188 lines
46 KiB
HTML
188 lines
46 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>16 December, 2022</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>SARS-CoV-2 causes periodontal fibrosis by deregulating mitochondrial beta-oxidation</strong> -
|
|||
|
|
<div>
|
|||
|
|
The global high prevalence of COVID-19 is a major challenge for health professionals and patients. SARS-CoV-2 virus mutate predominantly in the spike proteins, whilst the other key viral components remain stable. Previous studies have shown that the human oral cavity can potentially act as reservoir of the SARS-CoV-2 virus and COVID-19 is likely to be connected with poor periodontal health. However, the consequence of SARS-CoV-2 viral infection on human oral health has not been systematically examined. In this research, we aimed to study the pathogenicity of SARS-CoV-2 viral components on human periodontal health. We found that human periodontal tissues, particularly the fibroblasts highly expressed ACE2 and TMPRSS2. Exposure to SARS-CoV-2, especially by the viral envelope and membrane proteins induced fibrotic pathogenic phenotypes, including periodontal fibroblast hyperproliferation, concomitant with increased apoptosis and senescence. The fibrotic degeneration was mediated by a down-regulation of mitochondrial {beta}-oxidation. Fatty acid beta-oxidation inhibitor, etomoxir treatment could mirror the same pathological consequence on the fibroblasts, similar to SARS-CoV-2 infection. Our results therefore provide novel mechanistic insights into how SARS-CoV-2 infection can affect human periodontal health at the cell and molecular level.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.12.15.520561v1" target="_blank">SARS-CoV-2 causes periodontal fibrosis by deregulating mitochondrial beta-oxidation</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Understanding the mechanism of the SARS CoV-2 coinfection with other respiratory viruses</strong> -
|
|||
|
|
<div>
|
|||
|
|
Coinfections have a potential role in increased morbidity and mortality rates during pandemics. Our investigation is aimed at evaluating the viral coinfection prevalence in COVID-19 patients. Rapid diagnostic tests are tools with a paramount impact both on improving patient care. Particularly in the case of respiratory infections, it is of great importance to quickly confirm/exclude the involvement of pathogens. The COVID-19 pandemic has been associated with changes in respiratory virus infections worldwide, which have differed between virus types. In this paper, we systematically searched the percentage of coinfection of various respiratory viruses in COVID-19-positive samples. We included patients of all ages, in all settings. The main outcome was the proportion of patients with viral coinfection. By describing the differences in changes between viral species across different geographies over the course of the COVID-19 pandemic, we may better understand the complex factors involved in the community cocirculation of respiratory viruses.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.12.15.520197v1" target="_blank">Understanding the mechanism of the SARS CoV-2 coinfection with other respiratory viruses</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Progressive loss of conserved spike protein neutralizing antibody sites in Omicron sublineages is balanced by preserved T-cell recognition epitopes</strong> -
|
|||
|
|
<div>
|
|||
|
|
The continued evolution of the SARS-CoV-2 Omicron variant has led to the emergence of numerous sublineages with different patterns of evasion from neutralizing antibodies. We investigated neutralizing activity in immune sera from individuals vaccinated with SARS-CoV-2 wild-type spike (S) glycoprotein-based COVID-19 mRNA vaccines after subsequent breakthrough infection with Omicron BA.1, BA.2, or BA.4/BA.5 to study antibody responses against sublineages of high relevance. We report that exposure of vaccinated individuals to infections with Omicron sublineages, and especially with BA.4/BA.5, results in a boost of Omicron BA.4.6, BF.7, BQ.1.1, and BA.2.75 neutralization, but does not efficiently boost neutralization of sublineages BA.2.75.2 and XBB. Accordingly, we found in in silico analyses that with occurrence of the Omicron lineage a large portion of neutralizing B-cell epitopes were lost, and that in Omicron BA.2.75.2 and XBB less than 12% of the wild-type strain epitopes are conserved. In contrast, HLA class I and class II presented T-cell epitopes in the S glycoprotein were highly conserved across the entire evolution of SARS-CoV-2 including Alpha, Beta, and Delta and Omicron sublineages, suggesting that CD8+ and CD4+ T-cell recognition of Omicron BQ.1.1, BA.2.75.2, and XBB may be largely intact. Our study suggests that while some Omicron sublineages effectively evade B-cell immunity by altering neutralizing antibody epitopes, S protein-specific T-cell immunity, due to the very nature of the polymorphic cell-mediated immune, response is likely to remain unimpacted and may continue to contribute to prevention or limitation of severe COVID-19 manifestation.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.12.15.520569v1" target="_blank">Progressive loss of conserved spike protein neutralizing antibody sites in Omicron sublineages is balanced by preserved T-cell recognition epitopes</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Crowdsourcing Temporal Transcriptomic Coronavirus Host Infection Data: resources, guide, and novel insights.</strong> -
|
|||
|
|
<div>
|
|||
|
|
The emergence of SARS-CoV-2 reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of COVID-19 during the pandemic. This study coupled existing transcriptomic data from SARS-CoV-1 lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g., CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA Damage Response and Repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients that died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens. The data from this study is available in an interactive portal (http://18.222.95.219:8047).
