Fallonwade3806

Background Adults with celiac disease (CeD) show low bone mineral density (BMD) and high fracture risk. CeD guidelines suggest measurements of serum minerals and vitamin D. However, studies on vitamin levels in CeD patients are contradictory. Aim To investigate in CeD, 25-hydroxy-vitamin D [25(OH)D], 1,25-dihydroxy-vitamin D [1,25(OH)2D], and related analytes and to evaluate their relationships to peripheral BMD as assessed by peripheral quantitative computed tomography (pQCT). Methods Gluten-free diet (GFD)-treated, and untreated adult CeD patients naïve to vitamin D and calcium supplementation underwent measurements of serum 25(OH)D, 1,25(OH)2D, parathyroid hormone (PTH), total calcium, phosphate, and of radius BMD by pQCT. Results Complete data were collected in 105 patients for lab tests and 87 patients for BMD. For lab tests, untreated CeD differed from treated CeD for 22.0% lower serum 25(OH)D (p = 0.023), 42.5% higher serum PTH (p 0.13). Telaglenastat Independent correlates of diaphyseal cortical BMD were GFD treatment and body mass index (p less then 0.05). Conclusions Data indicated that, compared to CeD patients on a gluten-free diet, untreated adult CeD patients at diagnosis had lower 25(OH)D, higher PTH, and higher 1,25(OH)2D in the absence of difference in serum calcium and phosphorus. 25(OH)D and 1,25(OH)2D, even below the normal range, were not associated with BMD. Our findings do not support the use of vitamin D supplementation for all CeD adults.Immune response during sepsis is characterized by hyper-inflammation followed by immunosuppression. The crucial role of macrophages is well-known for both septic stages, since they are involved in immune homeostasis and inflammation, their dysfunction being implicated in immunosuppression. The cholinergic anti-inflammatory pathway mediated by macrophage α7 nicotinic acetylcholine receptor (nAChR) represents possible drug target. Although α7 nAChR activation on macrophages reduces the production of proinflammatory cytokines, the role of these receptors in immunological changes at the cellular level is not fully understood. Using α7 nAChR selective agonist PNU 282,987, we investigated the influence of α7 nAChR activation on the expression of cytokines and, for the first time, of the macrophage membrane markers cluster of differentiation 14 (CD14), human leukocyte antigen-DR (HLA-DR), CD11b, and CD54. Application of PNU 282,987 to THP-1Mϕ (THP-1 derived macrophages) cells led to inward ion currents and Ca2+ increase in cytoplasm showing the presence of functionally active α7 nAChR. Production of cytokines tumor necrosis factor-α (TNF-α), interleukin (IL)-6, and IL-10 was estimated in classically activated macrophages (M1) and treatment with PNU 282,987 diminished IL-10 expression. α7 nAChR activation on THP-1Mϕ, THP-1M1, and monocyte-derived macrophages (MDMs) increased the expression of HLA-DR, CD54, and CD11b molecules, but decreased CD14 receptor expression, these effects being blocked by alpha (α)-bungarotoxin. Thus, PNU 282,987 enhances the macrophage-mediated immunity via α7 nAChR by regulating expression of their membrane receptors and of cytokines, both playing an important role in preventing immunosuppressive states.Global climate change and urban heat islands have generated heat stress in summer, which does harm to people's health. The outdoor public commercial pedestrianized zone has an important role in people's daily lives, and the utilization of this space is evaluated by their outdoor thermal comfort and health. Using microclimatic monitoring and numerical simulation in a commercial pedestrianized zone in Tai Zhou, China, this study investigates people's outdoor thermal comfort in extreme summer heat. The final results provide a comprehensive system for assessing how to improve outdoor human thermal health. Under the guidance of this system, local managers can select the most effective strategy to improve the outdoor thermal environment.Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%.In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks' priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.