1.Incidence and Temporal Dynamics of Combined Infections in SARS-CoV-2-Infected Patients With Risk Factors for Severe Complications
Sin Young HAM ; Seungjae LEE ; Min-Kyung KIM ; Jaehyun JEON ; Eunyoung LEE ; Subin KIM ; Jae-Phil CHOI ; Hee-Chang JANG ; Sang-Won PARK
Journal of Korean Medical Science 2025;40(11):e38-
Background:
Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines.
Methods:
We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed.
Results:
A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%).
Conclusion
Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.
2.Incidence and Temporal Dynamics of Combined Infections in SARS-CoV-2-Infected Patients With Risk Factors for Severe Complications
Sin Young HAM ; Seungjae LEE ; Min-Kyung KIM ; Jaehyun JEON ; Eunyoung LEE ; Subin KIM ; Jae-Phil CHOI ; Hee-Chang JANG ; Sang-Won PARK
Journal of Korean Medical Science 2025;40(11):e38-
Background:
Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines.
Methods:
We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed.
Results:
A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%).
Conclusion
Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.
3.Incidence and Temporal Dynamics of Combined Infections in SARS-CoV-2-Infected Patients With Risk Factors for Severe Complications
Sin Young HAM ; Seungjae LEE ; Min-Kyung KIM ; Jaehyun JEON ; Eunyoung LEE ; Subin KIM ; Jae-Phil CHOI ; Hee-Chang JANG ; Sang-Won PARK
Journal of Korean Medical Science 2025;40(11):e38-
Background:
Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines.
Methods:
We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed.
Results:
A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%).
Conclusion
Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.
4.Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study
Heekyoung SONG ; Hong Yeon LEE ; Shin Ah OH ; Jaehyun SEONG ; Soo Young HUR ; Youn Jin CHOI
Cancer Research and Treatment 2025;57(2):547-557
Purpose:
We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL).
Materials and Methods:
Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates.
Results:
Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination.
Conclusion
In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.
5.Incidence and Temporal Dynamics of Combined Infections in SARS-CoV-2-Infected Patients With Risk Factors for Severe Complications
Sin Young HAM ; Seungjae LEE ; Min-Kyung KIM ; Jaehyun JEON ; Eunyoung LEE ; Subin KIM ; Jae-Phil CHOI ; Hee-Chang JANG ; Sang-Won PARK
Journal of Korean Medical Science 2025;40(11):e38-
Background:
Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease that needs further clinical investigation. Characterizing the temporal pattern of combined infections in patients with COVID-19 may help clinicians understand the clinical nature of this disease and provide valuable diagnostic and therapeutic guidelines.
Methods:
We retrospectively analyzed COVID-19 patients isolated in four study hospitals in Korea for one year period from May 2021 to April 2022 when the delta and omicron variants were dominant. The temporal characteristics of combined infections based on specific diagnostic tests were analyzed.
Results:
A total of 16,967 COVID-19 patients were screened, 2,432 (14.3%) of whom underwent diagnostic microbiologic tests according to the clinical decision-making, 195 of whom had positive test results, and 0.55% (94/16,967) of whom were ultimately considered to have clinically meaningful combined infections. The median duration for the diagnosis of combined infections was 15 (interquartile range [IQR], 5–25) days after admission. The proportion of community-acquired coinfections (≤ 2 days after admission) was 11.7% (11/94), which included bacteremia (10/94, 10.63%) and tuberculosis (1/94, 1.06%). Combined infections after 2 days of admission were diagnosed at median 16 (IQR, 9–26) days, and included bacteremia (72.3%), fungemia (19.3%), cytomegalovirus (CMV) diseases (8.4%), Pneumocystis jerovecii pneumonia (PJP, 8.4%) and invasive pulmonary aspergillosis (IPA, 4.8%).
Conclusion
Among COVID-19 patients with risk factors for severe complications, 0.55% had laboratory-confirmed combined infections, which included community and nosocomial pathogens in addition to unusual pathogens such as CMV disease, PJP and IPA.
6.Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study
Heekyoung SONG ; Hong Yeon LEE ; Shin Ah OH ; Jaehyun SEONG ; Soo Young HUR ; Youn Jin CHOI
Cancer Research and Treatment 2025;57(2):547-557
Purpose:
We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL).
Materials and Methods:
Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates.
Results:
Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination.
Conclusion
In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.
7.Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study
Heekyoung SONG ; Hong Yeon LEE ; Shin Ah OH ; Jaehyun SEONG ; Soo Young HUR ; Youn Jin CHOI
Cancer Research and Treatment 2025;57(2):547-557
Purpose:
We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL).
