An ultrasensitive electrochemical sensor for detecting porcine epidemic diarrhea virus based on a Prussian blue-reduced graphene oxide modified glassy carbon electrode.

This study developed a novel, ultrasensitive sandwich-type electrochemical immunosensor for detecting the porcine epidemic diarrhea virus (PEDV). By electrochemical co-deposition of graphene and Prussian blue, a Prussian blue-reduced graphene oxide-modified glassy carbon electrode was made, further modified with PEDV-monoclonal antibodies (mAbs) to create a new PEDV immunosensor using the double antibody sandwich technique. The electrochemical characteristics of several modified electrodes were investigated using cyclic voltammetry (CV). We optimized the pH levels and scan rate. Additionally, we examined specificity, reproducibility, repeatability, accuracy, and stability. The study indicates that the immunosensor has good performance in the concentration range of 1 × 101.88 to 1 × 105.38 TCID50/mL of PEDV, with a detection limit of 1 × 101.93 TCID50/mL at a signal-to-noise ratio of 3σ. The composite membranes produced via co-deposition of graphene and Prussian blue effectively increased electron transport to the glassy carbon electrode, boosted response signals, and increased the sensitivity, specificity, and stability of the immunosensor. The immunosensor could accurately detect PEDV, with results comparable to real-time quantitative PCR. This technique was applied to PEDV detection and served as a model for developing additional immunosensors for detecting hazardous chemicals and pathogenic microbes.

Serum metabolomic abnormalities in survivors of non-severe COVID-19.

Metabolic reprogramming is a distinctive characteristic of SARS-CoV-2 infection, which refers to metabolic changes in hosts triggered by viruses for their survival and spread. It is current urgent to understand the metabolic health status of COVID-19 survivors and its association with long-term health consequences of infection, especially for the predominant non-severe patients. Herein, we show systemic metabolic signatures of survivors of non-severe COVID-19 from Wuhan, China at six months after discharge using metabolomics approaches. The serum amino acids, organic acids, purine, fatty acids and lipid metabolism were still abnormal in the survivors, but the kynurenine pathway and the level of itaconic acid have returned to normal. These metabolic abnormalities are associated with liver injury, mental health, energy production, and inflammatory responses. Our findings identify and highlight the metabolic abnormalities in survivors of non-severe COVID-19, which provide information on biomarkers and therapeutic targets of infection and cues for post-hospital care and intervention strategies centered on metabolism reprogramming.

UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.

BACKGROUND: With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. METHODS: This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. RESULTS: The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models. CONCLUSION: In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.

Clinical course and outcomes of COVID-19 patients with a history of cerebrovascular disease: a retrospective study in Wuhan.

BACKGROUND: Data on patients with coronavirus disease 2019 (COVID-19) who have pre-existing cerebrovascular disease (CVD) are scarce. This study set out to describe the clinical course and outcomes of these patients. METHODS: This single-center retrospective study was performed at Huoshenshan Hospital in Wuhan, China. Patients with confirmed COVID-19 who had pre-existing CVD (N=69) were identified. COVID-19 patients without CVD were randomly selected and matched by age and sex to the patients with CVD. Clinical data were analyzed and compared between the 2 groups. The composite endpoint included intensive care unit admission, use of mechanical ventilation, and death. Multivariable Cox regression analyses with control for medical comorbidities were used to examine the relationship between pre-existing CVD and clinical outcome of COVID-19. RESULTS: Compared with patients without CVD, patients with pre-existing CVD were more likely to present with unapparent symptoms at first; however, at admission, these patients tended to be in a severer condition than those without CVD, with more underlying hypertension and diabetes. The levels of interleukin-6, creative kinase MB, aspartate transaminase, and creatinine, as well as prothrombin time, were also markedly higher in patients with CVD. Patients with pre-existing CVD were more likely to develop multi-organ dysfunction, deteriorate to critical condition, and yield poorer clinical outcomes than patients without CVD. Concerning therapeutics, greater proportions of patients with pre-existing CVD required mechanical ventilation, higher-order anti-bacterials, and drugs targeting underlying diseases and complications. In the multivariable analysis, pre-existing CVD was significantly associated with a poor clinical outcome. CONCLUSIONS: Patients with a history of CVD are more vulnerable to an over-activated inflammatory response and subsequent multi-organ dysfunction, resulting in a poor clinical outcome. Close monitoring is advisable for these patients.

