An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement.

Objective.In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN).Approach.The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios.Main results.The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were -0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms.Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.

Global Burden of Disease Study 2019 suggests that metabolic risk factors are the leading drivers of the burden of ischemic heart disease.

Metabolic dysfunction is becoming a predominant risk for the development of many comorbidities. Ischemic heart disease (IHD) still imposes the highest disease burden among all cardiovascular diseases worldwide. However, the contributions of metabolic risk factors to IHD over time have not been fully characterized. Here, we analyzed the global disease burden of IHD and 15 associated general risk factors from 1990 to 2019 by applying the methodology framework of the Global Burden of Disease Study. We found that the global death cases due to IHD increased steadily during that time frame, while the mortality rate gradually declined. Notably, metabolic risk factors have become the leading driver of IHD, which also largely contributed to the majority of IHD-related deaths shifting from developed countries to developing countries. These findings suggest an urgent need to implement effective measures to control metabolic risk factors to prevent further increases in IHD-related deaths.

Epidemiological Features of NAFLD From 1999 to 2018 in China.

With dramatic changes in lifestyles over the last 20 years, nonalcoholic fatty liver disease (NAFLD) has become the most prevalent liver disorder in China but has not received sufficient attention. NAFLD-related advanced liver disease and its mortality along with its overall disease burden are expected to increase substantially. There is thus an imperative need to clarify the epidemiological features of NAFLD to guide a holistic approach to management. We summarize eight epidemiological features of NAFLD in China over the past two decades using systematic review and meta-analysis methodology. Our data reveal a rapid growth in the NAFLD population, especially among younger individuals. Importantly, there is a strong ethnic difference in body mass index (BMI) and genetic risk of NAFLD compared with the US population. The etiology of advanced liver disease and its complications (e.g., hepatocellular carcinoma) has been altered because of a Westernized lifestyle and the implementation of effective vaccination strategies against viral hepatitis. Regional epidemiological patterns of NAFLD indicate that economics, environment, and lifestyle are critical factors in disease progression. The analysis also indicates that a large number of patients remain undiagnosed and untreated because of the inadequacy of diagnostic tools and the absence of effective pharmacologic therapies. Given the burden of NAFLD, future policy and research efforts need to address knowledge gaps to mitigate the risk burden.

Unexpected Rapid Increase in the Burden of NAFLD in China From 2008 to 2018: A Systematic Review and Meta-Analysis.

With rapid lifestyle transitions, the increasing burden of nonalcoholic fatty liver disease (NAFLD) in China has emerged as a major public health issue. To obtain a comprehensive overview of the status of NAFLD over the past decade, we evaluated the epidemiology, risk factors, complications, and management of NAFLD in China through a systematic review and meta-analysis. Five English literature databases and three Chinese databases were searched for relevant topics from 2008 to 2018. A total of 392 studies with a population of 2,054,554 were included. National prevalence of NAFLD was 29.2%, with a heavier disease burden among the middle-aged, males, those in northwest China and Taiwan, regions with a gross domestic product per capita greater than 100,000 yuan, and Uygur and Hui ethnic groups. Currently, original studies on natural history and complications of NAFLD in China are scarce. Several studies revealed that NAFLD is positively correlated with the incidence of extrahepatic tumors, diabetes, cardiovascular disease and metabolic syndrome. The Chinese population may have a higher hereditary risk of NAFLD due to more frequent nonsynonymous mutations in genes regulating lipid metabolism. Ultrasonography is the primary imaging tool in the detection of NAFLD in China. Serum tests and risk stratification algorithms for staging NAFLD remain under investigation. Specific pharmaceutical treatments for NAFLD are still undergoing clinical trials. It is noteworthy that the Chinese are underrepresented compared with their proportion of the NAFLD population in such trials. Conclusion: China experienced an unexpected rapid increase in the burden of NAFLD over a short period. Rising awareness and urgent actions need to be taken in order to control the NAFLD pandemic in China.

ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data.

BACKGROUND: ATAC-seq (Assays for Transposase-Accessible Chromatin using sequencing) is a recently developed technique for genome-wide analysis of chromatin accessibility. Compared to earlier methods for assaying chromatin accessibility, ATAC-seq is faster and easier to perform, does not require cross-linking, has higher signal to noise ratio, and can be performed on small cell numbers. However, to ensure a successful ATAC-seq experiment, step-by-step quality assurance processes, including both wet lab quality control and in silico quality assessment, are essential. While several tools have been developed or adopted for assessing read quality, identifying nucleosome occupancy and accessible regions from ATAC-seq data, none of the tools provide a comprehensive set of functionalities for preprocessing and quality assessment of aligned ATAC-seq datasets. RESULTS: We have developed a Bioconductor package, ATACseqQC, for easily generating various diagnostic plots to help researchers quickly assess the quality of their ATAC-seq data. In addition, this package contains functions to preprocess aligned ATAC-seq data for subsequent peak calling. Here we demonstrate the utilities of our package using 25 publicly available ATAC-seq datasets from four studies. We also provide guidelines on what the diagnostic plots should look like for an ideal ATAC-seq dataset. CONCLUSIONS: This software package has been used successfully for preprocessing and assessing several in-house and public ATAC-seq datasets. Diagnostic plots generated by this package will facilitate the quality assessment of ATAC-seq data, and help researchers to evaluate their own ATAC-seq experiments as well as select high-quality ATAC-seq datasets from public repositories such as GEO to avoid generating hypotheses or drawing conclusions from low-quality ATAC-seq experiments. The software, source code, and documentation are freely available as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/ATACseqQC.html .