Plos One
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Robust Artificial-neural-networks for k-space Interpolation (RAKI) is a recently proposed deep-learning-based reconstruction algorithm for parallel imaging. Its main premise is to perform k-space interpolation using convolutional neural networks (CNNs) trained on subject-specific autocalibration signal (ACS) data. Since training is performed individually for each subject, the reconstruction time is longer than approaches that pre-train on databases. In this study, we sought to reduce the computational time of RAKI. ⋯ The proposed implementations of RAKI bring the computational time towards clinically acceptable ranges. The new CNN architecture yields faster training, albeit at a slight performance loss, which may be acceptable for faster visualization in some settings.
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Comparative Study
Fecal and blood microbiota profiles and presence of nonalcoholic fatty liver disease in obese versus lean subjects.
Pathophysiological background in different phenotypes of nonalcoholic fatty liver disease (NAFLD) remains to be elucidated. The aim was to investigate the association between fecal and blood microbiota profiles and the presence of NAFLD in obese versus lean subjects. Demographic and clinical data were reviewed in 268 health checkup examinees, whose fecal and blood samples were available for microbiota analysis. ⋯ In the blood microbiota, Succinivibrionaceae showed opposite correlations in the lean (log2 coeff. = -1.349, P = 5.34E-06) and obese NAFLD groups (log2 coeff. = 2.215, P = 0.003). Notably, Leuconostocaceae was associated with the obese NAFLD in the gut (log2 coeff. = -1.168, P = 0.041) and blood (log2 coeff. = -2.250, P = 1.28E-10). In conclusion, fecal and blood microbiota profiles showed different patterns between subjects with obese and lean NAFLD, which might be potential biomarkers to discriminate diverse phenotypes of NAFLD.
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Tobacco smoking is often more prevalent among those with lower socio-economic status (SES) in high-income countries, which can be driven by the inequalities in initiation and cessation of smoking. Smoking is a leading contributor to socio-economic disparities in health. To date, the evidence for any socio-economic inequality in smoking cessation is lacking, especially in low- and middle-income countries (LMICs). This study examined the association between cessation behaviours and SES of smokers from eight LMICs. ⋯ Lack of clear evidence of the impact of lower SES on adult cessation behaviour in LMICs suggests that lower-SES smokers are not less successful in their attempts to quit than their higher-SES counterparts. Specifically, lack of employment, which is indicative of younger age and lower nicotine dependence for students, or lower personal disposable income and lower affordability for the unemployed and the retirees, may be associated with quitting. Raising taxes and prices of tobacco products that lowers affordability of tobacco products might be a key strategy for inducing cessation behaviour among current smokers and reducing overall tobacco consumption. Because low-SES smokers are more sensitive to price increases, tobacco taxation policy can induce disproportionately larger decreases in tobacco consumption among them and help reduce socio-economic disparities in smoking and consequent health outcomes.
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Comparative Study
Decreased psychomotor vigilance of female shift workers after working night shifts.
We compared psychomotor vigilance in female shift workers of the Bergmannsheil University Hospital in Bochum, Germany (N = 74, 94% nurses) after day and night shifts. ⋯ Our results add to the growing body of literature demonstrating that night-shift work is associated with decreased psychomotor vigilance. As the analysis of RTCV suggests, performance deficits may selectively be driven by few slow reactions at the lower end of the reaction time distribution function. Comparing intra-individual PVT-performances over three consecutive night and two consecutive day shifts, we observed performance improvements after the third night shift. Although a training effect cannot be ruled out, this finding may suggest better adaptation to the night schedule if avoiding fast-changing shift schedules.
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Radiomics studies require large patient cohorts, which often include patients imaged using different imaging protocols. We aimed to determine the impact of variability in imaging protocol parameters and interscanner variability using a phantom that produced feature values similar to those of patients. Positron emission tomography (PET) scans of a Hoffman brain phantom were acquired on GE Discovery 710, Siemens mCT, and Philips Vereos scanners. ⋯ The average ratio of the standard deviation of features on the phantom scans to that of the NSCLC patient scans was 0.73 using fixed-bin-width preprocessing and 0.92 using 64-level preprocessing. Most radiomics feature values had at least good reliability when imaging protocol parameters were within clinically used ranges. However, interscanner variability was about equal to interpatient variability; therefore, caution must be used when combining patients scanned on equipment from different vendors in radiomics data sets.