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Jiefeng Huang,1– 3,&ast; Lida Chen,4,&ast; Xiuling Li,1– 3,&ast; Menglan Chen,1– 3 Ting Lin,1– 3 Gongping Chen1– 3 1Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Fujian Medical University, Fujian Medical University, Fuzhou, People’s Republic of China; 2Institute of Respiratory Disease, Fujian Medical University, Fujian Medical University, Fuzhou, People’s Republic of China; 3Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, Zhangzhou Affiliated Hospital of Fujian Medical University, Xiangcheng, Zhangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Gongping Chen, Tel/Fax +86591-87981697, Email cgp2389@126.comAim: Emerging evidence has revealed that obstructive sleep apnea (OSA) is an independent risk factor for the development of a variety of adverse metabolic disease states. In this study, we evaluated the association between OSA severity and metabolic dysfunction-associated fatty liver disease (MAFLD) among Asian populations.Materials and Methods: This was a cross-sectional, single-center study. The study cohort consisted of patients undergoing polysomnography and abdominal ultrasonography. Logistic regression analysis was used to evaluate the independent risk factors of MAFLD in patients with OSA.Results: A total of 1065 patients (277 non-MAFLD and 788 MAFLD) were included in the study. The prevalence of MAFLD in non-OSA, mild-moderate OSA, and severe OSA patients was 58.16%, 72.41%, and 78.0%, respectively (p < 0.001). We identified significant differences in body mass index (BMI), apnea-hypopnea index (AHI), oxygen desaturation index (ODI), and lowest O2 saturation (LaSO2) between non-MAFLD and MAFLD patients (all p < 0.001). After adjusting for confounding variables, we used multivariate regression analysis to show that BMI, ODI, and triglyceride (TG) levels independently predicted the occurrence of MAFLD (odds ratio [OR] = 1.234, p < 0.001; OR = 1.022, p = 0.013; OR = 1.384, p = 0.001, respectively). Moreover, stratified analysis according to BMI indicated that TG levels were the predominant risk factor for MAFLD in a group of patients with a BMI < 23 kg/m2, while BMI, ODI, TG levels, and total cholesterol (TC) were the major risk factors for MAFLD in a group of patients with a BMI ≥ 23 kg/m2 (all p < 0.05).Conclusion: OSA-associated chronic intermittent hypoxia was independently associated with the risk of MAFLD, especially in OSA patients with a BMI ≥ 23 kg/m2, suggesting that oxidative stress might play an important role in the pathogenesis of MAFLD in patients with OSA.Keywords: obstructive sleep apnea, chronic intermittent hypoxia, metabolic dysfunction-associated fatty liver disease, obesity

Background: Hypoxia in obstructive sleep apnea (OSA) patients during sleep may have an effect on bone metabolism. Few data regarding evaluation of bone metabolism in young individuals diagnosed with OSA. In this study, we aim to identify the association between bone mineral density and OSA in young men (≤40 years old of age). Methods: Consecutive male subjects who underwent polysomnography were enrolled. Serum calcium, 25-hydroxyvitamin-D3, β-isomerized form C-terminal telopeptide of type I collagen, osteocalcin and procollagen type 1 N-propeptide were measured in all participants, and bone mineral density (BMD) at lumbar spine (L1-L4), femoral neck and hip total were determined by dual energy X-ray absorption (DXA). Results: The population consisted of 85 subjects (mean age 35.53 years). The BMD at lumbar spine (L1-L4) in moderate OSA patients was higher than control and severe OSA group significantly (p=0.036). After adjustment for confounding factors, stepwise multiple linear regression analyses showed LaSO2 (β = 0.340, p = 0.008) as an independent explanatory variable for Lumbar L1–L4 BMD, LaSO2 (β =0.304, p = 0.037), BMI (β = 0.393, p = 0.008) for femur neck BMD and BMI (β = 0.720, p = 0.002) for hip total BMD. Conclusion: Our finding indicates that there is a relationship between OSA and bone metabolism in younger men, suggesting OSA-related hypoxia was a protective factor for BMD, especially in moderate OSA group (10≤ AHI

Breast tumor segmentation is one of the key steps that helps us characterize and localize tumor regions. However, variable tumor morphology, blurred boundary, and similar intensity distributions bring challenges for accurate segmentation of breast tumors. Recently, many U-net variants have been proposed and widely used for breast tumors segmentation. However, these architectures suffer from two limitations: (1) Ignoring the characterize ability of the benchmark networks, and (2) Introducing extra complex operations increases the difficulty of understanding and reproducing the network. To alleviate these challenges, this paper proposes a simple yet powerful nested U-net (NU-net) for accurate segmentation of breast tumors. The key idea is to utilize U-Nets with different depths and shared weights to achieve robust characterization of breast tumors. NU-net mainly has the following advantages: (1) Improving network adaptability and robustness to breast tumors with different scales, (2) This method is easy to reproduce and execute, and (3) The extra operations increase network parameters without significantly increasing computational cost. Extensive experimental results with twelve state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that NU-net has more competitive segmentation performance on breast tumors. Furthermore, the robustness of NU-net is further illustrated on the segmentation of renal ultrasound images. The source code is publicly available on https://github.com/CGPzy/NU-net.

Most existing image restoration methods based on deep neural networks are developed for images which only degraded by a single degradation mode and imaging under an ideal condition. They cannot be directly used to restore the images degraded by multi-factor coupling. A complex task decomposition regularization optimization strategy (TDROS) is proposed to solve the problem. The restoration of images degraded by multi-factor coupling is a complex task that can be solved by separating these multiple factors, that is, breaking the complex task into numbers of simpler tasks to make the entire complex problem be overcome more easily. Motivated by this idea, the TDROS decomposes the complex task of image restoration into two sub-task: the potential task constrained by regularization and the main task for reconstructing high-definition images. In TDROS, the front of the neural network is focused on the restoration of images degraded by additive noise, while the other part of the network is focused mainly on the restoration of images degraded by blur. We applied the TDROS to an 11-layer convolutional neural network (CNN) and compared it with initial CNNs from the aspects of restoration accuracy and generalization ability. Based on these results, we used TDROS to design a novel network model for the restoration of atmospheric turbulence-degraded images. The experimental results demonstrate that the proposed TDROS can improve the generalization ability of the existing network more effectively than current popular methods, offering a better solution for the problem of severely degraded image restoration. Moreover, the TDROS concept provides a flexible framework for low-level visual complex tasks and can be easily incorporated into existing CNNs.

Due to speckle noise, changes in kidney shape and size between patients, and similar regions, segmenting kidneys in ultrasound images is challenging. To alleviate this challenge, we proposed a novel CNN model, namely multi-scale fusion network of structural features and detailed features (SDFNet), to segment kidneys accurately and robustly. Specifically, the architecture includes structure feature extraction network (S-Net), detail information extraction network (D-Net) and multi-scale fusion block (MCBlock), which are in charge of extracting structural features, capturing texture details and merging features, respectively. In S-Net, we designed a boundary detection (BD) module to obtain more complete kidney structural features. In addition, this paper also designed a step-by-step training mechanism to improve the generalization ability of the SDFNet. We validated the proposed method and compared the same kidney ultrasound dataset with several state-of-the-art methods using six quantitative indicators. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on kidney ultrasound image segmentation. It is worth noting that this paper quantitatively analyzes the loss function of segment kidney ultrasound images. This work is a good reference for future research.