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Prediction of Subsequent Vertebral Fracture After Acute Osteoporotic Fractures from Clinical and Paraspinal Muscle Features

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CALCIFIED TISSUE INTERNATIONAL 2024年第6期114卷 614-624页

作者： Xiong, Yuchao Zhang, Cici Chen, Xiaopei Wu, Li Liang, Shaohua Zhang, Ye huang, Junbing Guo, Wei Zeng, xuwen Xu, Fan Jinan Univ Guangzhou Red Cross Hosp Dept Radiol Guangzhou Peoples R China

To construct a nomogram based on clinical factors and paraspinal muscle features to predict vertebral fractures occurring after acute osteoporotic vertebral compression fracture (OVCF). We retrospectively enrolled 307 patients with acute OVCF between January 2013 and August 2022, and performed magnetic resonance imaging of the L3/4 and L4/5 intervertebral discs (IVDs) to estimate the cross-sectional area (CSA) and degree of fatty infiltration (FI) of the paraspinal muscles. We also collected clinical and radiographic data. We used univariable and multivariable Cox proportional hazards models to identify factors that should be included in the predictive nomogram. Post-OVCF vertebral fracture occurred within 3, 12, and 24 months in 33, 69, and 98 out of the 307 patients (10.8%, 22.5%, and 31.9%, respectively). Multivariate analysis revealed that this event was associated with percutaneous vertebroplasty treatment, higher FI at the L3/4 IVD levels of the psoas muscle, and lower relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. Area under the curve values for subsequent vertebral fracture at 3, 12, and 24 months were 0.711, 0.724, and 0.737, respectively, indicating remarkable accuracy of the nomogram. We developed a model for predicting post-OVCF vertebral fracture from diagnostic information about prescribed treatment, FI at the L3/4 IVD levels of the psoas muscle, and relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. This model could facilitate personalized predictions and preventive strategies.

关键词： Osteoporosis Compression fracture Paraspinal muscles Fat infiltration Nomograms

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Nomogram to screen older adult patients attending the radiology department for sarcopenia

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BMC GERIATRICS 2025年第1期25卷 1-9页

作者： Yuan, Guiying Ye, Guoxi Hu, Jianguang Hu, Huimin Shi, Chanmei Zhang, Ye huang, Junbing Li, Zhiqiong Zeng, xuwen Tan, Rongshao Xiong, Yuchao Jinan Univ Guangzhou Red Cross Hosp Dept Resp Guangzhou Peoples R China Jinan Univ Guangzhou Red Cross Hosp Dept Radiol 396 Tongfu Rd Guangzhou Guangzhou 510220 Guangdong Provi Peoples R China Jinan Univ Guangzhou Red Cross Hosp Dept Nephrol Guangzhou Peoples R China Jinan Univ Guangzhou Red Cross Hosp Inst Dis Oriented Nutr Res Dept Clin Nutr Guangzhou Peoples R China

BackgroundSarcopenia is an age-related syndrome that can impact the physical and mental health of older adults. However, it is often overlooked in clinical practice. Therefore, we aim to construct a nomogram based on simplified discriminant parameters for screening older adult patients for sarcopenia *** cross-sectional study included 654 patients aged >= 60 years who underwent an examination in the radiology department between October 2023 and June 2024. Patients were diagnosed with sarcopenia according to the method and cutoff value criteria proposed the Asian Working Group on Sarcopenia (AWGS) 2019 criteria. Calf circumference (CC), SARC-F score, mid-upper arm circumference (MUAC), and SARC-CalF score were used as simplified discriminant parameters for sarcopenia. The discriminative ability of these parameters for sarcopenia was assessed using receiver operating characteristic analysis. Additionally, we included each screening parameter and evaluated it's important for screening for the presence of sarcopenia via univariate and multivariate logistic regression analysis to develop a new screening nomogram model. The performance of the nomogram was evaluated using receiver operating characteristic curves, and the performance of the nomogram model was compared to that of CC, SARC-F, MUAC, and the SARC-CalF using the Delong *** the 654 subjects, 120 (18.3%) were diagnosed with sarcopenia, and the areas under the curve (AUCs) of the CC, SARC-F, MUAC, and SARC-CalF were 0.73, 0.61, 0.66, and 0.70, respectively. The multivariate analysis results revealed that older age, male sex, low CC, low MUAC, and low strength were related to sarcopenia. A nomogram model constructed with these five variables had an AUC of 0.84. The DeLong test showed that the diagnostic efficacy of the joint model was significantly higher than that of CC, SARC-F, MUAC, and *** simple nomogram based on simplified discriminant parameters offers personalized

