Neuroinflammatory and neurodegenerative aspects of Parkinson’s disease
Bibliographic record
Abstract
Objective. To evaluate the clinical features and the level of the inflammatory markers CCL5, slCAM-1, sVCAM-1, NCAM, PAI-1, and MPO in the groups of patients with Parkinson’s disease (PD) at various stages according to Hoehn and Yahr. Material and methods. The study included 533 patients with PD. All patients underwent a clinical neurological examination to determine the stage of PD, the severity of motor disorders according to the MDS-UPDRS scale (Unified Parkinson’s Disease Rating Scale of the Movement Disorder Society), and testing using validated questionnaires: Montreal Cognitive Assessment, Hospital Anxiety and Depression Rating Scale, Beck Depression Inventory-II, Fatigue Severity Scale, Scale for assessing autonomic disorders in PD patients. Behavioral disorders were evaluated using QUIP-RS. 144 PD patients had their serum concentration of several inflammatory markers measured (CCL5, slCAM-1, sVCAM-1, NCAM, PAI-1, and MPO) on the MAGPIX multiplex analyzer (Luminex, USA) using xMAP Technology. Genotyping of polymorphic variants of CCL5 (rs2107538) and PAI-1 (rs2227631) genes was performed using real-time PCR. Results. The serum levels of slCAM-1, sVCAM-1, CCL5, and NCAM varied in PD patients depending on the Hoehn and Yahr stage and disease duration. Correlations of serum marker levels were found both among themselves and with motor and non-motor disorders, which indicate a systemic inflammatory profile when increased peripheral production of CCL5, slCAM-1, sVCAM-1, NCAM, PAI-1, and MPO may play a role in the neurodegenerative process. Conclusion. The serum level of inflammatory markers, such as CCL5, slCAM-1, sVCAM-1, NCAM, PAI-1, and MPO, in PD patients varies depending on the stage of the progressive neurodegenerative process, indicating the importance of systemic inflammation during 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 | 0.000 | 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".