• Pediatric Infection and Vaccine
• /
• v.30 no.1
• /
• pp.20-32
• /
• 2023

Purpose: This study aimed to identify the etiology and risk factors of community-acquired pneumonia (CAP) requiring hospitalization in Korean children during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Clinical information of children admitted with CAP to Seoul National University Children's Hospital (SNUCH) between January 1, 2021, and February 28, 2022, was retrospectively collected and analyzed. In addition, the etiologic diagnosis and demographic data of children with CAP who were discharged at the other seven hospitals between January and February 2022 were collected. Pneumonia was diagnosed using strict criteria comprising clinical symptoms, physical examination findings, and chest radiographic findings. Results: Among 91 children hospitalized with CAP at SNUCH during the 14-month period, 68.4% were aged <5 years and 79.1% had underlying diseases. Among the 95 CAP cases, respiratory assistance was required in 70.5%, and the use of a ventilator was required in 20.0%. A total of five patients expired, all of whom were either immunocompromised or had underlying neurological diseases. Neurological diseases and immunosuppression were significantly correlated with respiratory assistance (P=0.003) and death (P=0.014). A total of 55% of the detected respiratory pathogens were viruses, the most common of which was rhinovirus at 35.9%. Among the 169 children hospitalized for CAP at the eight institutions, ≥1 respiratory virus was detected in 92.3%, among which respiratory syncytial virus (79.8%) was the most prevalent. Conclusions: Even during the COVID-19 pandemic, Korean children were hospitalized with CAP caused by seasonal respiratory viral pathogens. Although atypical and pyogenic bacteria were not detected, continuous clinical monitoring and further prospective studies should be conducted.

• Journal of the Korean Society of International Agriculture
• /
• v.31 no.4
• /
• pp.378-383
• /
• 2019

To determine whether FT-IR spectral analysis based on multivariate analysis for whole cell extracts can be used to discriminate papaya at metabolic level. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and 1,100-950 cm^-1, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins (1,700-1,500 cm^-1), phosphodiester groups from nucleic acid and phospholipid (1,500-1,300 cm^-1) and carbohydrate compounds (1,100-950 cm^-1). The result of PCA analysis showed that papaya leaves could be separated into clusters depending on different growth temperature. In this case, showed discrimination confirmed according to metabolite content of growth condition from papaya. And PLS-DA analysis also showed more clear discrimination pattern than PCA result. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful papaya cultivars.