关键词： Sarcopenia Nomogram Calf circumference SARC-F SARC-CalF

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Differences in Brain Structural Covariance Network Characteristics in Children and Adults With Autism Spectrum Disorder

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AUTISM RESEARCH 2021年第2期14卷 265-275页

作者： Cai, Suping Wang, xuwen Yang, Fan Chen, Dihui huang, Liyu Xidian Univ Sch Life Sci & Technol Xian 710071 Shaanxi Peoples R China

Systematically describing the structural topological configuration of human brain during development is an essential task. Autism spectrum disorder (ASD) represents a powerful challenge for psychiatry and neuroscience researchers. In this study, we investigated variations in the structural covariance network properties of 441 patients with ASD ranging in age from 7 to 45 years and in 426 age-matched healthy controls (HCs) using structural magnetic resonance neuroimaging from the ABIDE database. We applied a sliding window approach to study topological variation during development using comprehensive graph theoretical analysis. The main findings are as follows: (1) Cross-sectional trajectories of the network characteristics exhibited inverted U-shapes in both HCs and participants with ASD, with the latter exhibiting a 7-year delay in reaching the maximum value, (2) network resilience to targeted attacks peaked at 18 ' and 19 ' in the HCs and at 25 ' in the participants with ASD, and the weakest resilience occurred at age 7 ', (3) the HCs and participants with ASD exhibited normalized mean degree differences in the right amygdala, and (4) significant differences in the network characteristics were observed in the 18 ' age group at most of the densities analyzed. We used cross-sectional analysis to infer distinct neurodevelopmental trajectories in ASD in the brain structural connectome. Our findings are consistent with the notion that adolescence is a sensitive period of brain development with strong potential for brain plasticity, offering opportunities for environmental adaptation and social integration and for increasing vulnerability. ASD may be a product of susceptibility. Lay Summary We used cross-sectional analysis to preliminarily infer distinct neurodevelopmental trajectories in ASD in the brain structural connectome. The main findings are as follows: (1) Cross-sectional trajectories of the network characteristics exhibited inverted U-shapes in both HCs and pa

关键词： structure covariance network autism spectrum disorder sliding window analysis graph theoretical analysis brain development structural magnetic resonance neuroimaging

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IVDETR: A Multi-Source Image Pig Detection Method Based on Cross-Domain Feature Fusion

IVDETR: A Multi-Source Image Pig Detection Method Based on C...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Rui Song Hao Li Mengzhen huang Zihan huang Peng Li xuwen He School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In large-scale centralised pig farming, object detection algorithms can accurately track and monitor each pig. However, a significant proportion of current detection methods are based on visible images, and the colour differences between different pig breeds limit the applicability of the models. Furthermore, complex background interference, low illumination conditions, or high illumination intensity noise in visible scenes can likewise lead to reduced accuracy during detection. Therefore, we propose a cross-domain encoder based on the self-attention and design the IVDETR model, which integrates features from infrared and visible light images for pig object detection. In order to facilitate the training and evaluation of the model, we constructed a comprehensive, standardised infrared-visible light dataset for the first time. The experimental outcomes demonstrated that the detection accuracy was increased from 0.939 to 0.998 in a real pig breeding environment.

