Unravelling Atrioventricular Block Risk in Inflammatory Diseases: Systemic Inflammation Acutely Delays Atrioventricular Conduction via a Cytokine‐Mediated Inhibition of Connexin43 Expression
Bibliographic record
Abstract
Background Recent data suggest that systemic inflammation can negatively affect atrioventricular conduction, regardless of acute cardiac injury. Indeed, gap-junctions containing connexin43 coupling cardiomyocytes and inflammation-related cells (macrophages) are increasingly recognized as important factors regulating the conduction in the atrioventricular node. The aim of this study was to evaluate the acute impact of systemic inflammatory activation on atrioventricular conduction, and elucidate underlying mechanisms. Methods and Results We analyzed: (1) the PR-interval in patients with inflammatory diseases of different origins during active phase and recovery, and its association with inflammatory markers; (2) the existing correlation between connexin43 expression in the cardiac tissue and peripheral blood mononuclear cells (PBMC), and the changes occurring in patients with inflammatory diseases over time; (3) the acute effects of interleukin(IL)-6 on atrioventricular conduction in an in vivo animal model, and on connexin43 expression in vitro. In patients with elevated C-reactive protein levels, atrioventricular conduction indices are increased, but promptly normalized in association with inflammatory markers reduction, particularly IL-6. In these subjects, connexin43 expression in PBMC, which is correlative of that measured in the cardiac tissue, inversely associated with IL-6 changes. Moreover, direct IL-6 administration increased atrioventricular conduction indices in vivo in a guinea pig model, and IL-6 incubation in both cardiomyocytes and macrophages in culture, significantly reduced connexin43 proteins expression. Conclusions The data evidence that systemic inflammation can acutely worsen atrioventricular conduction, and that IL-6-induced down-regulation of cardiac connexin43 is a mechanistic pathway putatively involved in the process. Though reversible, these alterations could significantly increase the risk of severe atrioventricular blocks during active inflammatory processes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".