Acute pericardial postischemic inflammatory responses: Characterization using a preclinical porcine model
Bibliographic record
Abstract
BACKGROUND: Pericardial fluid (PF) contains cells, proteins, and inflammatory mediators, such as cytokines, chemokines, growth factors, and matrix metalloproteinases. To date, we lack an adequate understanding of the inflammatory response that acute injury elicits in the pericardial space. OBJECTIVE: To characterize the inflammatory profile in the pericardial space acutely after ischemia/reperfusion. METHODS: Pigs were used to establish a percutaneous ischemia/reperfusion injury model. PF was removed from pigs at different time points postanesthesia or postischemia/reperfusion. Flow cytometry was used to characterize the immune cell composition of PF, while multiplex analysis was performed on the acellular portion of PF to determine the concentration of inflammatory mediators. There was a minimum of 3 pigs per group. RESULTS: While native PF mainly comprises macrophages, we show that neutrophils are the predominant inflammatory cell type in the pericardial space after injury. The combination of acute ischemia/reperfusion (IR) and repeatedly accessing the pericardial space significantly increases the concentration of interleukin-1 beta (IL-1β) and interleukin-1 receptor antagonist (IL-1ra). IR significantly increases the pericardial concentration of TGFβ1 but not TGFβ2. We observed that repeated manipulation of the pericardial space can also drive a robust pro-inflammatory response, resulting in a significant increase in immune cells and the accumulation of potent inflammatory mediators in the pericardial space. CONCLUSION: In the present study, we show that both IR and surgical manipulation can drive robust inflammatory processes in the pericardial space, consisting of an increase in inflammatory cytokines and alteration in the number and composition of immune cells.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".