Background: Although the presence of both obesity and reduced muscle mass presents a dual metabolic burden and additively has a negative effect on a variety of cardiometabolic parameters, data regarding the associations between their combined effects and left ventricular diastolic function are limited. This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods: In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. SVR was calculated as an index of sarcopenic obesity by dividing the appendicular skeletal muscle mass by the visceral fat area. Results: In the random forest model, age and SVR were the most powerful predictors of LVDD. Multivariate logistic regression analysis demonstrated that older age (adjusted odds ratio [OR], 1.11; 95% confidence interval [CI], 1.07 to 1.15) and lower SVR (adjusted OR, 0.08; 95% CI, 0.01 to 0.57) were independent risk factors for LVDD.SVR showed a significant improvement in predictive performance and fair predictability for LVDD, with the highest area under the curve noted in both men and women, with statistical significance. In non-obese and metabolically healthy individuals, the lowest SVR tertile was associated with a greater risk of LVDD compared to the highest SVR tertile. Conclusion Decreased muscle mass and increased visceral fat were significantly associated with LVDD compared to obesity, body fat composition, and body muscle composition indices.

Background: Although the presence of both obesity and reduced muscle mass presents a dual metabolic burden and additively has a negative effect on a variety of cardiometabolic parameters, data regarding the associations between their combined effects and left ventricular diastolic function are limited. This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods: In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. SVR was calculated as an index of sarcopenic obesity by dividing the appendicular skeletal muscle mass by the visceral fat area. Results: In the random forest model, age and SVR were the most powerful predictors of LVDD. Multivariate logistic regression analysis demonstrated that older age (adjusted odds ratio [OR], 1.11; 95% confidence interval [CI], 1.07 to 1.15) and lower SVR (adjusted OR, 0.08; 95% CI, 0.01 to 0.57) were independent risk factors for LVDD.SVR showed a significant improvement in predictive performance and fair predictability for LVDD, with the highest area under the curve noted in both men and women, with statistical significance. In non-obese and metabolically healthy individuals, the lowest SVR tertile was associated with a greater risk of LVDD compared to the highest SVR tertile. Conclusion Decreased muscle mass and increased visceral fat were significantly associated with LVDD compared to obesity, body fat composition, and body muscle composition indices.

Background: Although the presence of both obesity and reduced muscle mass presents a dual metabolic burden and additively has a negative effect on a variety of cardiometabolic parameters, data regarding the associations between their combined effects and left ventricular diastolic function are limited. This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods: In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. SVR was calculated as an index of sarcopenic obesity by dividing the appendicular skeletal muscle mass by the visceral fat area. Results: In the random forest model, age and SVR were the most powerful predictors of LVDD. Multivariate logistic regression analysis demonstrated that older age (adjusted odds ratio [OR], 1.11; 95% confidence interval [CI], 1.07 to 1.15) and lower SVR (adjusted OR, 0.08; 95% CI, 0.01 to 0.57) were independent risk factors for LVDD.SVR showed a significant improvement in predictive performance and fair predictability for LVDD, with the highest area under the curve noted in both men and women, with statistical significance. In non-obese and metabolically healthy individuals, the lowest SVR tertile was associated with a greater risk of LVDD compared to the highest SVR tertile. Conclusion Decreased muscle mass and increased visceral fat were significantly associated with LVDD compared to obesity, body fat composition, and body muscle composition indices.

Valproic acid (VPA), widely used as an antiepileptic drug, exhibits developmental neurotoxicity when exposure occurs during early or late pregnancy, resulting in various conditions ranging from neural tube defects to autism spectrum disorders. However, toxicity during the very early stages of neural development has not been addressed. Therefore, we investigated the effects of VPA in a model where human pluripotent stem cells differentiate into anterior or posterior neural tissues. Exposure to VPA during the induction of neural stem cells induced different developmental toxic effects in a dose-dependent manner. For instance, VPA induced cell death more profoundly during anteriorly guided neural progenitor induction, while inhibition of cell proliferation and enhanced differentiation were observed during posteriorly guided neural induction. Furthermore, acute exposure to VPA during the posterior induction step also retarded the subsequent neurulation-like tube morphogenesis process in neural organoid culture. These results suggest that VPA exposure during very early embryonic development might exhibit cytotoxicity and subsequently disrupt neural differentiation and morphogenesis processes.

