ArticleCommunications biology2022
Tissue-level alveolar epithelium model for recapitulating SARS-CoV-2 infection and cellular plasticity.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 11 citations in OpenAlex.
- A Practical Gut-On-Chip Workflow for Imaging-Based Assessment of Epithelial Barrier Dynamics During E. coli Co-Culture.Biotechnology and bioengineering · 2026Article
- Mechanisms and impact of long COVID: pathophysiology, neuropsychiatric effects and vaccination.Frontiers in immunology · 2026Review
- Article
- Comprehensive characterization of human alveolar epithelial cells cultured for 28 days at the air-liquid interface.Scientific reports · 2025Article
- A Novel In Vitro Primary Human Alveolar Model (AlveolAir™) for H1N1 and SARS-CoV-2 Infection and Antiviral Screening.Microorganisms · 2025Article
- First contact: an interdisciplinary guide into decoding H5N1 influenza virus interactions with glycosaminoglycans in 3D respiratory cell models.Frontiers in cellular and infection microbiology · 2025Review
- Development of an in vitro human alveolar epithelial air-liquid interface model using a small molecule inhibitor cocktail.BMC molecular and cell biology · 2024Article
- Article
- A Monoclonal Human Alveolar Epithelial Cell Line ("Arlo") with Pronounced Barrier Function for Studying Drug Permeability and Viral Infections.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- SARS-CoV-2 cellular tropism and direct multiorgan failure in COVID-19 patients: Bioinformatic predictions, experimental observations, and open questions.Cell biology international · 2023Review
- The spatial dynamics of immune response upon virus infection through hybrid dynamical computational model.Frontiers in immunology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Pulmonary sequelae following COVID-19 pneumonia have been emerging as a challenge; however, suitable cell sources for studying COVID-19 mechanisms and therapeutics are currently lacking. In this paper, we present a standardized primary alveolar cell culture method for establishing a human alveolar epithelium model that can recapitulate viral infection and cellular plasticity. The alveolar model is infected with a SARS-CoV-2 pseudovirus, and the clinically relevant features of the viral entry into the alveolar type-I/II cells, cytokine production activation, and pulmonary surfactant destruction are reproduced. For this damaged alveolar model, we find that the inhibition of Wnt signaling via XAV939 substantially improves alveolar repair function and prevents subsequent pulmonary fibrosis. Thus, the proposed alveolar cell culture strategy exhibits potential for the identification of pathogenesis and therapeutics in basic and translational research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.