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.

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.

Objective:To investigate the factors influencing left atrial fibrosis in patients with atrial fibrillation(AF)and the association of Periostin protein,serum transforming growth factor-β2(TGF-β2)with left atrial fibrosis.Methods:We enrolled 100 AF patients admitted to Gansu Provincial People's Hospital between March 2021 and March 2023.They were divided into control group(＜10％,n=53)and fibrosis group(≥10％,n=47)according to their left atrial low voltage region.Univariate and multivariate Logistic regression were used to analyze the influ-encing factors of left atrial fibrosis in AF patients and construct a nomogram model.The diagnostic value of related factors and their combined detection for left atrial fibrosis in AF patients were analyzed by receiver operating char-acteristic curve(ROC).Spearman correlation analysis was used to analyze the association of Periostin protein,TGF-β2 with left atrial fibrosis in AF patients.Results:Compared to patients in the control group,those in the fibrosis group had significant higher left atrial diameter(LAD)[(37.08±3.19)mm vs.(33.45±2.45)mm],levels of ser-um uric acid(SUA)[(313.75±49.06)μmol/L vs.(279.88±38.15)μmol/L],Periostin protein[(83.27±3.98)ng/L vs.(75.21±3.04)ng/L],TGF-β2[(4346.84±321.34)ng/L vs.(4186.02±306.91)ng/L],and signifi-cant lower left atrial ejection fraction(LVEF)[(62.28±5.00)％vs.(67.24±3.07)％](P＜0.05 or＜0.01).Multivariate Logistic regression analysis showed that LAD(OR=1.663,95％CI 1.238～3.887,P=0.001),SUA(OR=1.586,95％CI 1.164～2.892,P＜0.001),Periostin protein(OR=1.997,95％CI 1.513～4.585,P=0.001),TGF-β2(OR=2.013,95％CI 1.543～5.864,P＜0.001)were independent risk factors for left atrial fi-brosis in AF patients,while LVEF was an independent protective factor(OR=0.524,95％CI 0.141～0.920,P=0.002).The nomogram model for left atrial fibrosis in AF patients:logit(P)=4.631+0.445 × LVEF+0.546 × LAD+0.575 × SUA+0.530 × Periostin protein+0.347 × TGF-β2.ROC curve showed that the area under the curve(AUC)of combined detection(0.893,95％CI 0.842～0.932)was significantly higher than SUA(AUC=0.637,95％CI 0.566～0.704),LVEF(AUC=0.701,95％CI 0.632～0.763),LAD(AUC=0.649,95％CI 0.579～0.715),Periostin protein(AUC=0.676,95％CI 0.606～0.740),TGF-β2(AUC=0.641,95％CI 0.570～0.707)alone(Z=5.265,6.399,6.379,6.040,6.483,P＜0.001 all).Spearman correlation analysis showed that Perios-tin protein and TGF-β2 were significantly positive correlated with left atrial fibrosis in AF patients(r=0.536,0.578,P＜0.001 all).Conclusion:Periostin protein and TGF-β2 were independent risk factors for left atrial fi-brosis in AF patients and were significantly positive correlated with it,a combination of above-mentioned indexes,cardiac function indexes and uric acid had good diagnostic value for left atrial fibrosis.

Aim To identify the underlying target of bullatine A(BA)against rheumatoid arthritis(RA)u-sing limited proteolysis-small molecule mapping(LiP-SMap)drug target proteomics and to provide a scientif-ic basis for clinical application of Aconiti brachypodi Radix in the treatment of RA.Methods LiP-SMap drug target proteomics was employed to perform bioin-formatics analysis for comparing and validating the dif-ferential protein expression after BA intervention.A collagen-induced arthritis(CIA)model was estab-lished in DBA/1 mice using bovine type Ⅱ collagen.The mice were then divided into the CIA model group,methotrexate-positive control group(MTX group),and BA groups(10 mg·kg-1 and 20 mg·kg-1)based on their clinical scores.After drug intervention,the thera-peutic efficacy against RA was assessed by joint index scores and foot thickness measurements.Histopatholog-ical changes in the arthritic joints of CIA mice were e-valuated using hematoxylin and eosin(HE)staining.Enzyme-linked immunosorbent assay(ELISA)was employed to detect inflammatory cytokines interleukin-17(IL-17)and total IgG and IgG3 anti-collagen-spe-cific antibodies levels from the serum of CIA mice.Flow cytometry was used to detect the expression levels of intracellular Th17 cells(IL-17+CD4+T cells)and Th1 cells(IFN-γ+CD4+T cells).Fluorescent quanti-tative PCR was performed to detect the expression of genes related to differential proteins.Results The proteomic analysis identified Serpinb1a as a protein with strong binding affinity to BA,and KEGG enrich-ment analysis indicated IL-17 signaling pathway was a crucial pathway of BA in against RA.BA treatment significantly reduced clinical scores and foot thickness,improved local arthritis symptoms in CIA mice,and al-leviated inflammatory cell infiltration into arthritic joints(P＜0.05).Differential protein validation re-sults showed that BA had strong affinity with Serpinb1a(-5.92 kJ·mol-1)and downregulated the expres-sion of Serpinb1a mRNA.Furthermore,the administra-tion of BA markedly reduced serum IL-17 A levels from CIA mice,inhibited the expression of intracellular IL-17 A and IFN-γ cytokines in splenic CD4+T cells(P＜0.05),and significantly downregulated the transcrip-tional expression of IL-17F(P＜0.05).Conclusion BA exhibits therapeutic effects on collagen-induced arthritis,and its mechanism of action may involve the regulation of Serpinb1a and the IL-17 signaling path-way.

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.

AIM To establish a UPLC method for the simultaneous content determination of liquiritin,ammonium glycyrrhizate,naringin,neohesperidin,hesperidin,rosmarinic acid,baicalin,wogonoside,baicalein,wogonin and praeruptorin A in Tongxuan Lifei Pills,and to make chemometric investigation.METHODS The analysis was performed on a 30 ℃ thermostatic SVEA C18 column(2.1 mm×150 mm,2.5 μm),with the mobile phase comprising of acetonitrile-0.1％phosphoric acid flowing at 0.5 mL/min in a gradient elution manner,and the detection wavelength was set at 250 nm.Subsequently,cluster analysis,principal component analysis and partial least square discriminant analysis were performed.RESULTS Eleven constituents showed good linear relationships within their own ranges(R2＞0.990 0),whose average recoveries were 90.00％-98.32％with the RSDs of 0.35％-1.89％.Forty batches of samples were clustered into 3 types.Baicalein,baicalin,liquiritin,wogonin,wogonoside and neohesperidin were taken as quality differential markers.CONCLUSION This simple and reproducible method can provide the basis for quality control and evaluation of Tongxuan Lifei Pills.

Hypothermia is a clinical syndrome characterized by core body temperature<35℃,caused by significant heat loss from body surface in cold environment.As a systemic cold injury,it can be lethal if treatment is delayed.Emergent diagnosis and treatment of hypothermia are expected to improve the prognosis of patients.In 2005,the U.S.Army Research Institute of Environmental Medicine(USARIEM)issued guidelines for the prevention and management of cold injuries,but there has been no corresponding standard in China.Therefore,Emergency Branch of Chinese Medical Rescue Association,Emergency Medical Equipment Society of China Association of Medical Equipment,Integrated Rehabilitation Medical Branch of Chinese Medical Rescue Association,and Pre-Hospital Emergency Care Working Committee of Chinese Aging Well Association jointly developed the Chinese Expert Consensus on Emergent Treatment of Hypothermia(2025 edition).The consensus covers the pathophysiology,etiology and epidemiology,diagnosis and severity grading,prehospital treatment,and in-hospital treatment of hypothermia,including 15 recommendations in total,aiming to provide guidance for the relevant clinical rescue work.

