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1,527 results on '"Chen, Yu-Hong"'

1. Clinical characteristics of membranous nephropathy after allogeneic hematopoietic stem cell transplantation: A real-world multicenter study

Author

Jin, Yue, Zhao, Peng, Zhang, Yuan-Yuan, Ye, Yi-Shan, Zhou, Fang, Wan, Ding-Ming, Chen, Yi, Zhou, Jian, Li, Xin, Wang, Yan, Liu, Yue, Bian, Zhi-Lei, Yang, Kai-Qian, Li, Zhen, Zhang, Jian, Xu, Wen-Wei, Zhou, Jian-Ying, An, Zhuo-Yu, Fu, Hai-Xia, Chen, Yu-Hong, Chen, Qi, Wu, Jin, Wang, Jing-Zhi, Mo, Xiao-Dong, Chen, Huan, Chen, Yao, Wang, Yu, Chang, Ying-Jun, Huang, He, Huang, Xiao-Jun, and Zhang, Xiao-Hui

Published
2024
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2. Salvage haploidentical transplantation for graft failure after first haploidentical allogeneic stem cell transplantation: an updated experience

Author

Ma, Rui, Zhu, Dan-Ping, Zhang, Xiao-Hui, Xu, Lan-Ping, Wang, Yu, Mo, Xiao-Dong, Lv, Meng, Zhang, Yuan-Yuan, Cheng, Yi-Fei, Yan, Chen-Hua, Chen, Yu-Hong, Chen, Yao, Wang, Jing-Zhi, Wang, Feng-Rong, Han, Ting-Ting, Kong, Jun, Wang, Zhi-Dong, Han, Wei, Chen, Huan, Chang, Ying-Jun, He, Yun, Xu, Zheng-Li, Zheng, Feng-Mei, Fu, Hai-Xia, Liu, Kai-Yan, Huang, Xiao-Jun, and Sun, Yu-Qian

Published
2024
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4. Clinical risk factors and prognostic model for patients with bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Author

Huang, Qiu-Sha, Han, Tian-Xiao, Chen, Qi, Wu, Jin, Zhao, Peng, Wu, Ye-Jun, He, Yun, Zhu, Xiao-Lu, Fu, Hai-Xia, Wang, Feng-Rong, Zhang, Yuan-Yuan, Mo, Xiao-Dong, Han, Wei, Yan, Chen-Hua, Wang, Jing-Zhi, Chen, Huan, Chen, Yu-Hong, Han, Ting-Ting, Lv, Meng, Chen, Yao, Wang, Yu, Xu, Lan-Ping, Liu, Kai-Yan, Huang, Xiao-Jun, and Zhang, Xiao-Hui

Published
2024
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10. Whole fresh fruit intake and risk of incident diabetes in different glycemic stages: a nationwide prospective cohort investigation

Author

Li, Li, Yang, Hai-Yan, Ma, Yan, Liang, Xing-Huan, Xu, Min, Zhang, Jie, Huang, Zhen-Xing, Meng, Li-Heng, Zhou, Jia, Xian, Jing, Suo, Ying-Jun, Huang, Song, Cai, Jin-Wei, Meng, Bi-Hui, Zhao, Zhi-Yun, Lu, Jie-Li, Xu, Yu, Wang, Tian-Ge, Li, Mian, Chen, Yu-Hong, Wang, Wei-Qing, Bi, Yu-Fang, Ning, Guang, Shen, Fei-Xia, Hu, Ru-Ying, Chen, Gang, Chen, Li, Chen, Lu-Lu, Deng, Hua-Cong, Gao, Zheng-Nan, Huo, Ya-Nan, Li, Qiang, Liu, Chao, Mu, Yi-Ming, Qin, Gui-Jun, Shi, Li-Xin, Su, Qing, Wan, Qin, Wang, Gui-Xia, Wang, Shuang-Yuan, Wang, You-Min, Wu, Sheng-Li, Xu, Yi-Ping, Yan, Li, Yang, Tao, Ye, Zhen, Yu, Xue-Feng, Zhang, Yin-Fei, Zhao, Jia-Jun, Zeng, Tian-Shu, Tang, Xu-Lei, Qin, Ying-Fen, and Luo, Zuo-Jie

Published
2023
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14. Machine learning study on organic solar cells and virtual screening of designed non-fullerene acceptors.

