ABSTRACT
The accumulation of protein sequence and structure data allows researchers to obtain large amount of descriptive information, simultaneously it poses an urgent need for researchers to extract information from existing data efficiently and apply it to downstream tasks. Protein design enables the development of novel proteins that are no longer restricted by experimental conditions, which is of great significance for drug target prediction, drug discovery, and material design. As an efficient method for data feature extraction, deep learning can be used to model protein data, and further add a priori information to design novel proteins. Therefore, protein design based on deep learning has become a promising approach despite of many challenges. This review summarizes the deep learning-based modeling and design methods of protein sequence and structure data, highlighting the strategies, principle, scope of application and case studies, with the aim to provide a valuable reference for relevant researchers.
Subject(s)
Amino Acid Sequence , Deep Learning , Drug Development , ProteinsABSTRACT
Objetivo: No Brasil, estudos sobre o tempo de vida das operadoras de planos de saúde são escassos. Assim, o artigo tem o objetivo de investigar fatores econômicos que explicam a sobrevida de empresas de plano de saúde. Métodos: Foi utilizada a técnica estatística denominada análise de sobrevivência, por meio do modelo semiparamétrico de Cox. Os dados foram obtidos no site da ANS (agência reguladora do setor) e referem-se a 929 operadoras de todas as regiões do país, em 2011-2018. As seguintes variáveis foram analisadas: Beneficiário (número médio de beneficiário por operadora), Porte (Porte 1: até 20 mil beneficiários, Porte 2: entre 20 mil e 100 mil beneficiários, Porte 3: acima de 100 mil beneficiários), Tempo no Mercado (quantidade de trimestres que a operadora permaneceu no mercado), Receita/Despesa (Muito baixa, Baixa, Alta e Muito alta), Lucro (Operadoras que lucraram e Operadoras que não lucraram), Tipo de Gestão (Gestão sem fins lucrativos e Gestão empresarial) e por último tem-se a variável (dependente) Falência, que indica se a operadora solicitou o encerramento das suas atividades. Resultados: Observou-se uma taxa de mortalidade para operadoras de pequeno porte maior comparativamente às demais, com probabilidade de não sobrevivência no mercado duas vezes menor em relação às empresas de médio porte e três vezes menor se comparada às de grande porte. Conclusões: Empresas de pequeno porte encontram-se em grande desvantagem no panorama brasileiro de operadoras de planos de saúde, qualquer que seja seu tempo de vida no mercado.
Objective: In Brazil, studies on the survival time of health insurance providers are scarce. Thus, this article aims to investigate economic factors that explain the survival of these companies. Methods: The statistical technique survival analysis was used (Cox semi-parametric model). Data were obtained from the ANS (regulatory agency) website and refer to 929 operators from all regions of the country, 2011-2018. The following variables were analyzed: Beneficiaries (average number of beneficiaries per provider), Size (Size 1: Up to 20 thousand beneficiaries, Size 2: Between 20 thousand and 100 thousand beneficiaries, Size 3: Above 100 thousand beneficiaries) Time in market (Number of quarters that the provider remained in the market), Revenue/Expenses (Very Low, Low, High and Very High), Profit (Providers that profited and providers that did not profit), Type of Management (Nonprofit Management and For profit/Business Management) and lastly the (dependent) Bankruptcy variable, which indicates if the operator requested the ending of its activities. Results: A higher mortality rate was observed for small providers compared to the others, with a probability of non-survival in the market two times smaller in relation to medium-sized companies and three times lower than the large ones. Conclusions: Small businesses are at a great disadvantage in the Brazilian panorama of health insurance providers, regardless of their life time in the market.
Subject(s)
Humans , Survival Analysis , Organizations , Insurance, HealthABSTRACT
The types of 3D printing rapid prototyping manufacturing technology were introduced,and the advantages and disadvantages of 3D printing technology and traditional printing technology were compared.Examples were taken to describe the problems and countermeasures of 3D printing technology in clinical medicine,and it's pointed out 3D printing technology may have of great significance for enhancing medical service,decreasing medical cost and promoting individualized medical service.
ABSTRACT
The types of 3D printing rapid prototyping manufacturing technology were introduced,and the advantages and disadvantages of 3D printing technology and traditional printing technology were compared.Examples were taken to describe the problems and countermeasures of 3D printing technology in clinical medicine,and it's pointed out 3D printing technology may have of great significance for enhancing medical service,decreasing medical cost and promoting individualized medical service.
ABSTRACT
Purpose: The aim of this article was to present the conceptual model and development of an oral health database for storage, organization, and recovery of data from dental clinical records of a university dental school. Method: This is a joint project of the Graduate Programs in Dentistry and in Computational Science of the Pontifical Catholic University of Rio Grande do Sul (PUCRS). During a period of three months in 2008, semi-structured interviews were conducted with the dental faculty of five clinical areas of the PUCRS Dental School (FO-PUCRS). Collected data were used to build the database language and architecture. Results: Conceptual, logical, and physical database models were developed, as well as the generation of a script in SQL (Structured Query Language). After generating the script, the tool Oracle SQL Developer (Oracle Corporation, Redwood Shores, CA, USA) was used to import and create the FO-PUCRS OHDB (Oral Health Database) implemented in the Oracle 10g, a relational database management system (RDBMS). Conclusion: The FO-PUCRS OHDB represents an important advancement towards automating the institutional operational demands and will contribute to organize the clinical and social information of the patient population for administrative, educational, and research purposes.
Objetivo: Este artigo teve como objetivo documentar o trabalho realizado em conjunto pelos Programas de Pós-Graduação em Odontologia (PPGO), e Ciência da Computação (PPGCC) da Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), visando à construção de um ambiente de armazenamento, organização e recuperação de dados provenientes de prontuários odontológicos. Método: Durante 3 meses em 2008 foram realizadas entrevistas semi-estruturadas com docentes de cinco áreas de atendimento odontológico da Faculdade de Odontologia (FO-PUCRS) para análise e coleta de requisitos. Resultados: Com base nas informações obtidas neste processo, foram desenvolvidas as modelagens conceitual, lógica e física do banco de dados, bem como a geração de um script em SQL (Structured Query Language). Após a geração do script, foi utilizada a ferramenta Oracle SQL Developer (Oracle Corporation, Redwood Shores, CA, USA) para sua importação e criação do BDSB (Banco de Dados em Saúde Bucal) implementado no SGBD (Sistema Gerenciador de Banco de Dados) relacional Oracle 10g. Conclusão: A construção do BDSB junto ao PPGO-PUCRS representa um importante avanço em direção a automatização das demandas operacionais da organização, mas principalmente a exploração de uma fonte valiosa de conhecimento, rica em informações clínicas e sociais.
Subject(s)
Databases, Factual , Dental Records , Oral HealthABSTRACT
OBJECTIVE: This study investigates consumers' choice regarding source of health information. Specifically, the relationships between consumers' socioeconomic characteristics, health status, attitude to health, information needs and source of health information were explored. METHODS: Type of health information sources includes web-based, health professional, and mass-media types. Structured questionnaires were completed by 1,189 consumers through a telephone survey, and 622(52.3%) responses were included in analysis. RESULTS: A key finding was the significant difference for accessing health information by customer's characteristics, although the strong preference was shown in web-based resource. Accessing channels were different by consumer's needs of health information and attitude to health as well as consumer's age, job, education, and economic status. CONCLUSION: Consumer behaviors are likely to optimize physical and psychosocial health potentials through health information. Despite a growing number of available web-based health information resources, consumers continue to face a variety of barriers as they attempt to access these resources. Meeting consumer's needs for health information is an important role for all providers of health information.