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Background: In the context of the outbreak of COVID-19 within mainland China, to understand the mental health status of university students during campus closure, this study analyzes the relationship between anxiety, depressive symptoms, and psychological capital and to reveals their central symptoms., Methods: A total of 12,945 university students were included in this study from April 10 to 19, 2022. Anxiety and depressive symptoms were measured by the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaires (PHQ-2). Psychological capital was measured using the Psychological Capital Questionnaire (PCQ-24). The centrality and bridge centrality indexes were used to identify central and bridge symptoms, respectively. Network Comparison Test (NCT) was also administered to check whether network traits differed by gender and place of residence., Results: The most influential node in this study was Trouble relaxing (GAD4), followed by Uncontrollable worry (GAD2) and Excessive worry (GAD3). The main bridging symptoms were Depressed mood (PHQ2), Psychological capital. There are no differences in the network structure of students by place of residence, while there are more significant differences in the network structure of students by gender., Conclusion: Central and bridging symptoms may be the core symptoms that trigger or maintain the development of anxiety and depression among university students during the COVID-19 campus closure. Timely and reasonable interventions targeting these symptoms may help reduce depression and anxiety in this population. In addition, improving university students' psychological capital may likewise contribute to the development of their good mental health., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Due to the continuous spread of the epidemic, some colleges and universities have implemented a campus lockdown management policy in China. In the context of the campus lockdown, this study aimed to explore whether anxiety mediated the association between interpersonal sensitivity and depression, and investigate whether psychological capital moderated the indirect or direct effect of mediation model., Methods: A total of 12945 undergrad students were recruited in China from April 10 to 19, 2022. These participants were asked to complete the online questionnaires measuring interpersonal sensitivity, anxiety, psychological capital, and depression. A moderated mediation model was examined by using PROCESS macro for SPSS 25.0, in which anxiety was a mediating variable, and psychological capital was a moderating variable., Results: Interpersonal sensitivity was positively associated with depression among Chinese college students (r = 0.47, P < 0.001). Anxiety partially mediated the association between interpersonal sensitivity and depression (indirect effect = 2.31, 95%CI [2.18, 2.44], accounting for 70% of the total effect). Moreover, the interaction effect of interpersonal sensitivity and psychological capital on anxiety (β = -0.04, t = -17.36, P < 0.001) and the interaction effect of anxiety and psychological capital on depression (β = 0.002, t = 1.99, P < 0.05) were statistically significant., Conclusions: The current study explained the mediation role of anxiety and the moderation role of psychological capital in the relation between interpersonal sensitivity and depression. The findings suggested that strict monitoring anxiety and promoting psychological capital may decrease the risk of depression among Chinese college students during the campus lockdown.

Introduction: The number of college students with mental problems has increased significantly, particularly during COVID-19. However, the clinical features of early-stage psychological problems are subclinical, so the optimal intervention treatment period can easily be missed. Artificial intelligence technology can efficiently assist in assessing mental health problems by mining the deep correlation of multi-dimensional data of patients, providing ideas for solving the screening of normal psychological problems in large-scale college students. Therefore, we propose a mental health assessment method that integrates traditional scales and multimodal intelligent recognition technology to support the large-scale and normalized screening of mental health problems in colleges and universities., Methods: Firstly, utilize the psychological assessment scales based on human-computer interaction to conduct health questionnaires based on traditional methods. Secondly, integrate machine learning technology to identify the state of college students and assess the severity of psychological problems. Finally, the experiments showed that the proposed multimodal intelligent recognition method has high accuracy and can better proofread normal scale results. This study recruited 1,500 students for this mental health assessment., Results: The results showed that the incidence of moderate or higher stress, anxiety, and depression was 36.3, 48.1, and 23.0%, which is consistent with the results of our multiple targeted tests., Conclusion: Therefore, the interactive multimodality emotion recognition method proposed provides an effective way for large-scale mental health screening, monitoring, and intervening in college students' mental health problems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Wu and Liu.)

