Relationships between neuropsychiatric symptoms, subtypes of astrocyte activities, and brain pathologies in Alzheimer's disease and Parkinson's disease
Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease (AD) and Parkinson's disease (PD) are neurodegenerative diseases (NDs). This study examined astrocytic contributions to neuropsychiatric symptoms (NPS), focusing on astrocytic protein activity and its relationship with NPS severity, accounting for clinical and pathological features of NDs. METHODS: Cerebrospinal astrocytic proteins (glial fibrillary acidic protein [GFAP], chitinase-3-like protein 1 [YKL-40], and aquaporin-4 [AQP4]) from Alzheimer's Disease Neuroimaging Initiative (ADNI) (AD) and Parkinson's Progression Markers Initiative (PPMI) (PD) were analyzed using K-means clustering. Six NPS domains, ND-specific pathologies (amyloid-beta/Aβ for AD, alpha-synuclein/αSyn for PD), and nonspecific pathology (phosphorylated tau/ptau) were assessed. RESULTS: In both samples, three astrocytic clusters were identified, and the "highYKL|lowOthers" cluster (high YKL-40, low GFAP/AQP4) consistently showed lower ptau and NPS severity compared to the "highAll" cluster (high GFAP, YKL-40, AQP4). In PPMI, the "highYKL|lowOthers" cluster also attenuated the relationship between αSyn and NPS compared to the "highAll" cluster. DISCUSSION: Astrocytic activity relates to NPS, highlighting astrocytic proteins as potential therapeutic targets for NPS in NDs. HIGHLIGHTS: Astrocytic protein clusters were linked to NPS severity in AD and PD cohorts. The "highYKL|lowOthers" cluster showed lower ptau and NPS severity than "allhigh" cluster in AD and PD cohorts. Astrocytic proteins may serve as therapeutic targets for managing NPS in NDs.
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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 | 0.000 | 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".