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Record W1791079802 · doi:10.4049/jimmunol.170.4.1625

Cutting Edge: Distinct Toll-Like Receptor 2 Activators Selectively Induce Different Classes of Mediator Production from Human Mast Cells

2003· article· en· W1791079802 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.

Bibliographic record

VenueThe Journal of Immunology · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMast cells and histamine
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDegranulationZymosanTLR2TLR4Leukotriene C4PeptidoglycanToll-like receptorMast cellBiologyCell biologyMicrobiologyActivator (genetics)ReceptorCD14LeukotrieneInnate immune systemImmunologySignal transductionBiochemistryIn vitroEnzyme

Abstract

fetched live from OpenAlex

Mast cells play a critical role in host defense against bacterial infection. Murine mast cells produce cytokines in response to bacterial peptidoglycan and LPS via Toll-like receptor (TLR) TLR2- and TLR4-dependent mechanisms. The expression of TLRs by human mast cells and responses to known TLR activators was examined. Human mast cells expressed mRNA for TLR1, TLR2, and TLR6 but not TLR4. Bacterial peptidoglycan and yeast zymosan were potent inducers of GM-CSF and IL-1beta and also induced substantial short-term cysteinyl leukotriene generation. In contrast, a synthetic triacylated lipopeptide induced short-term degranulation but failed to induce cysteinyl leukotriene production. The TLR4 activator Escherichia coli LPS did not induce a GM-CSF, IL-1beta leukotriene, or degranulation response. These data demonstrate highly selective production of different classes of mast cell mediators in response to distinct TLR activators of potential importance to the host response to bacterial or fungal 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 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 score0.037
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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