Acinetobacter baumannii (AB) has emerged as a major pathogen in vulnerable and severely ill patients. It remains unclear whether early mortality (EM) due to AB bacteremia is because of worse clinical characteristics of the infected patients or the virulence of the pathogen. In this study, we aimed to investigate the effect of AB virulence on EM due to bacteremia. This retrospective study included 138 patients with AB bacteremia (age: ≥ 18 years) who were admitted to a tertiary care teaching hospital in South Korea between 2015 and 2019. EM was defined as death occurring within 7 days of bacteremia onset. The AB clinical isolates obtained from the patients\' blood cultures were injected into 15 Galleria mellonella larvae each, which were incubated for 5 days. Clinical isolates were classified into high- and low-virulence groups based on the number of dead larvae. Patients\' clinical data were combined and subjected to multivariate Cox regression analyses to identify the risk factors for EM. In total, 48/138 (34.8%) patients died within 7 days of bacteremia onset. The Pitt bacteremia score was the only risk factor associated with EM. In conclusion, AB virulence had no independent effect on EM in patients with AB bacteremia.

This study aimed to develop and validate a machine learning (ML) model tailored to the Korean population with type 2 diabetes mellitus (T2DM) to provide a superior method for predicting the development of cardiovascular disease (CVD), a major chronic complication in these patients. We used data from two cohorts, namely the discovery (one hospital; n = 12,809) and validation (two hospitals; n = 2019) cohorts, recruited between 2008 and 2022. The outcome of interest was the presence or absence of CVD at 3 years. We selected various ML-based models with hyperparameter tuning in the discovery cohort and performed area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. CVD was observed in 1238 (10.2%) patients in the discovery cohort. The random forest (RF) model exhibited the best overall performance among the models, with an AUROC of 0.830 (95% confidence interval [CI] 0.818-0.842) in the discovery dataset and 0.722 (95% CI 0.660-0.783) in the validation dataset. Creatinine and glycated hemoglobin levels were the most influential factors in the RF model. This study introduces a pioneering ML-based model for predicting CVD in Korean patients with T2DM, outperforming existing prediction tools and providing a groundbreaking approach for early personalized preventive medicine.

OBJECTIVE: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR).
METHODS: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs.
RESULTS: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs.
CONCLUSIONS: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

BACKGROUND: Globally, over 39% of individuals are obese. Metabolic syndrome, usually accompanied by obesity, is regarded as a major contributor to noncommunicable diseases. Given this relationship, the concepts of metabolically healthy and unhealthy obesity, considering metabolic status, have been evolving. Attention is being directed to metabolically healthy people with obesity who have relatively low transition rates to noncommunicable diseases. As obesity rates continue to rise and unhealthy behaviors prevail among young adults, there is a growing need for obesity management that considers these metabolic statuses. A nomogram can be used as an effective tool to predict the risk of transitioning to metabolically unhealthy obesity from a metabolically healthy status.
OBJECTIVE: The study aimed to identify demographic factors, health behaviors, and 5 metabolic statuses related to the transition from metabolically healthy obesity to unhealthy obesity among people aged between 20 and 44 years and to develop a screening tool to predict this transition.
METHODS: This secondary analysis study used national health data from the National Health Insurance System in South Korea. We analyzed the customized data using SAS (SAS Institute Inc) and conducted logistic regression to identify factors related to the transition from metabolically healthy to unhealthy obesity. A nomogram was developed to predict the transition using the identified factors.
RESULTS: Among 3,351,989 people, there was a significant association between the transition from metabolically healthy to unhealthy obesity and general characteristics, health behaviors, and metabolic components. Male participants showed a 1.30 higher odds ratio for transitioning to metabolically unhealthy obesity than female participants, and people in the lowest economic status were also at risk for the transition (odds ratio 1.08, 95% CI 1.05-1.1). Smoking status, consuming >30 g of alcohol, and insufficient regular exercise were negatively associated with the transition. Each relevant variable was assigned a point value. When the nomogram total points reached 295, the shift from metabolically healthy to unhealthy obesity had a prediction rate of >50%.
CONCLUSIONS: This study identified key factors for young adults transitioning from healthy to unhealthy obesity, creating a predictive nomogram. This nomogram, including triglycerides, waist circumference, high-density lipoprotein-cholesterol, blood pressure, and fasting glucose, allows easy assessment of obesity risk even for the general population. This tool simplifies predictions amid rising obesity rates and interventions.

