175 lines
39 KiB
HTML
175 lines
39 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>02 March, 2023</title>
|
|||
|
|
<style>
|
|||
|
|
code{white-space: pre-wrap;}
|
|||
|
|
span.smallcaps{font-variant: small-caps;}
|
|||
|
|
span.underline{text-decoration: underline;}
|
|||
|
|
div.column{display: inline-block; vertical-align: top; width: 50%;}
|
|||
|
|
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
|
|||
|
|
ul.task-list{list-style: none;}
|
|||
|
|
</style>
|
|||
|
|
<title>Covid-19 Sentry</title><meta content="width=device-width, initial-scale=1.0" name="viewport"/><link href="styles/simple.css" rel="stylesheet"/><link href="../styles/simple.css" rel="stylesheet"/><link href="https://unpkg.com/aos@2.3.1/dist/aos.css" rel="stylesheet"/><script src="https://unpkg.com/aos@2.3.1/dist/aos.js"></script></head>
|
|||
|
|
<body>
|
|||
|
|
<h1 data-aos="fade-down" id="covid-19-sentry">Covid-19 Sentry</h1>
|
|||
|
|
<h1 data-aos="fade-right" data-aos-anchor-placement="top-bottom" id="contents">Contents</h1>
|
|||
|
|
<ul>
|
|||
|
|
<li><a href="#from-preprints">From Preprints</a></li>
|
|||
|
|
<li><a href="#from-clinical-trials">From Clinical Trials</a></li>
|
|||
|
|
<li><a href="#from-pubmed">From PubMed</a></li>
|
|||
|
|
<li><a href="#from-patent-search">From Patent Search</a></li>
|
|||
|
|
</ul>
|
|||
|
|
<h1 data-aos="fade-right" id="from-preprints">From Preprints</h1>
|
|||
|
|
<ul>
|
|||
|
|
<li><strong>SARS-CoV-2 post-vaccine surveillance studies in Australian children and adults with cancer: SerOzNET Statistical Analysis Plan</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
COVID-19 disease is associated with higher morbidity and mortality in cancer patients. Our study aimed to characterize the optimal strategy to improve vaccine induced protection against COVID-19 in children and adolescents with cancer. Results from The SerOzNET study will contribute comprehensive data on serology, cellular immune correlates from functional T-cell assays, quality of life data, and associated toxicity in relation to COVID-19 vaccination in children and adults with cancer. In this plan, we describe the statistics that will be used to report results of the SerOzNET study. SerOzNET examines COVID-19 vaccine response in children and adolescents with cancer.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.26.23286261v1" target="_blank">SARS-CoV-2 post-vaccine surveillance studies in Australian children and adults with cancer: SerOzNET Statistical Analysis Plan</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>COVID-19 or seasonal influenza? How to distinguish in people younger than 65 years old: A retrospective observational cohort study comparing the 2009 pandemic influenza A H1N1 with 2022 SARS-CoV-2 Omicron BA.2 outbreaks in China.</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Objective: This study attempted to explore the difference of clinical characteristics in H1N1 influenza infection and SARS-CoV-2 Omicron infection in people younger than 65 years old, in order to better identify the two diseases. Methods: A total of 127 H1N1 influenza patients diagnosed from May 2009 to July 2009 and 3265 patients diagnosed and identified as SARS-CoV-2 Omicron BA.2 variant from March 2022 to May 2022 were admitted in this study. Through the 1 : 2 match based on age (The difference is less than 2 years), gender and underlying diseases，115 patients with H1N1 infection and 230 patients with SARS-CoV-2 Omicron BA.2 infection (referred to as H1N1 group and Omicron group) were included in the statistics. The clinical manifestations of H1N1 group were compared with those of Omicron group. Logistic regression was performed to analyze the possible independent risk factors of H1N1 group and Omicron group. And multiple linear regression was used to analyze the factors for time for nucleic acid negativization (NAN) . Results: The median age of the two groups was 21 [11,26] years. Compared with the H1N1 group, the Omicron group had lower white blood cell count and CRP levels, less fever, nasal congestion, sore throat, cough, sputum and headache, while more olfactory loss, muscle soreness and LDH abnormalities. The Omicron group used less antibiotics and antiviral drugs, and the NAN time was longer (17 [ 14,20] VS 4 [ 3,5], P < 0.001). After logistic regression, it was found that fever, cough, headache, and increased white blood cell count were more correlated with the H1N1 group, while muscle soreness and LDH abnormalities were more correlated with the Omicron group. After analyzing the factors of NAN time, it was found that fever (B 1.529, 95 % CI [0.149,2.909], P = 0.030) significantly predicted longer NAN time in Omicron patients. Conclusion: This study comprehensively evaluated the similarities and differences in clinical characteristics between SARS-CoV-2 Omicron infection and 2009 H1N1 influenza infection, which is of great significance for a better understanding for these diseases.