The Type III Secretion System (T3SS) serves as a pivotal virulence apparatus for numerous Gram-negative bacterial pathogens, enabling them to infect both animal and plant hosts. Functioning as a molecular syringe, the T3SS directly translocates bacterial effector proteins from the bacterial cytoplasm into the interior of eukaryotic host cells. These effectors are central weapons that precisely manipulate a wide spectrum of host cellular physiological processes, ranging from cytoskeletal dynamics to immune signaling, to establish a favorable niche for bacterial survival and proliferation. Among the diverse arsenal of T3SS effectors, the YopJ family constitutes a critical group of virulence factors. Members of this family are characterized by a conserved catalytic triad structure—a hallmark of the CE clan of cysteine proteases that has been evolutionarily repurposed to confer acetyltransferase activity. A defining and intriguing feature of these enzymes is their stringent dependence on a host-derived eukaryotic cofactor, inositol hexakisphosphate (IP₆), for allosteric activation. This requirement acts as a sophisticated molecular safeguard, ensuring enzymatic activity only within the appropriate host environment, thereby preventing detrimental effects on the bacterium itself. While seminal studies on individual members such as Yersinia’s YopJ and Salmonella’s AvrA have provided deep mechanistic insights, a systematic and integrative understanding of the structure-function relationships across the entire family remains fragmented. Key questions persist regarding how a conserved catalytic core has diverged to recognize distinct host substrates in different kingdoms of life. To address this gap, this article provides a systematic review of the YopJ family, focusing on three interconnected aspects: their structural features, their catalytic mechanism, and their divergent immunosuppressive strategies in animal versus plant hosts. By conducting a comparative analysis of the sequences and resolved three-dimensional structures of three representative members (e.g., HopZ1a, PopP2, AvrA), we elucidate regions of significant variation embedded within the conserved core catalytic architecture. These variable regions, often involving surface loops and substrate-binding interfaces, are crucial determinants of target specificity and functional specialization. The functional divergence of this effector family is most apparent when comparing their modes of action in different hosts. In animal hosts, YopJ-family effectors primarily sabotage innate immune signaling pathways. They achieve this by acetylating key serine and threonine residues within the activation loops of critical kinases in the MAPK and NF‑κB pathways. This post-translational modification blocks the phosphorylation and subsequent activation of these kinases, leading to potent suppression of inflammatory cytokine production. Conversely, in plant hosts, the strategy broadens to dismantle the two-tiered plant immune system. YopJ homologs target a more diverse set of substrates, including immune-associated receptor-like cytoplasmic kinases (RLCKs), microtubule networks via tubulin acetylation (which disrupts cellular trafficking and signaling), and transcription factors central to defense gene regulation. This multi-target approach effectively suppresses both Pattern-Triggered Immunity (PTI) and Effector-Triggered Immunity (ETI). In conclusion, this synthesis aims to deepen the mechanistic understanding of YopJ family-mediated pathogenesis by integrating structural biology with cellular function across host kingdoms. Elucidating the precise molecular basis for substrate selection—how conserved platforms achieve target diversity—is a major frontier. Furthermore, this knowledge provides a vital theoretical foundation for developing novel anti-virulence strategies. Targeting the conserved IP₆-binding pocket or the catalytic acetyltransferase activity itself represents a promising avenue for designing broad-spectrum inhibitors that could disarm this critical family of bacterial effectors, potentially offering new therapeutic approaches against a range of pathogenic bacteria.

AIM To investigate the differences between boiled powder and decoction of Ermiao Powder.METHODS GC-MS was used to identify volatile constituents,after which the content determination of linalool,4-terpineol,α-terpineol,β-eucalyptol and taxifolin in distillate was performed,evaporation rate curve was drawn.The rat model for collagen-induced arthritis was established,then HE and SO/FG staining were conducted,and body weight,footpad swelling degree,arthritis score,immune organ(spleen,thymus)indices,serum inflammatory factors(IL-10,IL-6),ankle joint structure(foot claw swelling,micro-CT)and locomotor ability were detected.RESULTS Total 43 and 26 volatile constituents were identified in boiled powder and decoction,respectively.With the extension of boiling time,the average evaporation rates of 5 volatile constituents in the boiled powder distillate demonstrated the trends of first increase and then decrease,which reached a maximum at 15-20 min;those in the decoction distillate displayed the trends of decrease,which were not detected after 15 min except for β-eucalyptol.Compared with the decoction,the boiled powder exhibited stronger effects on improving foot swelling and arthritis score,alleviating pathological changes in joint tissues,inhibiting inflammatory factors and restoring motor ability.CONCLUSION More volatile constituents are observable in the boiled powder of Ermiao Powder than those in its decoction,along with stronger anti-rheumatoid arthritis activity.The optimal decocting endpoint is determined to be within 15 min in boiling water.

