Hemophagocytic lymphohistiocytosis (HLH) is a hyperinflammatory syndrome caused by excessive activation of lymphocytes and macrophages, leading to cytokine storm. Infection-associated HLH is most common, and Epstein-Barr virus is the leading triggers. Quick diagnosis is essential for starting the treatment before irreversible damage. We report a case of 16-year-old boy who presented with unremitted fever, jaundice, and erythematous maculopapular rash all over the body. Investigations showed thrombocytopenia, hyperferritinemia, hypertriglycemia, and the bone marrow biopsy showed hemophagocytosis. Epstein-Barr virus antibody was positive. He responded to chemotherapy as per the HLH-2004 protocol and supportive treatment, and was discharged without complication on day 17.
Mobile users spend a tremendous amount of time surfing multimedia contents over the Internet to pursue their interests. A resource-constrained smart device demands more intensive computing tasks and lessens the battery life. To address the resource limitations (i.e., memory, lower maintenance cost, easier access, computing tasks) in mobile devices, mobile cloud computing is needed. Several approaches have been proposed to confront the challenges of mobile cloud computing, but difficulties still remain. However, in the coming years, context collecting, processing, and interchanging the results on a heavy network will cause vast computations and reduce the battery life in mobiles. In this paper, we propose a "context-based intelligent multimedia system" (CIMS) for ubiquitous cloud computing. The main goal of this research is to lessen the computing percentage, storage complexities, and battery life for mobile users by using pervasive cloud computing. Moreover, to reduce the computing and storage concerns in mobiles, the cloud server collects several groups of user profiles with similarities by executing K-means clustering on users' data (context and multimedia contents). The distribution process conveys real-time notifications to smartphone users, according to what is stated in his/her profile. We considered a mobile cloud offloading system, which decides the offloading actions to/from cloud servers. Context-aware decision-making (CAD) customizes the mobile device
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