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.

This paper proposes a two-stage method integrating visual foundation models （VFM） and diffusion models. The segment anything model （SAM） as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema， inflammation， and thrombus regions in histopathological images of hemorrhoidal tissue， providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine （TCM） syndrome research. Trained and validated on multi-center retrospective data， the SAM-SegRefiner model achieved an average pixel accuracy of 95.32% and a mean intersection over union （mIoU） of 66.81% on an independent test set， significantly outperfor-ming comparative models such as U-Net， MixU-Net， and SAM-Med2D， and also demonstrating robust cross-center generalization capability. Furthermore， by correlating the quantitatively segmented results from the model with the patients' TCM syndrome types， the potential associations between pathomorphological features and TCM syndrome differentiation have been explored. The analysis revealed no statistically significant differences in the degree of inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

BACKGROUND:At present,the pathogenesis of osteoarthritis is still unclear,and there is a lack of effective means to control the disease.Research on osteoarthritis is mostly concentrated in the field of immunity,and there are few studies in the field of oxidative stress. OBJECTIVE:To explore the roles of oxidative stress and immune infiltration in osteoarthritis and to predict related miRNAs and therapeutic agents. METHODS:The GSE55235 dataset(10 samples of osteoarthritis and 10 healthy control samples)and the GSE55457 dataset(10 samples of osteoarthritis and 10 healthy control samples)were obtained from the GEO database for merging to obtain their differentially expressed genes that were combined with oxidative stress genes to get the differentially expressed genes of oxidative stress.The differentially expressed genes of oxidative stress were analyzed for KEGG and GO enrichment,and the osteoarthritis pathways and biological processes were evaluated using GSEA enrichment analysis.The protein-protein interaction network was constructed using the STRING online website and Cytoscape software,and the Degree algorithm was run to get the key genes.The GSE1919 dataset was obtained from the GEO database as a validation dataset,and the key genes were analyzed by variance analysis and receiver operating characteristic curve analysis to get the core genes.In addition,immune infiltration was evaluated by CIBERSORT and the correlation between core genes and immune cells was explored.miRNA prediction of core genes was performed using TargetScan and target drugs were predicted using the DSigDB database. RESULTS AND CONCLUSION:Sixty-five differentially expressed genes and five core genes(IL1B,CXCL8,MYC,NFKBIA,JUN)associated with oxidative stress were identified.Enrichment analysis showed that differentially expressed genes associated with oxidative stress were concentrated in the pathways of oxidative stress,interleukin-17,osteoclast differentiation,fluid shear stress and atherosclerosis.The area under the receiver operating characteristic curve for the five core genes exceeded 0.85,indicating their excellent specificity and sensitivity in diagnosing bone and joint conditions,as well as their close association with immune cells.The predicted miRNA was has-miR-3937,and the therapeutic small-molecule drugs were metformin,ionomycin and celecoxib.To conclude,oxidative stress and immune infiltration exist in osteoarthritis,and immune infiltration is involved in activating oxidative stress.The core genes and predicted miRNAs can be used as novel markers for the diagnosis of osteoarthritis,and small molecule drugs are predicted to treat osteoarthritis.

Objective To investigate the effects and related mechanisms of mitochondrial-derived peptide MOTS-c on glycochenodeoxycholic acid (GCDCA)-induced injury in human hepatocytes (THLE-3 cells). Methods THLE-3 cells were cultured in vitro and treated with different concentrations of GCDCA and MOTS-c. The optimal concentrations of GCDCA and MOTS-c were determined by cell counting kit (CCK)-8 method. Subsequently, THLE-3 cells were treated or pre-treated with GCDCA (200 µmol/L), MOTS-c (15, 30, 60 µmol/L), the multidrug resistance protein 2 (MRP2) inhibitor Probenecid (500 µmol/L), and the nuclear factor erythroid 2-related factor 2 (Nrf2) inhibitor ML385 (10 µmol/L). Cell proliferation was assessed by CCK-8 method. Lactate dehydrogenase (LDH) levels in the culture medium were measured by biochemical method. Cell apoptosis rates were determined by flow cytometry. MRP2 messenger RNA (mRNA) levels were detected by real-time quantitative polymerase chain reaction (RT-qPCR). MRP2 and Nrf2 protein expression levels were analyzed by Western blotting. Results As the concentration of GCDCA increased, the proliferation activity of THLE-3 cells gradually decreased, while LDH activity in the culture medium and apoptosis levels increased, and the expression levels of MRP2 in the cells decreased (all P<0.05). Treatment with 30 and 60 µmol/L MOTS-c significantly enhanced the proliferation activity of THLE-3 cells exposed to GCDCA, upregulated the expression of MRP2 and Nrf2, and reduced LDH activity and apoptosis levels (all P<0.05). Co-treatment with Probenecid partially reversed the protective effects of MOTS-c on GCDCA-induced THLE-3 cells injury, while co-treatment with ML385 partially inhibited the induction of MRP2 expression by MOTS-c in THLE-3 cells exposed to GCDCA. Conclusions MOTS-c may alleviate GCDCA-induced injury in human hepatocytes (THLE-3 cells), and its mechanism may be related to the upregulation of MRP2 expression mediated by Nrf2.

