A novel approach to detecting plasma synuclein aggregates for Parkinson’s disease diagnosis
Bibliographic record
Abstract
Alpha-synuclein (αSyn) aggregates are pathognomonic of Parkinson's disease (PD) and play a critical role in its pathogenesis. However, existing diagnostic approaches rely on invasive cerebrospinal fluid (CSF) sampling or tissue biopsies, limiting their accessibility and scalability in clinical practice. Here, we present the Constant Shake-Induced Conversion (CSIC) assay, a novel plasma-based technique for the detection of αSyn aggregates. A total of 102 participants, comprising 42 PD patients and 60 healthy controls (HCs), were enrolled. Plasma samples were subjected to CSIC and validated via αSyn depletion, enzyme-linked immunosorbent assay (ELISA), and Western blotting. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis, and clinical associations were examined using Spearman's correlation. The CSIC assay achieved an area under the curve (AUC) of 0.91, with 81% sensitivity and 85% specificity in distinguishing PD from HCs. Assay specificity was confirmed through αSyn depletion, and reproducibility assessments yielded intra- and inter-assay coefficients of variation below 10% and ~20%, respectively. Notably, plasma αSyn aggregate levels correlated with Hoehn and Yahr (H&Y) stage (r = 0.69), Unified Parkinson's Disease Rating Scale (UPDRS) (r = 0.68), and Montreal Cognitive Assessment scores (r = -0.47). These findings establish CSIC as a robust, non-invasive diagnostic method with strong potential for clinical implementation in PD.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".