A direct requirement of nuclear factor-κB for suppression of apoptosis in ventricular myocytes
Bibliographic record
Abstract
Nuclear factor-kappa B (NF-kappa B) is a ubiquitously expressed cellular factor regulated by the cytoplasmic factor inhibitor protein kappa B alpha (I kappa B alpha). Activation of NF-kappa B by cytokines, including tumor necrosis factor-alpha (TNF-alpha), requires the phosphorylation and degradation of I kappa B alpha. An anti-apoptotic role for NF-kappa B has recently been suggested. In the present study, we ascertained whether death-promoting signals and apoptosis mediated by TNF-alpha are suppressed by NF-kappa B in postnatal ventricular myocytes. Stimulation of myocytes with TNF-alpha resulted in a 12.1-fold increase (P < 0.01) in NF-kappa B-dependent gene transcription and DNA binding compared with controls. This was accompanied by a corresponding increase in the NF-kappa B target protein A20 as determined by Western blot analysis. Vital staining revealed that TNF-alpha was not cytotoxic to myocytes and did not provoke apoptosis. Adenovirus-mediated delivery of a nonphosphorylatable form of I kappa B alpha to inactivate NF-kappa B prevented TNF-alpha-stimulated NF-kappa B-dependent gene transcription and nuclear NF-kappa B DNA binding. Importantly, myocytes stimulated with TNF-alpha and defective for NF-kappa B activation resulted in a 2.2-fold increase (P < 0.001) in apoptosis. To our knowledge, the data provide the first indication that a functional NF-kappa B signaling pathway is crucial for suppressing death-promoting signals mediated by TNF-alpha in ventricular myocytes.
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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 | 0.001 |
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".