JAK-STAT signalling and the atrial fibrillation promoting fibrotic substrate
Bibliographic record
Abstract
AIMS: Left-atrial (LA) fibrosis is an important feature of many atrial fibrillation (AF) substrates. The JAK-STAT system contributes to cardiac remodelling, but its role in AF is unknown. Here we investigated JAK-STAT changes in an AF-model and their potential contributions to LA-fibrosis. METHODS AND RESULTS: LA-remodelling was studied in dogs with heart failure (HF) induced by ventricular tachypacing (VTP, 240 bpm), and in mice with left-ventricular (LV) dysfunction due to myocardial infarction (MI). The selective STAT-3 inhibitor S3I-201 was administered to fibroblasts in vitro or mice in vivo (10 mg/kg/d, osmotic mini-pump). HF-dogs developed LA-selective fibrosis and AF-susceptibility at 1-week VTP. The mRNA-expression of platelet-derived growth factor (PDGF, a JAK-STAT activator) isoforms A, C and D, as well as JAK2, increased in LA fibroblasts from 1-week VTP. HF upregulated protein-expression of PDGF-receptor-β and phosphorylated (activated) signal transducer and activator of transcription 3 (STAT3) in LA. PDGF-AB stimulation of LA fibroblasts increased PDGFR-α, STAT3 and phosphorylated-STAT3 expression, as well as collagen-1 and fibronectin-1 protein secretion (by 1.6- to 20-fold), with smaller changes in LV fibroblasts. Phosphorylated-STAT3 and collagen upregulation were suppressed by the JAK2 inhibitor AG-490, PDGF receptor inhibitor AG1296 and STAT3-inhibitor SI3-201. In vivo S3I-201 treatment of MI-mice attenuated LA-fibrosis, LA-dilation and P-wave duration changes versus vehicle-control. CONCLUSIONS: HF activates the LA JAK-STAT system and enhances PDGF-signalling. JAK-STAT inhibition reduces the profibrotic effects of PDGF stimulation on canine fibroblasts in vitro while attenuating in vivo LA-fibrosis and remodelling in post-MI mice, suggesting that the JAK/STAT pathway contributes to LA-fibrogenesis and might be a potential target for LA-fibrosis prevention.
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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) | 0.002 | 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".