Association studies between COVID-19 and SSc-ILD

Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases. © 2023 Editorial Office of Journal of Shenzhen University. All rights reserved.

A Survey on Machine Learning Techniques for Multimodal Biomedical Signal Processing

In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.

Project transition to the digital Middle Ages via COVID-19 pattern

The analysis of current trends in the implementation of effective socio-economic solutions and their development under the influence of COVID-19 is made. The prospects of using innovative and telecommunication technologies, robotics, big data processing methods and knowledge management methods in the formation and management of global economic clusters were noted. The clustering of delivery robots under pandemic conditions by methods of machine learning was carried out. The peculiarities of COVID-19 assessment as the main formative factor influencing socio-economic decision-making on a global scale are disclosed. The necessity and possible consequences of adopting and implementing new decisions designed to minimize the negative effects of COVID-19 on Russian and global economies are discussed. It is noted that the design and development of innovations in the system of management and transfer of knowledge is an indispensable condition for the successful development of future socio-economic relations. On the basis of the obtained results conclusions are made about the background of the applied solutions, about the vector of their direction and makes it clear what should be paid special attention to when assessing the current situation in society and determine which solutions are most effective and how the social order should be transformed to successfully withstand the new challenges. © 2023 SPIE.

The Science of COVID-19

Despite Steven Shapin's urging that science ought to include a "warts and all" picture of the process of knowledge making (1992, p. 28), students rarely get the chance to look closely at the methods, assumptions, and evolving nature of the field's understanding of phenomena. [...]nine secondary science teachers from a diverse set of geographical, content, and demographic contexts pilot tested the course. After verifying through multiple methods that there are significant similarities between the unknown virus and the SARS-CoV virus, students then use a more sophisticated bioinformatics tool known as the Basic Local Alignment Search Tool (BLAST;Figure 2, right). The students use JMOL simulations (Figure 3) to clearly see the importance of virologists in understanding basic life science crosscutting concepts like the relationship between structure and function and the disciplinary core ideas surrounding mutation and natural selection.

Exploration of potential mechanism of SARS-CoV-2 induced immune inflammatory response leading to progression of atherosclerosis and prediction of traditional Chinese medicine for preventing and treating based on bioinformatics.

Objective To explore the core targets and important pathways of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) induced atherosclerosis (AS) progression from the perspective of immune inflammation, so as to predict the potential prevention and treatment of traditional Chinese medicine (TCM). Methods Microarray data were obtained from the Gene Expression Omnibus (GEO) database for coronavirus disease 2019 (COVID-19) patients and AS patients, and the "limmar" and "Venn" packages were used to screen out the common differentially expressed genes (DEGs) genes in both diseases. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses were performed on the common DEGs to annotate their functions and important pathways. The two gene sets were scored for immune cells and immune function to assess the level of immune cell infiltration. The protein-protein interaction (PPI) network was constructed by STRING database, and the CytoHubba plug-in of Cytoscape was used to identify the hub genes. Two external validation datasets were introduced to validate the hub genes and obtain the core genes. Immuno-infiltration analysis and gene set enrichment analysis (GSEA) were performed on the core genes respectively. Finally the potential TCM regulating the core genes were predicted by Coremine Medical database. Results A total of 7898 genes related to COVID-19, 471 genes related to AS progression;And 51 common DEGs, including 32 highly expressed genes and 19 low expressed genes were obtained. GO and KEGG analysis showed that common DEGs, which were mainly localized in cypermethrin-encapsulated vesicles, platelet alpha particles, phagocytic vesicle membranes and vesicles, were involved in many biological processes such as myeloid differentiation factor 88 (MyD88)-dependent Toll-like receptor signaling pathway transduction, interleukin-8 (IL-8) production and positive regulation, IL-6 production and positive regulation to play a role in regulating nicotinamide adenine dinucleotide phosphate oxidase activity, Toll-like receptor binding and lipopeptide and glycosaminoglycan binding through many biological pathways, including Toll-like receptor signaling pathways, neutrophil extracellular trap formation, complement and coagulation cascade reactions. The results of immune infiltration analysis demonstrated the state of immune microenvironment of COVID-19 and AS. A total of 5 hub genes were obtained after screening, among which Toll-like receptor 2 (TLR2), cluster of differentiation 163 (CD163) and complement C1q subcomponent subunit B (C1QB) genes passed external validation as core genes. The core genes showed strong correlation with immune process and inflammatory response in both immune infiltration analysis and GSEA enrichment analysis. A total of 35 TCMs, including Chuanxiong (Chuanxiong Rhizoma), Taoren (Persicae Semen), Danggui (Angelicae Sinensis Radix), Huangqin (Scutellariae Radix), Pugongying (Taraxaci Herba), Taizishen (Pseudostellariae Radix), Huangjing (Polygonati Rhizoma), could be used as potential therapeutic agents. Conclusion TLR2, CD163 and C1QB were the core molecules of SARS-CoV-2-mediated immune inflammatory response promoting AS progression, and targeting predicted herbs were potential drugs to slow down AS progression in COVID-19 patients.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Liquid biopsy testing in metastatic or advanced breast cancer patients during the COVID-19 pandemic