Schizophrenia (SPR) is the most devastating mental illness that causes severe deterioration in social and occupational functioning, but, the etiology remains unknown. The objective of this study is to explore the genetic underpinnings of novelty seeking behavior in schizophrenic family within the Korean population. By conducting a family-based genome-wide association study, we aim to identify potential genetic markers and variations associated with novelty seeking traits in the context of SPR. We have recruited 27 probands (with SPR) with their parents and siblings whenever possible. DNA was extracted from blood sampling of 58 individuals in 27 families and analyzed in an Illumina core exome single nucleotide polymorphism (SNP) array. A family-based association test (qFAM) was used to derive SNP association values across all chromosomes. Although none of the final 800,000 SNPs reached the genome-wide significant threshold of 8.45 × 10-7, the most significant 4 SNPs were within the 10-5 to 10-7. This study identifies genetic associations between novelty seeking behavior and SPR within families. RAPGEF5 emerges as a significant gene, along with other neuropsychiatric-related genes. Noteworthy genes like DRD4 and COMT did not show associations, possibly due to the focus on schizophrenic family. While shedding light on this complex relationship, larger studies are needed for robust conclusions and deeper mechanistic insights.

Old age is associated with a higher risk of dementia. Psychosocial characteristics frequently affect cognitive function; however, the exact mechanism underlying the effect of psychosocial factors on cognitive function is unclear. Therefore, this study aimed to investigate the effects of psychosocial characteristics on cognitive function. The participants comprised 4809 middle-aged and older (years 50+) adults. The analysis used data from the Korean Longitudinal Study of Aging from 2014 to 2018. The effects of neighborhood interaction, depression, life satisfaction, and economic activity on cognitive function were examined, and a linear mixed model analysis was performed to assess the change in cognitive function by year. A statistically significant association was found between neighborhood interaction and time. Additionally, cognitive function decreased in the presence of depression and with time. In men, significant interactions were found between depression and time and between economic activity and time. In women, significant interactions were found between life satisfaction and time. The findings indicate that since active neighborhood interaction positively affects cognitive function, it is necessary to develop various community-wide social activity programs for middle-aged and older adults. As depression is a risk factor for cognitive impairment, it is crucial to prevent cognitive decline through continuous management of depression. Given the positive effects of economic activity on cognitive function in men, it is essential to expand infrastructure to sustain economic activity by developing educational programs and creating job opportunities for middle-aged and older men.

Different antiviral treatments for chronic hepatitis B (CHB) have been known to have different metabolic effects. This study aimed to reveal whether tenofovir alafenamide (TAF)-induced dyslipidemia and its associated outcomes are significant. This study utilized 15-year historical cohort including patients with CHB in Korea and consisted of two parts: the single-antiviral and switch-antiviral cohorts. In the single-antiviral cohort, patients were divided into four groups (entecavir [ETV]-only, tenofovir disoproxil fumarate [TDF]-only, TAF-only, and non-antiviral). Propensity score matching (PSM) and linear regression model were sequentially applied to compare metabolic profiles and estimated atherosclerotic cardiovascular disease (ASCVD) risks longitudinally. In the switch-antiviral cohort, pairwise analyses were conducted in patients who switched NAs to TAF or from TAF. In the single-antiviral cohort, body weight and statin use showed significant differences between groups before PSM, but well-balanced after PSM. Changes in total cholesterol were significantly different between groups (-2.57 mg/dL/year in the TDF-only group and +2.88 mg/dL/year in the TAF-only group; p = 0.002 and p = 0.02, respectively). In the TDF-only group, HDL cholesterol decreased as well (-0.55 mg/dL/year; p < 0.001). The TAF-only group had the greatest increase in ASCVD risk, followed by the TDF-only group and the non-antiviral group. In the switch-antiviral cohort, patients who switched from TDF to TAF had a higher total cholesterol after switching (+9.4 mg/dL/year) than before switching (-1.0 mg/dL/year; p = 0.047). Sensitivity analysis on data with an observation period set to a maximum of 3 years for NA treatment showed consistent results on total cholesterol (-2.96 mg/dL/year in the TDF-only group and +3.09 mg/dL/year in the TAF-only group; p = 0.001 and p = 0.005, respectively). Another sensitivity analysis conducted on statin-treated patients revealed no significant change in cholesterol and ASCVD risk. TAF was associated with increased total cholesterol, whereas TDF was associated with decreased total and HDL cholesterol. Both TAF and TDF were associated with increased ASCVD risks, and statin use might mitigate these risks.

