The advances of traditional Chinese medicine in the treatment of liver diseases in 2019

A comprehensive review of phytoconstituents in liver cancer prevention and treatment: targeting insights into molecular signaling pathways

Primary liver cancer is a type of cancer that develops in the liver. Hepatocellular carcinoma is a primary liver cancer that usually affects adults. Liver cancer is a fatal global condition that affects millions of people worldwide. Despite advances in technology, the mortality rate remains alarming. There is growing interest in researching alternative medicines to prevent or reduce the effects of liver cancer. Recent studies have shown growing interest in herbal products, nutraceuticals, and Chinese medicines as potential treatments for liver cancer. These substances contain unique bioactive compounds with anticancer properties. The causes of liver cancer and potential treatments are discussed in this review. This study reviews natural compounds, such as curcumin, resveratrol, green tea catechins, grape seed extracts, vitamin D, and selenium. Preclinical and clinical studies have shown that these medications reduce the risk of liver cancer through their antiviral, anti-inflammatory, antioxidant, anti-angiogenic, and antimetastatic properties. This article discusses the therapeutic properties of natural products, nutraceuticals, and Chinese compounds for the prevention and treatment of liver cancer.

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans

Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest. In this research study, a Traditional Chinese Medicine based diabetes diagnosis is presented based on analyzing the extracted features of panoramic tongue images such as color, texture, shape, tooth markings and fur. The feature extraction is done by Convolutional Neural Network (CNN)—ResNet 50 architecture, and the classification is performed by the proposed Deep Radial Basis Function Neural Network (RBFNN) algorithm based on auto encoder learning mechanism. The proposed model is simulated in MATLAB environment and evaluated with performance metrics—accuracy, precision, sensitivity, specificity, F1 score, error rate, and receiver operating characteristics (ROC). On comparing with existing models, the proposed CNN based Deep RBFNN machine learning classifier model outperformed with better classification performance and proving its effectiveness.