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.12.14.520483v1" target="_blank">Crowdsourcing Temporal Transcriptomic Coronavirus Host Infection Data: resources, guide, and novel insights.</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Long-term neutralizing antibody dynamics against SARS-CoV-2 in symptomatic and asymptomatic infections: a systematic review and meta-analysis</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Summary Background The kinetics of the neutralizing antibody response against SARS-CoV-2 is crucial for responding to the pandemic as well as developing vaccination strategies. We aimed to fit the antibody curves in symptomatic and asymptomatic individuals. Methods We systematically searched PubMed, Embase, Web of Science, and Europe PMC for articles published in English between Jan 1, 2020, and Oct 2, 2022. Studies evaluating neutralizing antibody from people who had a natural SARS-CoV-2 infection history were included. Study quality was assessed using a modified standardized scoring system. We fitted dynamic patterns of neutralizing antibody using a generalized additive model and a generalized additive mixed model. We also used linear regression model to conduct both univariate and multivariable analyses to explore the potential affecting factors on antibody levels. This study is registered with PROSPERO, CRD42022348636. Results 7,343 studies were identified in the initial search, 50 were assessed for eligibility after removal of duplicates as well as inappropriate titles, abstracts and full-text review, and 48 studies (2,726 individuals, 5,670 samples) were included in the meta-analysis after quality assessment. The neutralization titer of people who infected with SARS-CoV-2 prototype strain peaked around 27 days (217.4, 95%CI: 187.0-252.9) but remained below the Omicron BA.5 protection threshold all the time after illness onset or confirmation. Furthermore, neither symptomatic infections nor asymptomatic infections could provide over 50% protection against Omicron BA.5 sub-lineage. It also showed that the clinical severity and the type of laboratory assays may significantly correlated with the level of neutralizing antibody. Conclusions This study provides a comprehensive mapping of the dynamic of neutralizing antibody against SARS-CoV-2 prototype strain induced by natural infection and compared the dynamic patterns between prototype and variant strains. It suggests that the protection probability provided by natural infection is limited. Therefore, timely vaccination is necessary for both previously infected symptomatic and asymptomatic individuals.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.12.15.22283503v1" target="_blank">Long-term neutralizing antibody dynamics against SARS-CoV-2 in symptomatic and asymptomatic infections: a systematic review and meta-analysis</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Cell-type-specific co-expression inference from single cell RNA-sequencing data</strong> -
|
|||
|
|
<div>
|
|||
|
|
The inference of gene co-expressions from microarray and RNA-sequencing data has led to rich insights on biological processes and disease mechanisms. However, the bulk samples analyzed in most studies are a mixture of different cell types. As a result, the inferred co-expressions are confounded by varying cell type compositions across samples and only offer an aggregated view of gene regulations that may be distinct across different cell types. The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. However, the high sequencing depth variations and measurement errors in scRNA-seq data present significant challenges in inferring cell-type-specific gene co-expressions, and these issues have not been adequately addressed in the existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, built on a general expression-measurement model that explicitly accounts for sequencing depth variations and measurement errors in the observed single cell data. Systematic evaluations show that most existing methods suffer from inflated false positives and biased co-expression estimates and clustering analysis, whereas CS-CORE has appropriate false positive control, unbiased co-expression estimates, good statistical power and satisfactory performance in downstream co-expression analysis. When applied to analyze scRNA-seq data from postmortem brain samples from Alzheimer’s disease patients and controls and blood samples from COVID-19 patients and controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from other methods.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.12.13.520181v1" target="_blank">Cell-type-specific co-expression inference from single cell RNA-sequencing data</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Attitudes, behaviours and experiences of authors of COVID-19 preprints</strong> -
