中国细胞生物学学会功能基因组信息学和系统生物学分会生物医药专家研究组第三届会议暨数字医疗医药和大数据分析研讨会顺利召开


        中国细胞生物学学会功能基因组信息学和系统生物学分会生物医药专家研究组第三届会议暨数字医疗医药和大数据分析研讨会于2017年8月4日至9日在吉林省长春市举行。会议由中国细胞生物学学会功能基因组信息学和系统生物学分会主办,清华大学和澳门大学协办,并由吉林大学承办。


        本次会议共本次会议共收录摘要9篇,展示墙报10篇,并荣幸地邀请到中科院生物物理研究所陈润生院士、清华大学生物科学与技术系功能基因组信息学和系统生物学名誉会长孙之荣教授和哈尔滨医科大学生物信息科学与技术学院院长功能基因组信息学和系统生物学分会会长李霞教授到场并作特邀报告。


        首先澳门大学教授、中国细胞生物学学会功能基因组信息学与系统生物学分会生物医药专家研究组组长张晓华发表了开幕词,汇报了成立生物医药专家研究组以来获得的重大成果。随后10位来自学界以及产业界的专家也分享了各自领域内的研究成果。


        报告环节结束后,随后进入主题讨论,讨论主题包括diabetes糖尿病研究;COPD慢性肺阻等呼吸病研究;Asthma&allergy哮喘与过敏症研究;编程与算法研究。参会人员根据各自的研究课题内容,提炼出研究内容中可以相互合作的地方,并以交叉学科的研究角度去深入的讨论各自的主题,切实有效的加强了学术产业界的交流合作。


        会议最后进行了无记名形式投票产生了新一届的生物医药专家研究组成员。有效投票为22票,一致同意张晓华教授继任组长;李文婷任常务副组长;曹志新、石岩、Rui Wang、唐德钧为副组长;孙之荣、李霞为顾问;孟庆凯为秘书长。


        本次生物医药专家研究组第三届会议暨数字医疗医药和大数据分析研讨会的召开,促进了学术企业交流与合作,在一定范围内确定了我国生物医药领域未来研究方向和合作规划,对进一步推动生物医药在中国的发展具有重要的意义。


前排(从左到右):孟庆凯,薛梅,石岩,曹志新,张晓华,陈润生,孙之荣,李霞,魏冬青,刘远宁,刘小桥
后排(从左到右):张博伦,孟飞,林金香,李文婷,黄琛,黄志峰,李璐,董鑫正,陈畅,王丹丹,金俞,孙世学,冷栋梁


        图为陈润生院士作特邀报告


        图为孙之荣教授作特邀报告


        图为李霞教授作特邀报告

本次会议收录的海报英文摘要或简介如下::

1、  Primary author: Chen HUANG

Title: Identification of novel transcripts involved in ABPA by RNA-seq

Abstract: Allergic bronchopulmonary aspergillosis (ABPA) is an allergic inflammation lung worldwide-disease, caused by hypersensitivity to Aspergillus fumigatus. The current methods which have been applied to invest ABPA mainly rely on the traditional clinical experiments and bioassay. However, up to now, the detailed mechanism of ABPA is still poorly understood. In addition, with advancement of the next generation sequencing (NGS) technology, an increasing number of medical studies trend to utilize NGS technique to investigate the detailed molecular mechanisms of diverse diseases, such as tumor, parasitic disease, genetic disease and so on. On the other hand, long noncoding RNAs (lncRNAs), which has been defined as a novel class of functional RNAs longer than 200 nts, have been proved to involve in a diversity of biological pathways and processes, as well as the occurrence and development of many diseases. In the present study, we performed a comprehensive transcriptome analysis based on the deep RNA sequencing of 4 human (2 ABPP patients vs 2 healthy human), in order to unveil the potential lncRNAs related to ABPA, as well as their keys roles in pathogenesis of ABPA.

Acknowledgements: This work was supported by University of Macau through a Start-up Research Grant SRG2016-00083-FHS.

2、  Primary author: Dandan WANG

Title: Wearables for Continuous Glucose Monitoring and Cardiovascular Diseases

Abstract: As diabetes mellitus(DM) occurs all kinds of serious complications, people pay more and more attention to glucose measurement. The traditional approach such as finger prick to test blood sugar is painful, time consuming and inconvenient. Nowadays, Non-invasive wearable devices for continuous blood glucose monitoring(CGM) is developing rapidly. So, this paper gives a review of wearable devices for CGM and a brief comparison of their features. At the same time, we give prospects about the development and application of these devices. This paper may give instructions to patients and doctors to control glucose levels.

