Abstract 4372870: IL-37 contributes to the attenuation of inflammation and atrial fibrillation associated with right heart disease.
Bibliographic record
Abstract
Introduction: Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Age, obesity as well as heart failure, diabetes or right heart disease (RHD) induced by pulmonary arterial hypertension are important risk factors for AF. Studies have shown that patients with AF associated with RHD symptoms present high levels of circulating pro-inflammatory interleukin-(IL)-18. However, IL-37 an antagonist of the IL-18 receptor, has been shown to prevent inflammation. The role of IL-37 in the context of AF and RHD remains unexplored. Hypothesis: IL-37 reduces atrial inflammation and susceptibility to AF in the context of RHD. Methods: Right-sided cardiac hypertrophy and dilation were induced by pulmonary artery banding (PAB) on male and female Wistar rats (250-300g). PAB was not performed on Sham group. Animals were randomized into four groups and daily injection of IL-37 (1µg/kg) were administered to 50% of the PAB and Sham rats from day 0, 8-hour post-surgery until 3 weeks post-PAB. Echocardiography and electrophysiological studies were performed in vivo before sacrifices. Right atrial optical mapping was performed on Langendorff-perfused freshly excised hearts to analyze the atrial conduction. Protein and gene expression involved in AF were respectively analyzed by Western blot and qPCR. Results: Three weeks after surgery, although IL-37 did not prevent PAB-induced right-sided hypertrophy, PAB rats were more vulnerable to AF than Sham and PAB animals treated with IL-37. Optical mapping revealed reduced right atrial conduction velocity. Treatment with IL-37 induced a decrease in the expression of pro-inflammatory biomarkers (IL-18, IL-6, IL-1β) accompanied with a reduction of right-atrial fibrosis. Conclusion: IL-37 emerges as a potential therapeutic candidate for attenuating atrial inflammation and reducing susceptibility to AF in RHD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".