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Infectious bronchitis virus (IBV) has evolved through various mutation mechanisms, including antigenic drift and recombination. Four genotypic lineages of IBVs including GI-15, GI-16, GI-19, and GVI-1 have been reported in Korea. In this study, we isolated two IBVs from chicken farms, designated IBV/Korea/289/2019 (K289/19) and IBV/Korea/163/2021 (K163/21), which are two distinct natural recombinant viruses most likely produced by genetic reassortment between the S1 gene of K40/09 strain (GI-19 lineage) and IBV/Korea/48/2020 (GI-15 lineage) in co-infected commercial chickens. Comparative sequence analysis of hypervariable regions (HVRs) revealed that the K289/19 virus had similar HVR I and II with the K40/09 virus (100% and 99.2% nucleotide sequence identity, respectively), and HVR III with the IBV/Korea/48/2020 virus (100% nucleotide sequence identity). In contrast, the K163/21 virus had HVR I and II similar to the IBV/Korea/48/2020 virus (99.1% and 99.3% nucleotide sequence identity, respectively), and HVR III to the K40/09 virus (96.6% nucleotide sequence identity). The K289/19 virus exhibited similar histopathologic lesions, tissue tropism in trachea and kidney, and antigenicity with the parental K40/09 virus. The K163/21 exhibited similar pathogenicity and tissue tropism with the K40/09 virus, which were similar results with the isolate K289/19. However, it showed a lower antigenic relatedness with both parental strains, exhibiting R-value of 25 and 42, respectively. The continued emergence of the novel reassortant IBVs suggests that multiple recombination events have occurred between different genotypes within Korea. These results suggest that antigenic profiles could be altered through natural recombination in the field, complicating the antigenic match of vaccine strains to field strains. Enhanced surveillance and research into the characteristics of newly emerging IBVs should be carried out to establish effective countermeasures.

Interictal FDG-PET (iPET) is a core tool for localizing the epileptogenic focus, potentially before structural MRI, that does not require rare and transient epileptiform discharges or seizures on EEG. The visual interpretation of iPET is challenging and requires years of epilepsy-specific expertise. We have developed an automated computer-aided diagnostic (CAD) tool that has the potential to work both independent of and synergistically with expert analysis. Our tool operates on distributed metabolic changes across the whole brain measured by iPET to both diagnose and lateralize temporal lobe epilepsy. When diagnosing left temporal lobe epilepsy (LTLE) or right TLE (RTLE) versus non-epileptic seizures (NES), our accuracy in reproducing the results of the gold standard long term video-EEG monitoring was 82% (95% confidence interval [CI] 69-90%) or 88% (95% CI 76-94%), respectively. The classifier that both diagnosed and lateralized the disease had overall accuracy of 76% (95% CI 66-84%), where 89% (95% CI 77-96%) of patients correctly identified with epilepsy were correctly lateralized. When identifying LTLE, our CAD tool utilized metabolic changes across the entire brain. By contrast, only temporal regions and the right frontal lobe cortex, were needed to identify RTLE accurately, a finding consistent with clinical observations and indicative of a potential pathophysiological difference between RTLE and LTLE. The goal of CADs is to complement—not replace—expert analysis. In our dataset, the accuracy of manual analysis of iPET (~80%) was similar to CAD. The square correlation between our CAD tool and manual analysis, however, was only 30%, indicating that our CAD tool does not recreate manual analysis. The addition of clinical information to our CAD, however, did not substantively change performance. These results suggest that automated analysis might provide clinically valuable information to focus treatment more effectively.