190 lines
50 KiB
HTML
190 lines
50 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>12 October, 2022</title>
|
|||
|
<style type="text/css">
|
|||
|
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%;}
|
|||
|
</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>The SARS-CoV-2 envelope (E) protein forms a calcium- and voltage-activated calcium channel</strong> -
|
|||
|
<div>
|
|||
|
The function of ion channels is essential in the infectious cycle of many viruses. To facilitate viral uptake, maturation and export, viruses must modify the ionic balance of their host cells, in particular of calcium ions (Ca2+). Viroporins encoded in the viral genome play a key part in altering the cell’s ionic homeostasis. In SARS-Coronavirus-2 (SARS-CoV-2) - the causative agent of Covid-19 - the envelope (E) protein is considered to form ion channels in ERGIC organellar membranes, whose function is closely linked to disease progression and lethality. Deletion, blockade, or loss-of-function mutation of coronaviral E proteins results in propagation-deficient or attenuated virus variants. The exact physiological function of the E protein, however, is not sufficiently understood. Since one of the key features of the ER is its function as a Ca2+ storage compartment, we investigated the activity of E in the context of this cation. Molecular dynamics simulations and voltage-clamp electrophysiological measurements show that E exhibits ion channel activity that is regulated by increased luminal Ca2+ concentration, membrane voltage, post-translational protein modification, and negatively charged ERGIC lipids. Particularly, calcium ions bind to a distinct region at the ER-luminal channel entrance, where they activate the channel and maintain the pore in an open state. Also, alongside monovalent ions, the E protein is highly permeable to Ca2+. Our results suggest that the physiological role of the E protein is the release of Ca2+ from the ER, and that the distinct Ca2+ activation site may serve as a promising target for channel blockers, potentially inhibiting the infectious cycle of coronaviruses.
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.10.11.511775v1" target="_blank">The SARS-CoV-2 envelope (E) protein forms a calcium- and voltage-activated calcium channel</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Perceptions and predictors of COVID-19 vaccine hesitancy among health care providers across five countries in sub-Saharan Africa</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
The African continent has some of the world’s lowest COVID-19 vaccination rates. While the limited availability of vaccines is a contributing factor, COVID-19 vaccine hesitancy among health care providers (HCP) is another factor that could adversely affect efforts to control infections on the continent. We sought to understand the extent of COVID-19 vaccine hesitancy among HCP, and its contributing factors in Africa. We evaluated COVID-19 vaccine hesitancy among 1,499 HCP enrolled in a repeated cross-sectional telephone survey in Burkina Faso, Ethiopia, Nigeria, Tanzania and Ghana. We defined COVID-19 vaccine hesitancy among HCP as self-reported responses of definitely not, maybe, unsure, or undecided on whether to get the COVID-19 vaccine, compared to definitely getting the vaccine. We used Poisson regression models to evaluate factors influencing vaccine hesitancy among HCP. Approximately 65.6% were nurses and the mean age (±SD) of participants was 35.8 (±9.7) years. At least 67% of the HCP reported being vaccinated. Reasons for low COVID-19 vaccine uptake included concern about vaccine effectiveness, side effects and fear of receiving unsafe and experimental vaccines. COVID-19 vaccine hesitancy affected 45.7% of the HCP in Burkina Faso, 25.7% in Tanzania, 9.8% in Ethiopia, 9% in Ghana and 8.1% in Nigeria. Respondents reporting that COVID-19 vaccines are very effective (RR:0.21, 95% CI:0.08, 0.55), and older HCP (45 or older vs.20-29 years, RR:0.65, 95% CI: 0.44,0.95) were less likely to be vaccine-hesitant. Nurses were more likely to be vaccine-hesitant (RR 1.38, 95% CI: 1.00,1.89) compared to doctors. We found higher vaccine hesitancy among HCP in Burkina Faso and Tanzania. Information asymmetry among HCP, beliefs about vaccine effectiveness and the endorsement of vaccines by the public health institutions may be important. Efforts to address hesitancy should address information and knowledge gaps among different cadres of HCP and should be coupled with efforts to increase vaccine supply.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.11.22280952v1" target="_blank">Perceptions and predictors of COVID-19 vaccine hesitancy among health care providers across five countries in sub-Saharan Africa</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>A Third Dose COVID-19 Vaccination in Allogeneic Hematopoietic Stem Cell Transplantation Patients</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
