Type 1 diabetes (T1D) is an autoimmune disease. Different factors, including genetics and viruses may contribute to T1D, but the causes of T1D are not fully known, and there is currently no cure. The advent of high-throughput technologies has revolutionized the field of medicine and biology, and analysis of multi-source data along with clinical information has brought a better understanding of the mechanisms behind disease pathogenesis. The aim of this work was the development of a data repository linking clinical information and interactome studies in T1D. To address this goal, we analyzed the electronic health records and online databases of genes, proteins, miRNAs, and pathways to have a global view of T1D. There were common comorbid diseases such as anemia, hypertension, vitreous diseases, renal diseases, and atherosclerosis in the phenotypic disease networks. In the protein–protein interaction network, CASP3 and TNF were date-hub proteins involved in several pathways. Moreover, CTNNB1, IGF1R, and STAT3 were hub proteins, whereas miR-155-5p, miR-34a-5p, miR-23-3p, and miR-20a-5p were hub miRNAs in the gene-miRNA interaction network. Multiple levels of information including genetic, protein, miRNA and clinical data resulted in multiple results, which suggests the complementarity of multiple sources. With the integration of multifaceted information, it will shed light on the mechanisms underlying T1D; the provided data and repository has utility in understanding phenotypic disease networks for the potential development of comorbidities in T1D patients as well as the clues for further research on T1D comorbidities.
Heart rate variability (HRV) measurement in the field has not been widely studied due to the presence of substantial noises in certain circumstances even after signal processing. To overcome such a difficulty, a method, called VACA (Vote-And-Chain Algorithm) is proposed to obtain an approximate HRV measurement. With VACA, the contaminated ECGs can be patched to obtain HRV metric, such as SDNN, even when the arrival rate of noises has reached the same level of heart rate. The performance of this algorithm is evaluated with 27,000 contaminated ECGs which are synthesized by real ECGs in the Physio-Net and noises of Poisson process. The best parameters for VACA are explored so that it can reach an accuracy of (100±20)% for 97% of the 27000 contaminated ECG data. The experiment results show that VACA is an robust method for HRV measurement in applications that long-term multi-lead ECG is not feasible.
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