NLRs: Nucleotide-Binding Domain and Leucine-Rich-Repeat-Containing Proteins
Bibliographic record
Abstract
Eukaryotes have evolved strategies to detect microbial intrusion and instruct immune responses to limit damage from infection. Recognition of microbes and cellular damage relies on the detection of microbe-associated molecular patterns (MAMPs, also called PAMPS, or pathogen-associated molecular patterns) and so-called "danger signals" by various families of host pattern recognition receptors (PRRs). Members of the recently identified protein family of nucleotide-binding domain andleucine-rich-repeat-containing proteins (NLR), including Nod1, Nod2, NLRP3, and NLRC4, have been shown to detect specific microbial motifs and danger signals for regulating host inflammatory responses. Moreover, with the discovery that polymorphisms in NOD1, NOD2, NLRP1, and NLRP3 are associated with susceptibility to chronic inflammatory disorders, the view has emerged that NLRs act not only as sensors butalso can serve as signaling platforms for instructing and balancing host immune responses. In this chapter, we explore the functions of these intracellular innate immune receptors and examine their implication in inflammatory diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".