Author

Zhang, Cai-Rong, Li, Ming, Zhao, Miao, Gong, Ji-Jun, Liu, Xiao-Meng, Chen, Yu-Hong, Liu, Zi-Jiang, Wu, You-Zhi, and Chen, Hong-Shan

Subjects
SOLAR cells, MACHINE learning, STANDARD deviations, PHOTOVOLTAIC power systems, REGRESSION trees, DNA fingerprinting, BOOSTING algorithms, HUMAN fingerprints
Abstract

Machine learning (ML) is effective to establish the complicated trilateral relationship among structures, properties, and photovoltaic performance, which is fundamental issue in developing novel materials for improving power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, we constructed the database of 397 donor–acceptor pairs of OSCs with photovoltaic parameters and descriptor sets, which include donor–acceptor weight ratio within the active layer of the OSCs, root mean square of roughness, and 1024-bit Morgan molecular fingerprint for donor (Fp-D) and acceptor (Fp-A). The ML models random forest (RF), adaptive boosting (AdaBoost), extra trees regression, and gradient boosting regression trees were trained based on the descriptor set. The metrics determination coefficient (R2), Pearson correlation coefficient (r), root mean square error, and mean absolute error were selected to evaluate ML model performances. The results showed that the RF model exhibits the highest accuracy and stability for PCE prediction among these four ML models. Moreover, based on the decomposition of non-fullerene acceptors L8-BO, BTP-ec9, AQx-2, and IEICO, 20 acceptor molecules with symmetric A–D–A and A–π–D–π–A architectures were designed. The photovoltaic parameters of the designed acceptors were predicted using the trained RF model, and the virtual screening of designed acceptors was conducted based on the predicted PCE. The results indicate that six designed acceptors can reach the predicted PCE higher than 12% when P3HT was adopted as a donor. While PM6 was applied as a donor, five designed acceptors can achieve the predicted PCE higher than 16%. [ABSTRACT FROM AUTHOR]

Published
2023
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15. Clinical characteristics of membranous nephropathy after allogeneic hematopoietic stem cell transplantation: a real-world multicenter study

Author

Jin, Yue, primary, Zhao, Peng, additional, Zhang, Yuan-Yuan, additional, Ye, Yi-Shan, additional, Zhou, Fang, additional, Wan, Ding-Ming, additional, Chen, Yi, additional, Zhou, Jian, additional, Li, Xin, additional, Wang, Yan, additional, Liu, Yue, additional, Bian, Zhi-Lei, additional, Yang, Kai-Qian, additional, Li, Zhen, additional, Zhang, Jian, additional, Xu, Wen-Wei, additional, Zhou, Jian-Ying, additional, An, Zhuo-Yu, additional, Fu, Hai-Xia, additional, Chen, Yu-Hong, additional, Chen, Qi, additional, Wu, Jin, additional, Wang, Jing-Zhi, additional, Mo, Xiao-Dong, additional, Chen, Huan, additional, Chen, Yao, additional, Wang, Yu, additional, Chang, Ying-Jun, additional, Huang, He, additional, Huang, Xiao-Jun, additional, and Zhang, Xiao-Hui, additional

Published
2024
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16. Decreasing the steroid rapidly may help to improve the clinical outcomes of patients with intestinal steroid-refractory acute graft-versus-host disease receiving basiliximab treatment

Author

Cheng, Cong, primary, Deng, Dao-Xing, additional, Zhang, Xiao-Hui, additional, Xu, Lan-Ping, additional, Wang, Yu, additional, Yan, Chen-Hua, additional, Chen, Huan, additional, Chen, Yu-Hong, additional, Han, Wei, additional, Wang, Feng-Rong, additional, Wang, Jing-Zhi, additional, Sun, Yu-Qian, additional, Huang, Xiao-Jun, additional, and Mo, Xiao-Dong, additional

Published
2024
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26. Prognostic Factors and Outcomes in Patients With Septic Shock After Allogeneic Hematopoietic Stem Cell Transplantation

