ABSTRACT
Resumo O objetivo deste artigo é analisar a situação da Área Metropolitana de Brasília (AMB) antes do início da pandemia de COVID-19 com foco na disponibilidade e acessibilidade de recursos críticos para o tratamento da crise aguda respiratória causada pelo vírus SARS-CoV-2. Mapeamento geográfico da população e geolocalização dos estabelecimentos e recursos de saúde, construção de rede de relacionamentos entre a demanda potencial ao sistema de saúde público e a oferta de recursos existente em dez/2019. Análise baseada na teoria de redes complexas cruzando dados socioeconômicos disponíveis no CENSO, dados do Cadastro Nacional de Estabelecimentos de Saúde (CNES) e o micro relacionamento dos setores censitários e suas populações com o estoque e disponibilidade de recursos do tipo Leito de UTI Adulto Tipo II/III e Respiradores Mecânicos. Estabelecimentos do Distrito Federal (DF) concentram mais de 75% dos relacionamentos de acesso potencial aos recursos críticos para o tratamento de COVID-19. Embora as regiões do entorno do DF, pertencentes ao Goiás, apresentem a maior vulnerabilidade relativa no território estudado, são também as mais carentes de acessibilidade e disponibilidade de recursos, evidenciando um desequilíbrio assistencial dentro da região da AMB.
Abstract The objective was to analyze the situation of the Metropolitan Area of Brasília (AMB) before the onset of the COVID-19 pandemic, focusing on the availability and geographical accessibility of critical resources for the treatment of acute respiratory crises caused by the SARS-CoV-2 virus. Geographic mapping of the population within the territory and geolocation of health facilities and resources, construction of a relationship network between the potential demand simulated to the public health system and the supply of resources available in December 2019. The relationship analysis is based on the theory of complex networks crossing socioeconomic data available in the CENSUS and information from the National Registry of Health Establishments (CNES) and analyzing the micro relationship of census tracts with the stock and availability of health resources concerning Adult ICU Bed Type II/III and Respirators/Ventilators. The Federal District (DF) health facilities concentrate more than 75% of the relationships of potential access to critical resources for the treatment of COVID-19. Although the regions surrounding the DF, belonging to Goiás state, have the greatest relative vulnerability in the studied territory, they are also the most lacking in spatial accessibility and availability of resources, evidencing a care imbalance within the AMB region.
ABSTRACT
Abstract: Within the framework of Systems Biology, this paper proposes the complex network theory as a fundamental tool for determining the most critical dynamic variables in complex biochemical mechanisms. The Belousov-Zhabotinsky reaction is proposed as a study model and as a complex bipartite network. By determining the structural property authority, the most relevant dynamic variables are specified, and a mathematical model of the Belousov-Zhabotinsky reaction is obtained. The bidirectional coupling of the proposed model was made with other models associated with biological processes, finding synchronization phenomena when varying the coupling parameter. The time series obtained from the numerical solution of the coupled models were used to construct their images using the Gramian Angular Field technique. In the end, a supervised learning tool is proposed for the classification of the type of coupling by analyzing the images, obtaining score percentages above 94%. The hereby proposed methodology could be extended to the experimental field in order to determine anomalies in the coupling and synchronization of different physiological oscillators.
Resumen: En el marco de la Biología de sistemas, se propone en el presente trabajo a la teoría de redes complejas como una herramienta fundamental para la determinación de las variables dinámicas más importantes en mecanismos bioquímicos complejos. Se emplea como modelo de estudio la reacción de Belousov-Zhabotinsky y se plantea como una red compleja bipartita. Mediante la determinación de la propiedad estructural autoridad, se determinan las variables dinámicas con mayor relevancia y se obtiene un modelo matemático de la reacción de Belousov-Zhabotinsky. Se realizó el acoplamiento bidireccional del modelo planteado con otros modelos asociados a procesos biológicos, encontrándose fenómenos de sincronización al variar el parámetro de acoplamiento. Las series de tiempo obtenidas de la solución numérica de los modelos acoplados se emplearon para construir sus respectivas imágenes mediante la técnica de campo angular gramiano. Finalmente, se propone una herramienta de aprendizaje supervisado para la clasificación del tipo de acoplamiento mediante el análisis de las imágenes, obteniéndose porcentajes de exactitud por encima del 94%. La metodología propuesta en el presente trabajo podría extenderse y trasladarse al campo experimental con la finalidad de determinar anomalías en el acoplamiento y sincronización de distintos osciladores fisiológicos.
