α-Synuclein seed amplification assay positivity beyond synucleinopathies
Bibliographic record
Abstract
Neurodegenerative diseases are increasingly recognized as complex disorders involving multiple protein pathologies, with α-synuclein frequently observed beyond classical synucleinopathies such as Parkinson's disease and multiple system atrophy. Recent advances in seed amplification assays (SAAs) have enabled the highly sensitive and specific detection of misfolded α-synuclein in vivo, particularly in cerebrospinal fluid (CSF). This review focuses on CSF-based α-synuclein SAAs and their application in detecting co-pathology across non-synucleinopathies, including Alzheimer's disease, progressive supranuclear palsy, corticobasal syndrome, idiopathic normal pressure hydrocephalus, and traumatic brain injury. Evidence indicates a role for α-synuclein in clinical heterogeneity and disease progression. Emerging diagnostic frameworks increasingly support integrating co-pathologies into classification and therapeutic strategies. Addressing key knowledge gaps, such as α-synuclein interactions with other protein pathologies, and current limitations of α-syn SAA, such as the lack of quantification of misfolded α-synuclein seeds, will refine precision medicine and improve outcomes for patients with neurodegenerative diseases.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".