This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

Background: Acinar cell carcinoma (ACC) is a rare malignant epithelial neoplasm, which shares many cytomorphological features with other non-ductal pancreatic neoplasms such as pancreatic neuroendocrine neoplasm (PanNEN) and solid-pseudopapillary neoplasm (SPN). Due to the relative rarity of these tumors, pathologists are less familiar with the cytological features, especially on liquid-based cytology (LBC) which has been relatively recently introduced for endoscopic ultrasound-guided fine needle aspiration specimens. Methods: We evaluated the detailed cytological features of 15 histologically confirmed ACC (7 conventional smears [CS], 8 LBC), and compared them with the LBC features of SPN (n = 9) and PanNEN (n = 9). Results: Compared with CS, LBCs of ACC demonstrated significantly less bloody background. All ACCs demonstrated prominent nucleoli and macronucleoli on LBC. On comparison with the LBC features of SPN and PanNEN, most ACCs demonstrated a necrotic background with apoptotic debris while PanNEN and SPN did not show these features. Acinar structures were predominantly observed in ACC, while frequent pseudopapillary structures were seen only in SPN. Prominent nucleoli and macronucleoli were only seen in ACC. Conclusions ACC had characteristic cytological features that could be observed on LBC preparations, such as high cellularity, necrotic/apoptotic background, nuclear tangles, acinar arrangement of cells, and macronucleoli. These findings also help distinguish ACC from PanNEN and SPN on LBC. It is important to be familiar with these features, as an accurate diagnosis on endoscopic ultrasound–guided fine needle aspiration cytology would have impact on the management of the patient.

Objective: To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs.1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANSmid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

Humans ; Adult ; Cigarette Smoking/epidemiology* ; Prevalence ; Electronic Nicotine Delivery Systems ; Tobacco Products ; Republic of Korea/epidemiology*

Mainly due to the slanted focus on the mechanism and regulation of neuronal aging, research on astrocyte aging and its modulation during brain aging is scarce. In this study, we established aged astrocyte culture model by long-term culturing. Cellular senescence was confirmed through SA-β-gal staining as well as through the examination of morphological, molecular, and functional markers. RNA sequencing and functional analysis of astrocytes were performed to further investigate the detailed characteristics of the aged astrocyte model. Along with aged phenotypes, decreased astrocytic proliferation, migration, mitochondrial energetic function and support for neuronal survival and differentiation has been observed in aged astrocytes. In addition, increased expression of cytokines and chemokine-related factors including plasminogen activator inhibitor -1 (PAI-1) was observed in aged astrocytes. Using the RNA sequencing results, we searched potential drugs that can normalize the dysregulated gene expression pattern observed in long-term cultured aged astrocytes. Among several candidates, minoxidil, a pyrimidine-derived anti-hypertensive and anti-pattern hair loss drug, normalized the increased number of SA-β-gal positive cells and nuclear size in aged astrocytes. In addition, minoxidil restored up-regulated activity of PAI-1 and increased mitochondrial superoxide production in aged astrocytes.We concluded that long term culture of astrocytes can be used as a reliable model for the study of astrocyte senescence and minoxidil can be a plausible candidate for the regulation of brain aging.

Objectives: Relatively few studies have assessed risk factors for coronavirus disease 2019 (COVID-19) in public facilities used by children and adolescents. This study presents an analysis of a COVID-19 outbreak that occurred in a taekwondo gym in Korea, predominantly among children and adolescents, with the aim of providing insights on managing COVID-19 outbreaks in similar facilities. Methods: All 108 taekwondo gym students and staff received COVID-19 tests. A survey and closed-circuit television analyses were used to identify risk factors. A univariate analysis was conducted, followed by multivariate logistic regression analysis with backward elimination for variables with a significance level <0.10 in the univariate analysis. Results: COVID-19 was confirmed in 30 of 108 subjects at the taekwondo gym (attack rate, 27.8%). The outbreak started in an adult class student. This student transmitted the virus to the staff, who consequently transmitted the virus to adolescent students. In the univariate analysis, the relative risk for younger age (≤9 years) was 2.14 (95% confidence interval [CI], 1.01–4.54; p=0.054), and that for food consumption inside the gym was 2.12 (95% CI, 1.04–4.30; p=0.048). In the multivariate logistic regression analysis, the odds ratio for younger age was 2.96 (95% CI, 1.07–8.20; p=0.036), and that for food consumption inside the gym was 3.00 (95% CI, 1.10–8.17; p=0.032). Conclusion Food consumption inside the facility and young age were significant risk factors for COVID-19 transmission in this taekwondo gym. Food consumption should be prohibited in sports facilities, and infection prevention education for young students is also required.

Objective: To investigate demographic and skeletodental characteristics of one-jaw (1J-OGS) and two-jaw orthognathic surgery (2J-OGS) in patients with skeletal Class III malocclusion. Methods: 750 skeletal Class III patients who underwent OGS at 10 university hospitals in Korea between 2015 and 2019 were investigated; after dividing them into the 1J-OGS (n = 186) and 2J-OGS groups (n = 564), demographic and skeletodental characteristics were statistically analyzed. Results: 2J-OGS was more frequently performed than 1J-OGS (75.2 vs. 24.8%), despite regional differences (capital area vs. provinces, 86.6 vs. 30.7%, p < 0.001). Males outnumbered females, and their mean operation age was older in both groups. Regarding dental patterns, the most frequent maxillary arch length discrepancy (ALD) was crowding in the 1J-OGS group (52.7%, p < 0.001) and spacing in the 2J-OGS group (40.4%, p < 0.001). However, the distribution of skeletal pattern was not significantly different between the two groups (all p > 0.05). The most prevalent skeletal patterns in both groups were hyper-divergent pattern (50.0 and 54.4%, respectively) and left-side chin point deviation (both 49.5%).Maxillary spacing (odds ratio [OR], 3.645; p < 0.001) increased the probability of 2J-OGS, while maxillary crowding (OR, 0.672; p < 0.05) and normo-divergent pattern (OR, 0.615; p < 0.05) decreased the probability of 2J-OGS. Conclusions In both groups, males outnumbered females, and their mean operation age was older. The most frequent ALD was crowding in the 1J-OGS group, and spacing in the 2J-OGS group, while skeletal characteristics were not significantly different between the two groups.

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.