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.

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 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.

ObjectiveTo enhance the genetic diagnostic yield for fetal short femur length （FL） and to evaluate the integration of whole exome sequencing （WES） with chromosomal abnormality detection in prenatal diagnosis. MethodsA total of 27 mid-to-late trimester fetuses diagnosed with short FL ［FL < 5th percentile for gestational age or < -2 SD （Standard Deviation）］ at the Prenatal Diagnosis And Medical Genetics Center，Shenshan Medical Center，Memorial Hospital of Sun Yat-Sen University between November 2023 and May 2025 were included. Amniotic fluid or umbilical cord blood samples were collected and subjected to chromosomal karyotyping （19cases）， chromosomal microarray analysis （CMA） or low-coverage whole genome copy number variation（CNV-Seq） （27 cases）， and trio-based whole exome sequencing （trio-WES） （27 cases）. The pathogenicity of genetic variants were interpreted according to the American College of Medical Genetics and Genomics （ACMG） guidelines. ResultsTwenty-six genomic variants were detected in 17 of the 26 fetuses， including numerical chromosomal abnormalities （mosaic type）， copy number variations （CNVs）， single nucleotide variants （SNVs） and insertions-deletions （INDELs）. Genomic variants detected in 11 cases could explain the short femur phenotype.The overall diagnostic rate was 11/27， with pathogenic/likely pathogenic variants explaining 9 cases and variants of uncertain significance （VUS） accounting for 1 cases. In addition Chromosomal karyotype analysis revealed chromosome mosaicism explaining 1 case. The diagnostic rate of CNV detection （CMA/CNV-seq） was 2/27， while WES achieved a diagnostic rate of 10/27， with an incremental yield of 8/27 in cases with negative or inconclusive CNV results. Pathogenic mutations involved genes such as FGFR3， SHOX， DUOX2， CLCN5， and HBA1/HBA2. ConclusionTrio-WES significantly improves the genetic diagnostic rate for fetal short femur length， particularly in cases where chromosomal analysis fails to identify a cause. Karyotyping remains valuable for detecting low-level mosaicism in large-scale chromosomal abnormalities， thereby complementing WES and CNV detection. A stepwise or parallel genetic testing strategy is recommended in prenatal diagnosis to optimize diagnostic efficiency and support clinical decision-making.

The nucleotide binding oligomerization domain like receptor protein 3(NLRP3)inflamma-some is an intracellular polymeric protein complex that plays an important role in inflammation and immune response.In recent years,the study of NLRP3 inflammasome in hematological malignancies has become a hot topic.Leukemia is a large group of clonal hematologic malignancies that affect the maturation and/or prolifer-ation of bone marrow or lymphocytes.More and more studies have shown that NLRP3 inflammatory micro-molecules are involved in the occurrence and development of leukemia,and it is expected to become an impor-tant therapeutic target for leukemia in the future.In this review,the structure,biological function,activation pathway of NLRP3 inflammasome and its relationship with different types of leukemia were reviewed,and its potential application in the treatment of leukemia was discussed.

Objective To investigate the association between the variability of remnant cholesterol inflammatory index(RCII),a novel composite biomarker,and the risk of stroke,in order to provide a theoretical basis for stroke prevention.Methods A prospective cohort study was conducted on 38 659 Kailuan individuals who took annual physical examinations in 2006,2008,and 2010.These subjects were grouped based on the quartiles of RCII variability,which was represented by standard deviation(SD)and average real variability(ARV),and were followed up every 2 years,with the occurrence of stroke(including ischemic and hemorrhagic strokes),death,or the end of follow-up on December 31,2022 as the endpoints.Kaplan-Meier method was used to calculate the cumulative incidence rate of endpoint events across different groups,and log-rank test was used to compare the difference of cumulative incidence of endpoint events in each group.Multivariate Cox proportional hazards regression model was adopted to analyze the association between RCII variability and risk of stroke.Results Among the 38 659 participants,a total of 2 539 strokes occurred during a mean follow-up period of 11.22±2.26 years.After adjusting confounding factors,when the participants were grouped by the quartiles of RCII-SD,the hazard ratio(HR)for stroke was 1.034(95%CI:0.917～1.167,P=0.584),1.146(95%CI:1.018～1.290,P=0.025),and 1.209(95%CI:1.066～1.370,P=0.003),respectively in the Q2,Q3,and Q4 groups,when compared with the Q1 group(Ptrend<0.05).When they were grouped by the quartiles of RCII-ARV,the HR for stroke was 1.008(95%CI:0.894～1.136,P=0.901),1.109(95%CI:0.986～1.248,P=0.085),and 1.152(95%CI:1.018～1.303,P=0.025),respectively,in the Q2,Q3,and Q4 groups,when compared with the Q1 group.Furthermore,both sensitivity and stratified analyses yielded similar results.Conclusion RCII variability is significantly associated with stroke,and the risk of stroke is gradually increasing with increment of the variability.Countermeasures Relevant authorities can focus on reducing RCII variability as a central objective by establishing regular monitoring mechanism,strengthening lifestyle interventions,and standardizing dietary,exercise,and weight management in order to suppress the index fluctuations.The principle of stable lipid-lowering in medication and optimization of therapeutic regimens with stable efficacy should be emphasized to prevent the risk of additional vascular damage.

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.