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.

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.

This study employed the "disease-medicine-dose" pattern to mine the medication rules of traditional Chinese medicine(TCM) prescriptions containing Astragali Radix in ancient Chinese medical books, aiming to provide a scientific basis for the clinical application of Astragali Radix and the development of new medicines. The TCM prescriptions containing Astragali Radix were retrieved from databases such as Chinese Medical Dictionary and imported into Excel 2020 to construct the prescription library. Statical analysis were performed for the prescriptions regarding the indications, syndromes, medicine use frequency, herb effects, nature and taste, meridian tropism, dosage forms, and dose. SPSS statistics 26.0 and IBM SPSS Modeler 18.0 were used for association rules analysis and cluster analysis. A total of 2 297 prescriptions containing Astragali Radix were collected, involving 233 indications, among which sore and ulcer, consumptive disease, sweating disorder, and apoplexy had high frequency(>25), and their syndromes were mainly Qi and blood deficiency, Qi and blood deficiency, Yin and Yang deficiency, and Qi deficiency and collateral obstruction, respectively. In the prescriptions, 98 medicines were used with the frequency >25 and they mainly included Qi-tonifying medicines and blood-tonifying medicines. Glycyrrhizae Radix et Rhizoma, Angelicae Sinensis Radix, Ginseng Radix et Rhizoma, Atractylodis Macrocephalae Rhizoma, and Citri Reticulatae Pericarpium were frequently used. The medicines with high frequency mainly have warm or cold nature, and sweet, pungent, or bitter taste, with tropism to spleen, lung, heart, liver, and kidney meridians. In the treatment of sore and ulcer, Astragali Radix was mainly used with the dose of 3.73 g and combined with Glycyrrhizae Radix et Rhizoma to promote granulation and heal up sores. In the treatment of consumptive disease, Astragali Radix was mainly used with the dose of 37.30 g and combined with Ginseng Radix et Rhizoma to tonify deficiency and replenish Qi. In the treatment of sweating disorder, Astragali Radix was mainly used with the dose of 3.73 g and combined with Glycyrrhizae Radix et Rhizoma to consolidate exterior and stop sweating. In the treatment of apoplexy, Astragali Radix was mainly used with the dose of 7.46 g and combined with Glycyrrhizae Radix et Rhizoma to dispell wind and stop convulsions. Astragali Radix can be used in the treatment of multiple system diseases, with the effects of tonifying Qi and ascending Yang, consolidating exterior and stopping sweating, and expressing toxin and promoting granulation. According to the manifestations of different diseases, when combined with other medicines, Astragali Radix was endowed with the effects of promoting granulation and healing up sores, tonifying deficiency and Qi, consolidating exterior and stopping sweating, and dispelling wind and replenishing Qi. The findings provide a theoretical reference and a scientific basis for the clinical application of Astragali Radix and the development of new medicines.
Drugs, Chinese Herbal/history* ; Humans ; Medicine, Chinese Traditional/history* ; History, Ancient ; Astragalus Plant/chemistry* ; China ; Astragalus propinquus

