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Cancer is one of the main causes of mortality and morbidity worldwide. Using a single treatment plan for all of the patients is not efficient due to the biological heterogeneity in the individuals. In order to personalize the therapy plan, tumors behavior in each patient must be understood. For this purpose clinical information of the patients are used. Mathematical modeling has gained significant interest in tumor growth investigations, due to its higher flexibility than the other methods. Mass effect and the reaction terms are the key parameters that are investigated in this paper. This is the first time that the effects of these parameters are considered in brain tumor growth modeling and there are few researches that have used only MR images in this area. The mathematical models are used for predicting the growth of brain tumors based on personal MRIs and introducing intracellular fraction into the model. Results of the comparisons show that considering the mass effect in the growth model would improve the prediction. Furthermore, it is necessary to define the optimum formulation for reaction term according to patients' medical information, to be used in the personalized model of tumor growth prediction. The represented approach can be used as a basis for personalizing the therapy plan in patients with brain tumors.

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Cell culture is a vital method in biological and biomedical research. The global cell culture market, valued at around USD 26.54 billion in 2023, is projected to surpass USD 63.60 billion by 2032. While two-dimensional cell culture has led to significant advancements in biology, its simplicity does not accurately reflect the complex in vivo environment. This can result in misleading data with limited predictive value for in vivo applications, prompting increased interest in three-dimensional (3D) cultivation methods. The 3D cell culture mimics the behavior and organization of cells in vivo by emulating the extracellular matrix (ECM), providing better insights into 3D interactions among cells and between cells and the matrix, thus reconstructing their natural microenvironment. In this review we will outline the various types of 3D models (include spheroids, organoids, bio-printed structures, and tissue chips). Subsequently, we will examine the methodologies employed to develop 3D culture systems (include four category methods). Lastly, the practical applications and challenges of these 3D models will be addressed. The future research will likely concentrate on incorporating cutting-edge technologies to improve the reproducibility and applicability of 3D models in research.