Natriuretic peptide receptors and atrial-selective fibrosis: potential role in atrial fibrillation
Bibliographic record
Abstract
This editorial refers to ‘Absence of natriuretic peptide clearance receptor attenuates TGF-β1-induced selective atrial fibrosis and atrial fibrillation’ by D. Rahmutula et al., pp. 357–372. Atrial fibrillation (AF) is a significant cause of population morbidity, mortality, and preventable stroke.1 Underlying mechanisms are under active investigation, with a view to better understand pathophysiology and thereby improve therapeutic options.2 Cardiac fibrosis plays an important role in the occurrence of AF3 and its response to therapy.4 The atria appear to be particularly prone to fibrosis.3 For example, in dogs with cardiac remodelling due to tachycardiomyopathic heart failure (HF), atrial fibrous-tissue content increases much more than ventricular, reaching over 20-fold the ventricular level, accompanied by enhanced phosphorylation of mitogen-activated protein kinases and transforming growth factor-β (TGF-β) in atria.5 Transgenic mice engineered to overexpress constitutively active TGF-β show prominent atrial-selective fibrosis and AF susceptibility, despite similar transgene-expression in both atria and ventricles.6 The basis for the particular atrial susceptibility to fibrosis is poorly understood and might provide clues to novel fibrosis-prevention interventions of value in managing AF.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.028 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".