Purpose: Coronavirus disease 2019 (COVID-19) is a recently declared worldwide pandemic. Triaging of patients into severe and non-severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT-based radiomics signature for the prediction of these potential severe COVID-19 patients., Methods: One hundred fifty COVID-19 patients were enrolled and randomly divided into cross-validation and independent test sets. First, their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the inter- and intra-class correlation coefficients (ICC) analysis, minimum-redundancy maximum-relevance (mRMR) selection, and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow test and Delong test., Results: Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR, and LASSO methods resulted in the selection of ten radiomics features, which made up of the radiomics signature. The differences in the radiomics signature between the potential severe and non-severe groups in cross-validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models., Conclusions: A CT-based radiomics signature for the prediction of potential severe COVID-19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID-19 pandemic prevention and control., (© 2022 American Association of Physicists in Medicine.)

Background: There is a need to assess the long-term outcomes of survivors of critical illness from COVID-19., Methods: Ninety-two survivors of critical illness from COVID-19 from four hospitals in Hubei Province, China participated in this prospective cohort study. Multiple characteristics, including lung function (lung volumes, diffusing capacity for carbon monoxide, chest computed tomography scores, and walking capacity); immune status (SARS-CoV-2-neutralising antibody and all subtypes of immunoglobulin (Ig) G against SARS-CoV-2, immune cells in response to ex vivo antigen peptide stimuli, and lymphocyte count and its subtypes); liver, coagulation, and kidney functions; quality of life; cognitive function; and mental status, were assessed after 3, 6, and 12 months of follow-up., Results: Amongst the 92 enrolled survivors, 72 (78%) patients required mechanical ventilation. At 12 months, the predicted percentage diffusing capacity of lung for carbon monoxide was 82% (inter-quartile range [IQR]: 76-97%) with a residual volume of 77 (64-88)%. Other lung function parameters and the 6-min walk test improved gradually over time and were almost back to normal by 12 months. The titres of IgG and neutralising antibody to COVID-19 remained high at 12 months compared with those of controls who were not infected with COVID-19, although IgG titres decreased significantly from 34.0 (IQR: 23.8-74.3) to 15.0 (5.8-24.3) AU ml -1 (P<0.001), whereas neutralising antibodies decreased from 29.99 (IQR: 19.43-53.93) AU ml -1 at 6 months to 19.75 (13.1-29.8) AU ml -1 (P<0.001) at 12 months. In general, liver, kidney, physical, and mental functions also improved over time., Conclusions: Survivors of critical illness from COVID-19 show some persistent long-term impairments in lung function. However, a majority of these tests were normal by 12 months. These patients still had detectable levels of neutralising antibodies against SARS-CoV-2 and all types of IgG at 12 months, but the levels had declined over this time period., Clinical Trial Registration: None., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Till March 31st, 2021, the coronavirus disease 2019 (COVID-19) had reportedly infected more than 127 million people and caused over 2.5 million deaths worldwide. Timely diagnosis of COVID-19 is crucial for management of individual patients as well as containment of the highly contagious disease. Having realized the clinical value of non-contrast chest computed tomography (CT) for diagnosis of COVID-19, deep learning (DL) based automated methods have been proposed to aid the radiologists in reading the huge quantities of CT exams as a result of the pandemic. In this work, we address an overlooked problem for training deep convolutional neural networks for COVID-19 classification using real-world multi-source data, namely, the data source bias problem. The data source bias problem refers to the situation in which certain sources of data comprise only a single class of data, and training with such source-biased data may make the DL models learn to distinguish data sources instead of COVID-19. To overcome this problem, we propose MIx-aNd-Interpolate (MINI), a conceptually simple, easy-to-implement, efficient yet effective training strategy. The proposed MINI approach generates volumes of the absent class by combining the samples collected from different hospitals, which enlarges the sample space of the original source-biased dataset. Experimental results on a large collection of real patient data (1,221 COVID-19 and 1,520 negative CT images, and the latter consisting of 786 community acquired pneumonia and 734 non-pneumonia) from eight hospitals and health institutions show that: 1) MINI can improve COVID-19 classification performance upon the baseline (which does not deal with the source bias), and 2) MINI is superior to competing methods in terms of the extent of improvement.

