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Record W4280540681 · doi:10.1038/s41598-022-11700-z

Apical-out airway organoids as a platform for studying viral infections and screening for antiviral drugs

2022· article· en· W4280540681 on OpenAlexaff
Georgios Stroulios, Tyler Brown, Giulia Moreni, Douglas Kondro, Alessandro Dei, Allen Eaves, Sharon A. Louis, Juan Hou, Wing Y. Chang, Dasja Pajkrt, Katja C. Wolthers, Adithya Sridhar, Salvatore Simmini

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsTerry Fox Research InstituteStemcell Technologies
FundersHorizon 2020Horizon 2020 Framework ProgrammeEuropean Commission
KeywordsOrganoidCiliogenesisCell biologyAirwayBiologyIn vivoExtracellular matrixCiliumMedicineGenetics

Abstract

fetched live from OpenAlex

Airway organoids are polarized 3D epithelial structures that recapitulate the organization and many of the key functions of the in vivo tissue. They present an attractive model that can overcome some of the limitations of traditional 2D and Air-Liquid Interface (ALI) models, yet the limited accessibility of the organoids' apical side has hindered their applications in studies focusing on host-pathogen interactions. Here, we describe a scalable, fast and efficient way to generate airway organoids with the apical side externally exposed. These apical-out airway organoids are generated in an Extracellular Matrix (ECM)-free environment from 2D-expanded bronchial epithelial cells and differentiated in suspension to develop uniformly-sized organoid cultures with robust ciliogenesis. Differentiated apical-out airway organoids are susceptible to infection with common respiratory viruses and show varying responses upon treatment with antivirals. In addition to the ease of apical accessibility, these apical-out airway organoids offer an alternative in vitro model to study host-pathogen interactions in higher throughput than the traditional air-liquid interface model.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.066
GPT teacher head0.381
Teacher spread0.316 · 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.

Study designNot applicable
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

Citations69
Published2022
Admission routes1
Has abstractyes

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