|
|||
|
|
<div>
|
|||
|
|
The COVID-19 pandemic caused a rise in preprinting, apparently triggered by the need for open and rapid dissemination of research outputs. We surveyed authors of COVID-19 preprints to learn about their experience of preprinting as well as publishing in a peer-reviewed journal. A key aim was to consider preprints in terms of their effectiveness for authors to receive feedback on their work. We also aimed to compare the impact of feedback on preprints with the impact of comments of editors and reviewers on papers submitted to journals. We observed a high rate of new adopters of preprinting who reported positive intentions regarding preprinting their future work. This allows us to posit that the boost in preprinting may have a structural effect that will last after the pandemic. We also saw a high rate of feedback on preprints but mainly through “closed” channels – directly to the authors. This means that preprinting was a useful way to receive feedback on research, but the value of feedback could be increased further by facilitating and promoting “open” channels for preprint feedback. At the same time, almost a quarter of the preprints that received feedback received comments resembling journal peer review. This shows the potential of preprint feedback to provide valuable detailed comments on research. However, journal peer review resulted in a higher rate of major changes in the papers surveyed, suggesting that the journal peer review process has significant added value compared to preprint feedback.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://osf.io/preprints/socarxiv/d96yj/" target="_blank">Attitudes, behaviours and experiences of authors of COVID-19 preprints</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>The health impact of long COVID during the 2021-2022 Omicron wave in Australia: a quantitative burden of disease study</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Background Long COVID symptoms occur for a proportion of acute COVID-19 survivors, with reduced risk among the vaccinated, and for Omicron compared to Delta variant infections. The health loss attributed to pre-Omicron long COVID has previously been estimated using only a few major symptoms. Methods The years lived with disability (YLDs) due to long COVID in Australia during the 2021-2022 Omicron BA.1/BA.2 wave were calculated using inputs from previously published case-control, cross-sectional, or cohort studies examining the prevalence and duration of individual long COVID symptoms. This estimated health loss was compared with acute SARS-CoV-2 infection YLDs and years of life lost (YLLs) from SARS-CoV-2. The sum of these three components equalled COVID-19 disability-adjusted life years (DALYs), which was compared to DALYs from other diseases. Results 5200 (95% uncertainty interval [UI] 2100-8300) YLDs were attributable to long COVID and 1800 (95% UI 1100-2600) to acute-SARS-CoV-2 infection, suggesting long COVID caused 74% of the overall YLDs from SARS-CoV-2 infections in the BA.1/BA.2 wave. Total DALYs attributable to SARS-CoV-2 were 50 900 (95% UI 21 000-80 800), 2.4% of expected DALYs for all diseases in the same period. Conclusion This study provides a comprehensive approach to estimating the morbidity due to long COVID. Improved data on long COVID symptoms will improve the accuracy of these estimates. As data accumulates on SARS-CoV-2 infection sequalae (e.g., increased cardiovascular disease rates), total health loss is likely to be higher than estimated in this study. Nevertheless, this study demonstrates that long COVID requires consideration in pandemic policy planning given it is responsible for the majority of direct SARS-CoV-2 morbidity, including during an Omicron wave in a highly vaccinated population.