In silico modification of suberoylanilide hydroxamic acid (SAHA) as potential inhibitor for class II histone deacetylase (HDAC)

<p>Abstract</p> <p>Background</p> <p>The cervical cancer is the second most prevalent cancer for the woman in the world. It is caused by the oncogenic human papilloma virus (HPV). The inhibition activity of histone deacetylase (HDAC) is a potential strategy for cancer therapy. Suberoylanilide hydroxamic acid (SAHA) is widely known as a low toxicity HDAC inhibitor. This research presents <it>in silico</it> SAHA modification by utilizing triazole, in order to obtain a better inhibitor. We conducted docking of the SAHA inhibitor and 12 modified versions to six class II HDAC enzymes, and then proceeded with drug scanning of each one of them.</p> <p>Results</p> <p>The docking results show that the 12 modified inhibitors have much better binding affinity and inhibition potential than SAHA. Based on drug scan analysis, six of the modified inhibitors have robust pharmacological attributes, as revealed by drug likeness, drug score, oral bioavailability, and toxicity levels.</p> <p>Conclusions</p> <p>The binding affinity, free energy and drug scan screening of the best inhibitors have shown that 1c and 2c modified inhibitors are the best ones to inhibit class II HDAC.</p

Detection and Classification of Red Blood Cells Abnormality Using Faster R-cnn and Graph Convolutional Networks

Research in medical imagery field such as analysis of Red Blood Cells (RBCs) abnormalities can be used to assist laboratory's in determining further medical actions. Convolutional Neural Networks (CNN) is a commonly used method for the classification of RBCs abnormalities in blood cells images. However, CNN requires large number of labeled training data. A classification of RBCs abnormalities in limited data is a challenge. In this research we explore a semi-supervised learning using Graph Convolutional Networks (GCN) to classify RBCs abnormalities with limited number of labeled sample images. The proposed method consists of 3 stages, i.e., extraction of Region of Interest (ROI) of RBCs from blood images using Faster R-CNN, abnormality labeling and abnormality classification using GCN. The experiment was conducted on a publicly accessible blood sample image dataset to compare classification performance of pretrained CNN models (Resnet-101 and VGG-16) and GCN models (Resnet-101 + GCN and VGG-16 + GCN). The experiment showed that the GCN model build on VGG-16 features (VGG-16 + GCN) produced the best accuracy of 95%