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Record W2107009994 · doi:10.1684/ecn.2011.0274

TREM-1 interaction with the LPS/TLR4 receptor complex

2011· article· en· W2107009994 on OpenAlexaff
Rob J.W. Arts, Leo A. B. Joosten, Charles A. Dinarello, Bart Jan Kullberg, J.W.M. van der Meer, Mihai G. Netea

Bibliographic record

VenueEuropean Cytokine Network · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInflammation biomarkers and pathways
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsTLR4ReceptorCell biologyToll-like receptorInflammationBlocking (statistics)StimulationChemistryInterleukin-21 receptorBiologyImmunologyInnate immune systemNeuroscienceBiochemistry

Abstract

fetched live from OpenAlex

Triggering receptor expressed on myeloid cells 1 (TREM-1) is an activating receptor expressed on neutrophils and monocytes that amplifies inflammation induced by stimulation of pattern-recognition receptors. In this study, several lines of evidence are presented that TREM-1 interacts with the toll-like receptor 4 (TLR4) receptor complex, or is a component of this complex. Blocking anti-TREM-1 antibodies specifically inhibited LPS-induced TNF-α production, while the alternative approach of blocking TLR4 by a specific inhibitor led to a down-regulation of the effects of TREM-1 cross-linking. These data are in line with the TLR4-TREM1 co-localization in human neutrophils and suggests that, at least some of the biological effects of TREM-1 may be due to its interaction with the TLR4/LPS-receptor complex.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.207
Teacher spread0.165 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations66
Published2011
Admission routes1
Has abstractyes

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