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Low prevalence of germline hMSH6 mutations in colorectal cancer families from Spain

World Journal of Gastroenterology 2005年第37期11卷 5770-5776页

作者： Ana Sánchez de Abajo Miguel de la Hoya Alicia Tosar javier godino Juan Manuel Fernández Jose Lopez Asenjo Beatriz Perez Villamil Pedro Perez Segura Eduardo Diaz-Rubio Trinidad Caldes Laboratorio de Oncología Molecular Hospital Clinico San Carlos Madrid Spain Servicio de Anatomía Patológica Hospital Clinico San Carlos Madrid Spain Servicio de Oncología Médica Hospital Clinico San Carlos Madrid Spain

AIM: To investigate the prevalence and penetrance of hMSH6 mutations in Spanish HNPCC families that was negative for mutation in hMLH1 or hMSH2.METHODS: We used PCR-based DGGE assay and direct Sequencing to screen for hMSH6 gene in 91 HNPCC families.RESULTS: we have identified 10 families with germ-line mutations in the DNA sequence. These mutations included two intronic variation, three missense mutation, one nonsense mutation, and four silent mutations. Among the 10 germ-line mutations identified in the Spanish cohort,8 were novel, perhaps, suggesting different mutational spectra in the Spanish population. Detailed pedigrees were constructed for the three families with a possible pathogenic hMSH6 mutation. The two silent mutations H388H and L758L, detected in a person affected of colorectal cancer at age 29, produce loss of the wild-type allele in the tumor sample. Immunohistochemical analysis showed that expression of MSH6 protein was lost only in the tumors from the carriers of V878A and Q263X mutations.CONCLUSION: Altogether, our results indicate that disease-causing germ-line mutations of hMSH6 are very less frequent in Spanish HNPCC families.

关键词：基因突变 hMSH6 结肠癌直肠癌西班牙遗传因素

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Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

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World Journal of Gastroenterology 2023年第9期29卷 1427-1445页

作者： M Alvaro Berbís Felix Paulano godino javier Royuela del Val Lidia AlcaláMata Antonio Luna Department of Radiology HT MédicaSan Juan de Dios HospitalCórdoba 14960Spain Faculty of Medicine Autonomous University of MadridMadrid 28049Spain Department of Radiology HT MédicaClínica las NievesJaén 23007Spain

Artificial intelligence(AI)has experienced substantial progress over the last ten years in many fields of application,including healthcare.In hepatology and pancreatology,major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images,where AI can generate accurate and reproducible imaging diagnosis,reducing the physicians’workload.AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions.Furthermore,using radiomics,AI can introduce new quantitative information which is not visible to the human eye to radiological reports.AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas,such as neoplasms,chronic hepatic disease,or acute or chronic pancreatitis,among others.These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases,such as ultrasound,endoscopic ultrasonography,computerized tomography(CT),magnetic resonance imaging,and positron emission tomography/CT.However,AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient.AI can also be applied to choose the most convenient test prescription,to improve image quality or accelerate its acquisition,and to predict patient prognosis and treatment response.In this review,we summarize the current evidence on the application of AI to hepatic and pancreatic radiology,not only in regard to the interpretation of images,but also to all the steps involved in the radiological workflow in a broader sense.Lastly,we discuss the challenges and future directions of the clinical application of AI methods.

关键词： Artificial intelligence Machine learning Deep learning Imaging Liver Pancreas

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