Reprogramming of human somatic cells to pluripotency has been used to investigate disease mechanisms and to identify potential therapeutics. However, the methods used for reprogramming, in vitro differentiation, and phenotyping are still complicated, expensive, and time-consuming. To address the limitations, we first optimized a protocol for reprogramming of human fibroblasts and keratinocytes into pluripotency using single lipofection and the episomal vectors in a 24-well plate format. This method allowed us to generate multiple lines of integration-free and feeder-free induced pluripotent stem cells (iPSCs) from seven patients with cardiac diseases and three controls. Second, we differentiated human iPSCs derived from patients with Timothy syndrome into cardiomyocytes using a monolayer differentiation method. We found that Timothy syndrome cardiomyocytes showed slower, irregular contractions and abnormal calcium handling compared with the controls. The results are consistent with previous reports using a retroviral method for reprogramming and an embryoid body-based method for cardiac differentiation. Third, we developed an efficient approach for recording the action potentials and calcium transients simultaneously in control and patient cardiomyocytes using genetically encoded fluorescent indicators, ArcLight and R-GECO1. The dual optical recordings enabled us to observe prolonged action potentials and abnormal calcium handling in Timothy syndrome cardiomyocytes. We confirmed that roscovitine rescued the phenotypes in Timothy syndrome cardiomyocytes and that these findings were consistent with previous studies using conventional electrophysiological recordings and calcium imaging with dyes. The approaches using our optimized methods and dual optical recordings will improve iPSC applicability for disease modeling to investigate mechanisms underlying cardiac arrhythmias and to test potential therapeutics. STEM CELLS TRANSLATIONAL MEDICINE 2015;4:468-475 SIGNIFICANCEThis study found that dual optical recording using genetically encoded fluorescent indicators is a useful approach for identifying new lead chemical compounds in human induced pluripotent stem (iPS) cell-based models of not only cardiac diseases but also neuronal disorders. It will facilitate drug development and personalized medicine using iPS technology.

S urveillance Epidemiology Under Research Exclusion for Celiac Disease (SECURE-CELIAC) is an international, de-identified adult and pediatric database created to monitor and report on the severity of coronavirus disease 2019 (COVID-19) outcomes in patients with celiac disease (CD). MethodsThe SECURE-CELIAC registry (https://covidceliac. org/) was modeled on (and with support from) a comparable registry for patients with inflammatory bowel disease (https://covidibd.org/). It was established on March 31, 2020, and promoted via physician email lists, national societies, and social media. Clinicians worldwide were asked to report all confirmed cases of COVID-19 (through viral polymerase chain reaction swab or serology) in patients with CD using a secure online data entry platform. Clinicians were counseled to report cases only after a minimum of 7 days and resolution of illness or death. A choropleth map to illustrate geographic differences in reported cases of COVID-19 in those with CD was created using QGIS 3.44. An interactive online website was created using ArcGIS Online and ArcGIS Pro 2.4.1 to visualize current data by time, country, age, sex, hospitalizations, and deaths (https://arcg.is/1PyiXD). 1 This study was approved by the institutional review board at Columbia University Medical Center. Statistical analyses were conducted using SAS v9.4 (SAS Software, Cary, NC). We aimed to assess for CD-specific factors associated with severe COVID-19 outcomes, defined as hospitalization or death. Continuous and categorical variables were analyzed using the Mann-Whitney U test and the Fisher exact test, respectively. Multivariable analysis was performed using logistic regression.

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Purpose of review This review highlights literature from the past year and explores the impact on current understanding of celiac disease pathogenesis, diagnosis, and management. Recent findings In contrast to earlier clinical trials, recent data suggests that early gluten introduction may protect against the development of celiac disease. Celiac disease is underdiagnosed, associated with high burden of disease and linked to excess mortality risk, yet, there remains considerable uncertainty regarding the utility of mass screening in asymptomatic individuals. The gut microbiome is increasingly implicated in celiac disease pathogenesis, although the exact mechanism is undefined. Probiotics have been proposed as a disease-modifying option for celiac disease but most studies assessing efficacy are of low-quality. Patients with celiac disease do not appear to be at increased risk of contracting or developing adverse outcomes from COVID-19. Little is known about the pathogenesis of nonceliac gluten sensitivity; however, recent findings suggest an autoimmune basis for the condition. Summary Current understanding of celiac disease continues to advance, though significant knowledge gaps remain. Large, rigorous, prospectively designed studies are needed to further characterize celiac disease pathogenesis, management and therapeutic options.

Artificial intelligence (AI) and machine learning (ML) systems are increasingly used in medicine to improve clinical decision-making and healthcare delivery. In gastroenterology and hepatology, studies have explored a myriad of opportunities for AI/ML applications which are already making the transition to bedside. Despite these advances, there is a risk that biases and health inequities can be introduced or exacerbated by these technologies. If unrecognised, these technologies could generate or worsen systematic racial, ethnic and sex disparities when deployed on a large scale. There are several mechanisms through which AI/ML could contribute to health inequities in gastroenterology and hepatology, including diagnosis of oesophageal cancer, management of inflammatory bowel disease (IBD), liver transplantation, colorectal cancer screening and many others. This review adapts a framework for ethical AI/ML development and application to gastroenterology and hepatology such that clinical practice is advanced while minimising bias and optimising health equity.