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.

Background: Recurrent pregnancy loss undermines the physical and mental health of women. Recent randomized controlled trials have reported some effects of traditional Chinese medicine (TCM); however, whether various TCM methods have different effectiveness remains unclear. Objective: To comprehensively evaluate the efficacy and adverse events of TCM for patients with RPL and to explore whether various TCM methods have different effectiveness. Methods: Ten databases were searched up to May 27, 2024. The risk of bias was assessed using the RoB2 tool. The certainty of the evidence was evaluated using the grading of Recommendations, Assessment, Development, and Evaluation tool. Pairwise and network analyses were conducted using Stata 18.0. Results: A total of 47 randomized controlled trials enrolling 6678 women with RPL were included. Pairwise analysis showed that use of TCM had a significantly lower miscarriage rate (RR 0.50 [95% CI 0.45, 0.55]), lower preterm birth rate (RR 0.81 [95% CI 0.67, 0.98), and lower adverse event rate (RR 0.46 [95% CI 0.37, 0.58]). Moreover, use of TCM was associated with a higher alive-fetus rate (RR 1.21 [95% CI 1.15, 1.26]), live-birth rate (RR 1.20 [95% CI 1.15, 1.25]), and full-term rate (RR 1.37 [95% CI 1.23, 1.53]) compared with nonuse of TCM. Network analysis demonstrated that Bushenshugan combined with conventional Western medicine was ranked the best for the reduction of miscarriage rate. Discussion: Use of TCM is more likely to improve pregnancy outcomes and reduce adverse events compared with nonuse of TCM in patients with RPL. Different TCM methods have differences in reducing the miscarriage rate. The Bushenshugan method might be a potential optimal TCM therapy, but more high-quality evidence is needed to further validate and evaluate the efficacy and safety.

Triptolide （TP）， the core active component of the traditional Chinese medicine Tripterygium wilfordii ， exhibits remarkable pharmacological activities including anti-inflammatory， immunosuppressive and anti-tumor effects， and holds broad application prospects in the treatment of major diseases such as autoimmune diseases and malignant tumors. However， TP has a narrow therapeutic window and causes multi-organ toxicities including liver， kidney and reproductive toxicities， which severely restrict its safe clinical application and new drug development. Therefore， toxicity reduction and efficacy enhancement has become a core scientific problem urgently to be solved in this field. This paper systematically reviews the four core strategies for TP toxicity reduction and efficacy enhancement， including structural modification， dosage form improvement， herbal compatibility， and external therapies of traditional Chinese medicine. Among them， structural modification optimizes the toxic and efficacy characteristics of TP from the molecular structure level， with typica l derivatives including （5 R ）-5-hydroxy triptolide， ZT01， PG490-88， etc. Dosage form modification achieves toxicity reduction and efficacy enhancement via targeted and sustained-controlled drug release of diverse delivery systems. It includes triptolide preparations such as nanoparticles， liposomes， microemulsion gels and liquid crystals， possessing favorable clinical transformation potential. The herbal compatibility and external therapies of traditional Chinese medicine conform to the holistic view of traditional Chinese medicine and have a profound clinical application foundation， but their mechanisms of action are insufficiently elucidated， and they lack unified standardized specifications and high-quality evidence-based proof. In the future， we should rely on multi-omics technology to elucidate the toxic and efficacy mechanisms， integrate technologies to optimize preparations， improve the evaluation system and promote clinical transformation.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Objective To construct a clinical-radiological nomo-gram model for severe mycoplasma pneumoniae pneumonia coinfec-tion with other pathogens(Co-SMPP)in children.Methods The clinical and radiological data of children with severe mycoplasma pneumoniae pneumonia(SMPP)who underwent nucleic acid testing or bronchoalveolar lavage(BAL)were analyzed retrospectively.The data analysis were performed by using SPSS 27.0 software.The group comparison between simple SMPP and Co-SMPP children was conducted by using t-tests,Mann-Whitney U tests,or chi-square tests.Nomogram analysis was performed by using R software and rms packages.The predictive performance of the model was evaluated by using the receiver operating characteristic(ROC)curve.Results A total of 194 SMPP children were included in the study,including 136 cases(70.1％)with simple SMPP,58 cases(29.9％)with Co-SMPP.The fibrinogen and albumin levels were lower in Co-SMPP children[(3.53±0.85)g/L,41.00(39.03,43.68)g/L]than in simple SMPP children[(3.79±0.80)g/L,42.80(41.00,44.40)g/L],with P values of 0.047 and 0.036,respec-tively.The probability of bronchial stenosis and grid shadow were higher in Co-SMPP children than in simple SMPP children,and there were significant differences between the two groups(P＜0.001,P=0.010).The odds ratio of bronchial stenosis in predicting Co-SMPP children was 14.085.The clinical-radiological nomogram model had an area under the curve(AUC)of 0.840,with sensi-tivity and specificity of 0.756 and 0.848,respectively.Conclusion The nomogram model based on clinical-radiological features can effectively predict Co-SMPP.

