CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading.

In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading. Source codes and datasets are available at our GitHub repository (https://github.com/RobotvisionLab/COVID-19-severity-grading.git).

BCG-induced trained immunity: history, mechanisms and potential applications.

The Bacillus Calmette-Guérin (BCG) vaccine was discovered a century ago and has since been clinically applicable. BCG can not only be used for the prevention of tuberculosis, but also has a non-specific protective effect on the human body called trained immunity that is mediated by innate immune cells such as monocytes, macrophages, and natural killer cells. Mechanisms of trained immunity include epigenetic reprogramming, metabolic reprogramming, and long-term protection mediated by hematopoietic stem cells. Trained immunity has so far shown beneficial effects on cancer, viral-infections, autoimmune diseases, and a variety of other diseases, especially bladder cancer, respiratory viruses, and type 1 diabetes. The modulation of the immune response by BCG has led to the development of a variety of recombinant vaccines. Although the specific mechanism of BCG prevention on diseases has not been fully clarified, the potential role of BCG deserves further exploration, which is of great significance for prevention and treatment of diseases.

Safety and efficacy of glucocorticoids in the treatment of COVID-19: A meta-analysis of randomized control trials.

BACKGROUND: It is unclear the efficacy and safety of glucocorticoids compared with placebo or usual care for treatment of COVID-19. RESEARCH DESIGN AND METHODS: Randomized controlled trials (RCTs) of corticosteroids in COVID-19 patients from 1 December 2019, to 30 June 2022, were assessed using Cochrane bias risk assessment method and improved Jadad score scale. GRADEpro was used to rate the quality of evidence for outcomes. RESULTS: Fifteen RCTs were included, including 10,620 patients. Glucocorticoid treatment for severe and critical COVID-19 showed lesser all-cause mortality (OR = 0.85, 95% CI [0.76, 0.94], P = 0.002) than conventional treatment. However, for mildly ill patients, neither inhaled drugs nor intravenous drugs reduced mortality (OR = 0.64, 95% CI [0.24, 1.76], P = 0.39). Glucocorticoids had no significant effect on the adverse reactions of patients (OR = 1.18, 95% CI [0.77, 1.80], P = 0.44) compared with usual care/placebo. Subgroup analysis demonstrated that dexamethasone significantly reduced the mortality of COVID-19 patients. Low-dose glucocorticoids were also associated with lower all-cause mortality. CONCLUSION: Glucocorticoids (especially dexamethasone) reduce mortality of patients with severe and critical COVID-19 with no significant effect on the incidence of adverse reactions (moderate quality). In contrast, glucocorticoids do not benefit patients with mild symptoms (low quality).

Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review.

Since 2020, novel coronavirus pneumonia has been spreading rapidly around the world, bringing tremendous pressure on medical diagnosis and treatment for hospitals. Medical imaging methods, such as computed tomography (CT), play a crucial role in diagnosing and treating COVID-19. A large number of CT images (with large volume) are produced during the CT-based medical diagnosis. In such a situation, the diagnostic judgement by human eyes on the thousands of CT images is inefficient and time-consuming. Recently, in order to improve diagnostic efficiency, the machine learning technology is being widely used in computer-aided diagnosis and treatment systems (i.e., CT Imaging) to help doctors perform accurate analysis and provide them with effective diagnostic decision support. In this paper, we comprehensively review these frequently used machine learning methods applied in the CT Imaging Diagnosis for the COVID-19, discuss the machine learning-based applications from the various kinds of aspects including the image acquisition and pre-processing, image segmentation, quantitative analysis and diagnosis, and disease follow-up and prognosis. Moreover, we also discuss the limitations of the up-to-date machine learning technology in the context of CT imaging computer-aided diagnosis.

Lung Organoids as Model to Study SARS-CoV-2 Infection.

Coronavirus disease-2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic and has severely affected socio-economic conditions and people's life. The lung is the major target organ infected and (seriously) damaged by SARS-CoV-2, so a comprehensive understanding of the virus and the mechanism of infection are the first choices to overcome COVID-19. Recent studies have demonstrated the enormous value of human organoids as platforms for virological research, making them an ideal tool for researching host-pathogen interactions. In this study, the various existing lung organoids and their identification biomarkers and applications are summarized. At the same time, the seven coronaviruses currently capable of infecting humans are outlined. Finally, a detailed summary of existing studies on SARS-CoV-2 using lung organoids is provided and includes pathogenesis, drug development, and precision treatment. This review highlights the value of lung organoids in studying SARS-CoV-2 infection, bringing hope that research will alleviate COVID-19-associated lung infections.

FAM: focal attention module for lesion segmentation of COVID-19 CT images.

