Comparative analysis of 3D-culture system for murine neonatal heart regeneration: a systematic approach for big gene expression data

[[abstract]]Cardiovascular diseases are the leading cause of death worldwide. Loss or dysfunction of cardiomyocytes is associated with many forms of heart disease. The adult mammalian heart has a limited regenerative ability after damage, leading to the formation of fibrotic scar tissues, hypertrophy, contractile dysfunction and ul-timately, organ failure. In contrast, neonatal mammalian cardiomyocytes retain a significant replenishing potential briefly after birth. There is increasing enthusiasm to grow neonatal cardiomyocytes in 3D culture systems to artificially restore heart function. Various scaffolds and matrices are available, but the molecular and cellu-lar mechanisms underlying proliferation and differentiation of neonatal mammalian cardiomyocytes are not very well understood. Here, we utilize a systematic strategy to analyze the extensive genome-scale gene expression profiles of two different 3D constructs. We present a comprehensive comparison that may help improve the protocols for growing cardiomyocytes in a 3D culture system.[[notice]]補正完畢[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20140513~20140516[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa

Myocardial Infarction Classification by Morphological Feature Extraction from Big 12-Lead ECG Data

[[abstract]]Rapid and accurate diagnosis of patients with acute myocardial infarction is vital. The ST segment in Electrocardiography (ECG) represents the change of electric potential during the period from the end of ventricular depolarization to the beginning of repolarization and plays an important role in the detection of myocardial infarction. However, ECG monitoring generates big volumes of data and the underlying complexity must be extracted by a combination of methods. This study combines the advantages of polynomial approximation and principal component analysis. The proposed approach is stable for the 12-lead ECG data collected from the PTB database and achieves an accuracy of 98.07%.[[notice]]補正完畢[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20140513~20140516[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa

Cluster-based Classification of Diabetic Nephropathy among Type 2

[[abstract]]The prevalence of type 2 diabetes is increasing at an alarming rate. Various complications are associated with type 2 diabetes, with diabetic nephropathy being the leading cause of renal failure among diabetics. Often, when patients are diagnosed with diabetic nephropathy, their renal functions have already been significantly damaged, speeding up the progression towards end stage renal disease. Therefore, a risk prediction tool may be beneficial for the implementation of early treatment and prevention. In the present study, we propose to develop a prediction model integrating clustering and classification approaches for the identification of diabetic nephropathy among type 2 diabetes patients. Clinical and genotyping data are obtained from 345 type 2 diabetic patients(160 with non-diabetic nephropathy and 185 with diabetic nephropathy). The performance of using clinical features alone for cluster-based classification is compared with that of utilizing a combination of clinical and genetic attributes. We find that the inclusion of genetic features yield better prediction results. Further refinement of the proposed approach has the potential to facilitate the accurate identification of diabetic nephropathy and the development of better treatment in a clinical setting.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20140507~2014009[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Kyoto, Japa

Construction of a Prediction Model for Nephropathy among Obese Patients Using Genetic and Clinical Features

[[abstract]]Obesity is a complex disease arising from an excessive accumula-tion of body fat which leads to various complications such as diabetes, hyper-tension, and renal diseases. The growing prevalence of obesity is also becom-ing a major risk factor for nephropathy. When patients are diagnosed with nephropathy, their progression towards renal failure is usually inevitable. Therefore, a prediction tool will help medical doctors identify patients with a higher risk of developing nephropathy and implement early treatment or pre-vention. In this study, we attempted to construct a diagnostic support system for nephropathy using clinical and genetic traits. Our results show that pre-diction models involving the use of both genetic and clinical features yielded the best classification performance. Our finding is in accordance with the complex nature of obesity-related nephropathy and support the notion of us-ing genetic traits to design a personalized diagnostic model.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20150519~20150522[[booktype]]紙本[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Ho Chi Minh City, Vietna