Objective:To analyze the detection of obesity with reduced muscle mass under different evaluation criteria and its association with metabolic abnormalities.Methods:This cross-sectional study consecutively enrolled 3 740 participants who underwent health examinations at the Health Management Center of the First Affiliated Hospital of Guangxi Medical University from January to December in 2023. Body composition parameters, including body mass index (BMI), body fat percentage (BFP), visceral fat area (VFA), waist circumference (WC), waist-to-hip ratio (WHR), and skeletal muscle mass index (SMI) were obtained via bioelectrical impedance analysis (BIA) using a standardized body composition analyzer. The waist-to-height ratio (WHtR) was calculated from these measurements. The blood lipids, blood pressure, blood glucose, and serum uric acid were also assessed. The consistency in detecting obesity with reduced muscle mass under different evaluation criteria was analyzed using the kappa test, and the association between obesity with reduced muscle mass and metabolic abnormalities was examined via the chi-square test.Results:Among the 3 740 participants included in the analysis, 2 105 were male and 1 635 were female. The detection rates of obesity with reduced muscle mass ranged from 0-3.8% in males and 0-12.7% in females. When using BMI+SMI as the evaluation criterion, only one case (0.1%) was identified with obesity and reduced muscle mass. For other criteria-BFP+SMI, VFA+SMI, WC+SMI, WHR+SMI, and WHtR+SMI-the detection rates in males was 3.8%, 2.0%, 1.0%, 3.5%, and 3.0%, respectively, while in females, it was 12.7%, 6.5%, 0.2%, 10.1%, and 3.9%, respectively. Notably, the BFP+SMI, VFA+SMI, and WHtR+SMI criteria demonstrated moderate to good inter-rater agreement ( kappa>0.4), particularly in males ( kappa>0.6) (all P<0.05). In females, the prevalence of hypertension was significantly higher in the obesity with reduced muscle mass-positive group compared to the negative group when using BFP+SMI, VFA+SMI, WHR+SMI, and WHtR+SMI criteria (20.3% vs 10.9%, 26.4% vs 11.1%, 20.0% vs 11.2%, and 25.4% vs 11.6%)(all P<0.05). When BFP+SMI and VFA+SMI were used as evaluation criteria, the detection rate of glucose metabolism disorders were higher in individuals who are positive for obesity combined with reduced muscle mass than in those who were negative (43.9% vs 36.4%, 48.6% vs 36.6%) (both P<0.05). In males, the WHtR+SMI criterion identified a significantly elevated prevalence of abnormal serum uric acid levels in the obesity with reduced muscle mass-positive group compared to the negative group (42.9% vs 30.2%) ( P<0.05). Conclusion:Compared to alternative adiposity metrics, the evaluation criteria incorporating BFP, VFA and WHtR demonstrate significantly stronger associations between obesity with reduced muscle mass and metabolic abnormalities.

INTRODUCTION: In December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus emerged and caused a worldwide pandemic, leading to measures being imposed by many countries to reduce its transmission. Singapore implemented the 'circuit breaker', which restricted all movements except for access to necessities and healthcare services. We aimed to investigate the impact of lockdown measures on the pattern of trauma and its effects. METHODS: An observational, retrospective, single-centre descriptive study was conducted using the trauma registry in Singapore General Hospital. It included patients above 18 years old who presented to the emergency department with trauma and were subsequently admitted. Patients admitted from 1 February 2020 to 31 July 2020 and those admitted during the same timeframe in 2019 were studied. Subgroup analyses were performed for patients aged ≥65 years and those <65 years. RESULTS: A total of 1,037 patients were included for analysis. A 17.6% increase in trauma presentations was seen from 2019 to 2020. Patients aged ≥65 years accounted for the rise in admissions. The predominant mechanism of injury was falls at home for older patients and vehicular accidents in patients <65 years. There were no significant differences in injury severity score, intensive care/high-dependency unit admission rates, length of stay, mortality rate, and subsequent need for inpatient rehabilitation. CONCLUSION Our study provided information on differences in trauma presentations before and during the COVID-19 pandemic. Further studies are required to better inform on additional precautionary measures needed to reduce trauma and improve safety during future lockdowns and pandemics.
Humans ; COVID-19/prevention & control* ; Aged ; Retrospective Studies ; Singapore/epidemiology* ; Male ; Female ; Wounds and Injuries/epidemiology* ; Aged, 80 and over ; Middle Aged ; SARS-CoV-2 ; Hospitalization/statistics & numerical data* ; Adult ; Emergency Service, Hospital/statistics & numerical data* ; Registries ; Accidental Falls/statistics & numerical data* ; Pandemics ; Patient Admission/statistics & numerical data* ; Length of Stay ; Accidents, Traffic/statistics & numerical data*

To address issues such as loss of detailed information, blurred target boundaries, and unclear structural hierarchy in medical image fusion, this paper proposes an adaptive feature medical image fusion network based on a full-scale diffusion model. First, a region-level feature map is generated using a kernel-based saliency map to enhance local features and boundary details. Then, a full-scale diffusion feature extraction network is employed for global feature extraction, alongside a multi-scale denoising U-shaped network designed to fully capture cross-layer information. A multi-scale feature integration module is introduced to reinforce texture details and structural information extracted by the encoder. Finally, an adaptive fusion scheme is applied to progressively fuse region-level features, global features, and source images layer by layer, enhancing the preservation of detail information. To validate the effectiveness of the proposed method, this paper validates the proposed model on the publicly available Harvard dataset and an abdominal dataset. By comparing with nine other representative image fusion methods, the proposed approach achieved improvements across seven evaluation metrics. The results demonstrate that the proposed method effectively extracts both global and local features of medical images, enhances texture details and target boundary clarity, and generates fusion image with high contrast and rich information, providing more reliable support for subsequent clinical diagnosis.
Humans ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Neural Networks, Computer ; Diagnostic Imaging/methods* ; Image Interpretation, Computer-Assisted/methods*

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.