Author

Huang, Qiu-Sha, Han, Tian-Xiao, Fu, Hai-Xia, Meng, Han, Zhao, Peng, Wu, Ye-Jun, He, Yun, Zhu, Xiao-Lu, Wang, Feng-Rong, Zhang, Yuan-Yuan, Mo, Xiao-Dong, Han, Wei, Yan, Chen-Hua, Wang, Jing-Zhi, Chen, Huan, Chen, Yu-Hong, Han, Ting-Ting, Lv, Meng, Chen, Yao, Wang, Yu, Xu, Lan-Ping, Liu, Kai-Yan, Huang, Xiao-Jun, and Zhang, Xiao-Hui

Published
2024
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30. Mechanical properties of polycarbonate under intermediate strain rates compression

Author

WANG Bo-lun, WANG Tao, XIANG Ning, GE Yong, LANG Jian-lin, SUN Qi-wei, CHEN Yu-hong, and YAN Yue

Subjects
polycarbonate, compression property, strain rate, constitutive model, temperature, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

A high-speed hydraulic servo testing machine was used to carry out intermediate strain rate compression test of polycarbonate, and comparative experiments of low and high strain rate compression were carried out to verify the validity of the intermediate strain rate compression test. Based on the true stress-strain curves at various strain rates and temperatures, the mechanical properties of polycarbonate under compression at intermediate strain rates were obtained. The results show that polycarbonate undergoes four stages of elastic deformation, yielding, strain softening and strain hardening in intermediate strain rate. The strain rate and temperature dependence of material manifest as: yield strength and yield strain are increased with the enhancement of strain rate or reduction of temperature. On the contrary, when increasing temperature or reducing strain rate, the strain softening of the material are more apparent. Based on the experiments, a ZWT nonlinear viscoelastic constitutive model which can describe the compression process of polycarbonate in intermediate strain rates was constructed. This model can provide strong support for the structure design of transparent parts and the simulation of bird strike.

Published
2021
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31. Effect of ultraviolet accelerated aging on mechanical and optical properties of polycarbonate

Author

SUN Qi-wei, WANG Tao, CHEN Yu-hong, GE Yong, LANG Jian-lin, WANG Bo-lun, and YAN Yue

Subjects
uv accelerated aging, mechanical property, optical property, polycarbonate, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

In order to explore the effect of accelerated UV aging on the mechanical and optical properties of polycarbonate, the changes of polycarbonate's transmittance, yellow index, tensile properties and flexural properties with aging time and radiation intensity were studied by using the method of exposure to laboratory fluorescent ultraviolet lamps source. The results show that the light transmittance of polycarbonate is decreased with the increase of UV aging time and radiation intensity. According to FT-IR, several colored groups are created in polycarbonate because of prolonging the aging time and increasing the radiation intensity, which affects the optical property of polycarbonate. The tensile strength and elongation at break of polycarbonate are decreasd with the increase of UV aging time and irradiation intensity. The microcracks and holes on surface of polycarbonate are produced by longer aging time and higher radiation energy, which expand under tensile load and cause fracture. The flexural strength of polycarbonate is increased with the increase of UV aging time and irradiation intensity if the testing surface is irradiated. The degradation layer formed on the surface of the material increases the stiffness of polycarbonate, which leads to the increase of strength. The tensile modulus and bending modulus of polycarbonate are less affected by aging time and irradiation strength because UV accelerated aging has little effect on molecular weight.

Published
2021
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33. TRPML1 triggers ferroptosis defense and is a potential therapeutic target in AKT-hyperactivated cancer.

Author

Zhang, Hai-Liang, Hu, Bing-Xin, Ye, Zhi-Peng, Li, Zhi-Ling, Liu, Shan, Zhong, Wen-Qing, Du, Tian, Yang, Dong, Mai, Jia, Li, Li-Chao, Chen, Yu-Hong, Zhu, Xian-Ying, Li, Xuan, Feng, Gong-Kan, Zhu, Xiao-Feng, and Deng, Rong

Subjects
PEPTIDES, FUNCTIONAL analysis, CANCER treatment, CELL death, CANCER cells, KINASE inhibitors
Abstract

