Pro-inflammatory cytokines levels in tears and dry eye disease in Parkinson’s disease
Bibliographic record
Abstract
Background: Neuroinflammation is an essential event in Parkinson’s disease (PD). Identifying affordable and less invasive biomarkers to make an early diagnosis and monitor therapeutic strategies should be a priority among researchers. The study’s objective was to measure tear levels of cytokines in subjects with PD and their association with motor features and the presence of dry eye symptoms. Methods: A total of 16 subjects with PD and 16 age- and sex-matched controls were included. Movement Disorders Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Hoehn and Yahr (HY) stage scale, Montreal Cognitive Assessment (MoCA), tear break-up time (TBUT), blink rate (BR), Dry Eye Questionnaire 5 (DEQ-5) were examined, and pro-inflammatory cytokines [interleukin (IL)-1β, IL-6, IL-8, IL-10, IL-12p70 and tumor necrosis factor-alpha (TNF-α)] were quantified in tears using the BD Cytometric Bead Array Human Inflammatory Cytokine Kit. Results: Higher tear TNF-α were quantified in PD compared to controls (2.94±3.95 vs. 0.33±0.49 pg/mL, P=0.008). According to DEQ-5, 50.0% (n=8) of PD subjects and 12.5% (n=2) controls had dry eye disease (DED). No differences were found in cytokines concentrations between PD patients with DED compared to those without DED. IL-8 was associated with the HY stage, TBUT, DEQ-5, and a better MoCA score. A higher BR correlated moderately with a lower HY stage (r=−0.645, P=0.007), and DED patients have lower BR in PD (12.14±2.54 vs. 9.0±2.06 blinks/minute, P=0.031). Conclusions: PD patients have higher levels of TNF-α in tears than age- and sex-matched HC. IL-8 in tears may be both involved in the severity of the disease and in the development of DED in PD. In addition, our findings suggest that as HY stage increases, indicating a more advanced stage, BR decreases, indicating greater motor impairment. Conversely, the presence of DED is associated with higher levels of bradykinesia in PD patients, suggesting a potential relationship between DED and motor impairment severity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".