Ovarian cancer is one of the most common cancers in women in the world. It is also the 5th top cause of cancer-related death in the world. Despite chemotherapy being the primary treatment along with surgery, patients frequently suffer from a recurrence of ovarian cancer within a few years of the original treatment. The recurring nature of OC, therefore, necessitates the development of novel therapeutic interventions that can effectively tackle this disease. Immunotherapy has lately been found to offer significant clinical advantages. Some of the immunotherapy techniques being studied for ovarian cancer include adoptive T-cell treatment, immune checkpoint inhibition, and oncolytic virus. However, the most efficient way to increase longevity is through a combination of immunotherapy strategies with other disease therapeutic approaches such as radiotherapy, chemotherapy, and PARPi in additive or synergistic ways. To provide a more comprehensive insight into the current immunotherapies explored, this paper explores newly developed therapeutics for the disease with an emphasis on current outstanding immunotherapy. The current state of our understanding of how the disease interacts with host cells, current therapy options available, various advanced treatments present and the potential for combinatorial immuno-based therapies in the future have also been explored.

Diabetes mellitus is a long-term condition characterized by hyperglycaemia resulting in the emergence of a variety of health problems, such as diabetic retinopathy, kidney failure, dental problems, heart disease, nerve damage, etc.; and is governed by several factors, i.e. biological, genetics, food habits, sedentary lifestyle choices, poor diets and environments, etc. According to the recent morbidity figures, the global diabetic patient population is anticipated to reach 642 million by 2040, implying that one out of every ten people will be diabetic. The data generation and AI based methods—i.e., SVM, kNN, decision tree, Baysian method in medical health –have facilitated the effective prediction and classification of voluminous size of biological data of different types of BMI, skin thickness, glucose, age, tongue and retinal images apart from Omics data, for early diagnostics. The chapter summarizes the basic methods and applications of machine learning and soft computing techniques for diabetes diagnosis and prediction with limitations of integrative approaches.