We previously reported that a second dose of COVID-19 mRNA vaccine was safe and effective for allogeneic hematopoietic stem cell transplantation (HSCT) patients. However, some of these patients did not achieve seroconversion. Here, we investigated the safety and efficacy of a third dose of COVID-19 mRNA vaccine in Japanese allogeneic HSCT patients. Antibody titers against the S1 spike protein were measured using the QuaResearch COVID-19 Human IgM IgG ELISA kit. The previous study included 25 allogeneic HSCT patients who received two doses of COVID-19 mRNA vaccine. Following the exclusion of three patients because of the development of COVID-19 (n = 2) and loss to follow-up (n = 1), the study evaluated 22 allogeneic HSCT patients who received a third dose of COVID19 mRNA vaccine (BNT162b2 [n = 15] and mRNA1273 [n = 7]). Median age at the time of the first vaccination was 56 (range, 23-71) years. Median time from HSCT to the third vaccination and from the second to the third vaccination was 1842 (range, 378-4279) days and 219 (range, 194-258) days, respectively. Five patients were receiving immunosuppressants at the third vaccination, namely calcineurin inhibitors (CI) alone (n = 1), steroids alone (n = 2), or CI combined with steroids (n = 2). Median optical density of S1 IgG titers before and after the third dose was 0.099 (range, 0.001-0.713) and 1.315 (range, 0.006-1.730), respectively. Among 22 evaluable patients, 21 (95%) seroconverted after the third dose. Four of the five patients treated with steroids or CI seroconverted after the third vaccination. One patient with a serum IgG level of 173 mg/dL who received steroids did not achieve seroconversion. On one-week follow-up, none of our patients had > grade 3 or serious adverse events, new onset graft versus host disease (GVHD), or GVHD exacerbation after vaccination. The most frequent adverse event was mild pain at the injection site. A third dose of the BNT162b2 and mRNA-1273 COVID-19 vaccines was safe and effective for allogeneic HSCT patients.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.08.22280863v1" target="_blank">A Third Dose COVID-19 Vaccination in Allogeneic Hematopoietic Stem Cell Transplantation Patients</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Brain imaging and neuropsychological assessment of individuals recovered from mild to moderate SARS-CoV-2 infection</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
As SARS-CoV-2 infections have been shown to affect the central nervous system, the investigation of associated alterations of brain structure and neuropsychological sequelae is crucial to help address future health care needs. Therefore, we performed a comprehensive neuroimaging and neuropsychological assessment of 223 non-vaccinated individuals recovered from a mild to moderate SARS-CoV-2 infection (100 female/123 male, age [years], mean +- SD, 55.54 +- 7.07; median 9.7 months after infection) in comparison with 223 matched controls (93 female/130 male, 55.74 +- 6.60) within the framework of the Hamburg City Health Study. Primary study outcomes were advanced diffusion magnetic resonance imaging (MRI) measures of white matter microstructure, cortical thickness, white matter hyperintensity load and neuropsychological test scores. Among all 11 MRI markers tested, significant differences were found in global measures of mean diffusivity and extracellular free-water which were elevated in the white matter of post-SARS-CoV-2 individuals comparing to matched controls (free-water: 0.148 +- 0.018 vs. 0.142 +- 0.017, P<.001; mean diffusivity [10-3 mm2/s]: 0.747 +- 0.021 vs. 0.740 +- 0.020, P<.001). Group classification accuracy based on diffusion imaging markers was up to 80%. Neuropsychological test scores did not significantly differ between groups. Collectively, our findings suggest that subtle changes in white matter extracellular water content last beyond the acute infection with SARS-CoV-2. However, in our sample, a mild to moderate SARS-CoV-2 infection was not associated with neuropsychological deficits, significant changes in cortical structure or vascular lesions several months after recovery. External validation of our findings and longitudinal follow-up investigations are needed.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.07.08.22277420v3" target="_blank">Brain imaging and neuropsychological assessment of individuals recovered from mild to moderate SARS-CoV-2 infection</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Innate immune sensing of self-derived double-stranded RNA by RIG-I-MAVS-TNF-α regulates the survival and senescence fate of SARS-2-S syncytia</strong> -
|
|||
|
<div>
|
|||
|
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains an important health threat. Syncytial formation by infected cells mediated by the SARS-CoV-2 spike protein (SARS-2-S) is a hallmark of COVID-19-associated pathology. Although SARS-CoV-2 infection evokes cellular senescence, as in other viruses, the direct link between SARS-2-S-induced syncytia with senescence in the absence of viral infection and their senescence fate determinants remain unknown. Here, we show that syncytia formed by cells expressing exogenously delivered SARS-2-S exhibited a senescence-like phenotype in vitro and that SARS-2-S mRNA induced senescence phenotype in vivo. Extracellular vesicles (EVs) containing SARS-2-S also induced senescent syncytium formation independent of the de novo synthesis of SARS-2-S. Mechanistically, we show that the accumulation of endogenous dsRNA, partially that whose formation is induced by activation of the unfolded protein response (UPR), in SARS-2-S syncytia triggers RIG-I-MAVS signalling to drive the TNF–dependent survival and senescence fate of SARS-2-S syncytia. Our findings suggest that the fusogenic ability of SARS-2-S might contribute to the side effects of particular COVID-19 vaccines or perhaps long COVID-19 syndrome and provide insight into how these effects can be prevented.