Objective: To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method., Materials and Methods: One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups., Results: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas., Conclusion: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest., Key Points: • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae., (© 2020. The Author(s).)

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and has the ability to damage multiple organs. However, information on serum SARS-CoV-2 nucleic acid (RNAemia) in patients affected by coronavirus disease 2019 (COVID-19) is limited., Methods: Patients who were admitted to Zhongnan Hospital of Wuhan University with laboratory-confirmed COVID-19 were tested for SARS-COV-2 RNA in serum from 28 January 2020 to 9 February 2020. Demographic data, laboratory and radiological findings, comorbidities, and outcomes data were collected and analyzed., Results: Eighty-five patients were included in the analysis. The viral load of throat swabs was significantly higher than of serum samples. The highest detection of SARS-CoV-2 RNA in serum samples was between 11 and 15 days after symptom onset. Analysis to compare patients with and without RNAemia provided evidence that computed tomography and some laboratory biomarkers (total protein, blood urea nitrogen, lactate dehydrogenase, hypersensitive troponin I, and D-dimer) were abnormal and that the extent of these abnormalities was generally higher in patients with RNAemia than in patients without RNAemia. Organ damage (respiratory failure, cardiac damage, renal damage, and coagulopathy) was more common in patients with RNAemia than in patients without RNAemia. Patients with vs without RNAemia had shorter durations from serum testing SARS-CoV-2 RNA. The mortality rate was higher among patients with vs without RNAemia., Conclusions: In this study, we provide evidence to support that SARS-CoV-2 may have an important role in multiple organ damage. Our evidence suggests that RNAemia has a significant association with higher risk of in-hospital mortality., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Background: The 2019 novel coronavirus SARS-CoV-2 caused large outbreaks of COVID-19 worldwide. COVID-19 resembles community-acquired pneumonia (CAP). Our aim was to identify lymphocyte subpopulations to distinguish between COVID-19 and CAP., Methods: We compared the peripheral blood lymphocytes and their subsets in 296 patients with COVID-19 and 130 patients with CAP. Parameters for independent prediction of COVID-19 were calculated by logistic regression., Results: The main lymphocyte subpopulations (CD3 + CD4 + , CD16 + CD56 + , and CD4 + /CD8 + ratio) and cytokines (TNF- α and IFN- γ ) of COVID-19 patients were significantly different from that of CAP patients. CD16 + CD56 + %, CD4 + /CD8 + ratio, CD19 + , and CD3 + CD4 + were identified as predictors of COVID-19 diagnosis by logistic regression. In addition, the CD3 + CD4 + counts, CD3 + CD8 + counts, andTNF- α are independent predictors of disease severity in patients., Conclusions: Lymphopenia is an important part of SARS-CoV-2 infection, and lymphocyte subsets and cytokines may be useful to predict the severity and clinical outcomes of the disease., Competing Interests: The authors have no conflicts of interest to declare., (Copyright © 2021 Guohong Liu et al.)

Personalized assessment and treatment of severe patients with COVID-19 pneumonia have greatly affected the prognosis and survival of these patients. This study aimed to develop the radiomics models as the potential biomarkers to estimate the overall survival (OS) for the COVID-19 severe patients. A total of 74 COVID-19 severe patients were enrolled in this study, and 30 of them died during the follow-up period. First, the clinical risk factors of the patients were analyzed. Then, two radiomics signatures were constructed based on two segmented volumes of interest of whole lung area and lesion area. Two combination models were built depend on whether the clinic risk factors were used and/or whether two radiomics signatures were combined. Kaplan-Meier analysis were performed for validating two radiomics signatures and C-index was used to evaluated the predictive performance of all radiomics signatures and combination models. Finally, a radiomics nomogram combining radiomics signatures with clinical risk factors was developed for predicting personalized OS, and then assessed with respect to the calibration curve. Three clinical risk factors were found, included age, malignancy and highest temperature that influence OS. Both two radiomics signatures could effectively stratify the risk of OS in COVID-19 severe patients. The predictive performance of the combination model with two radiomics signatures was better than that only one radiomics signature was used, and became better when three clinical risk factors were interpolated. Calibration curves showed good agreement in both 15 d survival and 30 d survival between the estimation with the constructed nomogram and actual observation. Both two constructed radiomics signatures can act as the potential biomarkers for risk stratification of OS in COVID-19 severe patients. The radiomics+clinical nomogram generated might serve as a potential tool to guide personalized treatment and care for these patients., (Creative Commons Attribution license.)

