PALMD as a novel target for calcific aortic valve stenosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Novel medical treatments are urgently needed to stem the escalating socioeconomic burden of calcific aortic valve stenosis (CAVS). Herein, we describe the discovery of PALMD as a disease-causing gene for CAVS and discuss its implications for the understanding of disease pathogenesis and the development of new treatment options. RECENT FINDINGS: Large-scale genomic approaches are finally starting to yield genetic loci robustly associated with CAVS. PALMD was discovered using a transcriptome-wide association study, whereby the results of a genome-wide association study were integrated with the first expression quantitative trait loci mapping study in human aortic valve tissues. The direction of effect indicated that the CAVS risk alleles at the PALMD locus conferred susceptibility by decreasing the mRNA expression levels of PALMD in valve tissues. Further analyses, along with our limited knowledge on the biology of this gene, suggested that PALMD is a noncoronary aortic disease gene specific for CAVS that is likely to mediate its effect through pathways involved in cardiac development and/or remodeling. SUMMARY: Treatment modalities that increase the expression and/or function of PALMD in valve tissues must be evaluated. PALMD provides key insights into the genetics and pathogenesis of CAVS, will orient clinically relevant laboratory-based research and sets a turning point for gene discovery in CAVS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".