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598 results on '"Xu, Jiaying"'

155. Hierarchical integration of degradable mesoporous silica nanoreservoirs and supramolecular dendrimer complex as a general-purpose tumor-targeted biomimetic nanoplatform for gene/small-molecule anticancer drug co-delivery

Author

Fei, Yang, primary, Li, Menghuan, additional, Li, Yanan, additional, Wang, Xuan, additional, Xue, Chencheng, additional, Wu, Zuosu, additional, Xu, Jiaying, additional, Xiazeng, Zilu, additional, Cai, Kai-Yong, additional, and Luo, Zhong, additional

Published
2020
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162. Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China

Author

Su, Kun, primary, Xu, Liang, additional, Li, Guanqiao, additional, Ruan, Xiaowen, additional, Li, Xian, additional, Deng, Pan, additional, Li, Xinmi, additional, Li, Qin, additional, Chen, Xianxian, additional, Xiong, Yu, additional, Lu, Shaofeng, additional, Qi, Li, additional, Shen, Chaobo, additional, Tang, Wenge, additional, Rong, Rong, additional, Hong, Boran, additional, Ning, Yi, additional, Long, Dongyan, additional, Xu, Jiaying, additional, Shi, Xuanling, additional, Yang, Zhihong, additional, Zhang, Qi, additional, Zhuang, Ziqi, additional, Zhang, Linqi, additional, Xiao, Jing, additional, and Li, Yafei, additional

Published
2019
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164. QRS score: A simple marker to quantify the extent of myocardial scarring in patients with chronic total arterial occlusion

Author

Guo, Hongzhou, Zhou, Xuan, Xu, Jiaying, Ye, Zhishuai, Guo, Lei, and Huang, Rongchong

Abstract

Chronic total occlusion (CTO) is a critical and unique subgroup of coronary lesions. This study aimed to investigate the correlation between the Selvester QRS score and late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMRI) in quantifying myocardial scarring to provide a simple and feasible method for treating CTO. The medical records of 134 patients with absolute CTO who underwent coronary angiography between May 1, 2014 and December 30, 2017 were retrospectively reviewed. All patients were grouped according to the CTO location (right coronary artery [RCA] CTO, left artery descending [LAD] CTO, left circumflex [LCX] CTO, and multivessel CTO groups). The degree of myocardial scarring was determined according to the Selvester QRS score and using the LGE-CMRI. All patients were followed up for at least 12 months. Among the 62 CTO patients, 55 had occlusion of a single vessel and seven had occlusion of multiple vessels, of which 27 (43.55%) were in the RCA CTO group, 16 (25.81%) in the LAD CTO group, 12 (19.35%) in the LCX CTO group, and 7 (11.29%) in the multivessel CTO group. The area under the receiver operating characteristic curve for the QRS score that was used to determine the degree of myocardial scarring was 0.806, with a sensitivity and specificity of 94.7% and 42.1%, respectively. The Selvester QRS score and LGE-CMRI measures of scar size were correlated in the RCA CTO, LCX CTO, and multivessel CTO groups (r?=?0.466, 0.593, and 0.775, respectively). The Selvester QRS score was feasible for detecting myocardial scarring in patients with CTO.

Published
2022
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171. Does Body Mass Index and Height Influence the Incident Risk of Ischemic Stroke in Newly Diagnosed Type 2 Diabetes Subjects?

Author

Duan, Donghui, primary, Li, Hui, additional, Xu, Jiaying, additional, Wong, Liping, additional, Xu, Guodong, additional, Kong, Fanqian, additional, Li, Sixuan, additional, Gong, Qinghai, additional, Zhang, Xiaohong, additional, Zhao, Jinshun, additional, Zhang, Lina, additional, Xu, Guozhang, additional, Xing, Wenhua, additional, and Han, Liyuan, additional

Published
2019
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175. City-Wide Influenza Forecasting based on Multi-Source Data

Author

Su, Kun, primary, Xiong, Yu, additional, Qi, Li, additional, Xia, Yu, additional, Li, Baisong, additional, Yang, Lin, additional, Li, Qin, additional, Tang, Wenge, additional, Li, Xian, additional, Ruan, Xiaowen, additional, Lu, Shaofeng, additional, Chen, Xianxian, additional, Shen, Chaobo, additional, Xu, Jiaying, additional, Xu, Liang, additional, Han, Mei, additional, and Xiao, Jing, additional

Published
2018
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178. Self‐Supported Oxygen and Molybdenum Dual‐Doped Cobalt Phosphide Hierarchical Nanomaterials as Superior Bifunctional Electrocatalysts for Overall Water Splitting.

