The search for new chemical compounds with antitumor pharmacological activity is a necessary process for creating more effective drugs for each specific malignancy type. This review presents the outcomes of screening studies of natural compounds with high anti-glioma activity. Despite significant advances in cancer therapy, there are still some tumors currently considered completely incurable including brain gliomas. This review covers the main problems of the glioma chemotherapy including drug resistance, side effects of common anti-glioma drugs, and genetic diversity of brain tumors. The main emphasis is made on the characterization of natural compounds isolated from marine organisms because taxonomic diversity of organisms in seawaters significantly exceeds that of terrestrial species. Thus, we should expect greater chemical diversity of marine compounds and greater likelihood of finding effective molecules with antiglioma activity. The review covers at least 15 classes of organic compounds with their chemical formulas provided as well as semi-inhibitory concentrations, mechanisms of action, and pharmacokinetic profiles. In conclusion, the analysis of the taxonomic diversity of marine species containing bioactives with antiglioma activity is performed noting cytotoxicity indicators and to the tumor cells in comparison with similar indicators of antitumor agents approved for clinical use as antiglioblastoma chemotherapeutics.

The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough an indicator of COVID-19 severity and survival but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization of best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression.