Toward a biomarker panel measured in CNS-originating extracellular vesicles for improved differential diagnosis of Parkinson’s disease and multiple system atrophy
Bibliographic record
Abstract
Abstract Synucleinopathies are neurodegenerative diseases characterized by accumulation of misfolded α-synuclein (α-syn) inclusions in neuronal and/or glial cells. Different synucleinopathies may affect different brain regions and cell types. In Parkinson’s disease (PD) and dementia with Lewy bodies (DLB), α-syn deposits predominantly in neuronal Lewy bodies (LBs) and Lewy neurites (LNs), whereas in multiple system atrophy (MSA), α-syn-rich glial cytoplasmic inclusions (GCIs) are found in oligodendrocytes (1). Despite differences in the underlying pathophysiology, synucleinopathies often are misdiagnosed, especially by non-experts in the early-stages, due to the overlapping clinical symptoms (2, 3). Several studies have demonstrated the utility of measuring α-syn in neuronal EVs (nEVs) as a diagnostic biomarker for PD and atypical parkinsonian disorders (4). Recently, our group has shown that α-syn measured in both nEVs and oligodendroglial EVs (oEVs) in the same samples, and in particular the oEV:nEV α-syn concentration ratio, yielded a discriminative model distinguishing between PD and MSA with 89.8% sensitivity and 86.0% specificity and between healthy controls (HC) and MSA with 96.0% sensitivity and 84.3% specificity. In contrast, the model offered moderate separation between PD and HC – 71.4% sensitivity and 62.7% specificity (5).
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".