Coronavirus Disease 2019 Lung Research Articles

An efficient deep neural network framework for COVID-19 lung infection segmentation

Since the outbreak of Coronavirus Disease 2019 (COVID-19) in 2020, it has significantly affected the global health system. The use of deep learning technology to automatically segment pneumonia lesions from Computed Tomography (CT) images can greatly reduce the workload of physicians and expand traditional diagnostic methods. However, there are still some challenges to tackle the task, including obtaining high-quality annotations and subtle differences between classes. In the present study, a novel deep neural network based on Resnet architecture is proposed to automatically segment infected areas from CT images. To reduce the annotation cost, a Vector Quantized Variational AutoEncoder (VQ-VAE) branch is added to reconstruct the input images for purpose of regularizing the shared decoder and the latent maps of the VQ-VAE are utilized to further improve the feature representation. Moreover, a novel proportions loss is presented for mitigating class imbalance and enhance the generalization ability of the model. In addition, a semi-supervised mechanism based on adversarial learning to the network has been proposed, which can utilize the information of the trusted region in unlabeled images to further regularize the network. Extensive experiments on the COVID-SemiSeg are performed to verify the superiority of the proposed method, and the results are in line with expectations.

MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.

Severity of Pediatric COVID-19: Role of Vitamin D

Abstract Objective Vitamin D has many immune benefits and because its deficiency impacts most age groups, it became a nutrient of interest in the coronavirus disease 2019 (COVID-19) era. The objective of this study was to highlight the contribution of vitamin D status to the disease severity of hospitalized pediatric patients suffering from COVID-19 infection. Methods This was a cross-sectional study that was conducted on 42 children with documented positive polymerase chain reaction for COVID-19 infection. Detailed history taking and thorough clinical examination were done for each recruited patient. Besides the laboratory and radiological assessment done for COVID-19 patients, 25 hydroxy vitamin D levels [25(OH) D] in the serum were estimated using enzyme linked immunosorbent assay. Results Using the cutoff level of 10 ng/mL, only 40% of the patients were below this level and 60% had their vitamin D level more than or equal to 10 ng/mL. Significantly more patients of the first group needed oxygen support (denoting more severe COVID-19 infection and lung involvement). The older the patients, the more evident was vitamin D deficiency among them, and 25(OH) D values were not correlated to weight for length nor weight categories in the studied series of patients suffering from COVID-19. Conclusion Vitamin D deficiency affects the severity of pediatric COVID-19 infection in hospitalized patients. It is prudent to advise vitamin D level assessment in such cases and promptly manage the patients accordingly. We recommend further studies to assess the effect of vitamin D supplementation on the clinical outcome of COVID-19 in the pediatric population and other vulnerable groups.

COVID-19 Lung Damage in a Preterm Newborn

AbstractThe new coronavirus disease 2019 (COVID-19) was declared responsible for a global pandemic by the World Health Organization (WHO) on March 11, 2020. It created, since its emergence, a special concern in neonates given their immature immune system and their frequent contact with caregivers.Monochorionic very preterm female neonate (315/7 weeks of gestation) with growth retardation had at 3 weeks of age severe lung damage following an acute respiratory failure caused by horizontally transmitted severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Before this acute episode, she was without respiratory support apart from 7 days of positive airway pressure following a respiratory distress syndrome. Both of her parents, her twin sister, and her referent nurse tested positive for SARS-CoV-2. Chest radiographs showed bilateral alveolar infiltration. Chest computed tomography (CT) scan detected multiple ground-glass opacifications in both lungs along with ventilation disorders and bronchial wall thickening. Neonate paraclinical tests showed a negative C-reactive protein and hypogammaglobulinemia. Mechanical ventilation was needed for 1 week along with sedation, pharmaceutically induced paralysis, and inhaled nitric oxide. Severe bronchopulmonary dysplasia revealed by oxygen dependency at 36 weeks of gestation was treated with steroids and noninvasive respiratory support. The neonate was discharged with free oxygen at home.Her twin sister, also SARS-CoV-2–positive, only needed noninvasive ventilation for 19 days.Severe forms of COVID-19 in premature neonates, with long-term respiratory sequelae, are possible. Considering the lack of data in this age group, more studies are needed to investigate the risk factors associated with severe symptoms, the effects of specific treatments, and the long-term prognosis of the disease.

Framework for COVID-19 segmentation and classification based on deep learning of computed tomography lung images

Corona Virus Disease 2019 (COVID-19) has affected millions of people worldwide and caused more than 6.3 million deaths (World Health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography (CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task. ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also, VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique (ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprise of, end-to-end, VGG16, ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset which is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves 98.98% accuracy (ACC), 98.87% area under the ROC curve (AUC), 98.89% sensitivity (Se), 97.99% precision (Pr), 97.88% F1- score, and 1.8974-second computational time.

Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images

Coronavirus Disease 2019 (COVID-19) still presents a pandemic trend globally. Detecting infected individuals and analyzing their status can provide patients with proper healthcare while protecting the normal population. Chest CT (computed tomography) is an effective tool for screening of COVID-19. It displays detailed pathology-related information. To achieve automated COVID-19 diagnosis and lung CT image segmentation, convolutional neural networks (CNNs) have become mainstream methods. However, most of the previous works consider automated diagnosis and image segmentation as two independent tasks, in which some focus on lung fields segmentation and the others focus on single-lesion segmentation. Moreover, lack of clinical explainability is a common problem for CNN-based methods. In such context, we develop a multi-task learning framework in which the diagnosis of COVID-19 and multi-lesion recognition (segmentation of CT images) are achieved simultaneously. The core of the proposed framework is an explainable multi-instance multi-task network. The network learns task-related features adaptively with learnable weights, and gives explicable diagnosis results by suggesting local CT images with lesions as additional evidence. Then, severity assessment of COVID-19 and lesion quantification are performed to analyze patient status. Extensive experimental results on real-world datasets show that the proposed framework outperforms all the compared approaches for COVID-19 diagnosis and multi-lesion segmentation.

Functional Alterations Due to COVID-19 Lung Lesions-Lessons From a Multicenter V/Q Scan-Based Registry.

PurposeIn coronavirus disease 2019 (COVID-19) patients, clinical manifestations as well as chest CT lesions are variable. Lung scintigraphy allows to assess and compare the regional distribution of ventilation and perfusion throughout the lungs. Our main objective was to describe ventilation and perfusion injury by type of chest CT lesions of COVID-19 infection using V/Q SPECT/CT imaging.Patients and MethodsWe explored a national registry including V/Q SPECT/CT performed during a proven acute SARS-CoV-2 infection. Chest CT findings of COVID-19 disease were classified in 3 elementary lesions: ground-glass opacities, crazy-paving (CP), and consolidation. For each type of chest CT lesions, a semiquantitative evaluation of ventilation and perfusion was visually performed using a 5-point scale score (0 = normal to 4 = absent function).ResultsV/Q SPECT/CT was performed in 145 patients recruited in 9 nuclear medicine departments. Parenchymal lesions were visible in 126 patients (86.9%). Ground-glass opacities were visible in 33 patients (22.8%) and were responsible for minimal perfusion impairment (perfusion score [mean ± SD], 0.9 ± 0.6) and moderate ventilation impairment (ventilation score, 1.7 ± 1); CP was visible in 43 patients (29.7%) and caused moderate perfusion impairment (2.1 ± 1.1) and moderate-to-severe ventilation impairment (2.5 ± 1.1); consolidation was visible in 89 patients (61.4%) and was associated with moderate perfusion impairment (2.1 ± 1) and severe ventilation impairment (3.0 ± 0.9).ConclusionsIn COVID-19 patients assessed with V/Q SPECT/CT, a large proportion demonstrated parenchymal lung lesions on CT, responsible for ventilation and perfusion injury. COVID-19–related pulmonary lesions were, in order of frequency and functional impairment, consolidations, CP, and ground-glass opacity, with typically a reverse mismatched or matched pattern.

Anticoagulation as secondary prevention of massive lung thromboses in hospitalized patients with COVID-19

Anticoagulation as secondary prevention of massive lung thromboses in hospitalized patients with COVID-19

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality

Rationale and ObjectivesThe burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia.Materials and MethodsA previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest computed tomography scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes.ResultsInterobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). Probability of each outcome behaved as a logistic function of the opacity scoring (25% intensive care unit admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). Length of hospitalization, intensive care unit stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively).ConclusionThe tested dCNN was highly predictive of inpatient outcomes, performs at a near expert level, and provides added value for clinicians in terms of prognostication and disease severity.

A Novel Threshold-Based Segmentation Method for Quantification of COVID-19 Lung Abnormalities.