Objective:To investigate the risk factors and their predictive efficacy for early postoperative complications in elderly patients with hip fracture.Methods:A retrospective cohort study was conducted on the clinical data of 203 elderly patients with hip fracture admitted to the 908th Hospital of the Joint Logistics Support Force of the PLA and the First Affiliated Hospital of Nanchang University from January 2022 to December 2023, including 54 males and 149 females, aged 65-100 years [(80.5±7.7)years]. There were 96 patients with femoral neck fracture and 107 patients with intertrochanteric fracture. According to the AO/OTA classification, the fracture was classified as type 31A in 107 patients and type 31B in 96. Among them, 81 patients were treated with proximal femoral nail antirotation (PFNA), 65 with semi-hip arthroplasty, 52 with total hip arthroplasty (THA), and 5 with closed reduction and cannulated nail internal fixation. The patients were divided into complication group ( n=65) and non-complication group ( n=138) according to whether complications (mainly including delirium, lung infection, stress ulcer, and deep vein thrombosis of the lower limbs) occurred within 15 days after surgery. The gender, age, age stage, educational level, cause of injury, associated underlying diseases before surgery, AO/OTA classification, American Society of Anesthesiologists (ASA) classification, 5-factor modified frailty index (mFI-5) score, prognostic nutritional index (PNI), anesthesia method, operation method, operation time, intraoperative blood loss, length of hospital stay, etc., were recorded in the two groups. Univariate analysis and multivariate binary logistic regression analysis were used to evaluate the correlation between the above indexes and the occurrence of early postoperative complications in elderly patients with hip fracture and to determine their independent risk factors. The receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the predictive efficacy of each risk factor for the occurrence of early postoperative complications in elderly patients with hip fracture. Results:Univariate analysis showed a certain correlation between age, age stage, associated underlying diseases before surgery, AO/OTA classification, ASA classification, mFI-5 score, PNI, operation method, and length of hospital stay and the occurrence of early postoperative complications in elderly patients with hip fracture ( P<0.05), while gender, educational level, cause of injury, anesthesia method, operation time, and intraoperative blood loss were not correlated with the occurrence of early postoperative complications in elderly patients with hip fracture ( P>0.05). The results of multivariate binary logistic regression analysis showed that the associated underlying diseases before surgery ( OR=5.46, 95% CI 1.33, 22.39, P<0.05), mFI-5 score ( OR=15.90, 95% CI 5.36, 47.15, P<0.01), and PNI ( OR=0.70, 95% CI 0.60, 0.81, P<0.01) were significantly correlated with the occurrence of early postoperative complications in elderly patients with hip fracture. The results of ROC curve analysis showed that mFI-5 score (AUC=0.85, 95% CI 0.80, 0.91) and PNI (AUC=0.87, 95% CI 0.82, 0.93) had moderate predictive efficacy, while the early warning efficacy of associated underlying diseases was low (AUC=0.54, 95% CI 0.45, 0.62). The combination of the above risk factors was more effective in predicting early postoperative complications in elderly patients with hip fracture (AUC=0.95, 95% CI 0.92, 0.98). Conclusions:The mFI-5 score, PNI, and associated underlying diseases before surgery are independent risk factors for early postoperative complications in elderly patients with hip fracture. The mFI-5 score and PNI have a higher predictive efficacy than associated diseases before surgery on the occurrence of early postoperative complications in elderly patients with hip fracture, while the combination of the above risk factors provides a significantly better predictive performance.

Acute ischemic stroke (AIS) is a comprehensive syndrome characterized by neurological dysfunction, resulting from cerebral tissue ischemia and hypoxia due to impaired blood supply, which further leads to tissue softening or even necrosis. Restoring blood flow through recanalization of the occluded vessel is crucial for AIS treatment. Although more and more patients benefit from intravenous thrombolysis or endovascular therapy, some still have poor prognosis after vessel recanalization. Most studies indicate that ineffective recanalization, early neurological deterioration, and hemorrhagic transformation are the three main causes of adverse prognosis after recanalization therapy for AIS. This article systematically reviews the epidemiological characteristics, pathogenesis, and risk factors of the above three aspects based on previous studies, aiming to provide guidance for the diagnosis and treatment of adverse prognosis in AIS patients after recanalization therapy.