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242 lines
59 KiB
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<title>30 November, 2020</title>
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<title>Covid-19 Sentry</title><meta content="width=device-width, initial-scale=1.0" name="viewport"/><link href="styles/simple.css" rel="stylesheet"/><link href="../styles/simple.css" rel="stylesheet"/><link href="https://unpkg.com/aos@2.3.1/dist/aos.css" rel="stylesheet"/><script src="https://unpkg.com/aos@2.3.1/dist/aos.js"></script></head>
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<h1 data-aos="fade-down" id="covid-19-sentry">Covid-19 Sentry</h1>
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<h1 data-aos="fade-right" data-aos-anchor-placement="top-bottom" id="contents">Contents</h1>
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<ul>
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<li><a href="#from-preprints">From Preprints</a></li>
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<li><a href="#from-clinical-trials">From Clinical Trials</a></li>
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<li><a href="#from-pubmed">From PubMed</a></li>
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<li><a href="#from-patent-search">From Patent Search</a></li>
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</ul>
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<h1 data-aos="fade-right" id="from-preprints">From Preprints</h1>
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<li><strong>Predicting COVID-19 Dynamics Using SEIR-PADC Model</strong> -
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<div>
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There are a number of derivates of SIR type models developed in mathematics community with 5 to 8 ordinary differential equations to include detailed mechanisms. These models have included exposed, deceased, super-spreader, symptomatic and asymptomatic infected and hospitalized populations; but are mathematically complex and cumbersome. These methods rarely used actual clinical data in details and usually fitted with one or maximum two major clinical data. In this paper, we introduce SEIR-PADC model to include exposed, deceased, super-spreader and critical populations and divide infected population to symptomatic and asymptomatic. SEIR-PADC model is a set of 8 ordinary differential equations with 12 unknown coefficients. Along with, we used an optimization algorithm in MATLAB to find best fit coefficients to 5 set of COVID-19 data in Kuwait. Our focus is to track trends of COVID-19 in coming days. Initial conditions for 8 populations and initial guess values for 12 unknown coefficients are found in a way to best fit COVID-19 data. We used 136 days of COVID-19 data in Kuwait and obtained solutions to cumulative populations rather than daily population. Predictions for 5 different population of COVID-19 in Kuwait using SEIR-PADC model are promising and are discussed here.
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</div>
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<div class="article-link article-html-link">
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🖺 Full Text HTML: <a href="https://osf.io/yqcs2/" target="_blank">Predicting COVID-19 Dynamics Using SEIR-PADC Model</a>
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</div></li>
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<li><strong>Trends of COVID-19 (Coronavirus Disease) in GCC Countries using SEIR-PAD Dynamic Model</strong> -
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Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.
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</div>
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<div class="article-link article-html-link">
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🖺 Full Text HTML: <a href="https://osf.io/rean9/" target="_blank">Trends of COVID-19 (Coronavirus Disease) in GCC Countries using SEIR-PAD Dynamic Model</a>
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</div></li>
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<li><strong>Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD</strong> -
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<div>
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On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study’s main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.
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<div class="article-link article-html-link">
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🖺 Full Text HTML: <a href="https://osf.io/c7twb/" target="_blank">Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD</a>
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</div></li>
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<li><strong>Predicting COVID-19 (Coronavirus Disease) Outbreak Dynamics Using SIR-based Models: Comparative Analysis of SIRD and Weibull-SIRD</strong> -
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<div>
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The SIR type models are built by a set of ordinary differential equations (ODE), which are strongly initial value dependant. To fit multiple biological data with SIR type equations requires fitting coefficients of these equations by an initial guess and applying optimization methods. These coefficients are also extremely initial value-dependent. In the vast publication of these types, we hardly see, among simple to highly complicated SIR type methods, that these methods presented more than a maximum of two biological data sets. We propose a novel method that integrates an analytical solution of the infectious population using Weibull distribution function into any SIR type models. The Weibull-SIRD method has easily fitted 4 set of COVID-19 biological data simultaneously. It is demonstrated that the Weibull-SIRD method predictions for susceptible, infected, recovered, and deceased populations from COVID-19 in Kuwait and UAE are superior compared with SIRD original ODE model. The proposed method here opens doors for new deeper studying of biological dynamic systems with realistic biological data trends than providing some complicated, cumbersome mathematical methods with little insight into biological data’s real physics.
