ABSTRACT
Methods to prevent collisions between people to avoid traffic accidents are receiving significant attention. To measure the position in the non-line-of-sight (NLOS) area, which cannot be directly visually recognized, position-measuring methods use wireless-communication-type GPS and propagation characteristics of radio signals, such as received signal strength indication (RSSI). However, conventional position estimation methods using RSSI require multiple receivers, which decreases the position estimation accuracy, owing to the presence of surrounding buildings. This study proposes a system to solve this challenge using a receiver and position estimation method based on RSSI MAP simulation and particle filter. Moreover, this study utilizes BLE peripheral/central functions capable of advertising as the transmitter/receiver. By using the advertising radio waves, our method provides a framework for estimating the position of unspecified transmitters. The effectiveness of the proposed system is evaluated in this study through simulations and experiments in actual environments. We obtained an error average of the distance to be 1.6 m from the simulations, which shows the precision of the proposed method. In the actual environment, the proposed method showed an error average of the distance to be 3.3 m. Furthermore, we evaluated the accuracy of the proposed method when both the transmitter and receiver are in motion, which can be considered as a moving person in the outdoor NLOS area. The result shows an error of 4.5 m. Consequently, we concluded that the accuracy was comparable when the transmitter is stationary and when it is moving. Compared with conventional path loss, the model can measure distances of 3 m to 10 m, whereas the proposed method can estimate the "position" with the same accuracy in an outdoor environment. In addition, it can be expected to be used as a collision avoidance system that confirms the presence of strangers in the NLOS area.
Subject(s)
Algorithms , Radio Waves , Computer Simulation , HumansABSTRACT
INTRODUCTION: The spread of coronavirus disease 2019 (COVID-19) is having a profound effect on global health. In this study, we investigated early predictors of severe prognosis from the perspective of liver injury and risk factors for severe liver injury in patients with COVID-19. METHODS: We examined prognostic markers and risk factors for severe liver injury by analyzing clinical data measured throughout the course of the illness and the disease severity of 273 patients hospitalized for COVID-19. We assessed liver injury on the basis of aminotransferase concentrations and fibrosis-4 (FIB-4) index on admission, peak aminotransferase concentration during hospitalization, aminotransferase peak-to-average ratio, and albumin and total bilirubin concentrations. Furthermore, we analyzed age, aspartate aminotransferase (AST) concentrations, FIB-4 index on admission, hypertension, diabetes mellitus (DM), dyslipidemia, cerebral infarction, myocardial infarction, and body mass index as mortality risk factors. RESULTS: We identified advanced age as a risk factor. Among biochemical variables, AST concentration and FIB-4 index on admission were associated with high mortality. AST on admission and peak AST during hospitalization were significantly higher in the non-surviving (n = 45) than the discharged group (n = 228). Multivariable Cox hazards analyses for mortality showed significant hazard ratios for age, peak AST, and FIB-4 index on admission (p = 0.0001 and 0.0108, respectively), but not in a model including AST and FIB-4 index on admission. Furthermore, the AST peak was significantly higher among non-surviving patients with DM than in those without DM. CONCLUSIONS: We found that advanced age, high AST, and FIB-4 index on admission and a higher peak AST during hospitalization are risk factors for poor COVID-19 prognosis. Furthermore, DM was a risk factor for exacerbation of liver injury among non-surviving patients. The AST concentration and FIB-4 index should be assessed periodically throughout hospitalization, especially in patients with high AST values on admission and those with DM.
ABSTRACT
BACKGROUND: The effects of high-intensity immunity on coronavirus disease 2019 (COVID-19) remain unclear. Antibodies against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are preferentially induced in inpatients with COVID-19 compared with outpatients with milder disease, and immunosuppression is the standard therapy for severe cases. This study investigated the relationship between cross-reactive antibody production against seasonal human coronavirus and the clinical course of COVID-19. METHODS: Among the immunogenic epitopes of SARS-CoV-2, conserved peptides in human coronavirus OC43 were searched and synthesized. Enzyme-linked immunosorbent assay was designed to detect antibodies against synthesized peptides. Antibody titres against S2' cleavage site epitopes near fusion peptides of SARS-CoV-2 and OC43 were determined in the sera of 126 inpatients with COVID-19. The correlation between antibody titres and clinical data was analysed. RESULTS: Inpatients with COVID-19 who produced antibodies against OC43 did not develop severe or fatal pneumonia. Antibody titres against the corresponding epitope of SARS-CoV-2 did not differ between inpatients with severe and mild COVID-19. Antibody titres against the OC43 epitope increased more than those against SARS-CoV-2 during the first 2 weeks of COVID-19. CONCLUSIONS: Immunity to seasonal human coronavirus OC43 effectively enhanced recovery from COVID-19. Detecting cross-reactive antibodies to OC43 may help to predict prognosis for patients with COVID-19.