Nephropathia Epidemica (NE), endemic to several Volga regions of Russia, including the Republic of Tatarstan (RT) and the Republic of Mordovia (RM), is a mild form of hemorrhagic fever with renal syndrome caused by infection with rodent-borne orthohantaviruses. Although NE cases have been reported for decades, little is known about the hantavirus strains associated with human infection in these regions. There is also limited understanding of the pathogenesis of NE in the RT and the RM. To address these knowledge gaps, we conducted comparative analyses of patients with NE in the RT and the RM. Clinical symptoms were more severe in patients with NE from the RM with longer observed duration of fever symptoms and hospitalization. Analysis of patient sera showed changes in the levels of numerous cytokines, chemokines, and matrix metalloproteases (MMPs) in patients with NE from both the RT and the RM, suggesting leukocyte activation, extracellular matrix degradation, and leukocyte chemotaxis. Interestingly, levels of several cytokines were distinctly different between patients NE from the RT when compared with those from the RM. These differences were not related to the genetic variation of orthohantaviruses circulating in those regions, as sequence analysis showed that Puumala virus (PUUV) was the causative agent of NE in these regions. Additionally, only the “Russia” (RUS) genetic lineage of PUUV was detected in the serum samples of patients with NE from both the RT and the RM. We therefore conclude that differences in serum cytokine, chemokine, and MMP levels between the RT and the RM are related to environmental factors and lifestyle differences that influence individual immune responses to orthohantavirus infection.

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Every individual's perception of multimedia content varies based on their interpretation. Therefore, it is quite challenging to predict likability of any multimedia just based on its content. This paper presents a novel system for analysis of facial expressions of subject against the multimedia content to be evaluated. First, we developed a dataset by recording facial expressions of subjects under uncontrolled environment. These subjects are volunteers recruited to watch the videos of different genre, and provide their feedback in terms of likability. Subject responses are divided into three categories: Like, Neutral and Dislike. A novel multimodal system is developed using the developed dataset. The model learns feature representation from data based on the three provided categories. The proposed system contains ensemble of time distributed convolutional neural network, 3D convolutional neural network, and long short term memory networks. All the modalities in proposed architecture are evaluated independently as well as in distinct combinations. The paper also provides detailed insight into learning behavior of the proposed system.