Objective: To propose an improved method for constructing the internal quality control (IQC) framework for ELISA assays and validate its efficacy by statistically analyzing IQC data from nine blood center laboratories. Methods: 1) IQC data was collected from nine blood centers and analyzed using a domestic HBsAg ELISA detection kit as an example. 2) Differences between IQC values across batches within Blood Center 1 were assessed. 3) Statistical analyses were performed on batch usage, number of batches used, days of use, number of QC points, batch-specific means, and coefficients of variation (CV) across all nine centers. 4) Using the improved construction method for IQC framework, provisional and permanent frames were established for batches within Blood Center 1 and Blood Center 9, followed by outlier determination. Results: 1) Statistically significant differences were observed in IQC data between batches within Blood Center 1 (P<0.01). It is recommended that both the control material/reagents and the control chart framework be replaced simultaneously. 2) There were substantial differences among 9 blood centers regarding the control material/reagent lot numbers used, the number of QC runs per batch, and the QC values for identical lots. Therefore, individual laboratories should establish their own IQC chart frameworks. 3) The improved IQC framework construction method for ELISA assays is as follows: provisional frames are established via frame-shifting, using the pre-experimental mean and cumulative coefficient of variation (CV) from the preceding batch. For batches used >20 days with >20 QC points, permanent frames are constructed by aggregating in-control data accumulated over ≥20 days with ≥20 points to calculate cumulative mean and standard deviation. The provisional and permanent frames constructed by this method identified all 26 extreme outliers across Blood Centers 1 and 9 as out-of-control. Among the 218 general outliers, 10 were classified as normal by the provisional frames, while the remainder were designated as warnings or out-of-control. This method effectively monitors assay stability. Conclusion: Based on the statistical analysis of IQC practices across blood centers of varying scales, combined with the inherent characteristics of ELISA assays and the batch-to-batch instability of reagents/QC materials, it is recommended to reconstruct QC charts upon lot changes. The proposed method—utilizing frame-shifting for provisional frames and establishing permanent frames based on cumulative data—is applicable to blood center laboratories of differing sizes and effectively monitors the stability of the ELISA assay process.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Diffuse pediatric-type high-grade glioma (pHGG), H3- and isocitrate dehydrogenase (IDH)-wild-type, is a World Health Organization (WHO) grade 4 diffuse glioma with malignant histological features, which typically affects in children and adolescents. This multidisciplinary treatment case involves a 24-year-old young female patient initially diagnosed via biopsy. After concurrent chemoradiotherapy reduced the tumor size, complete resection was achieved, followed by adjuvant therapy with temozolomide and regorafenib. After recurrence, the tumor was resected again, and a second course of radiotherapy was administered, but the disease ultimately progressed rapidly, with an overall survival exceeding 32 months. pHGGs are highly malignant, aggressive, and associated with poor prognosis. Accurate diagnosis and targeted intervention require close collaboration among a multidisciplinary team, which highlights the critical value of multidisciplinary manage-ment in clinical practice.

Objective To analyze the medication rules of Chinese herbal concentrated-granule for the treatment of hypertension nationwide using data mining and knowledge graph methods,thus to provide evidence for expanding its application in treating hypertension.Methods From January 2022 to March 2023,Chinese herbal concentrated-granule prescriptions for hypertension prescribed by traditional Chinese medicine experts nationwide were collected.Microsoft Excel was used to analyze the frequency,properties and flavors,meridian tropism,and efficacy categories of the medicinals.Hierarchical clustering was applied for cluster analysis,and the Neo4j graph database was utilized to construct an information knowledge graph illustrating the relationships between regions and medicinals.Results A total of 2 600 Chinese herbal concentrated-granule prescriptions were included,involving 370 medicinals.The top six frequently-used medicinals were Poria(Fuling),Glycyrrhizae Radix et Rhizoma(Gancao),Atractylodis Macrocephalae Rhizoma(Baizhu),Citri Reticulatae Pericarpium(Chenpi),Paeoniae Radix Alba(Baishao),and Angelicae Sinensis Radix(Danggui).The properties of the medicinals were mainly warm and mild,and their flavors were usually sweet,pungent,and bitter.The medicinals frequently have the meridian tropism of the spleen,lung,and liver meridians.Hierarchical clustering yielded seven clusters.The information knowledge graph of region-medicinals relationships revealed that medicinals used in five or more regions were Baishao,Baizhu,Chenpi,Pinelliae Rhizoma(Banxia),Fuling,Gastrodiae Rhizoma(Tianma),Bupleuri Radix(Chaihu),Chuanxiong Rhizoma(Chuanxiong),Salviae Miltiorrhizae Radix et Rhizoma(Danshen),Danggui,Gancao,and Astragali Radix(Huangqi).Conclusion Chinese herbal concentrated-granule for hypertension usually consist of tonifying medicinals,with sweet and warm properties,and having the meridian tropism of the spleen meridian.And the medicinals composed of the prescriptions often have the actions of calming the liver and suppressing yang,strengthening the spleen and removing dampness,and nourishing the liver and kidney.