Objective: During the COVID-19 pandemic, cancer patients had restricted access to standard of care tissue biopsy. Liquid biopsy assays using next generation sequencing technology provides a less invasive method for determining circulating tumour mutations (ctDNA) associated with targeted treatments or prognosis. As part of deploying technology to help cancer patients obtain molecular testing, a clinical program was initiated to offer liquid biopsy testing for Canadian patients with advanced or metastatic breast cancer. Method(s): Blood was drawn in two 10 mL StreckTM DNA BCTs and sent to the CAP/CLIA/DAP accredited Imagia Canexia Health laboratory for testing using the clinically validated Follow ItTM liquid biopsy assay. Plasma was isolated using a double spin protocol and plasma cell-free DNA (cfDNA) extracted using an optimized Promega Maxwell RSC method. Extracted cfDNA was amplified using the multiplex amplicon-based hotspot 30 or 38 gene panel and sequenced. An inhouse developed bioinformatics pipeline and reporting platform were used to identify pathogenic single nucleotide variants (SNVs), indels (insertions and deletions), and gene amplification. Included in the panel are genes associated with metastatic breast cancer: AKT1, BRAF, ERBB2, ESR1, KRAS, PIK3CA, TP53. Result(s): To identify biomarkers, 1214 metastatic or advanced breast cancer patient cfDNA samples were tested. There were 15 cases sent for repeat testing. We reported 48% of samples harboring pathogenic ctDNA mutations in TP53 (22%), PIK3CA (19%), ESR1 (18%), AKT1 (2%), ERBB2 (1.5%). Co-occurring variants were identified in samples with ESR1/PIK3CA as well as TP53/PIK3CA (both p-values <0.001). Interestingly, 29% of samples with mutated ESR1 harbored >= 2 ESR1 ctDNA mutations. In 56% of cases, previous molecular testing indicated the cancer subtype as hormone receptor (ER, PR) positive with/without HER2 negative status. In this specific subgroup, 49% harbored ctDNA mutations with 63% of those being PIK3CA and/or ESR1 mutations. Conclusion(s): A population of Canadian women with metastatic breast cancer were tested using a liquid biopsy gene panel during the COVID-19 pandemic for identification of biomarkers for targeted therapeutic options. Over 50% of the samples were identified as hormone positive, with greater than 60% harboring PIK3CA and ESR1 ctDNA mutations. Studies have shown that metastatic PIK3CA mutated ER-positive/HER2-negative tumors are predictive to respond to alpelisib therapy and have FDA and Health Canada approval. Additionally, ESR1 mutations are associated with acquired resistance to antiestrogen therapies, and interestingly we identified 29% of ESR1 mutated samples with multiple mutations possibly indicating resistance subclones. In future studies, longitudinal monitoring for presence of multiple targetable and resistance mutations could be utilized to predict or improve clinical management.

IDDF2023-ABS-0045 Spatiotemporal immune landscape of Mas-driven pro-inflammatory microenvironment with hyperactivated glycolysis in acetaminophen-induced liver injury

IDDF2023-ABS-0045 Figure 1 IDDF2023-ABS-0045 Figure 2 IDDF2023-ABS-0045 Figure 3 IDDF2023-ABS-0045 Figure 4

Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study

This study aims to provide insights into predicting future cases of COVID-19 infection and rates of virus transmission in the UK by critically analyzing and visualizing historical COVID-19 data, so that healthcare providers can prepare ahead of time. In order to achieve this goal, the study invested in the existing studies and selected ARIMA and Fb-Prophet time series models as the methods to predict confirmed and death cases in the following year. In a comparison of both models using values of their evaluation metrics, root-mean-square error, mean absolute error and mean absolute percentage error show that ARIMA performs better than Fb-Prophet. The study also discusses the reasons for the dramatic spike in mortality and the large drop in deaths shown in the results, contributing to the literature on health analytics and COVID-19 by validating the results of related studies. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Identification of genetic determinants of COVID-19 severity by exome sequencing

