Association between serum NPTX2 and cognitive function in patients with vascular dementia
Bibliographic record
Abstract
OBJECTIVE: Neuronal Pentraxin 2 (NPTX2) has recently been widely reported as a novel biomarker for Alzheimer's disease (AD), but its correlation with vascular dementia (VaD) has not been elucidated. This study aimed to explore the correlation between NPTX2 and the cognitive function of VaD patients. METHODS: 112 VaD patients and 76 healthy controls were included in the study. Upon admission, clinical baseline data for all subjects were collected. Serum NPTX2 levels were determined using enzyme-linked immunosorbent assay (ELISA). At the same time, the Montreal cognitive assessment (MoCA) scale was used to measure cognitive function. Multivariate regression analysis was used to determine the relationship between serum NPTX2 level and the cognitive function of VaD patients. RESULTS: Compared with healthy controls, VaD patients had lower serum NPTX2 levels (p < .001). The results of Spearman's correlation analysis showed that serum NPTX2 levels in VaD patients were positively correlated with MoCA scores (r = .347, p = .042). The results of multivariate regression analysis showed that after adjusting for common risk factors, serum NPTX2 levels in VaD patients were still significantly associated with MoCA scores (β = 0.346, p = .039). CONCLUSIONS: Serum NPTX2 level was independently associated with cognitive function in patients with VaD. Serum NPTX2 level may be a novel predictor for cognitive function in VaD.
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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".