An endogenous inhibitor of angiogenesis downregulated by hypoxia in human aortic valve stenosis promotes disease pathogenesis
Bibliographic record
Abstract
Aortic valve stenosis is the most common valve disease in the western world. Central to the pathogenesis of this disease is the growth of new blood vessels (angiogenesis) within the aortic valve allowing infiltration of immune cells and development of intra-valve inflammation. Identifying the cellular mediators involved in this angiogenesis is important as this may reveal new therapeutic targets which could ultimately prevent the progression of aortic valve stenosis. Aortic valves from patients undergoing surgery for aortic valve replacement or dilation of the aortic arch were examined both ex vivo and in vitro. We now demonstrate that the anti-angiogenic protein, soluble fms-like tyrosine kinase 1 (sFlt1), a non-signalling soluble receptor for vascular endothelial growth factor, is constitutively expressed in non-diseased valves. sFlt-1 expression was, however, significantly reduced in aortic valve tissue from patients with aortic valve stenosis while protein markers of hypoxia were simultaneously increased. Exposure of primary-cultured valve interstitial cells to hypoxia resulted in a decrease in the expression of sFlt-1. We further reveal using a bioassay that siRNA knock-down of sFlt1 in valve interstitial cells directly results in a pro-angiogenic environment. Finally, incubation of aortic valves with sphingosine 1-phosphate, a bioactive lipid-mediator, increased sFlt-1 expression and inhibited angiogenesis within valve tissue. In conclusion, this study demonstrates that sFlt1 expression is directly correlated with angiogenesis in aortic valves and the observed decrease in sFlt-1 expression in aortic valve stenosis could increase valve inflammation, promoting disease progression. This could be a viable therapeutic target in treating this disease.
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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) | 0.001 | 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".