Background: Severe fever with thrombocytopenia syndrome virus (SFTSV) is a lethal threat.Increasing Severe fever with thrombocytopenia syndrome (SFTS) risk in Asia and the United States stems from the spread of natural host, Haemaphysalis longicornis. Rapid and accurate SFTSV molecular diagnosis is crucial for treatment decisions, reducing fatality risk. Methods: Blood samples from 17 suspected SFTS patients at Chuncheon Sacred Heart Hospital (September-December 2022) were collected. SFTSV was diagnosed using two reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays from Gangwon Institute of Health and Environment (RT-qPCR/GIHE) and Asan Medical Center (RT-qPCR/AMC). To address RT-qPCR disparities, amplicon-based MinION sequencing traced SFTSV genomic sequences in clinical samples. Results: In two samples (N39 and N50), SFTSV was detected in both RT-qPCR/GIHE and RTqPCR/AMC. Among 11 samples, RT-qPCR/AMC exclusively detected SFTSV. In four samples, both assays yielded negative results. Amplicon-based MinION sequencing enabled nearly whole-genome sequencing of SFTSV in samples N39 and N50. Among 11 discordant samples, five contained significant SFTSV reads, aligning with the RT-qPCR/AMC findings. However, another six samples showed insufficient viral reads in accordance with the negativity observed in RT-qPCR/GIHE. The phylogenetic pattern of SFTSV demonstrated N39 formed a genetic lineage with genotype A in all segments. SFTSV N50 grouped with the B-1 sub-genotype for L segment and B-2 sub-genotype for the M and S segments, indicating genetic reassortment. Conclusion The study demonstrates the robust sensitivity of amplicon-based MinION sequencing for the direct detection of SFTSV in clinical samples containing ultralow copies of viral genomes. Next-generation sequencing holds potential in resolving SFTSV diagnosis discrepancies, enhancing understanding of diagnostic capacity, and risk assessment for emerging SFTSV.

Background: Severe fever with thrombocytopenia syndrome virus (SFTSV) is a lethal threat.Increasing Severe fever with thrombocytopenia syndrome (SFTS) risk in Asia and the United States stems from the spread of natural host, Haemaphysalis longicornis. Rapid and accurate SFTSV molecular diagnosis is crucial for treatment decisions, reducing fatality risk. Methods: Blood samples from 17 suspected SFTS patients at Chuncheon Sacred Heart Hospital (September-December 2022) were collected. SFTSV was diagnosed using two reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays from Gangwon Institute of Health and Environment (RT-qPCR/GIHE) and Asan Medical Center (RT-qPCR/AMC). To address RT-qPCR disparities, amplicon-based MinION sequencing traced SFTSV genomic sequences in clinical samples. Results: In two samples (N39 and N50), SFTSV was detected in both RT-qPCR/GIHE and RTqPCR/AMC. Among 11 samples, RT-qPCR/AMC exclusively detected SFTSV. In four samples, both assays yielded negative results. Amplicon-based MinION sequencing enabled nearly whole-genome sequencing of SFTSV in samples N39 and N50. Among 11 discordant samples, five contained significant SFTSV reads, aligning with the RT-qPCR/AMC findings. However, another six samples showed insufficient viral reads in accordance with the negativity observed in RT-qPCR/GIHE. The phylogenetic pattern of SFTSV demonstrated N39 formed a genetic lineage with genotype A in all segments. SFTSV N50 grouped with the B-1 sub-genotype for L segment and B-2 sub-genotype for the M and S segments, indicating genetic reassortment. Conclusion The study demonstrates the robust sensitivity of amplicon-based MinION sequencing for the direct detection of SFTSV in clinical samples containing ultralow copies of viral genomes. Next-generation sequencing holds potential in resolving SFTSV diagnosis discrepancies, enhancing understanding of diagnostic capacity, and risk assessment for emerging SFTSV.

Background: Severe fever with thrombocytopenia syndrome virus (SFTSV) is a lethal threat.Increasing Severe fever with thrombocytopenia syndrome (SFTS) risk in Asia and the United States stems from the spread of natural host, Haemaphysalis longicornis. Rapid and accurate SFTSV molecular diagnosis is crucial for treatment decisions, reducing fatality risk. Methods: Blood samples from 17 suspected SFTS patients at Chuncheon Sacred Heart Hospital (September-December 2022) were collected. SFTSV was diagnosed using two reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays from Gangwon Institute of Health and Environment (RT-qPCR/GIHE) and Asan Medical Center (RT-qPCR/AMC). To address RT-qPCR disparities, amplicon-based MinION sequencing traced SFTSV genomic sequences in clinical samples. Results: In two samples (N39 and N50), SFTSV was detected in both RT-qPCR/GIHE and RTqPCR/AMC. Among 11 samples, RT-qPCR/AMC exclusively detected SFTSV. In four samples, both assays yielded negative results. Amplicon-based MinION sequencing enabled nearly whole-genome sequencing of SFTSV in samples N39 and N50. Among 11 discordant samples, five contained significant SFTSV reads, aligning with the RT-qPCR/AMC findings. However, another six samples showed insufficient viral reads in accordance with the negativity observed in RT-qPCR/GIHE. The phylogenetic pattern of SFTSV demonstrated N39 formed a genetic lineage with genotype A in all segments. SFTSV N50 grouped with the B-1 sub-genotype for L segment and B-2 sub-genotype for the M and S segments, indicating genetic reassortment. Conclusion The study demonstrates the robust sensitivity of amplicon-based MinION sequencing for the direct detection of SFTSV in clinical samples containing ultralow copies of viral genomes. Next-generation sequencing holds potential in resolving SFTSV diagnosis discrepancies, enhancing understanding of diagnostic capacity, and risk assessment for emerging SFTSV.