Objective To explore the effects of dodecanoylcarnitine(DA)and myristoleic acid(MA)on the function of mouse alveolar epithelial cell line MLE-12 and their underlying mechanisms.Methods An inflammatory model was established by stimulating MLE-12 cells with IL-4.The expression levels of DA,MA,and sphingosine-1-phosphate(S1P)in the cell supernatant were detected by ELISA.MLE-12 cells were separately intervened with DA and MA.RT-PCR and flow cytometry were used to detect the expression changes of inflammatory factors IL-6 and tumor necrosis factor-α(TNF-α)and the level of intracellular reactive oxygen species(ROS).Additionally,Western blot was performed to detect the expression of key proteins such as p38 mitogen-activated protein kinase(p-38 MAPK)and src homology 2 domain-containing phosphatase 1(SHP-1).To explore the role of S1PR2 in the effects of DA and MA,MLE-12 cells were pretreated with the S1PR2 inhibitor JTE-013,and the above experiments were repeated.Results IL-4 stimulation significantly upregulated the levels of DA,MA,and S1P in MLE-12 cells(P<0.05).DA/MA treatment groups exhibited significantly increased expression of IL-6 and TNF-α compared with the control group(P<0.05),along with elevated ROS levels(P<0.05).Western blot analysis revealed that DA/MA promoted SHP-1 dephosphorylation and phosphorylated p38 MAPK activation in MLE-12 cells.Notably,JTE-013 pre-treatment completely reversed these effects(P<0.05).Conclusion Asthma-related metabolites DA and MA exacerbate the inflammatory and oxidative stress responses of MLE-12 cells by activating the S1PR2 receptor,promoting the dephosphorylation of SHP-1 and the activation of the p-p38 MAPK pathway.This study reveals the core regulatory role of S1PR2 in this pathway as well.

Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

Objective:To provide novel genetic biomarkers for the diagnosis and treatment of sepsis,bioinformatics analysis was used to screen differentially expressed genes and identify Hub genes in sepsis.Methods:Gene Expression Omnibus(GEO)database was used to retrieve gene expression datasets of sepsis and screen for differentially expressed genes(DEGs).Protein-protein interaction(PPI)network analysis,Gene Ontology(GO)analysis,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were used to clarify the molecular mechanism of DEGs,and Hub genes were screened.Results:A total of 361 DEGs were identified,including 163 up-regulated genes and 198 down-regulated genes.Enrichment analysis revealed that these DEGs were primarily involved in antigen processing and presentation,T cell biology,cell adhesion molecules,and T cell receptor signaling pathways.CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1 were determined as optimal diagnostic biomarkers for sepsis.Conclusions:This study elucidated 10 Hub genes(CD4,TP53,PTPRC,LCK,ITGAM,ZAP70,CD247,CD2,CD3E,and HSP90AB1)as potential biomarkers for the diagnosis and treatment of sepsis.However,since the the generalizability of these Hub genes in patients with sepsis remains unvalidated,further experimental verification is still needed in the future.

