The digitization of traditional Chinese medicine (TCM) has generated vast amounts of data. However, these data are characterized by significant heterogeneity and complex semantic structures, posing substantial challenges for systematic integration and intelligent analysis, and limiting its potential for modern clinical and computational research. To address the challenges posed by the high heterogeneity and complex structure in TCM data, we designed and developed the TCM Data Hub platform, which is powered by the YiYuan large language models (LLMs). This platform aims to enhance intelligent data processing capabilities and unlock the potential for clinical application of TCM data through systematic integration and efficient utilization, thereby bridging the gap between traditional knowledge and modern computational research. This study first analyzed the heterogeneity and complexity of TCM information with respect to data types, structures, and semantics. A standardized data framework was constructed to enhance data integration and interoperability. Based on the TCM Intelligent Computing Platform of the China Academy of Chinese Medical Sciences, we trained the YiYuan LLMs to acquire domain-specific semantic understanding of TCM, thereby improving the platform’s comprehension of specialized terminology and knowledge systems. Leveraging the natural language processing capabilities of the LLMs, we developed a human-in-the-loop data processing system to enable efficient extraction, cleansing, and structured organization of TCM data. In addition, utilizing Vue and Java technologies, we developed multiple LLM-powered intelligent agents and systems, including a human-in-the-loop data processing system, as well as automated prescription mining and network pharmacology analysis agents. Task-specific agents tailored to TCM data processing were developed to enhance the model’s effectiveness in clinical knowledge discovery. System functionality and platform infrastructure were implemented using Java and Vue technologies.The TCM Data Hub platform has completed system construction and core functionality implementation. It supported integrated management and efficient access to 8 key types of TCM data: prescriptions, materia medica, ingredients, targets, diseases (Western medicine), diseases (TCM), syndromes, and therapeutic methods. The human-in-the-loop data processing system achieved an accuracy of 95.34% in structuring TCM data and supported annotation for data requiring manual labeling. The intelligent agent-driven big-data analytics module enabled 1-click, end-to-end workflows for TCM prescription mining, herb-syndrome association analysis, network pharmacology, and molecular biology research, completing a full data mining task in approximately 30 minutes. Users can interact with and manipulate data through a visual front-end interface. The system demonstrated stable performance, strong scalability, and a user-friendly experience. Empowered by the YiYuan LLMs, the TCM Data Hub platform significantly improves the accessibility, usability, and intelligence of TCM data. It effectively bridges traditional TCM knowledge with modern intelligent technologies, providing robust data support and intelligent tools for TCM research and clinical applications.

ObjectiveTo develop a full-process data platform of renal rehabilitation, diagnosis and quality control information. MethodsA hierarchical architectural design was proposed, adhering to clinical pathway models and standardized data protocols. The platform comprehensively covered assessment, intervention, follow-up and quality control for maintenance hemodialysis (MHD) patients. By integrating multidisciplinary resources and standardizing rehabilitation workflows, it delivered standardized and intelligent rehabilitation services. ResultsThe platform achieved standardized and intelligent management of rehabilitation services, effectively improved the physiological function, psychological state and quality of life convenience for MHD patients, while significantly reduced the economic and care burden on patients' families and society. ConclusionThe rehabilitation service model based on a full-process data platform may provide scientific and systematic support for MHD patients.

OBJECTIVE To explore the transformation of the dispensing and drug pickup process in traditional Chinese medicine pharmacy （TCM Pharmacy） in our hospital based on data-intelligence-driven， aiming to improve pharmacists’ work efficiency and patients’ drug pickup experience. METHODS Value stream mapping and journey mapping were used to systematically identify non-value-added links in pharmacists’ dispensing process and key pain points in patients’ drug pickup under the traditional process. An intelligent dispensing and drug pickup system for the TCM Pharmacy was developed based on the C# and Android television platforms， and a machine-learning model was adopted to predict patients’ drug pickup waiting time. A comprehensive evaluation was performed from three perspectives： system performance， prediction accuracy， and satisfaction of pharmacists and patients. RESULTS The system successfully streamlined non-value-added links such as “waiting for writing on the board” and “searching for drugs”， and realized multimodal dynamic prompts of dispensing status through auditory （number calling） and visual （television terminal） channels. The constructed model for predicting drug pickup waiting time exhibited good fitting degree and generalization ability （mean absolute error＝4.28 min， R 2 ＝0.882）. The comprehensive satisfaction scores of pharmacists and patients in the traditional mode were significantly increased from （70.99±1.74） and （73.58±1.98） to （90.02±1.30） and （88.61±2.08） in the new system， respectively （ P ＜0.01）. CONCLUSIONS The transformation of the intelligent drug dispensing and pickup system for TCM pharmacy based on data-intelligence-driven effectively improves the efficiency of pharmacists’ dispensing work， realizes process transparency and waiting time predictability， and significantly enhances patients’ drug pickup experience.

