Yi-Han Zhou

ShanghaiTech University

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i-10 index

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Co-Authors

Ze‐Sheng Li

Beijing Institute of Technology

Yuan Yao

Southwest University

Weiwei Han

Jilin University

Quan Luo

Jilin University

Qiang Li

Shandong University

Dafang Zhong

Shanghai Institute of Materia Medica

Luting Yu

China Pharmaceutical University

Xiaokang Yu

Sun Yat-sen University

Chun-Cheng Pu

China Pharmaceutical University

Yan Feng

Shanxi Agricultural University

Cooperative Institutions

Chinese Academy of Sciences

Jilin University

ShanghaiTech University

China Pharmaceutical University

Sun Yat-sen University

Shanghai Jiao Tong University

Chinese Academy of Medical Sciences & Peking Union Medical College

University of Chinese Academy of Sciences

Southern Medical University

Zhejiang University

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Papers

Citation

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Mechanism of CYP2C9 Inhibition by Flavones and Flavonols

Drug Metabolism and Disposition (2008)

Dayong Si Ying Wang Yi-Han Zhou Yingjie Guo Juan Wang

This article describes an in vitro investigation of the inhibition of cytochrome P450 (P450) 2C9 by a series of flavonoids made up of flavones (flavone, 6-hydroxyflavone, 7-hydroxyflavone, chrysin, baicalein, apigenin, luteolin, scutellarein, and wogonin) and flavonols (galangin, fisetin, kaempferol, morin, and quercetin). With the exception of flavone, all flavonoids were shown to inhibit CYP2C9-mediated diclofenac 4′-hydroxylation in the CYP2C9 RECO system, with K_i value ≤2.2 μM. In terms of the mechanism of inhibition, 6-hydroxyflavone was found to be a noncompetitive inhibitor of CYP2C9, whereas the other flavonoids were competitive inhibitors. Computer docking simulation and constructed mutants substituted at residue 100 of CYP2C9.1 indicate that the noncompetitive binding site of 6-hydroxyflavone lies beside Phe100, similar to the reported allosteric binding site of warfarin. The other flavonoids exert competitive inhibition through interaction with the substrate binding site of CYP2C9 accessed by flurbiprofen. These results suggest flavonoids can participate in interactions with drugs that act as substrates for CYP2C9 and provide a possible molecular basis for understanding cooperativity in human P450-mediated drug-drug interactions.

Baicalein

Chrysin

Flavones

Fisetin

Flavonols

Galangin

Myricetin

10.1124/dmd.108.023416

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Citations (152)

The metabolism of CYP2C9 and CYP2C19 for gliclazide by homology modeling and docking study

European Journal of Medicinal Chemistry (2008)

Yuan Yao Weiwei Han Yi-Han Zhou Ze‐Sheng Li Qiang Li

Docking (animal)

Gliclazide

Molecular model

10.1016/j.ejmech.2008.04.015

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Citations (18)

VGG-FusionNet: A Feature Fusion Framework from CT scan and Chest X-ray Images based Deep Learning for COVID-19 Detection

2022 IEEE International Conference on Data Mining Workshops (ICDMW) (2022)

Yi-Han Zhou Jiali Liu Zekun Yang Tianyi Liu Xinyue Meng

COVID-19 has been rampant across the globe since it was discovered in 2020, but the method of virus detection still lacks efficiency and requires human resources. Given the slow delivery of the PCR test and the many possible false negatives of the rapid tests, medical imaging such as a chest computed tomography (CT) scan or chest X-ray (CXR) is an alternative and efficient way to detect the coronavirus accurately. For the past two years, many researchers have proposed different deep learning methods for COVID-19 detection using CT scans or CXR images. Due to the lack of available data, our study aims to propose a new deep learning framework VGG-FusionNet that takes advantage of integrating features from both CT scan and CXR images while avoiding some pitfalls from previous studies, including a high risk of bias due to lack of demographic information for the dataset, poor reproducibility, and no evaluation on different data sources to study the generalizability. Specifically, we use the convolutional layers of GoogLeNet, ResNet, and VGG to extract features from CT scan and CXR images and fuse them before training through fully connected layers. The result shows that using VGG's convolutional layers achieves the best overall performance with an accuracy of 0.93. Our proposed framework outperforms the deep learning models, using features from CT scans or CXR.

Feature (linguistics)

10.1109/icdmw58026.2022.00054

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Citations (4)

A study of isomeric diglycosyl flavonoids by SORI CID of fourier transform ion cyclotron mass spectrometry in negative ion mode

Journal of the American Society for Mass Spectrometry (2007)

Cunyu Yan Shuying Liu Yi-Han Zhou Fengrui Song Meng Cui

High-resolution Sustained off resonance irradiation (SORI) CID was employed to distinguish four pairs of isomeric diglycosyl flavonoids in the negative mode using the electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FTICR MS). All of these isomers can be distinguished via MS/MS data. For these diglycosyl flavones and flavanones, the deprotonated alpha1-->6 linkage diglycosyl flavonoids produce fewer fragments than the alpha1-->2 linkage type compounds and the Retro-Diels-Alder (RDA) reaction in MS/MS only takes place when the aglycone is a flavanone and glycosylated with an alpha1-->2 intersaccharide linkage disaccharide. The deprotonation sites after collisional activation are discussed according to the high mass accuracy and high-resolution data of tandem spectrometry. Some of these high-resolution SORI CID product ions from alpha1-->2 linkage diglycosyl flavonoids involve multibond cleavages; the possible mechanism is discussed based on the computer modeling using Gaussian 03 program package at the B3LYP/6-31G level of theory. Unambiguous elementary composition data provides fragmentation information that has not been reported previously.

Fragmentation

Ion cyclotron resonance

Top-down proteomics

10.1016/j.jasms.2007.09.012

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Citations (13)

Study for Iris Classification Based on Multiple Machine Learning Models

Highlights in Science Engineering and Technology (2022)

Yi-Han Zhou

Classification algorithms in machine learning aim to classify data into different kinds, which is important in data mining. It is important and challenging to select the classification model with high accuracy and efficiency. To address this, this paper compares and analyzes Multilayer Perceptron (MLP)with different machine learning methods, including K-NearestNeighbor (KNN), Support Vector Machine (SVM), Logistic regression, decision tree, and random Forest. All models are trained to classify different kinds of Iris to show further comprehension of different models. All the related models are evaluated with metrics called accuracy and confusion matrices. The experimental results show that the confusion matrices based on various models are similar but MLP overperforms other models in terms of accuracy. With sufficient nodes per hidden layer for backpropagation, MLP has a remarkable ability to analyze and provide projections. With this superiority, it is expected that the neural network can have better performance in classification soon. After understanding the strength and limitations of various classification algorithms, the optimization models are expected to be developed to better solve real-world problems.

IRIS (biosensor)

10.54097/hset.v23i.3620

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Citations (2)

Analysis on Risks and Opportunities of Chinese Small and Micro Enterprises under Supply Chain Crises During COVID-19

Advances in economics, business and management research/Advances in Economics, Business and Management Research (2022)

Yi-Han Zhou

The impact of COVID-19 on supply chains across the country is significant, and this has led to the funding, employment issues and shutdown for small and micro-enterprises particularly.However, it also provides opportunities for small and micro enterprises to reorganize their business structures or transformation, as well as receiving supports from the government.Due to the importance of small and micro businesses to the domestic economy, this paper provides an overview of risks and opportunities faced by small and micro enterprises under the COVID-19 supply chain crisis, and gives suggestions for the improvement of the government and enterprises.It can be concluded that, under the COVID-19 supply chain crisis, small and micro enterprises bear the risks on working mode, payment, the break of capital chain, and the inefficient supply chain management.Meanwhile, they also face opportunities including management reorganization, self-reflection, digital transformation, and the government support.

10.2991/978-94-6463-036-7_107

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Citations (0)

Overview of Registered Trials on the Chinese Clinical Trial Registry (ChiCTR)

SSRN Electronic Journal (2021)

