RNA expression profile of calcified bicuspid, tricuspid, and normal human aortic valves by RNA sequencing
Bibliographic record
Abstract
The molecular mechanisms leading to premature development of aortic valve stenosis (AS) in individuals with a bicuspid aortic valve are unknown. The objective of this study was to identify genes differentially expressed between calcified bicuspid aortic valves (BAVc) and tricuspid valves with (TAVc) and without (TAVn) AS using RNA sequencing (RNA-Seq). We collected 10 human BAVc and nine TAVc from men who underwent primary aortic valve replacement. Eight TAVn were obtained from men who underwent heart transplantation. mRNA levels were measured by RNA-Seq and compared between valve groups. Two genes were upregulated, and none were downregulated in BAVc compared with TAVc, suggesting a similar gene expression response to AS in individuals with bicuspid and tricuspid valves. There were 462 genes upregulated and 282 downregulated in BAVc compared with TAVn. In TAVc compared with TAVn, 329 genes were up- and 170 were downregulated. A total of 273 upregulated and 147 downregulated genes were concordantly altered between BAVc vs. TAVn and TAVc vs. TAVn, which represent 56 and 84% of significant genes in the first and second comparisons, respectively. This indicates that extra genes and pathways were altered in BAVc. Shared pathways between calcified (BAVc and TAVc) and normal (TAVn) aortic valves were also more extensively altered in BAVc. The top pathway enriched for genes differentially expressed in calcified compared with normal valves was fibrosis, which support the remodeling process as a therapeutic target. These findings are relevant to understand the molecular basis of AS in patients with bicuspid and tricuspid valves.
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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 | 0.001 |
| 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".