Targeting ferroptosis for cancer therapy has slowed because of an incomplete understanding of ferroptosis mechanisms under specific pathological contexts such as tumorigenesis and cancer treatment. Here, we identify TRPML1-mediated lysosomal exocytosis as a potential anti-ferroptotic process through genome-wide CRISPR-Cas9 activation and kinase inhibitor library screening. AKT directly phosphorylated TRPML1 at Ser343 and inhibited K552 ubiquitination and proteasome degradation of TRPML1, thereby promoting TRPML1 binding to ARL8B to trigger lysosomal exocytosis. This boosted ferroptosis defense of AKT-hyperactivated cancer cells by reducing intracellular ferrous iron and enhancing membrane repair. Correlation analysis and functional analysis revealed that TRPML1-mediated ferroptosis resistance is a previously unrecognized feature of AKT-hyperactivated cancers and is necessary for AKT-driven tumorigenesis and cancer therapeutic resistance. TRPML1 inactivation or blockade of the interaction between TRPML1 and ARL8B inhibited AKT-driven tumorigenesis and cancer therapeutic resistance in vitro and in vivo by promoting ferroptosis. A synthetic peptide targeting TRPML1 inhibited AKT-driven tumorigenesis and enhanced the sensitivity of AKT-hyperactivated tumors to ferroptosis inducers, radiotherapy, and immunotherapy by boosting ferroptosis in vivo. Together, our findings identified TRPML1 as a therapeutic target in AKT-hyperactivated cancer. Editor's summary: Ferroptosis is a nonapoptotic form of regulated cell death and has been an attractive target for cancer therapy; however, the incomplete understanding of its underlying mechanism in tumorigenesis and resistance has limited its use. Here, Zhang et al. have identified transient receptor potential muclipin 1 (TRPML1) as a potential anti-ferroptotic process and target in AKT-driven cancer. They confirmed in vivo that TRPML1 inactivation using a synthetic peptide could increase sensitivity of AKT-hyperactivated tumors to ferroptosis inducers. This highlights TRPML1 as a promising therapeutic target that requires further evaluation. —Dorothy Hallberg [ABSTRACT FROM AUTHOR]

Published
2024
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34. Machine Learning Study on the Virtual Screening of Donor–Acceptor Pairs for Organic Solar Cells.

Author

Li, Ming, Zhang, Cai‐Rong, Zhang, Mei‐Ling, Gong, Ji‐Jun, Liu, Xiao‐Meng, Chen, Yu‐Hong, Liu, Zi‐Jiang, Wu, You‐Zhi, and Chen, Hong‐Shan

Subjects
ARTIFICIAL neural networks, SOLAR cells, MACHINE learning, REGRESSION trees, ELECTRON donors, PHOTOVOLTAIC power systems
Abstract

The selection of electron donors and nonfullerene acceptors (NFAs) in organic solar cells (OSCs) is crucial for improving photovoltaic performance. Machine learning (ML) has brought a breakthrough solution. Herein, 292 donor‐NFA pairs with experimental OSC parameters from the reported articles are collected. The ML descriptors include device processing parameters, molecular properties, and molecular structure. The five ML regression models, random forest (RF), extra tree regression, gradient boosting regression tree, adaptive boosting, and artificial neural network (ANN) are trained. GridSearchCV is used for hyperparameter optimization of ML regression models. The SHapley Additive exPlanation approach is employed to analyze descriptor importance. Among the trained five ML models, the RF model shows superior performance, achieving Pearson's correlation coefficient (r) of 0.81 on the test set. Based on the donors and NFAs in constructed dataset, the 9779 donor–NFA pairs for OSCs are generated by randomly combining donor and acceptor molecules. The trained RF model is utilized to predict the power conversion efficiency (PCE) of new donor–acceptor pairs for OSCs. The results indicate that the OSC composed of PBDB‐TF as donor and L8‐BO as acceptor can achieve the remarkable PCE of 17.9%. [ABSTRACT FROM AUTHOR]

Published
2024
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37. A prognostic model (BATAP) with external validation for patients with transplant-associated thrombotic microangiopathy

Author

Zhao, Peng, Wu, Ye-jun, He, Yun, Chong, Shan, Qu, Qing-yuan, Deng, Rui-xin, Sun, Xiao-wan, Huang, Qiu-sha, Liu, Xiao, Zhu, Xiao-lu, Wang, Feng-rong, Zhang, Yuan-yuan, Mo, Xiao-dong, Han, Wei, Wang, Jing-zhi, Wang, Yu, Chen, Huan, Chen, Yu-hong, Zhao, Xiang-yu, Chang, Ying-jun, Xu, Lan-ping, Liu, Kai-yan, Huang, Xiao-jun, and Zhang, Xiao-hui

