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Record W1604840148 · doi:10.1111/cmi.12165

Nutrient sensing and metabolic stress pathways in innate immunity

2013· review· en· W1604840148 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

VenueCellular Microbiology · 2013
Typereview
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchBurroughs Wellcome Fund
KeywordsBiologyInnate immune systemPI3K/AKT/mTOR pathwayAMPKSignal transductionNutrient sensingImmunityCell biologyAdaptation (eye)Metabolic pathwayPhosphorylationImmunologyImmune systemBiochemistryNeuroscienceEnzymeProtein kinase A

Abstract

fetched live from OpenAlex

Cells monitor nutrient availability through several highly conserved pathways that include the mTOR signalling axis regulated by AKT/PI3K, HIF and AMPK, as well as the GCN2/eIF2α integrated stress response pathway that provides cellular adaptation to amino acid starvation. Recent evidence has identified a critical interplay between these nutrient sensing pathways and innate immunity to bacterial pathogens, viruses and parasites. These observations suggest that, in addition to the well-characterized pro-inflammatory signalling mediated by pattern recognition molecules, a metabolic stress programme contributes to shape the global response to pathogens.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient 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 score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.257
Teacher spread0.226 · 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