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.

OBJECTIVE To summarize the implementation experiences of the China Hospital Association’s Clinical Pharmacist Instructor Training Program Reform， and to evaluate the effectiveness of the reform， thus continuously enhancing the quality and standards of clinical pharmacist instructor training. METHODS The study drew on project evaluation methodologies to summarize the main characteristics of the comprehensive system and new model for clinical pharmacist instructor training established through the reform by literature review. The “learning assessment” and “reaction assessment” were conducted by using Kirkpatrick’s four-level model of evaluation in order to evaluate the effectiveness of the clinical pharmacist instructor training reform through statistically processing and analyzing the performance data and teaching evaluation data of the instructor participants. Based on problem and trend analysis， the future development directions were anticipated for the reform of clinical pharmacist instructor training. RESULTS & CONCLUSIONS The latest round of clinical pharmacist instructor training reform initiated by the Chinese Hospital Association had initially established a four-pronged training system encompassing “recruitment， training， assessment， and management”. It had also forged a training 。 model “oriented towards enhancing clinical teaching competency， with practical learning and skill-based assessment conducted on clinical teaching sites as its core”. Following a period of over three years of gradual reform， the new training system and model became increasingly mature. In both 2023 and 2024， the participants achieved relatively high average total scores in their initial completion assessments [with scores of （84.05± 5.83） and （85.82±4.35） points， respectively]. They also reported a strong sense of gain from the training reform [with self- perceived gain scores of （4.80±0.44） and （4.85±0.39） points， respectively]. The operation and implementation effects of the reform were generally satisfactory. In the future， clinical pharmacist instructor training reforms should continue to address the issues remaining from the current phase， while aligning with global trends in pharmacy education and industry development. Additionally， sustained exploration and practice will be carried out around the core objective of “enhancing clinical teaching competence”.

Phosphatidylinositol 4 kinases are the initial and key molecules of the phosphatidyl inositol signaling pathway.Among them,the phosphatidylinositol 4-kinaseⅢ β(PI4KⅢβ)is in-volved in the synthesis of the Golgi PI4P pool,playing a vital role in numerous physiological processes.Meanwhile,the en-zyme is an important host factor mediating the replication of some pathogenic RNA viruses,and participating in other patho-logical processes such as bacterial infection and malaria.In ad-dition,studies have shown that the function of PI4KⅢβ is regu-lated by numerous factors,including host and viral protein bind-ing partners.This review will discuss the structure and the phys-iopathology regulatory mechanism of PI4KⅢβ.

This study was aimed at exploring the epidemiological characteristics of plague,and the evolutionary relation-ships among the isolated plague strains in the Yunnan border area,to provide clues for further studying epidemic causes and ep-idemiological patterns.Plague epidemic data in the border area during the second epidemic period(1982-2007)were collected and analyzed with descriptive epidemiological methods.Whole genome sequences of 262 strains of Yersinia pestis in the border area were obtained for phylogenetic analysis.Plague outbreaks occurred in 17 counties(cities)among 25 border counties(cit-ies);a total of 552 epidemic foci and 123 human cases were identified.The 1.ORI2,1.ORI3,1.IN3,2.ANT and 2.MED geno-types were identified among Yersinia pestis isolated from the Yunnan border area,among which the 1.ORI2 population was dominant.A total of 258 strains of Yersinia pestis from the 1.OR12 population belonged to four subclusters.The Myanmar and Vietnam clade was embedded within the Yunnan clade in the overall phylogeny.The above results indicated that during the sec-ond period of the epidemic,the intensity of plague epidemics in Yunnan's border areas was high,showing a trend of devel-opment from west to south and east.Our findings indicated a risk of cross-border transmission of plague between Yunnan and neighboring countries;therefore,the surveillance,pre-vention,and control of plague in border areas should be strengthened.

The biological characteristics and pathogenicity of Klebsiella oxytoca isolated from sick carrier pigeons in Gansu province were explored by morphological observations,biochemical testing,16S rRNA PCR analysis,and RNA sequencing.The drug resistance and pathogenicity of the isolated strains were studied by histopathological observation,drug susceptibility testing,and pathogenicity analysis.The livers,lungs,hearts,and other organs of the sick pigeons were bleeding.In addition,the livers were yellow and brittle,and the lungs were purulent.A Gram-negative,short,rod-shaped bacterium was successfully isolated from the sick pigeon.Pink,smooth,moist,and round colonies grew on MacConkey's agar.The result of the indigo matrix test was positive.The homology between the amplified 16S rRNA sequence and MN330093.1 was 100.00％,indicating that the sick pigeon was infected with K.oxytoca.The strain was named GS-BY-GG.K.Oxytoca GS-BY-GG was resistant to 10 drugs,including penicillin,ampicillin,and furazolidone,and sensitive to 5 others,which included florfenicol,meropenem,and gentamicin.Histopathological observation showed bleeding in multiple organs.The liver cells were irregu-larly arranged with brown-yellow pigmentation.Extensive cell necrosis and exfoliation were observed in the trachea and mucosal epithelium,with inflammatory cell infiltration in the mucosal layer.The isolates were highly pathogenic in specific pathogen-free chickens.These findings provide support for the clinical diagnosis and control of K.oxytoca GS-BY-GG.

Investigating the species/biovars,distribution patterns,and genetic correlations of Brucella from sheep in north-west China is critical to reveal the population and epidemiological characteristics of the Brucella melitensis.In this study,con-ventional identification and AMOS-PCR were used to determine the species/biovars of Brucella isolated from 13 regions in northwest China.MLST and MLVA-16 genotyping methods were used to analyze the genetic characteristics of the strains.Con-ventional identification and AMOS-PCR detection revealed that 59 Brucella melitensis were isolated in this study,in-cluding 22 strains from Inner Mongolia,17 strains from Xin-jiang,13 strains from Gansu,and 7 strains from Qinghai,of which 58 strains were B.melitensis biovar 3,and one strain was B.melitensis biovar 1.MLST analysis indicated that 90％(53/59)of B.melitensis were of ST8 sequence type,the dominant epidemic population.The MLVA-11 survey demonstrated that 59 B.melitensis strains clustered into six MLVA-11 genotypes,and 87％of the strains were of MLVA-11 genotype 116.Therefore,the predominant strains in the northwest region were from the Eastern Mediterranean lineage.MLVA-16 divided 59 strains of B.melitensis into 40 gen-otypes,eight of which were shared genotypes.Each genotype was composed of two to seven strains from the same region,thereby indicating that the cases of each shared genotype were outbreaks from a common source of infection.All shared MLVA-16 genotypes comprised strains from the same province,thus indicating apparent regional clustering characteristics of strains in each province.In a genetic comparison between populations and isolated strains from the spleens of sheep,multiple identical MLVA-16 genotypes were found to be composed of strains from different hosts.These findings indicated a transmission path-way from sheep/goats to humans.B.melitensis biovar 3 was the main pathogen causing animal brucellosis in the northwest re-gion,and infected sheep were the main brucellosis infection source in the regional population.The ST8 strains were the domi-nant epidemic population,and the MLVA genotype of strains in each region showed clear regional clustering characteristics.