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.08.01.22278219v4" target="_blank">The health impact of long COVID during the 2021-2022 Omicron wave in Australia: a quantitative burden of disease study</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Effect of Ivermectin 600 mcg/kg for 6 days vs Placebo on Time to Sustained Recovery in Outpatients with Mild to Moderate COVID-19: A Randomized Clinical Trial</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Background: Whether ivermectin, with a maximum targeted dose of 600 mcg/kg, shortens symptom duration or prevents hospitalization among outpatients with mild to moderate coronavirus disease 2019 (COVID-19) remains unknown. Our objective was to evaluate the effectiveness of ivermectin, dosed at 600 mcg/kg, daily for 6 days compared with placebo for the treatment of early mild to moderate COVID-19. Methods: ACTIV-6, an ongoing, decentralized, randomized, double-blind, placebo-controlled, platform trial, was designed to evaluate repurposed therapies in outpatients with mild to moderate COVID-19. A total of 1206 participants age >=30 years with confirmed COVID-19, experiencing >=2 symptoms of acute infection for <=7 days, were enrolled from February 16, 2022, through July 22, 2022, with follow-up data through November 10, 2022, at 93 sites in the US. Participants were randomized to ivermectin, with a maximum targeted dose of 600 mcg/kg (n=602), daily vs. placebo daily (n=604) for 6 days. The primary outcome was time to sustained recovery, defined as at least 3 consecutive days without symptoms. The 7 secondary outcomes included a composite of hospitalization, death, or urgent/emergent care utilization by day 28. Results: Among 1206 randomized participants who received study medication or placebo, median (interquartile range) age was 48 (38-58) years; 713 (59%) were women; and 1008 (84%) reported 2 or more SARS-CoV-2 vaccine doses. Median time to recovery was 11 (11-12) days in the ivermectin group and 11 (11-12) days in the placebo group. The hazard ratio (HR) (95% credible interval [CrI], posterior probability of benefit) for improvement in time to recovery was 1.02 (0.92-1.13; P[HR>1]=0.68). In those receiving ivermectin, 34 (5.7%) were hospitalized, died, or had urgent or emergency care visits compared with 36 (6.0%) receiving placebo (HR 1.0, 0.6-1.5; P[HR<1]=0.53). In the ivermectin group, 1 participant died and 4 were hospitalized (0.8%); 2 participants (0.3%) were hospitalized in the placebo group and there were no deaths. Adverse events were uncommon in both groups. Conclusions: Among outpatients with mild to moderate COVID-19, treatment with ivermectin, with a maximum targeted dose of 600 mcg/kg daily for 6 days, compared with placebo did not improve time to recovery. These findings do not support the use of ivermectin in patients with mild to moderate COVID-19. Trial registration: ClinicalTrials.gov Identifier: NCT04885530.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.12.15.22283488v1" target="_blank">Effect of Ivermectin 600 mcg/kg for 6 days vs Placebo on Time to Sustained Recovery in Outpatients with Mild to Moderate COVID-19: A Randomized Clinical Trial</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Long term anti-SARS-CoV-2 antibody kinetics and correlate of protection against Omicron BA.1/BA.2 infection</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Binding antibody levels against SARS-CoV-2 have shown to be correlates of protection against infection with pre-Omicron lineages. This has been challenged by the emergence of immune-evasive variants, notably the Omicron sublineages, in an evolving immune landscape with high levels of cumulative incidence and vaccination coverage. This in turn limits the use of commercially available high-throughput methods to quantify binding antibodies as a tool to monitor protection at the population-level. In this work, we leverage repeated serological measurements between April 2020 and December 2021 on 19083 participants of a population-based cohort in Geneva, Switzerland, to evaluate anti-Spike RBD antibody levels as a correlate of protection against Omicron BA.1/BA.2 infections during the December 2021-March 2022 epidemic wave. We do so by first modeling antibody dynamics in time with kinetic models. We then use these models to predict antibody trajectories into the time period where Omicron BA.1/BA.2 were the predominant circulating sub-lineages and use survival analyses to compare the hazard of having a positive SARS-CoV-2 test by antibody level, vaccination status and infection history. We find that antibody kinetics in our sample are mainly determined by infection and vaccination history, and to a lesser extent by demographics. After controlling for age and previous infections (based on anti-nucleocapsid serology), survival analyses reveal a significant reduction in the hazard of having a documented positive SARS-CoV-2 infection during the Omicron BA.1/BA.2 wave with increasing antibody levels, reaching up to a three-fold reduction for anti-S antibody levels above 800 IU/mL (HR 0.30, 95% CI 0.22-0.41). However, we did not detect a reduction in hazard among uninfected participants. Taken together these results indicate that anti-Spike RBD antibody levels, as quantified by the immunoassay used in this study, are an indirect correlate of protection against Omicron BA.1/BA.2 for individuals with a history of previous SARS-CoV-2 infection. Despite the uncertainty in what SARS-COV-2 variant will come next, these results provide reassuring insights into the continued interpretation of SARS-CoV-2 binding antibody measurements as an independent marker of protection at both the individual and population levels.