Acknowledgements: This work was supported by University of Macau through a Start-up Research Grant SRG2016-00083-FHS.

3、  Primary author: Yu JIN

Title: Entropy Change of Biological Dynamics in Chronic Obstructive Pulmonary Disease

Abstract: In this century, the rapid development of large data storage technologies, mobile network technology and portable medical devices makes it possible to measure, record, store, and track analysis of large amount of data in human physiological signals. Entropy is a key metric for quantifying the irregularity contained in physiological signals. In this review, we focus on how entropy changes in various physiological signals in chronic obstructive pulmonary disease (COPD). Our review concludes that the entropy change relies on the types of physiological signals under investigation. For major physiological signals related to respiratory diseases, such as airflow, heart rate variability and gait variability, the entropy of a patient with COPD is lower than that of a healthy person. However, for hormone secretion and respiratory sound, the entropy of a patient is higher than that of a healthy person. For mechanomyogram signal, the entropy increases with the increased severity of COPD. This result should give valuable guidance for the use of entropy for physiological signals measured by wearable medical device as well as for further research on entropy in COPD.

Acknowledgements: This work was supported by University of Macau through a Start-up Research Grant SRG2016-00083-FHS.

4、  Primary author: Chang CHEN

Title: Complexity Change in Cardiovascular Diseases

Abstract: With the fast development of wearable medical device in recent years, it becomes critical to conduct research on continuously measured physiological signals. Entropy is a key metric for quantifying the complexity contained in human physiological signals. In this review, we focus on exploring how complexity changes in various physiological signals in cardiovascular diseases. Our review concludes that the direction of complexity change relies on the physiological signals under investigation. For heart rate variability and pulse index, the complexity of a healthy person is higher than that of a patient with cardiovascular diseases. For diastolic period variability and diastolic heart sound, the direction of complexity change is reversed. Our conclusion should not only give valuable guidance for further research on the application of entropy in cardiovascular diseases but also provide a foundation for using entropy to analyze the complexity of physiological signals measured by wearable medical device.

Acknowledgements: This work was supported by University of Macau through a Start-up Research Grant SRG2016-00083-FHS.

5、  Primary author: Dongliang LENG

Title: A COPD database for precision medicine

Abstract: With the rapid development of digital medical in recent years, a huge volume of medical data which contains vast amount of information has been collected without fully exploration. So it is meaningful to manage these medical data for digging its potential value. This database is built to collect and share the real-world data of COPD patients on user-friendly web pages. This database stores continuously monitored biological data of COPD patients, such as PAW, flow and SpO2. The data were collected at a frequency of 5 Hz, and the data volume reaches 0.4 million for a full day continuously monitoring recording. Currently this database has more than 100 million (?) data points from 13 patients and more data is being collected. The data can be filtered and downloaded by their different attributes. For the further development of the database, users are encouraged to share their own data via the upload function. Besides, some mainstream algorithms and software are provided with in the website with a brief introduction for the uses to deep mining the big data.

Acknowledgements: This work was supported by University of Macau through a Start-up Research Grant SRG2016-00083-FHS.

6、  Primary author: Shixue SUN

Title: Change of Biological Dynamics in Asthma

Abstract: Asthma is a chronic respiratory disease featured with unpredictable flare-ups, for which continuous function monitoring is the key for symptoms control. Recently, the utilization of portable medical devices to monitor human physiological signals, especially for chronic disease control, is gaining its popularity. Entropy is now a crucial metric for quantifying the complexity contained in physiological signals. In this review, we focus on how complexity/irregularity changes in various physiological signals in asthma. Our review concludes that the entropy change depends on the physiological signals under investigation. For most of the respiration related physiological signals, such as airflow and heart rate variability, the entropy of a patient with asthma is lower than that of a healthy person. However, for respiratory sound, the entropy complexity of a patient is higher than that of a healthy person. For airway impedance, entropy increases with the increased severity of asthma. These results should give valuable guidance for the utilization of entropy in physiological signals measured by health monitoring devices as well as for further research on complexity in asthma.

Acknowledgements: This work was supported by University of Macau through a Start-up Research Grant SRG2016-00083-FHS.

7、  Primary author: Lu LI

8、  Primary author: Jinxiang LIN

9、  Primary author: Zhifeng HUANG

10、  Primary author: Peiyan ZHENG