Background and Aim: Patients with 2019 novel coronavirus disease (COVID-19) could present with gastrointestinal symptoms without fever or respiratory manifestations, which could be overlooked by health-care providers. We aimed to evaluate the clinical characteristics of COVID-19 in patients presenting with initial gastrointestinal symptoms., Methods: We evaluated all confirmed cases of COVID-19 in Zhongnan Hospital of Wuhan University between January 10 and February 29, 2020. We divided these patients into two groups: patients with initial gastrointestinal symptoms (group A, n = 183) and patients with respiratory syndrome and/or fever (group B, n = 1228). The clinical characteristics, radiological features, and laboratory data were assessed., Results: The clinical procedures of both groups underwent 1-2 weeks rising period and were downward trend at 3 weeks; less than 5% of patients progressed to critical illness. In both groups, mean leukocyte count (P = 0.354) and lymphocyte count (P = 0.386) were below normal, and C-reactive protein level was elevated (P = 0.412). There was mild liver function injury (aspartate aminotransferase, 65.8 ± 12.7 vs 67.4 ± 9.3 U/L, P = 0.246; alanine aminotransferase, 66.4 ± 13.2 vs 69.6 ± 12.7 U/L, P = 0.352), and normal renal function was intact (blood urea nitrogen 6.4 ± 2.5 vs 5.6 ± 2.8 mmol/L P = 0.358; creatinine 85.7 ± 37.2, 91.2 ± 32.6 μmol/L, P = 0.297). After a series of treatment, 176 and 1169 were stable and alive in groups A and B, respectively. The survival rate did not differ significantly between the groups (P = 0.313)., Conclusion: COVID-19 patients presented with initial gastrointestinal symptoms had similar clinical characteristics and outcomes, when compared with patients with fever and respiratory symptoms., (© 2020 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Background: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases., Objective: This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results., Methods: One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups., Results: The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups., Conclusions: The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportion of HU [-750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.

The outbreak of SARS-CoV-2 has become a pandemic with significant mortality. Published studies described clinical characteristics of the disease contain small cohorts from individual centers or larger series consisting of mixed series from multiple different hospitals. We report here analyses of mortality and disease severity among 402 patients from a single hospital. The cohort includes 297 patients with confirmed and 105 with clinical diagnosis. The latter group consists of cases with inconclusive nucleic acid test but meeting the criteria for clinical diagnosis. Data are compared between sexes and among different age groups. The overall case fatality is 5.2%. However, age at 70 years or older is associated with a significantly higher mortality (17.8%) and higher rate of severe and critical illness (57.5%). Case fatality is 8% in patients 50 years of age or older, and 1.2% in those younger than 50 years. In addition, case fatality is 7.6% in male patients, as opposed to 2.9% in females, demonstrating a clear sex difference., (© 2020 Wiley Periodicals LLC.)

