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 discuss the potential mechanisms by which programmed cell death(PCD)might contribute to the pathogenesis of diabetic kidney disease(DKD).Methods Retrieve the datasets GSE30529 and GSE30122 from the Gene Expression Omnibus database and analyze them to obtain differentially expressed genes(DEGs)associated with DKD.Utilize the Gene Set Enrichment Analysis website,the ferroptosis database,and the autophagy database,along with relevant literature,to identify genes associated with apoptosis,necroptosis,pyroptosis,autophagy,and ferroptosis.Cross-reference these genes with the DKD DEGs to identify PCD-related genes that are differentially expressed in DKD.Perform Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses to explore the biological functions and potential pathways of the core genes.Conduct a protein-protein interaction network analysis to examine the interaction relationships of the target genes,and use the CytoHubba plugin in Cytoscape to screen for Hub genes.Results In the GSE30529 dataset,a total of 460 DEGs were identified,while the GSE30122 dataset yielded 992 DEGs.After merging and removing duplicates,932 DEGs were obtained.By intersecting these DEGs with PCD-related genes,61 apoptosis-related genes,7 necroptosis-related genes,39 pyroptosis-related genes,18 autophagy-related genes,and 16 ferroptosis-related genes associated with DKD were identified.The KEGG analysis results indicated that the DEGs related to apoptosis,necroptosis,pyroptosis,and autophagy in PCD were primarily enriched in pathways associated with diabetic complications,including the AGE-RAGE,IL-17,NF-κB,and TNF signaling pathways.In contrast,DEGs related to ferroptosis were mainly enriched in the fatty acid degradation pathway.GO enrichment analysis revealed that the biological processes of the differentially expressed PCD related genes in DKD were primarily involved in the regulation of signals such as NF-κB-inducing kinase/NF-κB,IL-1,and IL-17.Conclusions Differentially expressed PCD-related genes in DKD are mainly enriched in related signal pathways such as AGE-RAGE,IL-17,NF-κB and TNF,suggesting a critical role of PCD in the pathogenesis of DKD.

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 discuss the potential mechanisms by which programmed cell death(PCD)might contribute to the pathogenesis of diabetic kidney disease(DKD).Methods Retrieve the datasets GSE30529 and GSE30122 from the Gene Expression Omnibus database and analyze them to obtain differentially expressed genes(DEGs)associated with DKD.Utilize the Gene Set Enrichment Analysis website,the ferroptosis database,and the autophagy database,along with relevant literature,to identify genes associated with apoptosis,necroptosis,pyroptosis,autophagy,and ferroptosis.Cross-reference these genes with the DKD DEGs to identify PCD-related genes that are differentially expressed in DKD.Perform Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses to explore the biological functions and potential pathways of the core genes.Conduct a protein-protein interaction network analysis to examine the interaction relationships of the target genes,and use the CytoHubba plugin in Cytoscape to screen for Hub genes.Results In the GSE30529 dataset,a total of 460 DEGs were identified,while the GSE30122 dataset yielded 992 DEGs.After merging and removing duplicates,932 DEGs were obtained.By intersecting these DEGs with PCD-related genes,61 apoptosis-related genes,7 necroptosis-related genes,39 pyroptosis-related genes,18 autophagy-related genes,and 16 ferroptosis-related genes associated with DKD were identified.The KEGG analysis results indicated that the DEGs related to apoptosis,necroptosis,pyroptosis,and autophagy in PCD were primarily enriched in pathways associated with diabetic complications,including the AGE-RAGE,IL-17,NF-κB,and TNF signaling pathways.In contrast,DEGs related to ferroptosis were mainly enriched in the fatty acid degradation pathway.GO enrichment analysis revealed that the biological processes of the differentially expressed PCD related genes in DKD were primarily involved in the regulation of signals such as NF-κB-inducing kinase/NF-κB,IL-1,and IL-17.Conclusions Differentially expressed PCD-related genes in DKD are mainly enriched in related signal pathways such as AGE-RAGE,IL-17,NF-κB and TNF,suggesting a critical role of PCD in the pathogenesis of DKD.

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.

Chronic kidney disease(CKD)is a chronic disease characterized by renal structural damage and dysfunction.At present,there is still a lack of effective therapeutic drugs and prevention and treatment methods for CKD in clinical practice.More and more studies have shown that necroptosis,as a new type of programmed cell death,plays a vital role in the onset and progression of CKD.Targeting key molecules in the necroptosis pathway,such as RIPK1,RIPK3 and MLKL,the development of small molecule inhibitors has become an emerging strategy for the treatment of CKD,and has shown significant potential to pro-tect the kidneys and alleviate renal fibrosis in a variety of in vitro and in vivo models.Therefore,this article summarizes the re-search progress of the mechanism of necroptosis in recent years,and focuses on the potential role of necroptosis in the pathogene-sis of CKD and the therapeutic potential of targeting this path-way,providing a new perspective and research direction for the prevention and treatment of CKD in the future.

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.

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.

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.