Plasma levels of soluble tumor necrosis factor receptors are associated with cognitive performance in Parkinson's disease
Bibliographic record
Abstract
Inflammatory mechanisms have been implicated in a series of neuropsychiatric conditions, including behavioral disturbances, cognitive dysfunction, and affective disorders. Accumulating evidence also strongly suggests their involvement in the pathophysiology of Parkinson's disease (PD). This study aimed to evaluate plasma levels of inflammatory biomarkers, and their association with cognitive performance and other non-motor symptoms of PD. PD patients and control individuals were subjected to various psychometric tests, including the Mini-Mental State Examination (MMSE), Frontal Assessment Battery (FAB), and Beck's Depression Inventory (BDI). Biomarker plasma levels were measured by enzyme-linked immunosorbent assay (ELISA). PD patients exhibited worse performance on MMSE and the programming task of FAB, and presented higher soluble tumor necrosis factor receptor (sTNFR) plasma levels than control individuals. Among PD patients, increased sTNFR1 and sTNFR2 concentrations were associated with poorer cognitive test scores. After multiple linear regression, sTNFR1 and education remained a significant predictor for FAB scores. Our data suggest that PD is associated with a proinflammatory profile, and sTNFRs are putative biomarkers of cognitive performance, with elevated sTNFR1 levels predicting poorer executive functioning in PD patients.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".