Materials and Methods:
Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates.
Results:
Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination.
Conclusion
In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.
8.Clinical Features of Mpox Patients in Korea: A Multicenter Retrospective Study
So Yun LIM ; Hyeon Jae JO ; Su-Yeon LEE ; Miyoung AHN ; Yeonjae KIM ; Jaehyun JEON ; Eu Suk KIM ; BumSik CHIN ; Jae-Phil CHOI ; Nam Joong KIM
Journal of Korean Medical Science 2024;39(4):e19-
Background:
Mpox is a viral illness with a characteristic skin rash caused by the monkeypox virus. In 2022, Mpox spread throughout the world, and an epidemic through domestic transmission started in South Korea in early 2023. This study aimed to summarize the clinical features of Mpox patients in South Korea.
Methods:
This is a multicenter retrospective study conducted at four hospitals in South Korea. All adult patients diagnosed with Mpox who were admitted to the study hospitals between June 1, 2022 and May 26, 2023 and were discharged by June 30, 2023 were reviewed.
Results:
Sixty patients were included, accounting for 65.9% of Mpox cases reported in South Korea during the study period. Median age was 32 years and 97% (58/60) of patients were male. In total, 85% (51/60) of patients reported their sexual orientation as homosexual or bisexual. The most common route of transmission was sexual or close contact (55/60). Every patient had a skin rash and 88% (53/60) had constitutional symptoms. In total, 42% (25/60) of patients had human immunodeficiency virus and 25% (15/60) had concomitant sexually transmitted infections. Severe manifestations of Mpox were identified in only two patients.
Conclusion
Mpox patients in South Korea were mainly young adult males and were infected through sexual contact. The clinical outcomes were favorable.
9.Genomic Analysis of Monkeypox Virus During the 2023 Epidemic in Korea
Chi-Hwan CHOI ; Minji LEE ; Sang Eun LEE ; Jin-Won KIM ; Hwachul SHIN ; Myung-Min CHOI ; Hwajung YI ; Min-Kyung KIM ; Jaehyun JEON ; Jun-Sun PARK ; Yeonjae KIM ; So Yun LIM ; BumSik CHIN ; Yoon-Seok CHUNG
Journal of Korean Medical Science 2024;39(18):e165-
We aimed to characterize the genomes of monkeypox virus isolates from the Far East, providing insights into viral transmission and evolution. Genomic analysis was conducted on 8 isolates obtained from patients with monkeypox virus disease in the Republic of Korea between May 2022 and early 2023. These isolates were classified into Clade IIb. Distinct lineages, including B.1.1, A.2.1, and B.1.3, were observed in 2022 and 2023 isolates, with only the B.1.3 lineage detected in six isolates of 2023. These genetic features were specific to Far East isolates (the Republic of Korea, Japan, and Taiwan), distinguishing them from the diverse lineages found in the Americas, Europe, Africa, and Oceania. In early 2023, the prevalence of the B.1.3 lineage of monkeypox virus identified in six patients with no overseas travel history is considered as an indicator of the potential initiation of local transmission in the Republic of Korea.
10.Aplastic Anemia, Mental Retardation, and Dwarfism Syndrome Associated with Aldh2 and Adh5 Mutations
Bomi LIM ; Anna CHO ; Jaehyun KIM ; Sang Mee HWANG ; Soo Yeon KIM ; Jong-Hee CHAE ; Hyoung Soo CHOI
Clinical Pediatric Hematology-Oncology 2024;31(2):52-55
Aplastic anemia, mental retardation, and dwarfism (AMeD) syndrome, also known as aldehyde degradation deficiency (ADD) syndrome, is an autosomal recessive disorder caused by mutations in the ALDH2 and ADH5 genes, leading to decreased activity of the aldehyde dehydrogenase 2 (ALDH2) and alcohol dehydrogenase 5 (ADH5) enzymes, subsequently triggering enhanced cellular levels of formaldehyde and diverse multisystem manifestations. Herein, we present the case of a 7-year-old girl with AMeD syndrome, characterized by pancytopenia, developmental delay, microcephaly, epilepsy, and myelodysplastic syndrome. Whole-exome sequencing revealed compound heterozygous variants (c.832G>C and c.678delA) in the ADH5 gene and a heterozygous pathogenic variant (c.1510G>A) in the ALDH2 gene. This case underscores the complexity of AMeD syndrome, emphasizing the importance of genetic testing to ensure diagnosis and aid in the development of potential targeted therapeutic approaches.

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