190 lines
48 KiB
HTML
190 lines
48 KiB
HTML
|
<!DOCTYPE html>
|
|||
|
|
<html lang="" xml:lang="" xmlns="http://www.w3.org/1999/xhtml"><head>
|
|||
|
|
<meta charset="utf-8"/>
|
|||
|
|
<meta content="pandoc" name="generator"/>
|
|||
|
|
<meta content="width=device-width, initial-scale=1.0, user-scalable=yes" name="viewport"/>
|
|||
|
|
<title>10 August, 2023</title>
|
|||
|
|
<style>
|
|||
|
|
code{white-space: pre-wrap;}
|
|||
|
|
span.smallcaps{font-variant: small-caps;}
|
|||
|
|
span.underline{text-decoration: underline;}
|
|||
|
|
div.column{display: inline-block; vertical-align: top; width: 50%;}
|
|||
|
|
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
|
|||
|
|
ul.task-list{list-style: none;}
|
|||
|
|
</style>
|
|||
|
|
<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>
|
|||
|
|
<body>
|
|||
|
|
<h1 data-aos="fade-down" id="covid-19-sentry">Covid-19 Sentry</h1>
|
|||
|
|
<h1 data-aos="fade-right" data-aos-anchor-placement="top-bottom" id="contents">Contents</h1>
|
|||
|
|
<ul>
|
|||
|
|
<li><a href="#from-preprints">From Preprints</a></li>
|
|||
|
|
<li><a href="#from-clinical-trials">From Clinical Trials</a></li>
|
|||
|
|
<li><a href="#from-pubmed">From PubMed</a></li>
|
|||
|
|
<li><a href="#from-patent-search">From Patent Search</a></li>
|
|||
|
|
</ul>
|
|||
|
|
<h1 data-aos="fade-right" id="from-preprints">From Preprints</h1>
|
|||
|
|
<ul>
|
|||
|
|
<li><strong>Verification Theatre at Borders and in Pockets</strong> -
|
|||
|
|
<div>
|
|||
|
|
To appear in: Colleen M. Flood, Y.Y. Brandon Chen, Raywat Deonandan, Sam Halabi, and Sophie Thériault (eds.) Pandemics, Public Health, and the Regulation of Borders: Lessons from COVID-19 (Routledge, forthcoming). This version: August 2023. Abstract The COVID-19 pandemic saw the creation of a wide array of digital infrastructures, underpinning both digital and paper systems, for proving attributes such as vaccination, test results or recovery. These systems were hotly debated. Yet this debate often failed to connect their social, technical and legal aspects, focussing on one area to the exclusion of the others. In this paper, I seek to bring them together. I argue that fraud-free “vaccination certificate” systems were a technical and social pipe-dream, but one that was primarily advantageous to organisations wishing to establish and own infrastructure for future ambitions as verification platforms. Furthermore, attempts to include features to ostensibly reduce fraud had, and risks further, broader knock-on effects on local digital infrastructures around the world, particularly in countries with low IT capacities easily captured by large firms and de facto excluded from and by global standardisation processes. The paper further reflects on the role of privacy in these debates, and how privacy, and more specifically confidentiality, was misconstrued as a main design aim of these systems, when the main social problems could manifest even in a system built with state of the art privacy-enhancing technologies. The COVID-19 pandemic should sharpen our senses towards the importance of infrastructures, and more broadly, how to use technologies in societies in crises.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://osf.io/preprints/socarxiv/h24uv/" target="_blank">Verification Theatre at Borders and in Pockets</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Transformative Effects of COVID-19 on Healthcare Systems</strong> -
|
|||
|
|
<div>
|
|||
|
|
The global healthcare landscape has undergone an unprecedented metamorphosis due to the COVID-19 pandemic, revealing vulnerabilities, sparking innovation, and necessitating adaptive strategies. This crisis’s impact extends beyond immediate health concerns, influencing healthcare delivery, policy, and infrastructure. Healthcare systems faced overwhelming strain, resulting in improvised medical units and resource allocation shifts. The pandemic prompted the deferral of non-urgent medical procedures, while telehealth integration surged, reshaping patient-doctor interactions. Fragile supply chains underscored the need for enhanced resilience measures. Mental health challenges highlighted the importance of comprehensive care, and healthcare workers exhibited remarkable resilience. Collective efforts accelerated scientific advancements, yielding swift vaccine development. Existing health inequalities were exacerbated, underscoring the urgency of equitable healthcare provision. Policy reforms and global cooperation played pivotal roles. A paradigm shift emerged, emphasizing digital innovation in healthcare. The pandemic’s repercussions underscore the significance of adaptability, collaboration, and ensuring fair access to care.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://osf.io/8x53a/" target="_blank">Transformative Effects of COVID-19 on Healthcare Systems</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>In silico thermodynamic evaluation of the effectiveness of RT-LAMP primers to SARS-CoV-2 variants detection</strong> -
|
|||
|
|
<div>
|
|||
|
|
Viral mutations are the primary cause of mismatches in primer-target hybridisation, affecting the sensibility of molecular techniques, potentially leading to detection dropouts. Despite its importance, little is known about the quantitative effect of mismatches in primer-target hybridisation. We use up-to-date and highly detailed thermodynamic model parameters of DNA mismatches to evaluate the sensibility to variants of SARS-CoV-2 RT-LAMP primers. We aligned 18 RT-LAMP primer sets, which were underwent clinical validation, to the genomes of Wuhan strain (ws), 7 variants and 4 subvariants, and calculated hybridisation temperatures allowing up to three consecutive mismatches. We calculate the coverage when the mismatched melting temperature falls by more than 5C in comparison to the matched alignments. If no mismatches are considered, the average coverage found would be 94% for ws, falling the lowest value for Omicron: 84%. However, considering mismatches the coverage is much higher: 97% (ws) to 88% (Omicron). Stabilizing mismatches (higher melting temperatures), account for roughly 1/3 of this increase. The number of primer dropouts increases for new each variant, however the effect is much less severe if mismatches are considered. We suggest using melting temperature calculations to continuously assess the trend of primer dropouts.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2023.08.08.552530v1" target="_blank">In silico thermodynamic evaluation of the effectiveness of RT-LAMP primers to SARS-CoV-2 variants detection</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Heart-on-a-chip model of immune-induced cardiac dysfunction reveals the involvement of free mitochondrial DNA and therapeutic effects of endothelial exosomes</strong> -
|
|||
|
|
<div>
|
|||
|
|
Cardiovascular disease continues to take more human lives than all cancer combined, prompting the need for improved research models and treatment options. Despite a significant progress in development of mature heart-on-a-chip models of fibrosis and cardiomyopathies starting from induced pluripotent stem cells (iPSCs), human cell-based models of myocardial inflammation are lacking. Here, we bioengineered a vascularized heart-on-a-chip system with circulating immune cells to model SARS-CoV-2-induced acute myocarditis. Briefly, we observed hallmarks of COVID-19-induced myocardial inflammation in the heart-on-a-chip model, as the presence of immune cells augmented the expression levels of proinflammatory cytokines, triggered progressive impairment of contractile function and altered intracellular calcium transient activities. An elevation of circulating cell-free mitochondrial DNA (ccf-mtDNA) was measured first in the in vitro heart-on-a-chip model and then validated in COVID-19 patients with low left ventricular ejection fraction (LVEF), demonstrating that mitochondrial damage is an important pathophysiological hallmark of inflammation induced cardiac dysfunction. Leveraging this platform in the context of SARS-CoV-2 induced myocardial inflammation, we established that administration of human umbilical vein-derived EVs effectively rescued the contractile deficit, normalized intracellular calcium handling, elevated the contraction force and reduced the ccf- mtDNA and chemokine release via TLR-NF-kB signaling axis.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2023.08.09.552495v1" target="_blank">Heart-on-a-chip model of immune-induced cardiac dysfunction reveals the involvement of free mitochondrial DNA and therapeutic effects of endothelial exosomes</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Oral SARS-CoV-2 host responses predict the early COVID-19 disease course</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Objectives: Oral fluids provide ready detection of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and host responses. This study sought to determine relationships between oral virus, oral anti-SARS-CoV-2-specific antibodies, and symptoms. Methods: Saliva/throat wash (saliva/TW) were collected from asymptomatic and symptomatic, nasopharyngeal (NP) SARS-CoV-2 RT-qPCR+, subjects (n=47). SARS-CoV-2 RT-qPCR, N-antigen detection by immunoblot and lateral flow assay (LFA) were performed. RT-qPCR targeting viral subgenomic RNA (sgRNA) was sequence confirmed. SARS-CoV-2-anti-S protein RBD LFA assessed IgM and IgG responses. Structural analysis identified host salivary molecules analogous to SARS-CoV-2-N-antigen. Statistical analyses were performed. Results: At baseline, LFA-detected N-antigen was immunoblot-confirmed in 82% of TW. However, only 3/17 were saliva/TW qPCR+. Sixty percent of saliva and 83% of TW demonstrated persistent N-antigen at 4 weeks. N-antigen LFA signal in three negative subjects suggested potential cross-detection of 4 structurally analogous salivary RNA binding proteins (alignment 19-29aa, RMSD 1-1.5 Angstroms). At entry, symptomatic subjects demonstrated replication-associated sgRNA junctions, were IgG+ (94%/100% in saliva/TW), and IgM+ (75%/63%). At 4 weeks, SARS-CoV-2 IgG (100%/83%) and IgM (80%/67%) persisted. Oral IgG correlated 100% with NP+PCR status. Cough and fatigue severity (p=0.0008 and 0.016), and presence of nausea, weakness, and composite upper respiratory symptoms (p=0.005, 0.037 and 0.017) were negatively associated with oral IgM. Female oral IgM levels were higher than male (p=0.056). Conclusion: Important to transmission and disease course, oral viral replication and persistence showed clear relationships with select symptoms, early Ig responses, and gender during early infection. N-antigen cross-reactivity may reflect mimicry of structurally analogous host proteins.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.03.06.23286853v2" target="_blank">Oral SARS-CoV-2 host responses predict the early COVID-19 disease course</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>The Role of Data Science in Navigating Future Pandemics</strong> -
|
|||
|
|
<div>
|
|||
|
|
The significance of data science in proficiently addressing and handling infectious disease outbreaks has been underscored by the COVID-19 pandemic. In today’s interconnected world, the integration of data-driven strategies is imperative to augment preparedness for and response to pandemics. Data science, which encompasses data analytics, machine learning, and predictive modelling, furnishes invaluable tools for early detection, surveillance, predictive modelling, drug discovery, contact tracing, real-time monitoring, resource allocation, global collaboration, and ethical considerations. By harnessing data-driven insights and fostering interdisciplinary cooperation, data science empowers us to proactively confront future pandemics, ensuring a more robust and well-prepared global community.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://osf.io/ekvc6/" target="_blank">The Role of Data Science in Navigating Future Pandemics</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>High-throughput detection of neutralizing antibodies to SARS-CoV-2 variants using flow cytometry</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Detecting neutralizing antibodies (NAbs) to SARS-CoV-2 variants is crucial for controlling the spread of COVID-19. In this work, we developed a high-throughput assay for the broad systematic examination of NAbs to eleven SARS-CoV variants of concern (VOCs), which include D614G, Alpha, Beta, Gamma, Delta, Kappa, and Omicron sub-lineages BA.1, BA.2, BA.3, BA.4, and BA.5. The assay is cost-effective, reliable, 35-fold more sensitive than Luminex technology, and can include the new variants during SARS-CoV-2 evolution. Importantly, our results highly correlated with a commercial IgG serological assay (R = 0.89) and cPass, a U.S. FDA-approved surrogate virus neutralization test (sVNT) assay (R = 0.93). With our high-throughput NAb platform, we constructed a comprehensive overview of the interactions between SARS-CoV-2 VOCs′ Spike trimer proteins and ACE2 receptors, leading to the identification of a monoclonal Ab with broad neutralizing activity. Furthermore, when compared to the D614G variant, we found that the serum NAbs elicited by the third dose vaccine (administered after 28 days) demonstrated decreased inhibition to multiple SARS-CoV-2 variants, including Gamma (0.94×), Alpha (0.91×), Delta (0.91×), Beta (0.81×), Kappa (0.81×), BA.2 (0.44×), BA.1 (0.43×), BA.3 (0.41×), BA.5 (0.35×) and BA.4 (0.33×), in cohort of 56 vaccinated individuals. Altogether, our proteomics platform proves to be an effective tool to detect broad NAbs in the population and aid in the development of future COVID-19 vaccines and vaccination strategies.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.08.07.23293304v1" target="_blank">High-throughput detection of neutralizing antibodies to SARS-CoV-2 variants using flow cytometry</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Diabetes following SARS-CoV-2 infection: Incidence, persistence, and implications of COVID-19 vaccination. A cohort study of fifteen million people.</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Background Type 2 diabetes (T2DM) incidence is increased after diagnosis of COVID-19. The impact of vaccination on this increase, for how long it persists, and the effect of COVID-19 on other types of diabetes remain unclear. Methods With NHS England approval, we studied diabetes incidence following COVID-19 diagnosis in pre-vaccination (N=15,211,471, January 2020-December 2021), vaccinated (N =11,822,640), and unvaccinated (N=2,851,183) cohorts (June-December 2021), using linked electronic health records. We estimated adjusted hazard ratios (aHRs) comparing diabetes incidence post-COVID-19 diagnosis with incidence before or without diagnosis up to 102 weeks post-diagnosis. Results were stratified by COVID-19 severity (hospitalised/non-hospitalised) and diabetes type. Findings In the pre-vaccination cohort, aHRS for T2DM incidence after COVID-19 (compared to before or without diagnosis) declined from 3.01 (95% CI: 2.76,3.28) in weeks 1-4 to 1.24 (1.12,1.38) in weeks 53-102. aHRS were higher in unvaccinated than vaccinated people (4.86 (3.69,6.41)) versus 1.42 (1.24,1.62) in weeks 1-4) and for hospitalised COVID-19 (pre-vaccination cohort 21.1 (18.8,23.7) in weeks 1-4 declining to 2.04 (1.65,2.51) in weeks 52-102), than non-hospitalised COVID-19 (1.45 (1.27,1.64) in weeks 1-4, 1.10 (0.98,1.23) in weeks 52-102). T2DM persisted for 4 months after COVID-19 for ~73% of those diagnosed. Patterns were similar for Type 1 diabetes, though excess incidence did not persist beyond a year post-COVID-19. Interpretation Elevated T2DM incidence after COVID-19 is greater, and persists longer, in hospitalised than non-hospitalised people. It is markedly less apparent post-vaccination. Testing for T2DM after severe COVID-19 and promotion of vaccination are important tools in addressing this public health problem.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.08.07.23293778v1" target="_blank">Diabetes following SARS-CoV-2 infection: Incidence, persistence, and implications of COVID-19 vaccination. A cohort study of fifteen million people.</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Equivalent Binding Of Sera From Omicron And Delta Period To Future Omicron Subvariants.</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Throughout the COVID-19 pandemic, virus evolution and large-scale vaccination programs have caused multiple exposures to SARS CoV-2 spike protein, resulting in complex antibody profiles. Binding of sera to spike protein of future variants in the context of heterogeneous exposure, has not been studied. We tested archival sera (delta and omicron period) stratified by anti-spike levels for reactivity to omicron subvariant (BA.1, BA.2, BA.2.12.1, BA.2.75, BA.4/5 and BF.7) spike. Antibody reactivity to wild-type (CLIA) and subvariants (ELISA) spike were similar between periods (p>0.05). Both High S group and Low S group of delta and omicron periods were closely related to future subvariants by Antigenic Cartography. Anti-spike antibodies to wild type (S1/S2 and Trimeric S) clustered with subvariant-binding antibodies. Low S group interspersed between High S group on hierarchical clustering. Hybrid immunity caused by cumulative virus exposure in delta or omicron periods caused equivalent binding to future variants. Low S antibody group demonstrating similar binding is a prominent finding.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.08.04.23293670v2" target="_blank">Equivalent Binding Of Sera From Omicron And Delta Period To Future Omicron Subvariants.</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
The COVID-19 pandemic brought not only global devastation but also an unprecedented infodemic of false or misleading information that spread rapidly through online social networks. Network analysis plays a crucial role in the science of fact-checking by modeling and learning the risk of infodemics through statistical processes and computation on mega-sized graphs. This paper proposes MEGA, <i>M</i>achine Learning-<i>E</i>nhanced <i>G</i>raph <i>A</i>nalytics, a framework that combines feature engineering and graph neural networks to enhance the efficiency of learning performance involving massive graphs. Infodemic risk analysis is a unique application of the MEGA framework, which involves detecting spambots by counting triangle motifs and identifying influential spreaders by computing the distance centrality. The MEGA framework is evaluated using the COVID-19 Infodemic Twitter dataset, demonstrating superior computational efficiency and classification accuracy.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2020.10.24.20215061v5" target="_blank">MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>National Changes in Diabetes Care Practices during the COVID-19 Pandemic: Prospective Study of US Adults</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Background There is a lack of nationally representative prospective data on the impact of the COVID-19 pandemic on diabetes care and management in adults with type 2 diabetes. We examined changes in diabetes care and management practices before and after the onset of the COVID-19 pandemic. Methods Using the National Health Interview Survey, we analyzed data from 870 adults living with type 2 diabetes who were interviewed in 2019 and re-interviewed between August and December 2020. Exposure to the COVID-19 pandemic was defined by year of survey (2019, pre-pandemic; 2020, pandemic). We estimated percent change in past year blood sugar check by a health professional and current use of blood sugar lowering medication overall and by sociodemographic subgroups. Results Receiving an annual blood sugar test fell by -3.3 percentage points (pp) (95% CI -5.7, -1.0), from 98.3% in 2019 to 95.0% in late 2020. The reduction in annual blood glucose testing was largely consistent across socio-demographic groups and was particularly pronounced among adults not working and adults aged 65 years and older. In the same time period, current use of diabetes medications increased by +3.8 pp (0.7, 6.9), from 85.9% to 89.7%. The increase in medication use was most pronounced among individuals aged 40-64-year old, employed, and those living in large central metropolitan areas. Conclusions Nationally, adults with Type 2 diabetes reported a reduction in annual blood glucose testing by a health professional and an increase in diabetes medication usage during the COVID-19 pandemic. If sustained after the end of the COVID-19 public health emergency, these changes have implications for national diabetes management and care.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.08.06.23293722v1" target="_blank">National Changes in Diabetes Care Practices during the COVID-19 Pandemic: Prospective Study of US Adults</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Young Healthcare Workers’ Employment Status and Mental Distress over SARS-CoV-2 in Bolivia</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Background Healthcare workers (HCW) have been particularly affected by the SARS-CoV-2 pandemic as it influenced employment conditions and unemployment/insecure employment. Their deterioration is associated with mental distress. Objective The aim of the study was to assess the trajectory of mental distress among HCW graduates during the COVID-19 pandemic in relation to their employment status. Methods We compared the change in mental distress over time among recent HCW graduates who were formally employed, to those who were unemployed/insecurely employed during the pandemic. In 2018 and 2022, we prospectively surveyed HCW who were in their final year of study in 2018 in Bolivia. Information was collected on socio-demographic characteristics, employment status, and mental distress. Mental distress was assessed using the 12-item General Health Questionnaire. Generalized Estimating Equations were implemented to examine changes in mental distress over time and the role of employment status in this development. Of the 663 HCW at baseline, 116 could be followed up. Findings Over the course of the pandemic, formal employment after graduation did not change the odds of mental distress (odds ratio (OR)=0.93 [95% confidence interval (CI) 0.13-6.83]). In contrast, unemployment/insecure employment statistically significantly increased the odds of mental distress (OR=2.10 [CI 1.05-4.24]) over time. Conclusions Especially in countries with limited social support for unemployed/insecurely employed citizens, interventions and policies to prevent mental distress among newly graduated HCW are important. This is particularly relevant in the face of crises such as the COVID-19 pandemic.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.08.07.23293747v1" target="_blank">Young Healthcare Workers’ Employment Status and Mental Distress over SARS-CoV-2 in Bolivia</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>PandoGen: Generating complete instances of future SARS-CoV2 sequences using Deep Learning</strong> -
|
|||
|
|
<div>
|
|||
|
|
Deep generative models have achieved breakthrough performance in generating computer code, instances of human language and images. We explore the use of these models to create as yet undiscovered instances of viral sequences in a pandemic situation. Towards this goal, we formulate a novel framework for training models to align the sequence generation problem to the characteristics of a pandemic. We applied our method to modeling the SARS-CoV2 Spike protein, the primary driver of the COVID-19 pandemic, and compared our method to models trained via prevalent practices applied to biological sequence modeling. Our method substantially out-performs a state-of-the-art generative model finetuned on SARS-CoV2 data, producing samples containing sequences which are four times as likely to be real, undiscovered sequences, and ten times as infectious. Our method can forecast novel lineages that will be reported over 10 weeks in the future. Given a limited sequence budget, our method also generates sequences belonging to the Delta variant and multiple dominant Omicron subvariants up to a month in advance.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2023.05.10.540124v3" target="_blank">PandoGen: Generating complete instances of future SARS-CoV2 sequences using Deep Learning</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Flexible Bayesian estimation of incubation times</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
The incubation period is of paramount importance in infectious disease epidemiology as it informs about the transmission potential of a pathogenic organism and helps to plan public health strategies to keep an epidemic outbreak under control. Estimation of the incubation period distribution from reported exposure times and symptom onset times is challenging as the underlying data is coarse. We develop a new Bayesian methodology using Laplacian-P-splines that provides a semi-parametric estimation of the incubation density based on a Langevinized Gibbs sampler. A finite mixture density smoother informs a set of parametric distributions via moment matching and an information criterion arbitrates between competing candidates. Our method has a natural nest within EpiLPS, a tool originally developed to estimate the time-varying reproduction number. Various simulation scenarios accounting for different levels of data coarseness are considered with encouraging results. Applications to real data on COVID-19, MERS-CoV and Mpox reveal results that are in alignment with what has been obtained in recent studies. The proposed flexible approach is an interesting alternative to classic Bayesian parametric methods for estimation of the incubation distribution.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.08.07.23293752v1" target="_blank">Flexible Bayesian estimation of incubation times</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Predicting Clinical Outcomes of SARS-CoV-2 Infection During the Omicron Wave Using Machine Learning</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk of adverse outcomes is a priority. The study objectives are to develop population-scale predictive models that: 1) identify predictors of adverse outcomes with Omicron surge SARS-CoV-2 infections, and 2) predict the impact of prioritized vaccination of high-risk groups for said outcome. We prepared a retrospective longitudinal observational study of a national cohort of 192,984 patients in the U.S. Veteran Health Administration who tested positive for SARS-CoV-2 from January 15 to August 15, 2022. We utilized sociodemographic characteristics, comorbidities, vaccination status, and prior COVID-19 infections, at time of testing positive for SARS-CoV-2 to predict hospitalization, escalation of care (high-flow oxygen, mechanical ventilation, vasopressor use, dialysis, or extracorporeal membrane oxygenation), and death within 30 days. Machine learning models demonstrated that advanced age, high comorbidity burden, lower body mass index, unvaccinated status, prior SARS-CoV-2 infection, and oral anticoagulant use were the important predictors of hospitalization and escalation of care. Similar factors predicted death. However, prior SARS-CoV-2 infection was associated with lower 30-day mortality, and anticoagulant use did not predict mortality risk. The all-cause death model showed the highest discrimination (Area Under the Curve (AUC) = 0.895, 95% Confidence Interval (CI): 0.885, 0.906) followed by hospitalization (AUC = 0.829, CI: 0.825, 0.834), then escalation of care (AUC=0.805, CI: 0.795, 0.814). Assuming a vaccine efficacy range of 70.8 to 78.7%, our simulations projected that targeted prevention in the highest risk group may have reduced 30-day hospitalization, care escalation, and death in more than 2 of 5 unvaccinated patients.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.08.06.23293725v1" target="_blank">Predicting Clinical Outcomes of SARS-CoV-2 Infection During the Omicron Wave Using Machine Learning</a>
|
|||
|
|
</div></li>
|
|||
|
|
</ul>
|
|||
|
|
<h1 data-aos="fade-right" id="from-clinical-trials">From Clinical Trials</h1>
|
|||
|
|
<ul>
|
|||
|
|
<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 2/3 Study to Evaluate the Safety and Immunogenicity of an (Omicron Subvariant) COVID-19 Vaccine Booster Dose of Previously Vaccinated Participants.</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: XBB.1.5 Vaccine (Booster); Biological: XBB.1.5 Vaccine (single dose)<br/><b>Sponsor</b>: Novavax<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Effect of Natural Food on Gut Microbiome and Phospholipid Spectrum of Immune Cells in COVID-19 Patients</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Dietary Supplement: Freeze-dried Mare Milk (Saumal)<br/><b>Sponsor</b>: Asfendiyarov Kazakh National Medical University<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Effects of Exercise Training on Patients With Long COVID-19</strong> - <b>Condition</b>: Long COVID-19<br/><b>Intervention</b>: Behavioral: Exercise training<br/><b>Sponsor</b>: Guangdong Provincial People’s Hospital<br/><b>Recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>EFFECT OF COGNITIVE BEHAVIORAL THERAPY ON DEPRESSION AND QUALITY OF LIFE IN PATIENTS WITH POST COVID-19</strong> - <b>Condition</b>: Post-COVID-19 Syndrome<br/><b>Intervention</b>: Behavioral: rehacom<br/><b>Sponsor</b>: Cairo University<br/><b>Enrolling by invitation</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Intradermal Administration of a COVID-19 mRNA Vaccine in Elderly</strong> - <b>Conditions</b>: Vaccination; Infection; COVID-19<br/><b>Intervention</b>: Biological: Comirnaty<br/><b>Sponsor</b>: Radboud University Medical Center<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>A Safety and Immune Response Study to Evaluate Varying Doses of an mRNA Vaccine Against Coronavirus Disease 2019 (COVID-19) in Healthy Adults</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: mRNA-CR-04 vaccine 10μg; Biological: mRNA-CR-04 vaccine 30μg; Biological: mRNA-CR-04 vaccine 100μg; Drug: Placebo<br/><b>Sponsor</b>: GlaxoSmithKline<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Phase 3 Adolescent Study for SARS-CoV-2 rS Variant Vaccines</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: NVX-CoV2601 co-formulated Omicron XBB.1.5 SARS-CoV-2 rS vaccine; Biological: Prototype/XBB.1.5 Bivalent Vaccine (5 µg)<br/><b>Sponsor</b>: Novavax<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Hyperbaric on Pulmonary Functions in Post Covid -19 Patients.</strong> - <b>Condition</b>: Post COVID-19 Patients<br/><b>Interventions</b>: Device: hyperbaric oxygen therapy; Device: breathing exercise; Drug: medical treatment<br/><b>Sponsor</b>: Cairo University<br/><b>Completed</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Dietary Intervention to Mitigate Post-Acute COVID-19 Syndrome</strong> - <b>Conditions</b>: Post-Acute COVID-19 Syndrome; Fatigue<br/><b>Interventions</b>: Other: Dietary intervention to mitigate Post-Acute COVID-19 Syndrome; Other: Attention Control<br/><b>Sponsor</b>: University of Maryland, Baltimore<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<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 Trial to Evaluate the Safety and Immunogenicity of BIMERVAX® When Coadministered With Seasonal Influenza Vaccine (SIIV) in Adults Older Than 65 Years of Age Fully Vaccinated Against COVID-19</strong> - <b>Conditions</b>: SARS CoV 2 Infection; Influenza, Human<br/><b>Interventions</b>: Biological: BIMERVAX; Biological: SIIV<br/><b>Sponsor</b>: Hipra Scientific, S.L.U<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>HD-Tdcs and Pharmacological Intervention For Delirium In Critical Patients With COVID-19</strong> - <b>Conditions</b>: COVID-19; Delirium; Critical Illness<br/><b>Interventions</b>: Combination Product: Active HD-tDCS; Combination Product: Sham HD-tDCS<br/><b>Sponsors</b>: Suellen Andrade; City University of New York<br/><b>Completed</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>RECOVER-VITAL: Platform Protocol, Appendix to Measure the Effects of Paxlovid on Long COVID Symptoms</strong> - <b>Conditions</b>: Long COVID-19; Long COVID<br/><b>Interventions</b>: Drug: Paxlovid 25 day dosing; Drug: Paxlovid 15 day dosing; Drug: Control<br/><b>Sponsor</b>: Kanecia Obie Zimmerman<br/><b>Enrolling by invitation</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>RECOVER-NEURO: Platform Protocol, Appendix_A to Measure the Effects of BrainHQ, PASC CoRE and tDCS Interventions on Long COVID Symptoms</strong> - <b>Conditions</b>: Long COVID; Long Covid19; Long Covid-19<br/><b>Interventions</b>: Other: BrainHQ/Active Comparator Activity; Other: BrainHQ; Other: PASC CoRE; Device: tDCS-active; Device: tDCS-sham<br/><b>Sponsor</b>: Duke University<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Directed Topical Drug Delivery for Treatment for PASC Hyposmia</strong> - <b>Condition</b>: Post Acute Sequelae Covid-19 Hyposmia<br/><b>Interventions</b>: Drug: Beclomethasone; Other: Placebo; Device: Microsponge<br/><b>Sponsor</b>: Duke University<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>RECOVER-NEURO: Platform Protocol to Measure the Effects of Cognitive Dysfunction Interventions on Long COVID Symptoms</strong> - <b>Conditions</b>: Long COVID; Long Covid19; Long Covid-19<br/><b>Interventions</b>: Other: BrainHQ/Active Comparator Activity; Other: BrainHQ; Other: PASC CoRE; Device: tDCS-active; Device: tDCS-sham<br/><b>Sponsor</b>: Duke University<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
</ul>
|
|||
|
|
<h1 data-aos="fade-right" id="from-pubmed">From PubMed</h1>
|
|||
|
|
<ul>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Greener approach for the isolation of oleanolic acid from <em>Nepeta leucophylla Benth</em>. Its derivatization and their molecular docking as antibacterial and antiviral agents</strong> - In the present study bioactive methanolic extract along with chloroform and hexane extracts obtained from shade dried leaves of the Himalayan aromatic medicinal plant Nepeta leucophylla Benth. Were screened for the presence of triterpenoids, especially oleanolic acid (OA). Total three compounds oleanolic acid, squalene and linoleic methyl ester were isolated from methanol extract. The percentage yield of OA was 0.11%. Out of these three, OA is more bioactive and was further subjected to…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Anti‑inflammatory effect of metformin against an experimental model of LPS‑induced cytokine storm</strong> - Cytokine storm is one of the leading causes of death in patients with COVID-19. Metformin has been shown to inhibit the action of a wide range of proinflammatory cytokines such as IL-6, and TNF-α which may ultimately affect cytokine storm due to Covid-19. The present study analyzed the anti-inflammatory effect of oral and intraperitoneal (IP) metformin administration routes in a mouse model of lipopolysaccharide (LPS)-induced cytokine storm. A total of 60 female BALB/c mice were randomly…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Immunogenicity and reactogenicity of inactivated SARS-CoV-2 vaccines in healthy adults</strong> - CONCLUSION: CD25, a late activation marker of lymphocytes and high-activity memory T cell subgroup, exhibited higher levels at the later stages after vaccination. COVID-19 booster vaccination in older adults and regular testing of SARS-CoV-2 neutralizing antibodies are recommended. Booster doses should be administered if the antibody level falls below the 30% inhibition rate.</p></li>
|
|||
|
|
<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-associated organs failure and inflammation: a focus on the role of cellular and viral microRNAs</strong> - SARS-CoV-2 has been responsible for the recent pandemic all over the world, which has caused many complications. One of the hallmarks of SARS-CoV-2 infection is an induced immune dysregulation, in some cases resulting in cytokine storm syndrome, acute respiratory distress syndrome and many organs such as lungs, brain, and heart that are affected during the SARS-CoV-2 infection. Several physiological parameters are altered as a result of infection and cytokine storm. Among them, microRNAs…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Valorization of Kappaphycus alvarezii through extraction of high-value compounds employing green approaches and assessment of the therapeutic potential of κ-carrageenan</strong> - This study utilizes different emerging green extraction technologies to recover maximum value-added products from Kappaphycus alvarezii and evaluate their bio-functional properties. Using the supercritical fluid extraction (SFE) method, the total lipid yield of 0.21 ± 0.2 % was obtained from the biomass. Linoleic acid, eicosapentaenoic acid, arachidonic acid, γ-linolenic acid, and docosahexaenoic acid were present in higher concentrations (9.12 %) in the lipid extracted with SFE as compared to…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Modifications of Lipid Pathways Restrict SARS-CoV-2 Propagation in Human Induced Pluripotent Stem Cell-derived 3D Airway Organoids</strong> - CONCLUSIONS: Together, our data demonstrated that modifications of lipid pathways restrict SARS-CoV-2 propagation in the iPSC-AOs, which the inhibition is speculated through the translocation of ACE2 from the cell membrane to the cytosol. Considering the highly frequent mutation and generation of SARS-CoV-2 variants, targeting host metabolisms of cholesterol or other lipids may represent an alternative approach against SARS-CoV-2 infection.</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Core mitochondrial genes are down-regulated during SARS-CoV-2 infection of rodent and human hosts</strong> - Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral proteins bind to host mitochondrial proteins, likely inhibiting oxidative phosphorylation (OXPHOS) and stimulating glycolysis. We analyzed mitochondrial gene expression in nasopharyngeal and autopsy tissues from patients with coronavirus disease 2019 (COVID-19). In nasopharyngeal samples with declining viral titers, the virus blocked the transcription of a subset of nuclear DNA (nDNA)-encoded mitochondrial OXPHOS genes, induced…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Comparison of SARS-CoV-2 entry inhibitors based on ACE2 receptor or engineered Spike-binding peptides</strong> - With increasing resistance of SARS-CoV-2 variants to antibodies, there is interest in developing entry inhibitors that target essential receptor-binding regions of the viral Spike protein and thereby present a high bar for viral resistance. Such inhibitors could be derivatives of the viral receptor, ACE2, or peptides engineered to interact specifically with the Spike receptor-binding pocket. We compared the efficacy of a series of both types of entry inhibitors, constructed as fusions to an…</p></li>
|
|||
|
|
<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 Omicron entry is type II transmembrane serine protease-mediated in human airway and intestinal organoid models</strong> - SARS-CoV-2 can enter cells after its spike protein is cleaved by either type II transmembrane serine proteases (TTSPs), like TMPRSS2, or cathepsins. It is now widely accepted that the Omicron variant uses TMPRSS2 less efficiently and instead enters cells via cathepsins, but these findings have yet to be verified in more relevant cell models. Although we could confirm efficient cathepsin-mediated entry for Omicron in a monkey kidney cell line, experiments with protease inhibitors showed that…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>TGF-β uncouples glycolysis and inflammation in macrophages and controls survival during sepsis</strong> - Changes in metabolism of macrophages are required to sustain macrophage activation in response to different stimuli. We showed that the cytokine TGF-β (transforming growth factor-β) regulates glycolysis in macrophages independently of inflammatory cytokine production and affects survival in mouse models of sepsis. During macrophage activation, TGF-β increased the expression and activity of the glycolytic enzyme PFKL (phosphofructokinase-1 liver type) and promoted glycolysis but suppressed the…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Escape from senescence: molecular basis and therapeutic ramifications</strong> - Cellular senescence constitutes a stress response mechanism in reaction to a plethora of stimuli. Senescent cells exhibit cell-cycle arrest and altered function. While cell-cycle withdrawal has been perceived as permanent, recent evidence in cancer research introduced the so-called escape-from-senescence concept. In particular, under certain conditions, senescent cells may resume proliferation, acquiring highly aggressive features. As such, they have been associated with tumour relapse,…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Comparative antibody and cell-mediated immune responses, reactogenicity, and efficacy of homologous and heterologous boosting with CoronaVac and BNT162b2 (Cobovax): an open-label, randomised trial</strong> - BACKGROUND: Few trials have compared homologous and heterologous third doses of COVID-19 vaccination with inactivated vaccines and mRNA vaccines. The aim of this study was to assess immune responses, safety, and efficacy against SARS-CoV-2 infection following homologous or heterologous third-dose COVID-19 vaccination with either one dose of CoronaVac (Sinovac Biotech; inactivated vaccine) or BNT162b2 (Fosun Pharma-BioNTech; mRNA vaccine).</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Post-infection treatment with the E protein inhibitor BIT225 reduces disease severity and increases survival of K18-hACE2 transgenic mice infected with a lethal dose of SARS-CoV-2</strong> - The Coronavirus envelope (E) protein is a small structural protein with ion channel activity that plays an important role in virus assembly, budding, immunopathogenesis and disease severity. The viroporin E is also located in Golgi and ER membranes of infected cells and is associated with inflammasome activation and immune dysregulation. Here we evaluated in vitro antiviral activity, mechanism of action and in vivo efficacy of BIT225 for the treatment of SARS-CoV-2 infection. BIT225 showed…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>The Role of the Tyrosine-Based Sorting Signals of the ORF3a Protein of SARS-CoV-2 on Intracellular Trafficking, Autophagy, and Apoptosis</strong> - The open reading frame 3a (ORF3a) is an accessory transmembrane protein that is important to the pathogenicity of SARS-CoV-2. The cytoplasmic domain of ORF3a has three canonical tyrosine-based sorting signals (YxxΦ; where x is any amino acid and Φ is a hydrophobic amino acid with a bulky -R group). They have been implicated in the trafficking of membrane proteins to the cell plasma membrane and to intracellular organelles. Previous studies have indicated that mutation of the ^(160) YSNV ^(163)…</p></li>
|
|||
|
|
<li data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom"><strong>Discovery of Hybrid Thiouracil-Coumarin Conjugates as Potential Novel Anti-SARS-CoV-2 Agents Targeting the Virus’s Polymerase “RdRp” as a Confirmed Interacting Biomolecule</strong> - The coronavirus (COVID-19) pandemic, along with its various strains, has emerged as a global health crisis that has severely affected humankind and posed a great challenge to the public health system of affected countries. The replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mainly depends on RNA-dependent RNA polymerase (RdRp), a key enzyme that is involved in RNA synthesis. In this regard, we designed, synthesized, and characterized hybrid thiouracil and coumarin…</p></li>
|
|||
|
|
</ul>
|
|||
|
|
<h1 data-aos="fade-right" id="from-patent-search">From Patent Search</h1>
|
|||
|
|
|
|||
|
|
|
|||
|
|
<script>AOS.init();</script></body></html>
|