres (CH)] and chronic active [11/12 within the brainstem; 6/15 within CH] lesions, but not in the inactive lesions or controls. This α‐synuclein immunoreactivity was preferentially expressed in lesions of the brainstem ( p < 0.05, vs. CH). Analysis of double‐immunofluorescence labeling shows co‐localization of α‐synuclein with neuronal (NeuN), microglial (Iba1) and oligodendroglial (Nogo‐A) markers, but not with astrocytes (GFAP). We suggest that α‐synuclein regulated by inflammatory signals may be involved in the pathology of MS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".