Valvular Prostaglandins Are Elevated in Severe Human Aortic Valve Stenosis
Bibliographic record
Abstract
BACKGROUND: Aortic valve stenosis (AVS) is the most common valvular disease in the developed world. AVS involves the progressive fibrocalcific remodeling of the aortic valve (AV), which impairs function and can ultimately lead to heart failure. Due to gaps in our understanding of the underlying mechanisms of AVS, there are no pharmacological treatments or dietary interventions known to slow AVS progression. Recent studies have begun to suggest oxylipins-a class of bioactive lipids-may be dysregulated in the valves of patients with AVS. METHODS: We utilized high-performance liquid chromatography-tandem mass spectrometry to conduct a targeted oxylipin analysis on human AV tissue and plasma from a cohort of 110 patients undergoing AV surgery. RESULTS: We identified 36 oxylipins in human AV tissue with all showing significant increase in patients with severe AVS. A multivariate model including patient characteristics and valvular oxylipins identified the arachidonic acid-COX (cyclooxygenase) pathway-derived prostanoids to be the most associated with AVS severity. Plasma oxylipin levels were measured in a subset of AV surgery patients and compared with a control group of healthy participants, showing distinct oxylipin profiles between control and disease. CONCLUSIONS: Our comprehensive analysis of oxylipins in the human AV identified the inflammatory and osteogenic regulating prostanoids to be positively correlated with AVS severity. This elucidation of prostanoid dysregulation warrants further research into COX inhibition to mitigate 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 | 0.001 |
| 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".