Author

Mu, Jianshuai, Xu, Jiaying, Zhou, Chenmin, Wang, Qian, Wang, Xiu‐Guang, Liu, Zheng‐Yu, Zhao, Xiao‐Jun, and Yang, En‐Cui

Subjects
HYDROGEN evolution reactions, COBALT phosphide, MOLYBDENUM, FISCHER-Tropsch process, NANOSTRUCTURED materials, OXYGEN evolution reactions, ELECTROCATALYSTS, SOLAR cells
Abstract

Electrocatalytic water splitting is considered an attractive way to achieve clean and sustainable energy production. Herein, a series of self‐supported oxygen and molybdenum dual‐doped cobalt phosphides grown on Ni foam (O,Mo−CoP/NFs) were prepared. The O,Mo−CoP/NFs exhibited 3D hierarchical morphologies including urchin‐like nanostructures assembled with nanorods and nanoflowers assembled with ultrathin nanosheets. The optimized O,Mo−CoP/NFs‐2 nanoflowers exhibited superior electrocatalytic performance for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), with an overpotential of only 59 mV at 10 mA cm−2 for HER and 301 mV overpotential at 100 mA cm−2 for OER. Moreover, the O,Mo−CoP/NFs‐2 nanoflowers exhibited efficient overall water splitting as low as 1.66 V for 100 mA cm−2 with outstanding long‐term stability, even superior to the commercial Pt−C/NF and RuO2/NF system. The superior activity of self‐supported O,Mo−CoP/NFs‐2 nanoflowers was attributed to the synergistic effect of O and Mo dual doping, which promoted its intrinsic catalytic activity, and its particular morphology of self‐supported 3D hierarchical nanoflowers, which could expose more catalytic sites and enable facile gas escape. Moreover, the device of overall water splitting can be powered by a single AA battery or a 1.5 V solar cell, showing its great potential for practical applications. [ABSTRACT FROM AUTHOR]

Published
2021
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179. Heterogeneous Analysis of Extracellular Vesicles for Osteosarcoma Diagnosis

Author

Zhai, Chunhui, Xu, Jiaying, Yang, Yuting, Xie, Feng, Cao, Li, Wang, Kai, Zhou, Yan, Ding, Xiaomin, Yin, Junyi, Ding, Xianting, Hu, Haiyan, and Yu, Hui

Abstract

Osteosarcoma (OS) is the most prevalent primary tumor of bones, often diagnosed late with a poor prognosis. Currently, few effective biomarkers or diagnostic methods have been developed for early OS detection with high confidence, especially for metastatic OS. Tumor-derived extracellular vesicles (EVs) are emerging as promising biomarkers for early cancer diagnosis through liquid biopsy. Here, we report a plasmonic imaging-based biosensing technique, termed subpopulation protein analysis by single EV counting (SPASEC), for size-dependent EV subpopulation analysis. In our SPASEC platform, EVs are accurately sized and counted on plasmonic sensor chips coated with OS-specific antibodies. Subsequently, EVs are categorized into distinct subpopulations based on their sizes, and the membrane proteins of each size-dependent subpopulation are profiled. We measured the heterogeneous expression levels of the EV markers (CD63, BMP2, GD2, and N-cadherin) in each of the EV subsets from both OS cell lines and clinical plasma samples. Using the linear discriminant analysis (LDA) model, the combination of four markers is applied to classify the healthy donors (n= 37), nonmetastatic OS patients (n= 13), and metastatic patients (n= 12) with an area under the curve of 0.95, 0.92, and 0.99, respectively. SPASEC provides accurate EV sensing technology for early OS diagnosis.

Published
2024
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193. Influencing factors of length of stay among repeatedly hospitalized patients with mood disorders: a longitudinal study in China.