OBJECTIVE To establish a scientific， systematic， multi-dimensional performance management system for pharmacy practice， so as to improve the efficiency and quality of pharmacy practice performance management in public hospitals. METHODS Based on the four dimensions of the balanced scorecard theory， finance， customer， internal process， learning and growth， reference indicators for pharmacy practice performance management were summarized. The Delphi method was used to screen indicators， and the analytic hierarchy process was applied to determine the weights of indicators. A pharmacy practice performance management system was then constructed. Based on this system， action plans were formulated and implemented. The effectiveness was evaluated from two aspects： customer reviews and changes in pharmacy practice outcomes. RESULTS A total of 28 reference indicators were summarized， and a performance management system for pharmacy practice was constructed， consisting of 4 primary indicators， 9 secondary indicators， and 20 tertiary indicators. Compared with action plans implementation before， the satisfaction of clinical departments was significantly improved， and 11 pharmacy practice performance management indicators were optimized after implementation. CONCLUSIONS A scientific and systematic performance management system for pharmacy practice has been successfully established， which can provide a reference for the innovation of hospital pharmacy practice management and the high quality development of pharmacy practice.

ObjectiveTo investigate the application of central auditory testing combined with electroencephalographic features in the early screening of mild cognitive impairment due to Alzheimer's disease. MethodsA convenience sampling method was used to conduct a field survey in the Qixia District of Nanjing City, Jiangsu Province, from July to September, 2025. A total of 40 older adults diagnosed as mild cognitive impairment due to Alzheimer's disease and 23 cognitively normal older adults were recruited. Auditory N-back and dichotic listening paradigms were employed for assessment, while the scores of Montreal Cognitive Assessment Beijing Version (MoCA-BJ) and 32-channel electroencephalogram (EEG) data were simultaneously collected. Binary logistic regression analysis was applied to identify predictive indicators, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. ResultsThe total score of dichotic listening was positively correlated with overall cognitive function (r = 0.475, P < 0.01), while right-ear advantage showed negative correlations with overall cognition, visuospatial ability, abstract reasoning, and delayed recall (r < -0.276, P < 0.05). Task performance across all conditions of the N-back task was positively correlated with overall cognitive function (r > 0.249, P < 0.05). Regarding EEG features, δ and θ band power and power peaks were positively correlated with delayed recall (r > 0.258, P < 0.05), whereas (α+θ)/(α+β) and θ/(α+β) were positively correlated with naming ability and visuospatial executive function, respectively (r > 0.261, P < 0.05). The results of the binary logistic regression analysis showed that the total score of binaural dichotic listening, the accuracy of 1-back task, the average power of δ waves, and the ratio of θ/(α+β) were independent influencing factors for mild cognitive impairment due to Alzheimer's disease (P < 0.05). The ROC analysis indicated that the AUC for auditory task performance and EEG features were 0.845 and 0.735 respectively, and when combined, the AUC increased to 0.862. ConclusionThe screening model based on the integration of auditory task performance and EEG features can be used in the early identification of mild cognitive impairment due to Alzheimer's disease.

