Avian influenza A viruses pose ongoing threats to human and animal health, with H7 subtypes causing outbreaks globally. In Mexico, highly pathogenic H7N3 viruses have circulated in poultry since 2012, causing sporadic human infections. Here we analyzed genetic markers in hemagglutinin sequences from Mexican H7N3 isolates and conducted serological assays on human populations with poultry exposure. Our results show conserved avian-like receptor binding sites, thus limiting human adaptation, alongside antigenic drift and acquisition of glycosylation sites likely driven by vaccination. Serological testing of 1103 individuals revealed no detectable antibodies against H7N3, indicating a naïve population. Phylogenetic analyses revealed multiple virus clades circulating regionally. These findings suggest that while current H7N3 viruses have limited capacity for sustained human transmission, the lack of population immunity underscores the importance of continued surveillance and risk assessment to mitigate potential pandemic threats.

Viruses can induce various mitochondrial morphological changes, which are associated with the type of immune response. Therefore, characterization and analysis of mitochondrial ultrastructural changes could provide insights into the kind of immune response elicited, especially when compared to uninfected cells. However, this analysis is highly time-consuming and susceptible to observer bias. This work presents the development of a deep learning-based approach for the automatic identification, segmentation, and analysis of mitochondria from thin-section transmission electron microscopy images of cells infected with two SARS-CoV-2 variants or the Zika virus, utilizing a convolutional neural network with a U-Net architecture. A comparison between manual and automatic segmentations, along with morphological metrics, was performed, yielding an accuracy greater than 85% with no statistically significant differences between the manual and automatic metrics. This approach significantly reduces processing time and enables a prediction of the immune response to viral infections by allowing the detection of both intact and damaged mitochondria. Therefore, the proposed deep learning-based tool may represent a significant advancement in the study and understanding of cellular responses to emerging pathogens. Additionally, its applicability could be extended to the analysis of other organelles, thereby opening up new opportunities for automated studies in cell biology.

In the original publication [1], Figure 1b was previously published by the authors in the PLoS One, but the citation (reference 14) was inadvertently omitted