Infectivity assays are essential for the development of viral vaccines, antiviral therapies, and the manufacture of biologicals. Traditionally, these assays take 2-7 days and require several manual processing steps after infection. We describe an automated viral infectivity assay (AVIA^TM), using convolutional neural networks (CNNs) and high-throughput brightfield microscopy on 96-well plates that can quantify infection phenotypes within hours, before they are manually visible, and without sample preparation. CNN models were trained on HIV, influenza A virus, coronavirus 229E, vaccinia viruses, poliovirus, and adenoviruses, which together span the four major categories of virus (DNA, RNA, enveloped, and non-enveloped). A sigmoidal function, fit between virus dilution curves and CNN predictions, results in sensitivity ranges comparable to or better than conventional plaque or TCID₅₀ assays, and a precision of ∼10%, which is considerably better than conventional infectivity assays. Because this technology is based on sensitizing CNNs to specific phenotypes of infection, it has potential as a rapid, broad-spectrum tool for virus characterization, and potentially identification.

As sequencing costs decrease, short-read and long-read technologies are indispensable tools for uncovering the genetic drivers behind bacterial pathogen resistance. This study explores the differences between the use of short-read (Illumina) and long-read (Oxford Nanopore Technologies [ONT]) sequencing in detecting antimicrobial resistance (AMR) genes in ESKAPE pathogens (ie, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae). Utilizing a dataset of 1385 whole genome sequences and applying commonly used bioinformatic methods in bacterial genomics, we assessed the differences in genomic completeness, pangenome structure, and AMR gene and point mutation identification. Illumina presented higher genome completeness, while ONT identified a broader pangenome. Hybrid assembly outperformed both Illumina and ONT at identifying key AMR genetic determinants, presented results closer to Illumina's completeness, and revealed ONT-like pangenomic content. Notably, Illumina consistently detected more AMR-related point mutations than its counterparts. This highlights the importance of method selection based on research goals, particularly when using publicly available data ranging a wide timespan. Differences were also observed for specific gene classes and bacterial species, underscoring the need for a nuanced understanding of technology limitations. Overall, this study reveals the strengths and limitations of each approach, advocating for the use of Illumina for common AMR analysis, ONT for studying complex genomes and novel species, and hybrid assembly for a more comprehensive characterization, leveraging the benefits of both technologies.

Porcine Reproductive and Respiratory Syndrome (PRRS) is the one of the most devastating diseases impacting the swine industry worldwide. Control and prevention methods rely on biosafety measures and vaccination. As an RNA virus with a high rate of mutation, vaccines are only partially effective against circulating and newly emerging strains. To reduce the burden of this disease, research on alternative control methods is needed. Here, we assess the in vitro antiviral effect of a novel platelet-rich plasma-derived biologic termed BIO-PLY^TM (for the BIOactive fraction of Platelet-rich plasma LYsate) from both swine and equine origin. Our results show that BIO-PLY^TM significantly reduces the amount of PRRSV viral load determined by RT-qPCR and the number of infectious viral particles measured by TCID50 in infected porcine alveolar and parenchymal macrophages. This study also showed limited toxicity of BIO-PLY^TM in vitro and aspects of its immunomodulatory capacity evaluating the regulation of reactive oxygen species and cytokines production in infected cells. Finally, this study presents promising data on the effect of BIO-PLY^TM on other RNA viruses such as human A influenza viruses and coronavirus.

Candidozyma auris (formerly known as Candida auris) (C auris) is a multidrug-resistant fungal pathogen that has emerged as a significant threat to global health. Shifts in climatic conditions may be driving its adaptation and pathogenicity. Its increased ability to tolerate higher temperatures has been suggested as the first adaptation led by anthropogenic climate change in a pathogenic organism. In this study, we analyzed 801 whole-genome sequences isolated in clinical settings from the New York-New Jersey region from 2016 to 2024. Using Bayesian hierarchical logistic regression models, we identified previously described antifungal resistance genes, their associated point mutations, heat tolerance genes, and their link with key climatic variables using mixed-effects logistic regression models. Our analysis revealed that the heat tolerance genes HSP90 and HSP104 were present in >98% of isolates. Among the antifungal resistance-related genes, several showed significant associations with climatic variables, particularly with precipitation and temperature. Elevated precipitation was consistently linked to increased prevalence in antifungal resistance genes and their associated point mutations, suggesting that elevated moisture levels may promote favorable conditions for fungal growth and biofilm formation. Additionally, the interaction between climatic variables showed a stronger association with the presence of resistance genes, evidencing the multifactorial nature of climate change in shaping pathogen adaptations. These findings emphasize the influence of climatic variables on the resistome of C auris, which is crucial for predicting the spread and resistance patterns of C auris as climate change continues.