Since December 2019, the novel coronavirus disease 2019 (COVID-19) has claimed the lives of more than 3.75 million people worldwide. Consequently, methods for accurate COVID-19 diagnosis and classification are necessary to facilitate rapid patient care and terminate viral spread. Lung infection segmentations are useful to identify unique infection patterns that may support rapid diagnosis, severity assessment, and patient prognosis prediction, but manual segmentations are time-consuming and depend on radiologic expertise. Deep learning-based methods have been explored to reduce the burdens of segmentation; however, their accuracies are limited due to the lack of large, publicly available annotated datasets that are required to establish ground truths. For these reasons, we propose a semi-automatic, threshold-based segmentation method to generate region of interest (ROI) segmentations of infection visible on lung computed tomography (CT) scans. Infection masks are then used to calculate the percentage of lung abnormality (PLA) to determine COVID-19 severity and to analyze the disease progression in follow-up CTs. Compared with other COVID-19 ROI segmentation methods, on average, the proposed method achieved improved precision (47.49%) and specificity (98.40%) scores. Furthermore, the proposed method generated PLAs with a difference of pm 3.89% from the ground-truth PLAs. The improved ROI segmentation results suggest that the proposed method has potential to assist radiologists in assessing infection severity and analyzing disease progression in follow-up CTs.

COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation

With the Coronavirus disease 2019 (COVID-19) spread, causing a world pandemic, and recently, the virus new variants continue to appear, making the situation more challenging and threatening, the visual assessment and quantification by expert radiologists have become costly and error-prone. Hence, there is a need to propose a model to predict the COVID-19 cases at the earliest possible to control the disease spread. In order to assist the medical professionals and reduce workload and the time the COVID-19 diagnosis cycle takes, this paper proposes a novel neural network architecture termed as O-Net to automatically segment chest Computerised Tomography Ct-scans infected by COVID-19 with optimised computing power and memory occupation. The O-Net consists of two convolutional autoencoders with an upsampling channel and a downsampling channel. Experimental tests show our proposal’s effectiveness and potential, with a dice score of 0.86, pixel accuracy, precision, specificity of 0.99, 0.99, 0.98, respectively. Performance on the external dataset illustrates generalisation and scalability capabilities of the O-Net model to Ct-scan obtained from different scanners with different sizes. The second objective of this work is to introduce our virtual reality platform, COVIR, that visualises and manipulates 3D reconstructed lungs and segmented infected lesions caused by COVID-19. COVIR platform acts as a reading and visualisation support for medical practitioners to diagnose COVID-19 lung infection. The COVIR platform could be used for medical education professional practice and training. It was tested by Thirteen participants (medical staff, researchers, and collaborators), they conclude that the 3D VR visualisation of segmented Ct-Scan provides an aid diagnosis tool for better interpretation.

Comparison of Chest Computed Tomography Between the Two Waves of Coronavirus Disease 2019 in Belgium Using Artificial Intelligence.

BackgroundIn this study, we aimed to compare two outbreaks of coronavirus disease 2019 (COVID-19) in Belgium in tomographic and biological-clinical aspects with artificial intelligence (AI).MethodologyWe performed an observational retrospective study. Adult patients who were symptomatic in the first seven days with COVID-19 infection, diagnosed by chest computed tomography (CT) and/or reverse transcription-polymerase chain reaction, were included in this study. The first wave of the pandemic lasted from March 25, 2020, to May 25, 2020, and the second wave lasted from October 7, 2020, to December 7, 2020. For each wave, two subgroups were defined depending on whether respiratory failure occurred during the course of the disease. The quantitative estimation of COVID-19 lung lesions was performed by AI, radiologists, and radiology residents. The chest CT severity score was calculated by AI.ResultsIn the 202 patients included in this study, we found statistically significant differences for obesity, hypertension, and asthma. The differences were predominant in the second wave. Moreover, a mixed distribution (central and peripherical) of pulmonary lesions was noted in the second wave, but no differences were noted regarding mortality, respiratory failure, complications, and other radiological and biological elements. Chest CT severity score was among the risk factors of mortality and respiratory failure. There was a mild agreement between AI and visual evaluation of pulmonary lesion extension (K = 0.4).ConclusionsBetween March and December 2020, in our cohort, for the majority of the parameters analyzed, we did not record significant changes between the two waves. AI can reduce the experience and performance gap of radiologists and better establish a hospitalization criterion.

Successful laparoscopic lavage and drainage for intestinal perforation in a patient with COVID-19: A case report.