Objective:To explore the clinical feature and genetic variation of NPHS1 variant-associated nephropathy ( NPHS1-VAN) in Chinese patients. Methods:This study was a case-series analysis. Patients with NPHS1-VAN, who were treated and/or followed in the Department of Pediatrics, Nanfang Hospital, Southern Medical University between 2018 and 2023 were recruited into this study. Genotype, phenotype and their relationship were analyzed. Results:Nine NPHS1-VAN patients from 8 non-consanguineous Chinese families were collected, including 5 males and 4 females. There were 7 cases with an onset age within 3 months and 2 cases with an onset age of 6 months and 13 years, respectively. Seven patients harbored compound heterozygous variants, two had homozygous variants, including 8 missense variations,3 frameshift variants, and 1 splicing site variant. Four patients in 3 families harbored missense variant c.928G>A, two of them experienced spontaneous remission of proteinuria at the age of 1 year and 2 years, respectively, another one had persistent proteinuria and entered end stage renal disease (ESRD) at 11 years old. The other one had an onset age of 6 months with no response to steroids initially. She got complete remission by tacrolimus administered, but relapse frequently and partially responded to steroids later. Two patients of this group died, one of them died of respiratory failure 3 days after birth. Excessive amniotic fluid and fetal edema were acknowledged at 28 weeks of gestational age. He harbored compound heterozygous variants of NPHS1, c.1135C>G (R379G) and c.1339G>A (E447K). His mother previously experienced fetal death at 28 weeks gestational age for her first pregnant and stillborn at 36 weeks of gestational age for her second pregnant, respectively. One patient in this study who harbored homozygous variant of c.1339G>A (E447K) presented with a mild phenotype, onset age was 13 years old and didn't progress to ESRD yet at 21 years. Thus, variant E447K was hypothesized to be weakly pathogenic, while R379G may be strongly pathogenic with a risk of death. Five novel variants were identified in this group of patients, 3 missense variants (c.1135C>G, c.1157A>T, c.3197T>A) and 2 frameshift variants (c.709_710delCT, c.3193delG). Renal biopsy was performed in 4 cases, of whom two were focal segmental glomerular sclerosis and another two were minimal change disease. Conclusions:NPHS1-VAN possesses remarkable clinical and genetic heterogeneity. Five novel variants were identified. Missense variant is the most common variant type and c.928G>A is the most common one in this group of patients, in consistent with previous report in China. Children harbor c.928G>A may have a mild phenotype with possible spontaneous remission and may be response to steroids and calcineurin inhibitor. Variant c.1135C>G (R379G) may have a strong pathogenicity, and patient who harbors this variant may have a severe phenotype.

Nanopore sequencing,as a representative of third-generation sequencing technology,is widely used in molecular biology research.It offers several advantages,including high throughput,ultra-long read lengths,real-time sequencing,and the absence of amplification and labeling requirements.This technology has been applied for the detection of various pathogens,genomic typing,structural variation analysis,and personalized treatment guidance,making it valuable for pathogen monitoring and medical research.This article focuses on the principles of nanopore sequencing technology,its advantages,and its applications in molecular biology and molecular medicine.The limitations of nanopore sequencing and its future development directions are also summarized,providing new perspectives for further research in this field.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Pediatric urological non-Hodgkin lymphoma is relatively rare.When a single renal or ureteral tumor with multiple lymph node or bone metastases is present, it is very easy to be misdiagnosed as urinary tumor with metastasis, resulting in unnecessary radical nephrectomy. This paper reports two cases of non-Hodgkin lymphoma presenting with single urinary nodules. Case 1 was diagnosed with a 4.5cm tumor in the right kidney due to fever and fatigue. CT and PET-CT showed multiple bone destruction and lymph node metastasis. Renal biopsy was performed and ALK-positive anaplastic large cell lymphoma was diagnosed. Chemotherapy was effective for 6 months. Case 2 was diagnosed with left hydronephrosis due to intermittent low back pain for 2 weeks. Ultrasound examination revealed ureteral dilation with left hydronephrosis and low echo occupying of the lower ureter. The patient had sudden blurred vision before surgery. MRI examination revealed space occupying in the right optic nerve frame, and PET-CT found multiple bone, lymph nodes and nerve invasion. A lymph node biopsy was performed and Burkitt lymphoma was diagnosed. After 6 months of chemotherapy and radiotherapy, the tumor shrank significantly.

Artificial intelligence(AI)has emerged as a cutting-edge technology leading the future and is a key engine for China's development.In the innovation and research of medical devices,AI has provided critical support in the areas of intelligent diagnostic assistance,intelligent therapeutic assis-tance,intelligent monitoring,life support,et al.Ma-chine learning-enabled device software functions(ML-DSFs)have become an essential component of many medical devices.Recently,the United States Food and Drug Administration(FDA)released a draft guidance titled"Marketing Submission Rec-ommendations for a Predetermined Change Con-trol Plan for Artificial Intelligence/Machine Learn-ing(AI/ML)-Enabled Device Software Functions(Draft)."that aimed to provide a forward-looking approach to foster the development of ML medical devices.By supporting iterative updates through modifications,this approach ensures the continu-ous safety and effectiveness of the devices.This guidance represents the latest in regulatory direc-tion and is especially beneficial for enhancing the quality and efficiency of clinical trials for AI prod-ucts.Therefore,we plan to provide a detailed intro-duction and interpretation of the guidance,with the aim of learning from international advanced regulatory concepts and experiences to promote the development of ML-DSFs with more profound international influence.