Circulating and tissue matricellular RNA and protein expression in calcific aortic valve disease
Bibliographic record
Abstract
Aortic valve sclerosis is a highly prevalent, poorly characterized asymptomatic manifestation of calcific aortic valve disease and may represent a therapeutic target for disease mitigation. Human aortic valve cusps and blood were obtained from 333 patients undergoing cardiac surgery ( n = 236 for severe aortic stenosis, n = 35 for asymptomatic aortic valve sclerosis, n = 62 for no valvular disease), and a multiplex assay was used to evaluate protein expression across the spectrum of calcific aortic valve disease. A subset of six valvular tissue samples ( n = 3 for asymptomatic aortic valve sclerosis, n = 3 for severe aortic stenosis) was used to create RNA sequencing profiles, which were subsequently organized into clinically relevant gene modules. RNA sequencing identified 182 protein-encoding, differentially expressed genes in aortic valve sclerosis vs. aortic stenosis; 85% and 89% of expressed genes overlapped in aortic stenosis and aortic valve sclerosis, respectively, which decreased to 55% and 84% when we targeted highly expressed genes. Bioinformatic analyses identified six differentially expressed genes encoding key extracellular matrix regulators: TBHS2, SPARC, COL1A2, COL1A1, SPP1, and CTGF. Differential expression of key circulating biomarkers of extracellular matrix reorganization was observed in control vs. aortic valve sclerosis (osteopontin), control vs. aortic stenosis (osteoprotegerin), and aortic valve sclerosis vs. aortic stenosis groups (MMP-2), which corresponded to valvular mRNA expression. We demonstrate distinct mRNA and protein expression underlying aortic valve sclerosis and aortic stenosis. We anticipate that extracellular matrix regulators can serve as circulating biomarkers of early calcific aortic valve disease and as novel targets for early disease mitigation, pending prospective clinical investigations.
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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".