Efficacy and Safety of Triazavirin Therapy for Coronavirus Disease 2019: A Pilot Randomized Controlled Trial

No therapeutics have been proven effective yet for the treatment of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To assess the efficacy and safety of Triazavirin therapy for COVID-19, we conducted a randomized, double-blinded controlled trial involving hospitalized adult patients with COVID-19. Participants were enrolled from ten sites, and were randomized into two arms of the study with a ratio of 1:1. Patients were treated with Triazavirin 250 mg versus a placebo three or four times a day for 7 d. The primary outcome was set as the time to clinical improvement, defined as normalization of body temperature, respiratory rate, oxygen saturation, cough, and absorption of pulmonary infection by chest computed tomography (CT) until 28 d after randomization. Secondary outcomes included individual components of the primary outcome, the mean time and proportion of inflammatory absorption in the lung, and the conversion rate to a repeated negative SARS-CoV-2 nucleic acid test of throat swab sampling. Concomitant therapeutic treatments, adverse events, and serious adverse events were recorded. Our study was halted after the recruitment of 52 patients, since the number of new infections in the participating hospitals decreased greatly. We randomized 52 patients for treatment with Triazavirin (n = 26) or a placebo (n = 26). We found no differences in the time to clinical improvement (median, 7 d versus 12 d; risk ratio (RR), 2.0; 95% confidence interval (CI), 0.7–5.6; p = 0.2), with clinical improvement occurring in ten patients in the Triazavirin group and six patients in the placebo group (38.5% versus 23.1%; RR, 2.1; 95% CI, 0.6–7.0; p = 0.2). All components of the primary outcome normalized within 28 d, with the exception of absorption of pulmonary infection (Triazavirin 50.0%, placebo 26.1%). Patients in the Triazavirin group used less frequent concomitant therapies for respiratory, cardiac, renal, hepatic, or coagulation supports. Although no statistically significant evidence was found to indicate that Triazavirin benefits COVID-19 patients, our observations indicated possible benefits from its use to treat COVID-19 due to its antiviral effects. Further study is required for confirmation.     

Coronavirus disease 2019 (COVID-19) in two pediatric patients with kidney disease on chronic immunosuppression: A case series

Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). While children appear to experience less severe disease than adults, those with underlying conditions such as kidney disease may be more susceptible to infection. Limited data are present for children with kidney disease, and there are limited prior reports of pediatric hemodialysis patients with COVID-19. This report describes the mild clinical disease course of COVID-19 in two pediatric patients with chronic kidney disease, one on hemodialysis and both on chronic immunosuppression. We review treatment in these patients, as well as our measures to reduce transmission among our hemodialysis patients and staff.     

Temporal Profiles of Antibody Responses,Cytokines, and Survival of COVID-19 Patients: A Retrospective Cohort

The longitudinal immunologic status of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients and its association with the clinical outcome are barely known. Thus, we sought to analyze the temporal profiles of specific antibodies, as well as the associations between the antibodies, proinflammatory cytokines, and survival of patients with coronavirus disease 2019 (COVID-19). A total of 1830 laboratory-confirmed COVID-19 cases were recruited. The temporal profiles of the virus, antibodies, and cytokines of the patients until 12 weeks since illness onset were fitted by the locally weighted scatter plot smoothing method. The mediation effect of cytokines on the associations between antibody responses and survival were explored by mediation analysis. Of the 1830 patients, 1435 were detectable for SARS-CoV-2, while 395 were positive in specific antibodies only. Of the 1435 patients, 2.4% presented seroconversion for neither immunoglobulin G (IgG) nor immunoglobulin M (IgM) during hospitalization. The seropositive rates of IgG and IgM were 29.6% and 48.1%, respectively, in the first week, and plateaued within five weeks. For the patients discharged from the hospital, the IgM decreased slowly, while high levels of IgG were maintained at around 188 AU·mL⁻¹ for the 12 weeks since illness onset. In contrast, in the patients who subsequently died, IgM declined rapidly and IgG dropped to 87 AU·mL⁻¹ at the twelfth week. Elevated interleukin-6, interleukin-8, interleukin-10, interleukin-1β, interleukin-2R, and tumor necrosis factor-α levels were observed in the deceased patients in comparison with the discharged patients, and 12.5% of the association between IgG level and mortality risk was mediated by these cytokines. Our study deciphers the temporal profiles of SARS-CoV-2-specific antibodies within the 12 weeks since illness onset and indicates the protective effect of antibody response on survival, which may help to guide prognosis estimation.     

