Conversion of cardiac myocytes to a proinflammtory phenotype in sepsis:role of p38 MAP kinase
Bibliographic record
Abstract
Sepsis is associated with an inflammatory response in the heart. Our previous studies demonstrated that cardiac myocytes challenged with septic plasma are converted to a proinflammatory phenotype; these myocytes promote neutrophil (PMN) transendothelial migration. The aim of the present study was to assess the role of p38 MAP kinase in the sepsis‐induced conversion of cardiac myocytes to a proinflammatory phenotype. Feces‐induced peritonitis (FIP) was used as a murine sepsis model. Cardiac myocytes were treated with plasma derived either from sham or septic mice. Supernatants collected from plasma‐conditioned myocytes were used to assess PMN transendothelial migration in a cell culture insert system. Both p38 MAP kinase and transcription factor, NFκB, in cardiac myocytes were activated after exposure of the myocytes to septic plasma as indicated by an increase in p38 MAP kinase phosphorylation (Western) and nuclear accumulation of NFκB (EMSA), respectively. Supernatants collected from cardiac myocytes conditioned with septic plasma increased PMN transendothelial migration. This response was prevented if the myocytes were pretreated with either the p38 MAP kinase inhibitor, SB202190 (10 μM) or the proteosome inhibitor MG132 (prevents NFκB activation; 2.5 μM). The increase in nuclear levels of NFκB in myocytes challenged with septic plasma was diminished by SB202190. Collectively, these findings indicate that the p38 MAP kinase/NFκB pathway plays an important role in conversion of cardiac myocytes to a proinflammatory phenotype in sepsis. (CIHR MOP‐13668, MGC‐12816)
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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) | 0.000 | 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 | 0.001 |
| 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".