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.

Purpose: Over the past 3 years, approximately 23,000 emergency surgeries were performed annually in South Korea, accounting for >1% of all surgeries nationwide. With the growing necessity for treating these emergency cases with dedication and proficiency, acute care surgery (ACS) teams were appointed at various hospitals. Regarding the implications of the ACS team, many studies showed promising results with a shorter time from the emergency department (ED) to the operating room (OR), shorter length of stay, and fewer complications. This study aimed to demonstrate the overall effect of ACS implementation at a single institution in South Korea. Methods: This was a single-center, retrospective observational study. Patients aged >18 years who visited the emergency room and received emergency surgery between July 2014 and December 2016 (pre-ACS) and between July 2017 and December 2019 (post-ACS) were included. Results: Among 958 patients, 497 were in the pre-ACS group and 461 in the post-ACS group. After propensity score matching by age, sex, underlying disease, and Emergency Surgery Acuity Score, 405 patients remained in each group. Our analysis showed a reduction in time from ED presentation to operation (547.8 ± 401.0 minutes vs. 476.6 ± 313.2 minutes, P = 0.005) and complication rates (24.7% vs. 16.8%, P < 0.001) in the post-ACS group. There were no significant differences in total operation duration, length of hospital stay, and mortality between the groups. Conclusion As expected, time from ED to OR and complication rates were significantly reduced in the post-ACS group.Implementing an ACS team dedicated to emergency surgery provides better clinical outcomes.

Purpose: Over the past 3 years, approximately 23,000 emergency surgeries were performed annually in South Korea, accounting for >1% of all surgeries nationwide. With the growing necessity for treating these emergency cases with dedication and proficiency, acute care surgery (ACS) teams were appointed at various hospitals. Regarding the implications of the ACS team, many studies showed promising results with a shorter time from the emergency department (ED) to the operating room (OR), shorter length of stay, and fewer complications. This study aimed to demonstrate the overall effect of ACS implementation at a single institution in South Korea. Methods: This was a single-center, retrospective observational study. Patients aged >18 years who visited the emergency room and received emergency surgery between July 2014 and December 2016 (pre-ACS) and between July 2017 and December 2019 (post-ACS) were included. Results: Among 958 patients, 497 were in the pre-ACS group and 461 in the post-ACS group. After propensity score matching by age, sex, underlying disease, and Emergency Surgery Acuity Score, 405 patients remained in each group. Our analysis showed a reduction in time from ED presentation to operation (547.8 ± 401.0 minutes vs. 476.6 ± 313.2 minutes, P = 0.005) and complication rates (24.7% vs. 16.8%, P < 0.001) in the post-ACS group. There were no significant differences in total operation duration, length of hospital stay, and mortality between the groups. Conclusion As expected, time from ED to OR and complication rates were significantly reduced in the post-ACS group.Implementing an ACS team dedicated to emergency surgery provides better clinical outcomes.

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.

Purpose: Over the past 3 years, approximately 23,000 emergency surgeries were performed annually in South Korea, accounting for >1% of all surgeries nationwide. With the growing necessity for treating these emergency cases with dedication and proficiency, acute care surgery (ACS) teams were appointed at various hospitals. Regarding the implications of the ACS team, many studies showed promising results with a shorter time from the emergency department (ED) to the operating room (OR), shorter length of stay, and fewer complications. This study aimed to demonstrate the overall effect of ACS implementation at a single institution in South Korea. Methods: This was a single-center, retrospective observational study. Patients aged >18 years who visited the emergency room and received emergency surgery between July 2014 and December 2016 (pre-ACS) and between July 2017 and December 2019 (post-ACS) were included. Results: Among 958 patients, 497 were in the pre-ACS group and 461 in the post-ACS group. After propensity score matching by age, sex, underlying disease, and Emergency Surgery Acuity Score, 405 patients remained in each group. Our analysis showed a reduction in time from ED presentation to operation (547.8 ± 401.0 minutes vs. 476.6 ± 313.2 minutes, P = 0.005) and complication rates (24.7% vs. 16.8%, P < 0.001) in the post-ACS group. There were no significant differences in total operation duration, length of hospital stay, and mortality between the groups. Conclusion As expected, time from ED to OR and complication rates were significantly reduced in the post-ACS group.Implementing an ACS team dedicated to emergency surgery provides better clinical outcomes.

