Prevention of atrial fibrillation after open-chest surgery with extracellular vesicle therapy
Bibliographic record
Abstract
Almost half of patients recovering from open-chest surgery experience atrial fibrillation (AF) that results principally from inflammation in the pericardial space surrounding the heart. Given that postoperative AF is associated with increased mortality, effective measures to prevent AF after open-chest surgery are highly desirable. In this study, we tested the concept that extracellular vesicles (EVs) isolated from human atrial explant-derived cells can prevent postoperative AF. Middle-aged female and male rats were randomized to undergo sham operation or induction of sterile pericarditis followed by trans-epicardial injection of human EVs or vehicle into the atrial tissue. Pericarditis increased the probability of inducing AF while EV treatment abrogated this effect in a sex-independent manner. EV treatment reduced infiltration of inflammatory cells and production of pro-inflammatory cytokines. Atrial fibrosis and hypertrophy seen after pericarditis were markedly attenuated by EV pretreatment, an effect attributable to suppression of fibroblast proliferation by EVs. Our study demonstrates that injection of EVs at the time of open-chest surgery shows prominent antiinflammatory effects and prevents AF due to sterile pericarditis. Translation of this finding to patients might provide an effective new strategy to prevent postoperative AF by reducing atrial inflammation and fibrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".