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.12.13.22283400v3" target="_blank">Long term anti-SARS-CoV-2 antibody kinetics and correlate of protection against Omicron BA.1/BA.2 infection</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>COVID-19 Associated Pulmonary Aspergillosis isolates are genomically diverse but similar to each other in their responses to infection-relevant stresses</strong> -
|
|||
|
|
<div>
|
|||
|
|
Secondary infections caused by the pulmonary fungal pathogen Aspergillus fumigatus are a significant cause of mortality in patients with severe Coronavirus Disease 19 (COVID-19). Even though epithelial cell damage and aberrant cytokine responses have been linked with susceptibility to COVID-19 associated pulmonary aspergillosis (CAPA), little is known about the mechanisms underpinning co-pathogenicity. Here, we analysed the genomes of 11 A. fumigatus isolates from patients with CAPA in three centres from different European countries. CAPA isolates did not cluster based on geographic origin in a genome-scale phylogeny of representative A. fumigatus isolates. Phenotypically, CAPA isolates were more similar to the A. fumigatus A1160 reference strain than to the Af293 strain when grown in infection-relevant stresses; except for interactions with human immune cells wherein macrophage responses were similar to those induced by the Af293 reference strain. Collectively, our data indicates that CAPA isolates are genomically diverse but are more similar to each other in their responses to infection-relevant stresses. A larger number of isolates from CAPA patients should be studied to identify genetic drivers of co-pathogenicity in patients with COVID-19.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2022.12.14.520265v1" target="_blank">COVID-19 Associated Pulmonary Aspergillosis isolates are genomically diverse but similar to each other in their responses to infection-relevant stresses</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>The estimated disease burden of COVID-19 in Japan from 2020 to 2021</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Background: To date, it is not fully understood to what extent COVID-19 has burdened society in Japan. This study aimed to estimate the total disease burden due to COVID-19 in Japan during 2020-2021. Methods: We stratify disease burden estimates by age group and present it as absolute Quality Adjusted Life Years (QALYs) lost and QALYs lost per 100,000 persons. The total estimated value of QALYs lost consists of (1) QALYs lost brought by deaths due to COVID-19, (2) QALYs lost brought by inpatient cases, (3) QALYs lost brought by outpatient cases, and (4) QALYs lost brought by long-COVID. Findings: QALYs lost due to COVID-19 was estimated as 286,781.7 for two years, 114.0 QALYs per 100,000 population per year. 71.3% of them were explained by the burden derived from deaths. Probabilistic sensitivity analysis showed that the burden of outpatient cases was the most sensitive factor. Interpretation: The large part of disease burden due to COVID-19 in Japan from the beginning of 2020 to the end of 2021 was derived from Wave 3, 4, and 5 and the proportion of QALYs lost due to morbidity in the total burden increased gradually. The estimated disease burden was smaller than that in other high-income countries. It will be our future challenge to take other indirect factors into consideration.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.12.14.22283492v1" target="_blank">The estimated disease burden of COVID-19 in Japan from 2020 to 2021</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Development and validation of MedDRA Tagger: a tool for extraction and structuring medical information from clinical notes</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Rapid and automated extraction of clinical information from patients9 notes is a desirable though difficult task. Natural language processing (NLP) and machine learning have great potential to automate and accelerate such applications, but developing such models can require a large amount of labeled clinical text, which can be a slow and laborious