Efficient management strategy of COVID-19 patients based on cluster analysis and clinical decision tree classification.

Early classification and risk assessment for COVID-19 patients are critical for improving their terminal prognosis, and preventing the patients deteriorate into severe or critical situation. We performed a retrospective study on 222 COVID-19 patients in Wuhan treated between January 23rd and February 28th, 2020. A decision tree algorithm has been established including multiple factor logistic for cluster analyses that were performed to assess the predictive value of presumptive clinical diagnosis and features including characteristic signs and symptoms of COVID-19 patients. Therapeutic efficacy was evaluated by adopting Kaplan-Meier survival curve analysis and cox risk regression. The 222 patients were then clustered into two groups: cluster I (common type) and cluster II (high-risk type). High-risk cases can be judged from their clinical characteristics, including: age > 50 years, chest CT images with multiple ground glass or wetting shadows, etc. Based on the classification analysis and risk factor analysis, a decision tree algorithm and management flow chart were established, which can help well recognize individuals who needs hospitalization and improve the clinical prognosis of the COVID-19 patients. Our risk factor analysis and management process suggestions are useful for improving the overall clinical prognosis and optimize the utilization of public health resources during treatment of COVID-19 patients.

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.

Special strategies and management of urological diseases during the COVID-19 Pandemic: initial experiences from a Medical Center of China

ABSTRACT Although urological diseases are not directly related to coronavirus disease 2019 (COVID-19), urologists need to make comprehensive plans for this disease. Urological conditions such as benign prostatic hyperplasia and tumors are very common in elderly patients. This group of patients is often accompanied by underlying comorbidities or immune dysfunction. They are at higher risk of COVID-19 infection and they tend to have severe manifestations. Although fever can occur along with urological infections, it is actually one of the commonest symptoms of COVID-19;urologists must always maintain a high index of suspicion in their clinical practices. As a urological surgeon, how we can protect medical staff during surgery is a major concern. Our hospital had early adoption of a series of strict protective and control measures, and was able to avoid cross-infection and outbreak of COVID-19. This paper discusses the effective measures that can be useful when dealing with urological patients with COVID-19.

Special strategies and management of urological diseases during the COVID-19 pandemic: initial experiences from a Medical Center of China.

Although urological diseases are not directly related to coronavirus disease 2019 (COVID-19), urologists need to make comprehensive plans for this disease. Urological conditions such as benign prostatic hyperplasia and tumors are very common in elderly patients. This group of patients is often accompanied by underlying comorbidities or immune dysfunction. They are at higher risk of COVID-19 infection and they tend to have severe manifestations. Although fever can occur along with urological infections, it is actually one of the commonest symptoms of COVID-19; urologists must always maintain a high index of suspicion in their clinical practices. As a urological surgeon, how we can protect medical staff during surgery is a major concern. Our hospital had early adoption of a series of strict protective and control measures, and was able to avoid cross-infection and outbreak of COVID-19. This paper discusses the effective measures that can be useful when dealing with urological patients with COVID-19.