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.10.10.511541v1" target="_blank">Innate immune sensing of self-derived double-stranded RNA by RIG-I-MAVS-TNF-α regulates the survival and senescence fate of SARS-2-S syncytia</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Bridging machine learning and compartment models to predict an epidemic</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
This work proposes a Physics-informed Machine learning method to model and emulate the progression of COVID-19. Besides the high accuracy, lower data need, and interpretability, the method also estimates hidden parameters from data, which are useful for policymakers to flatten the curve and better understand public healthcare system.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.07.22280853v1" target="_blank">Bridging machine learning and compartment models to predict an epidemic</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>COVID19 Diagnosis Using Chest X-rays and Transfer Learning</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
A pandemic of respiratory illnesses from a novel coronavirus known as Sars-CoV-2 has swept across the globe since December of 2019. This is calling upon the research community including medical imaging to provide effective tools for use in combating this virus. Research in biomedical imaging of viral patients is already very active with machine learning models being created for diagnosing Sars-CoV-2 infections in patients using CT scans and chest x-rays. We aim to build upon this research. Here we used a transfer-learning approach to develop models capable of diagnosing COVID19 from chest x-ray. For this work we compiled a dataset of 112120 negative images from the Chest X-Ray 14 and 2725 positive images from public repositories. We tested multiple models, including logistic regression and random forest and XGBoost with and without principal components analysis, using five-fold cross-validation to evaluate recall, precision, and f1-score. These models were compared to a pre-trained deep-learning model for evaluating chest x-rays called COVID-Net. Our best model was XGBoost with principal components with a recall, precision, and f1-score of 0.692, 0.960, 0.804 respectively. This model greatly outperformed COVID-Net which scored 0.987, 0.025, 0.048. This model, with its high precision and reasonable sensitivity, would be most useful as rule-in test for COVID19. Though it outperforms some chemical assays in sensitivity, this model should be studied in patients who would not ordinarily receive a chest x-ray before being used for screening.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.09.22280877v1" target="_blank">COVID19 Diagnosis Using Chest X-rays and Transfer Learning</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Infection-induced immunity is associated with protection against SARS-CoV-2 infection, but not decreased infectivity during household transmission</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
Background Understanding the impact of infection-induced immunity on SARS-CoV-2 transmission will provide insight into the transition of SARS-CoV-2 to endemicity. Here we estimate the effects of prior infection induced immunity and children on SARS-CoV-2 transmission in households. Methods We conducted a household cohort study between March 2020-June 2022 in Managua, Nicaragua where when one household member tests positive for SARS-CoV-2, household members are closely monitored for SARS-CoV-2 infection. Using a pairwise survival model, we estimate the association of infection period, age, symptoms, and infection-induced immunity with secondary attack risk. Results Overall transmission occurred in 72.4% of households, 42% of household contacts were infected and the secondary attack risk was 13.0% (95% CI: 11.7, 14.6). Prior immunity did not impact the probability of transmitting SARS-CoV-2. However, participants with pre-existing infection-induced immunity were half as likely to be infected compared to naive individuals (RR 0.53, 95% CI: 0.39, 0.72), but this reduction was not observed in children. Likewise, symptomatic infected individuals were more likely to transmit (RR 24.4, 95% CI: 7.8, 76.1); however, symptom presentation was not associated with infectivity of young children. Young children were less likely to transmit SARS-CoV-2 than adults. During the omicron era, infection-induced immunity remained protective against infection. Conclusions Infection-induced immunity is associated with protection against infection for adults and adolescents. While young children are less infectious, prior infection and asymptomatic presentation did not reduce their infectivity as was seen in adults. As SARS-CoV-2 transitions to endemicity, children may become more important in transmission dynamics.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.10.22280915v1" target="_blank">Infection-induced immunity is associated with protection against SARS-CoV-2 infection, but not decreased infectivity during household transmission</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Impact of the COVID-19 pandemic on exercise habits and overweight in Japan: a nation-wide panel survey</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
Introduction: A catastrophic disaster may cause distant health impacts like immobility and obesity. This research aims at analysing the impact of the COVID-19 pandemic on exercise habit and overweight in the Japanese population. Methods: Nation-wide online questionnaires were conducted five times from October 2020 to October 2021. The change in exercise habit, body mass index (BMI) and status of overweight (BMI>25kg/m2) were compared between the first questionnaire and later ones. Risk factors of losing exercise habit or developing overweight were analysed using multiple regression. Results: Data was obtained from 16,642 participants. In the early phase of the pandemic, people with high income and elderly females showed higher risk of decreased exercise days. Proportion of overweight was increased from 22.2% to 26.6% in males and from 9.3% to 10.8% in females. Middle aged males, elderly females, males who experienced SARS-CoV-2 infection were at higher risks of developing overweight. Conclusion: Our findings suggest that risks of immobility and overweight are homogeneous. Continuous intervention for elderly females and long-term intervention for males who were infected might be especially needed. As most disasters can cause similar social transformation, research and evaluation of immobility and obesity should be addressed in future disaster preparation/ mitigation plans.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.11.22280942v1" target="_blank">Impact of the COVID-19 pandemic on exercise habits and overweight in Japan: a nation-wide panel survey</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Epidemic graph diagrams as analytics for epidemic control in the data-rich era</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
