Is there any correlation between alpha-synuclein levels in tears and retinal layer thickness in Parkinson's disease?
Bibliographic record
Abstract
PURPOSE: To determine the total alpha-synuclein (αSyn) reflex tears and its association with retinal layers thickness in Parkinson's disease (PD). METHODS: Fifty-two eyes of 26 PD subjects and 52 eyes of age-and sex-matched healthy controls were included. Total αSyn in reflex tears was quantified using a human total αSyn enzyme-linked immunosorbent assay (ELISA) kit. The retinal thickness was evaluated with spectral-domain optical coherence tomography. The Movement Disorder Society-Unified Parkinsońs Disease Rating Scale (MDS-UPDRS), Non-Motor Symptoms Scale (NMSS), and Montreal Cognitive Assessment (MoCA) were used to assess motor, non-motor, and cognition. RESULTS: In PD, total αSyn levels were increased compared to control subjects [1.76pg/mL (IQR 1.74-1.80) vs 1.73pg/mL (IQR 1.70-1.77), p < 0.004]. The nerve fiber layer, ganglion cell layer, internal plexiform layer, inner nuclear layer, and outer nuclear layer were thinner in PD in comparison with controls (p < 0.05). The outer plexiform layer and retinal pigment epithelium were thicker in PD (p < 0.05). The total αSyn levels positively correlated with the central volume of the inner nuclear layer (r = 0.357, p = 0.009). CONCLUSION: Total αSyn reflex tear levels were increased in subjects with PD compared to controls. PD patients showed significant thinning of the inner retinal layers and thickening of outer retinal layers in comparison with controls. Total αSyn levels positively correlate with the central volume of the inner nuclear layer in PD. The combination of these biomarkers might have a possible role as a diagnostic tool in PD subjects.
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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 | 0.002 |
| 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".