MétaCan
Menu
Back to cohort
Record W4403073184 · doi:10.7554/elife.102068

β-Glucan reprograms alveolar macrophages via neutrophil/IFNγ axis in a murine model of lung injury

2024· article· en· W4403073184 on OpenAlexafffund
Renaud Prével, Erwan Pernet, Kim A. Tran, Abderrahmane Sadek, Mina Sadeghi, Elizabeth Lapshina, Leonardo F. Jurado, Arnold S. Kristof, Mohiéddine Moumni, Jérémie Poschmann, Maziar Divangahi

Bibliographic record

VenueeLife · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsMcGill University Health Centre
FundersCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéRoyal Society of Canada
KeywordsLungAlveolar macrophageMacrophageMicrobiologyImmunologyCell biologyChemistryMedicineBiologyBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

Alveolar macrophages (AMs) reside in the lower airways and play a crucial role in lung health and response to sterile inflammation and infections. AMs possess remarkable adaptability to different environmental challenges that can persist through their memory capacity (trained immunity). β-Glucan has been characterized as a potent inducer of central trained immunity by reprogramming haematopoietic stem cells in the bone marrow. In the present study, we show that systemic administration of β-glucan in mice induces peripheral trained immunity by reprogramming AMs in the lungs, in a Dectin1-independent manner. We furthermore demonstrate that AM reprogramming at both the transcriptional and metabolic levels exacerbate lung injury following bacterial (lipopolysaccharide) or viral (polyI:C) challenges via a neutrophil/IFN-γ-dependent manner. These findings identify an additional facet of β-glucan in trained immunity involving AM reprogramming and shed light on the potential detrimental effects of trained immunity.

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 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 score0.110
Threshold uncertainty score0.551

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 head0.276
Teacher spread0.266 · 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 teacher head, 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

Citations10
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueeLifeSame topicImmune responses and vaccinationsFrench-language works237,207