COVID-19 highlighted how modeling is an integral part of pandemic response. But it also exposed fundamental methodological challenges. As high-resolution data on disease progression, epidemic surveillance, and host behavior are now available, can models turn them into accurate epidemic estimates and reliable public health recommendations? Take the epidemic threshold, which estimates the potential for an infection to spread in a host population, quantifying epidemic risk throughout epidemic emergence, mitigation, and control. While models increasingly integrated realistic host contacts, no parallel development occurred with matching detail in disease progression and interventions. This narrowed the use of the epidemic threshold to oversimplified disease and control descriptions. Here, we introduce the epidemic graph diagrams (EGDs), novel representations to compute the epidemic threshold directly from arbitrarily complex data on contacts, disease and control efforts. We define a grammar of diagram operations to decompose, compare, simplify models, extracting new theoretical understanding and improving computational efficiency. We test EGDs on two public health challenges, influenza and sexually-transmitted infections, to (i) explain the emergence of resistant influenza variants in the 2007-2008 season, and (ii) demonstrate that neglecting non-infectious prodromic stages biases the predicted epidemic risk, compromising control. EGDs are however general, and increase the performance of mathematical modeling to respond to present and future public health challenges.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.10.22280897v1" target="_blank">Epidemic graph diagrams as analytics for epidemic control in the data-rich era</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Assessing the epidemic impact of protests during the COVID-19 pandemic</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
Protests during the COVID-19 pandemic present a complex trade-off between democratic rights of freedom of assembly and an epidemic risk, and have created a need for careful assessment of protest-driven infections. Here, we build a coupled disease transmission model and assess the impact of protests on the COVID-19 spread in the continental US using a dataset of 4,121 protests and 1.66 million protesters between April and June of 2020. We find that protests in 2020 had limited effects, creating tens of additional daily cases country-wide, due to their small size. However, a simple scaling relation of protest-driven infections derived from our simulations reveals that very large protests with over millions of participants can significantly boost outbreaks and impact the healthcare system. In the worst-case scenario, very large protests can add over 20,000 daily cases and over 7,000 thousand ICU admissions over the continental US. We hope our model can aid the policy rationale to maintain freedom of assembly in the current and future pandemics, while providing estimates for preparations for a healthcare surge in the worst-case setting.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.10.22280896v1" target="_blank">Assessing the epidemic impact of protests during the COVID-19 pandemic</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>SARS-CoV-2 seroprevalence and implications for population immunity: Evidence from two Health and Demographic Surveillance System sites in Kenya, February-June 2022</strong> -
|
|||
|
<div>
|
|||
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
Background Up-to-date SARS-CoV-2 antibody seroprevalence estimates are important for informing public health planning, including priorities for Coronavirus disease 2019 (COVID-19) vaccination programs. We sought to estimate infection- and vaccination-induced SARS-CoV-2 antibody seroprevalence within representative samples of the Kenyan population approximately two years into the COVID-19 pandemic and approximately one year after rollout of the national COVID-19 vaccination program. Methods We conducted cross-sectional serosurveys within random, age-stratified samples of Kilifi Health and Demographic Surveillance System (HDSS) and Nairobi Urban HDSS residents. Anti-spike (anti-S) immunoglobulin G (IgG) and anti-nucleoprotein (anti-N) IgG were measured using validated in-house ELISAs. Target-specific Bayesian population-weighted seroprevalence was calculated overall, by sex and by age, with adjustment for test performance as appropriate. Anti-S IgG concentrations were estimated with reference to the WHO International Standard (IS) for anti-SARS-CoV-2 immunoglobulin and their reverse cumulative distributions plotted. Results Between February and June 2022, 852 and 851 individuals within the Kilifi HDSS and the Nairobi Urban HDSS, respectively, were sampled. Only 11.0% (95% confidence interval [CI] 9.0-13.3) of all Kilifi HDSS participants and 33.4% (95%CI 30.2-36.6) of all Nairobi Urban HDSS participants had received any doses of COVID-19 vaccine. Population-weighted anti-S IgG seroprevalence was 69.1% (95% credible interval [CrI] 65.8-72.3) within the Kilifi HDSS and 88.5% (95%CrI 86.1-90.6) within the Nairobi Urban HDSS. Among COVID-unvaccinated residents of the Kilifi HDSS and Nairobi Urban HDSS, it was 66.7% (95%CrI 63.3-70.0) and 85.3% (95%CrI 82.1-88.2), respectively. Population-weighted, test-adjusted anti-N IgG seroprevalence within the Kilifi HDSS was 53.5% (95%CrI 46.5-61.1) and 65.5% (95%CrI 56.0-75.6) within the Nairobi Urban HDSS. The prevalence of anti-N antibodies was similar in vaccinated and unvaccinated subgroups in both HDSS populations. Anti-S IgG concentrations were significantly lower among Kilifi HDSS residents than among Nairobi Urban HDSS residents (p< 0.001). Conclusions Approximately, 7 in 10 Kilifi residents and 9 in 10 Nairobi residents were seropositive for anti-S IgG by May 2022 and June 2022, respectively. Given COVID-19 vaccination coverage, anti-S IgG seropositivity among COVID-unvaccinated individuals, and anti-N IgG seroprevalence, population-level anti-S IgG seroprevalence was predominantly derived from infection. Interventions to improve COVID-19 vaccination uptake should be targeted to individuals in rural Kenya who are at high risk of severe COVID-19.
