tetano
Editor, Senior Moderator
Sci Rep
. 2025 Jul 8;15(1):24342.
doi: 10.1038/s41598-025-09154-0. Predictive models of influenza A virus lethal disease yield insights from ferret respiratory tract and brain tissues
Troy J Kieran[SUP] 1 [/SUP], Xiangjie Sun[SUP] 2 [/SUP], Taronna R Maines[SUP] 2 [/SUP], Jessica A Belser[SUP] 3 [/SUP]
Affiliations
Collection of systemic tissues from influenza A virus (IAV)-infected ferrets at a fixed timepoint post-inoculation represents a frequent component of risk assessment activities to assess the capacity of IAV to replicate systemically. However, few studies have evaluated how the frequency and magnitude of IAV replication at discrete tissues contribute to within-host phenotypic outcomes, limiting our ability to fully contextualize results from scheduled necropsy into risk assessment settings. Employing aggregated data from ferrets inoculated with > 100 unique IAV (both human- and avian-origin viruses, spanning H1, H2, H3, H5, H7, and H9 subtypes), we examined relationships between infectious virus detection in four discrete tissue types (nasal turbinate, lung, brain, and olfactory bulb [BnOB]) to clinical outcomes of IAV-inoculated ferrets, and the utility of including these discrete tissue data as features in machine learning (ML) models. We found that addition of viral tissue titer data maintained high performance metrics of a predictive lethality classification ML model with or without inclusion of serially-collected virological and clinical data. Interestingly, infectious virus in BnOB was detected at higher frequency and magnitude among IAV associated with high pathogenicity phenotypes in ferrets, more so than tissues from the respiratory tract; in agreement, BnOB was the highest relative ranked individual tissue specimen in predictive classification models. This study highlights the potential role of BnOB viral titers in assessing IAV pathogenicity in ferrets, and highlights the role ML approaches can contribute towards understanding the predictive benefit of in vivo-generated data in the context of pandemic risk assessment.
. 2025 Jul 8;15(1):24342.
doi: 10.1038/s41598-025-09154-0. Predictive models of influenza A virus lethal disease yield insights from ferret respiratory tract and brain tissues
Troy J Kieran[SUP] 1 [/SUP], Xiangjie Sun[SUP] 2 [/SUP], Taronna R Maines[SUP] 2 [/SUP], Jessica A Belser[SUP] 3 [/SUP]
Affiliations
- PMID: 40624216
- DOI: 10.1038/s41598-025-09154-0
Collection of systemic tissues from influenza A virus (IAV)-infected ferrets at a fixed timepoint post-inoculation represents a frequent component of risk assessment activities to assess the capacity of IAV to replicate systemically. However, few studies have evaluated how the frequency and magnitude of IAV replication at discrete tissues contribute to within-host phenotypic outcomes, limiting our ability to fully contextualize results from scheduled necropsy into risk assessment settings. Employing aggregated data from ferrets inoculated with > 100 unique IAV (both human- and avian-origin viruses, spanning H1, H2, H3, H5, H7, and H9 subtypes), we examined relationships between infectious virus detection in four discrete tissue types (nasal turbinate, lung, brain, and olfactory bulb [BnOB]) to clinical outcomes of IAV-inoculated ferrets, and the utility of including these discrete tissue data as features in machine learning (ML) models. We found that addition of viral tissue titer data maintained high performance metrics of a predictive lethality classification ML model with or without inclusion of serially-collected virological and clinical data. Interestingly, infectious virus in BnOB was detected at higher frequency and magnitude among IAV associated with high pathogenicity phenotypes in ferrets, more so than tissues from the respiratory tract; in agreement, BnOB was the highest relative ranked individual tissue specimen in predictive classification models. This study highlights the potential role of BnOB viral titers in assessing IAV pathogenicity in ferrets, and highlights the role ML approaches can contribute towards understanding the predictive benefit of in vivo-generated data in the context of pandemic risk assessment.