Background: Severe fever with thrombocytopenia syndrome virus (SFTSV) is a lethal threat.Increasing Severe fever with thrombocytopenia syndrome (SFTS) risk in Asia and the United States stems from the spread of natural host, Haemaphysalis longicornis. Rapid and accurate SFTSV molecular diagnosis is crucial for treatment decisions, reducing fatality risk. Methods: Blood samples from 17 suspected SFTS patients at Chuncheon Sacred Heart Hospital (September-December 2022) were collected. SFTSV was diagnosed using two reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays from Gangwon Institute of Health and Environment (RT-qPCR/GIHE) and Asan Medical Center (RT-qPCR/AMC). To address RT-qPCR disparities, amplicon-based MinION sequencing traced SFTSV genomic sequences in clinical samples. Results: In two samples (N39 and N50), SFTSV was detected in both RT-qPCR/GIHE and RTqPCR/AMC. Among 11 samples, RT-qPCR/AMC exclusively detected SFTSV. In four samples, both assays yielded negative results. Amplicon-based MinION sequencing enabled nearly whole-genome sequencing of SFTSV in samples N39 and N50. Among 11 discordant samples, five contained significant SFTSV reads, aligning with the RT-qPCR/AMC findings. However, another six samples showed insufficient viral reads in accordance with the negativity observed in RT-qPCR/GIHE. The phylogenetic pattern of SFTSV demonstrated N39 formed a genetic lineage with genotype A in all segments. SFTSV N50 grouped with the B-1 sub-genotype for L segment and B-2 sub-genotype for the M and S segments, indicating genetic reassortment. Conclusion The study demonstrates the robust sensitivity of amplicon-based MinION sequencing for the direct detection of SFTSV in clinical samples containing ultralow copies of viral genomes. Next-generation sequencing holds potential in resolving SFTSV diagnosis discrepancies, enhancing understanding of diagnostic capacity, and risk assessment for emerging SFTSV.

Objectives: This study examined the changes in the characteristics of high-risk suicide groups in South Korea before and after the COVID-19 pandemic using the Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS) cohort. Methods: The K-COMPASS is a longitudinal cohort study that started in 2015. The participants included suicide attempters and individuals with suicidal ideation from various hospitals and mental health centers in South Korea. This study compared the sociodemographic and psychiatric characteristics of 800 participants from the first cohort (2015–2019) with 511 participants from the second and third cohorts (2019–2024). Data were collected through structured interviews and validated scales. Results: The second and third cohort participants were younger, had a higher proportion of females, and exhibited more severe psychiatric symptoms and higher suicidal risk than the first cohort. The prevalence of physical illnesses decreased, while the use of psychiatric medications and the severity of mental health issues increased. In addition, significant sociodemographic changes were observed, such as higher educational levels and urban residency. Conclusion Significant shifts in the characteristics of high-risk suicide groups were observed during the COVID-19 pandemic, highlighting the need for targeted mental health interventions focusing on younger individuals and females to prevent suicide in high-risk groups.

Objectives: This study aimed to identify trends in avoidable mortality (AVM) in 16 provincial and metropolitan regions of Korea and determine the factors influencing AVM. Methods: First, the avoidable mortality rate (AVMR) was calculated using the Statistics Korea cause-of-death and population data by age and region from 2013 to 2022. Second, a health determinants model was built, and we identified the factors influencing AVM using generalized estimating equations analysis. Results: Although the AVMR per 100 000 people displayed a steadily decreasing trend from 2013 to 2020, it began to increase in 2021. Meanwhile, Jeonnam, Jeonbuk, Gyeongnam, Gyeongbuk, Chungnam, Chungbuk, and Gangwon Provinces showed a higher AVMR than the national average. The analysis revealed that each 1-unit increase in the older adult population, smoking, perceived stress, or non-local medical utilization was associated with an increase in the AVMR. Conversely, 1-unit increases in the male-to-female ratio, marriage rate, positive self-rated health, local medical utilization, doctor population, influenza vaccination rate, cancer screening rate, or financial independence were associated with decrease in the AVMR. Conclusions This study established that the AVMR, which had been continuously decreasing across the 16 regions, shifted to an increasing trend in 2021. We also identified several factors influencing AVM. Further studies are needed to confirm the reasons for this shift in the AVMR and explore the factors that influence AVM across Korea’s 16 provincial and metropolitan regions.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.