Molecular basis of selective atrial fibrosis due to overexpression of transforming growth factor-β1
Bibliographic record
Abstract
AIMS: Animal studies show that transforming growth factor-β1 (TGF-β1) is an important mediator of atrial fibrosis and atrial fibrillation (AF). This study investigated the role of TGF-β1 in human AF and the mechanism of atrial-selective fibrosis. METHODS AND RESULTS: Atrial specimens from 17 open heart surgery patients and left atrial and ventricular specimens from 17 explanted hearts were collected to assess the relationship between TGF-β1, AF, and differential atrial vs. ventricular TGF-β1 levels. A transgenic mouse model overexpressing active TGF-β1 was used to study the mechanisms underlying the resultant atrial-selective fibrosis. Higher right atrial total TGF-β1 levels (2.58 ± 0.16-fold, P < 0.0001) and active TGF-β1 (3.7 ± 0.7-fold, P = 0.013) were observed in those that developed post-operative AF. Although no ventricular differences were observed, 11 explanted heart failure hearts exhibited higher atrial TGF-β1 levels than 6 non-failing hearts (2.30 ± 0.87 fold higher, P < 0.001). In the transgenic mouse, TGF-β1 receptor-1 kinase blockade resulted in decreased atrial expression of fibrosis-related genes. By RNA microarray analyses in that model, 80 genes in the atria and only 2 genes in the ventricle were differentially expressed. Although these mice atria, but not the ventricles, exhibited increased expression of fibrosis-related genes and phosphorylation of Smad2, there were no differences in TGF-β1 receptor levels or Smads in the atria compared with the ventricles. CONCLUSIONS: TGF-β1 mediates selective atrial fibrosis in AF that occurs via TGF-β Receptor 1/2 and the classical Smad pathway. The differential atrial vs. ventricular fibrotic response occurs at the level of TGF-β1 receptor binding or phosphorylation.
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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 | 0.000 | 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".