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.28.23286466v1" target="_blank">COVID-19 or seasonal influenza? How to distinguish in people younger than 65 years old: A retrospective observational cohort study comparing the 2009 pandemic influenza A H1N1 with 2022 SARS-CoV-2 Omicron BA.2 outbreaks in China.</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Bulk RNA-Sequencing of small airway cell cultures from IPF and post-COVID lung fibrosis patients illustrates disease signatures and differential responses to TGF-β1 treatment</strong> -
|
|||
|
|
<div>
|
|||
|
|
IPF is a condition in which an injury to the lung leads to the accumulation of scar tissue. This fibrotic tissue reduces lung compliance and impairs gas exchange. Studies have shown that infection with COVID-19 significantly worsens the clinical outcomes of IPF patients. The exact etiology of IPF is unknown, but recent evidence suggests that the distal small airways, (those having a diameter less than 2 mm in adults), play a role in the early pathogenesis of IPF. TGF-{beta}1 is a main driver of fibrosis in a variety of tissues; the binding of TGF-{beta}1 to its receptor triggers a signaling cascade that results in inflammatory signaling, accumulation of collagen and other components of the extracellular matrix, and immune system activation. This study aimed to investigate possible mechanisms that contribute to worsening lung fibrosis in IPF patients after being diagnosed with COVID-19, with a particular focus on the role of TGF-{beta}1. Small airway cell cultures derived from IPF and post-COVID-19 IPF patient transplant tissues were submitted for RNA-sequencing and differential gene expression analysis. The genetic signatures for each disease state were determined by comparing the differentially expressed genes present in the cells cultured under control conditions to cells cultured with TGF-{beta}1. The genes shared between the culture conditions laid the framework for determining the genetic signatures of each disease. Our data found that genes associated with pulmonary fibrosis appeared to be more highly expressed in the post-COVID fibrosis samples, under both control and TGF-{beta}1-treated conditions. A similar trend was noted for genes involved in the TGF-{beta}1 signaling pathway; the post-COVID fibrosis cell cultures seemed to be more responsive to treatment with TGF-{beta}1. Gene expression analysis, RT-PCR, and immunohistochemistry confirmed increased levels of BMP signaling in the IPF small airway cell cultures. These findings suggest that TGF-{beta}1 signaling in IPF small airway cells could be inhibited by BMP signaling, leading to the differences in genetic signatures between IPF and post-COVID fibrosis.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2023.03.01.530431v1" target="_blank">Bulk RNA-Sequencing of small airway cell cultures from IPF and post-COVID lung fibrosis patients illustrates disease signatures and differential responses to TGF-β1 treatment</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Type I interferon signaling induces a delayed antiproliferative response in Calu-3 cells during SARS-CoV-2 infection</strong> -
|
|||
|
|
<div>
|
|||
|
|
Disease progression during SARS-CoV-2 infection is tightly linked to the fate of lung epithelial cells, with severe cases of COVID-19 characterized by direct injury of the alveolar epithelium and an impairment in its regeneration from progenitor cells. The molecular pathways that govern respiratory epithelial cell death and proliferation during SARS-CoV-2 infection, however, remain poorly understood. We now report a high-throughput CRISPR screen for host genetic modifiers of the fitness of SARS-CoV-2-infected Calu-3 respiratory epithelial cells. The top 4 genes identified in our screen encode components of the same type I interferon signaling complex - IFNAR1, IFNAR2, JAK1, and TYK2. The 5th gene, ACE2, was an expected control encoding the SARS-CoV-2 viral receptor. Surprisingly, despite the antiviral properties of IFN-I signaling, its disruption in our screen was associated with an increase in Calu-3 cell fitness. We validated this effect and found that IFN-I signaling did not sensitize SARS-CoV-2-infected cultures to cell death but rather inhibited the proliferation of surviving cells after the early peak of viral replication and cytopathic effect. We also found that IFN-I signaling alone, in the absence of viral infection, was sufficient to induce this delayed antiproliferative response. Together, these findings highlight a cell autonomous antiproliferative response by respiratory epithelial cells to persistent IFN-I signaling during SARS-CoV-2 infection. This response may contribute to the deficient alveolar regeneration that has been associated with COVID-19 lung injury and represents a promising area for host-targeted therapeutic development.