In Silico and In Vitro Inhibition of SARS-CoV-2 PLpro with Gramicidin D.

Finding an effective drug to prevent or treat COVID-19 is of utmost importance in tcurrent pandemic. Since developing a new treatment takes a significant amount of time, drug repurposing can be an effective option for achieving a rapid response. This study used a combined in silico virtual screening protocol for candidate SARS-CoV-2 PLpro inhibitors. The Drugbank database was searched first, using the Informational Spectrum Method for Small Molecules, followed by molecular docking. Gramicidin D was selected as a peptide drug, showing the best in silico interaction profile with PLpro. After the expression and purification of PLpro, gramicidin D was screened for protease inhibition in vitro and was found to be active against PLpro. The current study's findings are significant because it is critical to identify COVID-19 therapies that are efficient, affordable, and have a favorable safety profile.

Novel COVID-19 vaccine hesitancy and acceptance, and associated factors, amongst medical students: a scoping review.

Medical students are likely to be exposed to COVID-19 patients so achieving high vaccination coverage rates for this group of healthcare workers is important, as is their potential as vaccination role models. The aim of this scoping review was to evaluate the current literature to determine the rates of COVID-19 vaccine hesitancy and acceptance, and associated factors, amongst medical students. Systematic searches of the Medline Ovid, Embase, PubMed, and Education Resources Information Centre (ERIC) online databases was conducted for relevant articles with keywords: 'COVID-19', 'vaccine hesitancy & acceptance' and 'medical students'. Articles were included for review if they reported the rates of vaccine hesitancy and acceptance, and associated factors, amongst medical students. Of the 258 articles identified, 52 met the inclusion criteria and underwent full-text review. Rates of vaccine hesitancy ranged from 5.4-86.7%, with generally positive attitudes towards COVID-19 vaccination. The main factors associated with vaccine hesitancy were concerns about the safety and efficacy of vaccines due to their accelerated development, being a pre-clinical medical student, and low perceived personal risk of COVID-19 infection. Inconsistencies were found for the influence of gender on attitudes towards vaccinations. Previous vaccination behaviours were predictive of willingness to receive the COVID-19 vaccine. Knowledge about COVID-19 vaccinations and their importance was found to be deficient amongst vaccine hesitant medical students. Generally, medical students express low levels of vaccine hesitancy. However, due to the variability in the factors associated with vaccine hesitancy across different populations and the dynamic and contextual nature of hesitancy, it is recommended that vaccination intent and associated attitudes are monitored on a longitudinal basis. It is important to map vaccine hesitancy at a local level to allow medical schools to develop strategies to encourage vaccination specific to their school's needs.

Global SARS-CoV-2 genomic surveillance: What we have learned (so far).

The COVID-19 pandemic has brought significant challenges for genomic surveillance strategies in public health systems worldwide. During the past thirty-four months, many countries faced several epidemic waves of SARS-CoV-2 infections, driven mainly by the emergence and spread of novel variants. In that line, genomic surveillance has been a crucial toolkit to study the real-time SARS-CoV-2 evolution, for the assessment and optimization of novel diagnostic assays, and to improve the efficacy of existing vaccines. During the pandemic, the identification of emerging lineages carrying lineage-specific mutations (particularly those in the Receptor Binding domain) showed how these mutations might significantly impact viral transmissibility, protection from reinfection and vaccination. So far, an unprecedented number of SARS-CoV-2 viral genomes has been released in public databases (i.e., GISAID, and NCBI), achieving 14 million genome sequences available as of early-November 2022. In the present review, we summarise the global landscape of SARS-CoV-2 during the first thirty-four months of viral circulation and evolution. It demonstrates the urgency and importance of sustained investment in genomic surveillance strategies to timely identify the emergence of any potential viral pathogen or associated variants, which in turn is key to epidemic and pandemic preparedness.

Is Gestational COVID-19 a Risk Factor for Congenital Hearing Loss?