COVID-19 In Shang Hai: It is Worth Learning from the Successful Experience in Preventing and Controlling the Overseas Epidemic Situation (preprint)

BackgroundCOVID-19 first appeared in Wuhan, Hubei Province, China in late December 2019 and spread rapidly in China. Currently, the spread of local epidemics has been basically blocked. The import of overseas epidemics has become the main form of growth in China ’s new epidemic. As an important international transportation hub in China, Shanghai is one of the regions with the highest risk of imported cases abroad. Due to imported of overseas cases are affected by the international epidemic trend. The traditional infectious disease model is difficult to accurately predict the cumulative trend of cumulative cases in the Shanghai areas. It is also difficult to accurately evaluate the effectiveness of the international traffic blockade.MethodsIn this situation, this study takes Shanghai as an example to propose a new type of infectious disease prediction model. The model first uses the sparse graph model to analyze the international epidemic spread network to find countries and regions related to Shanghai. Next, multiple regression models were used to fit the existing COVID-19 growth data in Shanghai. Finally, the model predicts the growth curve of the COVID-19 epidemic in Shanghai without an international traffic blockade.ResultsIn this study, by constructing a sparse graph network model, 30 countries and regions related to Shanghai's overseas epidemic input were obtained, such as the United States. Moreover, the three regression models in this paper have obtained a good fitting effect. Finally, using data from 30 countries and regions related to Shanghai from April 4 to April 19 for a 15-day short-term forecast and comparing with real data, the results show that Shanghai’s international traffic blockade is effective and necessary.ConclusionThis research show that the control measures taken by Shanghai are very effective. At present, more and more countries and regions will face the current situation in Shanghai. We recommend that other countries and regions learn from Shanghai ’s successful experience in preventing overseas imports in order to fully prepare for epidemic prevention and control.

COVID-19 In Shang Hai: It is Worth Learning from the Successful Experience in Preventing and Controlling the Overseas Epidemic Situation (preprint)

COVID-19 first appeared in Wuhan, Hubei Province,China in late December 2019 and spread rapidly in China. Currently, the spread of local epidemics has been basically blocked. The import of overseas epidemics has become the main form of growth in China's new epidemic. As an important international transportation hub in China, Shanghai is one of the regions with the highest risk of imported cases abroad. Due to imported of overseas cases are affected by the international epidemic trend. The traditional infectious disease model is difficult to accurately predict the cumulative trend of cumulative cases in the Shanghai areas. It is also difficult to accurately evaluate the effectiveness of the international traffic blockade. In this situation, this study takes Shanghai as an example to propose a new type of infectious disease prediction model. The model first uses the sparse graph model to analyze the international epidemic spread network to find countries and regions related to Shanghai. Next, multiple regression models were used to fit the existing COV-19 growth data in Shanghai. Finally, the model can predict the growth curve of Shanghai's epidemic without blocking traffic. The results show that the control measures taken by Shanghai are very effective. At present, more and more countries and regions will face the current situation in Shanghai. We recommend that other countries and regions learn from Shanghai's successful experience in preventing overseas imports in order to fully prepare for epidemic prevention and control.

A Sparse Gaussian Network Model for Prediction the Growth Trend of COVID-19 Overseas Import Case: When can Hong Kong Lift the International Traffic Blockad? (preprint)

The COVID-19 virus was first discovered from China. It has been widely spread internationally. Currently, compare with the rising trend of the overall international epidemic situation, China's domestic epidemic situation has been contained and shows a steady and upward trend. In this situation, overseas imports have become the main channel for china to increase the number of infected people. Therefore, how to track the spread channel of international epidemics and predict the growth of overseas case imports is become an open research question. This study proposes a Gaussian sparse network model based on lasso and uses Hong Kong as an example. To explore the COVID-19 virus from a network perspective and analyzes 75 consecutive days of COV-19 data in 188 countries and regions around the world. This article establishes an epidemic spread relationship network between Hong Kong and various countries and regions around the world and build a regression model based on network information to fit Hong Kong's COV-19 epidemic growth data. The results show that the regression model based on the relationship network can better fit the existing cumulative number growth curve. After combining the SEIJR model, we predict the future development trend of cumulative cases in Hong Kong (without blocking international traffic). Based on the prediction results, we suggest that Hong Kong can lift the international traffic blockade from early to mid-June