Higher Frequencies of T-Cells Expressing NK-Cell Markers and Chemokine Receptors in Parkinson’s Disease
Bibliographic record
Abstract
Immune cells are thought to be involved in a destructive cycle of sterile cerebral inflammatory responses in neurodegenerative diseases such as Parkinson’s Disease (PD). Despite their peripheral origin, immune cells may enter the CNS due to impaired blood–brain barrier function and may potentially contribute to neuronal damage. Hence, specific characteristics of peripherally activated immune cells could help in understanding neurodegeneration in PD and could potentially serve as accessible disease markers. To investigate immune cell activation status, the expression of receptors for cell surface molecules CD161, NKG2A, NKG2C and NKG2D as well as chemokine receptors CCR6, CXCR2, CXCR3 and CCR5 associated with neurodegenerative diseases was investigated. The frequencies of peripheral CD8+ T-cells expressing the inhibitory and activating receptors NKG2A and NKG2C, and the activating receptor NKG2D were higher in PD patients than in healthy matched controls. The frequencies of NKG2C+CD8− cells were also higher, whereas the frequencies of CD161+ cells were not significantly different. Of the chemokine receptor-expressing cells, only the proportion of CD4−CD56+CCR5+ T-cells was higher in PD patients than in the controls. These observations support the hypothesis that an imbalance in the activation state of T-cells plays a role in the pathological processes of PD and suggest that peripheral blood immune cell phenotypes could be specific early markers for inflammation in 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".