BAK1 gene variation and abdominal aortic aneurysms
Bibliographic record
Abstract
We sought to examine the role of genetics in the multifactorial disease, abdominal aortic aneurysm (AAA), by studying sequence variation in the BAK1 gene (BAK1) that codes for an apoptotic-promoting protein, as chronic apoptosis activation has been linked to AAA development and progression. BAK1 abdominal aorta cDNA from AAA patients and nondiseased individuals were compared with each other, as well as to the BAK1 genomic sequence obtained from matching blood samples. We found specific BAK1 single nucleotide polymorphism (SNP) containing alleles in both aneurysmic (31 cases) and healthy aortic tissue (5 cases) without seeing them in the matching blood samples. These same BAK1 SNPs have been reported, although rarely (average frequency <0.06%), in reference BAK1 DNA sequences. Based on this and other similar observations, we propose a novel hypothesis postulating that multiple variants of genes may preexist in "minority" forms within specific nondiseased tissues and be selected for, when intra- and/or extracellular conditions change. Therefore, the fact that different BAK1 variants can exist in both diseased and nondiseased AA tissues compared to matching blood samples, together with the rare occurrence of these same SNPs in reference sequences, suggests that selection may be a significant factor in AAA ontogeny.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".