|
|||
|
</p>
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.10.10.22280824v1" target="_blank">SARS-CoV-2 seroprevalence and implications for population immunity: Evidence from two Health and Demographic Surveillance System sites in Kenya, February-June 2022</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>MIGH-T: A Multi-Parametric and High-Throughput Platform for Host-Virus Binding Screens</strong> -
|
|||
|
<div>
|
|||
|
Speed is key during infectious disease outbreaks. It is essential, for example, to identify critical host binding factors to the pathogens as fast as possible. The complexity of host plasma membrane is often a limiting factor hindering fast and accurate determination of host binding factors as well as high-throughput screening for neutralizing antimicrobial drug targets. Here we describe MIGH-T, a multi-parametric and high-throughput platform tackling this bottleneck and enabling fast screens for host binding factors as well as new antiviral drug targets. The sensitivity and robustness of our platform was validated by blocking SARS-CoV-2 spike particles with nanobodies and IgGs from human serum samples.
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.10.10.511545v1" target="_blank">MIGH-T: A Multi-Parametric and High-Throughput Platform for Host-Virus Binding Screens</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>Comparative efficacy of antiviral strategies targeting different stages of the viral life cycle: A viral quasispecies dynamics study</strong> -
|
|||
|
<div>
|
|||
|
While the COVID-19 pandemic continues to impact public health worldwide significantly, the use of antiviral drugs and therapies has dramatically reduced the instances of severe disease and death. More broadly, the unprecedented use of antivirals also provides hope for preventing and mitigating similar viral outbreaks in the future. Here we ask: What are the comparative impact of antiviral therapeutics targeting different stages of the viral lifecycle? How do antiviral therapeutics impact the viral population in the bloodstream, or in other words, the viral load in high and low-immunity individuals? To address these questions, we use a model of viral quasispecies dynamics to examine the efficacy of antiviral strategies targeting three critical aspects of the viral life cycle, fecundity, reproduction rate, or infection rate. We find a linear relationship of the viral load with the change in fecundity and a power law with the change in the reproduction rate of the virus, with the viral load decreasing as the fecundity and the reproduction rates are decreased. Interestingly, however, for antivirals that target the infection rate, the viral load changes non-monotonically with the change in infection rate; the viral population initially increases and then decreases as the infection rate is decreased. The initial increase is especially pronounced for individuals with low immunity. By examining the viral population inside cells for such cases, we found that the therapeutics are only effective in such individuals if they stop the infection process entirely. Otherwise, the viral population inside cells does not go extinct. Our results predict the effectiveness of different antiviral strategies for COVID-19 and similar viral diseases and provide insights into the susceptibility of individuals with low immunity to effects like long covid.
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.10.10.511620v1" target="_blank">Comparative efficacy of antiviral strategies targeting different stages of the viral life cycle: A viral quasispecies dynamics study</a>
|
|||
|
</div></li>
|
|||
|
<li><strong>GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.</strong> -
|
|||
|
<div>
|
|||
|
Our work seeks to transform how new and emergent variants of pandemic causing viruses, specially SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences, and then finetuning a SARS-CoV-2 specific model on 1.5 million genomes, we show that GenSLM can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLM represents one of the first whole genome scale foundation models which can generalize to other prediction tasks. We demonstrate the scaling of GenSLMs on both GPU-based supercomputers and AI-hardware accelerators, achieving over 1.54 zettaflops in training runs. We present initial scientific insights gleaned from examining GenSLMs in tracking the evolutionary dynamics of SARS-CoV-2, noting that its full potential on large biological data is yet to be realized.
