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Record W3117279292 · doi:10.1097/shk.0000000000001704

Toll-Like Receptors, Associated Biochemical Signaling Networks, and S100 Ligands

2020· article· en· W3117279292 on OpenAlex
Sahil Gupta, James N. Tsoporis, Songhui Jia, Claúdia C. dos Santos, Thomas G. Parker, John C. Marshall

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

VenueShock · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsInnate immune systemPattern recognition receptorSignal transducing adaptor proteinReceptorSignal transductionCell biologyPathogen-associated molecular patternBiologyToll-like receptorTranscription factorIntracellularFunction (biology)Immune systemImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

ABSTRACT: Host cells recognize molecules that signal danger using pattern recognition receptors (PRRs). Toll-like receptors (TLRs) are the most studied class of PRRs and detect pathogen-associated molecular patterns and danger-associated molecular patterns. Cellular TLR activation and signal transduction can therefore contain, combat, and clear danger by enabling appropriate gene transcription. Here, we review the expression, regulation, and function of different TLRs, with an emphasis on TLR-4, and how TLR adaptor protein binding directs intracellular signaling resulting in activation or termination of an innate immune response. Finally, we highlight the recent progress of research on the involvement of S100 proteins as ligands for TLR-4 in inflammatory disease.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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