Plos One
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Renal involvement in Systemic Lupus Erythematous (SLE) patients is one of the leading causes of morbidity and a significant contributor to mortality. It's estimated that nearly 50% of SLE individuals develop kidney disease in the first year of the diagnosis. Class IV lupus nephritis (LN-IV) is the class of lupus nephritis most common in Colombian patients with SLE. ⋯ We identified 24 circulating microRNAs with differential abundance between LN-IV and CTL groups, fourteen of these microRNAs are described for the first time to lupus nephritis (hsa-miR-589-3p, hsa-miR-1260b, hsa-miR-4511, hsa-miR-485-5p, hsa-miR-584-5p, hsa-miR-543, hsa-miR-153-3p, hsa-miR-6087, hsa-miR-3942-5p, hsa-miR-7977, hsa-miR-323b-3p, hsa-miR-4732-3p and hsa-miR-6741-3p). These changes in the abundance of miRNAs could be interpreted as alterations in the miRNAs-mRNA regulatory network in the pathogenesis of LN, preceding the clinical onset of the disease. The findings thus contribute to understanding the disease process and are likely to pave the way towards identifying disease biomarkers for early diagnosis of LN.
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Emergency department (ED) crowding leads to prolonged emergency department length of stay (ED-LOS) and adverse patient outcomes. No uniform definition of ED crowding exists. Several scores have been developed to quantify ED crowding; the best known is the Emergency Department Work Index (EDWIN). Research on the EDWIN is often applied to limited settings and conducted over a short period of time. ⋯ The mEDWIN was applicable at a Dutch ED. The mEDWIN was able to identify fluctuations in ED occupancy. In addition, the mEDWIN had high discriminatory power for identification of a busy ED, when compared with the occupancy rate.
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The anaphylatoxin receptor C5aR1 plays an important role not only in innate immune responses, but also in bone metabolism and fracture healing, being highly expressed on immune and bone cells, including osteoblasts and osteoclasts. C5aR1 induces osteoblast migration, cytokine generation and osteoclastogenesis, however, the exact role of C5aR1-mediated signaling in osteoblasts is not entirely known. Therefore, we hypothesized that osteoblasts are essential target cells for C5a and that fracture healing should be disturbed in mice with an osteoblast-specific C5aR1 overexpression (Col1a1-C5aR1). ⋯ C5aR1 signaling in osteoblasts might possibly affect RANKL/OPG balance, leading to increased bone resorption. Additional trauma significantly impaired fracture healing, particularly in Col1a1-C5aR1 mice. In conclusion, the data indicate that C5aR1 signaling in osteoblasts plays a detrimental role in bone regeneration after fracture.
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On sedation motivated by the clinical needs for safety and reliability, recent studies have attempted to identify brain-specific signatures for tracking patient transition into and out of consciousness, but the differences in neurophysiological effects between 1) the sedative types and 2) the presence/absence of surgical stimulations still remain unclear. Here we used multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) measurements to observe electrical and hemodynamic responses during sedation simultaneously. Forty healthy volunteers were instructed to push the button to administer sedatives in response to auditory stimuli every 9-11 s. ⋯ Interestingly, the level of consciousness was switched on/off at significantly higher effect-site concentrations of sedatives in the brain according to the use of surgical stimuli, but the spatio-temporal EEG patterns were similar, regardless of the sedative used. We also observed sudden phase shifts in fronto-parietal connectivity at the LOC and the ROC as critical points. fNIRS measurement also revealed mild hemodynamic fluctuations. Compared with general anesthesia, our results provide insights into critical hallmarks of sedative-induced (un)consciousness, which have similar spatio-temporal EEG-fNIRS patterns regardless of the stage and the sedative used.
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The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. ⋯ Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy.