Rac1 activation induces tumour necrosis factor-α expression and cardiac dysfunction in endotoxemia
Bibliographic record
Abstract
Induction of tumour necrosis factor-α (TNF-α) expression leads to myocardial depression during sepsis. However, the underlying molecular mechanisms are not fully understood. The aim of this study was to investigate the role of Rac1 in TNF-α expression and cardiac dysfunction during endotoxemia and to determine the involvement of phosphoinositide-3 kinase (PI3K) in lipopolysaccharide (LPS)-induced Rac1 activation. Our results showed that LPS-induced Rac1 activation and TNF-α expression in cultured neonatal mouse cardiomyocytes. The response was inhibited in Rac1 deficient cardiomyocytes or by a dominant-negative Rac1 (Rac1N17). To determine whether PI3K regulates Rac1 activation, cardiomyocytes were treated with LY294002, a PI3K selective inhibitor. Treatment with LY294002 decreased Rac1 activity as well as TNF-α expression stimulated by LPS. Furthermore, inhibition of PI3K and Rac1 activity decreased LPS-induced superoxide generation which was associated with a significant reduction in ERK1/2 phosphorylation. To investigate the role of Rac1 in myocardial depression during endotoxemia in vivo, wild-type and cardiomyocyte-specific Rac1 deficient mice were treated with LPS (2 mg/kg, i.p.). Deficiency in Rac1 significantly decreased myocardial TNF-α expression and improved cardiac function during endotoxemia. We conclude that PI3K-mediated Rac1 activation is required for induction of TNF-α expression in cardiomyocytes and cardiac dysfunction during endotoxemia. The effect of Rac1 on TNF-α expression seems to be mediated by increased NADPH oxidase activity and ERK1/2 phosphorylation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".