Background: Health care workers (HCWs) are at increased risk of acquiring and transmitting COVID-19 infection. Moreover, they present role models for communities with regards to attitudes towards COVID-19 vaccination. Hence, hesitancy of HCWs towards vaccination can crucially affect the efforts aiming to contain the pandemic. Previously published studies paid little attention to HCWs in Arab countries, which have a population of over 440 million. Objectives: To assess the rates of COVID-19 vaccine hesitancy in Arabic-speaking HCWs residing in and outside Arab countries, and their perceived barriers towards vaccination. Methods: A cross-sectional study based on an online survey was conducted from 14–29 January 2021, targeting Arabic-speaking HCWs from all around the world. Results: The survey recruited 5708 eligible participants (55.6% males, 44.4% females, age 30.6 ± 10 years) from 21 Arab countries (87.5%) and 54 other countries (12.5%). Our analysis showed a significant rate of vaccine hesitancy among Arabic-speaking HCWs residing in and outside of Arab countries (25.8% and 32.8%, respectively). The highest rates of hesitancy were among participants from the western regions of the Arab world (Egypt, Morocco, Tunisia, and Algeria). The most cited reasons for hesitancy were concerns about side effects and distrust of the expedited vaccine production and healthcare policies. Factors associated with higher hesitancy included age of 30–59, previous or current suspected or confirmed COVID-19, female gender, not knowing the vaccine type authorized in the participant’s country, and not regularly receiving the influenza vaccine. Conclusion: This is the first large-scale multinational post-vaccine-availability study on COVID-19 vaccine hesitancy among HCWs. It reveals high rates of hesitancy among Arab-speaking HCWs. Unless addressed properly, this hesitancy can impede the efforts for achieving widespread vaccination and collective immunity.
Viruses show noticeable evolution to adapt and reproduce within their hosts. Theoretically, patterns and factors that affect the codon usage of viruses should reflect evolutionary changes that allow them to optimize their codon usage to their hosts. Some software tools can analyze the codon usage of organisms; however, their performance has room for improvement, as these tools do not focus on examining the codon usage co-adaptation between viruses and their hosts. This paper describes the R package, which is a crucial tool used to analyze the co-adaptation vhcub of codon usage between a virus and its host, with several implementations of indices and plots. The tool is available from: https://cran.r-project.org/web/packages/vhcub/. PubMed Abstract | Publisher Full Text | Free Full Text 2. Boël G, Letso R, Neely H, et al.: Codon influence on protein expression in E. coli correlates with mRNA levels. Nature. 2016; 529(7586): 358-363. PubMed Abstract | Publisher Full Text | Free Full Text 3. Burns CC, Shaw J, Campagnoli R, et al.: Modulation of poliovirus replicative fitness in HeLa cells by deoptimization of synonymous codon usage in the capsid region. J Virol. 2006; 80(7): 3259-3272. PubMed Abstract | Publisher Full Text | Free Full Text 4. Cladel NM, Hu J, Balogh KK, et al.: CRPV genomes with synonymous codon optimizations in the CRPV E7 gene show phenotypic differences in growth and altered immunity upon E7 vaccination. PLoS One. 2008; 3(8): e2947. PubMed Abstract | Publisher Full Text | Free Full Text 5. Elek A, Kuzman M, Vlahovicek K: coRdon: Codon Usage Analysis and Prediction of Gene Expressivity. R package version 1.0.3. 2019. Reference Source 6. Novembre JA: Accounting for background nucleotide composition when measuring codon usage bias. Mol Biol Evol. 2002; 19(8): 1390-1394. PubMed Abstract | Publisher Full Text 7. Sharp PM, Li WH: The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 1987; 15(3): 1281-1295. PubMed Abstract | Publisher Full Text | Free Full Text 8. Puigbò P, Aragonès L, Garcia-Vallvé S: RCDI/eRCDI: a web-server to estimate codon usage deoptimization. BMC Res Notes. 2010; 3(1): 87. PubMed Abstract | Publisher Full Text | Free Full Text 9. Zhou JH, Zhang J, Sun DJ, et al.: The distribution of synonymous codon choice in the translation initiation region of dengue virus. PLoS One. 2013; 8(10): e77239. PubMed Abstract | Publisher Full Text | Free Full Text 10. Wan XF, Xu D, Kleinhofs A, et al.: Quantitative relationship between synonymous codon usage bias and GC composition across unicellular genomes.
