This study compared key health indicators of poor, older urban adults attending a free clinic with those of the general older population, using data from the 2023 Korea National Health and Nutrition Examination Survey (KNHANES). Methods: This cross-sectional comparative study included 60 adults aged ≥60 years who attended the Raphael Nanum Homeless Clinic in Seoul. Participants completed a questionnaire, underwent anthropometric assessment, and provided fasting blood samples for measurement of total cholesterol, low density lipoprotein (LDL)-cholesterol, and triglycerides (TG). Obesity, current smoking, monthly alcohol use, poor self-rated health, and strength exercise (≥ 2 days/week) were defined according to 2023 KNHANES criteria and compared with age-matched 2023 KNHANES estimates for adults aged ≥ 60 years using independent t-tests and two-proportion z-tests. Results: Participants were predominantly men (80.0%) with a mean age of 79.9 years; 70.0% reported no regular income, and 46.7% rated their health as poor. Compared with their 2023 KNHANES counterparts, the clinic group had a higher prevalence of obesity (50.0% vs. 35.0%), particularly among men, and a more atherogenic lipid profile characterized by higher LDL-cholesterol despite similar total cholesterol levels and lower TG. The prevalence of current smoking (3.3% vs. 10.6%) and monthly alcohol use (31.7% vs. 53.0%) was significantly lower, whereas participation in strength exercise was low in both groups, with no significant differences observed. Conclusion: Poor, older urban adults exhibited multidimensional health disparities, including obesity, adverse lipid profiles, and markedly poorer self-rated health, despite lower levels of smoking and alcohol consumption. Community-based interventions targeting nutrition, physical activity, and chronic disease management are needed to reduce health inequalities in this vulnerable population.

Background: Peritoneal carcinomatosis is challenging to treat, with poor prognosis. Although pressurized intraperitoneal aerosol chemotherapy (PIPAC) and hyperthermal PIPAC (HPIPAC) aim to improve patients’ quality of life, there is a potential risk of antineoplastic drug exposure to healthcare workers during surgery. This study evaluates the risk of oxaliplatin exposure among healthcare workers during PIPAC and HPIPAC compared to systemic intravenous chemotherapy (IV). Methods: Oxaliplatin was administered to a swine model via IV, PIPAC, and HPIPAC in the operating room. Surface samples (n = 484) were collected from the operating room and personal protective equipment (PPE), while air samples (n = 144) were taken near the respiratory tract. Platinum, as an indicator of oxaliplatin, was measured using inductively coupled plasma mass spectrometry (ICP-MS). Results: Platinum was undetectable in 98.6% of the air samples, except one PIPAC leakage case. Surface contamination occurred in 8.7% of room samples and 13.9% of PPE, with gloves being most affected.Platinum contamination was also found on instruments like endoscope cameras and injectors, and cross-contamination on floors and injectors before PIPAC indicated suboptimal cleaning. No platinum was detected in the experimenter’s blood and urine. Conclusion Airborne platinum exposure was minimal when no drug leakage occurred, but surface contamination, particularly on gloves and instruments, highlighted the need for stringent protective measures to reduce occupational exposure risk. Although the possibility of inhalation exposure was low and there are no legal exposure standards for oxaliplatin, precautions should be taken to avoid potential exposure.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.