Role of cardiac fibroblasts in myocardial dysfunction in mice with sepsis: effect of NLRP3 inflammasome activation (1096.7)
Bibliographic record
Abstract
Background : Inflammasomes are intracellular platforms which promote maturation and releasing of IL‐1β and IL‐18. Cardiac fibroblast is the most abundant non‐cardiomyocytes within the heart. The aim of the present study was to evaluate whether sepsis activates NLRP3 inflammasome in cardiac fibroblasts and subsequently affects function of cardiomyocytes through IL‐1β thereby results in myocardial dysfunction. Materials and Methods: NLPR3, procaspase‐1, caspase‐1 p10, pro‐IL‐1β and IL‐1β in cardiac fibroblasts were assessed with Western blot or ELISA. Cardiomyocyte intracellular cAMP was assessed with direct immunoassay kit. Mouse model of sepsis was induced by intraperitoneal injection of LPS. Mouse myocardial function was evaluated with a mouse pressure‐volume loop analysis system. Results: Treatment of cardiac fibroblasts with LPS resulted in activation of capase‐1 (increased p10) and induced maturation and releasing of IL‐1β. Genetically (siRNA) and pharmacologically (Glyburide) inhibition of NLRP3 inflammasome in cardiac fibroblasts blocked the LPS‐induced activation of the caspase‐1, as well as the maturation and releasing of the IL‐1β. Inhibition of NLRP3 inflammasome in cardiac fibroblasts prevented the decrease in intracellular cAMP in cardiomyocyte conditioned with supernatants of cardiac fibroblasts with LPS. Inhibition of the NLRP3 inflammasome attenuated the myocardial dysfunction in mice with sepsis. Conclusions : Activation of NLRP3 inflammasome in cardiac fibroblasts results in increased IL‐1β production which mediates the cardiac fibroblast‐cardiomyocyte interaction and promotes the induction of myocardial dysfunction in mice with sepsis. (HSFO GIA 2012‐000212 to TR). Grant Funding Source : HSFO
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".