Nucleotide-Binding Oligomerization Domain-Like Receptors and Inflammasomes in the Pathogenesis of Non-Microbial Inflammation and Diseases
Bibliographic record
Abstract
The nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) or nucleotide-binding domain leucine-rich repeat-containing family of genes plays an important role in the development of innate immune responses. Some family members are known to form multiprotein complexes known as inflammasomes that regulate the processing and secretion of proinflammatory mediators, such as interleukin-1β and interleukin-18. Activity of the inflammasome is triggered not only by microbial infection, but also by a wide range of both exogenous and endogenous noninfectious stimuli. Consequently, the dysregulation of inflammasome activity is associated with numerous proinflammatory, non-microbial human diseases. The discovery of NLRP3 gene mutations in autoinflammatory diseases such as Muckle-Wells syndrome has led to the association of NLRs in the pathogenesis of many non-microbial diseases that include arthritis, neurodegenerative disorders, metabolic disorders (obesity and diabetes), cardiovascular disease (atherosclerosis, myocardial infarction), inflammatory bowel disease, kidney disease and hypersensitivity dermatitis. A number of NLRs are also associated with human disease in the absence of inflammasome activity, suggesting additional roles for NLRs in the regulation of inflammation and disease. This review serves to provide a summary of NLR-associated diseases and, where possible, the mechanisms behind the associations.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".