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2023.02.28.530557v1" target="_blank">Type I interferon signaling induces a delayed antiproliferative response in Calu-3 cells during SARS-CoV-2 infection</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>COVID-19 adenoviral vector vaccination elicits a robust memory B cell response with the capacity to recognize Omicron BA.2 and BA.5 variants</strong> -
|
|||
|
|
<div>
|
|||
|
|
Following the COVID-19 pandemic caused by SARS-CoV-2, novel vaccines have successfully reduced severe disease and death. Despite eliciting lower antibody responses, adenoviral vector vaccines are nearly as effective as mRNA vaccines. Therefore, protection against severe disease may be mediated by immune memory cells. We here evaluated plasma antibody and memory B cells (Bmem) targeting the Spike receptor binding domain (RBD) elicited by the adenoviral vector vaccine ChAdOx1 (AstraZeneca), their capacity to bind Omicron subvariants, and compared this to the response elicited by the mRNA vaccine BNT162b2 (Pfizer-BioNTech). Whole blood was sampled from 31 healthy adults pre-vaccination, and four weeks after dose one and dose two of ChAdOx1. Neutralizing antibodies (NAb) against SARS-CoV-2 were quantified at each timepoint. Recombinant RBDs of the Wuhan-Hu-1 (WH1), Delta, BA.2, and BA.5 variants were produced for ELISA-based quantification of plasma IgG and incorporated separately into fluorescent tetramers for flow cytometric identification of RBD-specific Bmem. NAb and RBD-specific IgG levels were over eight times lower following ChAdOx1 vaccination than BNT162b2. In ChAdOx1-vaccinated individuals, median plasma IgG recognition of BA.2 and BA.5 as a proportion of WH1-specific IgG was 26% and 17%, respectively. All donors generated resting RBD-specific Bmem, which were boosted after the second dose of ChAdOx1, and were similar in number to those produced by BNT162b2. The second dose of ChAdOx1 boosted Bmem that recognized VoC, and 37% and 39% of WH1-specific Bmem recognized BA.2 and BA.5, respectively. These data uncover mechanisms by which ChAdOx1 elicits immune memory to confer effective protection against severe COVID-19.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2023.02.28.530547v1" target="_blank">COVID-19 adenoviral vector vaccination elicits a robust memory B cell response with the capacity to recognize Omicron BA.2 and BA.5 variants</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>IgG4 serum levels are not elevated in cases of Post-COVID syndrome</strong> -
|
|||
|
|
<div>
|
|||
|
|
The different effector functions of human IgG are closely associated with its four subtypes. Class switch towards IgG4 occurs after long-term antigen exposure, downregulates immune responses and is associated with several autoimmune diseases. Interestingly, significantly elevated IgG4 levels have recently been detected after more than two mRNA vaccinations. We here study the distribution of IgG subtypes in the context of Post-COVID syndrome. To this end, we analyzed serum samples from two cohorts of 64 patients after COVID and 64 convalescent COVID-19 patients. We found differences in the absolute levels of Spike protein-specific IgG subtypes for both cohorts. IgG1 was the most abundant subtype, followed by IgG3 and IgG2 and IgG4 in declining order. A significant difference was only detected for IgG2. When further analyzing the IgG4 levels reactive against the Spike protein receptor-binding domain (RBD) and the nucleocapsid-protein of SARS-CoV-2, a small but significant difference was detected for the RBD but not nucleocapsid proteins. Since the total IgG4 levels are very low, we do not expect a biologically relevant role in the development and progression of post-COVID syndrome. However, low IgG2 levels, as seen in the Post-COVID cohort, could contribute to the persistent presence of SARS-CoV-2 antigens, causing chronic inflammation in the setting of post-COVID.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2023.03.01.530454v1" target="_blank">IgG4 serum levels are not elevated in cases of Post-COVID syndrome</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>SARS-CoV-2 ORF3c suppresses immune activation by inhibiting innate sensing</strong> -