Maidong products are categorized into "Hang Maidong" and "Chuan Maidong". Since the Ming and Qing Dynasties, "Hang Maidong" has been regarded as having superior quality, but currently, it remains in name only in the market. This article reviewed historical materia medica and local chronicles from the Ming and Qing Dynasties and analyzed the historical evolution of Maidong production areas. The history of Maidong production in Zhejiang can be traced back to the Song Dynasty, and cultivation had already developed by at least the Ming Dynasty. During the Ming and Qing Dynasties, it was consistently used as a tribute. Ming Dynasty chronicles record "Chuan Maidong", which had already been cultivated on a large scale by the Qing Dynasty. "Hang Maidong" and "Chuan Maidong" share the same origin, with the former identifiable by the "gourd waist" shape of its tuberous root. Based on this, it can be inferred that the "Maimendong" herb illustrated in the Origins of Materia Medica(Ben Cao Yuan Shi) and the Maidong stored in the Qing Palace Imperial Pharmacy were both "Hang Maidong". The protection and development of the authentic "Hang Maidong" medicinal herb are urgently needed.
China ; Drugs, Chinese Herbal/history* ; History, 17th Century ; History, Ancient ; Medicine, Chinese Traditional/history* ; History, Medieval ; History, 16th Century ; History, 18th Century ; History, 15th Century ; Plants, Medicinal/chemistry* ; History, 19th Century ; History, 20th Century ; Humans ; Materia Medica/history* ; History, 21st Century

To address the challenges faced by current brain midline segmentation techniques, such as insufficient accuracy and poor segmentation continuity, this paper proposes a deep learning network model based on a two-stage framework. On the first stage of the model, prior knowledge of the feature consistency of adjacent brain midline slices under normal and pathological conditions is utilized. Associated midline slices are selected through slice similarity analysis, and a novel feature weighting strategy is adopted to collaboratively fuse the overall change characteristics and spatial information of these associated slices, thereby enhancing the feature representation of the brain midline in the intracranial region. On the second stage, the optimal path search strategy for the brain midline is employed based on the network output probability map, which effectively addresses the problem of discontinuous midline segmentation. The method proposed in this paper achieved satisfactory results on the CQ500 dataset provided by the Center for Advanced Research in Imaging, Neurosciences and Genomics, New Delhi, India. The Dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), and normalized surface Dice (NSD) were 67.38 ± 10.49, 24.22 ± 24.84, 1.33 ± 1.83, and 0.82 ± 0.09, respectively. The experimental results demonstrate that the proposed method can fully utilize the prior knowledge of medical images to effectively achieve accurate segmentation of the brain midline, providing valuable assistance for subsequent identification of the brain midline by clinicians.
Humans ; Brain/diagnostic imaging* ; Deep Learning ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer

Objective To explore the application of DRG mortality risk stratification method in evaluating perioperative healthcare quality,analyzes the distribution characteristics and influencing factors of perioperative mortality patients,and provide decision-making support for reducing perioperative mortality.Methods Collect perioperative case data from a tertiary hospital in Jiangxi Province from 2021 to 2023,and classify the risk of death for different DRG group.A chi-square test was used for univariate analysis,and a logistic regression model was used to analyze the factors affecting perioperative patient mortality.Results Among the death cases,the medium-and high-risk groups accounted for the highest proportion(59.31%),and the ratio of male to female was 2.2∶1.0,the most cases were over 65 years old(57.60%),and the top five Core DRG disease groups are neurological disorders,circulatory disorders,early grouping disorders,digestive disorders,and hepatobiliary and pancreatic disorders.Critical condition,serious complications or comorbidities,operation grade,relative weight,age,gender,and hospital day were independent influencing factors of perioperative death.All differences were statistically significant(P＜0.05).Conclusion To reduce perioperative mortality risks,it is necessary to enhance the treatment capacity for critically ill patients,optimize the dynamic management mechanism for surgical grading,and strengthen the risk management of emergency surgical medical care.

