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Record W4385484256 · doi:10.1016/j.bj.2023.100637

The effects of NOD-like receptors on adaptive immune responses

2023· review· en· W4385484256 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

VenueBiomedical Journal · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanadian Association of Gastroenterology
KeywordsImmune systemReceptorNodImmune receptorImmunologyAcquired immune systemBiologyPattern recognition receptorMedicineImmunityDiabetes mellitusEndocrinologyGenetics

Abstract

fetched live from OpenAlex

It has long been appreciated that cues from the innate immune system orchestrate downstream adaptive immune responses. Although previous work has focused on the roles of Toll-like receptors in this regard, relatively little is known about how Nod-like receptors instruct adaptive immunity. Here we review the functions of different members of the Nod-like receptor family in orchestrating effector and anamnestic adaptive immune responses. In particular, we address the ways in which inflammasome and non-inflammasome members of this family affect adaptive immunity under various infectious and environmental contexts. Furthermore, we identify several key mechanistic questions that studies in this field have left unaddressed. Our aim is to provide a framework through which immunologists in the adaptive immune field may view their questions through an innate-immune lens and vice-versa.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.002

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 head0.030
GPT teacher head0.315
Teacher spread0.285 · 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