Conversion of cardiac myocytes to a proinflammtory phenotype in sepsis: role of peroxynitrite derived from eNOS
Bibliographic record
Abstract
Isolated cardiac myocytes challenged with septic plasma are converted to a proinflammatory phenotype; these myocytes generate chemokines and promote neutrophil (PMN) transendothelial migration. The aim of present study was to assess the role of peroxynitrite derived from eNOS in the sepsis‐induced conversion of cardiac myocytes to a proinflammatory phenotype. Sepsis was induced by i.p. injection of feces; saline (i.p.) served as a sham treatment. Cardiac myocytes were treated with plasma isolated from either sham or septic mice. Intracellular peroxynitrite (DHR oxidation) was increased in myocytes conditioned with septic plasma; an event prevented by a NOS inhibitor (L‐NAME). Supernatants from cardiac myocytes conditioned with septic plasma increased PMN transendothelial migration; an event prevented by L‐NAME or a peroxynitrite decomposition catalyst, Fe‐TPPs. Supernatants obtained from cardiac myocytes conditioned with septic plasma had higher levels of the chemokines, LIX and KC than those treated with sham plasma. Supernatanats obtained from the eNOS−/− myocytes conditioned with septic plasma 1) failed to promote PMN migration and 2) contained less LIX and KC than their wild type counterparts. Collectively, these findings indicate peroxynitrite derived from eNOS plays an important role in the conversion of cardiac myocytes to a proinflammatory phenotype in sepsis. (CIHR MOP‐81303, MOP‐13668 and 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".