OBJECTIVE: The aim of this study was to investigate whether COVID-19 during pregnancy is a risk factor for congenital hearing loss. STUDY DESIGN: Retrospective cohort. SETTING: Tertiary referral center. PATIENTS: Hearing screening test results of 60,223 newborns between March 2020 and May 2021 were screened using the national database. Newborn babies of 570 pregnant women with positive COVID-19 PCR test during pregnancy who met the study criteria were included in the gestational COVID-19 group, and 570 healthy newborns born in the same period were included in the control group. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURE: Results of up to three automatic auditory brainstem response tests in the first 30 days of life were used for newborn hearing screening. RESULTS: When the gestational COVID-19 and control groups were compared in terms of demographic data, there was no statistically significant difference for any of the variables (maternal age, gestational age, birth weight, neonate gender, mode of delivery, p > 0.05 for all variables). Of the mothers in the gestational COVID-19 group, 62 (10.9%) had COVID-19 in the first trimester, 181 (31.8%) in the second trimester, and 327 (57.3%) in the third trimester. When the first and second test results of newborn hearing screening were compared between the groups, the number of babies with hearing loss was higher in the gestational COVID-19 group than in the control group (p = 0.025; odds ratio, 1.357; 95% confidence interval, 1.039-1.774; p = 0.006; odds ratio, 4.924; 95% confidence interval, 1.410-17.193, respectively). For the third test results, hearing loss was detected in only one baby in both groups (p = 0.284). When the first, second, and third test results for newborn hearing screening were compared according to the trimesters when COVID-19 positivity was identified, the difference between trimesters was not found to be statistically significant (p > 0.05). CONCLUSION: To the best of our knowledge, this is the largest study in the literature of the impact of COVID-19 on newborn hearing. The findings in the study suggest that gestational COVID-19 is not a risk factor for permanent congenital hearing loss. However, because the risk of detecting hearing loss is high in the first 15 days, we emphasize the importance of the third screening test.

Perspective: Lessons from COVID-19 of countries in the European region in light of findings from the health system response monitor.

Introduction: Decision-makers initially had limited data to inform their policy responses to the COVID-19 pandemic. The research community developed several online databases to track cases, deaths, and hospitalizations; however, a major deficiency was the lack of detailed information on how health systems were responding to the pandemic and how they would need to be transformed going forward. Approach: In an effort to fill this information gap, in March 2020, the European Observatory on Health Systems and Policies, the WHO European Regional Office and the European Commission created the COVID-19 Health System Response Monitor (HSRM) to collect and organise up-to-date information on how health systems, mainly in the WHO European Region, were responding to the COVID-19 pandemic. Findings: The HSRM analysis and broader Observatory work on COVID-19 shone light on a range of health system challenges and weaknesses and catalogued policy options countries put in place during the pandemic to address these. Countries prioritised policies on investing in public health, supporting the workforce, maintaining financial stability, and strengthening governance in their response to COVID-19. Outlook: COVID-19 is likely to continue to impact health systems for the foreseeable future; the ability to cope with this pressure, and other shocks, depends on having good information on what other countries have done so that health systems develop adequate policy options. In support of this, the country information on the COVID-19 HSRM will remain available as a repository to inform decision makers on options for actions and possible measures against COVID-19 and other public health emergencies. Building on its previous work on health systems resilience, the European Observatory on Health Systems and Policies will sustain its focus on analysing key issues related to the recovery from the pandemic and making health systems more resilient. This includes policy knowledge transfer between countries and systematic resilience testing, aiming at contributing to an improved understanding of health system response, recovery, and preparedness. Contribution to the literature in non-technical language: The COVID-19 Health System Response Monitor (HSRM) was the first database in the WHO European Region to collect and organise up-to-date information on how health systems were responding to the COVID-19 pandemic. The HSRM provides a repository of policies which can be used to inform decision makers in health and other policy domains on options for action and possible measures against COVID-19 and other public health emergencies. This initiative proved particularly valuable, especially during the early phases of the pandemic, when there was limited information for countries to draw on as they formulated their own policy response to the pandemic. Our perspectives paper highlights some key challenges within health systems that the HSRM was able to identify during the pandemic and considers policy options countries put in place in response. Our research contributes to literature on emergency responses and recovery, health systems performance assessment, particularly health system resilience, and showcases the Observatory experience on how to design such a data collection tool, as well as how to leverage its findings to support cross-country learning.

Digital health interventions and quality of home-based primary care for older adults: A scoping review protocol.

Introduction: The use of digital health interventions has expanded, particularly in home-based primary care (HBPC), following the increase in the older adult population and the need to respond to the higher demand of chronic conditions, weakness and loss of autonomy of this population. There was an even greater demand with COVID-19 and subsequent isolation/social distancing measures for this risk group. The objective of this study is to map and identify the uses and types of digital health interventions and their reported impacts on the quality of HBPC for older adults worldwide. Methods and analysis: This is a scoping review protocol which will enable a rigorous, transparent and reliable synthesis of knowledge. The review will be developed from the theoretical perspective of Arksey and O'malley, with updates by Levac and Peters and respective collaborators based on the Joanna Briggs Institute manual, and guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Data from white literature will be extracted from multidisciplinary health databases such as: the Virtual Health Library, LILACS, MEDLINE/PubMed, Scopus, Web of Science, Cinahl and Embase; while Google Scholar will be used for gray literature. No date limit or language restrictions will be determined. The quantitative data will be analyzed through descriptive statistics and qualitative data through thematic analysis. The results will be submitted to stakeholder consultation for preliminary sharing of the study and will later be disseminated through publication in open access scientific journals, scientific events and academic and community journals. The full scoping review report will present the main impacts, challenges, opportunities and gaps found in publications related to the use of digital technologies in primary home care. Discussion: The organization of this protocol will increase the methodological rigor, quality, transparency and accuracy of scoping reviews, reducing the risk of bias.

Benefits of high-dose intravenous immunoglobulin on mortality in patients with severe COVID-19: An updated systematic review and meta-analysis.

Background: The clinical benefits of high-dose intravenous immunoglobulin (IVIg) in treating COVID-19 remained controversial. Methods: We systematically searched databases up to February 17, 2022, for studies examining the efficacy of IVIg compared to routine care. Meta-analyses were conducted using the random-effects model. Subgroup analysis, meta-regression, and trial series analysis w ere performed to explore heterogeneity and statistical significance. Results: A total of 4,711 hospitalized COVID-19 patients (1,925 IVIg treated and 2786 control) were collected from 17 studies, including five randomized controlled trials (RCTs) and 12 cohort studies. The application of IVIg was not associated with all-cause mortality (RR= 0.89 [0.63, 1.26], P= 0.53; I2 = 75%), the length of hospital stays (MD= 0.29 [-3.40, 6.44] days, P= 0.88; I2 = 96%), the needs for mechanical ventilation (RR= 0.93 ([0.73, 1.19], P= 0.31; I2 = 56%), or the incidence of adverse events (RR= 1.15 [0.99, 1.33], P= 0.06; I2 = 20%). Subgroup analyses showed that overall mortality among patients with severe COVID-19 was reduced in the high-dose IVIg subgroup (RR= 0.33 [0.13, 0.86], P= 0.02, I2 = 68%; very low certainty). Conclusions: Results of this study suggest that severe hospitalized COVID-19 patients treated with high-dose IVIg would have a lower risk of death than patients with routine care. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021231040, identifier CRD42021231040.

A generalized classification and coding system of Human Disease Animal Model Resource data with a case study to show improving database retrieval efficiency.