Valproic acid (VPA), widely used as an antiepileptic drug, exhibits developmental neurotoxicity when exposure occurs during early or late pregnancy, resulting in various conditions ranging from neural tube defects to autism spectrum disorders. However, toxicity during the very early stages of neural development has not been addressed. Therefore, we investigated the effects of VPA in a model where human pluripotent stem cells differentiate into anterior or posterior neural tissues. Exposure to VPA during the induction of neural stem cells induced different developmental toxic effects in a dose-dependent manner. For instance, VPA induced cell death more profoundly during anteriorly guided neural progenitor induction, while inhibition of cell proliferation and enhanced differentiation were observed during posteriorly guided neural induction. Furthermore, acute exposure to VPA during the posterior induction step also retarded the subsequent neurulation-like tube morphogenesis process in neural organoid culture. These results suggest that VPA exposure during very early embryonic development might exhibit cytotoxicity and subsequently disrupt neural differentiation and morphogenesis processes.

Valproic acid (VPA), widely used as an antiepileptic drug, exhibits developmental neurotoxicity when exposure occurs during early or late pregnancy, resulting in various conditions ranging from neural tube defects to autism spectrum disorders. However, toxicity during the very early stages of neural development has not been addressed. Therefore, we investigated the effects of VPA in a model where human pluripotent stem cells differentiate into anterior or posterior neural tissues. Exposure to VPA during the induction of neural stem cells induced different developmental toxic effects in a dose-dependent manner. For instance, VPA induced cell death more profoundly during anteriorly guided neural progenitor induction, while inhibition of cell proliferation and enhanced differentiation were observed during posteriorly guided neural induction. Furthermore, acute exposure to VPA during the posterior induction step also retarded the subsequent neurulation-like tube morphogenesis process in neural organoid culture. These results suggest that VPA exposure during very early embryonic development might exhibit cytotoxicity and subsequently disrupt neural differentiation and morphogenesis processes.

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.

Background/Aims: Ischemic colitis (IC), the most common ischemic syndrome affecting the gastrointestinal tract, results from a decreased blood supply to the colon. Persistent symptoms can lead to complications, necessitating surgery. This study assessed the clinical characteristics and risk factors for poor outcomes in IC. Methods: This retrospective observational study examined the medical records of 141 patients diagnosed pathologically with IC via surgery or colonoscopy at Chonnam National University Hwasun Hospital between April 2004 and August 2023. Results: Eighteen (12.8%) and 123 (87.2%) patients were diagnosed by surgical biopsy and biopsy with colonoscopy, respectively.Multivariate analysis identified right-sided colon involvement, fever, and the absence of hematochezia as risk factors for the progression to surgery (odds ratio [OR]=5.924, 95% confidence interval [CI] 1.009–34.767, p=0.049; OR=24.139, 95% CI 5.209– 111.851, p<0.001; and OR=0.076, 95% CI 0.013–0.446, p=0.004, respectively). The in-hospital mortality was 5.7% (8/141), and the patients who died exhibited higher rates of shock. The median (interquartile range) hospital stay was 11 (1–219) days. Patients who had longer hospital stays (≥14 days) had a significantly higher rate of fever but a lower rate of hematochezia. Conclusions A multidisciplinary approach is crucial for determining the need for surgery in patients with right-sided colon involvement, fever, or the absence of hematochezia.

Background: This study aimed to investigate the analgesic and preventive effect of low-level laser therapy (LLLT) on the incisional pain model and spinal nerve ligation (SNL) model in rats and identify the possible mechanisms of action. Methods: Male Sprague-Dawley rats were used, divided into different treatment groups. The single application group received LLLT before or after skin incision or SNL. The consecutive application group received LLLT for six consecutive days post-incision, three days pre-incision, or three consecutive days pre-SNL. The control group underwent skin incision or SNL without LLLT. The von Frey test was used to quantify the pain associated with mechanical allodynia. Pro-inflammatory cytokine level and alterations in nerve growth factor (NGF) expression were measured by using ELISA and immunohistochemistry, respectively in the skin, muscle of the paw, and spinal cord dorsal horn (SCDH). Results: In the incisional pain model, LLLT showed significant analgesic and preventive effect. LLLT ameliorated SNL-induced mechanical allodynia but LLLT had no preventive effect. LLLT decreased interleukin-1β (IL-1β) expression levels in the skin, muscle, and SCDH and reduced the optical density of skin and spinal cord NGF in the incisional pain model. Conclusions LLLT alleviated incisional pain and neuropathic pain caused by SNL in rats, and reduced the levels of IL-1β and NGF in the peripheral tissue and SCDH in the incisional pain model. LLLT might be effective in patients with post-operative pain and peripheral neuropathic pain.