The Scientific Foundation of Chinese Herbal Medicine against COVID-19

The recent coronavirus disease 2019 (COVID-19) pandemic outbreak has caused a serious global health emergency. Supporting evidence shows that COVID-19 shares a genomic similarity with other coronaviruses, such as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), and that the pathogenesis and treatment strategies that were applied 17 years ago in combating SARS-CoV and other viral infections could be taken as references in today’s antiviral battle. According to the clinical pathological features of COVID-19 patients, patients can suffer from five steps of progression, starting with severe viral infection and suppression of the immune system and eventually progressing to cytokine storm, multi-organ damage, and lung fibrosis, which is the cause of mortality. Therefore, early prevention of disease progression is important. However, no specific effective drugs and vaccination are currently available, and the World Health Organization is urging the development of novel prevention and treatment strategies. Traditional Chinese medicine could be used as an alternative treatment option or in combination with Western medicine to treat COVID-19, due to its basis on historical experience and holistic pharmacological action. Here, we summarize the potential uses and therapeutic mechanisms of Chinese herbal formulas (CHFs) from the reported literature, along with patent drugs that have been recommended by institutions at the national and provincial levels in China, in order to verify their scientific foundations for treating COVID-19. In perspective, more basic and clinical studies with multiple high-tech and translational technologies are suggested to further confirm the therapeutic efficacies of CHFs.     

Strategies and Advances in Combating COVID-19 in China

Coronavirus disease 2019 (COVID-19)—the third in a series of coronavirus infections—has caused a global public health event in the 21st century, resulting in substantial global morbidity and mortality. Building on its legacy of managing severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), China has played a key role in the scientific community by revealing the viral transmission routes and clinical characteristics of COVID-19 and developing novel therapeutic interventions and vaccines. Despite these rapid scientific and technological advances, uncertainties remain in tracing the original sources of infection, determining the routes of transmission and pathogenesis, and addressing the lack of targeted clinical management of COVID-19. Here, we summarize the major COVID-19 research advances in China in order to provide useful information for global pandemic control.     

关于新冠肺炎疫情防控中计量保障工作的几点思考

本文总结了新型冠状病毒肺炎疫情防控中所涉及的计量器具类别,以及对其进行检定、校准、检测等量值保障活动所适用的技术规范。围绕计量工作在新型冠状病毒肺炎疫情防控中的重要作用,文章重点从体温筛查、设备消毒、病毒检测、临床诊断、医疗救治、科学研究等方面分析了计量工作对新冠肺炎疫情防控的技术支撑和量值保障作用。为各疫情防控单位和医疗机构判断防控设备测量结果是否准确、可靠等问题是提供解决路径,也为各级计量技术机构和广大计量工作者针对疫情防控建立相关计量标准提供参考。     

Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome

In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV-2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’ complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.     

Automated contact tracing: a game of big numbers in the time of COVID-19

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.     

Clinical Characteristics and Outcomes of Type 2 Diabetes Patients Infected with COVID-19: A Retrospective Study

Diabetes and its related metabolic disorders have been reported as the leading comorbidities in patients with coronavirus disease 2019 (COVID-19). This clinical study aims to investigate the clinical features, radiographic and laboratory tests, complications, treatments, and clinical outcomes in COVID-19 patients with or without diabetes. This retrospective study included 208 hospitalized patients (≥ 45 years old) with laboratory-confirmed COVID-19 during the period between 12 January and 25 March 2020. Information from the medical record, including clinical features, radiographic and laboratory tests, complications, treatments, and clinical outcomes, were extracted for the analysis. 96 (46.2%) patients had comorbidity with type 2 diabetes. In COVID-19 patients with type 2 diabetes, the coexistence of hypertension (58.3% vs 31.2%), coronary heart disease (17.1% vs 8.0%), and chronic kidney diseases (6.2% vs 0%) was significantly higher than in COVID-19 patients without type 2 diabetes. The frequency and degree of abnormalities in computed tomography (CT) chest scans in COVID-19 patients with type 2 diabetes were markedly increased, including ground-glass opacity (85.6% vs 64.9%, P < 0.001) and bilateral patchy shadowing (76.7% vs 37.8%, P < 0.001). In addition, the levels of blood glucose (7.23 mmol·L⁻¹ (interquartile range (IQR): 5.80–9.29) vs 5.46 mmol·L⁻¹ (IQR: 5.00–6.46)), blood low-density lipoprotein cholesterol (LDL-C) (2.21 mmol·L⁻¹ (IQR: 1.67–2.76) vs 1.75 mmol·L⁻¹ (IQR: 1.27–2.01)), and systolic pressure (130 mmHg (IQR: 120–142) vs 122 mmHg (IQR: 110–137)) (1 mmHg = 133.3 Pa) in COVID-19 patients with diabetes were significantly higher than in patients without diabetes (P < 0.001). The coexistence of type 2 diabetes and other metabolic disorders is common in patients with COVID-19, which may potentiate the morbidity and aggravate COVID-19 progression. Optimal management of the metabolic hemostasis of glucose and lipids is the key to ensuring better clinical outcomes. Increased clinical vigilance is warranted for COVID-19 patients with diabetes and other metabolic diseases that are fundamental and chronic conditions.     

Facilities for Centralized Isolation and Quarantine for the Observation and Treatment of Patients with COVID-19

The coronavirus disease 2019 (COVID-19) pandemic increased the burden on many healthcare systems and in the process, exposed the need for medical resources and physical space. While few studies discussed the efficient utilization of medical resources and physical space so far. Therefore, this study aimed to summarize experiences related to facilities used for centralized isolation for medical observation and treatment during the COVID-19 pandemic in China and to provide suggestions to further improve the management of confirmed cases, suspected cases, and close contacts. In China, three types of facilities for centralized isolation (Fangcang shelter hospitals, refitted non-designated hospitals, and quarantine hotels) underwent retrofitting for the treatment and isolation of confirmed and suspected cases. These facilities mitigated the immediate high demand for space. Moreover, in order to minimize infection risks in these facilities, regulators and governmental agencies implemented new designs, management measures, and precautionary measures to minimize infection risk. Other countries and regions could refer to China's experience in optimally allocating social resources in response to the COVID-19 pandemic. As a conclusion, government should allocate social resources and construct centralized isolation and quarantine facilities for an emergency response, health authorities should issue regulations for centralized isolation facilities and pay strict attention to the daily management of these facilities, a multidisciplinary administration team is required to support the daily operation of a centralized isolation facility, in-depth studies and international collaboration on the centralized isolation policy are encouraged.     

Experimental Treatment with Favipiravir for COVID-19: An Open-Label Control Study

There is currently an outbreak of respiratory disease caused by a novel coronavirus. The virus has been named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease it causes has been named coronavirus disease 2019 (COVID-19). More than 16% of patients developed acute respiratory distress syndrome, and the fatality ratio was 1%–2%. No specific treatment has been reported. Herein, we examined the effects of favipiravir (FPV) versus lopinavir (LPV)/ritonavir (RTV) for the treatment of COVID-19. Patients with laboratory-confirmed COVID-19 who received oral FPV (Day 1: 1600 mg twice daily; Days 2–14: 600 mg twice daily) plus interferon (IFN)-α by aerosol inhalation (5 million international unit (IU) twice daily) were included in the FPV arm of this study, whereas patients who were treated with LPV/RTV (Days 1–14: 400 mg/100 mg twice daily) plus IFN-α by aerosol inhalation (5 million IU twice daily) were included in the control arm. Changes in chest computed tomography (CT), viral clearance, and drug safety were compared between the two groups. For the 35 patients enrolled in the FPV arm and the 45 patients in the control arm, all baseline characteristics were comparable between the two arms. A shorter viral clearance median time was found for the FPV arm versus the control arm (4 d (interquartile range (IQR): 2.5–9) versus 11 d (IQR: 8–13), P < 0.001). The FPV arm also showed significant improvement in chest CT compared with the control arm, with an improvement rate of 91.43% versus 62.22% (P = 0.004). After adjustment for potential confounders, the FPV arm also showed a significantly higher improvement rate in chest CT. Multivariable Cox regression showed that FPV was independently associated with faster viral clearance. In addition, fewer adverse events were found in the FPV arm than in the control arm. In this open-label before-after controlled study, FPV showed better therapeutic responses on COVID-19 in terms of disease progression and viral clearance. These preliminary clinical results provide useful information of treatments for SARS-CoV-2 infection.     

