Analysis of co-occurring and mutually exclusive amino acid changes and detection of convergent and divergent evolution events in SARS-CoV-2.

The inflation of SARS-CoV-2 lineages with a high number of accumulated mutations (such as the recent case of Omicron) has risen concerns about the evolutionary capacity of this virus. Here, we propose a computational study to examine non-synonymous mutations gathered within genomes of SARS-CoV-2 from the beginning of the pandemic until February 2022. We provide both qualitative and quantitative descriptions of such corpus, focusing on statistically significant co-occurring and mutually exclusive mutations within single genomes. Then, we examine in depth the distributions of mutations over defined lineages and compare those of frequently co-occurring mutation pairs. Based on this comparison, we study mutations' convergence/divergence on the phylogenetic tree. As a result, we identify 1,818 co-occurring pairs of non-synonymous mutations showing at least one event of convergent evolution and 6,625 co-occurring pairs with at least one event of divergent evolution. Notable examples of both types are shown by means of a tree-based representation of lineages, visually capturing mutations' behaviors. Our method confirms several well-known cases; moreover, the provided evidence suggests that our workflow can explain aspects of the future mutational evolution of SARS-CoV-2.

CoV2K model, a comprehensive representation of SARS-CoV-2 knowledge and data interplay.

Since the outbreak of the COVID-19 pandemic, many research organizations have studied the genome of the SARS-CoV-2 virus; a body of public resources have been published for monitoring its evolution. While we experience an unprecedented richness of information in this domain, we also ascertained the presence of several information quality issues. We hereby propose CoV2K, an abstract model for explaining SARS-CoV-2-related concepts and interactions, focusing on viral mutations, their co-occurrence within variants, and their effects. CoV2K provides a clear and concise route map for understanding different connected types of information related to the virus; it thus drives a process of data and knowledge integration that aggregates information from several current resources, harmonizing their content and overcoming incompleteness and inconsistency issues. CoV2K is available for exploration as a graph that can be queried through a RESTful API addressing single entities or paths through their relationships. Practical use cases demonstrate its application to current knowledge inquiries.

Protective Measures Practices Among Hospitals' Professionals Working in a Fragile Health System.

OBJECTIVE: This study aimed to assess the protective measures among health-care workers (HCWs) in a war-torn area during coronavirus disease 2019 (COVID-19) pandemic. METHODS: An online cross-sectional questionnaire was administrated to HCWs in Syria between April 1 and May 21, 2020. The questions aimed to assess the HCWs' application of safety, hygiene, and necessary protection considerations while attending to suspected or proven COVID-19 cases. Unpaired t-test and 1-way analysis of variance (ANOVA) were used for statistical analysis. RESULTS: Of the 290 participants included in the statistical analysis, 250 were medical doctors. Low scores of protective practices were noticed among the participants, as only 12% of doctors had a score above 6/15 points, and only 37.5% of nurses had a score of more than 4/12 points. Medical doctors who were not on the frontlines scored significantly higher than those who were on the frontlines (4.69 vs 3.80 points, respectively; P < 0.001). CONCLUSIONS: More courses and training sessions should be implemented to improve the practice of protective measures among HCWs (frontliners in particular) in areas with fragile health systems, such as Syria, during the COVID-19 pandemic, especially those on the frontlines. Moreover, specific COVID-19 protection measures guidelines to low-income countries are needed.

Assessment of medical students' preparedness and willingness for integration into a war-torn healthcare system: the example of COVID-19 pandemic scenario.

In Syria, medical students were placed on call to provide sufficient human resources during COVID-19 pandemic. This research aimed to explore the ability and willingness of the final-year medical students to assist during COVID-19 in the Syrian war-torn and fragile health-system. Final-year Syrian medical students were approached between 9th-17th April 2020 through an online questionnaire. Students' COVID-19-related medical knowledge (5-point score), clinical judgement (5-point score), and preparedness and willingness to integrate in healthcare facilities were assessed. A 10-point score was created, and linear regression and Tukey's HSD test were conducted. 1673 valid responses were received, of which 1199 (71.66%) responses were from the final-year students. Of the latter, 728 (60.71%) scored 4 points or higher in the medical knowledge score (mean 3.69 points), while 298 (24.85%) scored 4 or higher in the clinical judgement score. Final-year students scored significantly higher than the fourth-year students in the clinical judgement score. Finally, 682 (56.88%) of the final-year students expressed willingness to volunteer with healthcare teams. Final-year medical students may provide medical aid, on voluntary basis, by working in fragile health systems during pandemics. However, this should be undertaken in cases of extreme need. Sufficient personal protective measures, intensive training, and adequate supervision should be guaranteed.

CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning.

BACKGROUND: Literature about SARS-CoV-2 widely discusses the effects of variations that have spread in the past 3 years. Such information is dispersed in the texts of several research articles, hindering the possibility of practically integrating it with related datasets (e.g., millions of SARS-CoV-2 sequences available to the community). We aim to fill this gap, by mining literature abstracts to extract-for each variant/mutation-its related effects (in epidemiological, immunological, clinical, or viral kinetics terms) with labeled higher/lower levels in relation to the nonmutated virus. RESULTS: The proposed framework comprises (i) the provisioning of abstracts from a COVID-19-related big data corpus (CORD-19) and (ii) the identification of mutation/variant effects in abstracts using a GPT2-based prediction model. The above techniques enable the prediction of mutations/variants with their effects and levels in 2 distinct scenarios: (i) the batch annotation of the most relevant CORD-19 abstracts and (ii) the on-demand annotation of any user-selected CORD-19 abstract through the CoVEffect web application (http://gmql.eu/coveffect), which assists expert users with semiautomated data labeling. On the interface, users can inspect the predictions and correct them; user inputs can then extend the training dataset used by the prediction model. Our prototype model was trained through a carefully designed process, using a minimal and highly diversified pool of samples. CONCLUSIONS: The CoVEffect interface serves for the assisted annotation of abstracts, allowing the download of curated datasets for further use in data integration or analysis pipelines. The overall framework can be adapted to resolve similar unstructured-to-structured text translation tasks, which are typical of biomedical domains.

EpiSurf: metadata-driven search server for analyzing amino acid changes within epitopes of SARS-CoV-2 and other viral species.

EpiSurf is a Web application for selecting viral populations of interest and then analyzing how their amino acid changes are distributed along epitopes. Viral sequences are searched within ViruSurf, which stores curated metadata and amino acid changes imported from the most widely used deposition sources for viral databases (GenBank, COVID-19 Genomics UK (COG-UK) and Global initiative on sharing all influenza data (GISAID)). Epitopes are searched within the open source Immune Epitope Database or directly proposed by users by indicating their start and stop positions in the context of a given viral protein. Amino acid changes of selected populations are joined with epitopes of interest; a result table summarizes, for each epitope, statistics about the overlapping amino acid changes and about the sequences carrying such alterations. The results may also be inspected by the VirusViz Web application; epitope regions are highlighted within the given viral protein, and changes can be comparatively inspected. For sequences mutated within the epitope, we also offer a complete view of the distribution of amino acid changes, optionally grouped by the location, collection date or lineage. Thanks to these functionalities, EpiSurf supports the user-friendly testing of epitope conservancy within selected populations of interest, which can be of utmost relevance for designing vaccines, drugs or serological assays. EpiSurf is available at two endpoints. Database URL: http://gmql.eu/episurf/ (for searching GenBank and COG-UK sequences) and http://gmql.eu/episurf_gisaid/ (for GISAID sequences).