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Record W2116807191 · doi:10.4049/jimmunol.175.8.4834

Cutting Edge: TLR4 Deficiency Confers Susceptibility to Lethal Oxidant Lung Injury

2005· article· en· W2116807191 on OpenAlexaff
Xuchen Zhang, Peiying Shan, Salman T. Qureshi, Robert Homer, Ruslan Medzhitov, Paul W. Noble, Patty J. Lee

Bibliographic record

VenueThe Journal of Immunology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeme Oxygenase-1 and Carbon Monoxide
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Heart, Lung, and Blood Institute
KeywordsHyperoxiaTLR4Protein kinase BOxidative stressApoptosisImmunologyLungBiologyMedicineInflammationInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

TLRs have been studied extensively in pathogen-mediated host responses. We use a murine model of lethal oxidant-mediated injury to demonstrate for the first time that mammalian TLR4 is required for survival and lung integrity. Administering high levels of inspired oxygen, or hyperoxia, is commonly used as a life-sustaining measure in critically ill patients. However, prolonged exposures can lead to respiratory failure and death. TLR4-deficient mice exhibited increased mortality and lung injury during hyperoxia. The enhanced susceptibility of TLR4-deficient mice to hyperoxia was associated with an inability to up-regulate Bcl-2 and phospho-Akt. Restoration of Bcl-2 and phospho-Akt levels by the exogenous transfer of the antioxidant gene heme oxygenase-1 markedly attenuated hyperoxia-induced injury, apoptosis, and mortality in TLR4-deficient mice. Taken together, our results suggest a protective role of TLR4 in oxidant-mediated injury, providing novel mechanistic links among innate immunity, oxidant stress, and apoptosis.

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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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

Citations127
Published2005
Admission routes1
Has abstractyes

Explore more

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