The oncogenic role of hepatitis B virus X gene in hepatocarcinogenesis: recent updates

Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancers with high mortality rate. Among its various etiological factors, one of the major risk factors for HCC is a chronic infection of hepatitis B virus (HBV). HBV X protein (HBx) has been identified to play an important role in the HBV-induced HCC pathogenesis since it may interfere with several key regulators of many cellular processes. HBx localization within the cells may be beneficial to HBx multiple functions at different phases of HBV infection and associated hepatocarcinogenesis. HBx as a regulatory protein modulates cellular transcription, molecular signal transduction, cell cycle, apoptosis, autophagy, protein degradation pathways, and host genetic stability via interaction with various factors, including its association with various non-coding RNAs. A better understanding on the regulatory mechanism of HBx on various characteristics of HCC would provide an overall picture of HBV-associated HCC. This article addresses recent data on HBx role in the HBV-associated hepatocarcinogenesis

A Machine Learning-Based Virtual Screening for Natural Compounds Potential on Inhibiting Acetylcholinesterase in the Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease caused by neural cell death, characterized by the overexpression of acetylcholinesterase (AChE) and extracellular deposition of amyloid plaques. Currently, most of the FDA-approved AChE-targeting drugs can only relieve AD symptoms. There is no proven treatment capable to stop AD progression. Many natural products are isolated from several sources and analyzed through preclinical and clinical trials for their neuroprotective effects in preventing and treating AD. Therefore, this study aims to explore and determine potential candidates from natural bioactive compounds and their derivatives for AD treatment targeting AChE. In this study, feature extraction was carried out on 1730 compounds from six plants resulting from literature studies with limitations on international journals with a minimum publication year of 2018 and database searches, then classified using machine learning algorithms: Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). Hit compounds predicted to be active and inactive in the selected model were then processed through ensemble modelling. From 1730 compounds, there are 986 predicted active compounds and 370 predicted inactive compounds in the LR and RF ensemble modelling. Quercetin, Kaempferol, Luteolin, Limonene, γ-Terpinene, Nerolidol, and Linalool predicted active found overlapping in two to three plants in both LR and RF models

In-silico prediction of anti-breast cancer activity of ginger (Zingiber officinale) using machine learning techniques

INTRODUCTION: Indonesian civilization extensively uses traditional medicine to cure illnesses and preserve health. The lack of knowledge on the security and efficacy of medicinal plants is still a significant concern. Although the precise chemicals responsible for this impact are unknown, ginger is a common medicinal plant in Southeast Asia that may have anticancer qualities. METHOD: Using data from Dudedocking, a machine-learning model was created to predict possible breast anticancer chemicals from ginger. The model was used to forecast substances that block KIT and MAPK2 proteins, essential elements in breast cancer. RESULT: Beta-carotene, 5-Hydroxy-74′ -dimethoxyflavone, [12]-Shogaol, Isogingerenone B, curcumin, Trans-[10]-Shogaol, Gingerenone A, Dihydrocurcumin, and demethoxycurcumin were all superior to the reference ligand for MAPK2, according to molecular docking studies. Lycopene, [8]-Shogaol, [6]-Shogaol, and [1]-Paradol exhibited low toxicity and no Lipinski violations, but beta carotene had toxic predictions and Lipinski violations. It was anticipated that all three substances would have anticarcinogenic qualities. CONCLUSION: Overall, this study shows the value of machine learning in drug development and offers insightful information on possible anticancer chemicals from ginger

A Machine Learning-Based Virtual Screening for Natural Compounds Potential on Inhibiting Acetylcholinesterase in the Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease caused by neural cell death, characterized by the overexpression of acetylcholinesterase (AChE) and extracellular deposition of amyloid plaques. Currently, most of the FDA-approved AChE-targeting drugs can only relieve AD symptoms. There is no proven treatment capable to stop AD progression. Many natural products are isolated from several sources and analyzed through preclinical and clinical trials for their neuroprotective effects in preventing and treating AD. Therefore, this study aims to explore and determine potential candidates from natural bioactive compounds and their derivatives for AD treatment targeting AChE. In this study, feature extraction was carried out on 1730 compounds from six plants resulting from literature studies with limitations on international journals with a minimum publication year of 2018 and database searches, then classified using machine learning algorithms: Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). Hit compounds predicted to be active and inactive in the selected model were then processed through ensemble modelling. From 1730 compounds, there are 986 predicted active compounds and 370 predicted inactive compounds in the LR and RF ensemble modelling. Quercetin, Kaempferol, Luteolin, Limonene, γ-Terpinene, Nerolidol, and Linalool predicted active found overlapping in two to three plants in both LR and RF models