This study investigated the effects and underlying mechanisms of vanillic acid(VA) against cardiac fibrosis(CF) induced by isoproterenol(ISO) in mice. Male C57BL/6J mice were randomly divided into control group, VA group(100 mg·kg~(-1), ig), ISO group(10 mg·kg~(-1), sc), ISO + VA group(10 mg·kg~(-1), sc + 100 mg·kg~(-1), ig), ISO + dynamin-related protein 1(Drp1) inhibitor(Mdivi-1) group(10 mg·kg~(-1), sc + 50 mg·kg~(-1), ip), and ISO + VA + Mdivi-1 group(10 mg·kg~(-1), sc + 100 mg·kg~(-1), ig + 50 mg·kg~(-1), ip). The treatment groups received the corresponding medications once daily for 14 consecutive days. On the day after the last administration, cardiac functions were evaluated, and serum and cardiac tissue samples were collected. These samples were analyzed for serum aspartate aminotransferase(AST), lactate dehydrogenase(LDH), creatine kinase-MB(CK-MB), cardiac troponin I(cTnI), reactive oxygen species(ROS), interleukin(IL)-1β, IL-4, IL-6, IL-10, IL-18, and tumor necrosis factor-α(TNF-α) levels, as well as cardiac tissue catalase(CAT), glutathione(GSH), malondialdehyde(MDA), myeloperoxidase(MPO), superoxide dismutase(SOD), total antioxidant capacity(T-AOC) activities, and cytochrome C levels in mitochondria and cytoplasm. Hematoxylin-eosin, Masson, uranium acetate and lead citrate staining were used to observe morphological and mitochondrial ultrastructural changes in the cardiac tissues, and myocardial injury area and collagen volume fraction were calculated. Flow cytometry was applied to detect the relative content and M1/M2 polarization of cardiac macrophages. The mRNA expression levels of macrophage polarization markers [CD86, CD206, arginase 1(Arg-1), inducible nitric oxide synthase(iNOS)], CF markers [type Ⅰ collagen(Coll Ⅰ), Coll Ⅲ, α-smooth muscle actin(α-SMA)], and cytokines(IL-1β, IL-4, IL-6, IL-10, IL-18, TNF-α) in cardiac tissues were determined by quantitative real-time PCR. Western blot was used to detect the protein expression levels of Coll Ⅰ, Coll Ⅲ, α-SMA, Drp1, p-Drp1, voltage-dependent anion channel(VDAC), hexokinase 1(HK1), NOD-like receptor protein 3(NLRP3), apoptosis-associated speck-like protein(ASC), caspase-1, cleaved-caspase-1, gasdermin D(GSDMD), cleaved N-terminal gasdermin D(GSDMD-N), IL-1β, IL-18, B-cell lymphoma-2(Bcl-2), B-cell lymphoma-xl(Bcl-xl), Bcl-2-associated death promoter(Bad), Bcl-2-associated X protein(Bax), apoptotic protease activating factor-1(Apaf-1), pro-caspase-3, cleaved-caspase-3, pro-caspase-9, cleaved-caspase-9, poly(ADP-ribose) polymerase-1(PARP-1), and cleaved-PARP-1 in cardiac tissues. The results showed that VA significantly improved cardiac function in mice with CF, reduced myocardial injury area and cardiac index, and decreased serum levels of AST, CK-MB, cTnI, LDH, ROS, IL-1β, IL-6, IL-18, and TNF-α. VA also lowered MDA and MPO levels, mRNA expressions of IL-1β, IL-6, IL-18, and TNF-α, and mRNA and protein expressions of Coll Ⅰ, Coll Ⅲ, and α-SMA in cardiac tissues, and increased serum levels of IL-4 and IL-10, cardiac tissue levels of CAT, GSH, SOD, and T-AOC, and mRNA expressions of IL-4 and IL-10. Additionally, VA ameliorated cardiac pathological damage, inhibited myocardial cell apoptosis, inflammatory infiltration, and collagen fiber deposition, reduced collagen volume fraction, and alleviated mitochondrial damage. VA decreased the ratio of F4/80~+CD86~+ M1 cells and the mRNA expressions of CD86 and iNOS in cardiac tissue, and increased the ratio of F4/80~+CD206~+ M2 cells and the mRNA expressions of CD206 and Arg-1. VA also reduced protein expressions of p-Drp1, VDAC, NLRP3, ASC, caspase-1, cleaved-caspase-1, GSDMD, GSDMD-N, IL-1β, IL-18, Bad, Bax, Apaf-1, cleaved-caspase-3, cleaved-caspase-9, cleaved-PARP-1, and cytoplasmic cytochrome C, and increased the expressions of HK1, Bcl-2, Bcl-xl, pro-caspase-3, pro-caspase-9 proteins, as well as the Bcl-2/Bax and Bcl-xl/Bad ratios and mitochondrial cytochrome C content. These results indicate that VA has a significant ameliorative effect on ISO-induced CF in mice, alleviates ISO-induced oxidative damage and inflammatory response, and its mechanism may be closely related to the inhibition of Drp1/HK1/NLRP3 and mitochondrial apoptosis signaling pathways, suppression of myocardial cell inflammatory infiltration and collagen fiber deposition, reduction of collagen volume fraction and CollⅠ, Coll Ⅲ, and α-SMA expressions, thus mitigating CF.
Animals ; Isoproterenol/adverse effects* ; Male ; Mice ; Signal Transduction/drug effects* ; Vanillic Acid/administration & dosage* ; Dynamins/genetics* ; Mice, Inbred C57BL ; Fibrosis/genetics* ; Apoptosis/drug effects* ; Mitochondria/metabolism* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Myocardium/metabolism* ; Humans

