ObjectiveProtein-protein interactions (PPIs) are fundamental to the execution of biological functions within living cells. However, traditional biochemical methods, such as co-immunoprecipitation (Co-IP), often fail to capture transient, weak, or membrane-associated interactions due to the stringent detergent requirements for cell lysis. Proximity labeling (PL) has emerged in recent years as a transformative technology for mapping the proteomes of specific subcellular compartments and identifying dynamic interactomes in situ. Golgi protein 73 (GP73, also known as GOLPH2), a resident type II Golgi transmembrane protein, is a well-recognized clinical biomarker for liver diseases, including hepatocellular carcinoma (HCC). Despite its clinical significance, the comprehensive physiological and pathological functions of GP73 remain partially understood. This study aims to establish an APEX2-mediated proximity labeling system specifically targeting GP73 to map its interactome in a living cellular environment, thereby providing new insights into its molecular roles and regulatory mechanisms. MethodsTo achieve spatial specificity, we first constructed a stable cell line expressing a fusion protein consisting of GP73 and the engineered soybean peroxidase APEX2. The localization of the GP73-APEX2 fusion protein was validated to ensure it correctly targeted the Golgi apparatus. The proximity labeling reaction was initiated by incubating the cells with biotin-phenol (BP) for 30 min, followed by a brief (1 min) treatment with1 mmol/L hydrogen peroxide (H₂O₂). This catalytic reaction converts BP into highly reactive, short-lived biotin-phenoxyl radicals that covalently attach to endogenous proteins within a small labeling radius of the GP73-APEX2 enzyme. Subsequently, the cells were quenched, and biotinylated proteins were enriched using high-affinity streptavidin-coated magnetic beads. The captured “neighbor” proteins were subjected to on-bead digestion and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS) for high-throughput identification. Rigorous bioinformatics analysis, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction network mapping, was performed to interpret the biological significance of the identified candidates. ResultsOur results demonstrate the successful establishment of a robust and sensitive APEX2-based proximity labeling system for GP73. We identified a total of 95 high-confidence interacting proteins that were significantly enriched in the GP73 proximity proteome compared to control groups. Bioinformatics analysis revealed that these interactors were predominantly associated with biological processes such as vesicular transport, protein localization, and, most notably, molecular functions related to “ribosome binding” and “translation regulation”. This suggested an unexpected role for the Golgi-resident GP73 in the cellular translation machinery. To validate these findings, we performed targeted biochemical assays which confirmed a direct interaction between GP73 and the subunits of the eukaryotic translation initiation factor 3 (eIF3) complex, specifically EIF3G and EIF3I. Furthermore, functional validation using the surface sensing of translation (SUnSET) assay—a non-radioactive method to monitor protein synthesis—revealed that the overexpression of GP73 significantly promoted global protein translation levels in the cell, whereas its depletion or inhibition resulted in reduced translation efficiency. ConclusionThis study successfully utilized APEX2-mediated proximity labeling to provide the first systematic map of GP73 interactome in living cells. Our findings uncover a novel, unconventional function of GP73 as a regulator of cellular protein translation, likely mediated through its interaction with the eIF3 complex. This discovery significantly broadens our understanding of the biological roles of GP73 beyond its traditional function in the Golgi apparatus and suggests that it may act as a bridge between Golgi-related trafficking and the protein synthesis machinery. Furthermore, the technical framework established in this study provides a valuable template for investigating other complex organelle-associated protein networks and resolving transient macromolecular interactions in various physiological and pathological contexts.

Coronary CT-derived fractional flow reserve (CT-FFR) is a noninvasive alternative to invasive FFR for assessing the hemodynamic significance of coronary artery stenosis. CT-FFR uses routinely acquired coronary CT angiography (CCTA) with artificial intelligence and computational fluid dynamics to estimate pressure gradients, thereby supporting clinical decisionmaking without procedural risk. Adoption and implementation vary widely across regions. In Asia, South Korea exclusively uses HeartMedi+, which enables rapid analysis, streamlined workflow integration, and high user satisfaction. Japan relies primarily on FFR CT, which has national reimbursement and significantly influences treatment strategies, reducing unnecessary invasive procedures. Hong Kong, Singapore, Taiwan, Thailand, and Vietnam remain in early or exploratory phases due to cost, reimbursement, and infrastructure barriers. In the United States, CT-FFR is guideline-endorsed and reimbursed, where HeartFlow is the most widely used, supported by robust clinical validation and outcome data. Despite regional variability, CTFFR offers a noninvasive solution that enhances patient management, reduces unnecessary invasive testing, and is poised for broader clinical integration as technology and validation advance.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective: To investigate the regional differences in the incidence of urothelial carcinoma among kidney transplant recipients between northern and southern China，so as to provide reference for early diagnosis of this disease. Methods: A comprehensive search was conducted across multiple databases，including CNKI，Wanfang，CBM，and PubMed，using the keywords “kidney transplantation” and “tumor” to collect clinical data from qualified kidney transplant centers.The latest and most complete literature data published by 17 transplant centers in northern China and 14 in southern China were included.Statistical analyses were performed to compare the incidence of post-transplant urothelial carcinoma and non-urothelial malignancies. Results: A total of 37 475 kidney transplant recipients were included，among whom 837 (2.23%) developed post-transplant malignancies，including urothelial carcinoma (366/837，43.73%)，non-urothelial carcinoma (444/837，53.05%)，and malignancies with unspecified pathology (27/837，3.23%).The incidence of malignancies was significantly higher in northern China than in southern China [(2.82±1.39)% vs. (1.67±0.83)%，P=0.011]，with a particularly pronounced difference in the incidence of urothelial carcinoma [(1.68±1.12)% vs. (0.32±0.32)%，P<0.001].No significant difference was observed in the incidence of non-urothelial carcinoma between the two regions [(1.11±0.56)% vs. (1.35±0.65)%，P=0.279].Additionally，female transplant recipients exhibited a higher incidence of malignancies than males in both regions (southern China：2.38% vs. 1.80%; northern China：8.93% vs. 2.52%). Conclusion: The incidence of urothelial carcinoma following kidney transplantation is significantly higher in northern China than in southern China，underscoring the importance of implementing regular tumor screening for kidney transplant recipients，particularly for female patients in northern China，to facilitate early diagnosis and timely intervention.

Intracranial atherosclerotic stenosis(ICAS)is the most prevalent etiology of ischemic stroke in the Asian population.Predicting the risk of stroke in patients with ICAS and stratifying the stroke risk can help clinicians to take early interventional measures so as to improve patient outcomes.Optical coherence tomography(OCT)is a novel ultrahigh-resolution endovascular real-time imaging technique.OCT has multiple advantages in the assessment of atherosclerotic plaque characteristics and lumen morphology,especially in the display of the fine structural features of vulnerable plaques,which provides strong image support for the assessment of the plaque stability.Combined with hemodynamic assessment,OCT can judge the formation and development trend of plaques and predict the risk of stroke recurrence in patients with ICAS,which is of great significance in guiding targeted individualized interventional therapy.This paper aims to make a comprehensive review about the recent progress in OCT technology and its integration with hemodynamic assessment for the diagnosis and treatment of ICAS,to discuss the potential application prospects of OCT in the field of cerebrovascular diseases,so as to provide a scientific basis for the risk assessment,disease monitoring,and decision-making of treatment for patients with ICAS.

Objective This study aimed to investigate whether Trichinella spiralis Ts87 protein can facilitate immune evasion by degrading the mouse neutrophil extracellular traps(NETs).Methods First,we used bioinformatics tools to analyze the physicochemical properties of Ts87 and,through sequence homology alignment,confirmed its classification within the nuclease family.Molecular cloning and protein purification techniques were then employed to obtain high-purity Ts87 protein,and its nuclease activity was confirmed.Additionally,we compared the primary sequence of Ts87 with known active sites from human lysosomal DNase IIα and the structurally characterized Burkholderia thailandensis DNase II.This enabled us to predict potential active sites.Ts87 enzymatic mutants were generated using site-directed mutagenesis,and their functions were confirmed through enzymatic activity experiments.The extracellular nucleic acid content of mouse bone marrow neutrophils and the immunofluorescence staining of mouse NETs were assessed in vitro to verify whether the Ts87 protease mutants exhibited a decreased ability to degrade NETs.Finally,after immunizing mice with Ts87,calculated larvae burden and the degradation of NETs in vivo were observed by immunostaining the MPO and CitH3 expression in the intestinal NETs of the mice.Results High-purity Ts87 protein was successfully obtained and confirmed to possess nuclease activity,capable of degrading NETs in vitro.The Ts87 mutants exhibited reduced capacity in degrading NETs.In vivo experiments in mice demonstrated that the production of anti-Ts87 antibodies in immunized mice reduced the degradation of NETs,facilitating NET-mediated attack on the parasites and resulting in decreased larvae burden in the mice.Conclusions Trichinella spiralis can utilize Ts87 nuclease to evade capture by NETs,providing potential vaccine and drug targets for the prevention and treatment of trichinellosis.

Heart failure(HF)is a cardiovascular disease with a high prevalence and mortality rate worldwide,and despite the widespread use of existing drugs,device intervetions and surgical procedures,the clinical outcomes are still unsatisfactory.The exploration of new methods to treat HF is still an urgent problem.Gene therapy provides a new therapeutic strategy for HF by targeting the regulation of pathogenic genes.This article systematically reviewed the delivery system optimization,key targets and clinical translational challenges of gene therapy for HF,aiming to provide a theoretical basis for the optimization of treatment strategies.