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Have you followed the Go contest between\nGoogle AlphaGo and the world champion Ke Jie, or heard the news that the\nmedical robot Watson travelled to China and issued a diagnosis and prescription\nfor cancer patients in only ten seconds? What about the ability of artificial\nintelligence to outperform professional pathologists on recognising breast\ncancer?<\/p>\n\n
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In recent years, with the rapid development of\nbig data and artificial intelligence, and the encouragement from the Chinese\ngovernment of the advances in the healthcare industry and medical artificial\nintelligence (AI), it is perhaps inevitable that digital medicine will become\nthe new norm. Utilising AI technology, the government may save more budget in\norder to more effectively fund medical supplies for disease management and\nnursing services.<\/p>\n\n
Two Major Research Directions<\/b><\/p>\n\n
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Aside from digital medicine, as novel and\nbetter techniques for biological analysis emerge, the quantity of medical data\ngrows tremendously. Using big data, we can solve problems that were formerly\nunsolvable. Big data technologies and applications in bioinformatics include\npersonal genome sequencing using high- throughput sequencing, transcriptomic\nand proteomic research, genotypic and phenotypic studies at single-cell level,\nmetagenomics in human health, and medical image processing. Deriving hypotheses\nand mathematical models from the knowledge and observed patterns of biological\nsystems to analyse and interpret data is the fundamental initiative and goal of\nusing big data in bioinformatics.<\/p>\n\n
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Established in 2016, Prof Douglas Zhang\u2019s\nlaboratory at the University of Macau (UM) focuses on two areas: 1) Continuous\nmonitoring of human physiological signals with wearable devices, which in turn\nprovides data for studying the relationship between physiological dynamics and\ndiseases such as diabetes, respiratory and cardiac disorders; and 2) Genomic, transcriptomic\nand proteomic studies using high- throughput sequencing, from which the\ncorrelation between precision medicine and related diseases can be\ninvestigated. This research has been actively supported by the government of\nMacao, the Science and Technology Development Fund (FDCT), UM, and the Faculty\nof Health Sciences (FHS).<\/p>\n\n
Digital Health<\/b><\/p>\n\n
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Diabetes is a group of chronic metabolic\ndisorders. It occurs mainly due to the pancreas not producing enough insulin or\nthe cells of the body not responding properly to the insulin produced. Finally,\nthe body\u2019s blood glucose rises to a high level. China has the world\u2019s largest\nnumber of patients with diabetes. According to national surveys, the incidence\nof diabetes in China has increased dramatically over the past 30 years. Older\npeople, men, residents of cities or economically developed areas, and\noverweight or obese people have a higher prevalence of diabetes. How can we\nmonitor glucose in a convenient and quick way? How might we prevent the occurrence\nof diabetes? How is it possible to analyse and process the continuously\nmonitored data and explore information from the massive data? These are the\nproblems that Prof Zhang\u2019s laboratory studies.<\/p>\n\n
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Currently, patients with diabetes monitor\ntheir blood glucose by pricking their fingers in the hospital or at home.\nGenerally, they need to prick their fingers with a small needle no more than\nfour times to test the blood glucose levels. The testing method is painful and\ninconvenient, which may cause low frequency of glucose monitoring and even poor\nglucose control. For those patients in a pre-diabetes state, the continuous\nmonitoring of glucose can guide the provision of advance interventions and help\nto restore health. In a cooperative project with the Department of\nEndocrinology in the First Affiliated Hospital of Guangzhou Medical University,\nProf Zhang explored the complexity and fractality of glucose dynamics in\npatients with type-1 diabetes, type-2 diabetes, pre-diabetes, and normal\nhealth. The subjects applied minimally invasive wearable devices (see Figure 1)\nto continuously monitor the blood glucose. Prof Zhang also developed a R\npackage and related functions to analyse and compare the complexity and\nfractality. <\/p>\n\n
