Is Atrial Fibrillation a Risk Factor for Gastroesophageal Reflux Disease Occurrence?

Recent studies have reported an association between gastroesophageal reflux disease (GERD) and atrial fibrillation (AF). The objective of the present study was to evaluate whether AF is one of the risk factors for GERD occurrence.In this hospital-based, retrospective, case–control study, the patients were classified into 2 groups. The patients diagnosed with new AF were assigned to the AF group (n = 1612); those diagnosed without AF and GERD were assigned to the control group (n = 1612). The subjects in the control group were selected from outpatients of total healthcare center without a history of AF or GERD, and matched for age and gender. We evaluated the incidence of GERD and risk factors for GERD occurrence between the 2 groups.The number of patients experiencing occurrence of GERD during the follow-up period was significantly higher in the AF group than those in the control group, respectively (129 patients vs 98 subjects, P = 0.037). The incidence of GERD was significantly higher in the AF group than in the control group by Kaplan–Meier analysis with log-rank test (P = 0.008). The AF group''s adjusted hazard ratio of GERD occurrence against that of the control group was 1.37 (95% confidence interval [CI]: 1.16–1.57; P = 0.009) according to Cox''s proportional hazard model.The presence of AF appears to increase the incidence of GERD and may be considered a risk factor for the development of GERD. Further, large prospective and cohort studies will be required to better establish the correlation of GERD with AF.     

Monitoring Hepatocyte Dysfunction and Biliary Complication After Liver Transplantation Using Quantitative Hepatobiliary Scintigraphy

The significance of hepatobiliary scintigraphy (HBS) for hepatic graft function assessment was established mostly on retrospective studies and was not widely recognized due to the lack of quantitative data and variation in accuracy. This prospective study was performed to investigate the effectiveness of quantitative HBS for assessing hepatocyte dysfunction and biliary complication in liver transplant recipients.In 57 recipients who had undergone orthotopic liver transplantation, a total of 67 dynamic ^99mTc-EHIDA scans were performed and quantitative parameters including the hepatocyte extraction fraction (HEF), time to maximum hepatic radioactivity (T_max), and time for peak activity to decrease by 50% (T_1/2) were calculated. The scintigraphic results based on the 3 parameters were compared against the final diagnosis. A ROC curve analysis was carried out to identify the cutoff value of T_max for diagnosis of biliary stricture. Correlation between the parameters of postoperative HBS and conventional biochemical liver function indices were also analyzed.Quantitative ^99mTc-EHIDA HBS had an overall sensitivity of 94.12% (16/17), specificity of 93.33% (42/45), and diagnostic accuracy of 93.55% (58/62) for detecting hepatocyte dysfunction and biliary complication in liver transplant recipients. The recommended cutoff value of T_max for diagnosis of post-transplant biliary stricture was set at 15.75 min with a sensitivity of 100.0% and a specificity of 94.0%. The scintigraphic parameters (HEF, T_max) were statistically significantly associated with the conventional liver function parameters.Quantitative ^99mTc-EHIDA HBS offers a noninvasive imaging modality with high sensitivity and specificity to diagnose hepatocyte dysfunction as well as distinguish between patients with or without biliary stricture following liver transplantation. Furthermore, HEF and T_max values obtained from dynamic HBS show good correlation with conventional liver function parameters.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.