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.

Background: Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans. Methods: A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness. Conclusion A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Background: Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans. Methods: A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness. Conclusion A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Background: Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans. Methods: A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness. Conclusion A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Background: Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans. Methods: A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness. Conclusion A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

Cancer organoids are three-dimensional mini-organ analogues derived from cancer tissues and have been proposed as models capable of simulating the structure and function of human organs and tissues in vitro. We sought to establish gastric cancer patient-derived organoids (PDOs) from tissues obtained by endoscopic biopsies. Gastric cancer-PDOs were successfully established and cultured from cancer tissues with gastric adenocarcinoma by endoscopic biopsies. To confirm that gastric cancer-PDOs were derived from cancer tissue, the consistency of the original cancer tissue was assessed by histopathological examination.As a result, it was confirmed that the shape and internal structure of gastric cancer-PDO were derived from the original gastric cancer cells, and the tumor specificity of gastric cancerPDO was confirmed through Periodic acid-Schiff (PAS) and polyclonal carcinoembryonic antigen antibody staining. These results demonstrate that gastric cancer-PDO models show the characteristics of primary tumors and have potential for drug screening and providing a personalized medicine platform.