BACKGROUND: Currently there is no unified data classification and coding standard for the existing human disease animal model resource data worldwide. Different data classification and coding systems produce different retrieval methods. Some of these methods are inefficient and difficult to use. This research investigated the rules for the classification and coding of such data based on the Replication Methodology of Animal Models for Human Disease, the Classification and Coding Rules for Health Information Data Set (WS/T 306-2009), the Science and Technology Resource Identification (GB/T 32843-2016), the Scientific Data Management Measures (000014349/2018-00052), and The Generic Description Specification for Natural Science and Technology Resources. This research aimed to develop a classification and coding system for data obtained from human disease animal model resource based on the Internet environment to provide a standardized and unified foundation for the collection, saving, retrieval, and sharing of data from this resource. RESULTS: A complete data classification and coding table compiled in the form of letters and numbers was produced, with a classification infrastructure that expanded layer by layer according to the three dimensions (namely, system diseases, animal species, and modeling methods) and essential attributes. When necessary, it adopted the hierarchy of major, intermediate, and minor categories for certain layer and also one-to-one matched the code and classification result. CONCLUSION: Through this study, a sharing and joint construction mechanism for data from this resource can be developed by all research institutes in this field. As a case study, this research also offered technical support for constructing the database for the National Human Disease Animal Model Resource Center. The technological innovation of this paper is to derive a research oriented retrieval method, which provides technical support for the research on the current COVID-19 epidemic and on possible future epidemics.

Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU.

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.

HuCoPIA: An Atlas of Human vs. SARS-CoV-2 Interactome and the Comparative Analysis with Other Coronaviridae Family Viruses.

SARS-CoV-2, a novel betacoronavirus strain, has caused a pandemic that has claimed the lives of nearly 6.7M people worldwide. Vaccines and medicines are being developed around the world to reduce the disease spread, fatality rates, and control the new variants. Understanding the protein-protein interaction mechanism of SARS-CoV-2 in humans, and their comparison with the previous SARS-CoV and MERS strains, is crucial for these efforts. These interactions might be used to assess vaccination effectiveness, diagnose exposure, and produce effective biotherapeutics. Here, we present the HuCoPIA database, which contains approximately 100,000 protein-protein interactions between humans and three strains (SARS-CoV-2, SARS-CoV, and MERS) of betacoronavirus. The interactions in the database are divided into common interactions between all three strains and those unique to each strain. It also contains relevant functional annotation information of human proteins. The HuCoPIA database contains SARS-CoV-2 (41,173), SARS-CoV (31,997), and MERS (26,862) interactions, with functional annotation of human proteins like subcellular localization, tissue-expression, KEGG pathways, and Gene ontology information. We believe HuCoPIA will serve as an invaluable resource to diverse experimental biologists, and will help to advance the research in better understanding the mechanism of betacoronaviruses.

MVIP: multi-omics portal of viral infection.

Virus infections are huge threats to living organisms and cause many diseases, such as COVID-19 caused by SARS-CoV-2, which has led to millions of deaths. To develop effective strategies to control viral infection, we need to understand its molecular events in host cells. Virus related functional genomic datasets are growing rapidly, however, an integrative platform for systematically investigating host responses to viruses is missing. Here, we developed a user-friendly multi-omics portal of viral infection named as MVIP (https://mvip.whu.edu.cn/). We manually collected available high-throughput sequencing data under viral infection, and unified their detailed metadata including virus, host species, infection time, assay, and target, etc. We processed multi-layered omics data of more than 4900 viral infected samples from 77 viruses and 33 host species with standard pipelines, including RNA-seq, ChIP-seq, and CLIP-seq, etc. In addition, we integrated these genome-wide signals into customized genome browsers, and developed multiple dynamic charts to exhibit the information, such as time-course dynamic and differential gene expression profiles, alternative splicing changes and enriched GO/KEGG terms. Furthermore, we implemented several tools for efficiently mining the virus-host interactions by virus, host and genes. MVIP would help users to retrieve large-scale functional information and promote the understanding of virus-host interactions.

VThunter: a database for single-cell screening of virus target cells in the animal kingdom.

