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Record W4416446563 · doi:10.1093/jimmun/vkaf283.635

Alveolar macrophage function is impaired following inhalation of berry e-cigarette vapour 2750

2025· article· en· W4416446563 on OpenAlexaffabout
Ajitha Thanabalasuriar, Amelia Kulle, Antoine Dufour, Erika Penz, Ziyi Li, Ashley Kwak

Bibliographic record

VenueThe Journal of Immunology · 2025
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsMcGill University
Fundersnot available
KeywordsAlveolar macrophageMacrophageImmune systemInhalationPulmonary alveolusCellRespiratory systemEndocytosisCell growth

Abstract

fetched live from OpenAlex

Abstract Description In the lower respiratory tract, the alveolar spaces are divided from the bloodstream and the external environment by only a few microns of interstitial tissue. Alveolar macrophages (AMs) defend this delicate mucosal surface from invading infections by regularly patrolling the site. AMs have three behaviour modalities to achieve this goal: extending cell protrusions to sample surrounding areas, squeezing the whole cell body between alveoli, and patrolling by moving the cell body around each alveolus. In this study we found Rho GTPase, cell division control protein 42 (CDC42) expression significantly decreased after berry-flavoured e-cigarette (e-cig) exposure. This shifted AM behaviour from squeezing to probing. Changes in AM behaviour led to a reduction in the clearance of inhaled bacteria, Pseudomonas aeruginosa. These findings shed light on pathways involved in AM migration and highlight the harmful impact of e-cigarette vaping on alveolar macrophage function. Funding Sources Canadian Institute of Health Research Topic Categories Innate Immune Responses and Host Defense: Cellular Mechanisms (INC)

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 score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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