Role of stress granule activation in cardiomyocyte function in sepsis
Bibliographic record
Abstract
Sepsis is a life‐threatening organ dysfunction caused by a dysregulated host response to infection. It is the leading cause of mortality in Intensive Care Unit (ICU). Sepsis‐induced myocardial dysfunction (SIMD) is a major complication of the sepsis that contributes to high mortality of patients with sepsis. Stress granule (SG) is a cytoplasmic membraneless platform that is formed in response to cellular stresses. The role of SG in SIMD is unknown. The aim of present study is to understand whether activation of SG in cardiomyocytes (CM) plays a role in CM function in sepsis. Methods CMs were treated with lipopolysaccharide (LPS). CM SG activation (eIF2α phosphorylation) and TNFα production were assessed with Western blot and ELISA, respectively. CM function was evaluated with intracellular cAMP. ISRIB or over‐expression of G3BP1 was employed interfering SG. Results Challenging CMs with LPS results in eIF2α phosphorylation, an increase in TNFα production, and a decrease in intracellular cAMP in response to dobutamine. Pharmacologically (ISRIB) or genetically inhibition (G3BP1 knock down) of SG leads to an exaggerated increase in TNFα expression. Further, SG inhibition results in enhanced decrease in CM function (decreased intracellular cAMP) in CM with LPS. Over‐expression of G3BP1 in CMs prevents LPS‐induced CM TNFα production and improves CM contractility. Conclusion Our results indicate that the cardiomyocyte stress granule activation plays a protective role of myocardial function 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) | 0.000 | 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".