The novel coronavirus pneumonia (COVID-19) is the world's most serious public health crisis, posing a serious threat to public health. In clinical practice, automatic segmentation of the lesion from computed tomography (CT) images using deep learning methods provides an promising tool for identifying and diagnosing COVID-19. To improve the accuracy of image segmentation, an attention mechanism is adopted to highlight important features. However, existing attention methods are of weak performance or negative impact to the accuracy of convolutional neural networks (CNNs) due to various reasons (e.g. low contrast of the boundary between the lesion and the surrounding, the image noise). To address this issue, we propose a novel focal attention module (FAM) for lesion segmentation of CT images. FAM contains a channel attention module and a spatial attention module. In the spatial attention module, it first generates rough spatial attention, a shape prior of the lesion region obtained from the CT image using median filtering and distance transformation. The rough spatial attention is then input into two 7 × 7 convolution layers for correction, achieving refined spatial attention on the lesion region. FAM is individually integrated with six state-of-the-art segmentation networks (e.g. UNet, DeepLabV3+, etc.), and then we validated these six combinations on the public dataset including COVID-19 CT images. The results show that FAM improve the Dice Similarity Coefficient (DSC) of CNNs by 2%, and reduced the number of false negatives (FN) and false positives (FP) up to 17.6%, which are significantly higher than that using other attention modules such as CBAM and SENet. Furthermore, FAM significantly improve the convergence speed of the model training and achieve better real-time performance. The codes are available at GitHub (https://github.com/RobotvisionLab/FAM.git).

Influence of Internet Celebrity Medical Experts on COVID-19 Vaccination Intention of Young Adults: An Empirical Study From China.

With the continuous expansion of COVID-19, many medical experts with the characteristics of "Internet Celebrities" are increasingly influencing people's vaccination behavior, which is crucial for overall social welfare. To explore the influence of Internet celebrity medical experts on people's vaccination against COVID-19, this study constructed a conceptual model of COVID-19 vaccination intention based on the professionalism, morality, interaction dimension, and information content of Internet celebrity medical experts, to generate perceived value by establishing a trusting relationship between them and the influenced people. The empirical analysis shows that interactivity and information content are important factors determining the influence of Internet celebrity medical experts. In the context of high demands for COVID-19 vaccines, it is more effective to influence vaccination intention through strong demand than through generating trust. The empirical analysis shows that Internet celebrity medical experts have a significant role in COVID-19 vaccination, and interactivity and information content are two important factors determining the influence. Through the connection of information-demand, Internet celebrity medical experts can greatly influence the perceived value, by coaction with trust to influence the final intention. Therefore, the COVID-19 vaccination persuasion information released by Internet celebrity medical experts should be elaborately organized and demonstrated, especially from the demand aspect, and government could put more resources to support the information to spread.

Release and Demand of Public Health Information in Social Media During the Outbreak of COVID-19 in China.

Information release is a key to the macro-economy during the outbreak of the Coronavirus Diosease-2019 (COVID-19). To explore the relationship between information supply by the government and public information demand in the pandemic, this study collected over 4,000 posts published on the most popular social media platform, i.e., WeChat. Many approaches, such as text mining, are employed to explore the information at different stages during the pandemic. According to the results, the government attached great importance to the information related to the pandemic. The main topics of information released by the government included the latest situation of the pandemic, announcements by the State Council, and prevention policies for COVID-19. Information mismatch between the public and Chinese governments contributed to the economic depression caused by the pandemic. Specifically, the topics of "the latest situation" and "popular scientific knowledge regarding the pandemic" have gained the most attention of the public. The information demand of the public has changed from the pandemic itself to the recovery of social life and industrial activities after the authority announced the control of the pandemic. However, during the recession phase, the information demand has shifted to asymptomatic infections and global pandemic trends. By contrast, some of the main topics provided by the government, such as "How beautiful you are," were excessive because the public demand is insufficient. Therefore, severe mismatches existed between information release of the government and public information demand during the pandemic, which impeded the recovery of the economy. The results in this study provide strategical suggestions of information release and opinion guidance for the authorities.

Human Brain Organoids as an In Vitro Model System of Viral Infectious Diseases

Brain organoids, or brainoids, have shown great promise in the study of central nervous system (CNS) infection. Modeling Zika virus (ZIKV) infection in brain organoids may help elucidate the relationship between ZIKV infection and microcephaly. Brain organoids have been used to study the pathogenesis of SARS-CoV-2, human immunodeficiency virus (HIV), HSV-1, and other viral infections of the CNS. In this review, we summarize the advances in the development of viral infection models in brain organoids and their potential application for exploring mechanisms of viral infections of the CNS and in new drug development. The existing limitations are further discussed and the prospects for the development and application of brain organs are prospected.

Making the dynamic interactive map of COVID-19 by implementation of r software

Objective: To introduce the process and concrete steps of making dynamic interactive disease map using Leaflet program package of R software, and we provide assistance for demonstrating the regional distribution of the disease, epidemic analysis and field disposal.

Shift in the Dominant Sequence Type of Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infection from ST11 to ST15 at a Medical Center in Northeast China, 2015-2020.

