Relevance of Targeting Oxidative Stress, Inflammatory, and Pro-Resolution Mechanisms in the Prevention and Management of Postoperative Atrial Fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia. AF can lead to severe complications, including stroke, myocardial infarction, and sudden death. AF risk factors include pathological aging and conditions such as obesity, diabetes, and hypertension. Clinical data revealed that cardiothoracic and non-cardiothoracic surgeries are also important risk factors for AF. Post-operative AF (POAF) is associated with important public health costs caused by increased hospitalization, frequent emergency room visits, and enhanced healthcare utilization, which altogether lead to a low quality of life for the patients. Hence, POAF is a major clinical challenge, and there is an urgent need for the development of novel therapeutic strategies. Interestingly, evidence from clinical and fundamental research converges to identify cardiac oxidative stress and atrial inflammation as the common denominators of all AF risk factors. Unresolved inflammation is suspected to provoke cardiac fibrosis, which is an important contributor to cardiac arrhythmias and AF. Antioxidant, anti-inflammatory, and pro-resolution strategies may help to combat post-operative cardiac remodeling and POAF. This article aims to review the current scientific evidence supporting the role of inflammation in the pathogenesis of POAF and explore potential novel therapeutic strategies to prevent and mitigate inflammation in the management of AF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".