ABSTRACT
OBJECTIVE@#To obtain the subtypes of the clinical hypertension population based on symptoms and to explore the relationship between hypertension and comorbidities.@*METHODS@#The data set was collected from the Chinese medicine (CM) electronic medical records of 33,458 hypertension inpatients in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between July 2014 and May 2017. Then, a hypertension disease comorbidity network (HDCN) was built to investigate the complicated associations between hypertension and their comorbidities. Moreover, a hypertension patient similarity network (HPSN) was constructed with patients' shared symptoms, and 7 main hypertension patient subgroups were identified from HPSN with a community detection method to exhibit the characteristics of clinical phenotypes and molecular mechanisms. In addition, the significant symptoms, diseases, CM syndromes and pathways of each main patient subgroup were obtained by enrichment analysis.@*RESULTS@#The significant symptoms and diseases of these patient subgroups were associated with different damaged target organs of hypertension. Additionally, the specific phenotypic features (symptoms, diseases, and CM syndromes) were consistent with specific molecular features (pathways) in the same patient subgroup.@*CONCLUSION@#The utility and comprehensiveness of disease classification based on community detection of patient networks using shared CM symptom phenotypes showed the importance of hypertension patient subgroups.
ABSTRACT
Objective To analyze the application status and problems of PACS so as to facilitate its future development.Methods The application of PACS in some hospital from June 2015 to May 2016was reviewed,and considerations were taken on the hospital conditions to explore the application status and problems of PACS in China.Results There were a number of problems found in Department of International Diagnosis and Treatment,including 13-a active time of image acquisition devices,6.8％ of the workstations and 14.2％ of the image acquisition devices having no interface with PACS,and 2.9％ of the images non-uploaded to PACS.Conclusion PACS in China has to emphasize on stability,high efficiency,safety and reliability to facilitate its future application to medicine,scientific research and teaching.
ABSTRACT
Objective To analyze the application status and problems of PACS so as to facilitate its future development.Methods The application of PACS in some hospital from June 2015 to May 2016was reviewed,and considerations were taken on the hospital conditions to explore the application status and problems of PACS in China.Results There were a number of problems found in Department of International Diagnosis and Treatment,including 13-a active time of image acquisition devices,6.8％ of the workstations and 14.2％ of the image acquisition devices having no interface with PACS,and 2.9％ of the images non-uploaded to PACS.Conclusion PACS in China has to emphasize on stability,high efficiency,safety and reliability to facilitate its future application to medicine,scientific research and teaching.
ABSTRACT
Phenotypic characteristics of complex diseases such as rheumatoid arthritis are a consequence of interactions of genetic and environmental factors. Biomolecules closely interact with other molecular components and form functional modules, resulting in significant biologic action capability. While traditional biochemical research focuses on a single disease using narrowly constrained data, systems biology aims to interpret large volumes of highly complex and multilevel data obtained from high-through-put technologies to understand how biological systems function as a whole. Such a systems approach to complex diseases, so-called network medicine, can shape our comprehensive understanding of disease mechanisms by identifying modules temporally and spatially perturbed in the context of health and diseases. Given the unmet needs for diagnosis, monitoring, and treatment in rheumatoid arthritis, systems biology is obviously an emerging powerful tool to gain insight into disease mechanisms, study comorbidities, analyze therapeutic drugs and their targets, and discover novel network-based biomarkers.