process. To address this gap, we propose the MedDRA tagger, a fast annotation tool that makes use of industrial level libraries such as spaCy, biomedical ontologies and weak supervision to annotate and extract clinical concepts at scale. The tool can be used to annotate clinical text and obtain labels for training machine learning models and further refine the clinical concept extraction performance, or to extract clinical concepts for observational study purposes. To demonstrate the usability and versatility of our tool, we present three different use cases: we use the tagger to determine patients with a primary brain cancer diagnosis, we show evidence of rising mental health symptoms at the population level and our last use case shows the evolution of COVID-19 symptomatology throughout three waves between February 2020 and October 2021. The validation of our tool showed good performance on both specific annotations from our development set (F1 score 0.81) and open source annotated data set (F1 score 0.79). We successfully demonstrate the versatility of our pipeline with three different use cases. Finally, we note that the modular nature of our tool allows for a straightforward adaptation to another biomedical ontology. We also show that our tool is independent of EHR system, and as such generalizable.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.12.14.22283470v1" target="_blank">Development and validation of MedDRA Tagger: a tool for extraction and structuring medical information from clinical notes</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
When effective vaccines are available, vaccination programs are typically one of the best defences against the spread of an infectious disease. Unfortunately, vaccination rates may be suboptimal for a prolonged duration as a result of slow uptake of vaccines by the public. Key factors driving slow vaccination uptake can be a complex interaction of vaccine roll-out policies and logistics, and vaccine hesitancy behaviours potentially caused by an inflated sense of risk in adverse reactions in some populations or community complacency in communities that have not yet experienced a large outbreak. In the recent COVID-19 pandemic, public health responses around the world began to include vaccination programs from late 2020 to early 2021 with an aim of relaxing non-pharmaceutical interventions such as lockdowns and travel restrictions. For many jurisdictions there have been challenges in getting vaccination rates high enough to enable the relaxation of restrictions based on non-pharmaceutical interventions. A key concern during this time was vaccine hestitancy behaviours potentially caused by vaccine safety concerns fuelled by misinformation and community complacency in jurisdictions that had seen very low COVID-19 case numbers throughout 2020, such as Australia and New Zealand. We develop a novel stochastic epidemiological model of COVID-19 transmission that incorporates changes in population behaviour relating to responses based on non-pharmaceutical interventions and community vaccine uptake as functions of the reported COVID-19 cases, deaths, and vaccination rates. Through a simulation study, we develop a Bayesian analysis approach to demonstrate that different factors inhibiting the uptake of vaccines by the population can be isolated despite key model parameters being subject to substantial uncertainty. In particular, we are able to identify the presence of vaccine hesitancy in a population using reported case, death and vaccination count data alone. Furthermore, our approach provides insight as to whether the dominant concerns driving hesitancy are related to vaccine safety or complacency. While our simulation study is inspired by the COVID-19 pandemic, our tools and techniques are general and could be enable vaccination programs of various infectious diseases to be adapted rapidly in response to community behaviours moving forward into the future.