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2,922 results on '"Zhao, Yuliang"'

105. Immunogenicity and safety of a third dose of CoronaVac, and immune persistence of a two-dose schedule, in healthy adults: interim results from two single-centre, double-blind, randomised, placebo-controlled phase 2 clinical trials

Author

Zeng, Gang, Wu, Qianhui, Pan, Hongxing, Li, Minjie, Yang, Juan, Wang, Lin, Wu, Zhiwei, Jiang, Deyu, Deng, Xiaowei, Chu, Kai, Zheng, Wen, Wang, Lei, Lu, Wanying, Han, Bihua, Zhao, Yuliang, Zhu, Fengcai, Yu, Hongjie, and Yin, Weidong

Published
2022
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108. Computer-aided nanodrug discovery: recent progress and future prospects.

Author

Zheng, Jia-Jia, Li, Qiao-Zhi, Wang, Zhenzhen, Wang, Xiaoli, Zhao, Yuliang, and Gao, Xingfa

Subjects
COMPUTER workstation clusters, COMPUTER algorithms, MACHINE learning, DRUG resistance, TREATMENT effectiveness
Abstract

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio–nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs. [ABSTRACT FROM AUTHOR]

Published
2024
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111. Analysis of Image Quality Assessment Methods for Aerial Images

Author

Zhao, Yuliang, Zhang, Yuye, Han, Jikai, Wang, Yingying, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Liu, Qi, editor, Liu, Xiaodong, editor, Shen, Tao, editor, and Qiu, Xuesong, editor

Published
2021
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136. Diverse Applications of Nanomedicine.

Author

Pelaz, Beatriz, Alexiou, Christoph, Alvarez-Puebla, Ramon A, Alves, Frauke, Andrews, Anne M, Ashraf, Sumaira, Balogh, Lajos P, Ballerini, Laura, Bestetti, Alessandra, Brendel, Cornelia, Bosi, Susanna, Carril, Monica, Chan, Warren CW, Chen, Chunying, Chen, Xiaodong, Chen, Xiaoyuan, Cheng, Zhen, Cui, Daxiang, Du, Jianzhong, Dullin, Christian, Escudero, Alberto, Feliu, Neus, Gao, Mingyuan, George, Michael, Gogotsi, Yury, Grünweller, Arnold, Gu, Zhongwei, Halas, Naomi J, Hampp, Norbert, Hartmann, Roland K, Hersam, Mark C, Hunziker, Patrick, Jian, Ji, Jiang, Xingyu, Jungebluth, Philipp, Kadhiresan, Pranav, Kataoka, Kazunori, Khademhosseini, Ali, Kopeček, Jindřich, Kotov, Nicholas A, Krug, Harald F, Lee, Dong Soo, Lehr, Claus-Michael, Leong, Kam W, Liang, Xing-Jie, Ling Lim, Mei, Liz-Marzán, Luis M, Ma, Xiaowei, Macchiarini, Paolo, Meng, Huan, Möhwald, Helmuth, Mulvaney, Paul, Nel, Andre E, Nie, Shuming, Nordlander, Peter, Okano, Teruo, Oliveira, Jose, Park, Tai Hyun, Penner, Reginald M, Prato, Maurizio, Puntes, Victor, Rotello, Vincent M, Samarakoon, Amila, Schaak, Raymond E, Shen, Youqing, Sjöqvist, Sebastian, Skirtach, Andre G, Soliman, Mahmoud G, Stevens, Molly M, Sung, Hsing-Wen, Tang, Ben Zhong, Tietze, Rainer, Udugama, Buddhisha N, VanEpps, J Scott, Weil, Tanja, Weiss, Paul S, Willner, Itamar, Wu, Yuzhou, Yang, Lily, Yue, Zhao, Zhang, Qian, Zhang, Qiang, Zhang, Xian-En, Zhao, Yuliang, Zhou, Xin, and Parak, Wolfgang J

Subjects
Animals, Humans, Neoplasms, Drug Carriers, Drug Delivery Systems, Particle Size, Nanotechnology, Nanomedicine, Nanoparticles, Bioengineering, Prevention, Biotechnology, 5.1 Pharmaceuticals, Generic Health Relevance, Nanoscience & Nanotechnology
Abstract

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

Published
2017

137. A Nanoparticle-Based Artificial Ear for Personalized Classification of Emotions in the Human Voice Using Deep Learning

Author

Wang, Jianfei, Suo, Jiao, Liu, Dongdong, Zhao, Yuliang, Tian, Yanling, Bryanston-Cross, Peter, Li, Wen Jung, and Wang, Zuobin

Abstract

Artificial intelligence and human–computer interaction advances demand bioinspired sensing modalities capable of comprehending human affective states and speech. However, endowing skin-like interfaces with such intricate perception abilities remains challenging. Here, we have developed a flexible piezoresistive artificial ear (AE) sensor based on gold nanoparticles, which can convert sound signals into electrical signals through changes in resistance. By testing the sensor’s performance at both frequency and sound pressure level (SPL), the AE has a frequency response range of 20 Hz to 12 kHz and can sense sound signals from up to 5 m away at a frequency of 1 kHz and an SPL of 126 dB. Furthermore, through deep learning, the device achieves up to 96.9% and 95.0% accuracy in classification and recognition applications for seven emotional and eight urban environmental noises, respectively. Hence, on one hand, our device can monitor the patient’s emotional state by their speech, such as sudden yelling and screaming, which can help healthcare workers understand patients’ condition in time. On the other hand, the device could also be used for real-time monitoring of noise levels in aircraft, ships, factories, and other high-decibel equipment and environments.