Published
2021
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38. Basiliximab Treatment for Patients With Steroid-Refractory Acute Graft-Versus-Host Disease Following Matched Sibling Donor Hematopoietic Stem Cell Transplantation

Author

Jiang, Xin-Ya, primary, Zhang, Xiao-Hui, additional, Xu, Lan-Ping, additional, Wang, Yu, additional, Yan, Chen-Hua, additional, Chen, Huan, additional, Chen, Yu-Hong, additional, Han, Wei, additional, Wang, Feng-Rong, additional, Wang, Jing-Zhi, additional, Sun, Yu-Qian, additional, Mo, Xiao-Dong, additional, and Huang, Xiao-Jun, additional

Published
2024
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40. Prognostic factors and outcomes in patients with septic shock after allogeneic hematopoietic stem cell transplantation

Author

Huang, Qiu-Sha, primary, Han, Tian-Xiao, additional, Fu, Hai-Xia, additional, Meng, Han, additional, Zhao, Peng, additional, Wu, Ye-Jun, additional, He, Yun, additional, Zhu, Xiao-Lu, additional, Wang, Feng-Rong, additional, Zhang, Yuan-Yuan, additional, Mo, Xiao-Dong, additional, Han, Wei, additional, Yan, Chen-Hua, additional, Wang, Jing-Zhi, additional, Chen, Huan, additional, Chen, Yu-Hong, additional, Han, Ting-Ting, additional, Lv, Meng, additional, Chen, Yao, additional, Wang, Yu, additional, Xu, Lan-Ping, additional, Liu, Kai-Yan, additional, Huang, Xiao-Jun, additional, and Zhang, Xiao-Hui, additional

Published
2023
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41. Clinical risk factors and prognostic model for patients with bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Author

Huang, Qiu-Sha, primary, Han, Tian-Xiao, additional, Chen, Qi, additional, Wu, Jin, additional, Zhao, Peng, additional, Wu, Ye-Jun, additional, He, Yun, additional, Zhu, Xiao-Lu, additional, Fu, Hai-Xia, additional, Wang, Feng-Rong, additional, Zhang, Yuan-Yuan, additional, Mo, Xiao-Dong, additional, Han, Wei, additional, Yan, Chen-Hua, additional, Wang, Jing-Zhi, additional, Chen, Huan, additional, Chen, Yu-Hong, additional, Han, Ting-Ting, additional, Lv, Meng, additional, Chen, Yao, additional, Wang, Yu, additional, Xu, Lan-Ping, additional, Liu, Kai-Yan, additional, Huang, Xiao-Jun, additional, and Zhang, Xiao-Hui, additional

Published
2023
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43. Peripheral blood stem cell transplantation from haploidentical related donor could achieve satisfactory clinical outcomes for intermediate- or high-risk adult acute myeloid leukemia patients

Author

Cao, Le-Qing, primary, Huo, Wen-Xuan, additional, Zhang, Xiao-Hui, additional, Xu, Lan-Ping, additional, Wang, Yu, additional, Yan, Chen-Hua, additional, Chen, Huan, additional, Chen, Yu-Hong, additional, Han, Wei, additional, Wang, Feng-Rong, additional, Wang, Jing-Zhi, additional, Huang, Xiao-Jun, additional, and Mo, Xiao-Dong, additional

Published
2023
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46. Predictive Value of Dynamic Peri-Transplantation MRD Assessed By MFC Either Alone or in Combination with Other Variables for Outcomes of Patients with T-Cell Acute Lymphoblastic Leukemia

Author

Wang, Zhi-dong, Wang, Yue-wen, Xu, Lan-ping, Zhang, Xiao-hui, Wang, Yu, Chen, Huan, Chen, Yu-hong, Wang, Feng-rong, Han, Wei, Sun, Yu-qian, Yan, Chen-hua, Tang, Fei-fei, Mo, Xiao-dong, Wang, Ya-zhe, Liu, Yan-rong, Liu, Kai-yan, Huang, Xiao-jun, and Chang, Ying-jun

Published
2021
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