期刊：
Frontiers in Pharmacology,2023年14:1173747 ISSN：1663-9812
通讯作者：
Long, HP
作者机构：
[Zhou, Siqian; Long, HP; Liu, Jian; Long, Hongping] Hunan Univ Chinese Med, Ctr Med Res & Innovat, Hosp 1, Changsha, Peoples R China.;[Ding, Changsong; Zhou, Siqian; Wang, Yajing; Long, HP; Long, Hongping] Hunan Univ Chinese Med, Changsha, Peoples R China.;[Tan, Leihong] Hunan Univ Chinese Med, Dept Pharm, Hosp 2, Changsha, Peoples R China.;[Wang, Yikun] Cent South Univ, Xiangya Hosp 2, Dept Pharm, Changsha, Peoples R China.;[Li, Jing] Cent South Univ, Xiangya Hosp, Dept Pharm, Changsha, Peoples R China.
通讯机构：
[Long, HP ] H;Hunan Univ Chinese Med, Ctr Med Res & Innovat, Hosp 1, Changsha, Peoples R China.;Hunan Univ Chinese Med, Changsha, Peoples R China.
摘要：
Introduction: Corni Fructus (CF) is a Chinese herbal medicine used for medicinal and dietary purposes. It is available commercially in two main forms: raw CF (unprocessed CF) and wine-processed CF. Clinical observations have indicated that wine-processed CF exhibits superior hypoglycemic activity compared to its raw counterpart. However, the mechanisms responsible for this improvement are not well understood. Methods: To address this gap in knowledge, we conducted metabolomics analysis using ultra-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UPLC-QTOF-MS) to compare the chemical composition of raw CF and wine-processed CF. Subsequently, network analysis, along with immunofluorescence assays, was employed to elucidate the potential targets and mechanisms underlying the hypoglycemic effects of metabolites in CF. Results: Our results revealed significant compositional differences between raw CF and wine-processed CF, identifying 34 potential markers for distinguishing between the two forms of CF. Notably, wine processing led to a marked decrease in iridoid glycosides and flavonoid glycosides, which are abundant in raw CF. Network analysis predictions provided clues that eight compounds might serve as hypoglycemic metabolites of CF, and glucokinase (GCK) and adenylate cyclase (ADCYs) were speculated as possible key targets responsible for the hypoglycemic effects of CF. Immunofluorescence assays confirmed that oleanolic acid and ursolic acid, two bioactive compounds present in CF, significantly upregulated the expression of GCK and ADCYs in the HepG2 cell model. Discussion: These findings support the notion that CF exerted hypoglycemic activity via multiple components and targets, shedding light on the impact of processing methods on the chemical composition and hypoglycemic activity of Chinese herbal medicine.
作者机构：
[Zhou, Liang] Hunan Univ Chinese Med, Hosp 1, Dept Breast Surg, Changsha, Peoples R China.;[Liang, Lixin] Hunan Univ Chinese Med, Hosp 1, Dept Hlth Management, Changsha, Peoples R China.;[Zeng, Zhijun] Cent South Univ, Xiangya Hosp, Dept Gerat Surg, Changsha, Peoples R China.;[Gong, Houwu] DHC SOFTWARE CO LTD, DHC Mediway Technol Co Ltd, Beijing, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
NOV 18-21, 2019
会议地点：
San Diego, CA
会议主办单位：
[Zhou, Liang] Hunan Univ Chinese Med, Hosp 1, Dept Breast Surg, Changsha, Peoples R China.^[Liang, Lixin] Hunan Univ Chinese Med, Hosp 1, Dept Hlth Management, Changsha, Peoples R China.^[Zeng, Zhijun] Cent South Univ, Xiangya Hosp, Dept Gerat Surg, Changsha, Peoples R China.^[Gong, Houwu] DHC SOFTWARE CO LTD, DHC Mediway Technol Co Ltd, Beijing, Peoples R China.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
关键词：
Chinese Medicine;Breast Cancer;Electronic Medical Data;Association Rule
摘要：
Objective: To explore the association rules between medicines, core combinations and new prescriptions, and to analyze the medication rules of traditional Chinese medicine(TCM) in consolidation period of breast cancer based on data mining of medical records of TCM treatment in consolidation period of breast cancer in outpatient clinic of Hunan University of TCM in recent 10 years. Method: Use Python 3.7 software to select association rule algorithm with the minimum support degree greater than 60% and the minimum confidence greater than 0.9 to conduct data mining of the collected medical records.Results: A total of 9739 medical records and 178 Chinese herbal medicines were selected for data mining. The TCMs with higher frequency are atractylodes macrocephala, astragalus membranaceus, poria cocos, codonopsis pilosula, coix seed, hedyotis diffusa and scutellaria barbata. The top five bi-linked medicines in confidence are scutellaria barbata with atractylodes macrocephala, hedyotis diffusa with atractylodes macrocephala, liquor-saturated ligustrum lucidum with atractylodes macrocephala, coii with atractylodes macrocephala, and poria cocos with atractylodes macrocephala. The top five triple-linked medicines in confidence are liquor-saturated ligustrum lucidum with scutellaria barbata with atractylodes macrocephala, poria cocos with coix seed with atractylodes macrocephala, liquor-saturated ligustrum lucidum with coix seed with atractylodes macrocephala, and poria cocos with scutellaria barbata with atractylodes macrocephala. The top five quadr-linked medicine in confidence are poria cocos with liquor-saturated ligustrum lucidum with coix seed with atractylodes macrocephala, poria cocos with liquor-saturated ligustrum lucidum with scutellaria barbata with atractylodes macrocephala, hedyotis diffusa with poria cocos with liquor-saturated ligustrum lucidum with atractylodes macrocephala, codonopsis pilosula with poria cocos with liquor-saturated ligustrum lucidum with astragalus membranaceus, and codonopsis pilosula with atractylodes macrocephala with liquor-saturated ligustrum lucidum with astragalus membranaceus.Conclusion: The main pathogenesis of consolidation period of breast cancer is Qi deficiency and toxin stagnation, and the most important treatment is enriching Qi and detoxify, strengthening the spleen and tonifying the kidney.