ABSTRACT
The traditional Chinese medicine (TCM) as a treasure of the Chinese history, has deposited lots of precious experience and valuable data. As an effective mean of data processing and knowledge discovery, data mining and complex networks are used to describe the data from the statistics and complexity, and are widely used in the research of modern TCM. Data mining bias to explore the surface of the statistical law, lack for an in-depth discussion of the inner mechanism of the system; While the complex network mainly describes the system structure through the perspective of network and good at exploring the universal law in the functional complexity, but there are limitations in the large data processing. To analyze on the applications and problems of data mining and complex networks in the field of TCM, give two methods and frame of paradigms integration, construct Chinese medicine property development network in case of cerebral vascular disease, and excavate the regularity of compatibility of TCM combined with pharmaceutical association and improved COPRA algorithm. The experimental results show that the method can effectively explore the association rules of the infrequent itemsets, and provide the basis for the compatibility of Chinese herbal medicine.
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Based on clinical research and information-sharing system, this article was aimed to study both the syndrome distribution and Chinese medicine composition of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in the COPD base of our hospital using the complex networks. The analysis was made on the common rules of syndrome distribution and features of key Chinese medicine compositions in the diagnosis and treatment of AECOPD. The clinical research and information-sharing system was used on the AECOPD data study (3000 hospital electronic medical records from August 2012 to January 2015). Data was processed by ETL software. The database was constructed. And the complex networks were used in the data mining. The results showed that the distribution of AECOPD common syndromes was heat-phlegm obstructing lung of 41.94%, phlegm-turbid obstructing lung of 22.97%, phlegm-damp obstructing lung of 10.30%,qi-yin deficiency of 6.31%, phlegm-stasis obstructing lung of 5.72%, lung-kidneyqi-deficiency of 4.01%, lung-spleenqi-deficiency of 1.15%, and phlegm obstructing orifices of 1.15%. Chinese medicine compositions were obviously divided into two large groups, which were the core Chinese medicine group and the associated Chinese medicine group. The research results illustrated that the clinical research and information-sharing system had good analysis and classification effects, which not only analyzing the medication laws of senior Chinese medicine experts and single disease treatment by a hospital, but also using in the large data analysis (data collection of single or multiple diseases on Chinese medicine clinical research nationally). It provided a good scientific research platform for Chinese medicine.
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This article was aimed to explore the intelligent integration of database as the core-data and application of data mining as the core of knowledge discovery. On the basis of the existed set of information-sharing platform, this article elaborated from aspects of formatting HER intelligent integration, data integration and multi-method combined data mining intelligent integration. With the demonstration study on syndrome distribution and syndrome diagnostic criteria of acute exacerbations of chronic obstructive pulmonary disease (AECOPD), data integration and intelligence optimization of Crystal Reports and complex networks were applied to dig out the correlation between syndromes and symptoms, disease-syndromes-herb association analysis. Analysis mentioned above showed that based on the shared system, and in accordance to research programs, intelligent integration of data and data mining methods, the improvement of the sharing platform application is proved feasible.
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This study was aimed to discover core agent for the treatment of ulcerative colitis and explore the medication rules . A total of 525 ulcerative colitis medical records in the Jiangsu Province Hospital of TCM were selected from 2009 to 2013 . The records were input into the structured information acquisition system of clinical diagnosis and treatment . The complex network analysis was used to analyze core drugs of prescription and drug compatibility after data mining and rule processing . The results showed that the core drugs are Diyu , Huanglian, Muxiang, Baishao, Xianhecao, Danggui, Chaobaizhu, Huangqin, Zicao, Yiyiren, Fuling, Shanyao. It was concluded that data mining can be an objective method in the analysis of core drugs and compatibility in the treatment of ulcerative colitis. It can also be used to guide the clinical prescription medication.
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To discuss the application of complex networks models in preventing and controlling communicable disease, analyze and control the spread of infectious diseases by using the models and the software of complex networks based on its basic properties. Compared with conventional epidemiological approach, the complex networks theory, as a new theory, not only can describe the dynamic process of infections diseases spreading but also forecast the situation of infectious disease. The influence of the network's topology on the infections diseases transmission can be deeply understood through the research on disease spreading by its theory, so to control the spread of diseases. Complex networks theory approach can be used in epidemiological research for having much advantage compared with those conventional epidemiological approaches.
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Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.
ABSTRACT
As tremendous genomic data avalanches, exploring biological mechanism by data analysis and theory methods has become important for theoretical biology research. This method is significant for the study of complex gene functions and gene networks. Bowers used higher order logic relationships to decipher protein network organization, which is a systemic method called logic analysis of phylogenetic profiles (LAPP). LAPP is a data modeling and different from traditional computational methods. This computational approach identifies logic relationships of the elements (or components) in complex networks through the logic analysis of their expression data. The method can be used to infer functional relationships of two associated proteins to one another. It is important for discovering the new function mechanism of the protein. The clusters of orthologous groups (COGs) involved in a gene network usually are large groups and therefore LAPP is also an approach for complex gene logic networks. After the establishment of the gene logic network, it is convenient for the regulation of gene through the network. The method can used in many fields, such as species evolution, oncologic diagnosis and so on. LAPP was systematically described and analyzed and recent developments in methodologies and applications were highlighted. Some opinions of them were also given.