Imaging Striatal Microglial Activation in Patients with Parkinson’s Disease
Bibliographic record
Abstract
This study investigated whether the second-generation translocator protein 18kDa (TSPO) radioligand, [18F]-FEPPA, could be used in neurodegenerative parkinsonian disorders as a biomarker for detecting neuroinflammation in the striatum. Neuroinflammation has been implicated as a potential mechanism for the progression of Parkinson's disease (PD). Positron Emission Tomography (PET) radioligand targeting for TSPO allows for the quantification of neuroinflammation in vivo. Based on genotype of the rs6791 polymorphism in the TSPO gene, 16 mixed-affinity binders (MABs) (8 PD and age-matched 8 healthy controls (HCs)), 16 high-affinity binders (HABs) (8 PD and age-matched 8 HCs) and 4 low-affinity binders (LABs) (3 PD and 1 HCs) were identified. Total distribution volume (VT) values in the striatum were derived from a two-tissue compartment model with arterial plasma as an input function. There was a significant main effect of genotype on [18F]-FEPPA VT values in the caudate nucleus (p = 0.001) and putamen (p < 0.001), but no main effect of disease or disease x genotype interaction in either ROI. In the HAB group, the percentage difference between PD and HC was 16% in both caudate nucleus and putamen; in the MAB group, it was -8% and 3%, respectively. While this PET study showed no evidence of increased striatal TSPO expression in PD patients, the current findings provide some insights on the possible interactions between rs6791 polymorphism and neuroinflammation 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) | 0.000 | 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 | 0.000 | 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".