Refining Molecular Pathways Leading to Calcific Aortic Valve Stenosis by Studying Gene Expression Profile of Normal and Calcified Stenotic Human Aortic Valves
Bibliographic record
Abstract
BACKGROUND: Calcific aortic valve stenosis (AS) is a major societal and economic burden that is rising after the current shift toward an older population. Understanding the pathobiology of AS is crucial to implementing better preventive and therapeutic options. Research conducted during the past decade clearly points to active molecular and cellular processes involved in disease pathogenesis. However, no genomic approaches were used to identify genes and pathways that are differentially regulated in aortic valves of patients with and without AS. METHODS AND RESULTS: A large-scale quantitative measurements of gene expression was performed on 5 normal and 5 AS valves using Affymetrix GeneChips. A total of 409 and 306 genes were significantly up- and downregulated in AS valves, respectively. The 2 most highly upregulated genes were matrix metalloproteinase 12 and chitinase 3-like 1. The upregulation of these 2 biologically relevant genes in AS was validated by real-time polymerase chain reaction in 38 aortic valves (12 normal and 26 AS). To provide a global biological validation of the whole-genome gene expression analysis, the microarray experiment was repeated in a second set of aortic valves with (n=5) or without (n=5) AS. There was an overrepresentation of small P values among genes claimed significant in the first microarray experiment. A total of 223 genes were replicated (P<0.05 and fold change >1.2), including matrix metalloproteinase 12 and chitinase 3-like 1. CONCLUSIONS: This study reveals many unrecognized genes potentially implicated in the pathogenesis of AS. These new genes were overlaid on known pathological pathways leading to AS to refine our molecular understanding of 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 | 0.001 | 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) | 0.001 | 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".