A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Recent development of imidazole derivatives as potential anticancer agents

Cancer, one of the key health problems globally, is a group of related diseases that share a number of characteristics primarily the uncontrolled growth and invasive to surrounding tissues. Chemotherapy is one of the ways for the treatment of cancer which uses one or more anticancer agents as per chemotherapy regimen. Limitations of most anticancer drugs due to a variety of reasons such as serious side effects, drug resistance, lack of sensitivity and efficacy etc. generate the necessity towards the designing of novel anticancer lead molecules. In this regard, the synthesis of biologically active heterocyclic molecules is an appealing research area. Among heterocyclic compounds, nitrogen containing heterocyclic molecules has fascinated tremendous consideration due to broad range of pharmaceutical activity. Imidazoles, extensively present in natural products as well as synthetic molecules, have two nitrogen atoms, and are five membered heterocyclic rings. Because of their countless physiological and pharmacological characteristics, medicinal chemists are enthused to design and synthesize new imidazole derivatives with improved pharmacodynamic and pharmacokinetic properties. The aim of this present chapter is to discuss the synthesis, chemistry, pharmacological activity, and scope of imidazole-based molecules in anticancer drug development. Finally, we have discussed the current challenges and future perspectives of imidazole-based derivatives in anticancer drug development.

From NAFLD to MAFLD: Aligning Translational In Vitro Research to Clinical Insights

Although most same-stage non-alcoholic fatty liver disease (NAFLD) patients exhibit similar histologic sequelae, the underlying mechanisms appear to be highly heterogeneous. Therefore, it was recently proposed to redefine NAFLD to metabolic dysfunction-associated fatty liver disease (MAFLD) in which other known causes of liver disease such as alcohol consumption or viral hepatitis do not need to be excluded. Revised nomenclature envisions speeding up and facilitating anti-MAFLD drug development by means of patient stratification whereby each subgroup would benefit from distinct pharmacological interventions. As human-based in vitro research fulfils an irrefutable step in drug development, action should be taken as well in this stadium of the translational path. Indeed, most established in vitro NAFLD models rely on short-term exposure to fatty acids and use lipid accumulation as a phenotypic benchmark. This general approach to a seemingly ambiguous disease such as NAFLD therefore no longer seems applicable. Human-based in vitro models that accurately reflect distinct disease subgroups of MAFLD should thus be adopted in early preclinical disease modeling and drug testing. In this review article, we outline considerations for setting up translational in vitro experiments in the MAFLD era and allude to potential strategies to implement MAFLD heterogeneity into an in vitro setting so as to better align early drug development with future clinical trial designs.