OBJECTIVE To provide help for further exploring the mechanism of action of traditional Chinese herb by constructing a complex network of traditional Chinese herb-gene-protein,optimizing the mining method of potential associated genes of traditional Chinese herb and improving the mining efficiency of traditional Chinese herb system biology information.METHODS A graph neural network model HERBGAT with an attention mechanism was proposed.A small amount of traditional Chinese herb-related gene data in the public data platform was used as input,and deep mining was performed in the traditional Chinese herb-gene-protein complex net-work to output potential traditional Chinese herb-related genes.The prediction results were analyzed by disease association analysis and KEGG signaling pathway analysis on the bioinformatics platform to clarify their mechanism of action,and the prediction results were verified by the literature retrieval platform.RESULTS The training results showed that the average prediction accuracy of the HERB-GAT model could reach 94%.Compared with the other two advanced complex network mining methods,HERBGAT showed better per-formance in the three indicators of ACC,AUC and AUPR.In the literature verification stage,the model prediction results were verified by TCM clinical literature and modern pharmacology literature,showing the good effect of HERBGAT in practical application.At the end of this paper,taking the HERBGAT model and the improved EMOGI model to explore the mechanism of action of Pinellia ternata in treating lung cancer as an example,199 potential associated genes of Pinellia ternata in treating lung cancer were found,and these potential associated genes were preliminarily analyzed and discussed with the help of bioinformatics methods.CONCLUSION The HERBGAT model can effectively mine potential traditional Chinese herb-associated genes,improve the mining efficiency of traditional Chinese herb-gene-protein complex networks,provide new ideas and references for the optimization of traditional Chinese herb system biology information mining methods,and provide data basis and experimental direction for exploring the mechanism of action of tradi-tional Chinese herb.

OBJECTIVE To provide help for further exploring the mechanism of action of traditional Chinese herb by constructing a complex network of traditional Chinese herb-gene-protein,optimizing the mining method of potential associated genes of traditional Chinese herb and improving the mining efficiency of traditional Chinese herb system biology information.METHODS A graph neural network model HERBGAT with an attention mechanism was proposed.A small amount of traditional Chinese herb-related gene data in the public data platform was used as input,and deep mining was performed in the traditional Chinese herb-gene-protein complex net-work to output potential traditional Chinese herb-related genes.The prediction results were analyzed by disease association analysis and KEGG signaling pathway analysis on the bioinformatics platform to clarify their mechanism of action,and the prediction results were verified by the literature retrieval platform.RESULTS The training results showed that the average prediction accuracy of the HERB-GAT model could reach 94%.Compared with the other two advanced complex network mining methods,HERBGAT showed better per-formance in the three indicators of ACC,AUC and AUPR.In the literature verification stage,the model prediction results were verified by TCM clinical literature and modern pharmacology literature,showing the good effect of HERBGAT in practical application.At the end of this paper,taking the HERBGAT model and the improved EMOGI model to explore the mechanism of action of Pinellia ternata in treating lung cancer as an example,199 potential associated genes of Pinellia ternata in treating lung cancer were found,and these potential associated genes were preliminarily analyzed and discussed with the help of bioinformatics methods.CONCLUSION The HERBGAT model can effectively mine potential traditional Chinese herb-associated genes,improve the mining efficiency of traditional Chinese herb-gene-protein complex networks,provide new ideas and references for the optimization of traditional Chinese herb system biology information mining methods,and provide data basis and experimental direction for exploring the mechanism of action of tradi-tional Chinese herb.

Objective:To investigate the effect and preliminary mechanism of hypervirulent Klebsiella pneumoniae (hvKP) on the immune response to sepsis induced by liver abscess in mice. Methods:C57BL/6 mice were intraperitoneally injected with hvKP strain NTUH-K2044 or classical Klebsiella pneumoniae (cKP) strain HS11286 suspension to prepare the model of sepsis. The survivals rates of mice within 24 h were recorded. HE staining was used to observed the inflammatory cell infiltration in mouse liver tissues. The levels of neutrophil marker lymphocyte antigen 6G (Ly6G) in mouse liver tissues were detected by immunohistochemistry. The activity of reactive oxygen species (ROS) in mouse liver macrophages and peripheral blood monocytes was measured by ROS assay kit. The activation of p105/p50 and p65 in NF-κB signaling pathway in mouse liver macrophages and peripheral blood monocytes was detected by Western blot. The expression of IL-1β, IL-6 and TNF-α at mRNA and protein levels in mouse liver macrophages and peripheral blood monocytes were detected by qRT-PCR and ELISA, respectively. One-way analysis of variance and t test were used for statistical analysis. Results:Compared with cKP, hvKP infection could induce C57BL/6 mice to develop obvious liver abscess with massive inflammatory cell infiltration. Moreover, the level of Ly6G in liver tissues was significantly higher in hvKP-infected mice than in cKP-infected mice ( P<0.000 1), but the survival rate of hvKP-infected mice was significantly lower than that of cKP-infected mice ( P<0.000 1). hvKP significantly promoted the ROS activity ( P<0.000 1) and enhanced the phosphorylation of p105/p50 and p65 in NF-κB signaling pathway in mouse liver macrophages and peripheral blood monocytes as compared with cKP ( P<0.001). The expression of IL-1β, IL-6 and TNF-α at mRNA and protein levels in mouse liver macrophages and peripheral blood monocytes were significantly higher in hvKP-infected mice than in cKP-infected mice ( P<0.001). Conclusion:hvKP can promote the development of liver abscess and induce sepsis in mice.

Objective:To investigate the role of TcpC, a virulence factor of uropathogenic Escherichia coli (UPEC), in inhibiting the formation of neutrophil extracellular traps (NETs) in mouse bone marrow neutrophils, and to analyze its pathogenic mechanism. Methods:C57BL/6 mice were injected with either wild-type (CFT073 wt) or tcpc gene-knockout UPEC CFT073(CFT073 Δ tcpc) to establish a mouse model of cystitis. Mice were sacrificed 3 d post-infection, and their bladders were collected to observe gross pathological changes. Hematoxylin-eosin (HE) staining was used to assess histopathological changes in bladder tissues, and immunohistochemistry was performed to localize TcpC in bladder tissues. Bacterial loads in urine samples were quantified using the ten-fold dilution method, and the presence of tcpc gene in genomic DNA from bladder or urine samples was confirmed by PCR. The expression of TcpC at mRNA and protein levels in mouse bone marrow nuetrophils infected with CFT073 wt was detected by qRT-PCR and Western blot. The effects of UPEC infection on expression of NETs-related proteins and the production of pro-inflammatory factors in mouse bone marrow neutrophils were analyzed by Western blot and ELISA, respectively. Reactive oxygen species(ROS) level and bacterial viability in mouse bone marrow nuetrophils were measured using ROS and bacterial viability detection kits. Results:Compared to the CFT073 Δ tcpc group, the bladder of CFT073 wt group mice exhibited significant enlargement, extensive inflammatory cell infiltration, and the presence of TcpC in bladder tissue. The bacterial load in the urine of CFT073 wt -infected mice was significantly higher than that in the CFT073 Δ tcpc group ( P<0.01). PCR confirmed the presence of the tcpc gene in bladder and urine samples from CFT073 wt-infected mice. Increased expression of TcpC at both mRNA and protein levels was observed in CFT073 wt-infected mouse bone marrow neutrophils. CFT073 wt infection inhibited the mRNA and protein expression of NETs-related proteins and reduced the production of pro-inflammatory factors in mouse bone marrow neutrophils. TcpC suppressed ROS level and promoted the survival of CFT073 wt in mouse bone marrow neutrophils. Conclusions:TcpC enhances the pathogenicity of UPEC CFT073 by inhibiting the formation and activation of NETs in mouse bone marrow neutrophils. This study provides new insights into the pathogenic mechanisms of UPEC and the immune evasion strategies of other pathogenic bacteria, as well as potential targets for clinical prevention and treatment of UPEC-induced urinary tract infections.

Objective: To develop a facial image generation method based on a facial color-preserving generative adversarial network (FCP-GAN) that effectively decouples identity features from diagnostic facial complexion characteristics in traditional Chinese medicine (TCM) inspection, thereby addressing the critical challenge of privacy preservation in medical image analysis. Methods: A facial image dataset was constructed from participants at Nanjing University of Chinese Medicine between April 23 and June 10, 2023, using a TCM full-body inspection data acquisition equipment under controlled illumination. The proposed FCP-GAN model was designed to achieve the dual objectives of removing identity features and preserving colors through three key components: (i) a multi-space combination module that comprehensively extracts color attributes from red, green, blue (RGB), hue, saturation, value (HSV), and Lab spaces; (ii) a generator incorporating efficient channel attention (ECA) mechanism to enhance the representation of diagnostically critical color channels; and (iii) a dual-loss function that combines adversarial loss for de-identification with a dedicated color preservation loss. The model was trained and evaluated using a stratified 5-fold cross-validation strategy and evaluated against four baseline generative models: conditional GAN (CGAN), deep convolutional GAN (DCGAN), dual discriminator CGAN (DDCGAN), and medical GAN (MedGAN). Performance was assessed in terms of image quality [peak signal-to-noise ratio (PSNR) and structural similarity (SSIM)], distribution similarity [Fréchet inception distance (FID)], privacy protection (face recognition accuracy), and diagnostic consistency [mean squared error (MSE) and Pearson correlation coefficient (PCC)]. Results: The final analysis included facial images from 216 participants. Compared with baseline models, FCP-GAN achieved superior performance, with PSNR = 31.02 dB and SSIM = 0.908, representing an improvement of 1.21 dB and 0.034 in SSIM over the strongest baseline (MedGAN). The FID value (23.45) was also the lowest among all models, indicating superior distributional similarity to real images. The multi-space feature fusion and the ECA mechanism contributed significantly to these performance gains, as evidenced by ablation studies. The stratified 5-fold cross-validation confirmed the model’s robustness, with results reported as mean ± standard deviation (SD) across all folds. The model effectively protected privacy by reducing face recognition accuracy from 95.2% (original images) to 60.1% (generated images). Critically, it maintained high diagnostic fidelity, as evidenced by a low MSE (< 0.051) and a high PCC (> 0.98) for key TCM facial features between original and generated images. Conclusion The FCP-GAN model provides an effective technical solution for ensuring privacy in TCM diagnostic imaging, successfully having removed identity features while preserving clinically vital facial color features. This study offers significant value for developing intelligent and secure TCM telemedicine systems.

Objective:To investigate the effect and preliminary mechanism of hypervirulent Klebsiella pneumoniae (hvKP) on the immune response to sepsis induced by liver abscess in mice. Methods:C57BL/6 mice were intraperitoneally injected with hvKP strain NTUH-K2044 or classical Klebsiella pneumoniae (cKP) strain HS11286 suspension to prepare the model of sepsis. The survivals rates of mice within 24 h were recorded. HE staining was used to observed the inflammatory cell infiltration in mouse liver tissues. The levels of neutrophil marker lymphocyte antigen 6G (Ly6G) in mouse liver tissues were detected by immunohistochemistry. The activity of reactive oxygen species (ROS) in mouse liver macrophages and peripheral blood monocytes was measured by ROS assay kit. The activation of p105/p50 and p65 in NF-κB signaling pathway in mouse liver macrophages and peripheral blood monocytes was detected by Western blot. The expression of IL-1β, IL-6 and TNF-α at mRNA and protein levels in mouse liver macrophages and peripheral blood monocytes were detected by qRT-PCR and ELISA, respectively. One-way analysis of variance and t test were used for statistical analysis. Results:Compared with cKP, hvKP infection could induce C57BL/6 mice to develop obvious liver abscess with massive inflammatory cell infiltration. Moreover, the level of Ly6G in liver tissues was significantly higher in hvKP-infected mice than in cKP-infected mice ( P<0.000 1), but the survival rate of hvKP-infected mice was significantly lower than that of cKP-infected mice ( P<0.000 1). hvKP significantly promoted the ROS activity ( P<0.000 1) and enhanced the phosphorylation of p105/p50 and p65 in NF-κB signaling pathway in mouse liver macrophages and peripheral blood monocytes as compared with cKP ( P<0.001). The expression of IL-1β, IL-6 and TNF-α at mRNA and protein levels in mouse liver macrophages and peripheral blood monocytes were significantly higher in hvKP-infected mice than in cKP-infected mice ( P<0.001). Conclusion:hvKP can promote the development of liver abscess and induce sepsis in mice.

Objective:To investigate the role of TcpC, a virulence factor of uropathogenic Escherichia coli (UPEC), in inhibiting the formation of neutrophil extracellular traps (NETs) in mouse bone marrow neutrophils, and to analyze its pathogenic mechanism. Methods:C57BL/6 mice were injected with either wild-type (CFT073 wt) or tcpc gene-knockout UPEC CFT073(CFT073 Δ tcpc) to establish a mouse model of cystitis. Mice were sacrificed 3 d post-infection, and their bladders were collected to observe gross pathological changes. Hematoxylin-eosin (HE) staining was used to assess histopathological changes in bladder tissues, and immunohistochemistry was performed to localize TcpC in bladder tissues. Bacterial loads in urine samples were quantified using the ten-fold dilution method, and the presence of tcpc gene in genomic DNA from bladder or urine samples was confirmed by PCR. The expression of TcpC at mRNA and protein levels in mouse bone marrow nuetrophils infected with CFT073 wt was detected by qRT-PCR and Western blot. The effects of UPEC infection on expression of NETs-related proteins and the production of pro-inflammatory factors in mouse bone marrow neutrophils were analyzed by Western blot and ELISA, respectively. Reactive oxygen species(ROS) level and bacterial viability in mouse bone marrow nuetrophils were measured using ROS and bacterial viability detection kits. Results:Compared to the CFT073 Δ tcpc group, the bladder of CFT073 wt group mice exhibited significant enlargement, extensive inflammatory cell infiltration, and the presence of TcpC in bladder tissue. The bacterial load in the urine of CFT073 wt -infected mice was significantly higher than that in the CFT073 Δ tcpc group ( P<0.01). PCR confirmed the presence of the tcpc gene in bladder and urine samples from CFT073 wt-infected mice. Increased expression of TcpC at both mRNA and protein levels was observed in CFT073 wt-infected mouse bone marrow neutrophils. CFT073 wt infection inhibited the mRNA and protein expression of NETs-related proteins and reduced the production of pro-inflammatory factors in mouse bone marrow neutrophils. TcpC suppressed ROS level and promoted the survival of CFT073 wt in mouse bone marrow neutrophils. Conclusions:TcpC enhances the pathogenicity of UPEC CFT073 by inhibiting the formation and activation of NETs in mouse bone marrow neutrophils. This study provides new insights into the pathogenic mechanisms of UPEC and the immune evasion strategies of other pathogenic bacteria, as well as potential targets for clinical prevention and treatment of UPEC-induced urinary tract infections.

Objective This study proposes a Chinese Herbal Medicine(CHM)clustering method based on graph neural network(CHM-GCNK),aiming to discover potential compatibility of CHM at the biological network level.Methods Firstly,collect data of CHM,target,and their interactions,and construct a network of CHM and targets.Secondly,the graph neural network is used to learn the constructed network and obtain the embedded representation of CHM nodes.Then,use the Kmeans algorithm to clustering.Finally,use nonlinear dimensionality reduction technology t-SNE to visualize clustering results.Results The CHM-GCNK,Node2Vec-Kmeans,and SVD-Kmeans methods were applied to cluster 40 CHM for the treatment of lung cancer.The clustering results were five clusters,and CHM-GCNK was superior to the other two methods.The evaluation indicators SS,DBI,and CH showed results of 0.4006,0.7631,and 59.0001,respectively.Conclusion The clustering effect of CHM-GCNK is better and can be applied to the study of CHM compatibility,providing reference for the analysis methods of CHM biological networks in the era of artificial intelligence and multi omics data.

OBJECTIVE To analyze the differences in gait features between patients with cardiovascular and cerebrovascular dis-eases and normal people,and to explore new objective features of traditional Chinese medicine(TCM)whole-body inspection.METHODS A monocular camera was used to collect frontal walking videos of subjects,and the diagnosis results of TCM practitioners were used as disease annotation data;a deep learning model was used to estimate the three-dimensional coordinates of key points;the gait features were defined and calculated based on the three-dimensional coordinates of key points of the lower limbs;differences in gait features among people with cardiovascular and cerebrovascular diseases were collected and verified.RESULTS The three-di-mensional coordinates of key points of the lower limbs were automatically extracted and 8 types of TCM gait features were calculated:step width,stride length,foot lift height,limb angle,left and right hip joint angles,and left and right knee joint angles.It was found that there were significant differences in the features between people with cardiovascular and cerebrovascular diseases and healthy peo-ple(P<0.05).CONCLUSION The TCM inspection gait extracted by this study can effectively distinguish patients with cardiovas-cular and cerebrovascular diseases from healthy people,expands the research scope of TCM whole-body inspection,and provides new ideas for the early detection and prevention of cardiovascular and cerebrovascular diseases.

In recent years,graph representation learning methods have attracted significant attention for their ability to effectively handle graph-structured data.Chinese herbal medicine(CHM),with its multi-component,multi-target,and multi-pathway charac-teristics,demonstrates significant advantages in the treatment of complex diseases,particularly as different combinations of Chinese herbs can produce unique synergistic effects.Graph representation learning provides a new perspective for the in-depth study of CHM combinations.This paper first outlines the relevant methods of graph representation learning,explores the current application status of these methods in CHM combinations,and discusses the challenges and corresponding solutions.By reviewing the research dynamics and cutting-edge trends in this field,this paper aims to provide valuable references and insights for future in-depth research.