Objective To explore a nomogram of intratumor and peritumor radiomics based on enhanced CT venous phase to pre-operatively predict the pathological grade of poorly differentiated esophageal squamous cell carcinoma(ESCC).Methods A retro-spective selection was made of 266 ESCC patients confirmed by pathology(76 cases of poorly differentiated;190 cases of non-poorly differentiated),and all patients were randomly divided into training set(n=186),validation set(n=80),and full data set(n=266).Tumors were segmented on the enhanced CT venous phase to create three-dimensional region of interest(ROI)of intratumor,peritu-mor 0.3 cm,and intratumor+peritumor 0.3 cm.A total of 2 553 radiomics features were extracted.After feature dimensionality reduc-tion,XGboost machine learning algorithm was utilized to rank the top fifteen features.Stepwise forward multiple logistic regression was employed to identify the most significant features.The radiomics scores of the intratumor,peritumor 0.3 cm,and intratumor+peritu-mor 0.3 cm were calculated.The diagnostic efficacy of the model was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA).Results The nomogram constructed based on radiomics scores of intratumor,peritumor 0.3 cm,intratumor+peritumor 0.3 cm in the training set for preoperative prediction of poorly differentiated ESCC had an AUC of 0.899[95％confidence interval(CI)0.846-0.938],and it was well validated in the vali-dation set(AUC 0.869,95％CI 0.775-0.934)and the full data set(AUC 0.889,95％CI 0.845-0.924).Additionally,calibration curves and DCA indicated that the nomogram achieved good calibration ability in the three cohorts and offered greater clinical net benefit.Conclusion The nomogram based on enhanced CT venous phase intratumor and peritumor radiomics achieves a high and stable diagnostic efficacy for preoperatively predicting poorly differentiated ESCC,which may help with individualized surgical selec-tion and management before surgery.

Aim To explore the compatibility and po-tential mechanism of effective components of Biejia-Ezhu against triple negative breast cancer(TNBC)and verify it by experiments.Methods Effective compo-nents and targets of Biejia-Ezhu were obtained by TC-MSP and Swiss Target Prediction.Disease targets of TNBC were obtained from OMMI and GeneCards data-bases.The PPI network was constructed using STRING database.GO and KEGG path enrichment analysis was performed using DAVID database.Cytoscape3.9.1 software was used to construct the"drug-component-target-disease"network,screen key targets and compo-nents for molecular docking,and further verify the com-patibility of key components and targets in vitro.Re-sults ① A total of 71 effective components were iden-tified in the Biejia-Ezhu drug pair.There were 146 drug targets associated with the disease.A total of 113 signaling pathways were identified by KEGG analysis.The 71 potential active components of Biejia-Ezhu mainly acted on key targets such as mTORC1,ULK1,TNF,EGFR,ESR1,STAT3,HIF1A,and PTGS2.Mo-lecular docking results showed that glycine and curcu-min were the key active components of Biejia-Ezhu,and both had strong docking activity against key target proteins mTORC1 and ULK1.②The results of in vitro experiment showed that glycine combined with curcu-min significantly inhibited the proliferation and clonal formation ability of TNBC cells(P＜0.05),up-regula-ted the expression of autophagy marker LC3 Ⅱ/Ⅰ,down-regulated the expression of EGFR,down-regula-ted the expression of pathway protein mTORC1,p-mTOR,p-ULK1,and promoted the expression of path-way protein ULK1(P＜0.05).Conclusion The key component of Biejia-Ezhu against triple-negative breast cancer is glycine-curcumin,the mechanism of which may be related to the regulation of the mTORC1/ULK1 signaling pathway to promote autophagy.