Valvular oxidized phospholipids correlate with severity of human aortic valvular stenosis
Bibliographic record
Abstract
Calcific aortic valve stenosis (CAVS) is a degenerative disease characterized by progressive calcification and narrowing of the aortic valve, driven by a multifactorial inflammatory process. Oxidized phospholipids (OxPL) have been implicated in CAVS pathogenesis, but their presence within aortic valve tissue remains poorly defined. In this study, we developed a sensitive 2,4-dinitrophenylhydrazine (DNPH)-based LC/MS/MS method to identify and quantify 60 individual OxPL species across five phospholipid classes in plasma and tissue samples from patients with severe CAVS. Aortic valve tissue was collected from 70 patients undergoing valve replacement surgery and compared with tissue from 20 healthy donors. We identified 32 distinct OxPL species, including oxidized phosphatidylcholine (OxPC), phosphatidylethanolamine (OxPE), phosphatidylinositol (OxPI), and phosphatidylserine (OxPS). OxPC was the most abundant class, with 1-palmitoyl-2-(9-oxo-nonanoyl)-sn-glycero-3-phosphocholine (PONPC) being the predominant species, accounting for 35 % of total OxPL. We observed a significant increase in 30 OxPL species with advancing disease severity, with the most pronounced changes occurring between early (healthy and mild) and advanced (moderate and severe) stages of CAVS. Specifically, PONPC levels increased by 90 % (p = 0.012), and total OxPC levels rose by 83 % (p = 0.004) from mild to moderate disease. Compared to healthy valves, OxPC levels increased by 123 % (p < 0.0001) in moderate CAVS and by 239 % (p = 0.02) in severe CAVS. However, OxPL levels did not significantly increase between moderate and severe stages. These findings suggest that OxPL accumulation in valve tissue is an early event in CAVS progression, supporting the rationale for early intervention with OxPL-lowering therapies as a potential strategy to mitigate disease advancement.
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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".