Background: The persistent coronavirus disease 2019 (COVID-19) pandemic has had direct and indirect effects on mortality, making it essential to analyze excess mortality to fully understand the impact of the pandemic. In this study, we constructed a mathematical model using number of deaths from Statistics Korea and analyzed excess mortality between 2020 and 2022 according to age, sex, and dominant severe acute respiratory syndrome coronavirus 2 variant period. Methods: Number of all-cause deaths between 2010 and 2022 were obtained from the annual cause-of-death statistics provided by Statistics Korea. COVID-19 mortality data were acquired from the Korea Disease Control and Prevention Agency. A multivariate linear regression model with seasonal effect, stratified by sex and age, was used to estimate the number of deaths in the absence of COVID-19. The estimated excess mortality rate was calculated. Results: Excess mortality was not significant between January 2020 and October 2021.However, it started to increase monthly from November 2021 and reached its highest point during the omicron-dominant period. Specifically, in March and April 2022, during the omicron BA.1/BA.2-dominant period, the estimated median values for excess mortality were the highest at 17,634 and 11,379, respectively. Both COVID-19-related deaths and excess mortality increased with age. A notable increase in excess mortality was observed in individuals aged ≥ 65 years. In the context of excess mortality per 100,000 population based on the estimated median values in March 2022, the highest numbers were found among males and females aged ≥ 85 years at 1,048 and 910, respectively. Conclusion This study revealed that the prolonged COVID-19 pandemic coupled with its high transmissibility not only increased COVID-19-related deaths but also had a significant impact on overall mortality rates, especially in the elderly. Therefore, it is crucial to concentrate healthcare resources and services on the elderly and ensure continued access to healthcare services during pandemics. Establishing an excess mortality monitoring system in the early stages of a pandemic is necessary to understand the impact of infectious diseases on mortality and effectively evaluate pandemic response policies.

Background: The persistent coronavirus disease 2019 (COVID-19) pandemic has had direct and indirect effects on mortality, making it essential to analyze excess mortality to fully understand the impact of the pandemic. In this study, we constructed a mathematical model using number of deaths from Statistics Korea and analyzed excess mortality between 2020 and 2022 according to age, sex, and dominant severe acute respiratory syndrome coronavirus 2 variant period. Methods: Number of all-cause deaths between 2010 and 2022 were obtained from the annual cause-of-death statistics provided by Statistics Korea. COVID-19 mortality data were acquired from the Korea Disease Control and Prevention Agency. A multivariate linear regression model with seasonal effect, stratified by sex and age, was used to estimate the number of deaths in the absence of COVID-19. The estimated excess mortality rate was calculated. Results: Excess mortality was not significant between January 2020 and October 2021.However, it started to increase monthly from November 2021 and reached its highest point during the omicron-dominant period. Specifically, in March and April 2022, during the omicron BA.1/BA.2-dominant period, the estimated median values for excess mortality were the highest at 17,634 and 11,379, respectively. Both COVID-19-related deaths and excess mortality increased with age. A notable increase in excess mortality was observed in individuals aged ≥ 65 years. In the context of excess mortality per 100,000 population based on the estimated median values in March 2022, the highest numbers were found among males and females aged ≥ 85 years at 1,048 and 910, respectively. Conclusion This study revealed that the prolonged COVID-19 pandemic coupled with its high transmissibility not only increased COVID-19-related deaths but also had a significant impact on overall mortality rates, especially in the elderly. Therefore, it is crucial to concentrate healthcare resources and services on the elderly and ensure continued access to healthcare services during pandemics. Establishing an excess mortality monitoring system in the early stages of a pandemic is necessary to understand the impact of infectious diseases on mortality and effectively evaluate pandemic response policies.

Background: The persistent coronavirus disease 2019 (COVID-19) pandemic has had direct and indirect effects on mortality, making it essential to analyze excess mortality to fully understand the impact of the pandemic. In this study, we constructed a mathematical model using number of deaths from Statistics Korea and analyzed excess mortality between 2020 and 2022 according to age, sex, and dominant severe acute respiratory syndrome coronavirus 2 variant period. Methods: Number of all-cause deaths between 2010 and 2022 were obtained from the annual cause-of-death statistics provided by Statistics Korea. COVID-19 mortality data were acquired from the Korea Disease Control and Prevention Agency. A multivariate linear regression model with seasonal effect, stratified by sex and age, was used to estimate the number of deaths in the absence of COVID-19. The estimated excess mortality rate was calculated. Results: Excess mortality was not significant between January 2020 and October 2021.However, it started to increase monthly from November 2021 and reached its highest point during the omicron-dominant period. Specifically, in March and April 2022, during the omicron BA.1/BA.2-dominant period, the estimated median values for excess mortality were the highest at 17,634 and 11,379, respectively. Both COVID-19-related deaths and excess mortality increased with age. A notable increase in excess mortality was observed in individuals aged ≥ 65 years. In the context of excess mortality per 100,000 population based on the estimated median values in March 2022, the highest numbers were found among males and females aged ≥ 85 years at 1,048 and 910, respectively. Conclusion This study revealed that the prolonged COVID-19 pandemic coupled with its high transmissibility not only increased COVID-19-related deaths but also had a significant impact on overall mortality rates, especially in the elderly. Therefore, it is crucial to concentrate healthcare resources and services on the elderly and ensure continued access to healthcare services during pandemics. Establishing an excess mortality monitoring system in the early stages of a pandemic is necessary to understand the impact of infectious diseases on mortality and effectively evaluate pandemic response policies.