Viral infectious diseases are a devastating and continuing threat to human and animal health. Receptor binding is the key step for viral entry into host cells. Therefore, recognizing viral receptors is fundamental for understanding the potential tissue tropism or host range of these pathogens. The rapid advancement of single-cell RNA sequencing (scRNA-seq) technology has paved the way for studying the expression of viral receptors in different tissues of animal species at single-cell resolution, resulting in huge scRNA-seq datasets. However, effectively integrating or sharing these datasets among the research community is challenging, especially for laboratory scientists. In this study, we manually curated up-to-date datasets generated in animal scRNA-seq studies, analyzed them using a unified processing pipeline, and comprehensively annotated 107 viral receptors in 142 viruses and obtained accurate expression signatures in 2 100 962 cells from 47 animal species. Thus, the VThunter database provides a user-friendly interface for the research community to explore the expression signatures of viral receptors. VThunter offers an informative and convenient resource for scientists to better understand the interactions between viral receptors and animal viruses and to assess viral pathogenesis and transmission in species. Database URL: https://db.cngb.org/VThunter/.

Acute and Postacute Clinical Characteristics of Coronavirus Disease 2019 in Children in Japan.

BACKGROUND: The clinical features of coronavirus disease 2019 (COVID-19) in children have been changing because of the emergence and rapid spread of variants of concern (VOC). The increase in cases infected with VOC has brought concern with persistent symptoms after COVID-19 in children. This survey aimed to analyze the clinical manifestations and persistent symptoms of pediatric COVID-19 cases in Japan. METHODS: We analyzed the clinical manifestations of pediatric COVID-19 cases reported between February 2020 and April 2022 in Japan, using a dedicated database updated voluntarily by the members of the Japan Pediatric Society. Using the same database, we also analyzed persistent symptoms after COVID-19 in children who were diagnosed between February 2020 and November 2021. RESULTS: A total of 5411 and 1697 pediatric COVID-19 cases were included for analyzing clinical manifestations and persistent symptoms, respectively. During the Omicron variant predominant period, the percentage of patients with seizures increased to 13.4% and 7.4% in patient groups 1-4 and 5-11 years of age, respectively, compared with the pre-Delta (1.3%, 0.4%) or Delta period (3.1%, 0.0%). Persistent and present symptoms after 28 days of COVID-19 onset were reported in 55 (3.2%). CONCLUSIONS: Our survey showed that the rate of symptomatic pediatric COVID-19 cases increased gradually, especially during the Omicron variant predominant period, and a certain percentage of pediatric cases had persistent symptoms. Certain percentages of pediatric COVID-19 patients had severe complications or prolonged symptoms. Further studies are needed to follow such patients.

Emulating a Target Trial of Interventions Initiated During Pregnancy with Healthcare Databases: The Example of COVID-19 Vaccination.

BACKGROUND: Observational studies are often the only option to estimate effects of interventions during pregnancy. Causal inference from observational data can be conceptualized as an attempt to emulate a hypothetical pragmatic randomized trial: the target trial. OBJECTIVE: To provide a step-by-step description of how to use healthcare databases to estimate the effects of interventions initiated during pregnancy. As an example, we describe how to specify and emulate a target trial of COVID-19 vaccination during pregnancy, but the framework can be generally applied to point and sustained strategies involving both pharmacologic and non-pharmacologic interventions. METHODS: First, we specify the protocol of a target trial to evaluate the safety and effectiveness of vaccination during pregnancy. Second, we describe how to use observational data to emulate each component of the protocol of the target trial. We propose different target trials for different gestational periods because the outcomes of interest vary by gestational age at exposure. We identify challenges that affect (i) the target trial and thus its observational emulation (censoring and competing events), and (ii) mostly the observational emulation (confounding, immortal time, and measurement biases). CONCLUSION: Some biases may be unavoidable in observational emulations, but others are avoidable. For instance, immortal time bias can be avoided by aligning the start of follow-up with the gestational age at the time of the intervention, as we would do in the target trial. Explicitly emulating target trials at different gestational ages can help reduce bias and improve the interpretability of effect estimates for interventions during pregnancy.