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.12.13.22283297v1" target="_blank">Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Milk antibody response after 3rd dose of COVID-19 mRNA vaccine and SARS-CoV-2 breakthrough infection and implications for infant protection</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Anti-SARS-CoV-2 antibodies have been found in human-milk after COVID-19 infection and vaccination. However, little is known about their persistence in milk after booster vaccination and breakthrough infection. In this study, human-milk, saliva and blood samples were collected from 33 lactating individuals before and after mRNA-based vaccination and COVID-19 breakthrough infections. Antibody levels were measured using ELISA and symptoms were assessed using questionnaires. Evaluation of maternal and infant symptomatology revealed that infected mothers reported more symptoms than vaccinated mothers. We found that after vaccination, human-milk anti-SARS-CoV-2 antibodies persisted for up to 8 months. In addition, distinct patterns of human milk IgA and IgG production we observed after breakthrough infection compared to 3-dose vaccination series alone, indicating a differential central and mucosal immune profiles in hybrid compared with vaccine-induced immunity. To investigate passively-derived milk antibody protection in infants, we examined the persistence of these antibodies in infant saliva after breastfeeding. We found that IgA was more abundant in infant saliva compared to IgG and persist in infant saliva longer after feeding. Our results delineate the differences in milk antibody response to vaccination as compared to breakthrough infection and emphasize the importance of improving the secretion of IgA antibodies to human milk after vaccination to improve the protection of breastfeeding infants.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2022.12.12.22283367v1" target="_blank">Milk antibody response after 3rd dose of COVID-19 mRNA vaccine and SARS-CoV-2 breakthrough infection and implications for infant protection</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 Study for Immunocompromised Patients for Pre Exposure Prophylaxis of COVID-19 With AZD5156.</strong> - <b>Condition</b>: COVID 19<br/><b>Interventions</b>: Biological: Placebo; Biological: AZD5156; Biological: AZD7442 (EVUSHELD™)<br/><b>Sponsor</b>: AstraZeneca<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 Huashi Baidu Formula Clinical Study</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Drug: Huashi Baidu Granule; Drug: Monapiravir<br/><b>Sponsors</b>: Xiyuan Hospital of China Academy of Chinese Medical Sciences; Beijing YouAn Hospital; Kossamak Hospital; Kamuzu University of Health Sciences<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>Shaping Care Home COVID-19 Testing Policy</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Diagnostic Test: Lateral Flow Device<br/><b>Sponsor</b>: University College, London<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>Baldachin: Ceiling HEPA-filtration to Prevent Nosocomial Transmission of COVID-19</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Device: Baldachin<br/><b>Sponsor</b>: University Hospital Inselspital, Berne<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>Asunercept for the Treatment of Patients With Moderate to Severe COVID-19 Disease</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: Asunercept; Other: Placebo<br/><b>Sponsor</b>: Apogenix AG<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 in Adults to Assess the Safety and Efficacy of Inhaled IBIO123, for Post-exposure Prophylaxis of COVID-19</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: IBIO123; Other: Placebo<br/><b>Sponsor</b>: Immune Biosolutions Inc<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 PhaseⅡ Study to Evaluate the Safety & Immunogenicity of SARS-CoV-2 Alpha/Beta/Delta/Omicron Variants COVID-19 Vaccine</strong> - <b>Condition</b>: COVID-19 Pandemic<br/><b>Interventions</b>: Biological: SCTV01E; Biological: Placebo (normal saline)<br/><b>Sponsor</b>: Sinocelltech Ltd.<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>The Efficacy of Azvudine and Paxlovid in High-risk Patients With COVID-19: A Prospective Randomized Controlled Trial</strong> - <b>Condition</b>: SARS-CoV-2 Infection<br/><b>Interventions</b>: Drug: Azvudine; Drug: Paxlovid group<br/><b>Sponsors</b>: Southeast University, China; Hohhot First Hospital, Hohhot, Inner Mongolia, China<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>Effectiveness of Supportive Psychotherapy Through Internet-Based Teleconsultation on Psychological and Somatic Symptoms, Neutrophil-Lymphocyte Ratio, and Heart Rate Variability in Post Covid-19 Syndrome Patients</strong> - <b>Condition</b>: Post-COVID-19 Syndrome<br/><b>Intervention</b>: Behavioral: Supportive Psychotherapy<br/><b>Sponsor</b>: Indonesia 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>Graphene Photothermal Adjuvant Therapy for Mild Corona Virus Disease 2019: A Prospective Randomized Controlled Trial</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Device: Graphene spectrum light wave therapy room<br/><b>Sponsors</b>: Southeast University, China; Hohhot First 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>Post-COVID-19 