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Record W1979692855 · doi:10.1080/07853890701576172

Nod‐like receptors in innate immunity and inflammatory diseases

2007· review· en· W1979692855 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnals of Medicine · 2007
Typereview
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsNOD2NOD1Innate immune systemNodNALP3BiologyImmunityImmunologyPattern recognition receptorReceptorImmune systemAcquired immune systemPyrin domainInflammationInflammasomeGeneGenetics

Abstract

fetched live from OpenAlex

Over the past few years the field of innate immunity has undergone a revolution with the discovery of pattern recognition molecules (PRM) and their role in microbe detection. Among these molecules, the Nod-like receptors (NLRs) have emerged as key microbial sensors that participate in the global immune responses to pathogens and contribute to the resolution of infections. This growing group of proteins is divided into subfamilies with basis in their different signaling domains. Prominent among them are Nod1, Nod2, Nalp3, Ipaf, and Naip that have been shown to play important roles against intracellular bacteria. Furthermore, mutations in the genes that encode these proteins have been associated with complex inflammatory disorders including Crohn's disease, asthma, familial cold urticaria, Muckle-Wells syndrome, and Blau syndrome. In this review we will present the current knowledge on the role of these proteins in immunity and inflammatory diseases.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.229
GPT teacher head0.527
Teacher spread0.298 · 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