The COVID-19 pandemic continues to have an unprecedented impact on societies and economies worldwide. There remains an ongoing need for high-performance SARS-CoV-2 tests which may be broadly deployed for infection monitoring. Here we report a highly sensitive single molecule array (Simoa) immunoassay in development for detection of SARS-CoV-2 nucleocapsid protein (N-protein) in venous and capillary blood and saliva. In all matrices in the studies conducted to date we observe >98% negative percent agreement and >90% positive percent agreement with molecular testing for days 1–7 in symptomatic, asymptomatic, and pre-symptomatic PCR+ individuals. N-protein load decreases as anti-SARS-CoV-2 spike-IgG increases, and N-protein levels correlate with RT-PCR Ct-values in saliva, and between matched saliva and capillary blood samples. This Simoa SARS-CoV-2 N-protein assay effectively detects SARS-CoV-2 infection via measurement of antigen levels in blood or saliva, using non-invasive, swab-independent collection methods, offering potential for at home and point of care sample collection.

Severe COVID-19 disease caused by SARS-CoV-2 is frequently accompanied by dysfunction of the lungs and extrapulmonary organs. However, the organotropism of SARS-CoV-2 and the port of virus entry for systemic dissemination remain largely unknown. We profiled 26 COVID-19 autopsy cases from four cohorts in Wuhan, China, and determined the systemic distribution of SARS-CoV-2. SARS-CoV-2 was detected in the lungs and multiple extrapulmonary organs of critically ill COVID-19 patients up to 67 days after symptom onset. Based on organotropism and pathological features of the patients, COVID-19 was divided into viral intrapulmonary and systemic subtypes. In patients with systemic viral distribution, SARS-CoV-2 was detected in monocytes, macrophages, and vascular endothelia at blood–air barrier, blood–testis barrier, and filtration barrier. Critically ill patients with long disease duration showed decreased pulmonary cell proliferation, reduced viral RNA, and marked fibrosis in the lungs. Permanent SARS-CoV-2 presence and tissue injuries in the lungs and extrapulmonary organs suggest direct viral invasion as a mechanism of pathogenicity in critically ill patients. SARS-CoV-2 may hijack monocytes, macrophages, and vascular endothelia at physiological barriers as the ports of entry for systemic dissemination. Our study thus delineates systemic pathological features of SARS-CoV-2 infection, which sheds light on the development of novel COVID-19 treatment.

Purpose: This study compared the effect of whole lung lavage (WLL) at different time-points early after exposure of the respiratory system to insoluble radioactive particles. Materials and methods: Forty adult beagles were randomized into a control group and the 3-h, 8-h, 24-h, and 48-h lavage groups (n ¼ 8). A canine model of acute lung injury was established by spraying a depleted uranium (DU) suspension using a superfine fiber bronchoscope, at a dose of 20 mg/kg. The lavage groups were subjected to WLL at 3 h, 8 h, 24 h, and 48 h post-DU exposure, while the control group received no treatment after exposure. Measurement of U in serum was performed using inductively coupled plasma mass spectrometry; measurements in the lavage fluid and left lung tissue were performed using inductively coupled plasma atomic emission spectrometry. The color of the lavage fluid was analyzed using colorimetry, and shadow changes in the lung were observed using chest computed tomography (CT). Results: The lavage groups showed similarly increasing trends for serum U levels from DU exposure to 3 and 7 days after exposure; however, these values were significantly lower than those in the control group (p < .01). The U content in the lavage fluid was significantly higher in the 3-h group than in the 8-h, 24-h, and 48-h groups (p < .01), while that in the 8-h group was markedly higher than those in the 24-h and 48-h groups (p < .05). The average clearance rate of DU in the lungs varied in the range of 0.63-7.06%. The U content in the left lung tissue of each lavage group was significantly lower than that in the control group (p < .01), while the content in the 8h, 24-h, and 48-h groups was significantly higher than that in the 3-h group (p < .05). The colorimetric score of the lavage fluid in the 3-h group was significantly lower than those in the 8-h, 24h, and 48-h groups (p < .05). Chest CT showed different degrees of consolidation and ground glass shadow changes in all groups. The score of the left lung shadow volume in the 3-h group was significantly lower than in the control, 8-h, 24-h, and 48-h groups (p < .01), while the score in the 8-h group was significantly higher than those in the 48-h and control groups (p < .05). Conclusions: The best effect of WLL after exposure of the respiratory system to insoluble radioactive particles was achieved at 3 h, followed by 8 h; there was no difference in the effectiveness of lung lavage at 24 h and 48 h.

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The reliability of remote sensing (RS) image classification is crucial for applying RS image classification results. However, it has received minimal attention, especially the uncertainty of features extracted from RS images. The uncertainty of image features constantly accumulates, propagates, and ultimately affects the reliability and accuracy of image classification results. Thus, research on the quantitative modeling and measurement of the feature uncertainty of RS images is very necessary. To make up for the lack of research on quantitative modeling and measurement of uncertainty of image features, this study first investigates and summarizes the appearance characteristics of the feature uncertainty of RS images in geospatial and feature space domains based on the source and formation mechanisms of feature uncertainty. Then, a modeling and measurement approach for the uncertainty of image features is proposed on the basis of these characteristics. In this approach, a new Local Adaptive Multi-Feature Weighting Method based on Information Entropy and the Local Distribution Density of Points is proposed to model and measure the feature uncertainty of an image in the geospatial and feature space domains. In addition, a feature uncertainty index is also constructed to comprehensively describe and quantify the feature uncertainty, which can also be used to refine the classification map to improve its accuracy. Finally, we propose two effectiveness verification schemes in two perspectives, namely, statistical analysis and image classification, to verify the validity of the proposed approach. Experimental results on two real RS images confirm the validity of the proposed approach. Our study on the feature uncertainty of images may contribute to the development of uncertainty control methods or reliable classification schemes for RS images.