Sarcomas are rare malignant tumors that arise from transformed cells of mesenchymal origin. Despite the progress in diagnosis and treatment, sarcomas have a high mortality rate due to local recurrence, metastasis, and the development of drug resistance to chemotherapy. New models for sarcoma research are required to further understand the disease and to develop new therapies. In vitro sarcoma modeling is challenging because of significant genetic heterogeneities, diverse pathological, and overlapping clinical characteristics. Studies on the mechanisms of recurrence, metastasis, and drug resistance in sarcoma have resulted in the generation of novel three-dimensional (3D) culture models for sarcoma research. 3D culture models aim to recapitulate the tumor microenvironment that plays a critical role in the pathogenesis of sarcoma using biomaterial scaffolds of natural biological materials and artificial polymers. An ideal 3D culture model can properly mimic not only the microenvironment, oncogenesis, and maintenance of sarcoma cell growth, but also imitate the interactions between cells and to the extracellular matrix. More recently, 3D cell culture has been used to research the biological behavior and mechanism of chemotherapy and radiotherapy resistance in different sarcoma models. Ultimately, findings using 3D models that more accurately reflect human sarcoma biology are likely to translate into improved clinical outcomes. In this review, we discuss the most recent advances of 3D culture technologies in sarcoma research and emerging clinical applications.
The factors governing the impact sensitivity (H(50)) of nitrobenzenes and saturated nitro compounds were studied. It was observed that the oxygen balances (OB(100)) and nitro group charge (Q(NO2)) are two important factors influencing the stability of these nitro compounds. Employing the square of nitro group charge (Q(NO2)2) and OB(100) as the parameters, a good quantitative model was built for predicting H(50) of the above two sorts of nitro compounds. The predictive ability of the model was assessed by the cross-validation method (i.e., leave-one-out cross-validation). The cross-validation result shows that the model is significant and stable, and the predicted accuracy is within 0.21 m. This quantitative model may be a useful tool for the design of high-energy-density materials.
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