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Record W2107385964 · doi:10.1183/09031936.06.00010806

Toll-like receptor mRNA levels in alveolar macrophages after inhalation of endotoxin

2006· article· en· W2107385964 on OpenAlexaff
Nico A. Maris, Mark C. Dessing, Alex F. de Vos, Paul Bresser, Jaring S. van der Zee, H M Jansen, C. Arnold Spek, Tom van der Poll

Bibliographic record

VenueEuropean Respiratory Journal · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsBronchoalveolar lavageTLR4LipopolysaccharideCD14Alveolar macrophageReceptorImmunologyToll-like receptorInnate immune systemIn vivoInhalationImmune systemMacrophageBiologyMedicineIn vitroLungInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

Toll-like receptors (TLRs) are pattern-recognition receptors that have been implicated in the initiation of innate immune responses upon the first encounter with invading pathogens. The airways are frequently exposed to various types of lipopolysaccharide (LPS) from the environment or from pathogens. The current study was designed to determine the effect of LPS on TLR gene expression in human alveolar macrophages in vivo. In total, 16 healthy subjects were enrolled in a single-blinded, placebo-controlled study. Subjects inhaled 100 microg LPS or normal saline (n = 8 per group). Measurements were performed in alveolar macrophages purified from bronchoalveolar lavage fluid obtained 6 h post-challenge. Inhalation of LPS by healthy human volunteers resulted in enhanced alveolar macrophage expression of mRNAs encoding TLRs 1, 2, 7, 8 and CD14, and reduced expression of mRNAs encoding TLR4 and lymphocyte antigen 96. In conclusion, lipopolysaccharide differentially influences the toll-like receptor mRNA expression profile in human alveolar macrophages in vivo.

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 score0.001
Threshold uncertainty score0.004

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 science0.0000.000
Research integrity0.0000.000
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 head0.223
Teacher spread0.212 · 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

Citations62
Published2006
Admission routes1
Has abstractyes

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