This work presents an application of Wireless Sensor Network (WSN) of random access with one-way transmission to the monitoring of hospital patients. In the paper, we consider WSN single-hop network using one single radio frequency, such that all nodes are divided into several groups depending on the average time between the transmission due to the different state of health of patients. We apply the Poisson Arrivals See Time Averages (PASTA) to modeling of WSN. We present formula for the probability of collisions which has been verified by simulation studies of network.
The rapid growth in availability of new biomedical systems and devices capable of acquiring biosignals for disease diagnosis and health monitoring require rigorous processing. Biomedical research by nature depends on integrated problem solving software environment and often involves people located at different geographical positions. The reusability of different personalized tools are limited due to the complex architectural constrains and restricted interoperability among different devices mostly requiring individual tools. Thus, new computational environments are required to provide robust, user friendly, and scalable systems capable of interoperate seamlessly. This work proposes a service oriented architecture (SOA) based web application model for collaborative biosignal analysis and research to facilitate the seamless integration of various existing tools and different Health Information Systems.
Aim Many data showed a role of inflammation and dysfunction of immune system as important factors in the risk of schizophrenia. The TNFR2 receptor is a molecule that adapts to both areas. Tumor necrosis factor receptor 2 (TNFR2) is a receptor for the TNF-α cytokine which is a strong candidate gene for schizophrenia. The serum level of TNFR2 was significantly increased in schizophrenia and associated with more severe symptoms of schizophrenia. Methods We examined the association of the three single nucleotide polymorphisms (rs3397, rs1061622, and rs1061624) in TNFR2 gene with a predisposition to and psychopathology of paranoid schizophrenia in Caucasian population. The psychopathology was measured by a five-factor model of the PANSS scale. We also assessed a haplotype analysis with the -308G/A of TNF-α gene. Results Our case-control study (401 patients and 657 controls) revealed that the genetic variants of rs3397, rs1061622, and rs1061624 in the TNFR2 gene are associated with a higher risk of developing schizophrenia and more severe course in men. However, the genotypes with polymorphic allele for rs3397 SNP are protective for women. The rs1061624 SNP might modulate the appearance of the disease in relatives of people with schizophrenia. The CTGG haplotype build with tested SNPs of TNFR2 and SNP -308G/A of TNF-α has an association with a risk of schizophrenia in Caucasian population depending on sex. Our finding is especially true for the paranoid subtypes of schizophrenia.
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