Background: The persistent coronavirus disease 2019 (COVID-19) pandemic has had direct and indirect effects on mortality, making it essential to analyze excess mortality to fully understand the impact of the pandemic. In this study, we constructed a mathematical model using number of deaths from Statistics Korea and analyzed excess mortality between 2020 and 2022 according to age, sex, and dominant severe acute respiratory syndrome coronavirus 2 variant period. Methods: Number of all-cause deaths between 2010 and 2022 were obtained from the annual cause-of-death statistics provided by Statistics Korea. COVID-19 mortality data were acquired from the Korea Disease Control and Prevention Agency. A multivariate linear regression model with seasonal effect, stratified by sex and age, was used to estimate the number of deaths in the absence of COVID-19. The estimated excess mortality rate was calculated. Results: Excess mortality was not significant between January 2020 and October 2021.However, it started to increase monthly from November 2021 and reached its highest point during the omicron-dominant period. Specifically, in March and April 2022, during the omicron BA.1/BA.2-dominant period, the estimated median values for excess mortality were the highest at 17,634 and 11,379, respectively. Both COVID-19-related deaths and excess mortality increased with age. A notable increase in excess mortality was observed in individuals aged ≥ 65 years. In the context of excess mortality per 100,000 population based on the estimated median values in March 2022, the highest numbers were found among males and females aged ≥ 85 years at 1,048 and 910, respectively. Conclusion This study revealed that the prolonged COVID-19 pandemic coupled with its high transmissibility not only increased COVID-19-related deaths but also had a significant impact on overall mortality rates, especially in the elderly. Therefore, it is crucial to concentrate healthcare resources and services on the elderly and ensure continued access to healthcare services during pandemics. Establishing an excess mortality monitoring system in the early stages of a pandemic is necessary to understand the impact of infectious diseases on mortality and effectively evaluate pandemic response policies.

Purpose: We investigated the feasibility of preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/ computed tomography (CT) radiomics with machine learning to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients. Materials and Methods: Altogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating characteristic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters. Results: The incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively; p=0.490). Logistic regression analysis revealed that the rad_score was an independent predictor of MSI status in the training set. The rad_score performed better than metabolic tumor volume when assessed using the AUROC (0.867 vs. 0.794, p=0.015). Conclusion Our predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters.

Purpose: To examine the refractive errors, retinal manifestations, and genotype in tuberous sclerosis complex (TSC) patients in a Korean population. Materials and Methods: A total of 98 patients with TSC were enrolled in Severance Hospital for a retrospective cohort study. The number of retinal astrocytic hamartoma and retinal achromic patch within a patient, as well as the size, bilaterality, and morphological type were studied. In addition, the refractive status of patients and the comorbidity of intellectual disability and epilepsy were also examined. Results: Retinal astrocytic hamartoma was found in 37 patients, and bilateral invasion was observed in 20 patients (54%). TSC1 mutation was associated with myopia (p=0.01), while TSC2 mutation was associated with emmetropia (p=0.01). Retinal astrocytic hamartoma was categorized into three morphological types and examined as follows: type I (87%), type II (35%), and type III (14%). Single invasion of retinal astrocytic hamartoma was identified in 32% of the patients, and multiple invasions in 68%. The TSC1/ TSC2 detection rate was 91% (41/45). Among them, TSC1 variant was detected in 23 patients (54%), whereas TSC2 variant was detected in 18 patients (40%). The results showed that TSC2 mutations are correlated with a higher rate of retinal astrocytic hamartoma involvement (all p<0.05), and multiple and bilateral involvement of retinal hamartomas (all p<0.05). However, the size of retinal astrocytic hamartomas, comorbidity of epilepsy, or intellectual disability did not show correlation with the genetic variant. Conclusion TSC1 variant patients were more myopic, while TSC2 variant patients showed association with more extensive involvement of retinal astrocytic hamartoma.

