Activation of GSK‐3β inhibited TNF‐α expression in cardiomyocytes during LPS stimulation
Bibliographic record
Abstract
This study was to investigate the role of glycogen synthase kinase (GSK)‐3β in cardiomyocyte TNF‐α expression induced by lipopolysaccharides (LPS). Neonatal mouse cardiomyocytes were cultured and incubated with LPS in the presence of SB216763, a specific inhibitor of GSK‐3β, or DMSO for 4 hours. SB216763 increased both TNF‐α protein and mRNA expression, suggesting an inhibitory role of GSK‐3β. To substantiate the role of GSK‐3β, cardiomyocytes were infected with recombinant adenoviruses containing GSK‐3β(Ad‐GSK) or dominant active mutant of GSK‐3β (Ad‐AC‐GSK) or dominant negative mutant of GSK‐3β(Ad‐DN‐GSK) or Green Fluorescent Protein (GFP) (Ad‐GFP) for 24 hours, followed by incubation with LPS for 4 hours. Both Ad‐GSK and Ad‐AC‐GSK infection decreased whereas Ad‐DN‐GSK increased TNF‐α protein and mRNA expression, compared to Ad‐GFP. This result supported the inhibitory role of GSK‐3β in TNF‐α expression in LPS‐stimulated cardiomyocytes. In addition, inhibition of protein kinase B activation (AKT) by either LY294002 or over‐expression of dominant negative mutant of AKT significantly decreased TNF‐α protein and mRNA expression in LPS‐stimulated cardiomyocytes. As AKT suppresses GSK‐3β activation, our data further supports the notion that GSK‐3β inhibits TNF‐α expression. In conclusion, our study demonstrated an inhibitory role of GSK‐3β in LPS‐stimulated TNF‐α expression, suggesting that GSK‐3β may be a therapeutic target for sepsis.
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) | 0.002 | 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".