Nitric oxide and calcium signaling regulate myocardial tumor necrosis factor-α expression and cardiac function in sepsisThis article is one of a selection of papers published in this special issue on Calcium Signaling.
Bibliographic record
Abstract
Myocardial tumor necrosis factor-alpha (TNF-alpha), a proinflammatory cytokine, is a critical inducer of myocardial dysfunction in sepsis. The purpose of this review is to summarize the mechanisms through which TNF-alpha production is regulated in cardiomyocytes in response to lipopolysaccharide (LPS), a key pathogen-associated molecular pattern (PAMP) in sepsis. These mechanisms include Nox2-containing NAD(P)H oxidase, phospholipase C (PLC)gamma1, and Ca2+ signaling pathways. Activation of these pathways increases TNF-alpha expression via activation of extracellular signal-regulated kinases 1 and 2 (ERK1/2) and p38 mitogen-activated protein kinase (MAPK). Conversely, activation of c-Jun NH2-terminal kinase 1 (JNK1) negatively regulates TNF-alpha production through inhibition of ERK1/2 and p38 MAPK activity. Interestingly, endothelial nitric oxide synthase (eNOS) promotes TNF-alpha expression by enhancing p38 MAPK activation, whereas neuronal NOS (nNOS) inhibits TNF-alpha production by reducing Ca2+-dependent ERK1/2 activity. Therefore, the JNK1 and nNOS inhibitory pathways represent a "brake" that limits myocardial TNF-alpha expression in sepsis. Further understanding of these signal transduction mechanisms may lead to novel pharmacological therapies in sepsis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".