Metabolic syndrome induces neovascularization in calcific aortic stenosis
Bibliographic record
Abstract
Introduction Aortic stenosis (AS) is an inflammatory disease in which neovascularization process develops. We hypothesized that the metabolic syndrome (MS) would influence the neovascularization process in AS valves. Methods In 40 patients a quantitative analysis of blood vessels in AS valves along with the blood lipid profile were determined to establish relationships between the clinical atherosclerotic risk factors including the MS. Results Age, gender, hypercholesterolemia, smoking, diabetes, as well as treatment with statins or ACE inhibitors had no significant effect on the extent of neovascularization. Factors associated with the neovascularization process were obesity (1.6±0.5 blood vessels/400x field vs 0.7±0.3 blood vessels/400x field; p=0.14), hypertension (1.5±0.5 blood vessels/400x field vs 0.6±0.4 blood vessels/400x field; p=0.13) and MS (2.3±0.6 blood vessels/400x field vs 0.4±0.3 blood vessels/400x field; p=0.0002). In multivariate analysis, the MS was the only independent predictor of valve neovascularization. HDL‐cholesterol level was inversely correlated with neovascularization (r=‐0.49; p=0.009). We documented in 53% of MS patients, rich cellular islands in which blood vessels were abundant. Cellular islands were densely infiltrated by endothelial progenitor cells (EPC) (CD45+ CD133+) which correlated with the number of blood vessels(r=0.65; p=0.0006). Conclusion AS is a disease characterized by the formation of new blood vessels which is independently determined by the MS. The level of HDL and the number of EPC are among the mechanisms influencing the neovasacularization process in AS.
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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) | 0.002 | 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".