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Psoriasis is a recurrent, chronic, immune-mediated, systemic inflammatory disease of the skin, joints, and other organic systems. After atopic dermatitis, chronic stationary psoriasis is the most common inflammatory skin disease, affecting an average of 2–4% of the world's population. The disease carries a significant burden due to its numerous comorbidities and the major impact on patients' social and emotional aspects of life. According to current knowledge, psoriasis is a multifactorial disease that occurs in genetically predisposed individuals under various environmental factors, which trigger an immune response disorder with a series of complex inflammatory cascades. The disease is initiated and maintained by mutual interaction of the innate and adaptive immune cells, primarily dendritic cells, T lymphocytes, and keratinocytes, whose leading role alternates at different stages of the disease, consisting mainly in the IL-23/Th17 pathway. Inflammatory events result in consequent epidermal and dermal changes and evolution of the characteristic psoriatic phenotype, respectively. This paper aims to present a comprehensive overview of current knowledge on psoriasis genetic and environmental etiological factors, immunopathogenesis, and the leading cellular and cytokine participants in the inflammatory pathways of this disease. [ABSTRACT FROM AUTHOR]

Simple Summary: Osteosarcoma is the most common primary bone tumor and has a poor prognosis. Therefore, it is important to understand the mechanism of the development of osteosarcoma to overcome therapy resistance. Several mathematical models have been developed to study the initiation and progression of many cancer types. However, there are currently no mathematical models for the progression of osteosarcoma, to the best of our knowledge. In this work, we develop a data-driven mathematical model to analyze the impact of the immune cell interactions on the growth of osteosarcoma tumors that have distinct immune patterns. Our model provides a foundation for investigating the effect of various treatments on the dynamics of key players in the primary tumor, including immune cells and cytokines, and ultimately the whole tumor. As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN- γ , switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments. [ABSTRACT FROM AUTHOR]