Potential Association of Neutrophil Extracellular Traps With Cognitive Impairment in Cerebral Small Vessel Disease
Bibliographic record
Abstract
No acceptable biomarker can facilitate the early identification of cognitive impairment associated with cerebral small vessel disease (CSVD) in the older persons. The neutrophil extracellular traps (NETs) in the inflammation response of circulatory and central systems are essential in destroying the blood-brain barrier. The present study aims to explore the potential associations of plasma NETs with cognitive performance in CSVD. We recruited 146 CSVD patients and 66 healthy controls (HCs), and comprehensive neuropsychological assessments and multimodal magnetic resonance imaging were conducted. Three NETs markers, namely citrullination of histone H3, neutrophil elastase-DNA, and myeloperoxidase (MPO)-DNA, and 4 oxidative stress-related indexes in plasma samples, were measured. The plasma levels of 3 NETs markers were more significantly elevated in CSVD patients than in HCs. Significant correlations of the 3 NETs markers were observed with multiple cognitive domain scores. Furthermore, higher plasma malondialdehyde and NETs levels were significantly associated with the worse Montreal Cognitive Assessment scores among CSVD patients. Moreover, plasma MPO-DNA levels significantly mediated the effect of the amplitude of low-frequency fluctuation value within the bilateral caudate and the scores of global cognitive function, executive function, and information processing speed. Additionally, a panel of 3 NETs markers had the highest area under the curve value to distinguish the cognitively impaired CSVD patients from HCs and nonimpaired ones. Therefore, plasma NETs may be potential biomarkers for early diagnosis of CSVD-related cognitive impairment. Activated lipid peroxidation in circulation and impaired caudate function support potential associations of plasma NETs in cognitively impaired CSVD patients.
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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".