Objective To explore the characteristic changes in white matter microstructure in Parkinson's disease(PD)patients via automated fiber quantification(AFQ)technology,providing a basis for the identification and diagnosis of PD,and to analyze the feasibility of combining the AFQ method with support vector machine(SVM)in the diagnosis of PD.Methods Forty patients with primary PD(PD group)and 20 healthy controls(HC)(HC group)were prospectively selected.The AFQ technology was applied for white matter fiber tract analysis.Statistical analyses were performed using FSL(v6.0)software and SPSS 27.0 software.Independent-sample t-tests were conducted for comparisons between groups in AFQ analysis.The AFQ method was used to analyze the relationship between diffusion tensor imaging(DTI)parameters and Montreal Cognitive Assessment(MoCA)scores.Results(1)The results of AFQ analysis revealed that compared with the HC group,the PD group exhibited significantly lower fractional anisotropy(FA)values in the right cingulum bundle,left cingulum bundle hippocampus,and left uncinate fasciculus,with no differences in the FA values of the remaining 17 fiber tracts.Moreover,PD group demonstrated higher mean diffusivity(MD)values in the left cingulum bundle,left cingulum bundle hippocampus,left inferior frontal occipital fasciculus,left inferior longitudinal fasciculus,left superior longitudinal fasciculus,and left uncinate fasciculus.These differences were statistically significant(P<0.05),while no significant differences were found in the MD values of the remaining 14 fiber tracts.Furthermore,the MD values of the left inferior frontal occipital fasciculus,and left inferior longitudinal fasciculus were negatively correlated with the MoCA scores.(2)The classification results of SVM showed that the best results were achieved when combining the differential nodes of FA and MD as classification features,with an area under the curve(AUC)of 0.922,an accuracy of 84.81%,a sensitivity of 87.50%,and a specificity of 82.05%.Conclusion The DTI parameters in PD patients can serve as potential biomarkers for diagnosis.The AFQ methods provides an effective approach for detecting alterations white matter tract integrity,offering important insights for the identification and diagnosis of PD.The best results are achieved when combining the differential nodes of FA and MD as classification features.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective:To evaluate the accuracy of a self-developed extraoral scanning system based on photogrammetry technology, and to provide evidence for advancing the development and clinical application evaluation of domestically produced scanning devices.Methods:This research group developed a photogrammetry-based implant extraoral scanning system with customized scan bodies. Two distinct edentulous implant resin models were designed and three-dimensional (3D)-printed by Center of Digital Dentistry, Peking University School and Hospital of Stomatology, containing 6 (Model 1) and 8 (Model 2) abutment analogs respectively. Reference data acquisition was performed using a high-precision denture 3D scanner with scan caps mounted on the analogs. Specialized scan bodies were then mounted on the analogs for 3D positional data acquisition using both the self-developed system (experimental group) and the clinically established system (control group). Each system conducted 10 repeated scans per model. Trueness was assessed through root mean square error (RMSE), linear deviation (LD), and angular deviation (AD) relative to reference data, while precision was determined through intra-group RMSE analysis. Systematic comparisons included inter-group performance on identical models and intra-group variability across different models.Results:For Model 1, the experimental group showed statistically significant advantages over controls in intra-group RMSE [(3.10±0.71) μm vs (4.61±1.51) μm, P<0.001], reference-data RMSE [(21.48±0.60) μm vs (32.50±0.63) μm, P<0.001], linear deviation [23.64 (32.35) μm vs 44.86 (55.73) μm, P<0.001], and angular deviation [0.29° (0.29°) vs 0.23° (0.33°), P<0.001]. In Model 2, significant improvements were observed in intra-group RMSE [(4.47±1.58) μm vs (6.21±2.07) μm, P<0.001], reference-data RMSE [(38.84±0.86) μm vs (43.69±1.34) μm, P<0.001], and linear deviation [37.95 (50.68) μm vs 49.71 (58.89) μm, P<0.001]. Both groups exhibited model-dependent variability, with RMSE of precision and trueness of both groups, linear deviation of experimental group, angular deviation of control group showing statistically significant increases (all P<0.001) corresponding to abutment analog quantity. Conclusions:The self-developed scanning system demonstrates superior accuracy in 3D positional acquisition of abutment analogs compared to the contral group system, with implant number identified as a critical determinant of extraoral scanning accuracy.