关键词： Training Accuracy Noise Lighting Object detection Feature extraction Pattern recognition Detection algorithms Monitoring Farming

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Evaluation of cultivars diversity and lipid composition properties of Idesia polycarpa var. vestita Diels

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JOURNAL OF FOOD SCIENCE 2022年第9期87卷 3841-3855页

作者： Wen, Leyan Xiang, xuwen Wang, Zhirong Yang, Qingqing Guo, Zehang huang, Pimiao Mao, Jianmei An, Xiaofeng Kan, Jianquan Southwest Univ Coll Food Sci 2 Tiansheng Rd Chongqing 400715 Peoples R China Minist Agr & Rural Affairs Peoples Republ China Lab Qual & Safety Risk Assessment Agroprod Storag Chongqing Peoples R China Chinese Hungarian Cooperat Res Ctr Food Sci Chongqing Peoples R China Chongqing Key Lab Special Food Cobuilt Sichuan & Chongqing Peoples R China Chongqing Shanlinyuan Forestry Comprehens Dev Co Chongqing Peoples R China

Idesia polycarpa var. vestita Diels is a perennial deciduous tree widely distributed throughout China. Four I. polycarpa fruit of different cultivars with different fruit issues during the growth process were compared, which were on the basis of morphological characteristics and chemical compositions. The influencing factors of oil accumulation in I. polycarpa fruit and the correlation between different components were investigated, and the results revealed a negative correlation between oil content and total sugar (r = -0.930), ash (r = -0.606), and crude fiber (r = -0.952). Except for oil, none of the chemical components changed substantially during the growing phase, and most cultivars showed higher oil content in the pulp portion (14.14-43.99 g/100 g). Linoleic acid was the most abundant fatty acid in I. polycarpa oil (IPO), with values ranging from 52.18% to 66.65% (fruit), 55.44% to 65.15% (pulp), and 68.99% to 78.76% (seed). Principal component analysis revealed that Hubei varieties are more advantageous. Besides, the lipid composition of IPO was identified by ultrahigh-performance liquid chromatography coupled with electrospray ionization Q-Exactive Focus mass spectrometry. The glyceride components of IPO were mostly composed of 22 triacylglycerols, 30 diacylglycerols, and 8 monoacyglycerols. Meanwhile, linoleic acid was mainly distributed in sn-2 and sn-3 positions. Practical Application Morphologic characteristics, chemical compositions, oil fatty acid profiles, and oil correlation factors of I. polycarpa were analyzed. The comprehensive score of oil quality by PCA in Hb fruit was the highest. The glyceride components of I. polycarpa oil were mainly composed of 22 triacylglycerols, 30 diacylglycerols, and eight monoacyglycerols.

关键词： chemical properties fatty acid profile Idesia polycarpa var vestita Diels lipid composition principal component analysis

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Accurate multiclassification and segmentation of gastric cancer based on a hybrid cascaded deep learning model with a vision transformer from endoscopic images

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INFORMATION SCIENCES 2024年 670卷

作者： Ul Haq, Ejaz Yong, Qin Yuan, Zhou huang, Jianjun Ul Haq, Rizwan Qin, xuwen Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Shenzhen Univ Sch Elect & Informat Engn Shenzhen Peoples R China Khushal Khan Khattak Univ KPK Karak Pakistan Shenzhen Int Exchange Coll Shenzhen Grade A1 Shenzhen Peoples R China

Compared with other forms of cancer, gastric cancer has high mortality and incidence rates, making it a major cause of death worldwide. Accurate diagnosis is crucial in the treatment of stomach cancer. Researchers have used deep learning techniques facilitated by developments in artificial intelligence to classify and segment endoscopic images of stomach cancer. Most recent research examining endoscopic images of stomach cancer has used a binary classification system, which is insufficient for practical use. False -positives and computational costs become problematic when segmentation is applied to all the images of healthy patients obtained throughout the evaluation. Hence, the expected level of performance has not been attained in the real-time multiclassification and segmentation of stomach cancer. In this study, we present a deep learning -based technique for multiclassification of endoscopic images by combining modified GoogLeNet and vision transformer (ViT) models and identifying invasive areas based on Faster RCNN. The classification of endoscopic images into three categories, namely, normal, early gastric cancer, and advanced gastric cancer, is accomplished by using a hybrid approach including modified GoogLeNet and vision transformer (ViT) models. Gastric cancer regions are then identified and segmented in the endoscopic images using the Faster R -CNN method. The Faster R -CNN algorithm is used with an endoscopic image as input, resulting in the generation of a bounding box and label image that accurately represents the gastric cancerous area. The proposed model achieved an accuracy, sensitivity and F1 -score of 97.4%, 97.5% and 95.9%, respectively, for the classification of noncancerous, early gastric cancer and advanced gastric cancer. Furthermore, the performance of the segmentation method was also validated based on evaluation metrics and achieved 96.7%, 96.6% and 95.5% accuracy, sensitivity and F1 -score, respectively, for the segmentation of noncancer

关键词： Gastric cancer Transformer Classification Segmentation Deep learning

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A Three-Dimensional Cyclic Multiplication - Convolution Neural Network for Hyperspectral Image Classification 19

A Three-Dimensional Cyclic Multiplication - Convolution Neur...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Xiaorui huang, Zihan Sheng, Zhongyin Xu, Zihan He, xuwen Lin, Qiaoling Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan China

ISBN: (纸本)9798350346558

Hyperspectral images have a large amount of spectral and spatial information, and play a significant role in remote sensing. However, the traditional image classification techniques are no longer applicable to the developing hyperspectral remote sensing images, and how to reasonably utilize the rich feature information of hyperspectral images to obtain high-accuracy classification results has become one of the most important research topics at present. This paper proposes a new 3D convolutional neural network to solve the problem of feature wastage in hyperspectral images. The framework cycles the convolutional kernel size, and the number of convolutional kernels increases in a multiplicative relationship for hyperspectral image classification (CM-CNN). Compared with existing neural network frameworks on different datasets, CMCNN achieves the highest Kappa coefficient and confusion matrix accuracy, which are stable at above 95%. From the classification results, there are almost no obvious classification errors. © 2022 IEEE.

关键词： Image classification

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XGB-SEGA coupled energy saving method for wastewater treatment plants

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APPLIED WATER SCIENCE 2024年第2期14卷 29页

作者： Wang, Zijian Zhou, xuwen Wang, Hao huang, Zehan Ji, Jiantao Peng, Zhaoxu Jiang, Kun Beijing Univ Technol Fac Architecture Civil & Transportat Engn Beijing 100124 Peoples R China Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Harbin Inst Technol Sch Civil & Environm Engn Shenzhen 518055 Peoples R China Cent Plains Environm Protect Co Ltd Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Ecol & Environm Zhengzhou 450001 Peoples R China CSD Water Serv CO Ltd Beijing 100124 Peoples R China

To reduce the amount of energy consumed in wastewater treatment plants, nine methods were used to select the key operation parameters that affected energy consumption according to daily operation records, and an intelligent operation management system based on a genetic algorithm was constructed by mapping the relationships between energy consumption and the key operation parameters. The results showed that the prediction and management of energy consumption could be achieved by incorporating the strengthened elastic genetic algorithm into the extreme gradient boosting model. The main parameters affecting energy consumption were the influent flow rate, effluent total nitrogen, NH4+-N loading rate, etc., and the energy consumption could be reduced by 13-27% (with an average of 22%). The parameters were all selected from the daily operation records of the wastewater treatment plant, and no additional complex data acquisition system was needed to collect specific parameters. This study provided a cost-effective strategy to reduce energy consumption in wastewater treatment plants.

