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Record W2001590535 · doi:10.1002/path.2271

Nod‐like proteins in inflammation and disease

2007· review· en· W2001590535 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Pathology · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsUniversity of Toronto
FundersHoward Hughes Medical Institute
KeywordsNOD1NOD2Innate immune systemInflammationBiologyPattern recognition receptorNodDiseaseImmunityImmunologyImmune systemSignal transductionNeuroscienceReceptorIntracellularCell biologyMedicineGeneticsPathologyGene

Abstract

fetched live from OpenAlex

The field of innate immunity has undergone an enormous upheaval during the last decade. The discovery of different groups of proteins, called pattern recognition molecules (PRMs), which detect microbial components, so-called pathogen-associated molecular patterns (PAMPs) and trigger protective responses, had a huge impact on the understanding of innate immune responses. Among the PRMs, the intracellular Nod-like receptors (NLRs) have recently been identified as key mediators of inflammatory and immune responses. The NLR family is divided into subfamilies on the basis of their different signal transduction domains, and recent studies have highlighted the role of certain NLRs, including Nod1, Nod2, Nalp3, Ipaf and Naip5, in the detection of intracellular microbes and possibly 'danger signals'. In this review, we summarize the current knowledge on the function of these proteins in immunity and inflammation, with a focus on their participation in different disease pathologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.300
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it