Chronic Fatigue Syndrome</strong> - <b>Conditions</b>: Post-COVID-19 Syndrome; Post-COVID Syndrome<br/><b>Intervention</b>: Drug: Synthetic Vitamin B1<br/><b>Sponsors</b>: ClinAmygate; As-Salam Center, Maadi, Cairo, Egypt<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 Study to Evaluate the Efficacy, Safety, and Immunogenicity of SARS-CoV-2 Variant (BA.4 /5) mRNA Vaccine</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: ABO1020; Biological: Placebo<br/><b>Sponsor</b>: Suzhou Abogen Biosciences 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>Prednisolone and Vitamin B1/6/12 in Patients With Post-Covid-Syndrome</strong> - <b>Condition</b>: Post-COVID-19 Syndrome<br/><b>Interventions</b>: Drug: Prednisolone 20 mg/ 5 mg; Drug: Vitamin B compound (100mg B1, 50 mg B6, 500 µg B12); Drug: Placebo for Vitamin B compound; Drug: Placebo for Prednisolon<br/><b>Sponsors</b>: Wuerzburg University Hospital; University Hospital Tuebingen; University Hospital Schleswig-Holstein<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>Effects of an Immersive Virtual Reality Intervention</strong> - <b>Conditions</b>: Nurse’s Role; COVID-19 Pandemic; Mental Stress<br/><b>Interventions</b>: Behavioral: Mindfulness-based Stress Reduction by Therapeutic VR; Behavioral: Mindfulness-based Stress Reduction<br/><b>Sponsor</b>: Nanjing University of Traditional Chinese Medicine<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>Communicating Uncertainty - Protocol for Randomized Trial</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Other: Overall uncertainty language<br/><b>Sponsors</b>: Trustees of Dartmouth College; Norwegian Institute of Public Health<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>SARS-CoV-2 viral protein ORF3A injures renal tubules by interacting with TRIM59 to induce STAT3 activation</strong> - No abstract</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>In vitro screening of anti-viral and virucidal effects against SARS-CoV-2 by Hypericum perforatum and Echinacea</strong> - No abstract</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>Serum 25-hydroxycholesterol levels are increased in patients with coronavirus disease 2019</strong> - No abstract</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 leap towards personalised therapy of acute lung injury</strong> - No abstract</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>Ginkgolic acids inhibit SARS-CoV-2 and its variants by blocking the spike protein/ACE2 interplay</strong> - No abstract</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 signalome: Potential therapeutic interventions</strong> - No abstract</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>Astegolimab or Efmarodocokin Alfa in Patients With Severe COVID-19 Pneumonia: A Randomized, Phase 2 Trial</strong> - No abstract</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>Prophylactic Administration of the Monoclonal Antibody Adintrevimab Protects against SARS-CoV-2 in Hamster and Non-Human Primate Models of COVID-19</strong> - No abstract</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>Inhibited personality traits, internalizing symptoms, and drinking to cope during the COVID-19 pandemic among emerging adults</strong> - No abstract</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>Pattern enrichment analysis for phage selection of stapled peptide ligands</strong> - No abstract</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>Pea eggplant (<em>Solanum torvum</em> Swartz) is a source of plant food polyphenols with SARS-CoV inhibiting potential</strong> - No abstract</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>Identification of two specific transcriptomic clusters of COVID-19 ARDS patients with different immune profiles and different outcomes</strong> - No abstract</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>Covalent CES2 Inhibitors Protect against Reduced Formation of Intestinal Organoids by the Anticancer Drug Irinotecan</strong> - No abstract</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>Host-directed therapy with 2-Deoxy-D-glucose inhibits human rhinoviruses, endemic coronaviruses, and SARS-CoV-2</strong> - No abstract</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>Identification of a pharmacological approach to reduce ACE2 expression and development of an <em>in vitro</em> COVID-19 viral entry model</strong> - No abstract</p></li>
|
|||
|
|
</ul>
|
|||
|
|
<h1 data-aos="fade-right" id="from-patent-search">From Patent Search</h1>
|
|||
|
|
|
|||
|
|
|
|||
|
|
<script>AOS.init();</script></body></html>
|