|
|||
|
</div>
|
|||
|
<div class="article-link article-html-link">
|
|||
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.10.10.511571v1" target="_blank">GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics.</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>Clinical Study of Recombinant Omicron-Delta COVID-19 Vaccine (CHO Cell)</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: Recombinant Omicron-Delta COVID-19 Vaccine (CHO Cell); Biological: Inactivated COVID-19 vaccine (Vero Cell)<br/><b>Sponsors</b>: Anhui Zhifei Longcom Biologic Pharmacy Co., Ltd.; First Affiliated Hospital Bengbu Medical College<br/><b>Active, not 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 Study to Learn About a Repeat 5-Day Treatment With the Study Medicines (Called Nirmatrelvir/Ritonavir) in People 12 Years Old or Older With Return of COVID-19 Symptoms and SARS-CoV-2 Positivity After Finishing Treatment With Nirmatrelvir/Ritonavir</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Drug: nirmatrelvir; Drug: ritonavir; Drug: placebo for nirmatrelvir<br/><b>Sponsor</b>: Pfizer<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 III Study to Evaluate Immunogenicity and Safety of COVID-19 Vaccine EuCorVac-19 in Healthy Adults</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: EuCorVac-19; Biological: ChAdOx1<br/><b>Sponsor</b>: EuBiologics Co.,Ltd<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>Study Evaluating Diltiazem in Combination With Standard Treatment in the Management of Patients Hospitalized With COVID-19 Pneumonia</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Drug: DILTIAZEM TEVA 60 mg or placebo<br/><b>Sponsors</b>: Hospices Civils de Lyon; Signia Therapeutics<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>COVID-19 Booster Dose Reminder/Recall for Adolescents</strong> - <b>Condition</b>: COVID-19 Vaccines<br/><b>Intervention</b>: Behavioral: Reminder/Recall Sent Via Preferred Method of Communication<br/><b>Sponsor</b>: Marshfield Clinic Research Foundation<br/><b>Active, not 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>Research on Community Based ATK Test Study to Control Spread of COVID-19 in Migrant Community</strong> - <b>Condition</b>: COVID-19 Pandemic<br/><b>Intervention</b>: Device: STANDARD Q COVID-19 Ag Test<br/><b>Sponsor</b>: University of Oxford<br/><b>Active, not 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>COVID-19 Simulation Education on Nursing Students</strong> - <b>Conditions</b>: COVID-19 Pandemic; Simulation of Physical Illness<br/><b>Interventions</b>: Behavioral: Simulation training; Other: Control Group<br/><b>Sponsor</b>: Mehmet Akif Ersoy 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>VAX-MOM COVID-19: Increasing Maternal COVID-19 Vaccination</strong> - <b>Conditions</b>: Immunization; Infection; Pregnancy Related; COVID-19<br/><b>Interventions</b>: Behavioral: VAX-MOM COVID-19 Intervention; Other: Standard of Care<br/><b>Sponsors</b>: University of Rochester; Centers for Disease Control and Prevention; University of California, Los Angeles<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>Safety and Immunogenicity of COVID-19 Vaccine, AdCLD-CoV19-1 OMI, as a Booster</strong> - <b>Conditions</b>: COVID-19; Vaccines<br/><b>Interventions</b>: Biological: AdCLD-CoV19-1 OMI (Part A); Biological: AdCLD-CoV19-1 OMI (Part B); Other: Placebo (Part B)<br/><b>Sponsor</b>: Cellid Co., Ltd.<br/><b>Active, not 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>Personalized Computerized Training Program for Cognitive Dysfunction After COVID-19</strong> - <b>Conditions</b>: Post-Acute COVID-19; Long COVID<br/><b>Intervention</b>: Device: CogniFit’s CCT Post COVID-19<br/><b>Sponsor</b>: Universidad Antonio de Nebrija<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>Sequential Enhanced Safety Study of a Novel Coronavirus Messenger RNA (mRNA) Vaccine in Adults Aged 18 Years and Older.</strong> - <b>Condition</b>: Corona Virus Disease 2019(COVID-19)<br/><b>Intervention</b>: Biological: 0.3ml of mRNA vaccine<br/><b>Sponsor</b>: Yu Qin<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>Understanding the Impact of Death Conditions Linked to the COVID-19 Crisis on the Grieving Process in Bereaved Families</strong> - <b>Condition</b>: Psychological Disorder<br/><b>Intervention</b>: Other: Qualitative research interview<br/><b>Sponsor</b>: Assistance Publique - Hôpitaux de Paris<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 Study to Evaluate Immunogenicity and Safety of BBV154 Booster Dose</strong> - <b>Condition</b>: COVID-19 Respiratory Infection<br/><b>Interventions</b>: Biological: BBV154 Intranasal Vaccine; Biological: Intramuscular vaccine COVAXIN; Biological: Covishield<br/><b>Sponsor</b>: Bharat Biotech International Limited<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>Impact of Teeth Brushing in Ventilated COVID-19 Patients.