Calycosin Rebalances Advanced Glycation End Products-Induced Glucose Uptake Dysfunction of Hepatocyte In Vitro.

Diabetes mellitus (DM) often accompanies liver dysfunction. Astragali Radix is a traditional Chinese herbal medicine that is widely administrated to ameliorate the symptoms of diabetes as well as liver dysfunction, but its acting mechanism is still not yet fully recognized. Advanced glycation end products (AGEs) play a key role in promoting diabetic organ dysfunction. Both hyperglycemia and AGEs can induce insulin resistance, hepatocyte damage and liver dysfunction. We designed this study to explore the effects of the phytoestrogen Calycosin, a major active component of Astragali Radix, on AGEs-induced glucose uptake dysfunction in the hepatocyte cell line and relevant mechanisms. MTT and BrdU methods were applied to evaluate cell viability. 2-NBDG was used to observe glucose uptake by a live cell imaging system. Immunofluorescence method was carried out to investigate GLUT1, GLUT4, and RAGE protein expressions on cell membrane. cAMP content was determined by an EIA method. We found Calycosin concentration-dependently ameliorated AGEs-induced hepatocyte viability damage. AGEs dramatically reduced basal glucose uptake in hepatocytes, and this reduction could be reversed by Calycosin administration. By immunofluorescence detection, we observed that Calycosin could inhibit AGEs-induced GLUT1 expression down-regulation via estrogen receptor (ER). Furthermore, Calycosin decreased AGEs-promoted RAGE and cAMP elevation in hepatocytes. These findings strongly suggest that Calycosin can ameliorate AGEs-promoted glucose uptake dysfunction in hepatocytes; the protection of cell viability and ER-RAGE and GLUT1 pathways play a significant role in this modulation.

Novel harmonic regularization approach for variable selection in Cox's proportional hazards model.

Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq (1/2 < q < 1) regularizations, to select key risk factors in the Cox's proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL), the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.

Application of L1/2 regularization logistic method in heart disease diagnosis.

Heart disease has become the number one killer of human health, and its diagnosis depends on many features, such as age, blood pressure, heart rate and other dozens of physiological indicators. Although there are so many risk factors, doctors usually diagnose the disease depending on their intuition and experience, which requires a lot of knowledge and experience for correct determination. To find the hidden medical information in the existing clinical data is a noticeable and powerful approach in the study of heart disease diagnosis. In this paper, sparse logistic regression method is introduced to detect the key risk factors using L(1/2) regularization on the real heart disease data. Experimental results show that the sparse logistic L(1/2) regularization method achieves fewer but informative key features than Lasso, SCAD, MCP and Elastic net regularization approaches. Simultaneously, the proposed method can cut down the computational complexity, save cost and time to undergo medical tests and checkups, reduce the number of attributes needed to be taken from patients.