Completeness of notifications of severe acute respiratory syndrome at the national level and in a regional health care in the state of Minas Gerais, during the COVID-19 pandemic, 2020. / Completude das notificações de síndrome respiratória aguda grave no âmbito nacional e em uma regional de saúde de Minas Gerais, durante a pandemia de COVID-19, 2020.

OBJECTIVE: To analyze the completeness of notifications of severe acute respiratory syndrome cases on the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) during the COVID-19 pandemic, on the national database and on the Regional Health Center database of the state of Minas Gerais, Brazil, in 2020. METHODS: This was a descriptive study of the completeness of sociodemographic variables and those related to etiology, clinical condition, evolution and diagnostic criteria of SIVEP-Gripe. The level of completeness was classified as excellent (> 95%), good (90% to 95%), regular (80% to 90%), poor (50% to 80%) or very poor (< 50%). RESULTS: The percentage of variables with excellent completeness was only 18.1% on the national database, and 27.8% on the regional database. CONCLUSION: Both SIVEP-Gripe databases presented low completeness, making it necessary to improve the work process and routine training of professionals aimed at the correct filling.

The role of social circle COVID-19 illness and vaccination experiences in COVID-19 vaccination decisions: an online survey of the United States population.

BACKGROUND: Around the world, policymakers have clearly communicated that COVID-19 vaccination programs need to be accepted by a large proportion of the population to allow life return to normal. However, according to the Center for Disease Control, about 31% of the United States population had not completed the primary vaccination series as of November 2022. AIMS: The primary aim of this work is to identify the factors associated by American citizens with the decision to be vaccinated against COVID-19. In addition, the proportion of fatal events from COVID-19 vaccinations was estimated and compared with the data in the VAERS database. METHODS: An online survey of COVID-19 health experiences was conducted. Information was collected regarding reasons for and against COVID-19 inoculations, experiences with COVID-19 illness and COVID-19 inoculations by survey respondents and their social circles. Logit regression analyses were carried out to identify factors influencing the likelihood of being vaccinated. RESULTS: A total of 2840 participants completed the survey between December 18 and 23, 2021. 51% (1383 of 2840) of the participants were female and the mean age was 47 (95% CI 46.36-47.64) years. Those who knew someone who experienced a health problem from COVID-19 were more likely to be vaccinated (OR: 1.309, 95% CI 1.094-1.566), while those who knew someone who experienced a health problem following vaccination were less likely to be vaccinated (OR: 0.567, 95% CI 0.461-0.698). 34% (959 of 2840) reported that they knew at least one person who had experienced a significant health problem due to the COVID-19 illness. Similarly, 22% (612 of 2840) of respondents indicated that they knew at least one person who had experienced a severe health problem following COVID-19 vaccination. With these survey data, the total number of fatalities due to COVID-19 inoculation may be as high as 278,000 (95% CI 217,330-332,608) when fatalities that may have occurred regardless of inoculation are removed. CONCLUSION: Knowing someone who reported serious health issues either from COVID-19 or from COVID-19 vaccination are important factors for the decision to get vaccinated. The large difference in the possible number of fatalities due to COVID-19 vaccination that emerges from this survey and the available governmental data should be further investigated.

Implementation of a centralized pharmacovigilance system in academic pan-European clinical trials: Experience from EU-Response and conect4children consortia.