期刊：
2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI),2017年:1-7
通讯作者：
Wang, Guojun
作者机构：
[Dai, Yinglong] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.;[Xing, Xiaofei; Wang, Guojun] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China.;[Chen, Sihong] Hunan Univ Chinese Med, Hosp 1, Prevent Treatment Ctr, Changsha 410007, Hunan, Peoples R China.
会议名称：
IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation Conference (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
会议时间：
AUG 04-08, 2017
会议地点：
San Francisco, CA
会议主办单位：
[Dai, Yinglong] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.^[Wang, Guojun;Xing, Xiaofei] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China.^[Chen, Sihong] Hunan Univ Chinese Med, Hosp 1, Prevent Treatment Ctr, Changsha 410007, Hunan, Peoples R China.
关键词：
knowledge representation;deep learning;feature learning;traditional Chinese medicine;diagnosis
摘要：
Traditional Chinese medicine (TCM), which is built on a foundation of a long history of Chinese medical practice, provides a quite different approach with western medicine. It has formed a deep knowledge of medical science, theory, diagnostic methods, prescriptions and cures. Fully grasping the deep knowledge that mostly based on a great amount of experience is beyond anyone's capability. May we train a big electronic brain to assist diagnosis and healthcare suggestion from TCM approach? Thanks to the advances in deep learning that provides us a powerful method for fusing huge volume data into effective representations, we propose a framework of deep architectures that directly maps the heterogeneous high-dimensional sensory data into a common low-dimensional latent feature space, which is quite approaching the analysis of a TCM practitioner. The information process includes images, audios, text and other sensory data. We propose to use a hierarchically supervised training method to train the deep architectures and obtain the interpretable representations. It can facilitate the non-invasive, painless and inexpensive TCM diagnostic methods to check people's health state with a computer assistant.
期刊：
2017 15TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS AND 2017 16TH IEEE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING AND COMMUNICATIONS (ISPA/IUCC 2017),2017年:959-966 ISSN：2158-9178
会议名称：
15th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) / 16th IEEE International Conference on Ubiquitous Computing and Communications (IUCC)
会议时间：
DEC 12-15, 2017
会议地点：
Guangzhou, PEOPLES R CHINA
会议主办单位：
[Dai, Yinglong] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China.^[Wang, Guojun;Xie, Dongqing;Chen, Shuhong] Guangzhou Univ, Sch Comp Sci & Educ Software, Guangzhou 510006, Guangdong, Peoples R China.^[Chen, Sihong] Hunan Univ Chinese Med, Hosp 1, Prevent Treatment Ctr, Changsha 410007, Hunan, Peoples R China.
会议论文集名称：
IEEE International Symposium on Parallel and Distributed Processing with Applications
关键词：
Deep Neural Networks;Simulation;Healthcare;Deep Reinforcement Learning;Closed Loop
摘要：
In this paper, we present a human body simulator for healthcare research. In this special environment, human body is regarded as a black-box system that generates different outputs corresponding to different external inputs. The inputs can be healthcare interventions, and the outputs can be phenotypes that reflect latent health states. The healthcare purpose is to find effective strategies that can make the human body transfer to a healthy state from any other unhealthy states. At first, we propose to use deep neural networks (DNNs) to model the human body system. After some analyses, we discover that the models of neural networks can reflect some real cases. Then, we implement a virtual human body simulator and a deep reinforcement learning (DRL) module. These two modules form a closed loop to do some healthcare experiments. The experiments compare different architectures of the body simulator and illustrate some attributes of the models.
作者机构：
[Xu, G. C.; Jiao, L. Y.] Gannan Med Univ, Ganzhou 341000, Peoples R China.;[Li, Y. C.; Wang, J. X.] Hunan Univ Tradit Chinese Med, Postgrad Sch, Changsha 410208, Hunan, Peoples R China.;[Li, Y. C.] Hunan Univ Tradit Chinese Med, Affiliated Hosp 1, Dept Neurol, Changsha 410007, Hunan, Peoples R China.
会议名称：
5th Chinese Congress on Gerontology and Health Industry (CCGI)
作者机构：
[Huang, Z. Q.] Gannan Med Univ, Coll Pharm, Ganzhou 341000, Peoples R China.;[Li, Y. C.; Wang, J. X.] Hunan Univ Tradit Chinese Med, Postgrad Sch, Changsha 410208, Hunan, Peoples R China.;[Li, Y. C.] Hunan Univ Tradit Chinese Med, Affiliated Hosp 1, Dept Neurol, Changsha 410007, Hunan, Peoples R China.
会议名称：
5th Chinese Congress on Gerontology and Health Industry (CCGI)
