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.

INTRODUCTION: Alzheimer's disease (AD) shows significant sex differences in prevalence and clinical manifestations, but the underlying molecular mechanisms remain unclear. METHODS: This study used a large‐scale, single‐cell transcriptomic atlas of the human prefrontal cortex to investigate sex‐dependent molecular changes in AD. Our approach combined cell type–specific and sex‐specific differential gene expression analysis, pathway enrichment, gene regulatory network construction, and cell–cell communication analysis to identify sex‐dependent changes. RESULTS: We found significant sex‐specific gene expression patterns and pathway alterations in AD. Male astrocytes showed changes in cell death pathways, with RPTOR as a key regulator, while female astrocytes had alterations in Wnt signaling and cell cycle regulation. Cell–cell communication analysis uncovered sex‐specific intercellular signaling patterns, with male‐specific impacts on apoptosis‐related signaling and female‐specific alterations in Wnt and calcium signaling. DISCUSSION: This study reveals sex‐dependent gene expression patterns, pathway alterations, and intercellular communication changes, suggesting the need for sex‐specific approaches in AD research. Highlights: Single‐cell transcriptomics reveals significant sex‐specific molecular signatures in Alzheimer's disease (AD). Male astrocytes show enhanced modulation of apoptotic and cell death pathways in AD; female astrocytes exhibit distinct alterations in Wnt signaling and cell‐cycle regulation. Sex‐dimorphic changes in mitochondrial gene expression are observed in excitatory neurons, suggesting divergent energy metabolism profiles may contribute to AD sex differences. RPTOR is identified as a key regulator of male‐specific changes in astrocytes, implicating the mechanistic target of rapamycin pathway in sex‐dependent AD pathology. New cell–cell communication analyses reveal sex‐specific patterns of intercellular signaling, providing insights into how cellular interactions may differentially contribute to AD pathology in males and females.

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 vhcub R package, which is a crucial tool used to analyze the co-adaptation 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/.

Complex diseases often share genetic susceptibility factors, molecular pathways, and pathological mechanisms. Understanding these commonalities through systematic cross‐disease comparisons can reveal both disease‐specific and shared biomarkers, potentially suggesting new therapeutic targets and opportunities for drug repurposing. In recent years, the growth of multi‐omics datasets across diverse diseases, coupled with advances in computational systems biology, has enabled sophisticated cross‐disease analyses. New methodological frameworks have emerged for integrating and comparing disease‐specific molecular signatures, from gene‐level analyses to complex network‐based approaches. Here, we present a comprehensive framework for computational cross‐disease comparison and integration of omics data, covering established and emerging methodologies. These include gene‐level comparative analyses, pathway‐based approaches, network biology methods, matrix factorization techniques, and machine learning approaches. We examine important aspects of data preprocessing, normalization, and integration, suggesting practical solutions to common technical challenges. We provide a detailed overview of relevant software tools and databases, discussing their strengths, limitations, and optimal use cases for cross‐disease analysis. Finally, we explore current trends in cross‐disease omics analysis, particularly through deep learning methods, highlighting new opportunities for methodological innovation and biological discovery in this field. This compilation of computational methods and practical insights aims to serve as a resource both for bioinformaticians seeking guidance on optimal method selection and biomedical researchers interested in applied cross‐disease analyses. In addition to highlighting practical recommendations and common pitfalls, it provides an entry point to the extensive literature in the field, supporting readers in identifying and further exploring suitable methods for their research needs.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Databases and Expert Systems

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.

Scene classification is one of the most complex tasks in computer-vision. The accuracy of scene classification is dependent on other subtasks such as object detection and object classification. Accurate results may be accomplished by employing object detection in scene classification since prior information about objects in the image will lead to an easier interpretation of the image content. Machine and transfer learning are widely employed in scene classification achieving optimal performance. Despite the promising performance of existing models in scene classification, there are still major issues. First, the training phase for the models necessitates a large amount of data, which is a difficult and time-consuming task. Furthermore, most models are reliant on data previously seen in the training set, resulting in ineffective models that can only identify samples that are similar to the training set. As a result, few-shot learning has been introduced. Although few attempts have been reported applying few-shot learning to scene classification, they resulted in perfect accuracy. Motivated by these findings, in this paper we implement a novel few-shot learning model—GenericConv—for scene classification that has been evaluated using benchmarked datasets: MiniSun, MiniPlaces, and MIT-Indoor 67 datasets. The experimental results show that the proposed model GenericConv outperforms the other benchmark models on the three datasets, achieving accuracies of 52.16 ± 0.015, 35.86 ± 0.014, and 37.26 ± 0.014 for five-shots on MiniSun, MiniPlaces, and MIT-Indoor 67 datasets, respectively.