Objective:To develop an original-mirror alignment associated deep learning algorithm for intelligent registration of three-dimensional maxillofacial point cloud data,by utilizing a dynamic graph-based registration network model(maxillofacial dynamic graph registration network,MDGR-Net),and to provide a valuable reference for digital design and analysis in clinical dental applications.Methods:Four hundred clinical patients without significant deformities were recruited from Peking University School of Stomatology from October 2018 to October 2022.Through data augmentation,a total of 2 000 three-dimensional maxillofacial datasets were generated for training and testing the MDGR-Net algorithm.These were divided into a training set(1 400 cases),a validation set(200 cases),and an internal test set(200 cases).The MDGR-Net model constructed feature vectors for key points in both original and mirror point clouds(X,Y),established correspondences between key points in the X and Y point clouds based on these feature vectors,and calculated rotation and translation matrices using singular value decomposi-tion(SVD).Utilizing the MDGR-Net model,intelligent registration of the original and mirror point clouds were achieved,resulting in a combined point cloud.The principal component analysis(PCA)algorithm was applied to this combined point cloud to obtain the symmetry reference plane associated with the MDGR-Net methodology.Model evaluation for the translation and rotation matrices on the test set was performed using the coefficient of determination(R2).Angle error evaluations for the three-dimensional maxillofacial symmetry reference planes were constructed using the MDGR-Net-associated method and the"ground truth"iterative closest point(ICP)-associated method were conducted on 200 cases in the inter-nal test set and 40 cases in an external test set.Results:Based on testing with the three-dimensional maxillofacial data from the 200-case internal test set,the MDGR-Net model achieved an R2 value of 0.91 for the rotation matrix and 0.98 for the translation matrix.The average angle error on the internal and external test sets were 0.84°±0.55° and 0.58°±0.43°,respectively.The construction of the three-dimensional maxillofacial symmetry reference plane for 40 clinical cases took only 3 seconds,with the model performing optimally in the patients with skeletal Class Ⅲ malocclusion,high angle cases,and Angle Class Ⅲ orthodontic patients.Conclusion:This study proposed the MDGR-Net association method based on intelligent point cloud registration as a novel solution for constructing three-dimensional maxillo-facial symmetry reference planes in clinical dental applications,which can significantly enhance diagnos-tic and therapeutic efficiency and outcomes,while reduce expert dependence.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

AIM To investigate the differences between boiled powder and decoction of Ermiao Powder.METHODS GC-MS was used to identify volatile constituents,after which the content determination of linalool,4-terpineol,α-terpineol,β-eucalyptol and taxifolin in distillate was performed,evaporation rate curve was drawn.The rat model for collagen-induced arthritis was established,then HE and SO/FG staining were conducted,and body weight,footpad swelling degree,arthritis score,immune organ(spleen,thymus)indices,serum inflammatory factors(IL-10,IL-6),ankle joint structure(foot claw swelling,micro-CT)and locomotor ability were detected.RESULTS Total 43 and 26 volatile constituents were identified in boiled powder and decoction,respectively.With the extension of boiling time,the average evaporation rates of 5 volatile constituents in the boiled powder distillate demonstrated the trends of first increase and then decrease,which reached a maximum at 15-20 min;those in the decoction distillate displayed the trends of decrease,which were not detected after 15 min except for β-eucalyptol.Compared with the decoction,the boiled powder exhibited stronger effects on improving foot swelling and arthritis score,alleviating pathological changes in joint tissues,inhibiting inflammatory factors and restoring motor ability.CONCLUSION More volatile constituents are observable in the boiled powder of Ermiao Powder than those in its decoction,along with stronger anti-rheumatoid arthritis activity.The optimal decocting endpoint is determined to be within 15 min in boiling water.

Objective To explore the characteristic changes in white matter microstructure in Parkinson's disease(PD)patients via automated fiber quantification(AFQ)technology,providing a basis for the identification and diagnosis of PD,and to analyze the feasibility of combining the AFQ method with support vector machine(SVM)in the diagnosis of PD.Methods Forty patients with primary PD(PD group)and 20 healthy controls(HC)(HC group)were prospectively selected.The AFQ technology was applied for white matter fiber tract analysis.Statistical analyses were performed using FSL(v6.0)software and SPSS 27.0 software.Independent-sample t-tests were conducted for comparisons between groups in AFQ analysis.The AFQ method was used to analyze the relationship between diffusion tensor imaging(DTI)parameters and Montreal Cognitive Assessment(MoCA)scores.Results(1)The results of AFQ analysis revealed that compared with the HC group,the PD group exhibited significantly lower fractional anisotropy(FA)values in the right cingulum bundle,left cingulum bundle hippocampus,and left uncinate fasciculus,with no differences in the FA values of the remaining 17 fiber tracts.Moreover,PD group demonstrated higher mean diffusivity(MD)values in the left cingulum bundle,left cingulum bundle hippocampus,left inferior frontal occipital fasciculus,left inferior longitudinal fasciculus,left superior longitudinal fasciculus,and left uncinate fasciculus.These differences were statistically significant(P<0.05),while no significant differences were found in the MD values of the remaining 14 fiber tracts.Furthermore,the MD values of the left inferior frontal occipital fasciculus,and left inferior longitudinal fasciculus were negatively correlated with the MoCA scores.(2)The classification results of SVM showed that the best results were achieved when combining the differential nodes of FA and MD as classification features,with an area under the curve(AUC)of 0.922,an accuracy of 84.81%,a sensitivity of 87.50%,and a specificity of 82.05%.Conclusion The DTI parameters in PD patients can serve as potential biomarkers for diagnosis.The AFQ methods provides an effective approach for detecting alterations white matter tract integrity,offering important insights for the identification and diagnosis of PD.The best results are achieved when combining the differential nodes of FA and MD as classification features.