Purpose: The COVID-19 pandemic has greatly affected people's mental health. The direct and indirect pathways between social support and suicidal ideation in the period are still unclear. This study explores the pathways from social support to suicidal ideation through resilience and depressive symptoms among undergraduates during the COVID-19 campus lockdown. Methods: During two weeks of the COVID-19 campus lockdown, a total of 12,945 undergraduates at a university in eastern China completed the questionnaire including sociodemographic characteristics, suicidal ideation, social support, resilience, and depressive symptoms. A structural equation modeling (SEM) approach was used to analyze the direct and indirect pathways from social support to suicidal ideation via the mediators of resilience and depressive symptoms. Results: Of the 12,917 undergraduates included in this study, 7.4% (n = 955) reported they sometimes had suicidal ideation, 0.8% (n = 109) reported they often had suicidal ideation, 0.9% (n = 122) reported they always had suicidal ideation, and 13.2% (n = 1704) reported they had depressive symptoms. Social support exerted significant direct (β = − 0.058), indirect (β = − 0.225), and total (β = − 0.283) effects on suicidal ideation; 20.5% of the total effect was direct, and 79.5% was indirect. Social support predicted suicidal ideation through resilience (β = − 0.038), and depressive symptoms (β = − 0.087), explaining 13.4%, and 30.7% of the total effect, respectively. Social support predicted suicidal ideation through the sequential mediation of resilience and depressive symptoms (β = − 0.099), explaining 35.0% of the total effect. Conclusion: This is the first study to provide the evidence of pathways from social support to suicidal ideation through resilience and depressive symptoms during the COVID-19 campus lockdown among undergraduates in China. Both direct and indirect pathways from social support to suicidal ideation were identified as intervention targets to reduce suicidal ideation. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related mis-diagnosis. Reliable automatic classification algorithms can be really helpful; however, they usually require a considerable number of COVID-19 cases for training, which is difficult to acquire in a timely manner. Meanwhile, how to effectively utilize the existing archive of non-COVID-19 data (the negative samples) in the presence of severe class imbalance is another challenge. In addition, the sudden disease outbreak necessitates fast algorithm development. In this work, we propose a novel approach for effective and efficient training of COVID-19 classification networks using a small number of COVID-19 CT exams and an archive of negative samples. Concretely, a novel self-supervised learning method is proposed to extract features from the COVID-19 and negative samples. Then, two kinds of soft-labels (‘difficulty’ and ‘diversity’) are generated for the negative samples by computing the earth mover's distances between the features of the negative and COVID-19 samples, from which data ‘values’ of the negative samples can be assessed. A pre-set number of negative samples are selected accordingly and fed to the neural network for training. Experimental results show that our approach can achieve superior performance using about half of the negative samples, substantially reducing model training time. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This case series describes reverse transcriptase–polymerase chain reaction (RT-PCR) test results in 4 health professionals discharged from hospitalization or quarantine after 2 negative RT-PCR test results and resolution of clinical COVID-19 infection. [ABSTRACT FROM AUTHOR]

• Joint segmentation and detection framework for COVID-19. • Context enhancement module for semantic segmentation. • Context enhancement at multiple resolutions. • Optimize segmentation and detection results through post-processing. • Obtained state-of-the-art results for COVID-19 segmentation and detection. The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database. [ABSTRACT FROM AUTHOR]

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases. • We build a system for detecting COVID-19 infection regions. • We build a dataset of COVID-19. • It is easy to distinguish pneumonia from healthy cases. [ABSTRACT FROM AUTHOR]

Lonicerae japonicae flos (LJF), known as Jin Yin Hua in Chinese, is one of the most commonly used traditional Chinese herbs and nutraceuticals. Nowadays, LJF is broadly applied in an array of afflictions, such as fever, sore throat, flu infection, cough, and arthritis, with the action mechanism to be elucidated. Here, we strove to summarize the main phytochemical components of LJF and review its updated pharmacological effects, including inhibition of inflammation, pyrexia, viruses, and bacteria, immunoregulation, and protection of the liver, nervous system, and heart, with a focus on the potential efficacy of LJF on coronavirus disease–2019 based on network pharmacology so as to fully underpin the utilization of LJF as a medicinal herb and a favorable nutraceutical in daily life. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is highly contagious, mainly causing inflammatory lesions in the lungs, and can also cause damage to the intestine and liver. The rapid spread of the virus that causes coronavirus disease 2019 (COVID-19) pneumonia has posed complex challenges to global public health. Early detection, isolation, diagnosis, and treatment are the most effective means of prevention and control. At present, the epidemic situation of new coronavirus infection has tended to be controlled in China, and it is still in a period of rapid rise in much of the world. The current gold standard for the diagnosis of COVID-19 is the detection of coronavirus nucleic acids, but imaging has an important role in the detection of lung lesions, stratification, evaluation of treatment strategies, and differentiation of mixed infections. This Chinese expert consensus statement summarizes the imaging features of COVID-19 pneumonia and may help radiologists across the world to understand this disease better. [ABSTRACT FROM AUTHOR]