Bibliographic record
Abstract
2][3][4] The underlying mechanisms responsible for this heritability are complex, with evidence supporting the involvement of both rare and common genetic variants. 5,6Although considerable progress has been made since the first genetic culprit for AF was identified in 2003, translating our improved genetic understanding into clinically actionable treatment strategies remains a vision rather than a reality. 7,8Leveraging insights gleaned from rare variants identified in familial AF cases may be limited by their generalizability to the greater AF population, whereas a major challenge faced with common variants identified through genome-wide association studies (GWAS) has been clarifying their functional relevance.Single nucleotide polymorphisms (SNPs) identified through GWAS have been predominantly nonprotein coding, leaving experts to hypothesize their mechanism of action. 9s the list of AF GWAS SNPs progressively expands, there is a mounting need to clarify their functional effects to translate their identification into clinically actionable tools. 10,11The predominant belief has been that these SNPs reside within regulatory regions that modulate expression of nearby (and potentially remote) genes.Genomic loci that associate with altered mRNA expression levels are referred to as expression quantitative trait loci (eQTLs), with the terms cis and trans indicating regulation of nearby and distant genes, respectively. 12Notably, regulation of gene expression varies across cell types, and hence eQTL values are tissue-specific, a notion highlighting the importance of evaluating disease-associated SNPs in a disease-relevant tissue context.Given the limited availability of many human tissue types, a large-scale National Institutes of Health-funded initiative termed the Genotype-Tissue Expression project was launched in 2010 to serve as a publically available resource to facilitate investigation into the relationship between genetic variation and gene expression. 13ince its initial launch, the Genotype-Tissue Expression project has accumulated 11 688 samples across 53 tissue types from a total of 714 donors.On entry into the database, tissues undergo massively parallel RNA sequencing to quantitate genome-wide mRNA expression levels, whereas SNP genotyping is performed on DNA from peripheral blood. 14Relevant to cardiac disease, tissues samples have been collected from the right atrial appendage (264 with donor genotype), the left ventricle, and the coronary arteries.Although right atrial appendage samples have been accrued, left atrial tissue has not been collected, which may limit the ability to leverage Genotype-Tissue Expression project data for AF research given that AF pathophysiology is felt to predominantly reside within the left atrium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".