OBJECTIVE: To investigate the clinical characteristics and molecular epidemiology of carbapenem-resistant Klebsiella pneumoniae (CRKP) bloodstream infection at a medical center in northeast China, especially after coronavirus disease (COVID-19) pandemic. METHODS: Fifty-one patients were diagnosed with CRKP bloodstream infection between January 2015 and December 2020, among which 42 isolates were available for further study. Species identification and antibiotic susceptibilities were tested with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and VITEK 2 systems. Carbapenemase genes, virulence genes and MLST genes were detected by polymerase chain reaction. Moreover, the string test and serum killing assay were performed to evaluate the virulence of the CRKP isolates. RESULTS: During the six-year period, the detection rate of CRKP in bloodstream infection showed an increasing trend, with the intensive care unit, hematology and respiratory medicine wards mainly affected. Molecular epidemiology analyses showed that KPC-2 was the dominant carbapenemase gene. In addition, the dominant sequence type (ST) of CRKP shifted from ST11 to ST15 strains, which were all sensitive to amikacin in contrast to the ST11 stains. Furthermore, ST15 CRKP strains were positive for the KfuB virulence gene and more resistant to serum killing compared to the ST11 CRKP strains. Nonetheless, the mortality rate of patients infected with ST11 and ST15 CRKP did not show any significant differences. CONCLUSION: A shift in the dominant sequence type of CRKP bloodstream infections from ST11 to ST15 was observed during the years 2015-2020. Compared to ST11, the ST15 CRKP strains showed amikacin sensitivity, positivity for KfuB gene, and serum resistance, which may indicate stronger virulence.

Does Asthma Increase the Mortality of Patients with COVID-19?: A Systematic Review and Meta-Analysis.

The purpose of this systematic review and meta-analysis was to explore the literature and collate data comparing the mortality of coronavirus disease 2019 (COVID-19) patients with and without asthma. The databases PubMed, Scopus, Embase, Google Scholar, and medRxiv.org were searched for studies comparing the clinical outcomes of asthmatic patients with those of nonasthmatic patients diagnosed with COVID-19. Mortality data were summarized using the Mantel-Haenszel OR with 95% CI in a random-effects model. Five retrospective studies met the inclusion criteria. A meta-analysis of data from 744 asthmatic patients and 8,151 nonasthmatic patients indicated that the presence of asthma had no significant effect on mortality (OR = 0.96; 95% CI 0.70-1.30; I2 = 0%; p = 0.79). Results were stable in a sensitivity analysis. A descriptive analysis of other clinical outcomes indicated no difference in the duration of hospitalization and the risk of intensive care unit (ICU) transfer between asthmatic and nonasthmatic patients. To conclude, preliminary data indicates that asthma as a comorbidity may not increase the mortality of COVID-19. Data on the influence of asthma on the risk of hospitalization, the duration of hospitalization, the requirement of ICU admission, and disease severity is still too limited to draw any strong conclusions. Further studies with a larger sample size are required to establish strong evidence.

Epidemiological characteristics of COVID-19 in Hainan Province, China

The epidemiology characteristics of 2019 novel coronavirus diseases (COVID-19) cases in Hainan were collected and analyzed for providing next stage control and prevention strategy in next stage. Spatial and temporal distribution, population characteristic, cluster, the interval between onset, visiting clinic, admitted were analyzed. Local cases and severe cases were also included in the analysis. Result showed that a total of 168 confirmed cases, including 36 severe cases and 5 fatal cases were reported. Cases were mainly distributed in Haikou, Sanya etc tourism cities and counties. The first case occurred in Jan 13th and the epidemic peak occurred in Jan 24th. Since Feb 6th, onset of illness has declined. The male-to-female ratio was 0.9:1. The median age was 51 years. Cases older than 50 years accounted for 54.8%. Retirees accounted for 36.9%, which was highest in all cases. Since Feb, the proportion of local cases rose dramatically. The period from onset to visiting clinic (OTV), from first visiting clinic to diagnosis (VTF), from onset to diagnosis (OTD) and from onset to be admitted (OTA) was longer in local cases than imported cases. Median age and the percentage of underlying diseases of severe/extreme cases were higher than mild/ordinary cases. OTV of severe/extreme cases was longer than mild/ordinary cases, while for VTF, the former was shorter than latter. The epidemic was divided into three stages. Most of cases in the first stage were imported cases, while in the second stage most of cases were local cases. There were few cases in the third stages. We should strengthen personal protection and health monitoring for people in service industry, isolate the close contacts, and carry out publicity and education to raise the awareness of medical treatment for people, especially for old people. Clinical doctors should monitor the state of the patients older than 60 years and with underlying diseases. We should step up epidemic monitoring prevention and control measure for people return from holiday and immigrant to consolidate the effects of prevention and control work.

Inhibitors of the renin-angiotensin system: The potential role in the pathogenesis of COVID-19.

Coronavirus disease 2019 (COVID-19), which initially began in China, has spread to other countries of Asia, Europe, America, Africa and Oceania, with the number of confirmed cases and suspected cases increasing each day. According to recently published research, it was found that the majority of the severe cases were elderly, and many of them had at least one chronic disease, especially cardiovascular diseases. Angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs) are the most widely used drugs for cardiovascular diseases. The clinical effect of ACEIs/ARBs on patients with COVID-19 is still uncertain. This paper describes their potential role in the pathogenesis of COVID-19, which may provide useful in the advice of cardiologists and physicians.