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Record W2977549946 · doi:10.1371/journal.pone.0223339

In vivo clearance of nanoparticles by transcytosis across alveolar epithelial cells

2019· article· en· W2977549946 on OpenAlexafffund
Pascal Detampel, Anutosh Ganguly, Sara Tehranian, Francis Green, Santiswarup Singha, Pere Santamaría, Ayodeji A. Jeje, Clifford S. Cho, Björn Petri, Matthias Amrein

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of Calgary
FundersUniversity of CalgarySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAlberta InnovatesCanadian Institutes of Health ResearchU.S. Department of Veterans AffairsNational Science Foundation
KeywordsTranscytosisIn vivoIntravital microscopyLungNanoparticleAlveolar EpitheliumEpitheliumA549 cellChemistryBiophysicsAerosolizationCell biologyEndocytosisPathologyInhalationMaterials scienceBiologyNanotechnologyIn vitroCellAnatomyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Nanoparticles in polluted air or aerosolized drug nanoparticles predominantly settle in the alveolar lung. Here, we describe a novel, highly effective pathway for the particles to cross the alveolar epithelium and reach the lymph and bloodstream. Amorphous silica nanoparticles, suspended in perfluorocarbon, were instilled into the lungs of mice for intravital microscopy. Particles formed agglomerates that settled on the alveolar wall, half of which were removed from the lung within 30 minutes. TEM histology showed agglomerates in stages of crossing the alveolar epithelium, in large compartments inside the epithelial cells and crossing the basal membrane into the interstitium. This pathway is consistent with published kinetic studies in rats and mice, using a host of (negatively) charged and polar nanoparticles.

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 score0.004
Threshold uncertainty score0.723

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)0.0010.000

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 head0.020
GPT teacher head0.241
Teacher spread0.221 · 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

Citations33
Published2019
Admission routes2
Has abstractyes

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