BACKGROUND: Many factors affecting the spread of spinal anesthesia have been investigated. But L3-4 or L4-5 interspace was choosen which was known as the site of buffering, in their study. We investigated the effect of some of these factors on sensory blockade level by using L2-3 interspace. METHODS: Eightyfive patients, ASA physical status I - Il, were involved in our study. Sensory blockade level was checked with pinprick test at 10 minutes and 30 minutes. The effect of age, sex, height, weight, CSF pressure and pressure difference generated when full flexed and non-full flexed lateral position on sensory blockade level was studied whereas other factors such as puncture technique, dosage and concentration of drug and patients position after injection, were kept constant under the same condition. And also studied the effect of degree of flexion at injection on the sensory blockade level. RESULTS: Height and CSF pressure were correlated with sensory blockade level at 10 minutes after injection(R2=0.14, P<0.01). Only height was correlated with sensory blockade level at 30 minutes after injection(R2=0.09, P<0.0l). CONCLUSIONS: Only height was correlated with sensory blokade level at 30 minutes. So, height might be considered as the most impressive minor factor affecting the extent of sensory blockade level.
Anesthesia, Spinal* ; Humans ; Punctures

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

BACKGROUND: Children today are exposed to various media devices, and their usage of these is increasing. Prior studies have outlined forms of harm this can potentially cause. However, there has been little empirical research on the use of media devices among preschool children in Asia. The aim of this study was to examine and analyze longitudinal trends in media device use among Korean preschool children, focusing on the frequency of engagement, time spent with, and ownership of media devices, delineated by sex. METHODS: Four hundred parents of children aged 2–5 years were invited to enroll. The baseline assessment, Wave 1, was conducted between December 2015 and June 2016, and follow-up assessments, Wave 2 and Wave 3, were conducted annually for the following 2 years. Time of media use, frequency of media use, and ownership of media devices (TV, tablet PCs, and smartphones) were investigated. RESULTS: Ownership of tablet PCs increased significantly between Wave 1 and Wave 3 for boys and girls (corrected P < 0.001). Frequency of media use increased significantly between Wave 1 and Wave 3 only in boys' use of tablet PCs (mean difference 0.8 day/wk). Time of media use increased significantly between Wave 1 and Wave 3 for both sexes in all devices, measured by mean difference on weekdays and weekends (TV by 0.6 and 0.7 hr/day, tablet PCs by 0.6 and 0.8 hr/day, and smartphones by 0.4 and 0.4 hr/day). Children spent more time using media devices during weekends than on weekdays. CONCLUSION: This study observed an increase in the tendency of media device use among preschool children in Korea. The patterns of use indicate that paying attention to the types of devices children use is needed, as well as vigilance on weekends.
Asia ; Child ; Child, Preschool ; Empirical Research ; Female ; Follow-Up Studies ; Humans ; Korea ; Ownership ; Parents ; Smartphone

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.

Multiple simultaneous cranial neuropathies occur rarely in diabetes patients. In general, diabetic cranial neuropathy presents in an isolated form and frequently involves oculomotor or facial nerves. We report a 73-year-old man with known type 2 diabetes mellitus who presented with severe dizziness, diplopia and third, fourth and sixth nerve ophthalmoplegia of both eyes. Radiological, laboratory and ophthalmic work-up including magnetic resonance imaging and angiography (MRI and MRA) revealed no specific tumor, aneurysm, or inflammation findings, except for a previous cerebral infarction and atherosclerotic changes in the internal carotid and vertebral arteries. After strict blood glucose control, the multiple cranial nerve palsies spontaneously resolved in 12 weeks. We report the case with a review of the literature.
Abducens Nerve* ; Aged ; Aneurysm ; Angiography ; Blood Glucose ; Cerebral Infarction ; Cranial Nerve Diseases ; Diabetes Mellitus ; Diabetes Mellitus, Type 2 ; Diabetic Neuropathies* ; Diplopia ; Dizziness ; Facial Nerve ; Humans ; Inflammation ; Magnetic Resonance Imaging ; Ophthalmoplegia ; Vertebral Artery

OBJECTIVE: The object of this study was to compare the cure rate and confirm the clinical efficacy of three most frequent surgical procedures for stress urinary incontinence (Burch colposuspension, pubovaginal sling operation, tension-free vaginal tape). MATERIALS AND METHODS: We collected datas from the records of ninety-one patients who were diagnosed as stress urinary incontinence from Jan. 1999 to May 2001. Burch colposuspension was performed by department of gynecology, Severance hospital in thirty-three patients, pubovaginal sling operation was performed by department of urology in twenty-eight patients, and tension-free vaginal tape was performed by department of urology in thirty-one patients. We investigated the characteristics of patients, preoperative urodynamic study results, cure rates and complication rates for the result, and compared them by x2-test. RESULTS: There were statistically no significant differences between the cure rate of each operation after 3, 6 month of operation but after 12 months of follow up, the cure rate of pubovaginal sling operation was significantly higher than that of Burch operation and tension-free vaginal tape. CONCLUSION: The cure rate of pubovaginal sling operation was significantly higher after 12 months of follow up after surgery. There was no significant difference between cure rates of Burch operation and tension- free vaginal tape. We propose randomized prospective study with larger population in the future.
Female ; Follow-Up Studies ; Gynecology ; Humans ; Suburethral Slings* ; Urinary Incontinence* ; Urodynamics ; Urology

Stem cells can give rise to various cell types and are capable of regenerating themselves over multiple cell divisions. Pluripotency and self-renewal potential of stem cells have drawn vast interest from different disciplines, with studies on the molecular properties of stem cells being one example. Current investigations on the molecular basis of stem cells pluripotency and self-renewal entail traditional techniques from chemistry and molecular biology. In this mini review, we discuss progress in stem cell research that employs proteomics approaches. Specifically, we focus on studies on human stem cells from proteomics perspective. To our best knowledge, only the following types of human stem cells have been examined via proteomics analysis: human neuronal stem cells, human mesenchymal stem cells, and human embryonic stem cells. Protein expression serves as biomarkers of stem cells and identification and expression level of such biomarkers are usually determined using two-dimensional electrophoresis coupled mass spectrometry or non-gel based mass spectrometry.
*Electrophoresis, Gel, Two-Dimensional ; Genetic Techniques ; Humans ; Mass Spectrometry/*methods ; Neurons/*cytology/physiology ; Proteomics/*methods ; Stem Cells/*metabolism