关键词： Energy saving Extreme gradient boosting (XGB) Genetic algorithm optimization Carbon neutrality Wastewater treatment

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Latex microspheres lateral flow immunoassay with smartphone-based device for rapid detection of Cryptococcus

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TALANTA 2025年 284卷 127254页

作者： Zang, Xuelei Zhou, Yangyu Li, Shuming Shi, Gang Deng, Hengyu Zang, Xuefeng Cao, Jingrong Yang, Ruonan Lin, xuwen Deng, Hui huang, Yemei Yang, Chen Wu, Ningxin Song, Chao Wu, Lidong Xue, Xinying Capital Med Univ Beijing Shijitan Hosp Emergency & Crit Care Med Ctr Dept Resp & Crit Care Beijing 100038 Peoples R China Shandong Second Med Univ Weifang 261053 Peoples R China Datang Telecom Convergence Commun Technol Co Ltd Beijing 100094 Peoples R China Chinese Acad Fishery Sci Beijing 100141 Peoples R China Capital Med Univ Xuanwu Hosp Dept Clin Lab Beijing 100053 Peoples R China Nanjing Normal Univ Sch Food Sci & Pharmaceut Engn Nanjing 210023 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Lab Ctr Med Ctr 1 Beijing 1000853 Peoples R China 971 Hosp Chinese Peoples Liberat Army Navy Dept Cadres Qingdao 266000 Peoples R China Chinese Acad Fishery Sci Freshwater Fisheries Res Ctr Wuxi 214081 Peoples R China

Cryptococcus is a pathogenic fungus that poses a threat to human health. Conventional detection methods have limited the rapid and accurate qualitative and quantitative analysis of Cryptococcus, affecting early diagnosis and treatment. In this study, we developed a Point-of-Care Testing (POCT) platform that integrates lateral flow immunoassay (LFIA) with smartphones, enabling both rapid qualitative and quantitative detection of Cryptococcus. The LFIA strip utilizes latex microspheres (LMs) as labeling probes, achieving a detection limit of 3000 CFU/mL and presenting higher sensitivity than the Colloidal Gold Nanoparticles Lateral Flow Immunoassay (AuNPs-LFIA) strip, and approximately eight times that of the AuNPs-LFIA strip. Additionally, it exhibiting no cross-reactivity with over 24 common pathogens and validated in clinical samples. For quantitative analysis, artificial intelligence algorithms were employed to convert smartphone-captured images into grayscale values. Eleven feature values were utilized as a dataset for machine learning to construct a linear regression model, with Mean Squared Error (MSE) and R2 reaching 0.45 and 0.91, respectively. Moreover, the recovery rates in the serum samples ranged from 90.0 % to 108 %, indicating a good practicability. This research presents a rapid diagnostic technology for Cryptococcus and lays the theoretical and technical groundwork for detecting other pathogens.

关键词： Gold nanoparticles

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Materiality, Experience and the Body: The Catholic Pilgrimage of Sheshan in Shanghai, China

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RELIGIONS 2023年第1期14卷 40页

作者： huang, Jianbo Zheng, xuwen Lee, Christine East China Normal Univ Inst Anthropol Minhang Campus Shanghai Peoples R China

This paper foregrounds the Turnerian experiences of pilgrims themselves, for whom pilgrimage is perhaps first and foremost the process of experiencing faith with their whole body and mind. At the Chinese Catholic pilgrimage site of Sheshan, located in western Shanghai, multiple meanings and possibilities are written onto the body of the pilgrim as it interacts with sacred materialities. In the process, the pilgrim materially orients themselves towards the transcendent other and to people and events throughout time. The boundary between subject and object is increasingly blurred in the pilgrim's imagination, and pilgrimage becomes a 'porous' mind-body experience for them. In the process, as pilgrims repeatedly physically enact doctrine and doctrinal texts in the course of pilgrimage-while simultaneously rooting them in their own personal lives-Sheshan is, through the concrete actions of worshippers layered up over time, continually being re-made as sacred.

关键词： Sheshan pilgrimage religious experience materiality body anthropology of experience

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