ABSTRACT
Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.
ABSTRACT
Viral infections constitute a fundamental and continuous challenge for the global scientific and medical community, as highlighted by the ongoing COVID-19 pandemic. In combination with prophylactic vaccines, the development of safe and effective antiviral drugs remains a pressing need for the effective management of rare and common pathogenic viruses. The design of potent antivirals can be informed by the study of the three-dimensional structure of viral protein targets. Structure-based design of antivirals in silico provides a solution to the arduous and costly process of conventional drug development pipelines. Furthermore, rapid advances in high-throughput computing, along with the growth of available biomolecular and biochemical data, enable the development of novel computational pipelines in the hunt of antivirals. The incorporation of modern methods, such as deep-learning and artificial intelligence, has the potential to revolutionize the structure-based design and repurposing of antiviral compounds, with minimal side effects and high efficacy. The present review aims to provide an outline of both traditional computational drug design and emerging, high-level computing strategies.
ABSTRACT
The COVID-19 pandemic has complicated current healthcare services for cancer patients. Patients with haematological malignancies specifically seem vulnerable to SARS-CoV-2 infection due to their immunosuppressed status. The COVID-19 pandemic influences every step of the assessment and treatment of a haematological malignancy. Clinicians must adhere to strict policies to not spread the virus to their patients while they must also adjust their workflow for maximum productivity. These difficulties accentuate the ever-present need to improve the healthcare services for cancer patients. This improvement is needed not only to combat the problems that arose from the COVID-19 pandemic but also to establish a framework for the management of patients with haematological malignancies in potential future pandemics.
ABSTRACT
The novel coronavirus SARS-CoV-2 has emerged as a severe threat against public health and global economies. COVID-19, the disease caused by this virus, is highly contagious and has led to an ongoing pandemic. SARS-CoV-2 affects, mainly, the respiratory system, with most severe cases primarily showcasing acute respiratory distress syndrome. Currently, no targeted therapy exists, and since the number of infections and death toll keeps rising, it has become a necessity to study possible therapeutic targets. Antiviral drugs can target various stages of the viral infection, and in the case of SARS-CoV-2, both structural and non-structural proteins have been proposed as potential drug targets. This review focuses on the most researched SARS-CoV-2 proteins, their structure, function, and possible therapeutic approaches.