Junqi Liu Runze Zhou Narasimha M. Beeraka Olga Sukocheva Baosheng Li

Background: Clinical trial registry (CTR) data analysis provides reliable background and insights for large-scale development of health regulations and promotion of transparency in clinical trial support. Chinese Clinical Trial Registry (ChiCTR) platform, the third largest CTR in the world, was used in this study for analysis of the data collected during the last 13 years in China.Methods: A multi-dimensional grouping analysis was conducted to estimate trends in annual trial registration, geographical distribution, sources of funding, targeted diseases, and trial types. We have analysed all available clinical trial records (over 30,000) registered on ChiCTR platform from 2007 to 2020 via ChiCTR.gov.Results: A steady upward trend in clinical trial registrations was observed from 2008 to 2017. The year-on-year (yoy%) growth of registration was the highest in 2014 and 2018. Public funding represented the largest category of funding sources in Chinese healthcare system. The top five ChiCTR registration sites for all disease types were highly populated urban regions of China, including Shanghai, Beijing, Guangdong, Sichuan, and Jiangsu. Trials targeting neoplastic diseases accounted for the largest portion of registrations, followed by cardio/cerebrovascular and orthopaedic diseases-related trials.Conclusions: In China, research on oncological and cardiovascular diseases receives the highest share of national public funding for medical research. Publicly funded trials occupied a major segment in the registry. The analysed records reflect escalation of clinical research activities in China and distribution of public funding resources towards highly populated areas with the highest incidence of oncological and cardiovascular diseases.Funding Information: This study was supported by National Natural Science Foundation of China (No. 81703158).Declaration of Interests The authors declare no conflict of interest.

10.2139/ssrn.3988012

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Citations (1)

A Malicious PDF File Detection Method Based on Improved Ensemble Learning Stacking

Yidan Tang Jinjin Dong Yixuan Guo Yi-Han Zhou Feifan Lu

A malicious PDF file detection method based on ensemble learning is proposed to address the problem that malicious PDF files are highly concealed and difficult to detect. In order to efficiently identify malicious PDF files that are highly concealed, the detection range of malicious PDF files by machine learning models is improved by combining the conventional features of PDF files with structural features. The recognition module adopts Stacking method of ensemble learning, adds weighting operation to improve the combination performance of multiple base learners, and finds the best combination of base learners and meta learner through experiments. After experiments, NB, RF and DT are selected as the optimal Stacking model base learners and Logistic Regression as the meta learner. the optimal Stacking model achieves 98.70% accuracy on the test set, which is better than Adaboost model and deep learning DNN model.

Ensemble Learning

AdaBoost

Base (topology)

10.1109/icftic57696.2022.10075332

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Citations (1)

CATALYTIC REACTION MECHANISM OF HUMAN PHOTORECEPTOR RETINOL DEHYDROGENASE: A THEORETICAL STUDY

Journal of Theoretical and Computational Chemistry (2008)

Yuan Yao Weiwei Han Yi-Han Zhou Quan Luo Ze‐Sheng Li

Human photoreceptor retinol dehydrogenase (hRDH8) catalyzes the reduction of all-trans-retinal to all-trans-retinol with NADPH as a rate-limiting step in the visual cycle. Based on the docking results of the substrate to the 3D structure of hRDH8 which is generated by homology modeling method, three quantum chemical calculation models with different sizes were used to investigate the catalytic reaction mechanism of hRDH8 with the aid of density functional theory. The calculations indicate that hRDH8 employs a general acid/base mechanism that a proton is transferred to the keto oxygen of the substrate after the pro-S hydride of NADPH transfer to keto carbon of the substrate. The H-transfer order is converse to that in the proposed mechanism of 17ß-hydroxysteroid dehydrogenase 1, which is highly related to the hRDH8 sequence. Tyr155 always provides the proton to the keto oxygen of the substrate whether unprotonated Lys159 is considered or not in the calculation models. However, protonated Lys159 changes the initial mechanism and replaces Tyr155 to provide the proton to the keto oxygen of the substrate. Moreover, protonated Lys159 can also decrease very effectively the Gibbs free energy barrier to make the reaction indeed energetically feasible. The role of Lys159 in hRDH8 is different from that in 17ß-hydroxysteroid dehydrogenase 1. The solvent effect calculations indicate that the reaction is more feasible energetically in the protein electrostatic environment than in the gas phase.

10.1142/s0219633608003964

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Citations (5)

The three-dimensional structure of human aurora-C kinase predicted by homology modeling

Journal of Molecular Structure THEOCHEM (2007)

Weiwei Han Yi-Han Zhou Yuan Yao Ze‐Sheng Li

Docking (animal)

Aurora B kinase

Molecular model

Molecular mechanics

Homology

10.1016/j.theochem.2007.03.020

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Citations (1)