ABSTRACT
Cancer immunotherapy harnesses the immune system by targeting tumor cells that express antigens recognized by immune system cells, thus leading to tumor rejection. These tumor-associated antigens include tumor-specific shared antigens, differentiation antigens, protein products of mutated genes and rearrangements unique to tumor cells, overexpressed tissue-specific antigens, and exogenous viral proteins. However, the development of effective therapeutic approaches has proven difficult, mainly because these tumor antigens are shielded, and cells primarily express self-derived antigens. Despite innovative and notable advances in immunotherapy, challenges associated with variable patient response rates and efficacy on select tumors minimize the overall effectiveness of immunotherapy. Variations observed in response rates to immunotherapy are due to multiple factors, including adaptative resistance, competency, and a diversity of individual immune systems, including cancer stem cells in the tumor microenvironment, composition of the gut microbiota, and broad limitations of current immunotherapeutic approaches. New approaches are positioned to improve the immune response and increase the efficacy of immunotherapies, highlighting the challenges that the current global COVID-19 pandemic places on the present state of immunotherapy.
ABSTRACT
The occupant density in buildings is one of the major and overlooked parameters affecting the energy consumption and virus transmission risk in buildings. HVAC systems energy consumption is highly dependent on the number of occupants. Studies on the transmission of COVID-19 virus have indicated a direct relationship between occupant density and COVID-19 infection risk. This study aims to seek the optimum occupant distribution patterns that account for the lowest number of infected people and minimum energy consumption. A university building located in Tehran has been chosen as a case study, due to its flexibility in performing various occupant distribution patterns. This multi-objective optimization problem, with the objective functions of energy consumption and COVID-19 infected people, is solved by NSGA-II algorithm. Energy consumption is evaluated by EnergyPlus, then it is supplied to the algorithm through a co-simulation communication between EnergyPlus and MATLAB. Results of this optimization algorithm for 5 consequent winter and summer days, represent optimum occupant distribution patterns, associated with minimum energy consumption and COVID-19 infected people for winter and summer. Building air exchange rate, class duration, and working hours of the university, as the COVID-19 controlling approaches were studied, and promising results have been obtained. It was concluded that an optimal population distribution can reduce the number of infected people by up to 56% and energy consumption by 32%. Furthermore, it was concluded that virtual learning is an excellent approach in universities to control the number of infections and energy consumption.