Epstein-Barr Virus-Positive Leiomyosarcoma in Immunocompetent Patients.

OBJECTIVE: Epstein-Barr Virus-Associated Smooth Muscle Tumor (EBV-SMT) is a rare tumor with a higher rate of occurrence in unusual locations in the setting of immunodeficiency. In this study, we evaluated a cohort of ordinary leiomyosarcomas (LMS) for the presence of EBV and described the clinicopathological features deviating from routinely diagnosed cases of EBV-SMT. MATERIAL AND METHOD: The sections of tissue microarrays including 93 classical LMS occurring in various locations were hybridized with EBER and stained for LMP1 antibody using the Leica Bond Autostainer. EBV real-time PCR assay was performed in 2 EBER-positive cases. RESULTS: Among the 93 LMS cases, 2 non-uterine cases (2.2%) were positive for EBER and negative for LMP1, and were referred to as `EBV-positive LMS`. Both were females in their 6th decade without immunosuppression. EBV real-time PCR assay revealed the presence of EBV in one of the cases. Tumors were located in the pancreas and chest wall. Morphologically, tumors were rather myxoid, multinodular, and composed of long fascicles of spindle cells with intermediate- to high-grade features. High mitotic activity and focal necrosis were present, whereas no accompanying lymphocytes were detected. One of the patients developed metastatic disease after 3 years. CONCLUSION: EBV-positive LMS occurring in immunocompetent patients has features distinct from classical EBV-SMT seen in immunosuppressed patients.

Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in the wild. The fundamental inspiration in the design of GAO is derived from the hunting strategy of giant armadillos in moving towards prey positions and digging termite mounds. The theory of GAO is expressed and mathematically modeled in two phases: (i) exploration based on simulating the movement of giant armadillos towards termite mounds, and (ii) exploitation based on simulating giant armadillos' digging skills in order to prey on and rip open termite mounds. The performance of GAO in handling optimization tasks is evaluated in order to solve the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that GAO is able to achieve effective solutions for optimization problems by benefiting from its high abilities in exploration, exploitation, and balancing them during the search process. The quality of the results obtained from GAO is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that GAO presents superior performance compared to competitor algorithms by providing better results for most of the benchmark functions. The statistical analysis of the Wilcoxon rank sum test confirms that GAO has a significant statistical superiority over competitor algorithms. The implementation of GAO on the CEC 2011 test suite and four engineering design problems show that the proposed approach has effective performance in dealing with real-world applications.