Author

Xu, Feng, Cheng, Peixia, Xu, Jiaying, Wang, Xiaonan, Jiang, Zhen, Zhu, Huiping, Fan, Hua, Wang, Qian, and Gao, Qi

Subjects
*PSYCHOTHERAPY patients, *PSYCHIATRIC treatment, *T-test (Statistics), *PSYCHOLOGICAL distress, *INCOME, *RESEARCH funding, *HOSPITAL care, *PATIENT readmissions, *MEDICAL care, *AFFECTIVE disorders, *HOSPITAL patients, *RETROSPECTIVE studies, *CHI-squared test, *STRUCTURAL equation modeling, *SYMPTOMS, *AGE distribution, *EVALUATION of medical care, *LONGITUDINAL method, *SUICIDAL behavior, *ANTIDEPRESSANTS, *ELECTRONIC health records, *MEDICAL records, *ACQUISITION of data, *LENGTH of stay in hospitals, *ADVERSE health care events, *PSYCHOSOCIAL factors, *HEALTH care rationing, *COMORBIDITY, *MENTAL depression
Abstract

Background: Patients with mood disorders usually require repeated and prolonged hospitalization, resulting in a heavy burden on healthcare resources. This study aims to identify variables associated with length of stay(LOS) of repeatedly hospitalized patients with mood disorders and to provide information for optimizing psychiatry management and healthcare resource allocation. Methods: Electronic medical records (EMRs) of repeatedly hospitalized patients with mood disorders from January 2010 to December 2018 were collected and retrospectively analyzed. Chi-square and t-test were adopted to investigate the differences in characteristics between the two groups of short LOS and long LOS. Generalized estimating equation (GEE) was conducted to investigate potential factors influencing LOS. Results: A total of 2,009 repeatedly hospitalized patients with mood disorders were enrolled, of which 797 (39.7%) had a long LOS and 1,212 (60.3%) had a short LOS. Adverse effects of treatment, continuous clinical manifestation, chronic onset type, suicide attempt, comorbidity and use of antidepressants were positively associated with long LOS among all repeatedly hospitalized patients with mood disorders (P < 0.050). For patients with depression, factors associated with long LOS consisted of age, monthly income, adverse effects of treatment, continuous clinical manifestation, suicide attempt and comorbidity (P < 0.050). Whereas, for patients with bipolar disorder (BD), adverse effects of treatment, four or more hospitalizations and use of antidepressants contributed to the long LOS (P < 0.050). Influencing factors of LOS also vary among patients with different effectiveness of treatment. Conclusion: The LOS in repeatedly hospitalized patients with mood disorders was influenced by multiple factors. There were discrepancies in the factors affecting LOS in patients with different diagnoses and effectiveness of treatment, and specific factors should be addressed when evaluating the LOS. [ABSTRACT FROM AUTHOR]

Published
2024
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194. Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data.

Author

He, Zhengda, Chen, Linjie, Xu, Jiaying, Lv, Hao, Zhou, Rui-ning, Hu, Jianhua, Chen, Yadong, and Gao, Yang

Subjects
*ELECTROENCEPHALOGRAPHY, *TRANSFORMER models, *DIAGNOSIS, *DRUG development, *DRUG discovery, *TREATMENT effectiveness
Abstract

Simple Summary: Electroencephalograms provide valuable insights into brain activity with diverse medical utility that encompasses diagnosis, monitoring, drug discovery, and treatment evaluation. We propose an artificial intelligence model that is uniquely optimized to analyze electroencephalogram signals by directly processing raw data. This model captures intricate spatial and temporal patterns in electroencephalograms through dedicated components like spatial channel attention and sparse transformer encoding. Through being evaluated extensively, our model demonstrates high accuracy in detecting brain diseases and classifying psychotropic drugs. By automatically learning the representations from raw electroencephalogram data, it adapts well across diseases, subjects, and tasks. The model's end-to-end learning aptitude and task versatility constitutes a robust and widely applicable automated electroencephalogram analytics solution. We believe it has the potential to significantly advance electroencephalogram-based diagnosis and personalized medicine. Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects. [ABSTRACT FROM AUTHOR]

Published
2024
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