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.

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.

Internal hernia (IH) is a rare cause of small bowel obstruction occurs when there is protrusion of an internal organ into a retroperitoneal fossa or a foramen in the abdominal cavity. IH can be presented with acute or chronic abdominal symptom and discovered by accident in operation field. However, various kinds of imaging modalities often do not provide the assistance to diagnose IH preoperatively, but computed tomography (CT) scan has a high diagnostic accuracy. We report a case of congenital IH in a 6-year-old boy who experienced life threatening shock. CT scan showed large amount of ascites, bowel wall thickening with poor or absent enhancement of the strangulated bowel segment. Surgical exploration was performed immediately and had to undergo over two meters excision of strangulated small bowel. To prevent the delay in the diagnosis of IH, we should early use of the CT scan and take urgent operation.
Abdominal Cavity ; Ascites ; Child ; Hernia ; Humans ; Intestinal Obstruction ; Shock

BACKGROUND: The purpose of the study was to estimate the mental health problems and other related factors in residents that experienced the Samsung-Hebei spirits oil spill by surveying PTSD symptoms. METHOD: Trained interviewers performed direct interviews of the residents approximately 70 days after the oil spill. We investigated PTSD symptoms through PTSD Symptom Scale Interview Version (PSS-I). To determine those factors related to PTSD symptoms, the following factors were determined as part of the survey: gender, age, occupation, duration of cleanup activity, monthly income and amount of debt. Logistic regression analysis was used to analyze all factors. RESULTS: Of the 318 residents investigated, 56.6% of the subjects had PTSD symptoms related to the spirits oil spill (about 70 days post spill). With regard to occupation, those subjects answering fishery (POR:3.05) and commerce (POR:4.24) as their occupations experienced higher PTSD symptoms than residents answering farming as their occupation. Residents who answered that they had debt over 10 million KRW (POR:2.61) were more vulnerable to PTSD symptoms compared to residents without debt; residents with acute physical symptoms were vulnerable (POR:5.11) to PTSD symptom compared to residents without acute physical symptoms. The results of multiple logistic analysis, including the cleanup activity, age, gender, occupation, acute physical symptoms and amount of debt in the model suggest that only cleanup activity increased PTSD symptoms. The subjects who had engaged in cleanup activities for longer periods of time had more PTSD symptoms and an additional dose-response relationship. CONCLUSION: Many residents in Samsung-Hebei spirits oil spill area had PTSD symptoms. This suggests that there were serious mental health problems among the residents, who might require specific social supports and psychiatric interventions as a result of the oil spill.
Commerce ; Environmental Remediation ; Fisheries ; Logistic Models ; Mental Health ; Occupations ; Petroleum ; Petroleum Pollution ; Stress Disorders, Post-Traumatic

Esophageal atresia (EA) with proximal tracheoesophageal fistula (TEF; gross type B) is a rare defect. Although most patients have long-gap EA, there are still no established surgical guidelines. A premature male infant with symmetric intrauterine growth retardation (birth weight, 1,616 g) was born at 35 weeks and 5 days of gestation. The initial diagnosis was pure EA (gross type A) based on failure to pass an orogastric tube and the absence of stomach gas. A “feed and grow” approach was implemented, with gastrostomy performed on postnatal day 2. A fistula was detected during bronchoscopy for recurrent pneumonia; thus, we confirmed type B EA and performed TEF excision and cervical end esophagostomy. As the infant's stomach volume was insufficient for bolus feeding after reaching a body weight of 2.5 kg, continuous tube feeding was provided through a gastrojejunal tube. On the basis of these findings, esophageal reconstruction with gastric pull-up was performed on postnatal day 141 (infant weight, 4.7 kg), and he was discharged 21 days postoperatively. At 12 months after birth, there was no catch-up growth; however, he is currently receiving a baby food diet without any complications. In patients with EA, bronchoscopy is useful for confirming TEF, whereas for those with long-gap EA with a small stomach volume, esophageal reconstruction with gastric pull-up after continuous feeding through a gastrojejunal tube is worth considering.
Body Weight ; Bronchoscopy ; Diagnosis ; Diet ; Enteral Nutrition ; Esophageal Atresia* ; Esophagostomy ; Fetal Growth Retardation ; Fistula ; Gastrostomy ; Humans ; Infant ; Infant, Newborn ; Infant, Premature* ; Male ; Parturition ; Pneumonia ; Pregnancy ; Stomach ; Tracheoesophageal Fistula

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.