Background: Health Care Workers (HCWs) are at increased risk of acquiring and transmitting COVID-19 infection. Also, they present role models for communities with regards to attitudes towards COVID-19 vaccination. Hence, hesitancy of HCWs towards vaccination can crucially affect the efforts aiming to contain the pandemic. Previously published studies paid little attention to HCWs in Arab countries, which has a population of over 440 million. Objectives: to assess the rates of COVID-19 vaccine hesitancy in Arabic-speaking HCWs residing in and outside the Arab countries, and their perceived barriers towards vaccination. Methods: a cross-sectional study based on an online survey was conducted from 14-Jan 2021 to 29-Jan 2021, targeting Arabic-speaking HCWs from all around the world. Results: the survey recruited 5,708 eligible participants (55.6% males, 44.4% females, age 30.6±10 years) from 21 Arab countries (87.5%) and 54 other countries (12.5%). Our analysis shows a significant rate of vaccine hesitancy among Arabic-speaking HCWs residing in and outside Arab countries (25.8% and 32.8%, respectively). The highest rates of hesitancy were among participants from the west region of the Arab world (Egypt, Morocco, Tunisia, and Algeria). The most cited reasons for hesitancy were concerns about side effects and distrust in vaccine expedited production and healthcare policies. Factors associated with higher hesitancy included age of 30-59, previous or current suspected or confirmed COVID-19, female gender, not knowing the vaccine type authorized in the participant’s country, and not regularly receiving the influenza vaccine. Conclusion: this is the first large-scale, multinational, post-vaccine-availability study on COVID-19 vaccine hesitancy among HCWs. It reveals high rates of hesitancy among Arab-speaking HCWs. Unless addressed properly, this hesitancy can impede the efforts for achieving widespread vaccination and collective immunity.
Image understanding and scene classification are keystone tasks in computer vision. The development of technologies and profusion of existing datasets open a wide room for improvement in the image classification and recognition research area. Notwithstanding the optimal performance of exiting machine learning models in image understanding and scene classification, there are still obstacles to overcome. All models are data-dependent that can only classify samples close to the training set. Moreover, these models require large data for training and learning. The first problem is solved by few-shot learning, which achieves optimal performance in object detection and classification but with a lack of eligible attention in the scene classification task. Motivated by these findings, in this paper, we introduce two models for few-shot learning in scene classification. In order to trace the behavior of those models, we also introduce two datasets (MiniSun; MiniPlaces) for image scene classification. Experimental results show that the proposed models outperform the benchmark approaches in respect of classification accuracy.
Purpose Protein misfolding and aggregation result in proteotoxic stress and underlie the pathogenesis of many diseases. To overcome proteotoxicity, cells compartmentalize misfolded and aggregated proteins in different inclusion bodies. The aggresome is a paranuclear inclusion body that functions as a storage compartment for misfolded proteins. Choroid plexus tumors (CPTs) are rare neoplasms comprised of three pathological subgroups. The underlying mechanisms of their pathogenesis remain unclear. This study aims to elucidate the prognostic role and the biological effects of aggresomes in pediatric CPTs. Methods We examined the presence of aggresomes in 42 patient-derived tumor tissues by immunohistochemistry and we identified their impact on patients’ outcomes. We then investigated the proteogenomics signature associated with aggresomes using whole-genome DNA methylation and proteomic analysis to define their role in the pathogenesis of pediatric CPTs. Results Aggresomes were detected in 64.2% of samples and were distributed among different pathological and molecular subgroups. The presence of aggresomes with different percentages was correlated with patients’ outcomes. The ≥ 25% cutoff had the most significant impact on overall and event-free survival (p-value < 0.001) compared to the pathological and the molecular stratifications. Conclusions These results support the role of aggresome as a novel prognostic molecular marker for pediatric CPTs that was comparable to the molecular classification in segregating samples into two distinct subgroups, and to the pathological stratification in the prediction of patients’ outcomes. Moreover, the proteogenomic signature of CPTs displayed altered protein homeostasis, manifested by enrichment in processes related to protein quality control.
Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. Thus, GeneCloudOmics is a one-stop open-source tool for gene expression data analysis and visualization. It is freely available at http://combio-sifbi.org/GeneCloudOmics.
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