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</div>
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🖺 Full Text HTML: <a href="https://osf.io/b9nj7/" target="_blank">Predicting COVID-19 (Coronavirus Disease) Outbreak Dynamics Using SIR-based Models: Comparative Analysis of SIRD and Weibull-SIRD</a>
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<li><strong>The Kids Are Not Alright: A Preliminary Report of Post-COVID Syndrome in University Students</strong> -
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Background: Post-COVID syndrome is increasingly recognized by the medical community but has not been studied exclusively in young adults. This preliminary report investigates the prevalence and features of protracted symptoms in non-hospitalized university students who experienced mild-to-moderate acute illness. Methods: 148 students completed an online study to earn research credit for class. Data from COVID-19 positive participants with symptoms ≥28 days (N=22) were compared to those who fully recovered (N=21) and those not diagnosed with COVID-19 (N=58). Results: 51% of participants who contracted COVID-19 (N=43) experienced symptoms ≥28 days and were classified as having post-COVID syndrome; all but one (96%) were female. During acute illness the post-COVID group, compared to those who fully recovered, experienced significantly more chest pain (64% vs 14%; P=.002), fatigue (86% vs 48%; P=.009), fever (82% vs 48%; P=.02), olfactory impairment (82% vs 52%; P=.04), headaches (32% vs 5%; P<.05), and diarrhea (32% vs 5%; P<.05). Compared to those not diagnosed with COVID-19, the post-COVID syndrome group more frequently experienced exercise intolerance (43% vs. 0%; P<.001), dyspnea (43% vs. 0%; P<.001), chest pain (31% vs 7%; P=.002), olfactory impairment (19% vs 0%; P=.004), lymphadenopathy (19% vs 0%; P=.004), gustatory impairment (14% vs 0%; P=.02), and appetite loss (36% vs 14%; P=.02). Conclusions: Our results contradict the perception that this yet-to-be-defined post-COVID syndrome predominantly affects middle-aged adults and suggest that exercise intolerance, dyspnea, chest pain, chemosensory impairment, lymphadenopathy, rhinitis, and appetite loss may differentiate post-COVID syndrome from general symptoms of pandemic, age, and academic related stress. These findings are also consistent with previous reports that females are more vulnerable to this post viral syndrome. Large-scale population-based studies are essential to discerning the magnitude and characterization of post-COVID syndrome in young adults as well as more diverse populations.
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<div class="article-link article-html-link">
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🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.11.24.20238261v1" target="_blank">The Kids Are Not Alright: A Preliminary Report of Post-COVID Syndrome in University Students</a>
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</div></li>
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<li><strong>COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model</strong> -
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<div>
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Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm to fit 4-set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible. Trends of COVID-19 in Trends in Gulf Cooperation Council (GCC) countries are successfully predicted using available data from outbreak until 23rd June 2020. Promising results of SEIRPAD model provide insight into better management of COVID-19 pandemic in GCC countries.
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<div class="article-link article-html-link">
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🖺 Full Text HTML: <a href="https://osf.io/xevck/" target="_blank">COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model</a>
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</div></li>
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<li><strong>Sensitivity Analysis on Predictive Capability of SIRD Model for Coronavirus Disease (COVID-19)</strong> -
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SIR model is one of the simplest methods used in prediction of endemic/pandemic outbreaks. We examined SIRD model for development of COVID-19 in Kuwait which was started on 24 February 2020 by 5 patients in Kuwait. This paper investigates sensitivity of SIRD model for development of COVID-19 in Kuwait based on duration of progressed days of data. For Kuwait, we have fitted SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed sensitivity of the model with number of days of data. The parameters of SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of SIRD model indicates that after 40 days the peak infectious day can be adequately predicted; althogh, error percentage from sensetivity analysis indicates that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19 and more precise methods are needed to tackle nonlinear dynamics of a pandemic.
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</div>
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<div class="article-link article-html-link">
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🖺 Full Text HTML: <a href="https://osf.io/vren6/" target="_blank">Sensitivity Analysis on Predictive Capability of SIRD Model for Coronavirus Disease (COVID-19)</a>
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<li><strong>Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction</strong> -
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The susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.
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<div class="article-link article-html-link">
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🖺 Full Text HTML: <a href="https://osf.io/upjns/" target="_blank">Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction</a>
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</div></li>
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<li><strong>Spatially resolved simulations of the spread of COVID-19 in European countries</strong> -
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We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governamental interventions, changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country.Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle countries which remain understudied.
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🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.11.25.20238600v1" target="_blank">Spatially resolved simulations of the spread of COVID-19 in European countries</a>
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<li><strong>The Impact of COVID-19 on Care Seeking Behavior of Patients at Tertiary Care Follow-up Clinics: A Cross-Sectional Telephone Survey. Addis Ababa, Ethiopia.</strong> -
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Abstract Background: COVID-19, the disease caused by the new coronavirus SARS-CoV-2 is among the most obscure global pandemics resulting in diverse health and economic disruptions. It adversely affects the routine health care delivery and health service uptake by patients. However, its impact on care-seeking behavior is largely unknown in Ethiopia. Objective: This study was to determine the impact of the pandemic on the care-seeking behavior of patients with chronic health conditions at Tikur Anbessa Specialized Hospital in Addis Ababa. Methods: A cross-sectional hospital-based survey conducted between May and July 2020 on patients whose appointment was between March to June 2020. A sample of 750 patients was approached using phone calls and data collection was done using a pretested questionnaire. After cleaning, the data entered into the IBM SPSS software package for analysis. Results: A total of 644 patients with a median age of 25 years, and an M: F ratio of 1:1.01 was described with a response rate of 86%. A loss to follow-up missed medication and death occurred in 70%, 12%, and 1.3% of the patients respectively. In the multivariable logistic regression analysis, patients above 60 years old were more likely to miss follow-up (OR-23.28 (9.32-58.15), P<001). Patients who reported fear of COVID-19 at the hospital were 19 times more likely to miss follow-up (adjusted OR=19.32, 95% CI:10.73-34.79, P<0.001), while patients who reported transportation problems were 6.5 times more likely to miss follow-up (adjusted OR=6.11, 95% CI:3.06-12.17, P<0.001). Conclusions: COVID-19 pandemic affected the care-seeking behavior of patients with chronic medical conditions adversely and the impact was more pronounced among patients with severe disease, fear of COVID19, and transportation problems. Education on preventive measures of COVID-19, use of phone clinics, and improving chronic illness services at the local health institutions may reduce loss to follow-up among these patients.
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🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.11.25.20236224v1" target="_blank">The Impact of COVID-19 on Care Seeking Behavior of Patients at Tertiary Care Follow-up Clinics: A Cross-Sectional Telephone Survey. Addis Ababa, Ethiopia.</a>
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<li><strong>Airway antibodies wane rapidly after COVID-19 but B cell memory is generated across disease severity</strong> -
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Understanding immune responses following SARS-CoV-2 infection in relation to COVID-19 severity is critical to predicting the effects of long-term immunological memory on viral spread. Here we longitudinally assessed systemic and airway immune responses against SARS-CoV-2 in a well-characterized cohort of 147 infected individuals representing the full spectrum of COVID-19 severity; from asymptomatic infection to fatal disease. High systemic and airway antibody responses were elicited in patients with moderate to severe disease, and while systemic IgG levels were maintained after acute disease, airway IgG and IgA declined significantly. In contrast, individuals with mild symptoms showed significantly lower antibody responses but their levels of antigen-specific memory B cells were comparable with those observed in patients with moderate to severe disease. This suggests that antibodies in the airways may not be maintained at levels that prevent local virus entry upon re-exposure and therefore protection via activation of the memory B cell pool is critical.
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🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.11.25.20238592v1" target="_blank">Airway antibodies wane rapidly after COVID-19 but B cell memory is generated across disease severity</a>
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<li><strong>Self-harm during the early period of the COVID-19 Pandemic in England: comparative trend analysis of hospital presentations</strong> -
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Background The COVID-19 pandemic and public health measures necessary to address it may have major effects on mental health, including on self-harm. We have used well-established monitoring systems in two hospitals in England to investigate trends in self-harm presentations to hospitals during the early period of the pandemic. Method Data collected in Oxford and Derby on patients aged 18 years and over who received a psychosocial assessment after presenting to the emergency departments following self-harm were used to compare trends during the three-month period following lockdown in the UK (23rd March 2020) to the period preceding lockdown and the equivalent period in 2019. Results During the 12 weeks following introduction of lockdown restrictions there was a large reduction in the number of self-harm presentations to hospitals by individuals aged 18 years and over compared to the pre-lockdown weeks in 2020 (mean weekly reduction of 13.5 (95% CI 5.6 - 21.4) and the equivalent period in 2019 (mean weekly reduction of 18.0 (95% CI 13.9 - 22.1). The reduction was greater in females than males, occurred in all age groups, with a larger reduction in presentations following self-poisoning than self-injury. Conclusions A substantial decline in hospital presentations for self-harm occurred during the three months following the introduction of lockdown restrictions. Reasons could include a reduction in self-harm at the community level and individuals avoiding presenting to hospital following self-harm. Longer-term monitoring of self-harm behaviour during the pandemic is essential, together with efforts to encourage help-seeking and the modification of care provision.
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🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.11.25.20238030v1" target="_blank">Self-harm during the early period of the COVID-19 Pandemic in England: comparative trend analysis of hospital presentations</a>
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<li><strong>Autosomal Dominant Polycystic Kidney Disease does not significantly alter major COVID-19 outcomes among veterans</strong> -
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Chronic kidney disease (CKD), as well as its common causes (e.g., diabetes and obesity), are recognized risk factors for severe COVID-19 illness. To explore whether the most common inherited cause of CKD, autosomal dominant polycystic kidney disease (ADPKD), is also an independent risk factor, we studied data from the VA health system and the VA COVID-19-shared resources (e.g., ICD codes, demographics, pre-existing conditions, pre-testing symptoms, and post-testing outcomes). Among 61 COVID-19-positive ADPKD patients, 21 (34.4%) were hospitalized, 10 (16.4%) were admitted to ICU, 4 (6.6%) required ventilator, and 4 (6.6%) died by August 18, 2020. These rates were comparable to patients with other cystic kidney diseases and cystic liver-only diseases. ADPKD was not a significant risk factor for any of the four outcomes in multivariable logistic regression analyses when compared with other cystic kidney diseases and cystic liver-only diseases. In contrast, diabetes was a significant risk factor for hospitalization [OR 2.30 (1.61, 3.30), p<0.001], ICU admission [OR 2.23 (1.47, 3.42), p<0.001], and ventilator requirement [OR 2.20 (1.27, 3.88), p=0.005]. Black race significantly increased the risk for ventilator requirement [OR 2.00 (1.18, 3.44), p=0.011] and mortality [OR 1.60 (1.02, 2.51), p=0.040]. We also examined the outcome of starting dialysis after COVID-19 confirmation. The main risk factor for starting dialysis was CKD [OR 6.37 (2.43, 16.7)] and Black race [OR 3.47 (1.48, 8.1)]. After controlling for CKD, ADPKD did not significantly increase the risk for newly starting dialysis comparing with other cystic kidney diseases and cystic liver-only diseases. In summary, ADPKD did not significantly alter major COVID-19 outcomes among veterans when compared to other cystic kidney and liver patients.
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🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.11.25.20238675v1" target="_blank">Autosomal Dominant Polycystic Kidney Disease does not significantly alter major COVID-19 outcomes among veterans</a>
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<li><strong>Symptom-based prediction model of SARS-1 CoV-2 infection developed from self-reported symptoms of SARS-CoV-2-infected individuals in an online survey</strong> -
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Background: Infections with the newly emerged severe acute respiratory syndrome virus 2 (SARS-CoV-2) have quickly reached pandemic proportions and are causing a global health crisis. First recognized for the induction of severe disease, the virus also causes asymptomatic infections or infections with mild symptoms that can resemble common colds. Since infections with mild course are probably a major contributor to the spread of SARS-CoV-2, better detection of such cases is important. To provide better understanding of these mild SARS-CoV-2 infections and to improve information for potentially infected individuals, we performed a detailed analysis of self-reported symptoms of SARS-CoV-2 positive and SARS-CoV-2 negative individuals. Methods: In an online-based survey, 963 individuals provided information on symptoms associated with an acute respiratory infection, 336 of the participants had tested positive for SARS-CoV-2 infection, 107 had tested negative, and 520 had not been tested for SARS-CoV-2 infection. Results: The symptoms reported most frequently by SARS-CoV-2 infected individuals were tiredness, loss of appetite, impairment of smell or taste and dry cough. The symptoms with the highest odds ratios between SARS-CoV-2 positive and negative individuals were loss of appetite and impairment of smell or taste. Based on the most distinguishing symptoms, we developed a Bayesian prediction model, which had a positive predictive value of 0.80 and a negative predictive value of 0.72 on the SARS-CoV-2 tested individuals. The model predicted 56 of 520 non-tested individuals to be SARS-CoV-2 positive with more than 75% probability, and another 84 to be SARS-CoV-2 positive with probability between 50% and 75%. Conclusions: A combination of symptoms can provide a good estimate of the probability of SARS-CoV-2 infection.
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🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.11.25.20236752v1" target="_blank">Symptom-based prediction model of SARS-1 CoV-2 infection developed from self-reported symptoms of SARS-CoV-2-infected individuals in an online survey</a>
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<li><strong>The Relationship between Weekly Periodicity and COVID-19 Progression</strong> -
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COVID-19 is extraordinary both as once-in-a-lifetime pandemic and having abundant real-time case data, thus providing an extraordinary opportunity for timely independent analysis and novel perspectives. We investigate the weekly periodicity in the daily reported new cases and new deaths with the implied relationships to the societal and institutional responses using autocorrelation and Fourier transformation. The results show significant linear correlations between the weekly periodicity and the total cases and deaths, ranging from 50% to 84% for sizable groups of countries with population normalized deaths spanning nearly three orders of magnitude, from a few to approaching a thousand per million. In particular, the Strength Indicator of the periodicity in the new cases, defined by the autocorrelation with a 7-day lag, is positively correlated strongly to the total deaths per million in respective countries. The Persistence Indicator of the periodicity, defined as the average of three autocorrelations with 7-, 14- and 21-day lags, is an overall better indicator of the progression of the pandemic. For longer time series, Fourier transformation gives similar results. This analysis begins to fill the gap in modeling and simulation of epidemics with the inclusion of high frequency modulations, in this case most likely from human behaviors and institutional practices, and reveals that they can be highly correlated to the magnitude and duration of the pandemic. The results show that there is significant need to understand the causes and effects of the periodicity and its relationship to the progression and outcome of the pandemic, and how we could adapt our strategies and implementations to reduce the extent of the impact of COVID-19.
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🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.11.24.20238295v1" target="_blank">The Relationship between Weekly Periodicity and COVID-19 Progression</a>
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<h1 data-aos="fade-right" id="from-clinical-trials">From Clinical Trials</h1>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Ivermectin for Severe COVID-19 Management</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Drug: Ivermectin<br/><b>Sponsors</b>: Afyonkarahisar Health Sciences University; NeuTec Pharma<br/><b>Completed</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>A Phase Ⅱ Clinical Trial of Recombinant Corona Virus Disease-19 (COVID-19) Vaccine (Sf9 Cells)</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: Low-dose Recombinant COVID-19 vaccine (Sf9 cells) (18-59 years) & Two dose regimen; Biological: Low-dose Recombinant COVID-19 vaccine (Sf9 cells) (18-59 years) & Three dose regimen; Biological: High-dose Recombinant COVID-19 vaccine (Sf9 cells) (18-59 years) & Two dose regimen; Biological: High-dose Recombinant COVID-19 vaccine (Sf9 cells) (18-59 years) & Three dose regimen; Biological: Low-dose Recombinant COVID-19 vaccine (Sf9 cells) (60-85 years) & Two dose regimen; Biological: Low-dose Recombinant COVID-19 vaccine (Sf9 cells) (60-85 years) & Three dose regimen; Biological: High-dose Recombinant COVID-19 vaccine (Sf9 cells) (60-85 years) & Two dose regimen; Biological: High-dose Recombinant COVID-19 vaccine (Sf9 cells) (60-85 years) & Three dose regimen; Biological: Low-dose placebo (18-59 years) & Two dose regimen; Biological: Low-dose placebo (18-59 years) & Three dose regimen; Biological: High-dose placebo (18-59 years) & Two dose regimen; Biological: High-dose placebo (18-59 years) & Three dose regimen; Biological: Low-dose placebo (60-85 years) & Two dose regimen; Biological: Low-dose placebo (60-85 years) & Three dose regimen; Biological: High-dose placebo (60-85 years) & Two dose regimen; Biological: High-dose placebo (60-85 years) & Three dose regimen<br/><b>Sponsors</b>: Jiangsu Province Centers for Disease Control and Prevention; West China Hospital<br/><b>Recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Adaptive COVID-19 Treatment Trial 4 (ACTT-4)</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Drug: Baricitinib; Drug: Dexamethasone; Other: Placebo; Drug: Remdesivir<br/><b>Sponsor</b>: National Institute of Allergy and Infectious Diseases (NIAID)<br/><b>Recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Vitamin D and Zinc Supplementation for Improving Treatment Outcomes Among COVID-19 Patients in India</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Dietary Supplement: Vitamin D3 (cholecalciferol); Dietary Supplement: Zinc (zinc gluconate); Dietary Supplement: Zinc (zinc gluconate) & Vitamin D (cholecalciferol); Other: Placebo<br/><b>Sponsors</b>: Harvard School of Public Health; Foundation for Medical Research; University Health Network, Toronto<br/><b>Not yet recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>A Study to Compare the Efficacy of GNS561 Versus Standard of Care in Patients With SARS-CoV-2 (COVID-19) Infection</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Drug: GNS561<br/><b>Sponsor</b>: Genoscience Pharma<br/><b>Recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>A Study of Immune System Proteins in Participants With Mild to Moderate COVID-19 Illness</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Drug: LY3819253; Drug: LY3832479; Drug: Placebo<br/><b>Sponsors</b>: Eli Lilly and Company; AbCellera Biologics Inc.; Shanghai Junshi Bioscience Co., Ltd.<br/><b>Recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>A Synthetic MVA-based SARS-CoV-2 Vaccine, COH04S1, for the Prevention of COVID-19</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Drug: Placebo Administration; Biological: Vaccine Therapy<br/><b>Sponsors</b>: City of Hope Medical Center; National Cancer Institute (NCI)<br/><b>Recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Early Versus Delayed Intubation of Patients With COVID-19</strong> - <b>Conditions</b>: COVID-19; Acute Hypoxemic Respiratory Failure<br/><b>Intervention</b>: Other: Endotracheal intubation<br/><b>Sponsor</b>: Evangelismos Hospital<br/><b>Not yet recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Phase I Trial of a Recombinant SARS-CoV-2 Vaccine (CHO Cell)</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: Two doses of middle-dose recombinant SARS-CoV-2 vaccine (CHO Cell) at the schedule of day 0, 14; Biological: Three doses of middle-dose recombinant SARS-CoV-2 vaccine (CHO Cell) at the schedule of day 0, 14, 28; Biological: Two doses of high-dose recombinant SARS-CoV-2 vaccine (CHO Cell) at the schedule of day 0, 14; Biological: Three doses of high-dose recombinant SARS-CoV-2 vaccine (CHO Cell) at the schedule of day 0, 14, 28; Biological: Two doses of placebo at the schedule of day 0, 14 #middle-dose group#; Biological: Three doses of placebo at the schedule of day 0, 14, 28 #middle-dose group#; Biological: Two doses of placebo at the schedule of day 0, 14 #High-dose group#; Biological: Three doses of placebo at the schedule of day 0, 14, 28 #High-dose group#<br/><b>Sponsors</b>: Jiangsu Province Centers for Disease Control and Prevention; Academy of Military Medical Sciences，Academy of Military Sciences，PLA; ZHONGYIANKE Biotech Co, Ltd.; LIAONINGMAOKANGYUAN Biotech Co, Ltd<br/><b>Recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Convalescent Plasma Transfusion in Severe COVID-19 Patients in Jamaica</strong> - <b>Condition</b>: COVID-19, Convalescent Plasma Treatment<br/><b>Intervention</b>: Biological: Convalescent Plasma Infusion<br/><b>Sponsor</b>: The University of The West Indies<br/><b>Not yet recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Losartan and Spironolactone Treatment for ICU Patients With COVID-19 Suffering From ARDS</strong> - <b>Conditions</b>: COVID-19; ARDS<br/><b>Intervention</b>: Drug: Losartan 50 mg and Spironolactone 25 mg pillules oral use<br/><b>Sponsor</b>: Assistance Publique Hopitaux De Marseille<br/><b>Recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Study to Assess Adverse Events and How Intravenous (IV) ABBV-47D11 Moves Through the Body of Adult Participants Hospitalized With Coronavirus Disease 2019 (COVID-19)</strong> - <b>Condition</b>: CoronaVirus Disease-2019 (COVID-19)<br/><b>Interventions</b>: Drug: ABBV-47D11; Drug: Placebo for ABBV-47D11<br/><b>Sponsor</b>: AbbVie<br/><b>Not yet recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>BCG Vaccination to Prevent COVID-19</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Drug: Tice® BCG (for intravesical use) BCG LIVE strain of the BCG (Merck) vaccine; Drug: Preservative-free saline<br/><b>Sponsors</b>: Henry M. Jackson Foundation for the Advancement of Military Medicine; Harvard Medical School; Uniformed Services University of the Health Sciences; United States Department of Defense; Defense Health Agency<br/><b>Not yet recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Application of Convalescent Plasma in the Treatment of SARS CoV-2 Disease (COVID-19) With Evaluation of Therapy Effectiveness</strong> - <b>Condition</b>: COVID-19 Convalescent Plasma Treatment<br/><b>Intervention</b>: Biological: COVID-19 convalescent plasma treatment<br/><b>Sponsors</b>: Wroclaw Medical University; Medical Research Agency<br/><b>Not yet recruiting</b></p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Fase I Clinical Trial on NK Cells for COVID-19</strong> - <b>Conditions</b>: Covid19; Sars-cov 2<br/><b>Intervention</b>: Biological: Natural Killer Cells infusion<br/><b>Sponsor</b>: Hospital de Clinicas de Porto Alegre<br/><b>Not yet recruiting</b></p></li>
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<h1 data-aos="fade-right" id="from-pubmed">From PubMed</h1>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>RAC1 nitration at Y(32) IS involved in the endothelial barrier disruption associated with lipopolysaccharide-mediated acute lung injury</strong> - Acute lung injury (ALI), a devastating illness induced by systemic inflammation e.g., sepsis or local lung inflammation e.g., COVID-19 mediated severe pneumonia, has an unacceptably high mortality and has no effective therapy. ALI is associated with increased pulmonary microvascular hyperpermeability and alveolar flooding. The small Rho GTPases, RhoA and Rac1 are central regulators of vascular permeability through cytoskeleton rearrangements. RhoA and Rac1 have opposing functional outcome: RhoA…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Ionophore antibiotic X-206 is a potent inhibitor of SARS-CoV-2 infection in vitro</strong> - Pandemic spread of emerging human pathogenic viruses, such as the current SARS-CoV-2, poses both an immediate and future challenge to human health and society. Currently, effective treatment of infection with SARS-CoV-2 is limited and broad spectrum antiviral therapies to meet other emerging pandemics are absent leaving the World population largely unprotected. Here, we have identified distinct members of the family of polyether ionophore antibiotics with potent ability to inhibit SARS-CoV-2…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Nucleocapsid protein of SARS-CoV-2 phase separates into RNA-rich polymerase-containing condensates</strong> - The etiologic agent of the Covid-19 pandemic is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The viral membrane of SARS-CoV-2 surrounds a helical nucleocapsid in which the viral genome is encapsulated by the nucleocapsid protein. The nucleocapsid protein of SARS-CoV-2 is produced at high levels within infected cells, enhances the efficiency of viral RNA transcription, and is essential for viral replication. Here, we show that RNA induces cooperative liquid-liquid phase…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Integrative Imaging Reveals SARS-CoV-2-Induced Reshaping of Subcellular Morphologies</strong> - Pathogenesis induced by SARS-CoV-2 is thought to result from both an inflammation-dominated cytokine response and virus-induced cell perturbation causing cell death. Here, we employ an integrative imaging analysis to determine morphological organelle alterations induced in SARS-CoV-2-infected human lung epithelial cells. We report 3D electron microscopy reconstructions of whole cells and subcellular compartments, revealing extensive fragmentation of the Golgi apparatus, alteration of the…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>MEK inhibitors reduce cellular expression of ACE2, pERK, pRb while stimulating NK-mediated cytotoxicity and attenuating inflammatory cytokines relevant to SARS-CoV-2 infection</strong> - COVID-19 affects vulnerable populations including elderly individuals and patients with cancer. Natural Killer (NK) cells and innate-immune TRAIL suppress transformed and virally-infected cells. ACE2, and TMPRSS2 protease promote SARS-CoV-2 infectivity, while inflammatory cytokines IL-6, or G-CSF worsen COVID-19 severity. We show MEK inhibitors (MEKi) VS-6766, trametinib and selumetinib reduce ACE2 expression in human cells. In some human cells, remdesivir increases ACE2-promoter…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>N-Glycan Modification in Covid-19 Pathophysiology: In vitro Structural Changes with Limited Functional Effects</strong> - In 2014, we reported two siblings with a rare congenital disorder of glycosylation due to mutations in mannosyl-oligosaccharide glucosidase (MOGS). The glycan alteration derived from this disease resulted in an in vitro infection resistance to particular enveloped, N-glycosylation-dependent viruses as influenza and HIV. As part of the global effort to find safe and effective antiviral therapies for Covid-19, we assessed the in vitro activity of the FDA-approved α-glucosidase inhibitor miglustat…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>JAK-STAT pathway inhibition and their implications in COVID-19 therapy</strong> - As the incidence of COVID-19 increases with time, more and more efforts are made to pave a way out for the therapeutic strategies to deal with the disease progression. Inflammation being a significant influencer has implicated us to re-look into its signaling cascades drawing attention towards the JAK/STAT pathway. Considered as a major signaling mediator of cytokines and chemokines, the JAK/STAT pathway has significantly contributed to the worsening of COVID-19. JAK phosphorylation mediated by…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>A Case-Control Study of the 2019 Influenza Vaccine and Incidence of COVID-19 Among Healthcare Workers</strong> - CONCLUSIONS: Significant findings suggest that the 2019 influenza vaccine may have a protective association against COVID-19 among HCW.</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>HDL-scavenger receptor B type 1 facilitates SARS-CoV-2 entry</strong> - Responsible for the ongoing coronavirus disease 19 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells through binding of the viral spike protein (SARS-2-S) to the cell-surface receptor angiotensin-converting enzyme 2 (ACE2). Here we show that the high-density lipoprotein (HDL) scavenger receptor B type 1 (SR-B1) facilitates ACE2-dependent entry of SARS-CoV-2. We find that the S1 subunit of SARS-2-S binds to cholesterol and possibly to HDL…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Analysis of the efficacy of HIV protease inhibitors against SARS-CoV-2’s main protease</strong> - CONCLUSION: Targeting of SARS-CoV-2 M^(pro) by some of the HIV PIs might be of limited clinical potential, given the high concentration of the drugs required to achieve significant inhibition. Therefore, given their weak inhibition of the viral protease, any potential beneficial effect of the PIs in COVID-19 context might perhaps be attributed to acting on other molecular target(s), rather than SARS-CoV-2 M^(pro).</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Can Host Cell Proteins Like ACE2, ADAM17, TMPRSS2, Androgen Receptor be the Efficient Targets in SARS-CoV-2 Infection?</strong> - A novel betacoronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused a large disease outbreak in Wuhan, China in December 2019, is currently spreading across world’s many of the countries. Along with binding of the virus spike with the host cell receptor, fusion of the viral envelope with host cell membranes is a critical step in establishing successful infection of SARS-CoV-2. In this entry process, a diversity of host cell proteases and andro-gen receptor play a…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing</strong> - Motivated by the current COVID-19 epidemic, this work introduces an epidemiological model in which separate compartments are used for susceptible and asymptomatic “socially distant” populations. Distancing directives are represented by rates of flow into these compartments, as well as by a reduction in contacts that lessens disease transmission. The dynamical behavior of this system is analyzed, under various different rate control strategies, and the sensitivity of the basic reproduction number…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Translation of Mycobacterium Survival Strategy to Develop a Lipo-peptide based Fusion Inhibitor</strong> - The entry of enveloped viruses requires the fusion of viral and host cell membranes. An effective fusion inhibitor aiming at impeding such membrane fusion may emerge as a broad-spectrum antiviral agent against a wide range of viral infections. Mycobacterium survives inside the phagosome by inhibiting phagosome-lysosome fusion with the help of a coat protein coronin 1. Structural analysis of coronin 1 and other WD40-repeat protein suggest that the trp-asp (WD) sequence is placed at distorted…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Repurposing Anti-Cancer Drugs for COVID-19 Treatment</strong> - The novel coronavirus disease 2019 (COVID-19) pandemic has caused catastrophic damage to human life across the globe along with social and financial hardships. According to the Johns Hopkins University Coronavirus Resource Center, more than 41.3 million people worldwide have been infected, and more than 1,133,000 people have died as of October 22, 2020. At present, there is no available vaccine and a scarcity of efficacious therapies. However, there is tremendous ongoing effort towards…</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Gammacoronavirus Avian Infectious Bronchitis Virus and Alphacoronavirus Porcine Epidemic Diarrhea Virus Exploit a Cell-Survival Strategy via Upregulation of cFOS to Promote Viral Replication</strong> - Coronaviruses have evolved a variety of strategies to optimize cellular microenvironment for efficient replication. In this study, we report the induction of AP-1 transcription factors by coronavirus infection based on genome-wide analyses of differentially expressed genes in cells infected with avian coronavirus infectious bronchitis virus (IBV). Most members of the AP-1 transcription factors were subsequently found to be upregulated during the course of IBV and porcine epidemic diarrhea virus…</p></li>
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<h1 data-aos="fade-right" id="from-patent-search">From Patent Search</h1>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>AN EFFICIENT METHODOLOGY TO MANAGE THE ADMISSIONS IN HOSPITALS DURING THE PANDEMICS SUCH AS COVID 19</strong> -</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>SARS-CoV-2 예방을 위한 mRNA기반 항원보강제 혼합물 합성 방법</strong> - 본 발명은 SARS-CoV-2(코로나 바이러스) 예방을 위한 mRNA 항원보강제에 관한 것으로 코로나 바이러스에 대한 백신으로서 상기의 항원에 대한 예방을 목적으로 하고 있다. 아이디어에는 보강제에 해당하는 완전프로인트항원보강제(CFA)와 불완전프로인트항원보강제(IFA), 번역과 안정성의 최적화가 된 mRNA, mRNA 운반체, 양이온성 지질 나노입자(lipid nanoparticles)로 구성되며 기존의 백신에 비해 효율성과 안정성의 측면에서 더 향상된 효과를 가지고 있다.</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Vorrichtung zum Reinigen und/oder Desinfizieren von Objekten</strong> -</p>
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<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
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</p><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">Vorrichtung (1) zum Desinfizieren von Objekten mit einer Basiseinheit (2), mit einem Aufnahmebehälter (4) für Wasser, welcher an der Basiseinheit (2) montierbar und von der Basiseinheit demontierbar ist, mit einer Objekthalterung (6) zum Halten und/oder Stützen der Objekte (10), wobei diese Objekthalterung (6) in dem Aufnahmebehälter montierbar ist und mit einer elektrisch betriebenen Reinigungseinrichtung (8), welche in dem Wasser befindliche Objekte zumindest mittelbar reinigt oder desinfiziert, wobei diese Reinigungseinrichtung in der Basiseinheit befindliche Erzeugungsmittel zum Erzeugen einer elektrischen Spannung aufweist sowie einen Plasmagenerator und/oder eine Ultraschallerzeugungseinheit.</p></li>
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</ul>
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<ul>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Methods for treating Arenaviridae and Coronaviridae virus infections</strong> - Provided are methods for treating Arenaviridae and Coronaviridae virus infections by administering nucleosides and prodrugs thereof, of Formula I:</li>
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</ul>
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<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">wherein the ’ position of the nucleoside sugar is substituted. The compounds, compositions, and methods provided are particularly useful for the treatment of Lassa virus and Junin virus infections.</p>
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<ul>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Atemschutz-Baukastensystem</strong> -
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<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
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Atemschutz-Baukastensystem, das aufweist:</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">eine auf zumindest Mund und Nase einer Person aufsetzbare Maske (1), die einen Eingang (11) und einen Ausgang (12) aufweist, und</li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">mindestens einen Schlauch (3, 31, 32),</li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">wobei sämtliche Komponenten des Atemschutz-Baukastensystems modular ausgebildet und über Steckverbindungen oder Schraubverbindungen (115, 125, 155, 165, 175, 215, 315, 75, 915) miteinander verbindbar sind, um der Maske (1) Luft über deren Eingang (11) zuzuführen und/oder ausgeatmete Luft vom Ausgang (12) der Maske (1) wegzuführen.</li>
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</ul>
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<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"></p>
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<ul>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Vorrichtung zur Übergabe und Dekontamination von mit Krankheitserregern kontaminierten Gegenständen oder Erzeugnissen</strong> -
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<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
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Vorrichtung zur Übergabe von mit Krankheitserregern kontaminierten Gegenständen oder Erzeugnissen nach einer Dekontamination, umfassend eine Einrichtung zur Dekontamination der mit Krankheitserregern kontaminierten Gegenstände oder Erzeugnisse mit mindestens einer UV-Strahlungsquelle (24), eine Durchzugseinrichtung mit Ein- und/oder Ausgabebereichen für die kontaminierten bzw. dekontaminierten Gegenstände oder Erzeugnisse, dadurch gekennzeichnet, dass die Durchzugseinrichtung im Eingang bzw. im Ausgang zum Ein- und/oder Ausgabebereich angeordnete sich paarweise gegenüberliegende Walzen (17) und Räder (4) umfasst, die zum Einzug bzw. zur Ausgabe der kontaminierten bzw. dekontaminierten Gegenstände oder Erzeugnisse vorgesehen sind, wobei die Walzen (17) und die Räder (4) durch im Ein- und/oder Ausgabebereich angeordnete Sensoren (23) und einer elektronische Kontrolleinheit (27) in Bewegung bringbar sind, wobei die Gegenstände oder Erzeugnisse in den Bereich der Einrichtung zur Dekontamination förderbar sind, der zwischen den paarweise angeordneten Walzen (17) und Rädern (4) vorgesehen ist, welcher sich gegenüberliegende Platten (25) aus Quarzglas oder einem UV-transparenten Polymermaterial, wie Graphen oder Kunstglas umfasst, über bzw. unter welchen die UV-Strahlungsquelle (24) angeordnet ist, welche als UVC-LED-Leiste und/oder Modul mit mindestens einer LED-Lampe ausgebildet ist.</p></li>
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</ul>
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<img alt="embedded image" id="EMI-D00000"/>
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<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"></p>
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<ul>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>제2형 중증급성호흡기증후군 코로나바이러스 감염 질환의 예방 또는 치료용 조성물</strong> - 본 발명은 화학식 1로 표시되는 화합물, 또는 이의 약학적으로 허용가능한 염을 유효성분으로 포함하는 제2형 중증급성호흡기증후군 코로나바이러스 감염 질환 예방 또는 치료용 약학적 조성물을 제공한다. [화학식 1] .</p>
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<pre><code> JPEG
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112020094463686-pat00017.jpg
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48
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135</code></pre></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>新型冠状病毒中和性抗体滴度检测ELISA试剂盒</strong> - 本发明提供一种新型冠状病毒中和性抗体滴度检测ELISA试剂盒，其中包括：包被有生物素‑链霉亲和素标记的人ACE2蛋白的酶标板、辣根过氧化酶标记的新型冠状病毒RBD蛋白、新型冠状病毒中和性抗体阳性对照、包被液、洗涤液、稀释液、封闭液、显色液和终止液等。该试剂盒具有成本低，操作简单，高灵敏度、高特异性、高准确度的特点，可用于新型冠状病毒中和抗体的批量、快速检测。</p></li>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Reagenzien und Verwendungen zur Diagnose einer SARS-CoV-2-Infektion</strong> -</p>
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<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
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</p><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">Diagnostisch nützlicher Träger umfassend ein Polypeptid umfassend SEQ ID NO1 oder eine Variante davon, die an einen Antikörper gegen SEQ ID NO1 aus einer Probe von einem Patienten binden kann, der an einer SARS-CoV-2-Infektion leidet, wobei das Polypeptid bevorzugt auf der Festphase des Trägers immobilisiert ist.</p></li>
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</ul>
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<ul>
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<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Reagenzien und Verwendungen zur Diagnose einer SARS-CoV-2-Infektion</strong> -
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<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
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Verwendung eines Polypeptides umfassend SEQ ID NO1 oder eine Variante davon, die an einen Antikörper gegen SED ID NO1 aus einer Probe von einem Patienten binden kann, zur Herstellung eines diagnostischen Kits.</p></li>
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</ul>
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