A novel coronavirus recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn). Source codes and datasets are available at our GitHub.
Background A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and Methods We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. Results The experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019. Conclusions The established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30 mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. Detailed descriptions and quantitative analysis on key components of the system were provided. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to show the effect of ensemble size and the effectiveness of the ensemble model. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.
Porous Fe3O4/SnO2 core/shell nanorods are successfully fabricated, in which the width and the length of the pores are 5−10 and 10−60 nm, respectively. We prepared 80 wt % of porous Fe3O4/SnO2 core/shell nanorod-wax composites in order to measure their electromagnetic parameters. The measured results indicate that effective complementarities between the dielectric loss and the magnetic loss are realized over 2−18 GHz frequency range, suggesting the porous Fe3O4/SnO2 core/shell nanorods have excellent electromagnetic wave absorption properties. The reflection loss was calculated in terms of the transmit-line theory. The absorption range under −20 dB is from 3.2 to 16.88 GHz for an absorber thickness of 2−5 mm. Moreover, the porous core/shell nanorods exhibit dual-frequency absorption characteristics and their maximum reflection loss reaches −27.38 dB at 16.72 GHz as the absorber thickness is 4 mm. The excellent microwave absorption properties of the porous Fe3O4/SnO2 core/shell nanorods are attributed to effective complementarities between the dielectric loss and the magnetic loss and the special core−shell structures.
Base editors (BEs) enable the generation of targeted single-nucleotide mutations, but currently used rat APOBEC1-based BEs are relatively inefficient in editing cytosines in highly methylated regions or in GpC contexts. By screening a variety of APOBEC and AID deaminases, we show that human APOBEC3A-conjugated BEs and versions we engineered to have narrower editing windows can mediate efficient C-to-T base editing in regions with high methylation levels and GpC dinucleotide content.
Multiplexed detection of extracellular vesicle (EV)-derived microRNAs (miRNAs) plays a critical role in facilitating disease diagnosis and prognosis evaluation. Herein, we developed a highly specific nucleic acid detection platform for simultaneous quantification of several EV-derived miRNAs in constant temperature by integrating the advantages of a clustered regularly interspaced short palindromic repeats/CRISPR associated nucleases (CRISPR/Cas) system and rolling circular amplification (RCA) techniques. Particularly, the proposed approach demonstrated single-base resolution attributed to the dual-specific recognition from both padlock probe-mediated ligation and protospacer adjacent motif (PAM)-triggered cleavage. The high consistency between the proposed approach RCA-assisted CRISPR/Cas9 cleavage (RACE) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) in detecting EV-derived miRNAs’ abundance from both cultured cancer cells and clinical lung cancer patients validated its robustness, revealing its potentials in the screening, diagnosis, and prognosis of various diseases. In summary, RACE is a powerful tool for multiplexed, specific detection of nucleic acids in point-of-care diagnostics and field-deployable analysis.
Abstract. We propose a novel, multi-task, fully convolutional network (FCN) architecture for automatic segmentation of brain tumor. This network extracts multi-level contextual information by concatenating hierarchical feature representations extracted from multimodal MR images along with their symmetric-difference images. It achieves improved segmentation performance by incorporating boundary information directly into the loss function. The proposed method was evaluated on the BRATS13 and BRATS15 datasets and compared with competing methods on the BRATS13 testing set. Segmented tumor boundaries obtained were better than those obtained by single-task FCN and by FCN with CRF. The method is among the most accurate available and has relatively low computational cost at test time.
A unique 1D nanostructure of Pt@CeO 2 −BDC was prepared from Pt@CeBDC MOF. The Pt@CeO 2 −BDC was rich in oxygen vacancies (i.e., XPS O β /(O α + O β ) = 39.4%), and on the catalyst, the 2 nm Pt clusters were uniformly deposited on the 1D mesoporous polycrystalline CeO 2 . Toluene oxidation was conducted in a spectroscopic operando Raman−online FTIR reactor to elucidate the reaction mechanism and establish the structure−activity relationship. The reaction proceeds as follows: (I) adsorption of toluene as benzoate intermediates on Pt@CeO 2 −BDC at low temperature by reaction with surface peroxide species; (II) reaction activation and ring-opening involving lattice oxygen with a concomitant change in defect densities indicative of surface rearrangement; (III) complete oxidation to CO 2 and H 2 O by lattice oxygen and reoxidation of the reduced ceria with consumption of adsorbed oxygen species. The Pt clusters, which mainly exist as Pt 2+ with minor amounts of Pt 0 and Pt 4+ on the surface, facilitated the adsorption and reaction activation. The Pt-CeO 2 interface generates reduced ceria sites forming nearby adsorbed peroxide at low temperature that oxidize toluene into benzoate species by a Langmuir−Hinshelwood mechanism. As the reaction temperature increases, the role of lattice oxygen becomes important, producing CO 2 and H 2 O mainly by the Mars-van Krevelen mechanism.
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