|
|||
|
|
<div>
|
|||
|
|
SARS-CoV-2 proteins are translated from subgenomic RNAs (sgRNAs). While most of these sgRNAs are monocistronic, some viral mRNAs encode more than one protein. For example, the ORF3a sgRNA also encodes ORF3c, an enigmatic 41-amino acid peptide. Here, we show that ORF3c suppresses RIG-I- and MDA5-mediated immune activation and interacts with the signaling adaptor MAVS. In line with this, ORF3c inhibits IFN-{beta} induction. This immunosuppressive activity of ORF3c is conserved among members of the subgenus sarbecovirus, including SARS-CoV and coronaviruses isolated from bats. Notably, however, the SARS-CoV-2 delta and kappa variants harbor premature stop codons in ORF3c demonstrating that this reading frame is not essential for efficient viral replication in vivo. In agreement with this, disruption of ORF3c did not significantly affect SARS-CoV-2 replication in CaCo-2 or CaLu-3 cells. In summary, we here identify ORF3c as an immune evasion factor that suppresses IFN-{beta} induction, but is dispensable for efficient replication of SARS-CoV-2.
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.biorxiv.org/content/10.1101/2023.02.27.530232v1" target="_blank">SARS-CoV-2 ORF3c suppresses immune activation by inhibiting innate sensing</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Elevated symptoms of depression and anxiety among family members and friends of critically ill COVID-19 patients - An observational study of five cohorts across four countries</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Background. Little is known regarding the mental health impact of having a significant person (family member and/or close friend) with COVID-19 of different severity. Methods. The study included five prospective cohorts from four countries (Iceland, Norway, Sweden, and the UK) with self-reported data on COVID-19 and symptoms of depression and anxiety during March 2020-March 2022. We calculated the prevalence ratio (PR) of depression and anxiety in relation to having a significant person with COVID-19 and performed a longitudinal analysis in the Swedish cohort to describe the temporal patterns of the results. Results. 162,237 and 168,783 individuals were included in the analysis of depression and anxiety, respectively, of whom 24,718 and 27,003 reported a significant person with COVID- 19. Overall, the PR was 1.07 (95% CI: 1.05-1.10) for depression and 1.08 (95% CI: 1.03-1.13) for anxiety among significant others of COVID-19 patients. The respective PRs for depression and anxiety were 1.04 (95% CI: 1.01-1.07) and 1.03 (95% CI: 0.98-1.07) if the significant person was never hospitalized, 1.15 (95% CI: 1.08-1.23) and 1.24 (95% CI: 1.14-1.34) if the patient was hospitalized, 1.42 (95% CI: 1.27-1.57) and 1.45 (95% CI: 1.31-1.60) if admitted to the ICU, and 1.34 (95% CI: 1.22-1.46) and 1.36 (95% CI: 1.22-1.51) if the significant person died. Individuals of hospitalized, ICU admitted, or deceased patients showed higher prevalence of depression and anxiety during the entire 12 months after the COVID-19 diagnosis of the significant person. Conclusions. Close friends and family members of critically ill COVID-19 patients show elevated prevalence of depression and anxiety throughout the first year after the diagnosis.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.28.23286559v1" target="_blank">Elevated symptoms of depression and anxiety among family members and friends of critically ill COVID-19 patients - An observational study of five cohorts across four countries</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Mixed methods approach to examining the implementation experience of a phone-based health research survey investigating risk factors for SARS-CoV-2 infection in California</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Objective: To describe the implementation of a test-negative design case-control study in California during the Coronavirus Disease 2019 (COVID-19) pandemic. Methods: Between February 24, 2021 - February 24, 2022, 34 interviewers called 38,470 SARS-CoV-2-tested Californians to enroll 1,885 cases and 1,871 controls in a 20-minute telephone survey. We estimated adjusted odds ratios for