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The machinery of Nod‐like receptors: refining the paths to immunity and cell death

2011· review· en· W1551045512 on OpenAlex
Maya Saleh

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

VenueImmunological Reviews · 2011
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsBiologyInnate immune systemEffectorPattern recognition receptorReceptorImmune systemImmunityNodCell biologyInflammationAcquired immune systemSignal transductionImmunologyAutophagyProgrammed cell deathInflammasomeNeuroscienceGeneticsGene

Abstract

fetched live from OpenAlex

One of the fundamental aspects of the innate immune system is its capacity to discriminate between self and non-self or altered self, and to quickly respond by eliciting effector mechanisms that act in concert to restore normalcy. This capacity is determined by a set of evolutionarily conserved pattern recognition receptors (PRRs) that sense the presence of microbial motifs or endogenous danger signals, including tissue damage, cellular transformation or metabolic perturbation, and orchestrate the nature, duration and intensity of the innate immune response. Nod-like receptors (NLRs), a group of intracellular PRRs, are particularly essential as evident by the high incidence of genetic variations in their genes in various diseases of homeostasis. Here, I overview the signaling mechanisms of NLRs and discuss the mounting evidence of evolutionary conservation between their pathways and the cell death machinery. I also describe their effector functions that link the sensing of danger to the induction of inflammation, autophagy or cell death.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.078
GPT teacher head0.315
Teacher spread0.237 · 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