Objective: To improve our understanding of EPEC, this study focused on analyzing and comparing the genomic characteristics of EPEC isolates from humans and companion animals in Korea. Methods: The whole genome of 26 EPEC isolates from patients with diarrhea and 20 EPEC isolates from companion animals in Korea were sequenced using the Illumina HiSeq X (Illumina, USA) and Oxford Nanopore MinION (Oxford Nanopore Technologies, UK) platforms. Results: Most isolates were atypical EPEC, and did not harbor the bfpA gene. The most prevalent virulence genes were found to be ompT (humans: 61.5%; companion animals:60.0%) followed by lpfA (humans: 46.2%; companion animals: 60.0%). Although pangenome analyses showed no apparent correlation among the origin of the strains, virulence profiles, and antimicrobial resistance profiles, isolates included in clade A obtained from both humans and companion animals exhibited high similarity. Additionally, all the isolates included in clade A encoded the ompT gene and did not encode the hlyE gene. The two isolates from companion animals harbored an incomplete bundle-forming pilus region encoding bfpA and bfpB. Moreover, the type IV secretion system-associated genes tra and trb were found in the bfpA-encoding isolates from humans. Conclusions and Relevance: Whole-genome sequencing enabled a more accurate analysis of the phylogenetic structure of EPEC and provided better insights into the understanding of EPEC epidemiology and pathogenicity.

Objective: To improve our understanding of EPEC, this study focused on analyzing and comparing the genomic characteristics of EPEC isolates from humans and companion animals in Korea. Methods: The whole genome of 26 EPEC isolates from patients with diarrhea and 20 EPEC isolates from companion animals in Korea were sequenced using the Illumina HiSeq X (Illumina, USA) and Oxford Nanopore MinION (Oxford Nanopore Technologies, UK) platforms. Results: Most isolates were atypical EPEC, and did not harbor the bfpA gene. The most prevalent virulence genes were found to be ompT (humans: 61.5%; companion animals:60.0%) followed by lpfA (humans: 46.2%; companion animals: 60.0%). Although pangenome analyses showed no apparent correlation among the origin of the strains, virulence profiles, and antimicrobial resistance profiles, isolates included in clade A obtained from both humans and companion animals exhibited high similarity. Additionally, all the isolates included in clade A encoded the ompT gene and did not encode the hlyE gene. The two isolates from companion animals harbored an incomplete bundle-forming pilus region encoding bfpA and bfpB. Moreover, the type IV secretion system-associated genes tra and trb were found in the bfpA-encoding isolates from humans. Conclusions and Relevance: Whole-genome sequencing enabled a more accurate analysis of the phylogenetic structure of EPEC and provided better insights into the understanding of EPEC epidemiology and pathogenicity.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Objective: To improve our understanding of EPEC, this study focused on analyzing and comparing the genomic characteristics of EPEC isolates from humans and companion animals in Korea. Methods: The whole genome of 26 EPEC isolates from patients with diarrhea and 20 EPEC isolates from companion animals in Korea were sequenced using the Illumina HiSeq X (Illumina, USA) and Oxford Nanopore MinION (Oxford Nanopore Technologies, UK) platforms. Results: Most isolates were atypical EPEC, and did not harbor the bfpA gene. The most prevalent virulence genes were found to be ompT (humans: 61.5%; companion animals:60.0%) followed by lpfA (humans: 46.2%; companion animals: 60.0%). Although pangenome analyses showed no apparent correlation among the origin of the strains, virulence profiles, and antimicrobial resistance profiles, isolates included in clade A obtained from both humans and companion animals exhibited high similarity. Additionally, all the isolates included in clade A encoded the ompT gene and did not encode the hlyE gene. The two isolates from companion animals harbored an incomplete bundle-forming pilus region encoding bfpA and bfpB. Moreover, the type IV secretion system-associated genes tra and trb were found in the bfpA-encoding isolates from humans. Conclusions and Relevance: Whole-genome sequencing enabled a more accurate analysis of the phylogenetic structure of EPEC and provided better insights into the understanding of EPEC epidemiology and pathogenicity.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Objective: To improve our understanding of EPEC, this study focused on analyzing and comparing the genomic characteristics of EPEC isolates from humans and companion animals in Korea. Methods: The whole genome of 26 EPEC isolates from patients with diarrhea and 20 EPEC isolates from companion animals in Korea were sequenced using the Illumina HiSeq X (Illumina, USA) and Oxford Nanopore MinION (Oxford Nanopore Technologies, UK) platforms. Results: Most isolates were atypical EPEC, and did not harbor the bfpA gene. The most prevalent virulence genes were found to be ompT (humans: 61.5%; companion animals:60.0%) followed by lpfA (humans: 46.2%; companion animals: 60.0%). Although pangenome analyses showed no apparent correlation among the origin of the strains, virulence profiles, and antimicrobial resistance profiles, isolates included in clade A obtained from both humans and companion animals exhibited high similarity. Additionally, all the isolates included in clade A encoded the ompT gene and did not encode the hlyE gene. The two isolates from companion animals harbored an incomplete bundle-forming pilus region encoding bfpA and bfpB. Moreover, the type IV secretion system-associated genes tra and trb were found in the bfpA-encoding isolates from humans. Conclusions and Relevance: Whole-genome sequencing enabled a more accurate analysis of the phylogenetic structure of EPEC and provided better insights into the understanding of EPEC epidemiology and pathogenicity.