Predicting Diabetes Mellitus With Machine Learning Techniques

Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical examination data in Luzhou, China. It contains 14 attributes. In this study, five-fold cross validation was used to examine the models. In order to verity the universal applicability of the methods, we chose some methods that have the better performance to conduct independent test experiments. We randomly selected 68994 healthy people and diabetic patients’ data, respectively as training set. Due to the data unbalance, we randomly extracted 5 times data. And the result is the average of these five experiments. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used

Suppressing miRNA-15a/-16 expression by interleukin-6 enhances drug-resistance in myeloma cells

The bone marrow microenvironment facilitates the survival, differentiation, and proliferation of myeloma (MM) cells. This study identified that microRNA-15a and -16 expressions tightly correlated with proliferation and drug sensitivity of MM cells. miRNA-15a/-16 expression in MM cells was significantly increased after treatment with cytotoxic agents. The interaction of bone marrow stromal cells (BMSC) with MM cells resulted in decreased miRNA-15a/-16 expression and promoted the survival of the MM cells. Interleukin-6 (IL-6) produced by BMSCs suppressed the expression of miRNA-15a and 16 in a time- and dose- dependent pattern, with the suppression on miRNA-15a being more significant than on miRNA-16. miRNA-15a-transfected MM cells were found to be arrested in G1/S checkpoint, and the transfected MM cells had decreased growth and survival. In conclusion, our data suggest that via suppressing miRNA-15a and -16 expressions, IL-6 secreted by BMSCs promotes drug-resistance in myeloma cells

Detection of recurrent cytogenetic aberrations in multiple myeloma: A comparison between MLPA and iFISH

Multiple myeloma (MM) is a genetically heterogeneous disease with diverse clinical characteristics and outcomes. Recently, multiplex ligation-dependent probe amplification (MLPA) has emerged as an effective and robust method for the detection of cytogenetic aberrations in MM patients. In the present study, MLPA analysis was applied to analyze cytogenetics of CD138 tumor cells of 59 MM samples, and its result was compared, retrospectively, with the interphase fluorescence in situ hybridization (iFISH) data. We firstly established the normal range of each of the 42 diagnostic probes using healthy donor samples. A total of 151 aberrations were detected in 59 patient samples, and 49/59 cases (83.1%) harbored at least one copy number variation. Overall, 0–7 aberrations were detected per case using MLPA, indicating the heterogeneity and complexity of MM cytogenetics. We showed the high efficiency of MLPA and the high congruency of the two methods to assess cytogenetic aberrations. Considering that MLPA analysis is not reliable when the aberration only exits in a small population of tumor cells, it is essential to use both MLPA and iFISH as complementary techniques for the diagnosis of MM

Analysis of the decomposition of factors affecting energy-related carbon emissions in Guangxi province, China

Present study also indicated the industrial structure of Guangxi was characterized with high-energy consuming industries since 2004. The rapid urbanization changed the population structure of Guangxi, being a leading driver of carbon emissions, by examining the regional development inequalities in China and Guangxi, this study suggested the governments should develop special policies for creating a pathway towards sustainable, low-carbon development for under-developed minority-inhabited areas rather than setting non-regional differences emissions targets

Srovnání metod měření plynných emisí S02 a NOx

Import 20/04/2006Prezenční výpůjčkaVŠB - Technická univerzita Ostrava. Fakulta metalurgie a materiálového inženýrství. Katedra (616) ochrany životního prostředí v průmysl

Enhanced Low-Neutron-Flux Sensitivity Effect in Boron-Doped Silicon

Space particle irradiation produces ionization damage and displacement damage in semiconductor devices. The enhanced low dose rate sensitivity (ELDRS) effect caused by ionization damage has attracted wide attention. However, the enhanced low-particle-flux sensitivity effect and its induction mechanism by displacement damage are controversial. In this paper, the enhanced low-neutron-flux sensitivity (ELNFS) effect in Boron-doped silicon and the relationship between the ELNFS effect and doping concentration are further explored. Boron-doped silicon is sensitive to neutron flux and ELNFS effect could be greatly reduced by increasing the doping concentration in the flux range of 5 × 109–5 × 1010 n cm−2 s−1. The simulation based on the theory of diffusion-limited reactions indicated that the ELNFS in boron-doped silicon might be caused by the difference in the concentration of remaining vacancy-related defects (Vr) under different neutron fluxes. The ELNFS effect in silicon becomes obvious when the (Vr) is close to the boron doping concentration and decreased with the increase in boron doping concentration due to the remaining vacancy-related defects being covered. These conclusions are confirmed by the p+-n-p Si-based bipolar transistors since the ELNFS effect in the low doping silicon increased the reverse leakage of the bipolar transistors and the common-emitter current gain (β) dominated by highly doped silicon remained unchanged with the decrease in the neutron flux. Our work demonstrates that the ELNFS effect in boron-doped silicon can be well explained by noise diagnostic analysis together with electrical methods and simulation, which thus provide the basis for detecting the enhanced low-particle-flux damage effect in other semiconductor materials