The DOI matches its Crossref record; 144 of the 144 references it cites were found in a registry or an index.
Autophagy, Inflammation, and Immune Dysfunction in the Pathogenesis of Pancreatitis
Open accessAbstract
Pancreatitis is a common disorder with significant morbidity and mortality, yet little is known about its pathogenesis, and there is no specific or effective treatment. Its development involves dysregulated autophagy and unresolved inflammation, demonstrated by studies in genetic and experimental mouse models. Disease severity depends on whether the inflammatory response resolves or amplifies, leading to multi-organ failure. Dysregulated autophagy might promote the inflammatory response in the pancreas. We discuss the roles of autophagy and inflammation in pancreatitis, mechanisms of deregulation, and connections among disordered pathways. We identify gaps in our knowledge and delineate perspective directions for research. Elucidation of pathogenic mechanisms could lead to new targets for treating or reducing the severity of pancreatitis.
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Cites
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Cited by
Diagnosis and Management of Chronic Pancreatitis: A Review
Singh, V. K., Yadav, D., & Garg, P. K. (2019) · JAMA · doi:10.1001/jama.2019.19411 · Cited by 486
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Self-citation
Metadata
- Authors
- Gukovskaya, A. S., Gukovsky, I., Algül, H., & Habtezion, A.
- Venue
- Gastroenterology
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 153
- Issue
- 5
- Pages
- 1212-1226
- ISSN
- 0016-5085 (ISSN-L)
- OpenAlex
- W2755524842
- Cited by
- 357
- References
- 144
- FWCI
- 19.71
- Type
- journal article
- Language
- en
- Open access
- green
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- Gukovskaya, A. S., Gukovsky, I., Algül, H., & Habtezion, A. (2017). Autophagy, Inflammation, and Immune Dysfunction in the Pathogenesis of Pancreatitis. Gastroenterology. https://doi.org/10.1053/j.gastro.2017.08.071
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.