Abstract 4141276: Valvular oxidized phospholipids correlate with severity of human aortic valvular stenosis
Bibliographic record
Abstract
Introduction: Aortic valve stenosis (AVS) is a degenerative disease characterized by progressive calcification and stenosis, driven by a multifactorial inflammatory process. Oxidized phospholipids (OxPL) have been implicated in this process, yet their presence in aortic valve tissue remains unexplored. This study aims to fill this gap by developing a sensitive method to identify and quantify OxPL in the plasma and tissue of patients with severe AVS. Methods: We obtained aortic valve tissue from 70 patients undergoing aortic valve replacement surgery. The tissue samples were subjected to derivatization with DNPH, followed by LC/MS/MS analysis, enabling the identification and quantification of 60 individual OxPL across five different phospholipid classes. Results: Our analysis identified 34 OxPL species across five phospholipid classes in human valvular tissue, including oxidized phosphatidylcholine (OxPC), phosphatidylethanolamine (OxPE), phosphatidylinositol (OxPI), and phosphatidylserine (OxPS). OxPC was the most abundant class, with PONPC being the most prevalent molecule, constituting 35% of total OxPL. We observed a significant increase (p<0.05) in 20 OxPL species in valvular tissue when comparing mild to moderate to severe valve stenosis based on pressure gradient. The most significant change occurred between mild and moderate AVS as PONPC increased by 90% (p=0.012) and total OxPC increased by 83% (p=0.004). Compared to plasma, valvular tissue had more OxPS species present and an increase in longer chain fatty acid aldehydes in the SN-2 position of OxPCs. Conclusion: Our findings indicate that OxPL infiltration of the valve tissue is an early event in the progression of AVS. This suggests that early intervention with OxPL-lowering therapies may prove beneficial in managing AVS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
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".