University course scheduling (UCS) is one of the most important and time-consuming issues that all educational institutions face yearly. Most of the existing techniques to model and solve UCS problems have applied approximate methods, which are different in terms of efficiency, performance, and optimization speed. Accordingly, this research aims to apply an exact optimization method to provide an optimal solution to the course scheduling problem. In other words, in this research, an integer programming model is presented to solve the USC problem. In this model, hard and soft constraints include the facilities of classrooms, courses of different levels and compression of students' curriculum, courses outside the faculty and planning for them, and the limited time allocated to the professors. The objective is to maximize the weighted sum of allocating available times to professors based on their preferences in all periods. To evaluate the presented model's feasibility, it is implemented using the GAMS software. Finally, the presented model is solved in a larger dimension using a real data set from a college in China and compared with the current program in the same college. The obtained results show that considering the mathematical model's constraints and objective function, the faculty courses' timetable is reduced from 4 days a week to 3 working days. Moreover, master courses are planned in two days, and the courses in the educational groups do not interfere with each other. Furthermore, by implementing the proposed model for the real case study, the maximum teaching hours of the professors are significantly reduced. The results demonstrate the efficiency of the proposed model and solution method in terms of optimization speed and solution accuracy.

Background: Tumor microenvironment (TME) plays a crucial role in virtually every aspect of tumorigenesis of glioblastoma multiforme (GBM). The dysfunctional TME promotes drug resistance, disease recurrence and distant metastasis. Recent evidence indicates that exosomes released by stromal cells within TME may promote oncogenic phenotypes via transferring signaling molecules such as cytokines, proteins and microRNAs. Results: In this study, clinical GBM samples were collected and analyzed. We found that GBM-associated macrophages (GAMs) secreted exosomes which were enriched with oncomiR-21. Co-culture of GAMs (and GAM derived exosomes) and GBM cell lines resulted in the increased GBM cells’ resistance against temozolomide (TMZ) by upregulating pro-survival gene, PDCD4 and stemness markers Sox2, STAT3, Nestin and miR-21-5p and increased M2 cytokines, IL-6 and TGF-β1 secreted by GBM cells, promoting the M2 polarization of GAMs. Subsequently, pacritinib treatment suppressed GBM tumorigenesis and stemness; more importantly, pacritinib-treated GBM cells showed markedly reduced ability to secret M2 cytokines and reduced miR-21 enriched exosomes secreted by GAMs. Pacritinib-mediated effects were accompanied by a reduction of oncomiR miR-21-5p, by which tumor suppressor PDCD4 was targeted. We subsequently established a patient-derived xenograft models where mice bore patient GBM and GAMs. The treatment of pacritinib, and the combination of pacritinib/TMZ appeared to significantly reduce tumorigenesis of GBM/GAM PDX mice, overcome TMZ-resistance, and M2 polarization of GAMs. Conclusion: In summation, we showed that potential of pacritinib alone or in combination with TMZ for suppressing GBM tumorigenesis via modulating STAT3/miR-21/PDCD4 signaling. Further investigations are warranted for adopting pacritinib for the treatment of TMZ-resistant GBM in the clinical settings.