Network Analysis of MPO and Other Relevant Proteins Involved in Diabetic Foot Ulcer and Other Diabetic Complications

Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

Retinal Image Analysis for Ocular Disease Prediction Using Rule Mining Algorithms

Medical image processing is now gaining a significant momentum in clinical situation to undertake diagnosis of different anatomical defects. However, with regard to eye diseases, there is no such well-defined image processing technique in medical image analysis. The scope of this study is to automate computer analysis of ocular disease-related retinal images, which may ease the job of ophthalmologists to rule out the diseased condition. In this present work, eye images are subjected for developing a reliable tool for processing the eye retinal fundus images. The primary objective is to effectively probe retinal image data for providing a holistic approach in automatic fundus disease detection and screening to help clinicians in addition with a developed reliable image processing technique combined with a rule-based clustering method for automatic analysis of fundus images in a reduced time frame. More than 400 eye images available in online are examined. The images were preprocessed by grayscale conversion, retinal segmentation, ROI and crop ROI, image resizing, and extraction in RGB channels. Then these images were segmented by NRR from RGB channels, centroids of rows and columns, and NRR to binary image conversion. Then extraction of features like cup to disc area, optic cup area, and NRR calculations prior to measuring ISNT. A unique algorithm named as EARMAM was introduced for the prediction of diseased image from healthy eye image pool is envisaged in this paper. The functional significance of the EARMAM algorithm was compared with other common classification algorithm of current practice such as SVM, naïve Bayes, random forest, and SMO. The results of confusion matrix have shown that there was 93% prediction accuracy which was higher than the predictive values of other algorithms. The above results are discussed with future improvement and application in clinical field.

Revealing the Mechanism of EGCG, Genistein, Rutin, Quercetin, and Silibinin Against hIAPP Aggregation via Computational Simulations

To inhibit hIAPP aggregation and reduce toxicity of its oligomers are one of the potential strategies for the treatment of Type 2 diabetes (T2D). It has been reported that there is an effective inhibitory effect on hIAPP aggregation by five natural flavonoids, including Genistein, Rutin, Quercetin, Epigallocatechin gallate (EGCG), and Silibinin, which are widely found in our daily food. However, the detailed mechanisms to inhibit hIAPP aggregation remain unclear. Here, we explore the mechanisms of the five flavonoids against hIAPP aggregation by molecular docking and molecular dynamics simulations. We show that these flavonoids can disaggregate Chain A and Chain B of hIAPP to reduce the extended conformation by binding with two regions of hIAPP, Leu₁₂-Ala₁₃-Asn₁₄ and Asn₃₁-Val₃₂-Gly₃₃-Ser₃₄-Asn₃₅, with the inhibitory ability of Genistein > Rutin > Quercetin > EGCG > Silibinin. These five compounds exhibit a common mechanism for disaggregation of the hIAPP pentamer; that is, they loosen the two nearest peptide chains to potentially destroy the hIAPP oligomer. Mutations of eight key residues remarkably affected by the flavonoids indicate that the secondary structures of the hIAPP pentamer change from β-sheet to be random coil, thereby to destroy its structural stability; moreover, the 28th (Ser), 12th (Leu) and 32nd (Val) amino acids exhibit significant effects on structural stability of the hIAPP pentamer, providing an important hint that these amino acids can be considered as potential targets for design of new candidate inhibitors against hIAPP oligomers. This work is beneficial to understanding of mechanism of these inhibits against hIAPP aggregation and will facilitate screening, modification, and design of new inhibitors.