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In this project, Prof Zhang developed an R\npackage CGM analyzer and he found that there was differentiation of complexity\nand fractality among type-1 diabetes, type-2 diabetes, prediabetes and health\nsubjects (Figure 2). These results prompted Prof Zhang to conduct research to\nexplore the use of complexity and fractality to assess the conditions of\npatients with prediabetes and then help doctors to treat the patients and\nadjust their life style, diet, and exercise. The developed R package contains functions\nfor analysing data from a CGM study. It covers a wide and comprehensive range\nof data analysis methods including reading a series of datasets, obtaining summary\nstatistics of glucose levels, plotting data, transforming the time stamp\nformat, fixing missing values, evaluating the mean of daily difference and continuous\noverlapping net glycaemic action, calculating multiscale sample entropy,\nconducting pairwise comparison, and displaying results using various plots.\nThis package is published in the journal Bioinformatics. This package has\ngreatly facilitated the analysis of various CGM studies and helped scientists\nconduct research about diabetes. <\/p>\n\n
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In addition to cooperation with hospitals,\nProf Zhang\u2019s laboratory has conducted an experiment in Macao to explore the\neffects of exercise, heart rate, sleep and diet on healthy people and\ndiabetics. In the experiment, they used Fitbit charge 2 device to monitor the\nheart rate, sleep quality and exercise status, while the glucose fluctuations\nwere continuously monitored by using the Freestyle blood glucose meter, and the\ndiet was recorded and photographed by the subjects themselves. The research\nattracted many participants. The project is currently in the process of data\nanalysis.<\/p>\n\n
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Prof Zhang also uses wearable medical devices\nto conduct studies on cardiovascular diseases and respiratory diseases such as\nchronic obstructive pulmonary disease, asthma and allergic rhinitis.<\/p>\n\n
Bioinformatics<\/b><\/p>\n\n
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Prof Zhang\u2019s laboratory has also initiated\nseveral research projects on bioinformatics. In the integrative transcriptomic\nanalysis on the in-depth exploration of the mechanisms of allergic\nbronchopulmonary aspergillosis (ABPA), we utilise next-generation sequencing to\nstudy respiratory disease-related non-coding RNA, such as long non-coding RNA\n(lncRNA) and circular RNA (circRNA). We have collaborated with Guangzhou Medical\nUniversity First Affiliated Hospital and Guangzhou Respiratory Tract Disease\nResearch Centre. Through comparative transcriptomic studies between healthy individuals\nand patients, we discover disease-related non-coding RNA, which acts as\ncandidates of biomarkers for molecular diagnosis as well as targets in drug\ndesign. Moreover, collaborating with Prof Ge Wei in the FHS, employing\nzebrafish with different mutations (eg YX-1 gene knockout with CRISPR\/Cas9), we\nperform transcriptomic analysis to explore gene-related lncRNA, which helps\nclarify the regulation of the gene and thus provides the molecular mechanism of\nthe specific gene during zebrafish development. <\/p>\n\n
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Through\ncollaboration with Prof Chuxia Deng in the FHS, we investigate the\nclassification and differentiation of breast tumour cells with rats as model\nusing the new high-throughput RNA sequencing technique, Drop-seq. Deng\u2019s\nlaboratory carries out the bioassays and RNA sequencing to obtain Drop-seq\ndataset, and our laboratory performs data analysis, including expression\nquantification, cluster analysis, and marker gene identification, to explore\nthe classification and possible molecular mechanisms underlying differentiation\nof breast tumour cells of rats. <\/p>\n\n
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In the\nproject of library construction of whole genome and high-throughput screening\nbased on CRISPR\/Cas9, Zhang\u2019s laboratory and RiboBio Co., Guangzhou began\ncooperation which is supported by a Science Innovation and Development Grant of\nthe Guangzhou government. The project focuses on the development of application\nusing CRISPR\/Cas9 gene editing and accessible synthetic chemistry for RNA\nsynthesis in CRISPR\/Cas9 editing, so that it progressively builds the world\u2019s\nfirst human genome library which constitutes a scientific and reliable system\nof CRISPR\/Cas9 high-throughput screening analysis, to support the mass\napplication and industrialisation of CRISPR\/Cas9 technology. Zhang\u2019s laboratory\nis responsible for developing data analysis platform for high-throughput\nscreening with CRISPR\/Cas9 editing. More specifically, our laboratory employs\nand improves the early high-throughput screening system with siRNA library, and\ndesigns mathematical models for target library, reference setup, microplate\nselection and sample quantification compliant with CRISPR\/Cas9 editing. Also,\nwe introduce quality control on data in experimental design to reduce systemic\nerrors. <\/p>
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