The antidepressant activity and molecular mechanisms of Yueju Wan volatile oil were investigated. The Yueju Wan volatile oil was extracted by using supercritical CO_2. Gas chromatography-mass spectrometry(GC-MS) combined with network pharmacology identified 28 chemical constituents in Yueju Wan volatile oil, primarily terpenes and lactones. A total of 123 overlapping targets were associated with depression, including core targets of interleukin-1β(IL-1β), signal transducer and activator of transcription 3(STAT3), and caspase-3(CASP3). These targets were mainly involved in the prolactin, advanced glycation end products/receptor(AGE/RAGE), and phosphoinositide 3-kinase/protein kinase B(PI3K/Akt) signaling pathways. A reserpine-induced depression mouse model was established to evaluate the therapeutic effects and mechanisms of Yueju Wan volatile oil. The effects of Yueju Wan volatile oil on depression-like behavior in mice were evaluated by analyzing body mass, body temperature index, tail suspension immobility time, forced swimming immobility time, and sucrose preference. Hematoxylin-eosin(HE) staining revealed neuronal protection of Yueju Wan volatile oil in the brain of mice. Enzyme-linked immunosorbent assay(ELISA) and Western blot were employed to detect the protein expression of AGEs, IL-1β, phosphorylated PI3K(p-PI3K), Akt, phosphorylated Akt(p-Akt), nuclear factor κB(NF-κB), and brain-derived neurotrophic factor(BDNF). Behavioral evaluation showed that Yueju Wan volatile oil could effectively control the decline of body mass and body temperature of depressed mice, reduce tail suspension and swimming immobility time, and enhance their preference for sucrose. Histopathological examination showed that Yueju Wan volatile oil could alleviate the neuronal damage in CA1 and dentate gyrus(DG) of the hippocampus of mice. ELISA and Western blot results showed that Yueju Wan volatile oil could significantly increase the protein expression levels of PI3K, Akt, and BDNF and significantly decrease the protein expression levels of AGEs, IL-1β, p-PI3K, p-Akt, and NF-κB in the hippocampus of mice. Furthermore, the p-PI3K/PI3K and p-Akt/Akt ratios were significantly decreased at medium and high doses. These findings suggest that the aromatherapy of Yueju Wan volatile oil can significantly improve reserpine-induced depression-like behavior in mice, which may be related to reducing the expression of neuronal membrane protein AGEs, reducing the phosphorylation levels of PI3K and Akt, inhibiting NF-κB entry into the nucleus, and alleviating the release of pro-inflammatory factors and nerve injury.
Animals ; Antidepressive Agents/chemistry* ; Mice ; Proto-Oncogene Proteins c-akt/immunology* ; Phosphatidylinositol 3-Kinases/immunology* ; Oils, Volatile/chemistry* ; Male ; Drugs, Chinese Herbal/chemistry* ; Signal Transduction/drug effects* ; Depression/metabolism* ; Glycation End Products, Advanced/immunology* ; Humans

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks