Comprehensive characterization of the postoperative pericardial inflammatory response: Potential implications for clinical outcomes
Bibliographic record
Abstract
Objective: There is a paucity of data on the inflammatory response that takes place in the pericardial space after cardiac surgery. This study provides a comprehensive assessment of the local postoperative inflammatory response. Methods: Forty-three patients underwent cardiotomy, where native pericardial fluid was aspirated and compared with postoperative pericardial effluent collected at 4, 24, and 48 hours' postcardiopulmonary bypass. Flow cytometry was used to define the levels and proportions of specific immune cells. Samples were also probed for concentrations of inflammatory cytokines, matrix metalloproteinases (MMPs), and tissue inhibitors of metalloproteinases (TIMPs). Results: Preoperatively, the pericardial space mainly contains macrophages and T cells. However, the postsurgical pericardial space was populated predominately by neutrophils, which constituted almost 80% of immune cells present, and peaked at 24 hours. When surgical approaches were compared, minimally invasive surgery was associated with fewer neutrophils in the pericardial space at 4 hours' postsurgery. Analysis of the intrapericardial concentrations of inflammatory mediators showed interleukin-6, MMP-9, and TIMP-1 to be highest postsurgery. Over time, MMP-9 concentrations decreased significantly, whereas TIMP-1 levels increased, resulting in a significant reduction of the ratio of MMP:TIMP after surgery, suggesting that active inflammatory processes may influence extracellular matrix remodeling. Conclusions: These results show that cardiac surgery elicits profound alterations in the immune cell profile in the pericardial space. Defining the cellular and molecular mediators that drive pericardial-specific postoperative inflammatory processes may allow for targeted therapies to reduce immune-mediated complications.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".