The SARS-CoV-2 Omicron (B.1.1.529) variant of concern emerged in South Africa late in 2021 and rapidly spread across the world causing a significant increase in the number of infections. Importantly, this variant was also associated with an increased risk of reinfections.
Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a “fingerprint” for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening—faster and more accurate—with improved cost-effectiveness when compared to existing technologies.
Influenza A viruses (IAV-S) belonging to the H1 subtype are endemic in swine worldwide. Antigenic drift and antigenic shift lead to a substantial antigenic diversity in circulating IAV-S strains. As a result, the most commonly used vaccines based on whole inactivated viruses (WIVs) provide low protection against divergent H1 strains due to the mismatch between the vaccine virus strain and the circulating one. Here, a consensus coding sequence of the full-length of HA from H1 subtype was generated in silico after alignment of the sequences from IAV-S isolates obtained from public databases and was delivered to pigs using the Orf virus (ORFV) vector platform. The immunogenicity and protective efficacy of the resulting ORFVΔ121conH1 recombinant virus were evaluated against divergent IAV-S strains in piglets. Virus shedding after intranasal/intratracheal challenge with two IAV-S strains was assessed by real-time RT-PCR and virus titration. Viral genome copies and infectious virus load were reduced in nasal secretions of immunized animals. Flow cytometry analysis showed that the frequency of T helper/memory cells, as well as cytotoxic T lymphocytes (CTLs), were significantly higher in the peripheral blood mononuclear cells (PBMCs) of the vaccinated groups compared to unvaccinated animals when they were challenged with a pandemic strain of IAV H1N1 (CA/09). Interestingly, the percentage of T cells was higher in the bronchoalveolar lavage of vaccinated animals in relation to unvaccinated animals in the groups challenged with a H1N1 from the gamma clade (OH/07). In summary, delivery of the consensus HA from the H1 IAV-S subtype by the parapoxvirus ORFV vector decreased shedding of infectious virus and viral load of IAV-S in nasal secretions and induced cellular protective immunity against divergent influenza viruses in swine.
Omicron (B.1.1.529) is the most recent SARS-CoV-2 variant of concern (VOC), which emerged in late 2021 and rapidly achieved global predominance in early 2022. In this study, we compared the infection dynamics, tissue tropism and pathogenesis and pathogenicity of SARS-CoV-2 D614G (B.1), Delta (B.1.617.2) and Omicron BA.1.1 sublineage (B.1.1.529) variants in a highly susceptible feline model of infection. While D614G- and Delta-inoculated cats became lethargic, and showed increased body temperatures between days 1 and 3 post-infection (pi), Omicron-inoculated cats remained subclinical and, similar to control animals, gained weight throughout the 14-day experimental period. Intranasal inoculation of cats with D614G- and the Delta variants resulted in high infectious virus shedding in nasal secretions (up to 6.3 log10 TCID50.ml-1), whereas strikingly lower level of viruses shedding (<3.1 log10 TCID50.ml-1) was observed in Omicron-inoculated animals. In addition, tissue distribution of the Omicron variant was markedly reduced in comparison to the D614G and Delta variants, as evidenced by in situ viral RNA detection, in situ immunofluorescence, and quantification of viral loads in tissues on days 3, 5, and 14 pi. Nasal turbinate, trachea, and lung were the main - but not the only - sites of replication for all three viral variants. However, only scarce virus staining and lower viral titers suggest lower levels of viral replication in tissues from Omicron-infected animals. Notably, while D614G- and Delta-inoculated cats had severe pneumonia, histologic examination of the lungs from Omicron-infected cats revealed mild to modest inflammation. Together, these results demonstrate that the Omicron variant BA.1.1 is less pathogenic than D614G and Delta variants in a highly susceptible feline model.
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