Abstract 6970: Extracellular Vesicles Prevent Post-Operative Atrial Fibrillation
Bibliographic record
Abstract
Half of patients recovering from cardiac surgery experience a malignant form of atrial fibrillation that results from inflammation in the pericardial space surrounding the heart. Given that post-operative atrial fibrillation (POAF) increases mortality, effective measures to prevent or treat POAF are highly desirable. In this study, we built on previous work to see if extracellular vesicles (EVs) isolated from human atrial explant-derived cell conditioned media can prevent POAF. Hypothesis: Human EVs will reduce AF inducibility in a rat model of sterile pericarditis by reducing inflammation and the malignant pro-fibrillatory transformation of atrial fibroblasts. Methods and Results: Middle aged female and male Sprague-Dawley rats (6 months old) were randomized to sham operation (n=24; 12 females, 12 males) or induction of sterile pericarditis followed by intra-atrial injection of 10 8 human EVs (n=34; 17 females, 17 males) or vehicle (n=34; 17 females, 17 males). Three days later, all rats underwent invasive electrophysiological testing prior to sacrifice. As expected, pericarditis increased the probability of inducing AF (Figure Panel A). EV treatment reduced the probability of AF. Recipient sex did not alter this effect. Analysis of atrial tissue histology (H&E staining) and lysate (Luminex assays for IL-2, Il-18, MCP-1 and PDGF-AB) revealed that EV treatment abrogated the pro-inflammatory effects of pericarditis. As shown in Panel B, pericarditis increased both atrial fibrosis and mass while EV treatment limited the effect of pericarditis on atrial fibrosis and mass. These effects were attributable to the anti-fibrotic effect of EV treatment on atrial fibroblast proliferation profiled using in-vitro cultures exposed to pro-fibrillatory stimuli (i.e., IL-6 and TGFβ1). Conclusion: Intramyocardial injection of EVs at the time of open chest surgery may provide a facile acellular strategy to prevent POAF by reducing inflammation and fibrosis.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".