Setting-up a high quality, compliant and efficient pharmacovigilance (PV) system in multi-country clinical trials can be more challenging for academic sponsors than for companies. To ensure the safety of all participants in academic studies and that the PV system fulfils all regulations, we set up a centralized PV system that allows sponsors to delegate work on PV. This initiative was put in practice by our Inserm-ANRS MIE PV department in two distinct multinational European consortia with 19 participating countries: conect4children (c4c) for paediatrics research and EU-Response for Covid-19 platform trials. The centralized PV system consists of some key procedures to harmonize the complex safety processes, creation of a local safety officer (LSO) network and centralization of all safety activities. The key procedures described the safety management plan for each trial and how tasks were shared and delegated between all stakeholders. Processing of serious adverse events (SAEs) in a unique database guaranteed the full control of the safety data and continuous evaluation of the risk-benefit ratio. The LSO network participated in efficient regulatory compliance across multiple countries. In total, there were 1312 SAEs in EU-Response and 83 SAEs in c4c in the four trials. We present here the lessons learnt from our experience in four clinical trials. We managed heterogeneous European local requirements and implemented efficient communication with all trial teams. Our approach builds capacity for PV that can be used by multiple academic sponsors.

Global COVID-19 vaccine acceptance rate: Systematic review and meta-analysis.

Background: A vaccine against COVID-19 is a vital tool in managing the current pandemic. It is becoming evident that an effective vaccine would be required to control COVID-19. Effective use of vaccines is very important in controlling pandemics and paving the way for an acceptable exit strategy. Therefore, this systematic review and meta-analysis aims to determine the global COVID-19 acceptance rate that is necessary for better management of COVID-19 pandemic. Methods: This review was conducted based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocols and considered the studies conducted on acceptance and/or hesitancy of COVID-19 vaccine. Articles were searched using electronic databases including PubMed, Scopus, Web of Science, Embase, CINAHL, and Google Scholar. The quality of the study was assessed using the Joanna Briggs Institute (JBI) critical assessment tool to determine the relevance of each included article to the study. Results: Of the 6,021 articles identified through the electronic database search, 68 articles were included in the systematic review and meta-analysis. The global pooled acceptance rate of the COVID-19 vaccine was found to be 64.9% [95% CI of 60.5 to 69.0%]. Based on the subgroup analysis of COVID-19 vaccine acceptance rate by the World Health Organization's region, the countries where the study was conducted, occupation, and survey period, the prevalence of COVID-19 vaccine acceptance rate was 60.8% [95% CI: 56.3, 65.2%], 61.9% [95% CI: 61.3, 62.4%], 81.6% [95% CI: 79.7, 83, 2%] and 64.5% [95% CI: 60.3, 68.5%], respectively. Conclusions: This review revealed the variation in the level of COVID-19 vaccine acceptance rate across the world. The study found that the overall prevalence of COVID-19 vaccine acceptance was 64.9%. This finding indicated that even if the COVID-19 vaccine is developed, the issue of accepting or taking the developed vaccine and managing the pandemic may be difficult.

Mapping the output of the global literature on the links between gut microbiota and COVID-19.

BACKGROUND: The term "human microbiota" refers to populations of microorganisms that live harmoniously in co-existence with humans. They contribute significantly to the host's immunological response when confronted with a respiratory viral infection. However, little is known about the relationship between the human microbiome and COVID-19. Therefore, our objective is to perform a bibliometric analysis to explore the overall structure and hotspots of research activity on the links between microbiota and COVID-19 at the global level. METHODS: The research literature on the microbiota and COVID-19 published between 2020 and 2022 was obtained from the Scopus database. Bibliometric analysis and network visualization were performed with VOSviewer. RESULTS: Of the 701 publications selected, the USA contributed the most (n = 157, 22.40%), followed by China (n = 118, 16.83%) and Italy (n = 82, 11.70%). Hotspots in this field were "COVID-19 is associated with an altered upper respiratory tract microbiome," "the effect of antibiotics on the gut microbiome," as well as "patient nutrition and probiotic therapy in COVID-19." CONCLUSIONS: The links between microbiota and COVID-19 remain an urgent concern at present, and the use of probiotics or/and antibiotics during the pandemic needs to be further improved. This landscape analysis of the links between the microbiota and COVID-19 will provide a basis for future research.

Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest.

As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.