</strong> - <b>Conditions</b>: Microbial Colonization; COVID-19 Respiratory Infection; Dysbiosis; VAP - Ventilator Associated Pneumonia; HAI<br/><b>Intervention</b>: Procedure: Oral Procedure<br/><b>Sponsor</b>: University Hospital in Krakow<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>PAPR: PAP + MBSR for Front-line Healthcare Provider COVID-19 Related Burnout</strong> - <b>Conditions</b>: Depression; Burnout, Professional<br/><b>Interventions</b>: Drug: Psilocybin; Behavioral: Mindfulness-Based Stress Reduction (MBSR)<br/><b>Sponsors</b>: University of Utah; Heffter Research Institute; Usona Institute<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>In silico analysis of the antidepressant fluoxetine and related drugs at SARS-CoV-2 main protease (Mpro) and papain-like protease (PLpro)</strong> - CONCLUSION: In an in silico perspective, it is likely that the SSRIs and other anti-depressants could interact with Mpro and cause the enzyme to malfunction. Unfortunately, the same drugs did not present similar results on PLpro crystal, therefore no inhibition is expected on an in vitro trial. Anyway, in vitro test are necessary for the better understanding the links between SARS-CoV-2 proteases and anti-depressants.</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>Investigation of Streptomyces sp. Strain EMB24 Secondary Metabolite Profile Has Unraveled Its Extraordinary Antibacterial Potency Against Drug-Resistant Bacteria</strong> - With the overuse and misuse of antibiotics amid COVID-19 pandemic, the antimicrobial resistance, which is already a global challenge, has accelerated its pace significantly. Finding novel and potential antibiotics seems one of the probable solutions. In this work, a novel Streptomyces sp. strain EMB24 was isolated and found to be an excellent source of antimicrobials as confirmed by agar-plug assay. It showed antibacterial activity against infection-causing bacteria, namely Staphylococcus…</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 First in man study to Evaluate the Safety, Pharmacokinetics, and Pharmacodynamics of RP7214, a Dihydroorotate Dehydrogenase (DHODH) inhibitor in Healthy Subjects</strong> - Dihydroorotate dehydrogenase (DHODH) is a mitochondrial enzyme that is essential for pyrimidine de-novo synthesis. Rapidly growing cancer cells and replicating viruses are dependent on host cell nucleotides, the precursors of which are provided by DHODH. Hence DHODH becomes an ideal target for pharmacological intervention. RP7214 is a potent and selective inhibitor of human DHODH and has shown anti-viral and anti-leukemic activity in preclinical studies. This paper describes the Phase I study…</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>Face masks inhibit facial cues for approachability and trustworthiness: an eyetracking study</strong> - Wearing face masks during the Covid-19 pandemic has undeniable benefits from our health perspective. However, the interpersonal costs on social interactions may have been underappreciated. Because masks obscure critical facial regions signaling approach/avoidance intent and social trust, this implies that facial inference of approachability and trustworthiness may be severely discounted. Here, in our eyetracking experiment, we show that people judged masked faces as less approachable and…</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>Rapid Generation of Circulating and Mucosal Decoy Human ACE2 using mRNA Nanotherapeutics for the Potential Treatment of SARS-CoV-2</strong> - Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can cause lethal pulmonary damage in humans. It contains spike proteins on its envelope that bind to human angiotensin-converting enzyme 2 (hACE2) expressed on airway cells, enabling entry of the virus, and causing infection. The soluble form of hACE2 binds SARS-CoV-2 spike protein, prevents viral entry into target cells, and ameliorates lung injury; however, its short half-life limits therapeutic utilities. Here, synthetic mRNA is…</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>Therapeutic potential of kaempferol on Streptococcus pneumoniae infection</strong> - Co-infections with pathogens and secondary bacterial infections play significant roles during the pandemic coronavirus disease 2019 (COVID-19) pathogenetic process, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Notably, co-infections with Streptococcus pneumoniae (S. pneumoniae), as a major Gram-positive pathogen causing pneumonia or meningitis, severely threaten the diagnosis, therapy, and prognosis of COVID-19 worldwide. Accumulating evidences have emerged indicating…</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>Impact of the COVID-19 pandemic on changes in temperature-sensitive cardiovascular and respiratory disease mortality in Japan</strong> - Some cardiovascular and respiratory diseases are triggered by changes in ambient temperature or extremes of temperature. This study aimed to clarify the changes in mortality associated with temperature-sensitive diseases in Japan during the COVID-19 pandemic. We used data from three major cities (Sapporo City, Tokyo 23 wards, and Osaka City) from 2010 to 2019 to determine disease mortality rates and monthly mean temperatures from April to December. If the pandemic had not occurred in 2020, 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>Immunogenicity to SARS-CoV-2 Omicron variant among school-aged children with 2-dose of inactivated SARS-CoV-2 vaccines followed by BNT162b2 booster</strong> - CONCLUSION: A regimen of 2-dose of inactivated vaccine followed by BNT162b2 booster dose elicited high neutralizing antibody against the Omicron variants in healthy school-aged children.