RESUMO
With the development of omics technology， the construction of disease networks has been widely used in the study of complex diseases. It has been widely used to construct disease networks using systems biology technology to study complex diseases. The mechanism exploration model of disease molecular network which uses the method of constructing disease networks， simulates the occurrence of diseases， explores the core development mechanism of complex diseases， and then predicts biomarkers and exploits the mechanism of drug action provided many new thoughts for the prevention and treatment of complex diseases. Nowadays， the research on the mechanism of myocardial infarction caused by myocardial ischemia and heart failure after myocardial infarction is still very important. However， the research of the molecular network of myocardial infarction and heart failure diseases is usually limited to a few targets and pathways， so it is not able to comprehensively and systematically explain the disease process. Furthermore， authors outlined the typical biological process of "myocardial infarction-heart failure" and related targets from the pathophysiological level， and summarized the existing methods of constructing dynamic networks for heart diseases and other diseases. Based on the dynamic molecular network construction methods of cardiac diseases and other diseases， this paper discusses the construction of the dynamic molecular network of myocardial infarction and heart failure， in order to understand the evolution of myocardial infarction and heart failure more accurately and explore the importance of the dynamic molecular network of the disease process for the study of disease mechanism.
RESUMO
Breast cancer is a malignant tumor with the highest morbidity and mortality in female in recent years, and it is a complex disease that affects human health. Studies have shown that dynamic network biomarkers (DNB) can effectively identify critical states at which complex diseases such as breast cancer change from a normal state to a disease state. However, the traditional DNB method requires data from multiple samples in the same disease state, which is usually unachievable in clinical diagnosis. This paper quantitatively analyzes the time series data of MCF-7 breast cancer cells and finds the DNB module of a single sample in the time series based on landscape DNB (L-DNB) method. Then, a comprehensive index is constructed to detect its early warning signals to determine the critical state of breast cancer cell differentiation. The results of this study may be of great significance for the prevention and early diagnosis of breast cancer. It is expected that this paper can provide references for the related research of breast cancer.
Assuntos
Feminino , Humanos , Biomarcadores Tumorais , Neoplasias da Mama , Diagnóstico , Diferenciação Celular , Progressão da Doença , Detecção Precoce de Câncer , Células MCF-7RESUMO
Objective: To research the application of dynamic-network probation in combination with traditional clinic probation in surgical practice teaching. Methods:Sixty four people in the undergraduate 2012(1, 2 session) set as the experimental group. 40 people in the undergraduate 2012(biochemical class) set as the control group. The traditional clinic probation was applied to the control group. The traditional clinic probation was applied to the control group. The dynamic-network probation in combination with traditional clinic probation was applied to experimental group. The teaching effects were evaluated and compared roundly. Results:The teaching effects of experimental group was significantly better than control group in all respects (x2=7.512, x2=6.797, x2=7.778, x2=4.768, x2=8.541;P<0.05). Satisfaction ratio of experimental group was significantly higher than that of control group in all respects of surgery teaching (x2=4.475, x2=5.900, x2=5.005, x2=5.904, x2=4.502; P<0.05). The examination performance of experimental group was significantly better than control group in theoretical knowledge and clinical skills testing (t=25.840, t=19.319;P<0.05). Conclusion:Dynamic-network probation in combination with traditional clinical probation has a great effect on surgical practice teaching and worth to be applied.