Multiple Facets of NF-κB in the Heart
Bibliographic record
Abstract
The progression from cardiac injury to symptomatic heart failure has been intensely studied over the last decade, and is largely attributable to a loss of functional cardiac myocytes through necrosis, intrinsic and extrinsic apoptosis pathways and autophagy. Therefore, the molecular regulation of these cellular programs has been rigorously investigated in the hopes of identifying a potential cell target that could promote cell survival and/or inhibit cell death to avert, or at least prolong, the degeneration toward symptomatic heart failure. The nuclear factor (NF)-κB super family of transcription factors has been implicated in the regulation of immune cell maturation, cell survival, and inflammation in many cell types, including cardiac myocytes. Recent studies have shown that NF-κB is cardioprotective during acute hypoxia and reperfusion injury. However, prolonged activation of NF-κB appears to be detrimental and promotes heart failure by eliciting signals that trigger chronic inflammation through enhanced elaboration of cytokines including tumor necrosis factor α, interleukin-1, and interleukin-6, leading to endoplasmic reticulum stress responses and cell death. The underlying mechanisms that account for the multifaceted and differential outcomes of NF-κB on cardiac cell fate are presently unknown. Herein, we posit a novel paradigm in which the timing, duration of activation, and cellular context may explain mechanistically the differential outcomes of NF-κB signaling in the heart that may be essential for future development of novel therapeutic interventions designed to target NF-κB responses and heart failure following myocardial injury.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".