Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142904/1/hep29800.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142904/2/hep29800_am.pd

COVID-19 Vaccine Mandates: Impact on Radiology Department Operations and Mitigation Strategies

OBJECTIVE: Coronavirus disease 2019 (COVID-19) vaccine mandates are being implemented in health systems across the United States, and the impact on the radiology department workforce and operations becuase of vaccine hesitancy among health care workers is currently unknown. This article discusses the potential impact of the COVID-19 vaccine mandate on a large multicenter radiology department as well as strategies to mitigate those effects. METHODS: Weekly vaccine compliance data were obtained for employees across the entire health system from August 17, 2021, through September 13, 2021, and radiology department-specific data were extracted. Vaccine compliance data was mapped to specific radiology job titles and the five different hospital locations. RESULTS: A total of 6% of radiology department employees were not fully vaccine compliant by the initial deadline of September 10, 2021. MR technologists and radiology technology assistants had the highest initial rates of noncompliance of 37% and 38%, respectively. Vaccine noncompliance rates by the mandate deadline ranged from 0.5% to 7.0% at the five hospital sites. Only one hospital required a decrease in imaging hours of operation because of the vaccine mandate. CONCLUSION: Despite initial concerns about the impact of vaccine mandate noncompliance on departmental operations, there was ultimately little effect because of improved vaccine compliance after the mandate. Understanding individual employee and locoregional differences in vaccine compliance can help leaders proactively develop mitigation strategies to manage this new challenge during the COVID-19 pandemic

The correlation between statistical descriptors of heterogeneous materials

Heterogeneous materials such as rocks and composites are comprised of multiple material phases of different sizes and shapes that are randomly distributed through the medium. The random microstructure is typically described by using various statistical descriptors, which include volume fraction, two-point correlation function, and tortuosity, to name a few. Capturing different morphological features, a large number of statistical descriptors are proposed in different research fields, such as material science, geoscience and computational engineering. It is well known that these statistical descriptors are not independent from each other, but until recently it remains unclear what descriptors are more similar or more different. In particular, it is extremely difficult to look for quantified relations between various descriptors, since they are often defined in very different formats. The lack of quantified understanding of descriptors’ relations can cause uncertainties or even systematic errors in heterogeneous materials studies. To address this issue, we propose a novel and generic correlation analysis strategy and establish, for the first time, the quantified relations between various statistical descriptors for heterogeneous materials. Based on data science techniques, our approach consists of three operational steps: data regularization, dimension reduction and correlation analysis. A total of 41 statistical descriptors are collected and analysed in this study, which is readily extensible to include other new descriptors. The generic and quantified correlation results are compared with other established descriptor relations that are obtained from analytical analysis or physical intuition, and good agreements are observed in all cases. The quantified relations between various descriptors are summarized in a single correlation graph, which provides useful guiding information for the characterization, reconstruction and property prediction of heterogeneous materials