A role of altered inflammation-related gene expression in cerebral small vessel disease with cognitive impairment
Bibliographic record
Abstract
Objective. To identify the role of changes in the expression of inflammation-related genes in cerebral microangiopathy/cerebral small vessel disease (cSVD). Material and methods. Forty-four cSVD patients (mean age 61.4±9.2) and 11 controls (mean age 57.3±9.7) were studied. Gene expression was assessed on an individual NanoString nCounter panel of 58 inflammation-related genes and 4 reference genes. A set of genes was generated based on converging results of complete genome-wide association studies (GWAS) in cSVD and Alzheimer’s disease (AD) and circulating markers associated with vascular wall and Brain lesions in cSVD. RNA was isolated from blood leukocytes and analyzed with the nCounter Analysis System, followed by analysis in nSolver 4.0. Results were verified by real-time PCR. Results. cSVD patients had a significant decrease in BIN1 (log2FC=–1.272; p=0.039) and VEGFA (log2FC=–1.441; p=0.038) expression compared to controls, which showed predictive ability for cSVD. The cut-off for BIN1 expression was 5.76 a.u. (sensitivity 73%; specificity 75%) and the cut-off for VEGFA expression was 9.27 a.u. (sensitivity 64%; specificity 86%). Reduced expression of VEGFA (p=0.011), VEGFC (p=0.017), CD2AP (p=0.044) was associated with cognitive impairment (CI). There was a significant direct correlation between VEGFC expression and the scores on the Montreal Cognitive Assessment test and between BIN1 and VEGFC expression and delayed memory. Conclusion. The possible prediction of cSVD by reduced expression levels of BIN1, VEGFA and the association of clinically significant CI with reduced VEGFA and VEGFC expression indicate their importance in the development and progression of the disease. The established importance of these genes in the pathogenesis of AD suggests that similar changes in their expression profile in cSVD may be one of the conditions for the comorbidity of the two pathologies.
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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".