Colitis Following Initiation of Sofosbuvir and Simeprevir for Genotype 1 Hepatitis C.

Sofosbuvir and simeprevir are used for the treatment of chronic hepatitis C (HCV) genotype 1. Both drugs have been well-tolerated, with diarrhea noted in 6% cases with sofosbuvir, 16% with sofosbuvir plus simeprevir, and 0% with simeprevir. No prior reports exist of colitis secondary to either drug or their combination. We report a patient with no prior history of inflammatory bowel disease who developed significant bloody diarrhea within 2 weeks of sofosbuvir/simeprevir initiation. Colonoscopy and biopsy confirmed pancolitis, which responded to mesalamine and completion of sofosbuvir/simeprevir

American Gastroenterological Association Institute Clinical Practice Update—Expert Review: Care of Patients Who Have Achieved a Sustained Virologic Response After Antiviral Therapy for Chronic Hepatitis C Infection

Chronic hepatitis C virus infection is well-recognized as a common blood-borne infection with global public health impact affecting 3 to 5 million persons in the United States and more than 170 million persons worldwide. Chronic hepatitis C virus infection is associated with significant morbidity and mortality due to complications of liver cirrhosis and hepatocellular carcinoma. Current therapies with all-oral direct-acting antiviral agents are associated with high rates of sustained virologic response (SVR), generally exceeding 90%. SVR is associated with a reduced risk of liver cirrhosis, hepatic decompensation, need for liver transplantation, and both liver-related and all-cause mortality. However, a subset of patients who achieve SVR will remain at long-term risk for progression to cirrhosis, liver failure, hepatocellular carcinoma, and liver-related mortality. Limited evidence is available to guide clinicians on which post-SVR patients should be monitored vs discharged, how to monitor and with which tests, how frequently should monitoring occur, and for how long. In this clinical practice update, available evidence and expert opinion are used to generate best practice recommendations on the care of patients with chronic hepatitis C virus who have achieved SVR

Adaptive User Interface for a Camera Aperture within an Active Display Area

This publication describes systems and techniques to account for an active display area around a camera aperture in a “hole-punch” style display of an electronic device to reduce a light-leaking effect caused by pixels surrounding the camera aperture. Illuminated pixels that are proximate to the camera aperture can degrade a quality of an image captured by a camera sensor by preventing the sensor from properly detecting light from a targeted image, such as a user’s face. To counteract this image degradation, techniques described herein override the illumination control for pixels surrounding the hole in the display. For example, responsive to the camera being engaged, one or more rings of pixels around the display hole can be controlled to have a decreased illumination level based on ambient brightness. The decreased illumination can involve being commanded to be turned off or being commanded to illuminate at a lower level. With less light emanating from pixels that are proximate to the display hole, there is less light pollution funneled into the camera aperture to affect the camera sensor

Deeply-Learned Generalized Linear Models with Missing Data

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, \textit{dlglm}, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data

Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders

Electronic Health Records (EHRs) are commonly used to investigate relationships between patient health information and outcomes. Deep learning methods are emerging as powerful tools to learn such relationships, given the characteristic high dimension and large sample size of EHR datasets. The Physionet 2012 Challenge involves an EHR dataset pertaining to 12,000 ICU patients, where researchers investigated the relationships between clinical measurements, and in-hospital mortality. However, the prevalence and complexity of missing data in the Physionet data present significant challenges for the application of deep learning methods, such as Variational Autoencoders (VAEs). Although a rich literature exists regarding the treatment of missing data in traditional statistical models, it is unclear how this extends to deep learning architectures. To address these issues, we propose a novel extension of VAEs called Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not At Random (MNAR) patterns in the Physionet data. Our proposed method models the missingness mechanism using an embedded neural network, eliminating the need to specify the exact form of the missingness mechanism a priori. We show that the use of our method leads to more realistic imputed values relative to the state-of-the-art, as well as significant differences in fitted downstream models for mortality.Comment: 37 pages, 3 figures, 3 tables, under review (Journal of the American Statistical Association

Cysteinyl Leukotriene Levels in Esophageal Mucosal Biopsies of Children with Eosinophilic Inflammation: Are They All the Same?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75498/1/j.1572-0241.2006.00557.x.pd

Divergent lineage of a novel hantavirus in the banana pipistrelle (Neoromicia nanus) in Côte d'Ivoire

Recently identified hantaviruses harbored by shrews and moles (order Soricomorpha) suggest that other mammals having shared ancestry may serve as reservoirs. To investigate this possibility, archival tissues from 213 insectivorous bats (order Chiroptera) were analyzed for hantavirus RNA by RT-PCR. Following numerous failed attempts, hantavirus RNA was detected in ethanol-fixed liver tissue from two banana pipistrelles (Neoromicia nanus), captured near Mouyassué village in Côte d'Ivoire, West Africa, in June 2011. Phylogenetic analysis of partial L-segment sequences using maximum-likelihood and Bayesian methods revealed that the newfound hantavirus, designated Mouyassué virus (MOUV), was highly divergent and basal to all other rodent- and soricomorph-borne hantaviruses, except for Nova virus in the European common mole (Talpa europaea). Full genome sequencing of MOUV and further surveys of other bat species for hantaviruses, now underway, will provide critical insights into the evolution and diversification of hantaviruses

Predicting Risk of End-Stage Liver Disease in Antiretroviral-Treated HIV/Hepatitis C Virus-Coinfected Patients

Background. End-stage liver disease (ESLD) is an important cause of morbidity among HIV/hepatitis C virus (HCV)-coinfected patients. Quantifying the risk of this outcome over time could help determine which coinfected patients should be targeted for risk factor modification and HCV treatment. We evaluated demographic, clinical, and laboratory variables to predict risk of ESLD in HIV/HCV-coinfected patients receiving antiretroviral therapy (ART). Methods. We conducted a retrospective cohort study among 6,016 HIV/HCV-coinfected patients who received ART within the Veterans Health Administration between 1997 and 2010. The main outcome was incident ESLD, defined by hepatic decompensation, hepatocellular carcinoma, or liver-related death. Cox regression was used to develop prognostic models based on baseline demographic, clinical, and laboratory variables, including FIB-4 and aspartate aminotransferase-to-platelet ratio index, previously validated markers of hepatic fibrosis. Model performance was assessed by discrimination and decision curve analysis. Results. Among 6,016 HIV/HCV patients, 532 (8.8%) developed ESLD over a median of 6.6 years. A model comprising FIB-4 and race had modest discrimination for ESLD (c-statistic, 0.73) and higher net benefit than alternative strategies of treating no or all coinfected patients at relevant risk thresholds. For FIB-4 \u3e3.25, ESLD risk ranged from 7.9% at 1 year to 26.0% at 5 years among non-blacks and from 2.4% at 1 year to 14.0% at 5 years among blacks. Conclusions. Race and FIB-4 provided important predictive information on ESLD risk among HIV/HCV patients. Estimating risk of ESLD using these variables could help direct HCV treatment decisions among HIV/HCV-coinfected patients