Houpo Sanwutang， included in the Catalogue of Ancient Classical Prescriptions （Second Batch）， was first recorded in the Synopsis of Golden Chamber written by ZHANG Zhongjing from the Eastern Han dynasty and was modified by successive generations of medical experts. A total of 37 pieces of effective data involving 37 ancient Chinese medical books were retrieved from different databases. Through literature mining， statistical analysis， and data processing， combined with modern articles， this study employed bibliometrics to investigate the historical origin， composition， decoction methods， clinical application， and other key information. The results showed that the medicinal origin of Houpo Sanwutang was clearly documented in classic books. Based on the conversion of the measurements from the Han Dynasty， it is recommended that 110.4 g Magnolia Officinalis Cortex， 55.2 g Rhei Radix et Rhizoma， and 72 g Aurantii Fructus Immaturus should be taken. Magnolia Officinalis Cortex and Aurantii Fructus Immaturus should be decocted with 2 400 mL water first， and 1 000 mL should be taken from the decocted liquid. Following this， Rhei Radix et Rhizoma should be added for further decoction， and then 600 mL should be taken from the decocted liquid. A single dose of administration is 200 mL， and the medication can be stopped when patients restore smooth bowel movement. Houpo Sanwutang has the effect of moving Qi， relieving stuffiness and fullness， removing food stagnation， and regulating bowels. It can be used in treating abdominal distending pain， guarding， constipation， and other diseases with the pathogenesis of stagnated heat and stagnated Qi in the stomach. The above results provide reference for the future development and research of Houpo Sanwutang.

Pre-admission is a critical initiative to optimize medical service processes and alleviate the challenge of "difficult access to healthcare. "However, there is currently a lack of standardized protocols for pre-admission procedures. This study aims to systematically analyze key nodes and risk factors in pre-admission process design and propose optimization strategies, providing a foundation for policy formulation and hospital practices. By constructing a "forward-reverse" dual-process model of pre-admission and identifying risk points based on stakeholder theory (patients, hospitals, healthcare administration, and insurance), the study reveals that while pre-admission can reduce the average length of stay, improve bed turnover rates, and enhance patient satisfaction, it also presents risks such as cross-period financial settlement, challenges in insurance policy adaptability, demands for information system integration, and the need for defining medical safety boundaries. To optimize the pre-admission process and mitigate these risks, this study explores framework improvements in areas including eligibility criteria, mode selection, cost settlement, transition between pre-admission and inpatient status, and cancellation of pre-admission, offering practical guidance for public hospitals. The authors argue that pre-admission requires tripartite collaboration among hospitals, insurers, and healthcare administrations: hospitals should establish top-level design, continuously refine processes, and implement dynamic risk assessment mechanisms; insurance providers should support cross-period settlement policies; and healthcare administrations should issue guiding policies or standardized protocols. Through multi-department coordination and collaborative efforts, the optimization and innovation of pre-admission processes can be advanced, ultimately delivering more efficient and convenient healthcare experiences for patients.

The construction of a medical safety system based on the medical core quality and safty systems is the foundation of the hospital. Multi-campus operation coordinated development is key to the high-quality development of public hospitals and the balanced distribution of high-quality medical resources. Building a medical safety system that comfoms to the specialized layout and operation of branch districts is very important the construction of multi-campus hospitals. In January 2023, Xidan Campus of Peking Union Medical College Hospital established a medical safety system that was compatible with its development, based on the construction of the medical core quality and safty system. The system covered four dimensions: early identification, early assessment, early intervention, and fast response. It included high-risk surgical evaluation and filing, early warning of nursing rooms, periodic medical safety rounds, and rapid response teams and cross hospital transportation of critical care rapid response teams. As of June 2024, the hospital had recorded 570 high-risk surgeries with no unplanned secondary surgeries or unplanned readmissions; Reported nursing warnings 68 times, initiated 93 emergency treatments and cross hospital transfers. All emergency patients received early warning assessments and completed graded and classified transfers and management, effectively ensuring patient safety. This practice could provide references for other multi-campus hospitals to promote the construction and development of medical safety systems.