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The emergence of the Covid-19 pandemic has drawn great attention to vulnerable people affected by major diseases. Among them, Alzheimer's disease (AD) is the most prevalent disease. However, a long-standing challenge is to achieve early diagnosis of AD by detecting biomarkers such as amyloid beta (Aβ42), thus avoiding the labor of specialized hospital personnel and the high cost of imaging examinations using positron emission tomography. In this paper, we report a straightforward approach to realize a non-invasive lab-around fiber (LaF) optical sensor for AD biomarker detection, which is based on a tilted fiber Bragg grating (TFBG) combined with a nanoscale metallic thin film. We successfully demonstrated the detection of Aβ42 in complex biological matrices with a detection limit of 5 pg/mL. Therefore, our TFBG-SPR biosensor platform enables large-scale early disease screening and has great potential for clinical applications in early AD diagnosis. © 2022 IEEE.
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[Purposes[ Through the cluster analysis and the SEIR Model established, to study the trend of COVID-19 outbreak in all the regions of Beijing and analyze the prevention and control effects to provide basis for its prevention and control. [Methods] We collected the data related to COVID-19 outbreak in Beijing from January 21 to March 31, 2020, used SPSS19.0 and MetaboAnalysis software to conduct a cluster analysis on the outbreak data of the cases in all the regions of Beijing, the cases from others places to Beijing and the imported cases and built a SEW Model through Matlab to simulate the development trend of COVID-19 outbreak in Beijing. [Results] According to the cluster analysis, the high incidence was mainly distributed in Chaoyang District, Haidian District, Changping District, Fengtai District, Dazing District, Xicheng District and among the people from other places to Beijing;according to the SEIR Model, in mid-to-late February, the inflection point of the local outbreak appeared but there were still the enormous risks of imported cases and the prevention and control was still in a severe situation. [Conclusion] At present, the outbreak situation has tended to be gentle but the comprehensive and strict prevention and control shall still be strengthened to strictly prevent the imported cases and resolutely curb the spread of the outbreak.
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OBJECTIVE: To investigate the risk factors for acute myocardial injury in coronavirus disease 2019 (COVID-19) patients. METHODS: This is a retrospective analysis of a COVID-19 cohort, in which 149 confirmed COVID-19 patients enrolled were divided into the group of myocardial injury (19 cases) and the group of non-myocardial injury (130 cases). Myocardial injury was defined according to Fourth universal definition of myocardial infarction released by European Society of Cardiology (ESC) in 2018, that cardiac troponin (cTn) was above 99th percentile of the reference level. Clinical information and results of laboratory tests of the eligible patients were collected. Factors associated with myocardial injury in COVID-19 patients were evaluated. RESULTS: Compared with the group of non-injury, the patients in the group of injury were older and had a larger proportion of severe or critical cases (P < 0.05), higher respiratory rate and lower percutaneous oxygen saturation (SpO(2)) without oxygen therapy on admission (P < 0.05). All inflammatory indexes except for tumor necrosis factor α (TNF-α) showed significant elevation in the patients of the group of injury (P < 0.05). Analyzed by Spearman correlation test, we showed that the levels of circulatory cTnI were in positive correlation with the levels of high-sensitivity C-reactive protein (hs-CRP), ferritin, receptor of interleukin-2 (IL-2R), interleukin-6 (IL-6) and interleukin-8 (IL-8) (ρ > 0, P < 0.05). Lower SpO(2) without oxygen therapy on admission (OR: 0.860, 95%CI: 0.779-0.949, P=0.003) and higher plasma IL-6 levels (OR: 1.068, 95%CI: 1.019-1.120, P=0.006) were independent risk factors for acute myocardial injury in the patients with COVID-19 by multivariate Logistic regression analyses. CONCLUSION: Hypoxic state and inflammation may play a key role in the pathogenesis of acute myocardial injury in COVID-19 patients.
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Purpose: Use artificial intelligence technology to identify the characteristics of covid-19 in CT images, quickly screen COVID-19 patients, achieve rapid diversion and treatment of suspected patients, reduce the risk of infection and control the spread of the disease.Materials and methods: This article combines deep learning target detection and image classification methods to study the CT images of COVID-19 patients. By extracting and analyzing the features of lesions in different periods, a new detection model of covid-19 based on time-spatial sequence convolution is obtained. The algorithm is based on a recurrent neural network structure and a 2D convolutional layer structure.Results: The spatiotemporal sequence convolution kernel based on time and space attributes can effectively extract the latent image semantic features of multiple image data of COVID-19 patients. By comparing with Faster RCNN, YOLO3 and SSD algorithm models, the detection method proposed in this paper can obtain more accurate comprehensive detection results.Conclusion: The time-spatial sequence convolution model can quickly complete the automatic detection of COVID-19 and improve the efficiency of preliminary diagnosis. By correlating images from different stages of the same patient, more accurate auxiliary preliminary screening results can be obtained. © 2020 IEEE.