</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 photoelectrochemical immunosensor based on magnetic all-solid-state Z-scheme heterojunction for SARS-CoV-2 nucleocapsid protein detection</strong> - Rapid, convenient and accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is urgently needed to timely diagnosis of coronavirus pandemic (COVID-19) and control of the epidemic. In this study, a signal-off photoelectrochemical (PEC) immunosensor was constructed for SARS-CoV-2 nucleocapsid (N) protein detection based on a magnetic all-solid-state Z-scheme heterojunction (Fe(3)O(4)<span class="citation" data-cites="SiO">@SiO</span>(2)<span class="citation" data-cites="TiO">@TiO</span>(2)<span class="citation" data-cites="CdS/Au">@CdS/Au</span>, FSTCA). Integrating the advantages of magnetic materials and…</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>Chemical screen uncovers novel structural classes of inhibitors of the papain-like protease of coronaviruses</strong> - The papain-like protease (PLpro) of coronaviruses is an attractive antiviral target to inhibit both viral replication and interference of the host immune response. We have identified and characterized three novel classes of small molecules, thiophene, cyanofuran, and triazoloquinazoline, as PLpro inhibitors Thiophene inhibited the PLpro of two major coronaviruses, Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV) including…</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>Impact of SARS-CoV-2-specific memory B cells on the immune response after mRNA-based Comirnaty vaccine in seronegative health care workers</strong> - CONCLUSION: IgG<sup>(-)MBC</sup>(-) individuals showed the worst humoral and cellular responses, both in frequency and magnitude, after vaccination. Individuals whose antibodies wane and become undetectable after a given period of time post vaccination and show no specific MBCs are less protected and hence are good candidates for boosting vaccine. On the other hand, seronegative individuals with specific MBC showed faster and higher responses compared to the IgG<sup>(-)MBC</sup>(-) group.</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>Synthesis, spectroscopic, topological, hirshfeld surface analysis, and anti-covid-19 molecular docking investigation of isopropyl 1-benzoyl-4-(benzoyloxy)-2,6-diphenyl-1,2,5,6-tetrahydropyridine-3-carboxylate</strong> - Isopropyl 1-benzoyl-4-(benzoyloxy)-2,6-diphenyl-1,2,5,6-tetrahydropyridine-3-carboxylate (IDPC) was synthesized and characterized via spectroscopic (FT-IR and NMR) techniques. Hirshfeld surface and topological analyses were conducted to study structural and molecular properties. The energy gap (E(g)), frontier orbital energies (E(HOMO), E(LUMO)) and reactivity parameters (like chemical hardness and global hardness) were calculated using density functional theory with B3LYP/6-311++G (d,p) level…</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 immunophilin inhibitors and non-immunosuppressive analogs on coronavirus replication in human infection models</strong> - CONCLUSION: The immunophilin inhibitors CsA and ALV display robust anti-coronaviral properties in multiple infection models, including phBECs, reflecting a primary site of HCoV infection. In contrast, FK506 displayed cell-type specific effects, strongly affecting CoV replication in Huh7.5 and HEK293, but inconsistently and less pronounced in phBECs.</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 potential molecular implications of adiponectin in the evolution of SARS-CoV-2: Inbuilt tendency</strong> - Adiponectin (APN) is an adipokine concerned in the regulation of glucose metabolism, insulin sensitivity and fatty acid oxidation. APN plays a critical role in viral infections by regulating the immune response through its anti-inflammatory/pro-inflammatory axis. Reduction of APN may augment the severity of viral infections because APN inhibits immune cells’ response via suppression of inflammatory signaling pathways and stimulation of adenosine monophosphate protein kinase (AMPK). Moreover, APN…</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>Pathophysiology of Post-COVID syndromes: a new perspective</strong> - Most COVID-19 patients recovered with low mortality; however, some patients experienced long-term symptoms described as “long-COVID” or “Post-COVID syndrome” (PCS). Patients may have persisting symptoms for weeks after acute SARS-CoV-2 infection, including dyspnea, fatigue, myalgia, insomnia, cognitive and olfactory disorders. These symptoms may last for months in some patients. PCS may progress in association with the development of mast cell activation syndrome (MCAS), which is a distinct kind…</p></li>
|
|||
|
</ul>
|
|||
|
<h1 data-aos="fade-right" id="from-patent-search">From Patent Search</h1>
|
|||
|
|
|||
|
|
|||
|
<script>AOS.init();</script></body></html>
|