ABSTRACT
Cervical cancer is the common cancer among women, where early-stage diagnoses of cervical cancer lead to recovery from the deadly cervical cancer. Correct cervical cancer staging is predominant to decide the treatment. Hence, cervical cancer staging is an important problem in designing automatic detection and diagnosing applications of the medical field. Convolutional Neural Networks (CNNs) often plays a greater role in object identification and classification. The performance of CNN in medical image classification can already compete with radiologists. In this paper, we planned to build a deep Capsule Network (CapsNet) for medical image classification that can achieve high accuracy using cervical cancer Magnetic Resonance (MR) images. In this study, a customized deep CNN model is developed using CapsNet to automatically predict the cervical cancer from MR images. In CapsNet, each layer receives input from all preceding layers, which helps to classify the features. The hyper parameters are estimated and it controls the backpropagation gradient at the initial learning. To improve the CapsNet performance, residual blocks are included between dense layers. Training and testing are performed with around 12,771 T2-weighted MR images of the TCGA-CESC dataset publicly available for research work. The results show that the accuracy of Customized CNN using CapsNetis higher and behaves well in classifying the cervical cancer. Thus, it is evident that CNN models can be used in developing automatic image analysis tools in the medical field.
ABSTRACT
Notch1 activation triggers significant oncogenic signaling that manifests as enhanced metastatic potential and tumorigenesis in colorectal cancer. Novel small-molecule inhibitors, mainly plant-derived analogs, have low toxicity profiles and higher bioavailability. In this study, we have developed a small molecule, ASR490, by modifying structure of naturally occurring compound Withaferin A. ASR490 showed a growth-inhibitory potential by downregulating Notch1 signaling in HCT116 and SW620 cell lines. Docking studies and thermal shift assays confirmed that ASR490 binds to Notch1, whereas no changes in Notch2 and Notch3 expression were seen in colorectal cancer cells. Notch1 governs epithelial-to-mesenchymal transition signaling and is responsible for metastasis, which was abolished by ASR490 treatment. To further confirm the therapeutic potential of ASR490, we stably overexpressed Notch1 in HCT-116 cells and determined its inhibitory potential in transfected colorectal cancer (Notch1/HCT116) cells. ASR490 effectively prevented cell growth in both the vector (P = 0.005) and Notch1 (P = 0.05) transfectants. The downregulation of Notch1 signaling was evident, which corresponded with downregulation of mesenchymal markers, including N-cadherin and ß-catenin and induction of E-cadherin in HCT-116 transfectants. Intraperitoneal administration of a 1% MTD dose of ASR490 (5 mg/kg) effectively suppressed the tumor growth in control (pCMV/HCT116) and Notch1/HCT116 in xenotransplanted mice. In addition, downregulation of Notch1 and survival signaling in ASR-treated tumors confirmed the in vitro results. In conclusion, ASR490 appears to be a potent agent that can inhibit Notch1 signaling in colorectal cancer.