Background/Objectives: The severity of the symptoms of coronavirus disease 2019 (COVID-19) has been associated to age, comorbidity, and male sex. Besides virus characteristics, host genetic factors influence the infection outcome. Different genome-wide association studies and meta-analyses investigated the contribution of common variants, whereas the role of rare variants just started to be elucidated. Our goal is to determine the contribution of rare variants to the development of severe COVID-19 in the Italian population. Method(s): We compared the genetic background of 215 severe COVID-19 patients with 1764 individuals from the general population. Rare variants (minor allele frequency <1%) from wholeexome sequencing data were retrieved using a bioinformatics variant discovery pipeline. We tested the impact of rare variants (classified according to their predicted effect on the gene product) both using a burden test design, and an iterative machine learning (ML) approach. Result(s): We identified a total of 690,000 rare variants in the entire examined population. Among them, 250 were associated with COVID-19 severity at a nominal P < 0.05. Gene-based burden test revealed a gene with an excess of loss-of-function mutations at P < 0.05. Finally, the ML approach, analysing all the 690,000 rare variants, identified the best combination of variants that is able to predict COVID-19 severity in our cohort. Conclusion(s): Our work provides new insights on the genetic signature of COVID-19 in the Italian population. The most informative rare variants could be exploited to define individuals' risk profiles to COVID-19 severity for the Italian population.

Pathogen genomics in public health laboratories: successes, challenges, and lessons learned from California's SARS-CoV-2 Whole-Genome Sequencing Initiative, California COVIDNet.

The capacity for pathogen genomics in public health expanded rapidly during the coronavirus disease 2019 (COVID-19) pandemic, but many public health laboratories did not have the infrastructure in place to handle the vast amount of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequence data generated. The California Department of Public Health, in partnership with Theiagen Genomics, was an early adopter of cloud-based resources for bioinformatics and genomic epidemiology, resulting in the creation of a SARS-CoV-2 genomic surveillance system that combined the efforts of more than 40 sequencing laboratories across government, academia and industry to form California COVIDNet, California's SARS-CoV-2 Whole-Genome Sequencing Initiative. Open-source bioinformatics workflows, ongoing training sessions for the public health workforce, and automated data transfer to visualization tools all contributed to the success of California COVIDNet. While challenges remain for public health genomic surveillance worldwide, California COVIDNet serves as a framework for a scaled and successful bioinformatics infrastructure that has expanded beyond SARS-CoV-2 to other pathogens of public health importance.

Paper and screen media in current health education practices aimed at older adults: a scoping review protocol.

INTRODUCTION: With technological advancement and the COVID-19 pandemic, paper-based media are giving way to screen-based media to promote healthy ageing. However, there is no review available covering paper and screen media use by older people, so the objective of this review is to map the current use of paper-based and/or screen-based media for health education aimed at older people. METHODS AND ANALYSIS: The literature will be searched in Scopus, Web of Science, Medline, Embase, Cinahl, The ACM Guide to Computing Literature and Psyinfo databases. Studies in English, Portuguese, Italian or Spanish published from 2012 to the date of the search will be examined. In addition, an additional strategy will be carried out, which will be a Google Scholar search, in which the first 300 studies according to Google's relevance algorithm will be verified. The terms used in the search strategy will be focused on older adults, health education, paper-based and screen-based media, preferences, intervention and other related terms. This review will include studies where the average age of the participants was 60 years or older and were users of health education strategies through paper-based or screen-based media. Two reviewers will carry out the selection of studies in five steps: identification of studies and removal of duplicates, pilot test, selection by reading titles and abstracts, full-text inclusion and search for additional sources. A third reviewer will resolve disagreements. To record information from the included studies, a data extraction form will be used. The quantitative data will be presented in a descriptive way and the qualitative data through Bardin's content analysis. ETHICS AND DISSEMINATION: Ethical approval is not applicable to the scoping review. The results will be disseminated through presentations at significant scientific events and published in journals in the area. PROTOCOL REGISTRATION NUMBER: Open science framework (DOI: DOI 10.17605/OSF.IO/GKEAH).