The gastrointestinal (GI) tract of shrimp, which is comprised of the stomach, hepatopancreas, and intestine, houses microbial communities that play crucial roles in immune defense, nutrient absorption, and overall health. While the intestine\'s microbiome has been well-studied, there has been limited research investigating the stomach and hepatopancreas. The present study addresses this gap by profiling the bacterial community in these interconnected GI segments of Pacific whiteleg shrimp. To this end, shrimp samples were collected from a local aquaculture farm in South Korea, and 16S rRNA gene amplicon sequencing was performed. The results revealed significant variations in bacterial diversity and composition among GI segments. The stomach and hepatopancreas exhibited higher Proteobacteria abundance, while the intestine showed a more diverse microbiome, including Cyanobacteria, Actinobacteria, Bacteroidetes, Firmicutes, Chloroflexi, and Verrucomicrobia. Genera such as Oceaniovalibus, Streptococcus, Actibacter, Ilumatobacter, and Litorilinea dominated the intestine, while Salinarimonas, Sphingomonas, and Oceaniovalibus prevailed in the stomach and hepatopancreas. It is particularly notable that Salinarimonas, which is associated with nitrate reduction and pollutant degradation, was prominent in the hepatopancreas. Overall, this study provides insights into the microbial ecology of the Pacific whiteleg shrimp\'s GI tract, thus enhancing our understanding of shrimp health with the aim of supporting sustainable aquaculture practices.

Previous studies have reported associations between newly diagnosed diabetes and poor outcomes after percutaneous coronary intervention (PCI), but there is limited data focusing on elderly patients (age ≥ 65). This study aimed to analyze the prevalence and clinical implications of newly diagnosed diabetes in elderly patients who underwent PCI. From 2004 to 2021, a total of 2456 elderly patients who underwent invasive PCI at Korea University Guro Hospital were prospectively enrolled and followed up for a median of five years. The primary endpoint was five-year major adverse cardiovascular events (MACE). Cox regression was used to evaluate whether newly diagnosed diabetes impacted on long-term clinical outcomes. Newly diagnosed diabetes was presented in approximately 8.1% to 10.9% of elderly patients who underwent PCI. Those who had a new diagnosis of diabetes had a higher risk of MACE than previously known diabetes (25.28% vs. 19.15%, p = 0.039). After adjusting for significant factors, newly diagnosed diabetes remained an independent predictor of MACE (HR [hazard ratio] 1.64, 95% confidence interval [CI] 1.24-2.17, p < 0.001), cardiac death (HR 2.15, 95% CI 1.29-3.59, p = 0.003) and repeat revascularization (HR 1.52, 95% CI 1.09-2.11, p = 0.013), but not for non-fatal myocardial infarction (HR 1.66, 95% CI 0.94-2.12, p = 0.081). Newly diagnosed diabetes was associated with an increased risk of 5-year MACE compared with non-diabetes and previously diagnosed diabetes in elderly patients underwent PCI. More attention should be given to those elderly newly diagnosed diabetes population.

Moderately elevated albuminuria (30-300 mg/g) is a marker of renal dysfunction and a risk factor of cardiovascular disease. Additionally, several recent studies have reported a relationship between moderately elevated albuminuria and triglyceride (TG) levels. Therefore, we aimed to evaluate the relationship between the urine albumin-to-creatinine ratio (UACR) and total cholesterol (TC), TG, and high-density lipoprotein C (HDL-C) levels. We analyzed data from 19,340 patients from the 2011-2014 and 2019-2020 from the Korea National Health and Nutrition Examination Surveys. Multivariate linear regression analysis showed that the UACR was positively associated with TC and TG levels and negatively associated with HDL-C levels in both Korean women and men. These results were reanalyzed according to the degree of proteinuria (normal, moderately elevated albuminuria, and severely elevated albuminuria (≥ 300 mg/g)). We found a positive relationship between UACR and TC and TG levels, but a negative association with HDL-C levels, except for TC (moderately elevated albuminuria) and HDL-C (moderately elevated albuminuria) in Korean men and TC (severely elevated albuminuria), TG (severely elevated albuminuria), and HDL-C (normal range albuminuria) in Korean women. The correlation between albuminuria and lipid profiles became more evident as albuminuria shift from normal to the severely elevated albuminuria. Thus our multivariate linear regression analysis showed that lipid profiles (TG, TC, and HDL-C levels) were associated with the UACR.