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.

Legumes comprise one of the world’s largest, most diverse, and economically important plant families, known for their nutritional and medicinal benefits. Legumes are susceptible to a wide range of diseases, similar to other agricultural crops. Diseases have a considerable impact on the production of legume crop species, resulting in large yield losses worldwide. Due to continuous interactions between plants and their pathogens in the environment and the evolution of new pathogens under high selection pressure; disease resistant genes emerge in plant cultivars in the field against those pathogens or disease. Thus, disease resistant genes play critical roles in plant resistance responses, and their discovery and subsequent use in breeding programmes aid in reducing yield loss. The genomic era, with its high-throughput and low-cost genomic tools, has revolutionised our understanding of the complex interactions between legumes and pathogens, resulting in the identification of several critical participants in both the resistant and susceptible relationships. However, a substantial amount of existing information about numerous legume species has been disseminated as text or is preserved across fractions in different databases, posing a challenge for researchers. As a result, the range, scope, and complexity of these resources pose challenges to those who manage and use them. Therefore, there is an urgent need to develop tools and a single conjugate database to manage genetic information for the world’s plant genetic resources, allowing for the rapid incorporation of essential resistance genes into breeding strategies. Here, developed the first comprehensive database of disease resistance genes named as LDRGDb - LEGUMES DISEASE RESISTANCE GENES DATABASE comprises 10 legumes [Pigeon pea (Cajanus cajan), Chickpea (Cicer arietinum), Soybean (Glycine max), Lentil (Lens culinaris), Alfalfa (Medicago sativa), Barrelclover (Medicago truncatula), Common bean (Phaseolus vulgaris), Pea (Pisum sativum),Faba bean (Vicia faba), and Cowpea (Vigna unguiculata)]. The LDRGDb is a user-friendly database developed by integrating a variety of tools and software that combine knowledge about resistant genes, QTLs, and their loci, with proteomics, pathway interactions, and genomics (https://ldrgdb.in/).