Background: It is estimated that between 5% and 10% of pancreatic cancer (PC) cases are due to hereditary factors.
Methods: Review of the literature.
Results: In families with clustering of PC, germline mutations in specific genes might be responsible for the disease. It is suggested that PC progresses from precursor lesions, the pancreatic intraepithelial neoplasias (PanINs). Several key genetic alterations in oncogenes (K-ras, Her2/neu) and tumour suppressor genes (p16, p53, SMAD4) occur in the progression from PanIN lesions towards PC. PC is mostly diagnosed on clinical presentation at an advanced, no longer resectable, stage. The overall 5-year survival rate is extremely poor. Recent studies report a better survival rate of PC, providing surgery takes place at an early stage. Surveillance of family members at increased risk for PC might lead to detection of tumours at an early stage and improve overall survival.
Conclusion: Clinicians should be aware of the tumour syndromes that are associated with an increased risk of PC. Efforts to improve PC survival must focus on identification of high-risk patients, detection of early stage disease and novel screening strategies.
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http://dx.doi.org/10.1080/00855920310002762 | DOI Listing |
Viruses
December 2024
Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool L3 5RF, UK.
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Pharmalex India Pvt. Ltd., Noida 201301, India.
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November 2024
Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
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December 2024
Faculty of Computer Science, Polish-Japanese Academy of Information Technology, 86 Koszykowa Street, 02-008 Warsaw, Poland.
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December 2024
College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210046, China.
Early identification of concrete cracks and multi-class detection can help to avoid future deformation or collapse in concrete structures. Available traditional detection and methodologies require enormous effort and time. To overcome such difficulties, current vision-based deep learning models can effectively detect and classify various concrete cracks.
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