In Silico Evaluation of SARS-CoV-2 K417N, L452R, and E484K Detection Assays Against Omicron Variants.

The study's objective was to assess whether the performance of the DIAGNOVITAL SARS-CoV-2 Mutation Detection Assays is affected by Omicron mutations. In silico evaluation of 67,717 Variant of Concern, Variant of Interest sequences and 6,612 sequences of the Omicron variants involving BA1., BA2., BA3 sub-lineages downloaded from the GISAID database by 17 December 2021, were performed. The sequences were aligned according to the reference genome MN908947.3 using MAFFT multiple sequence alignment software version 7. Our findings showed that among 6,612 Omicron, 41 Spike gene mutations with a frequency of ≥70% were identified. Some of the Omicron mutations (R408S, N440K, G446S, Q493S, Q498R) could affect the diagnostic performance of K417N, L452R, and E484K assays against the Omicron sub-lineages. However, L452R and K417N mutation tests allow differentiation of the Delta and Omicron variants mutation profile. The COVID-19 pandemic lasted longer than expected, and the rapid modification of diagnostic kits seems necessary to combat the pandemic.

In silico design and evaluation of a novel therapeutic agent against the spike protein as a novel treatment strategy for COVID-19 treatment.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that is associated with severe damage to other human organs. It causes by a novel coronavirus, and it is spreading all over the world. To date, there is some approved vaccine or therapeutic agent which could be effective against this disease. But their effectiveness against mutated strains is not studied completely. The spike glycoprotein on the surface of the coronaviruses gives the virus the ability to bind to host cell receptors and enter cells. Inhibition of attachment of these spikes can lead to virus neutralization by inhibiting viral entrance. AIMS: In this study, we tried to use the virus entrance strategy against itself by utilizing virus receptor (ACE-2) in order to design an engineered protein consisting of a human Fc antibody fragment and a part of ACE-2, which reacts with virus RBD, and we also evaluated this interaction by computational methods and in silico methods. Subsequently, we have designed a new protein structure to bind with this site and inhibit the virus from attaching to its cell receptor, mechanically or chemically. METHODS: Various in silico software and bioinformatic databases were used to retrieve the requested gene and protein sequences. The physicochemical properties and possibility of allergenicity were also examined. Three-dimensional structure prediction and molecular docking were also performed to develop the most suitable therapeutic protein. RESULTS: The designed protein consisted of a total of 256 amino acids with a molecular weight of 28984.62 and 5.92 as a theoretical isoelectric point. Instability and aliphatic index and grand average of hydropathicity are 49.99, 69.57 and -0.594, respectively. CONCLUSIONS: In silico studies can provide a good opportunity to study viral proteins and new drugs or compounds since they do not need direct exposure to infectious agents or equipped laboratories. The suggested therapeutic agent should be further characterized in vitro and in vivo.

A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion.

Given the COVID-19 pandemic, there is interest in understanding ligand-receptor features and targeted antibody-binding attributes against emerging SARS-CoV-2 variants. Here, we developed a large-scale structure-based pipeline for analysis of protein-protein interactions regulating SARS-CoV-2 immune evasion. First, we generated computed structural models of the Spike protein of 3 SARS-CoV-2 variants (B.1.1.529, BA.2.12.1, and BA.5) bound either to a native receptor (ACE2) or to a large panel of targeted ligands (n = 282), which included neutralizing or therapeutic monoclonal antibodies. Moreover, by using the Barnes classification, we noted an overall loss of interfacial interactions (with gain of new interactions in certain cases) at the receptor-binding domain (RBD) mediated by substituted residues for neutralizing complexes in classes 1 and 2, whereas less destabilization was observed for classes 3 and 4. Finally, an experimental validation of predicted weakened therapeutic antibody binding was performed in a cell-based assay. Compared with the original Omicron variant (B.1.1.529), derivative variants featured progressive destabilization of antibody-RBD interfaces mediated by a larger set of substituted residues, thereby providing a molecular basis for immune evasion. This approach and findings provide a framework for rapidly and efficiently generating structural models for SARS-CoV-2 variants bound to ligands of mechanistic and therapeutic value.

Histone mRNA polyadenylation-mediated inflammation underlies various virus infections and cancers.

The mechanistic understanding of virus infection and inflammation in many diseases is incomplete. Normally, messenger RNA (mRNA) tails of replication-dependent histones (RDH) that safeguard naked nuclear DNAs are protected by a specialized stem-loop instead of polyadenylation. Here, we showed that infection by various RNA viruses (including severe acute respiratory syndrome coronavirus 2) induced aberrant polyadenylation of RDH mRNAs (pARDH) that resulted in inflammation or cellular senescence, based on which we constructed a pARDH inflammation score (pARIS). We further investigated pARIS elevation in various disease conditions, including different types of virus infection, cancer, and cellular senescence. Notably, we found that pARIS was positively correlated with coronavirus disease 2019 severity in specific immune cell types. We also detected a subset of HIV-1 elite controllers characterized by pARDH "flipping" potentially mediated by HuR. Importantly, pARIS was positively associated with transcription of endogenous retrovirus but negatively associated with most immune cell infiltration in tumors of various cancer types. Finally, we identified and experimentally verified two pARIS regulators, ADAR1 and ZKSCAN1, which was first linked to inflammation. The ZKSCAN1 was known as a transcription factor but instead was shown to regulate pARIS as a novel RNA binding protein. Both regulators were upregulated under most infection and inflammation conditions. In conclusion, we unraveled a potential antiviral mechanism underlying various types of virus infections and cancers.

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins.

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25µM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

How helpful were molecular dynamics simulations in shaping our understanding of SARS-CoV-2 spike protein dynamics?

The SARS-CoV-2 spike protein (S) represents an important viral component that is required for successful viral infection in humans owing to its essential role in recognition of and entry to host cells. The spike is also an appealing target for drug designers who develop vaccines and antivirals. This article is important as it summarizes how molecular simulations successfully shaped our understanding of spike conformational behavior and its role in viral infection. MD simulations found that the higher affinity of SARS-CoV-2-S to ACE2 is linked to its unique residues that add extra electrostatic and van der Waal interactions in comparison to the SARS-CoV S. This illustrates the spread potential of the pandemic SARS-CoV-2 relative to the epidemic SARS-CoV. Different mutations at the S-ACE2 interface, which is believed to increase the transmission of the new variants, affected the behavior and binding interactions in different simulations. The contributions of glycans to the opening of S were revealed via simulations. The immune evasion of S was linked to the spatial distribution of glycans. This help the virus to escape the immune system recognition. This article is important as it summarizes how molecular simulations successfully shaped our understanding of spike conformational behavior and its role in viral infection. This will pave the way to us preparing for the next pandemic as the computational tools are tailored to help fight new challenges.

Molecular Detection of Canine Parvovirus-2c (Cpv-2c) in Diseased Coyote Pups (Canis Latrans) at Northeastern Mexico

Intro: Canine parvovirus type 2 (PVC-2), Protoparvovirus genus of the Parvoviridae family, is a worldwide distribution virus that affects the Canidae family. In free-living coyotes (Canis latrans), the presence of the PCV-2a, PCV-2b and PCV-2c subtypes of PVC-2 has been reported, but there are no reports of their presence as a cause of clinical damage. The objective of this study is to report the presence of PVC-2c in an outbreak of mild gastroenteritis in three coyote pups detected in northeastern Mexico Methods: During the fall of 2019, in the suburban area of Monterrey, N.L., 3 affected coyote pups were detected with a mild gastroenteric condition consisting of mild diarrhea with loose stools, vomiting, dehydration, loss of appetite, pale mucous membranes, and low weight. Stool samples were tested for Canine Parvovirus (CPV-2), Canine Coronavirus (CCV) or Giardia antigens with a commercial kit. All samples were positive for CPV-2 and these were subsequently analyzed by PCR and sequencing of the CPV-2 VP2 gene. Using bioinformatics, the VP2 gene sequence data obtained were used to establish phylogenetic relationships with homologous sequences reported in coyotes and CPV-2 vaccines. Finding(s): The genetic sequence of VP2 obtained showed a high homology (98.1 to 100%) with CPV-2c. The sequences obtained from the pups showed 100% homology to each other. The phylogenetic tree showed that the sequences reported in coyotes are grouped in different clades and that the sequence of the VP2 gene of CPV-2c from coyote pups is grouped in a different monophyletic group. Conclusion(s): Information suggests that wild coyotes may not only act as asymptomatic reservoir hosts but may also be clinically affected by PVC-2c. It is necessary to carry out studies to know the effects of the genetic subtypes of PVC-2 in the population of coyotes and other wild canids of northeastern Mexico.Copyright © 2023