NOD-Like Receptors: Versatile Cytosolic Sentinels
Bibliographic record
Abstract
Nucleotide binding oligomerization domain (NOD)-like receptors are cytoplasmic pattern-recognition receptors that together with RIG-I-like receptor (retinoic acid-inducible gene 1), Toll-like receptor (TLR), and C-type lectin families make up the innate pathogen pattern recognition system. There are 22 members of NLRs in humans, 34 in mice, and even a larger number in some invertebrates like sea urchins, which contain more than 200 receptors. Although initially described to respond to intracellular pathogens, NLRs have been shown to play important roles in distinct biological processes ranging from regulation of antigen presentation, sensing metabolic changes in the cell, modulation of inflammation, embryo development, cell death, and differentiation of the adaptive immune response. The diversity among NLR receptors is derived from ligand specificity conferred by the leucine-rich repeats and an NH2-terminal effector domain that triggers the activation of different biological pathways. Here, we describe NLR genes associated with different biological processes and the molecular mechanisms underlying their function. Furthermore, we discuss mutations in NLR genes that have been associated with human 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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".