Poxviridae is a family of large, complex, enveloped, and double-stranded DNA viruses. The members of this family are ubiquitous and well known to cause contagious diseases in humans and other types of animals as well. Taxonomically, the poxviridae family is classified into two subfamilies, namely Chordopoxvirinae (affecting vertebrates) and Entomopoxvirinae (affecting insects). The members of the Chordopoxvirinae subfamily are further divided into 18 genera based on the genome architecture and evolutionary relationship. Of these 18 genera, four genera, namely Molluscipoxvirus, Orthopoxvirus, Parapoxvirus, and Yatapoxvirus, are known for infecting humans. Some of the popular members of poxviridae are variola virus, vaccine virus, Mpox (formerly known as monkeypox), cowpox, etc. There is still a pressing demand for the development of effective vaccines against poxviruses. Integrated immunoinformatics and artificial-intelligence (AI)-based methods have emerged as important approaches to design multi-epitope vaccines against contagious emerging infectious diseases. Despite significant progress in immunoinformatics and AI-based techniques, limited methods are available to predict the epitopes. In this study, we have proposed a unique method to predict the potential antigens and T-cell epitopes for multiple poxviruses. With PoxiPred, we developed an AI-based tool that was trained and tested with the antigens and epitopes of poxviruses. Our tool was able to locate 3191 antigen proteins from 25 distinct poxviruses. From these antigenic proteins, PoxiPred redundantly located up to five epitopes per protein, resulting in 16,817 potential T-cell epitopes which were mostly (i.e., 92%) predicted as being reactive to CD8+ T-cells. PoxiPred is able to, on a single run, identify antigens and T-cell epitopes for poxviruses with one single input, i.e., the proteome file of any poxvirus.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

During infectious disease outbreaks, acquiring genetic data across various kingdoms offers essential information to tailor precise treatment methodologies and bolster clinical, epidemiological, and public health awareness. Metagenomics sequencing has paved the way for personalized treatment approaches and streamlined the monitoring process for both co-infections and opportunistic infections. In this study, we conducted long-read metagenomic DNA sequencing on mpox-like lesion pustules from six suspected patients who were positive and confirmed to be infected with MPXV during the MPXV subclade Ib outbreak in the Eastern Democratic Republic of the Congo. The sequenced data were taxonomically classified as bacterial, fungal, and viral in composition. Our results show a wide spectrum of microorganisms present in the lesions. Bacteria such as Corynebacterium amycolatum, Gardnerella vaginalis, Enterococcus faecium, Enterobacter clocae, Staphylococcus epidermidis, and Stenotrophomonas maltophilia were found in the lesions. The viral classification of the reads pointed out the absolute predominance of the monkeypox virus. Taken together, the outcomes of this investigation underscore the potential involvement of microorganisms in mpox lesions and the possible role that co-infections played in exacerbating disease severity and transmission during the MPXV subclade Ib outbreak.

The ongoing second-largest Ebola outbreak in the Democratic Republic of the Congo (DRC) was caused by contagious Bundibugyo virus (BDBV). So far, seven provinces of the DRC including Bas-Uele, Haut-Uele, Ituri, Nord-Kivu (North Kivu), Sud-Kivu (South Kivu), Sud-Ubangi (South Ubangi), and Tshopo provinces have confirmed cases of BDBV. Here, we report the clinical characteristics of the provincial index case of BDBV 2026 infection in South Kivu, DRC, and provide information about contacts who tested positive for BDBV. Collected clinical specimens tested with Polymerase Chain Reactions confirmed BDBV in the infected index case and its connections.

The ongoing second-largest Ebola outbreak in the Democratic Republic of the Congo (DRC) is caused by contagious Bundibugyo virus (BDBV). So far, seven provinces of DRC including Bas-Uele, Haut-Uele, Ituri, Nord-Kivu (North Kivu), Sud-Kivu (South Kivu), Sud-Ubangi (South Ubangi), and Tshopo provinces have confirmed the cases of the BDBV. Here, we report the clinical characterises of the first index case of BDBV 2026 infection in South Kivu, DRC and provide information about its connections those also later found positive with the Ebola. Collected clinical specimens tested with Polymerase Chain Reactions confirmed the BDBV in the infected index case and its connections.

Background: Poxviruses constitute a family of large dsDNA viruses that can infect a plethora of species including humans. Historically, poxviruses have caused a health burden in multiple outbreaks. The large genome of poxviruses favors reverse vaccinology approaches that can determine potential antigens and epitopes. Here, we propose the modeling of a user-friendly database containing the predicted antigens and epitopes of a large cohort of poxvirus proteomes using the existing PoxiPred method for reverse vaccinology of poxviruses. Methods: In the present study, we obtained the whole proteomes of as many as 37 distinct poxviruses. We utilized each proteome to predict both antigenic proteins and T-cell epitopes of poxviruses with the aid of an Artificial Intelligence method, namely the PoxiPred method. Results: In total, we predicted 3966 proteins as potential antigen targets. Of note, we considered that this protein may exist in a set of proteoforms. Subsets of these proteins constituted a comprehensive repository of 54,291 linear T-cell epitopes. We combined the outcome of the predictions in the format of a web tool that delivers a database of antigens and epitopes of poxviruses. We also developed a comprehensive repository dedicated to providing access to end-users to obtain AI-based screened antigens and T-cell epitopes of poxviruses in a user-friendly manner. These antigens and epitopes can be utilized to design experiments for the development of effective vaccines against a plethora of poxviruses. Conclusions: The TCEPVDB repository, already deployed to the web under an open-source coding philosophy, is free to use, does not require any login, does not store any information from its users.