sIntroductionSome patients with coronavirus disease 2019 (COVID-19) have acute abdomen and need surgery. However, surgery in the acute phase of COVID-19 is associated with worse postoperative outcomes and an increased risk of mortality. We report a case of a patient with COVID-19 who developed intestinal perforation that was treated acutely with antibiotics and delayed surgical intervention.Presentation of caseA 79-year-old man with COVID-19 was treated with remdesivir and dexamethasone, and his respiratory symptoms and hypoxia improved. However, abdominal symptoms developed, and intestinal perforation occurred. As the nasopharyngeal swab PCR test was positive for SARS-CoV-2, conservative treatment with tazobactam/piperacillin was started to avoid surgery in the acute phase of COVID-19. An intraperitoneal abscess was confirmed on follow-up computed tomography. Emergent laparoscopic lavage and drainage, and transverse colon stoma construction were performed with medical staff using full personal protective equipment. Bacterial culture from the ascites detected Escherichia coli and Bacteroides. The SARS-CoV-2 PCR test of the ascites sample was negative. No infection was observed in the medical staff.DiscussionCOVID-19 has been associated with a higher perioperative risk and postoperative mortality. There has also been a report of ascitic fluid testing positive for SARS-CoV-2 on PCR, suggesting the possibility of intraoperative aerosolization. Avoiding surgical treatment in the acute phase of COVID-19 may reduce deaths from perioperative complications.ConclusionOur case suggests that in acute COVID-19 lung infection, careful observation and delayed surgical treatment could prevent worsening of the COVID-19 and reduce the risk of infection to the medical staff.

CapsNet-COVID19: Lung CT image classification method based on CapsNet model.

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.

Diurnal Variation in the Mortality of Section Patients with COVID-19 Pneumonia: A Retrospective Study

Introduction: Coronavirus Disease 2019 (COVID-19), the new contagious novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), pandemic in 2020-21 has had a devastating impact on human race. The most common cause of death among hospitalised patient was COVID-19 pneumonia or lung injury. Various studies have shown diurnal variation in human mortality due to all causes with or without intervention. Aim: To identify existence of diurnal variations for mortality among the hospitalised patients with COVID-19 pneumonia. Materials and Methods: This hospital-record based, retrospective study was conducted in a tertiary referral centre of north east India (Assam Medical College, Dibrugarh, Assam, India) which was a dedicated COVID-19 hospital during the pandemic. The study was conducted from September 2021 to December 2021 and the data was collected and recorded from the Cadaver slips issued to families of patient dying of COVID-19 pneumonia during the period January 2021 to August 2021. The data were generated by plotting the number of deaths of COVID-19 cases for each two hour interval as a percent of the mean number of deaths per twohour interval and as a percentage of cumulative deaths per twohour interval on a 24 hour scale. The deaths were sub grouped according to gender, age, and reported co-morbid causes of death along with pneumonia. Comparisons of data i.e., mean deaths/2 hour interval (mean±SD) were performed by one-way Analysis of Variance (ANOVA), followed by Bartlett's test for equal variances. The p-value <0.05 was considered as statistically significant. Results: Total 743 deaths, with 537 males and 206 females were included in the study. Mean age of the deaths was 56.39 years. There was rise of deaths during 4 PM to 6 PM (16:00 to 18:00) interval for all deaths due to COVID-19 pneumonia. The increase in deaths during this period was mainly due to deaths among males equal or above 65 years and females below age 65 years. However, the deaths of females equal or above the age of 65 years did not show significant diurnal variation. Only 26.51% (n=197) of pneumonia deaths were without co-morbidity. Conclusion: There exists a diurnal variation in mortality among COVID-19 pneumonia patients with evening rise of deaths. Diurnal variation is significantly more among males rather than females above 65 years.

Delayed lung function testing after COVID-19 to detect persistent lung function abnormalities - The DELTA - COVID-19 lung function study.

Coronavirus disease 2019 (COVID-19) has made its presence felt as the worst pandemic witnessed till date in the 21st century. The mortality and morbidity associated with COVID-19 does not end with the acute pneumonia and respiratory failure and consequences extend well into the subsequent weeks to months in a minority. After recovery from severe disease, symptoms, lung function abnormalities and radiological changes are known to persist for varying length of time in a small proportion of patients. Various studies report different incidences of lung function abnormalities post-COVID-19. The present study describes the occurrence, severity, pattern and risk factors for persistent lung function abnormalities post-COVID-19 patients. The present study was aimed to find out the occurrence of persistent lung function abnormalities in patients hospitalised with COVID-19 at three months of discharge who had normal previous lung function prior to COVID-19. In those with persisting abnormal lung function, the severity, pattern and risk factors for persistent lung function abnormalities were also studied. The present study was a retrospective study in patients hospitalised with COVID-19 who had radiological evidence of pneumonia at admission. Patients with prior abnormal lung function were excluded from the study. Lung function was analysed with spirometry, 6-min walk test and diffusion capacity between day 85 and 95 of hospital discharge and the occurrence, severity and pattern of impairment was described. Lung function impairment was correlated with baseline characteristics and univariate regression analysis was done to identify risk factors for persisting functional impairment. 39 patients were included in the study. Spirometry at follow up showed a restrictive ventilatory defect in 26 out of 39 patients (64%) and a normal study was noted in 12 patients. One patient had an obstructive ventilatory defect. Diffusion impairment was present in 27 patients and normal transfer factor was seen in 12 patients. The degree of diffusion impairment was mild in 16 patients and moderate in 11 patients. Univariate regression analysis revealed that age, history of systemic hypertension, severe hypoxia at presentation, and extent of lung involvement by CT chest were associated with lung function impairment. Almost two-thirds of patients hospitalised with COVID-19 pneumonia have persistent lung function abnormalities at three months post-discharge. Advanced age, severe disease and medical comorbidities increase the risk of persistent functional abnormalities.

Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways.

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases' lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

Effect and Mechanism of Qingfei Paidu Decoction in the Management of Pulmonary Fibrosis and COVID-19.

Qingfei Paidu decoction (QFPD) has been repeatedly recommended for the clinical treatment of novel coronavirus disease 2019 (COVID-19) in multiple provinces throughout China. A possible complication of COVID-19 lung involvement is pulmonary fibrosis, which causes chronic breathing difficulties and affects the patient's quality of life. Therefore, there is an important question regarding whether QFPD can alleviate the process of pulmonary fibrosis and its potential mechanisms. To explore this issue, this study demonstrated the anti-pulmonary fibrosis activity and mode of action of QFPD in vivo and in vitro pulmonary fibrosis models and network pharmacology. The results showed that QFPD effectively ameliorated the bleomycin-induced inflammation and collagen deposition in mice and significantly improved the epithelial-mesenchymal transition in pulmonary fibrosis in mice. In addition, QFPD inhibited bleomycin-induced M2 polarization of macrophages in pulmonary tissues. An in-depth study of the mechanism of QFPD in the treatment of pulmonary fibrosis based on network pharmacology and molecular simulation revealed that SRC was the main target of QFPD and sitosterol (a key compound in QFPD). QFPD and sitosterol regulate the EMT process and M2 polarization of macrophages by inhibiting the activation of SRC, thereby alleviating pulmonary fibrosis in mice. COVID-19 infection might produce severe fibrosis, and antifibrotic therapy with QFPD may be valuable in preventing severe neocoronavirus disease in patients with IPF, which could be a key factor explaining the role of QFPD in the treatment of COVID-19.

Single-cell RNA analysis reveals the potential risk of organ-specific cell types vulnerable to SARS-CoV-2 infections

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease 2019 (COVID-19) since December 2019 that has led to more than 160 million confirmed cases, including 3.3 million deaths. To understand the mechanism by which SARS-CoV-2 invades human cells and reveal organ-specific susceptible cell types for COVID-19, we conducted comprehensive bioinformatic analysis using public single-cell RNA sequencing datasets. Utilizing the expression information of six confirmed COVID-19 receptors (ACE2, TMPRSS2, NRP1, AXL, FURIN and CTSL), we demonstrated that macrophages are the most likely cells that may be associated with SARS-CoV-2 pathogenesis in lung. Besides the widely reported ‘chemokine storm’, we identified ribosome related pathways that may also be potential therapeutic target for COVID-19 lung infection patients. Moreover, cell-cell communication analysis and trajectory analysis revealed that M1-like macrophages showed the highest relation to severe COVID-19 patients. And we also demonstrated that up-regulation of chemokine pathways generally lead to severe symptoms, while down-regulation of ribosome and RNA activity related pathways are more likely to be mild. Other organ-specific susceptible cell type analyses could also provide potential targets for COVID-19 therapy. This work can provide clues for understanding the pathogenesis of COVID-19 and contribute to understanding the mechanism by which SARS-CoV-2 invades human cells.

Multi-feature Multi-Scale CNN-Derived COVID-19 Classification from Lung Ultrasound Data.

The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools. However, difficulty in eliminating the risk of disease transmission, radiation exposure and not being cost-effective are some of the challenges for CT and CXR imaging. This fact induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due to its practical advantages of noninvasiveness, repeatability, and sensitive bedside property. In this paper, we utilize a deep learning model to perform the classification of COVID-19 from LUS data, which could produce objective diagnostic information for clinicians. Specifically, all LUS images are processed to obtain their corresponding local phase filtered images and radial symmetry transformed images before fed into the multi-scale residual convolutional neural network (CNN). Secondly, image combination as the input of the network is used to explore rich and reliable features. Feature fusion strategy at different levels is adopted to investigate the relationship between the depth of feature aggregation and the classification accuracy. Our proposed method is evaluated on the point-of-care US (POCUS) dataset together with the Italian COVID-19 Lung US database (ICLUS-DB) and shows promising performance for COVID-19 prediction.