Published
2024
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138. Tailoring thermal conductivity and microstructure of Al–Fe alloys by addition of Sc2O3and variable cooling rates

Author

Liu, Huan, Zhao, Yuliang, Wei, Qiuyun, He, Weixiang, Song, Dongfu, Sun, Zhenzhong, Fu, Yanan, Xiao, Fei, and Shu, Da

Abstract

Al–Fe alloy is a kind of high thermal conductivity alloy, which are widely utilized in the communication and automobile industries. The thermal conductivity is greatly affected by the morphology, size, and distribution of iron-rich intermetallic phases (Fe-rich phases), pores, and Al grains. The evolution of microstructure and thermal conductivity of Al–Fe alloys by adding Sc2O3particles and adjusting cooling rates are systematically studied by optical and scanning electron microscopy, and synchrotron X-ray imaging. The results show that Sc2O3addition reduces the size and increases the number of primary Fe-rich phases, while increasing the grain size of Al matrix. The Sc2O3promote their nucleation of primary Fe-rich phases and partial Sc solutes inhibit their growth, resulting in the improvement of thermal conductivity and mechanical properties. The remaining Sc2O3particles have large lattice misfit with Al, led to the formation of coarsen size Al. The highest thermal conductivity is 214 W/(m·K) in the alloy with Sc2O3and slow cooling rate. Sc2O3addition increase the thermal conductivity of the studied alloys, i.e., an increment of 3 W/(m K) (by 1.1 %) at slow cooling rate by and an increment of 11 W/(m K) (by 6.2 %) at fast cooling rate. This study provides a new strategy to improving thermal conductivity of Al alloy for both academia and industry.

Published
2024
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142. Prognostic value of neutrophil-to-lymphocyte ratio dynamics in patients with septic acute kidney injury: a cohort study.

Author

Wei, Wei, Liu, Caihong, Song, Guojiao, Yang, Letian, Li, Jian, Wang, Bo, Yin, Ting, Yang, Yingying, Ma, Liang, Zhang, Ling, Fu, Ping, and Zhao, Yuliang

Subjects
ACUTE kidney failure, NEUTROPHIL lymphocyte ratio, PROGNOSIS, COHORT analysis, RENAL replacement therapy
Abstract

Neutrophil-to-lymphocyte ratio (NLR) has been suggested to be a prognostic marker for various diseases, but whether NLR dynamics (ΔNLR) is related to mortality and disease severity in patients with septic acute kidney injury (AKI) has not been determined. Between August 2013 and August 2021, septic AKI patients at our center were retrospectively enrolled. ΔNLR was defined as the difference between the NLR at septic AKI diagnosis and at hospital admission. The relationship between the ΔNLR and mortality was evaluated by Kaplan–Meier curves, Cox proportional hazards, and cubic spline analyses. The prediction values were compared by area under the receiver-operating characteristic curve (AUROC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) analyses. Of the 413 participants, the mean age was 63 ± 17 years, and 134 were female (32.4%). According to the median value, patients in the high-ΔNLR group had significantly greater 90-d mortality (74.4% vs. 46.6%, p < 0.001). After adjustment for potential confounders, high ΔNLR remained an independent predictor of 90-d mortality (HR = 2.80; 95% CI = 1.74–4.49, p < 0.001). Furthermore, ΔNLR had the highest AUROC for 90-d mortality (0.685) among the various biomarkers and exhibited an improved NRI (0.314) and IDI (0.027) when incorporated with PCT and CRP. For secondary outcomes, patients with high ΔNLR had increased risk of 30-d mortality (p = 0.004), need for renal replacement therapy (p = 0.011), and developing stage-3 AKI (p = 0.040) according to the adjusted models. High ΔNLR is independently associated with increased risk of patient mortality and adverse outcomes. ΔNLR might be utilized to facilitate risk stratification and optimize septic AKI management. [ABSTRACT FROM AUTHOR]

Published
2024
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150. Influence of Nickel on Microstructure and Mechanical Properties in Medium-Carbon Spring Steel.

Author

Yu, Qian, Zhao, Yuliang, and Zhao, Feiyu

Subjects
*NICKEL, *STEEL, *PHASE transitions, *MICROSTRUCTURE, *YIELD stress
Abstract

The effects of adding nickel on the phase transition temperature, microstructure, and mechanical properties of medium-carbon spring steel have been investigated. The results show that adding nickel reduces the martensite start (Ms) temperature, improves hardenability, and refines the sub-microstructure of the martensite, thereby improving yield stress. The yield strength of martensitic steel increases by approximately 100 MPa due to a synergistic combination of grain refinement strengthening and dislocation strengthening, with an increase in the nickel content from 0 wt.% to 1 wt.%. The cryogenic impact toughness of martensitic steel also improved with a higher nickel content due to packet and block refinement and an increase in the proportion of high-angle grain boundaries (HAGBs). [ABSTRACT FROM AUTHOR]

Published
2024
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