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.     

Cefoperazone-sulbactam and risk of coagulation disorders or bleeding: a retrospective cohort study

ABSTRACT

Objectives: Limited evidence has suggested that cefoperazone-sulbactam causes coagulation disorders and bleeding.

Methods: The authors conducted a retrospective study to compare patients receiving cefoperazone-sulbactam versus those treated with cefoperazone-tazobactam or ceftazidime. Propensity-score matching was used to explore whether treatment with cefoperazone-sulbactam increased the risk of prothrombin time (PT) prolongation, coagulation disorders, and bleeding, or decreased platelets (PLT).

Results: The cohort included 23,242 patients. Among patients receiving cefoperazone-sulbactam, the risk of PT prolongation, coagulation disorders, decreased PLT, and bleeding was 5.3%, 9.2%, 15.7%, and 4.2%, respectively. Propensity-score matching analyses suggested that cefoperazone-sulbactam increased the risk of PT prolongation (aOR 2.26, 95% CI 1.61–3.18), coagulation disorders (aOR 1.81, 95% CI 1.43–2.30), and decreased PLT (aOR 1.46, 95% CI 1.25–1.72), but not increase bleeding (aOR 1.05, 95% CI 0.79–1.40) compared with ceftazidime. Patients receiving cefoperazone-sulbactam had higher risk of PT prolongation (aOR 1.53, 95% CI 1.11–2.10), coagulation disorders (aOR 1.53, 95% CI 1.21–1.95), but not decreased PLT (aOR 0.93, 95% CI 0.81–1.07) or bleeding (aOR 1.11, 95% CI 0.87–1.42), compared with those receiving cefoperazone-tazobactam.

Conclusion: Cefoperazone-sulbactam may be associated with a higher risk of PT prolongation and coagulation disorders compared with cefoperazone-tazobactam and ceftazidime.