answering the phone and consenting to participate using mixed effects logistic regression. We used a web-based anonymous survey to compile interviewer experiences. Results: Cases had 1.29-fold (95% CI: 1.24-1.35) higher adjusted odds of answering the phone and 1.69-fold (1.56-1.83) higher adjusted odds of consenting to participate compared to controls. Calls placed from 4pm to 6pm had the highest adjusted odds of being answered. Interviewers who faced participants with dire need for social services or harassment experienced poor mental health. Conclusions: We suggest calling during afternoons and allocating more effort towards enrolling controls when designing a case-control study. Remaining adaptive to the dynamic needs of the team is critical to a successful study, especially in a pandemic setting.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.27.23286454v1" target="_blank">Mixed methods approach to examining the implementation experience of a phone-based health research survey investigating risk factors for SARS-CoV-2 infection in California</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>The gray swan: model-based assessment of the risk of sudden failure of hybrid immunity to SARS-CoV-2</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
In the fourth year of the COVID-19 pandemic, public health authorities worldwide have adopted a strategy of learning to live with SARS-CoV-2. This has involved the removal of measures for limiting viral spread, resulting in a large burden of recurrent SARS-CoV-2 infections. Crucial for managing this burden is the concept of the so-called wall of hybrid immunity, through repeated reinfections and vaccine boosters, to reduce the risk of severe disease and death. Protection against both infection and severe disease is provided by the induction of neutralizing antibodies (nAbs) against SARS-CoV-2. However, pharmacokinetic (PK) waning and rapid viral evolution both degrade nAb binding titers. The recent emergence of variants with strongly immune evasive potential against both the vaccinal and natural immune responses raises the question of whether the wall of population-level immunity can be maintained in the face of large jumps in nAb binding potency. Here we use an agent-based simulation to address this question. Our findings suggest large jumps in viral evolution may cause failure of population immunity resulting in sudden increases in mortality. As a rise in mortality will only become apparent in the weeks following a wave of disease, reactive public health strategies will not be able to provide meaningful risk mitigation. Learning to live with the virus could thus lead to large death tolls with very little warning. Our work points to the importance of proactive management strategies for the ongoing pandemic, and to the need for multifactorial approaches to COVID-19 disease control.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.26.23286471v1" target="_blank">The gray swan: model-based assessment of the risk of sudden failure of hybrid immunity to SARS-CoV-2</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Combining models to generate a consensus effective reproduction number R for the COVID-19 epidemic status in England</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
The effective reproduction number R was widely accepted as a key indicator during the early stages of the COVID-19 pandemic. In the UK, the R value published on the UK Government Dashboard has been generated as a combined value from an ensemble of fourteen epidemiological models via a collaborative initiative between academia and government. In this paper we outline this collaborative modelling approach and illustrate how, by using an established combination method, a combined R estimate can be generated from an ensemble of epidemiological models. We show that this R is robust to different model weighting methods and ensemble size and that using heterogeneous data sources for validation increases its robustness and reduces the biases and limitations associated with a single source of data. We discuss how R can be generated from different data sources and is therefore a good summary indicator of the current dynamics in an epidemic.