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.

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*

This study was to investigate the relationship between spring pollen distribution concentration, species and the detection results of air-borne pollen allergens in Taiyuan City, Shanxi Province during March to May 2022 and March to May 2023.A retrospective study was conducted in the Otorhinolaryngology Head and Neck Surgery Clinic of the First Hospital of Shanxi Medical University.Pollen particles will be monitored by gravity sedimentation method on the roof of the outpatient department of the First Hospital of Shanxi Medical University in downtown Taiyuan from March to May 2022-2023, and pollen species and quantity will be observed and recorded under an optical microscope.The air-borne pollen allergen detection results of all allergic rhinitis patients in the otolaryngology Head and Neck surgery Department of the First Hospital of Shanxi Medical University were extracted from the relevant outpatient system. SPSS software and Pearson correlation analysis were used to compare the correlation between the allergens and the dominant air-borne pollen monitoring results. Results are as follows: (1)A total of 18 species of spring pollen in Taiyuan City were monitored in 2022-2023, with 101 177.5 grains, and the dominant airborne pollen was poplar (16.69%) and pine (29.06%) pollen. The pollen of poplar (11.96%), elm (7.89%) and cypress (8.68%) were dominant in early spring; Pine (25.16%) pollen predominated in late spring. The two peaks of pollen dispersal in Taiyuan were in late March (15 479 grains) and early and mid May (15 094/15 343 grains).(2) The positive rates of allergens in serum specific IgE detection were: wormwood (46%, 248/541 cases), tree combination (26%, 143/541 cases), ragweed (19%, 101/541 cases), humulus scandens (9%, 49/541 cases).(3)There was a linear positive correlation between the positive rate of air-borne pollen allergens in allergic rhinitis patients in the Department of Otolaryngology Head and Neck Surgery in the First Hospital of Shanxi Medical University and the dominant air-borne pollen concentration in the same period ( P<0.05, r=0.999). In conclusion, two spring pollen dispersal peaks were formed in late March and early to mid May in Taiyuan City, and the dominant air-borne pollens were poplar and pine pollens. The positive rate of air borne pollen allergen sIgE showed that wormwood allergy was the highest.There was a positive correlation between the concentration of air-borne pollen and the positive rate of air-borne pollen allergens in patients with allergic rhinitis in the Department of otorhinolaryngology and head and neck surgery in Taiyuan in 2022 and 2023.The monitoring of pollen distribution in spring can provide an important scientific basis for clinical workers to formulate prevention and treatment plans for patients with allergic rhinitis in the season, and provide data reference for the epidemiological investigation of allergic diseases in Taiyuan in the future.