Aged ; Depression ; Follow-Up Studies ; Humans ; Korea ; Longitudinal Studies ; Odds Ratio ; Surveys and Questionnaires ; Weights and Measures

OBJECTIVE: To assess the effectiveness of Internet of Things (IoT)-based behavioral intervention for reducing depressive symptom of older adults with major depressive disorder. METHODS: A 12-week randomized cross-over controlled study was conducted at community mental health center. We recruited 39 participants with major depressive disorder aged 60 years or older. As a multidomain intervention, four evidence-based therapeutic factors (physical activity, healthy diet, social activity, and emotional regulation) were approached. To maintain motivation of participants, we applied contingency management using IoT device based on operant conditioning theory. RESULTS: The primary outcome was change of depressive symptom measured by Montgomery-Asberg Depression Rating Scale (MADRS). Mixed-effect model compared the effectiveness of intervention and usual care management (intervention by time and period interaction, p=0.017). And during the study period consisting of a total of visit 8, significant group difference was shown in post hoc test at visit 4 (MADRS score of intervention group : MADRS score of control group=7.7±3.4 : 21.1±11.5, p=0.008). CONCLUSION: Community-implementable IoT-based behavioral intervention resulted in greater reduction of depressive symptom of elderly with major depressive disorder.
Adult ; Aged ; Conditioning, Operant ; Depression ; Depressive Disorder, Major ; Diet ; Humans ; Internet ; Mental Health ; Motivation

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Brain-MR studies are sensitive to intracranial ischemia and vascular flow. However, brain MR study for brain death is clinically limited because keeping the ventilation is difficult during study. In our hospital, three 'brain death patients' brain-MR studies were performed under the anesthetic Mapleson's circuit-F system. Three patients' clinical states were not changed after the studies. We confirmed that brain herniation, absent intracranial flow void, no intracranial contrast enhancement, poor gray/white matter differentiation, and prominent nasal enhancement findings. The value of brain-MR study for brain death may be possible.
Brain ; Brain Death ; Ischemia ; Ventilation

PURPOSE: The Korean society has moved rapidly toward becoming a multicultural society. This study aimed to estimate the seroprevalence of hepatitis viruses and investigate hepatitis B virus (HBV) genotypic diversity in female marriage immigrants. MATERIALS AND METHODS: Screening program was conducted at support centers for multicultural families in 21 administrative districts in Korea between July 2011 and January 2017. A total of 963 female marriage immigrants were included in this study. Blood samples were tested for hepatitis viral markers and HBV genotype. RESULTS: Subjects' median age was 33 years (20–40 years), and they originated from nine countries including Vietnam (n=422, 43.8%), China (n=311, 32.3%), the Philippines (n=85, 8.8%), Cambodia (n=58, 6.0%), and Japan (n=39, 4.0%). About 30% (n=288) of subjects required hepatitis A vaccination. HBsAg positive rate was 5.4% (n=52). Positive HBsAg results were the highest in subjects from Southeast Asia (6.6%, n=38). Anti-HBs positive rate was 60.4% (n=582). About 34% (n=329) of subjects who were negative for anti-HBs and HBsAg required HBV vaccinations. Genotypes B and C were found in 54.6% (n=12) and 45.4% (n=10) of the 22 subjects with HBV, in whom genotypes were tested. Eight (0.8%) subjects were positive for anti-HCV. Positive anti-HCV results were the highest in subjects from Central Asia (7.9%, n=3). CONCLUSION: Testing for hepatitis viral marker (hepatitis A virus IgG and HBsAg/anti-HBs) is needed for female marriage immigrants. Especially, HBV genotype B is different from genotype C of Koreans. Therefore, interest and attention to vaccination programs for female marriage immigrants are necessary for both clinicians and public health institutes.
Academies and Institutes ; Asia ; Asia, Southeastern ; Biomarkers ; Cambodia ; China ; Emigrants and Immigrants* ; Female* ; Genotype* ; Hepatitis A ; Hepatitis B Surface Antigens ; Hepatitis B virus ; Hepatitis B* ; Hepatitis Viruses* ; Hepatitis* ; Humans ; Immunoglobulin G ; Japan ; Korea* ; Marriage* ; Mass Screening ; Philippines ; Prevalence ; Public Health ; Seroepidemiologic Studies ; Vaccination ; Vietnam

PURPOSE: Bleaching agents containing sodium hypochlorite are widely used at home to bleach laundry and to disinfect hard surfaces. A retrospective study, with a literature review, was conducted to focus attention on the clinical outcome after accidental or intentional ingestion of sodium hypochlorite. METHODS: The medical records of 67 patients presented to the Inha University emergency department for sodium hypochlorite ingestion between June 1996 and July 2003 were retrospectively examined. RESULTS: The Mean volume of the bleach in the 56 patients who ingested the bleach in a suicide attempt was significantly larger than that of the 11 patients with accidental ingestion (P=0.001). Nausea and vomiting was present in 79% of the patients. The volume of ingestion in patients with vomiting was significantly larger than that in patients without vomiting (P=0.001). Patients with epigastric pain ingested larger volumes of bleach compared to patients without the pain (P=0.01). Endoscopic examinations were performed in seven patient, and normal findings were seen in three patients. Grade 1 caustic injury was observed in two patients, and Grade 2 injuries in the rest. CONCLUSION: The solution of the sodium hypochlorite may cause mild symptoms and seems to be safe after ingestion. However, careful evaluation with endoscopy and hospital admission should be considered if there are symptoms or signs suggesting caustic injury of the esophagus and/or stomach or if the ingested volume is large.
Bleaching Agents ; Caustics ; Eating ; Emergency Service, Hospital ; Endoscopy ; Esophagus ; Family Characteristics* ; Humans ; Medical Records ; Nausea ; Poisons ; Retrospective Studies* ; Sodium Hypochlorite* ; Sodium* ; Stomach ; Suicide ; Vomiting

BACKGROUND/AIMS: The insulin-like growth factor (IGF) system has been implicated in tumor growth, invasion, and metastasis. However, reports on the IGF-1 receptor (IGF-1R) based on radioimmunoassays are conflicting, and its prognostic implications in non-small-cell lung cancer (NSCLC) are still controversial. METHODS: Seventy-one paraffin-embedded tissue sections from stage I NSCLC patients were stained using a mouse monoclonal antibody against human IGF-1R. RESULTS: The intensity and frequency of IGF-1R expression on the membrane and cytoplasm of cancer cells was evaluated and scored using a semiquantitative system. IGF-1R expression was detected in nine of 71 (12.7%) cases. No significant relationship was found between clinical/histopathological parameters and IGF-1R expression. None of the patients whose tumor expressed IGF-1R had experienced distant metastasis or cancer-related death, although the difference did not reach statistical significance. CONCLUSIONS: We conclude that IGF-1R expression may not be a major prognostic factor for stage I NSCLC.
Adult ; Aged ; Aged, 80 and over ; Animals ; Carcinoma, Non-Small-Cell Lung/*immunology/mortality/pathology ; Female ; Humans ; Immunohistochemistry ; Insulin-Like Growth Factor I/*biosynthesis ; Male ; Mice ; Middle Aged ; Neoplasm Metastasis ; Neoplasm Staging ; Prognosis ; Receptor, IGF Type 1/*biosynthesis