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.27.23286501v1" target="_blank">Combining models to generate a consensus effective reproduction number R for the COVID-19 epidemic status in England</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Influence of COVID-19 on liver attenuation from two computed tomography scans over time: a retrospective study</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Introduction. There is no unequivocal opinion concerning the influence of decreased liver attenuation on the COVID-19 severity, but its widespread occurrence among these patients has been shown. There has been no evaluation of the liver status both before and after COVID-19. Study objective. To assess the prognostic value of liver attenuation on CT scan in patients with COVID-19. Material and methods. A retrospective cohort study. Data of COVID-19 outpatients were analyzed. Inclusion criteria: two chest CT scans, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in blood, and polymerase chain reaction results to verify SARS-CoV-2. Subjects were categorized into four comparison groups depending on the severity of lung involvement. Liver attenuation was analyzed by automatic segmentation, where the values less than 40 HU were considered pathological. Results. Data from 499 subjects were included. The groups differed in age and the level of liver attenuation on both CT scans. No correlation between ALT, AST and changes in liver attenuation was found. On follow-up CT, low liver attenuation was observed in males (odds ratio (OR) 2.79 (95% CI 1.42-5.47), p-value = 0.003) and in patients with a baseline reduced liver density (OR 60.59 (95% CI 30.51-120.33), p-value < 0.001). Age over 60 years was associated with the development of lung lesions (OR 1.04 (95% CI 1.02-1.06) for extent of lung injury < 25%, OR 1.08 (95% CI 1.05-1.11) for 25-50%, OR 1.1 (95% CI 1.06-1.15) for 25-50%, p-value < 0.001). Low liver attenuation on the baseline CT scan increased the odds of severe lung injury (OR 6.9 (95% CI 2.06-23.07), p-value = 0.002). Conclusion. In COVID-19, patients with low liver attenuation are more likely to develop severe lung damage.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.27.23286488v1" target="_blank">Influence of COVID-19 on liver attenuation from two computed tomography scans over time: a retrospective study</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>A survey and antibody test following the surge of SARS-CoV-2 Omicron infection in China</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
The surge of SARS-CoV-2 Omicron infection in most Chinese residents at the end of 2022 provided a unique opportunity to understand how the immune system responds to the Omicron infection in a population with limited contact to prior SARS-CoV-2 variants. Moreover, whether the prototype SARS-CoV-2 booster vaccination could help induce the antibody against Omicron variants? Here, we tested the level of IgG, IgA, and IgM specific to the prototype SARS-CoV-2 spike RBD (Receptor Binding Domain) from the collected blood samples from 636 individuals. Sequential inoculation of different vaccines showed higher IgG levels after infection. As the antibody level against Omicron BA.5, BF.7, and XBB 1.5 of the individuals has highly positive correlation with the antibody level against prototype SARS-CoV2, the IgG level specific to the prototype SARS-CoV-2 spike RBD could also represent the IgG level against Omicron variants. Furthermore, the 4th booster vaccination could induce a comparable antibody level against prototype, Omicron BA.5, BF.7, and XBB 1.5 variants in the patients with 2 or 3-dose vaccination and protect people from being infected. In conclusion, these data suggest that the prototype SARS-CoV-2 booster vaccination helps induce a high level of antibody against prototype, BA.5, BF.7, and XBB 1.5 variants after Omicron infection.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.28.23286535v1" target="_blank">A survey and antibody test following the surge of SARS-CoV-2 Omicron infection in China</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>The relationship between mental health, sleep quality, and the immunogenicity of COVID-19 vaccinations</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
Sleep modulates the immune response and sleep loss can reduce the immunogenicity of certain vaccinations. Vice versa immune responses impact sleep. We aimed to investigate the influence of mental health and sleep quality on the immunogenicity of COVID-19 vaccinations and, conversely, of COVID-19 vaccinations on sleep quality. The prospective CoVacSer study monitored mental health, sleep quality, and Anti-SARS-CoV-2-Spike IgG titres in a cohort of 1,082 healthcare workers from the 29th of September 2021 to the 19th of December 2022. Questionnaires and blood samples were collected before, 14 days, and three months after the third COVID-19 vaccination. In 154 participants the assessments were also conducted before and 14 days after the fourth COVID-19 vaccination. Healthcare workers with psychiatric disorders had slightly lower Anti-SARS-CoV-2-Spike IgG levels before the third COVID-19 vaccination. However, this effect was mediated by higher median age and body mass index in this subgroup. Antibody titres following the third and fourth COVID-19 vaccination (booster vaccinations) were not significantly different between subgroups with and without psychiatric disorders. Sleep quality did not affect the humoral immunogenicity of the COVID-19 vaccinations. Moreover, the COVID-19 vaccinations did not impact self-reported sleep quality. Our data suggests that in a working population neither mental health nor sleep quality relevantly impact the immunogenicity of COVID-19 vaccinations and that COVID-19 vaccinations are not a precipitating factor for insomnia. The findings from this large-scale real-life cohort study will inform clinical practice regarding the recommendation of COVID-19 booster vaccination for individuals with mental health and sleep problems.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.26.23286402v1" target="_blank">The relationship between mental health, sleep quality, and the immunogenicity of COVID-19 vaccinations</a>
|
|||
|
|
</div></li>
|
|||
|
|
<li><strong>Association of SARS-CoV-2 Nucleocapsid Protein Mutations with Patient Demographic and Clinical Characteristics during the Delta and Omicron Waves</strong> -
|
|||
|
|
<div>
|
|||
|
|
<p data-aos="fade-left" data-aos-anchor-placement="bottom-bottom">
|
|||
|
|
SARS-CoV-2 genomic mutations outside the spike protein that may increase transmissibility and disease severity have not been well characterized. This study identified mutations in the nucleocapsid protein and their possible association with patient characteristics. We analyzed 695 samples from patients with confirmed COVID-19 in Saudi Arabia between April 1, 2021, and April 30, 2022. Nucleocapsid protein mutations were identified through whole genome sequencing. χ2 tests and T tests assessed associations between mutations and patient characteristics. Logistic regression estimated risk of intensive care unit (ICU) admission or death. Of 60 mutations identified, R203K was most common followed by G204R, P13L, and E31del, R32del, and S33del. These mutations were associated with reduced risk of ICU admission. P13L, E31del, R32del, and S33del were also associated with reduced risk of death. By contrast, D63G, R203M, and D377Y were associated with increased risk of ICU admission. Most mutations were detected in the SR-rich region, which was associated with low risk of death. C-tail and central linker regions were associated with increased risk of ICU admission, whereas the N-arm region was associated with reduced ICU admission risk. Some SARS-CoV-2 nucleocapsid amino acid mutations may enhance viral infection and COVID-19 disease severity.
|
|||
|
|
</p>
|
|||
|
|
</div>
|
|||
|
|
<div class="article-link article-html-link">
|
|||
|
|
🖺 Full Text HTML: <a href="https://www.medrxiv.org/content/10.1101/2023.02.26.23285573v1" target="_blank">Association of SARS-CoV-2 Nucleocapsid Protein Mutations with Patient Demographic and Clinical Characteristics during the Delta and Omicron Waves</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>Exercise Training Six-Months After Discharge in Post-COVID-19 Syndrome</strong> - <b>Condition</b>: COVID-19 Pneumonia<br/><b>Intervention</b>: Other: Aerobic exercise and strength training<br/><b>Sponsor</b>: Ukbe Sirayder<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>ACTIV-6: COVID-19 Study of Repurposed Medications - Arm C (Fluticasone)</strong> - <b>Condition</b>: Covid19<br/><b>Interventions</b>: Drug: Fluticasone; Other: Placebo<br/><b>Sponsors</b>: Susanna Naggie, MD; National Center for Advancing Translational Sciences (NCATS); Vanderbilt University Medical Center<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>ACTIV-6: COVID-19 Study of Repurposed Medications - Arm A (Ivmermectin 400)</strong> - <b>Condition</b>: Covid19<br/><b>Interventions</b>: Drug: Ivermectin; Other: Placebo<br/><b>Sponsors</b>: Susanna Naggie, MD; National Center for Advancing Translational Sciences (NCATS); Vanderbilt University Medical Center<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>Counter-Regulatory Hormonal and Stress Systems in Patients With COVID-19</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Diagnostic Test: Blood sampling<br/><b>Sponsor</b>: Fondazione Policlinico Universitario Agostino Gemelli IRCCS<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>Exploratory Efficacy of N-Acetylcysteine in Patients With History of COVID-19</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Drug: N-Acetylcysteine; Drug: Placebo<br/><b>Sponsor</b>: Fondazione Policlinico Universitario Agostino Gemelli IRCCS<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 Specific miRNA Encoded by SARS-CoV-2 as a Diagnostic Tool to Predict Disease Severity in COVID-19 Patients</strong> - <b>Condition</b>: COVID-19<br/><b>Intervention</b>: Diagnostic Test: miRNA analysis in plasma<br/><b>Sponsor</b>: Fondazione Policlinico Universitario Agostino Gemelli IRCCS<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>Telerehabilitation in the Post-COVID-19 Patient (TRIALS)</strong> - <b>Condition</b>: Post-COVID-19 Syndrome<br/><b>Intervention</b>: Other: Telerehabilitation program<br/><b>Sponsor</b>: Istituto Auxologico Italiano<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>Application and Research of Mesenchymal Stem Cells in Alleviating Severe Development of COVID-19 Infection</strong> - <b>Condition</b>: COVID-19<br/><b>Interventions</b>: Biological: Umbilical cord mesenchymal stem cells implantation; Other: Comparator<br/><b>Sponsor</b>: Hebei Medical University<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>Immunogenicity and Reactogenicity of the Beta-variant Recombinant Protein Booster Vaccine (VidPrevtyn Beta, Sanofi) Compared to a Bivalent mRNA Vaccine (Comirnaty Original/Omicron BA.4-5, BioNTech-Pfizer) in Adults Previously Vaccinated With at Least 3 Doses of COVID-19 mRNA Vaccine</strong> - <b>Conditions</b>: Vaccine Reaction; COVID-19<br/><b>Interventions</b>: Biological: Comirnaty® BNT162b2 /Omicron BA.4-5 vaccine (Pfizer-BioNTech); Biological: VidPrevtyn® Beta vaccine (Sanofi/GSK)<br/><b>Sponsors</b>: Assistance Publique - Hôpitaux de Paris; IREIVAC/COVIREIVAC Network<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>MGC Health COVID-19 & Flu A+B Home Multi Test Usability Study</strong> - <b>Conditions</b>: COVID-19; Influenza A; Influenza B<br/><b>Interventions</b>: Diagnostic Test: MGC Health COVID-19 & Flu A+B Home Multi Test; Diagnostic Test: MGC Health COVID-19 & Flu A+B Home Multi Test (2 to 13 y/o)<br/><b>Sponsors</b>: Medical Group Care, LLC; CSSi Life Sciences<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>Cognitive Rehabilitation for People With Cognitive Covid19</strong> - <b>Condition</b>: Long Covid19<br/><b>Intervention</b>: Behavioral: Cognitive rehabilitation<br/><b>Sponsors</b>: University College, London; Bangor University; St George’s University Hospitals NHS Foundation Trust; University of Brighton; University Hospital Southampton NHS Foundation Trust; Greater Manchester Mental Health NHS Foundation Trust<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>A Study of HH-120 Nasal Spray in Close Contacts of Those Diagnosed With COVID-19</strong> - <b>Conditions</b>: COVID-19; SARS-CoV-2 Infection<br/><b>Intervention</b>: Drug: HH-120 Nasal Spray<br/><b>Sponsor</b>: Beijing Ditan Hospital<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>Mitigating Mental and Social Health Outcomes of COVID-19: A Counseling Approach</strong> - <b>Conditions</b>: Social Determinants of Health; Mental Health Issue; COVID-19<br/><b>Interventions</b>: Other: Individual Counseling; Other: Group Counseling; Other: Resources<br/><b>Sponsors</b>: New Mexico State University; National Institutes of Health (NIH)<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>Post COVID-19 REspiratory Mechanisms and the Efficacy of a Breathing Exercise Intervention for DYsregulated Breathing</strong> - <b>Conditions</b>: COVID-19; Respiratory Disease<br/><b>Intervention</b>: Other: Breathing techniques over 12 sessions / 6 weeks inc yoga<br/><b>Sponsor</b>: University of Nottingham<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>A Phase 1/2 Study to Assess the Safety and Immunogenicity of JCXH-221, an mRNA-based Broadly Protective COVID-19 Vaccine</strong> - <b>Conditions</b>: COVID-19; Infectious Disease<br/><b>Interventions</b>: Biological: JCXH-221; Biological: Active Comparator; Other: Placebo<br/><b>Sponsors</b>: Immorna Biotherapeutics, Inc.; ICON plc<br/><b>Not yet recruiting</b></p></li>
|
|||
|
|
</ul>
|
|||
|
|
<h1 data-aos="fade-right" id="from-pubmed">From PubMed</h1>
|
|||
|
|
<h1 data-aos="fade-right" id="from-patent-search">From Patent Search</h1>
|
|||
|
|
|
|||
|
|
|
|||
|
|
<script>AOS.init();</script></body></html>
|