Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.

Background: Impaired executive function is common in older adults. This study examined the causal relationship between the use of information and communication technology (ICT) and executive function in older adults over time.Method: This study performed a secondary analysis of data from four waves (2016–2019) of the National Health and Aging Trends Study. A fixed-effect analysis was conducted to examine the effects of ICT on the executive function of older adults without dementia aged ≥65 years. This study analyzed data from 3,334 respondents. Results: We observed significant positive effects of ICT use on executive function over time (standardized β=0.043–0.045; 95% confidence interval, 0.001–0.043; p<0.05). Conclusion The current findings support the use of ICT as a protective approach to prevent decline in executive function in community-dwelling older adults.

This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.