Objective To determine the effect of knee extension constraint training on bilateral knee biomechanics and bilateral symmetry in running after anterior cruciate ligament reconstruction(ACLR).Methods A total of 33 male patients with unilateral ACL injuries were randomly assigned to a BRACE group of 14 wearing a brace with limitation of knee extension,a PLACEBO group of 10 wear-ing a brace without limitation of knee extension,and a CONTROL group of 9,wearing no brace.All groups underwent unilateral hamstring-auto graft ACLR surgery,immediately followed by 12-week rou-tine rehabilitation.Between week 13 and 48,both the BRACE and PLACEBO groups wore braces for one hour on Mondays.Then,running biomechanical tests were performed at the ends of Week 12 and Week 48,and the bilateral knee extension/flexion angle,moment and inter-leg difference(ILD)were calculated.One-dimensional statistical parametric mapping(SPM1d)two-way ANOVA with repeated measures on one factor was used to identify differences in bilateral knee biomechanics and ILD among the three groups before and after the intervention.Results There was no significant interaction effect of group and time on bilateral knee flexion angle,knee extension moment,and ILD in running(P>0.05).Moreover,no significant effect of group was found on the bilateral knee biomechanics and ILD in running(P>0.05).Additionally,significant effects of time were observed on bilateral knee flexion angle and extension moment in running.However,bilateral knee flexion angle decreased during termi-nal stance(ACLR leg:89%～100%,P=0.036;non-ACLR leg:94%～100%,P=0.046),while the bi-lateral knee extension moment increased during mid-stance(ACLR leg:17%～59%,P<0.001;non-ACLR leg:38%～61%,P<0.001)between week 12 and 48.Conclusion In this study,no improvement was found in the abnormal knee biomechanics and symmetry during running in male patients after uni-lateral ACL reconstruction through long-term knee extension constraint training.Moreover,within one year after ACL reconstruction,the knee extension moment of the surgical limb increased gradually over time,with no changes in the knee flexion angle of the surgical limb and bilateral knee symme-try,suggesting that abnormal knee biomechanics and bilateral symmetry should be paid attention to in the post-surgery rehabilitation.

Distinguishing pseudoprogression (PsP) from true progression (TP) in patients with high-grade gliomas after treatment remains a critical challenge, as their appearances on conventional MRI are often similar. Accurate differentiation between the two is of great clinical significance for guiding and adjusting therapeutic strategies. MRI functional imaging can reveal key tissue characteristics such as water molecule diffusion, microvascular perfusion, and metabolic changes within lesions after treatment, thereby providing valuable objective evidences for differentiating PsP from TP. Especially in recent years, the radiomics analysis method based on large-scale MRI image data has further improved the accuracy in distinguishing PsP from TP. This article reviews the research progress on applications of various MRI techniques and combination of radiomics with other artificial intelligence methods in differentiating PsP from TP in high-grade gliomas, aiming to enhance the clinicians' understanding of differential diagnosis of PsP and TP in high-grade gliomas, and offer ideas and directions for emerging MRI application in this field.

Objective To explore the application of DRG mortality risk stratification method in evaluating perioperative healthcare quality,analyzes the distribution characteristics and influencing factors of perioperative mortality patients,and provide decision-making support for reducing perioperative mortality.Methods Collect perioperative case data from a tertiary hospital in Jiangxi Province from 2021 to 2023,and classify the risk of death for different DRG group.A chi-square test was used for univariate analysis,and a logistic regression model was used to analyze the factors affecting perioperative patient mortality.Results Among the death cases,the medium-and high-risk groups accounted for the highest proportion(59.31%),and the ratio of male to female was 2.2∶1.0,the most cases were over 65 years old(57.60%),and the top five Core DRG disease groups are neurological disorders,circulatory disorders,early grouping disorders,digestive disorders,and hepatobiliary and pancreatic disorders.Critical condition,serious complications or comorbidities,operation grade,relative weight,age,gender,and hospital day were independent influencing factors of perioperative death.All differences were statistically significant(P＜0.05).Conclusion To reduce perioperative mortality risks,it is necessary to enhance the treatment capacity for critically ill patients,optimize the dynamic management mechanism for surgical grading,and strengthen the risk management of emergency surgical medical care.