Cardiac surgery elicits pericardial inflammatory responses that are distinct compared with postcardiopulmonary bypass systemic inflammation
Bibliographic record
Abstract
Objectives: Cardiac surgery using cardiopulmonary bypass contributes to a robust systemic inflammatory process. Local intrapericardial postsurgical inflammation is believed to trigger important clinical implications, such as postoperative atrial fibrillation and postsurgical intrathoracic adhesions. Immune mediators in the pericardial space may underlie such complications. Methods: In this prospective pilot clinical study, 12 patients undergoing isolated coronary artery bypass graft surgery were enrolled. Native pericardial fluid and venous blood samples (baseline) were collected immediately after pericardiotomy. Postoperative pericardial fluid and venous blood samples were collected 48-hours after cardiopulmonary bypass and compared with baseline. Flow cytometry determined proportions of specific immune cells, whereas multiplex analysis probed for inflammatory mediators. Results: macrophages in native pericardial fluid compared with baseline blood, after surgery there are significantly fewer of these cells present in the pericardial space compared with blood. Postoperatively, concentration of interleukin receptor antagonist 6, and interleukin 8 were significantly higher in the pericardial space compared with blood. After surgery, compared with blood, the pericardial space has a significantly higher concentration of matrix metalloproteinase 3, matrix metalloproteinase 8, and matrix metalloproteinase 9. The same trend was observed with transformational growth factor β. Conclusions: Cardiac surgery elicits an inflammatory response in the pericardial space, which differs from systemic inflammatory responses. Future work should determine whether or not this distinct local inflammatory response contributes to postsurgical complications and could be modified to influence clinical outcomes.
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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 | 0.001 |
| 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) | 0.002 | 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".