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/OBJECTIVES: Mushroom consumption, rich in diverse nutrients and bioactive compounds, is suggested as a potential significant contributor to preventing cardiometabolic diseases (CMDs). This systematic review aimed to explore the association between mushrooms and cardiometabolic health outcomes, utilizing data from prospective cohort studies and clinical trials focusing on the general population, with mushrooms themselves as a major exposure. SUBJECTS/METHODS: All original articles, published in English until July 2023, were identified through searches on PubMed, Ovid-Embase, and google scholar. Of 1,328 studies, we finally selected 5 prospective cohort studies and 4 clinical trials. RESULTS: Existing research is limited, typically consisting of 1 to 2 studies for each CMD and cardiometabolic condition. Examination of articles revealed suggestive associations in some cardiometabolic conditions including blood glucose (both fasting and postprandial), high-density lipoprotein cholesterol related indices, high-sensitivity C-reactive protein, and obesity indices (body weight, body mass index, and waist circumference). However, mushroom consumption showed no association with the mortality and morbidity of cardiovascular diseases, stroke, and type 2 diabetes, although there was a potentially beneficial connection with all cause-mortality, hyperuricemia, and metabolic syndrome. CONCLUSION Due to the scarcity of available studies, drawing definitive conclusions is premature. Further comprehensive investigations are needed to clarify the precise nature and extent of this relationship before making conclusive recommendations for the general population.

BACKGROUND: In guided bone regeneration (GBR), there are various problems that occur in the bone defect after the wound healing period. This study aimed to investigate the enhancement of the osteogenic ability of the dual scaffold complex and identify the appropriate concentration of growth factors (GF) for new bone formation based on the novel GBR concept that is applying rapid bone forming GFs to the membrane outside of the bone defect. METHODS: Four bone defects with a diameter of 8 mm were formed in the calvaria of New Zealand white rabbits each to perform GBR. Collagen membrane and biphasic calcium phosphate (BCP) were applied to the bone defects with the four different concetration of BMP-2 or FGF-2. After 2, 4, and 8 weeks of healing, histological, histomorphometric, and immunohistochemical analyses were conducted. RESULTS: In the histological analysis, continuous forms of new bones were observed in the upper part of bone defect in the experimental groups, whereas no continuous forms were observed in the control group. In the histomorphometry, The group to which BMP-2 0.5 mg/ml and FGF-2 1.0 mg/ml was applied showed statistically significantly higher new bone formation. Also, the new bone formation according to the healing period was statistically significantly higher at 8 weeks than at 2, 4 weeks. CONCLUSION The novel GBR method in which BMP-2, newly proposed in this study, is applied to the membrane is effective for bone regeneration. In addition, the dual scaffold complex is quantitatively and qualitatively advantageous for bone regeneration and bone maintenance over time.

Purpose: This study aimed to identify prognostic factors based on treatment outcomes for congenital diaphragmatic hernia (CDH) at a single-center and to identify factors that may improve these outcomes. Methods: Thirty-five neonates diagnosed with CDH between January 2011 and December 2021 were retrospectively analyzed. Pre- and postnatal factors were correlated and analyzed with postnatal clinical outcomes to determine the prognostic factors. Highest oxygenation index (OI) within 24 hours of birth was also calculated. Treatment strategy and outcome analysis of published literatures were also performed. Results: Overall survival rate of this cohort was 60%. Four patients were unable to undergo anesthesia and/or surgery. Three patients who commenced extracorporeal membrane oxygenation (ECMO) post-surgery were non-survivors. Compared to the survivor group, the non-survivor group had a significantly higher occurrence of pneumothorax on the first day, need for high-frequency ventilator and inhaled nitric oxide use, and high OI within the first 24 hours. The non-survivor group showed an early trend towards the surgery timing and a greater number of patch closures. Area under the receiver operating characteristic curve was 0.878 with a sensitivity of 76.2% and specificity of 92.9% at an OI cutoff value of 7.75. Conclusion OI within 24 hours is a valuable predictor of survival. It is expected that the application of ECMO based on OI monitoring may help improve the opportunity for surgical repair, as well as the prognosis of CDH patients.