Purpose: To compare anterior biometry measurements using placido-scanning-slit topography, rotating Scheimpflug tomography, and swept-source optical coherence tomography. Methods: A retrospective review consisted of 80 eyes of 49 participants who underwent anterior chamber depth (ACD), central corneal thickness (CCT), and keratometry examination on the same day. We used placido-scanning-slit topography (ORBscan II), rotating Scheimpflug tomography (Pentacam HR), and swept-source optical coherence tomography (CASIA SS1000). The intraclass correlation coefficients and Bland-Altman plots were used to evaluate the agreement and differences between measurements. Results: The mean ACD values were 2.88 ± 0.43, 2.82 ± 0.50, and 2.68 ± 0.44 mm; and the mean CCT values were 536.96 ± 31.19, 543.79 ± 31.04, and 561.41 ± 32.60 μm; and the mean keratometry (Km) were 43.81 ± 1.69, 43.81 ± 1.77, and 44.65 ± 1.95 diopters; as measured by CASIA SS-1000, Pentacam HR, and ORBscan II, respectively. Among the three devices, ACD was deepest to shallowest in the order of CASIA SS-1000, Pentacam HR, and ORBscan II (p < 0.05). The CCT was thickest to thinnest in the order of ORBscan II, Pentacam HR, and CASIA SS-1000 (p < 0.05). No significant differences in Km values were examined between CASIA SS-1000 and Pentacam HR, whereas ORBscan II overestimated Km with a statistically significant difference compared to the other two devices. Conclusions High level of agreement was found between CASIA SS-1000 and Pentacam HR for anterior parameters, including ACD, CCT, and Km, suggesting interchangeability. However, ORBscan II measurements differed considerably with the measurements obtained from the other two devices; therefore, it should not be used interchangeably. However, further studies with repeatability test should be considered in order to elucidate the reliability of each device.

Purpose: To evaluate the accuracy of the Kane formula for intraocular lens (IOL) power calculation in comparison with existing formulas by incorporating optional variables into calculation. Materials and Methods: This retrospective review consisted of 78 eyes of patients who had undergone uneventful phacoemulsification with intraocular implantation at Severance Hospital in Seoul, Korea between February 2020 and January 2021. The Kane formula was compared with six of the existing IOL formulas (SRK/T, Hoffer-Q, Haigis, Holladay1, Holladay2, Barrett Universal II) based on the mean absolute error (MAE), median absolute error (MedAE), and the percentages of eyes within prediction errors of ±0.25D, ±0.50D, and ±1.00D. Results: The Barrett Universal II formula demonstrated the lowest MAEs (0.26±0.17D), MedAEs (0.28D), and percentage of eyes within prediction errors of ±0.25D, ± 0.50D, and ±1.00D, although there was no statistically significant difference between Barrett Universal II-SRK/T (p=0.06), and Barrett Universal II-Kane formula (p<0.51). Following the Barrett Universal II formula, the Kane formula demonstrated the second most accurate formula with MAEs (0.30±0.19D) and MedAEs (0.28D). However, no statistical difference was shown between Kane-Barrett Universal II (p=0.51) and Kane-SRK/T (p=0.14). Conclusion Although slightly better refractory outcome was noted in the Barrett Universal II formula, the performance of the Kane formula in refractive prediction was comparable in IOL power calculation, marking its superiority over many conventional IOL formulas, such as HofferQ, Haigis, Holladay1, and Holladay2.

ELISAs and rapid diagnostic tests (RDTs) are widely used for diagnosing dengue virus (DENV) infection. Using 138 single blood samples, we compared the ability to detect non-structural (NS)-1 antigen and anti-DENV IgM/IgG antibodies among (1) DENV Detect NS1 ELISA, DENV Detect IgM capture ELISA and DENV Detect IgG ELISA (InBios International, Inc.); (2) Anti-Dengue virus IgM Human ELISA and Anti-Dengue virus IgG Human ELISA (Abcam); (3) Dengue virus NS1 ELISA, Anti-Dengue virus ELISA (IgM) and Anti-Dengue virus ELISA (IgG) (Euroimmun); (4) Asan Easy Test Dengue NS1 Ag 100 and Asan Easy Test Dengue IgG/IgM (Asan Pharm); (5) SD BIOLINE Dengue Duo (Standard Diagnostics); and (6) Ichroma Dengue NS1 and Ichroma Dengue IgG/IgM (Boditech Med). For NS1 antigen detection, InBios and Euroimmun showed higher sensitivities (100%) than the RDTs (42.9–64.3%). All tests demonstrated variable sensitivities for IgM (38.1–90.5%) and IgG (65.7–100.0%). InBios and Boditech Med demonstrated higher sensitivity (95.6% and 88.2%, respectively) than the other tests for combined NS1 antigen and IgM antibody. Five NS1 antigen tests had good agreement (92.8–98.6%) without showing positivity for chikungunya. However, all IgG tests demonstrated potential false-positivity with variable ranges. Clinical laboratories should note performance variations across tests and potential cross-reactivity.
Antibodies ; Chungcheongnam-do ; Dengue Virus ; Dengue ; Diagnosis ; Diagnostic Tests, Routine ; Enzyme-Linked Immunosorbent Assay ; Humans ; Immunoglobulin G ; Immunoglobulin M