Horse model of spontaneous atrial fibrillation share proteomic changes with humans
Bibliographic record
Abstract
Horses and humans are among the few mammals susceptible to spontaneous atrial fibrillation (AF), both suffering from high recurrence rates after treatment. Treatment resistance is often attributed to progressive atrial remodeling, but current treatment options fail to effectively address this aspect. Here, we introduce a novel horse model of spontaneous AF to investigate the biological pathway changes in early stages of the disease. Through data-independent acquisition mass spectrometry on biopsies from the right and left atrium and left ventricular chamber of horses with early-stage persistent AF (n = 8) and controls (n = 8), we identify several differentially regulated proteins across all three chambers. Pathway enrichment analyses and histological stainings highlight a significant role of atrial extracellular matrix (ECM) remodeling in early AF. Other key proteomic changes relate to metabolism, contractility, and protein-folding, and overlap with findings from publicly available human datasets. Our results demonstrate that horses and humans share several AF-related proteomic changes, providing translational insights into the early atrial remodeling processes that are likely to contribute to treatment resistance. These protein-level changes could serve as biomarkers or pharmacological targets for preventing AF-associated atrial remodeling and improve treatment outcomes across species.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".