The NF-κB signaling system in the immunopathogenesis of inflammatory bowel disease
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is an idiopathic, chronic condition characterized by episodes of inflammation in the gastrointestinal tract. The nuclear factor κB (NF-κB) system describes a family of dimeric transcription factors. Canonical NF-κB signaling is stimulated by and enhances inflammation, whereas noncanonical NF-κB signaling contributes to immune organogenesis. Dysregulation of NF-κB factors drives various inflammatory pathologies, including IBD. Signals from many immune sensors activate NF-κB subunits in the intestine, which maintain an equilibrium between local microbiota and host responses. Genetic association studies of patients with IBD and preclinical mouse models confirm the importance of the NF-κB system in host defense in the gut. Other studies have investigated the roles of these factors in intestinal barrier function and in inflammatory gut pathologies associated with IBD. NF-κB signaling modulates innate and adaptive immune responses and the production of immunoregulatory proteins, anti-inflammatory cytokines, antimicrobial peptides, and other tolerogenic factors in the intestine. Furthermore, genetic studies have revealed critical cell type-specific roles for NF-κB proteins in intestinal immune homeostasis, inflammation, and restitution that contribute to the etiopathology of IBD-associated manifestations. Here, we summarize our knowledge of the roles of these NF-κB pathways, which are activated in different intestinal cell types by specific ligands, and their cross-talk, in fueling aberrant intestinal inflammation. We argue that an in-depth understanding of aberrant immune signaling mechanisms may hold the key to identifying predictive or prognostic biomarkers and developing better therapeutics against inflammatory gut pathologies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".