The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Air−Liquid Interface Cultures of the Healthy and Diseased Human Respiratory Tract: Promises, Challenges, and Future Directions
Open accessAbstract
Air−liquid interface (ALI) culture models currently represent a valid instrument to recreate the typical aspects of the respiratory tract in vitro in both healthy and diseased state. They can help reducing the number of animal experiments, and hence support the 3R principle. This review discusses ALI cultures and co‐cultures derived from immortalized as well as primary cells, which are used to study the most common disorders of the respiratory tract, in terms of both pathophysiology and drug screening. The article displays ALI models used to simulate inflammatory lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, cystic fibrosis, lung cancer, and viral infections. It also focuses on ALI cultures described in literature studying respiratory viruses such as SARS‐CoV‐2 causing the global Covid‐19 pandemic at the time of writing this review. Additionally, commercially available models of ALI cultures are presented. Ultimately, the aim of this review is to provide a detailed overview of ALI models currently available and to critically discuss them in the context of the most prevalent diseases of the respiratory tract.
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Cited by
Basement membrane properties and their recapitulation in organ-on-chip applications
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Metadata
- Authors
- Baldassi, D., Gabold, B., & Merkel, O. M.
- Venue
- Advanced NanoBiomed Research
- Publisher
- Wiley
- Published
- 2021-05-06
- Volume
- 1
- Issue
- 6
- ISSN
- 2699-9307 (ISSN-L)
- OpenAlex
- W3160793930
- Cited by
- 160
- References
- 16
- FWCI
- 10.31
- Type
- journal article
- Language
- en
- Open access
- gold
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- Baldassi, D., Gabold, B., & Merkel, O. M. (2021). Air−Liquid Interface Cultures of the Healthy and Diseased Human Respiratory Tract: Promises, Challenges, and Future Directions. Advanced NanoBiomed Research. https://doi.org/10.1002/anbr.202000111
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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