Potential Inhibitory Effect of Vitamins Against COVID-19

Coronavirus disease 2019 (COVID-19) is a current pandemic that has affected more than 195 countries worldwide. In this severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, when treatment strategies are not yet clear and vaccines are not available, vitamins are an excellent choice to protect against this viral infection. The rationale behind this study was to examine the inhibitory effect of vitamins B, C, and D against the main protease of SARSCoV-2 and angiotensin-converting enzyme 2 (ACE2), which have critical rolesin the immune system. Molecular docking, performed by using MOE-Dock of the Chemical Computing Group, was used to understand the mechanism. The vitamins all docked within the active sites of the M^pro (PDB ID:6LU7) and ACE2 receptor proteins (PDB ID:6VW1). Vitamins B and C delivered maximum energy scores against both targets, while vitamin D displayed a binding energy score of −7.9532 kcal/mol for M^pro and −7.9297 for ACE2. The efficiency of all three vitamins is higher than the binding energy score of chloroquine (−6.889 kcal/mol), which is now under clinical trials. The use of vitamins is beneficial, being immune system restorative, and they also act as anti-COVID agents. Although the potential beneficial effects of vitamin B and C are revealed through docking studies, further clinical trials are required for the validation of these results.     

Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners. Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22, 2020, to May 30, 2020. The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method. The results reveal that the GBR model achieves 0.00686 root mean square error, the lowest among several comparative models.     

Nanotheranostics and its role in diagnosis,treatment and prevention of COVID-19

Microbe-related, especially viral-related pandemics have currently paralyzed the world and such pathogenesis is expected to rise in the upcoming years. Although tremendous efforts are being made to develop antiviral drugs, very limited progress has been made in this direction. The nanotheranostic approach can be a highly potential rescue to combat this pandemic. Nanoparticles (NPs) due to their high specificity and biofunctionalization ability could be utilized efficiently for prophylaxis, diagnosis and treatment against microbial infections. In this context, titanium oxide, silver, gold NPs, etc. have already been utilized against deadly viruses like influenza, Ebola, HIV, and HBV. The discovery of sophisticated nanovaccines is under investigation and of prime importance to induce reproducible and strong immune responses against difficult pathogens. This review focuses on highlighting the role of various nano-domain materials such as metallic NPs, magnetic NPs, and quantum dots in the biomedical applications to combat the deadly microbial infections. Further, it also discusses the nanovaccines those are already available for various microbial diseases or are in clinical trials. Finally, it gives a perspective on the various nanotechnologies presently employed for efficient diagnosis and therapy against disease causing microbial infections, and how advancement in this field can benefit the health sector remarkably.     

Deep Learning Approach for Analysis and Characterization of COVID-19

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images of patients, developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases, while 397 belong to negative cases of COVID-19. Our experiment resulted in an accuracy of 98.4%, sensitivity of 98.5%, specificity of 98.3%, precision of 97.1%, and F1-score of 97.8%. The additional parameters of classification error, mean absolute error (MAE), root-mean-square error (RMSE), and Matthew’s correlation coefficient (MCC) are used to evaluate our proposed work. The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.     

Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

Lightweight deep convolutional neural networks (CNNs) present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients. Recently, advantages of portable Ultrasound (US) imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases. In this paper, a new framework of lightweight deep learning classifiers, namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images. Compared to traditional deep learning models, lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources. Four main lightweight deep learning models, namely MobileNets, ShuffleNets, MENet and MnasNet have been proposed to identify the health status of lungs using US images. Public image dataset (POCUS) was used to validate our proposed COVID-LWNet framework successfully. Three classes of infectious COVID-19, bacterial pneumonia, and the healthy lung were investigated in this study. The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0% and 647.0 s, respectively. This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobile-based radiological tool for clinical diagnosis of COVID-19 and other lung diseases.