Glycogen synthase kinase‐3beta suppresses tumor necrosis factor‐alpha expression in cardiomyocytes during lipopolysaccharide stimulation
Bibliographic record
Abstract
This study was to investigate the role of glycogen synthase kinase-3beta (GSK-3beta) in cardiomyocyte tumor necrosis factor-alpha (TNF-alpha) expression induced by lipopolysaccharide (LPS). In cultured neonatal mouse cardiomyocytes, LPS induced TNF-alpha expression and increased GSK-3beta activation. Inhibition of GSK-3beta by SB216763 or by over-expression of a dominant negative mutant of GSK-3beta significantly enhanced TNF-alpha expression in LPS-stimulated cardiomyocytes, in association with an increase in p65 phosphorylation. In contrast, over-expression of GSK-3beta by adenoviral vectors containing wild-type GSK-3beta or a constitutively active GSK-3beta attenuated TNF-alpha expression induced by LPS. Further evidence to support the inhibitory role of GSK-3beta in TNF-alpha expression is that protein kinase B (Akt) signaling, an upstream inhibitor of GSK-3beta, promotes TNF-alpha expression in LPS-stimulated cardiomyocytes and this action of Akt signaling can be mimicked by GSK-3beta inactivation. Our study demonstrates that GSK-3beta plays an inhibitory role in cardiomyocyte TNF-alpha expression during LPS stimulation, and it may be a potential therapeutic target for 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 | 0.000 |
| 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".