Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly across the USA, causing extensive morbidity and mortality, particularly in the African American community. Autopsy can considerably contribute to our understanding of many disease processes and could provide crucial information to guide management of patients with coronavirus disease 2019 (COVID-19). We report on the relevant cardiopulmonary findings in, to our knowledge, the first autopsy series of ten African American decedents, with the cause of death attributed to COVID-19.Methods Autopsies were performed on ten African American decedents aged 44-78 years with cause of death attributed to COVID-19, reflective of the dominant demographic of deaths following COVID-19 diagnosis in New Orleans. Autopsies were done with consent of the decedents' next of kin. Pulmonary and cardiac features were examined, with relevant immunostains to characterise the inflammatory response, and RNA labelling and electron microscopy on representative sections.Findings Important findings include the presence of thrombosis and microangiopathy in the small vessels and capillaries of the lungs, with associated haemorrhage, that significantly contributed to death. Features of diffuse alveolar damage, including hyaline membranes, were present, even in patients who had not been ventilated. Cardiac findings included individual cell necrosis without lymphocytic myocarditis. There was no evidence of secondary pulmonary infection by microorganisms.Interpretation We identify key pathological states, including thrombotic and microangiopathic pathology in the lungs, that contributed to death in patients with severe COVID-19 and decompensation in this demographic. Management of these patients should include treatment to target these pathological mechanisms.
Summary Embryonic gene expression intricately reflects anatomical context, developmental stage, and cell type. To address whether the precise spatial origins of cardiac cells can be deduced solely from their transcriptional profiles, we established a genome-wide expression database from 118, 949, and 1166 single murine heart cells at embryonic days (e)8.5, 9.5, and 10.5, respectively. We segregated these cells by type using unsupervised bioinformatic analysis and identified chamber-specific genes. Using a random forest algorithm, we reconstructed the spatial origin of single e9.5 and e10.5 cardiomyocytes with 92.0+/-3.2% and 91.2+/-2.8% accuracy respectively (99.4+/-1.0% and 99.1+/-1.1% if a +/-1 zone margin is permitted) and predicted the second heart field distribution of Isl1-lineage descendants. When applied to Nkx2-5-/- cardiomyocytes from murine e9.5 hearts, we showed their transcriptional alteration and lack of ventricular phenotype. Our database and zone classification algorithm will enable the discovery of novel mechanisms in early cardiac development and disease.
Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from lowresolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this deep imaging process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel 1 × 1 1 × 1 1 × 1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for superresolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three largescale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.