In vivo distribution of α-synuclein in multiple tissues and biofluids in Parkinson disease
Bibliographic record
Abstract
OBJECTIVE: The Systemic Synuclein Sampling Study (S4) measured α-synuclein in multiple tissues and biofluids within the same patients with Parkinson disease (PD) vs healthy controls (HCs). METHODS: S4 was a 6-site cross-sectional observational study of participants with early, moderate, or advanced PD and HCs. Motor and nonmotor measures and dopamine transporter SPECT were obtained. Biopsies of skin, colon, submandibular gland (SMG), CSF, saliva, and blood were collected. Tissue biopsy sections were stained with 5C12 monoclonal antibody against pathologic α-synuclein; digital images were interpreted by neuropathologists blinded to diagnosis. Biofluid total α-synuclein was quantified using ELISA. RESULTS: The final cohort included 59 patients with PD and 21 HCs. CSF α-synuclein was lower in patients with PD vs HCs; sensitivity/specificity of CSF α-synuclein for PD diagnosis was 87.0%/63.2%, respectively. Sensitivity of α-synuclein immunoreactivity for PD diagnosis was 56.1% for SMG and 24.1% for skin; specificity was 92.9% and 100%, respectively. There were no significant relationships between different measures of α-synuclein within participants. CONCLUSIONS: S4 confirms lower total α-synuclein levels in CSF in patients with PD compared to HCs, but specificity is low. In contrast, α-synuclein immunoreactivity in skin and SMG is specific for PD but sensitivity is low. Relationships within participants across different tissues and biofluids could not be demonstrated. Measures of pathologic forms of α-synuclein with higher accuracy are critically needed. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that total CSF α-synuclein does not accurately distinguish patients with PD from HCs, and that monoclonal antibody staining for SMG and skin total α-synuclein is specific but not sensitive for PD diagnosis.
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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 | 0.001 | 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".