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.

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 the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

Objective: To investigate the association between uric acid level and elevated blood pressure among children and adolescents in Guangzhou, so as to provide a scientific basis for prevention and control of hypertension in the pediatric population. Methods: From March to December 2019, using stratified cluster random sampling, 3 626 children and adolescents aged 6-17 years without elevated blood pressure at baseline were recruited from 6 primary and secondary schools in Guangzhou. Serum uric acid levels were measured at baseline, and divided into four groups ( Q1-Q 4) based on age and sex specific quartiles for analyses. Two waves of follow up were carried out from October 2020 to May 2021 and from October 2021 to May 2022, respectively. Multilevel robust Poisson regression was used to evaluate the association between uric acid level and elevated blood pressure among children and adolescents. The additive interaction between high uric acid level and sugar sweetened beverage (SSB) intake on elevated blood pressure was assessed by stratification analysis and calculation of the relative excess risk due to interaction (RERI). Results: A total of 1 131 new cases of elevated blood pressure were identified during the two follow ups, with an incidence density of 130.5 cases 1 000 person years. Multilevel robust Poisson regression analysis showed that after adjusting for confounders such as gender, age and annual family income, compared with those in the uric acid Q 1 level, the risk of elevated blood pressure in individuals in the Q 4 level increased by 25% ( RR=1.25, 95%CI =1.08-1.44), and the risk of elevated systolic blood pressure increased by 20% ( RR=1.20, 95%CI =1.05-1.36) (both P <0.05); no statistical association was observed between uric acid and elevated diastolic blood pressure (all P >0.05). In the stratified analysis, the statistical associations of uric acid Q 4 level with elevated blood pressure ( RR=1.34, 95%CI =1.13-1.58) and elevated systolic blood pressure ( RR=1.33, 95%CI =1.14-1.55) were only observed in the group with SSB intake ≥ 250 mL/week (both P <0.05). The RERI values for the effect of uric acid Q 4 and Q 3 levels with SSB intake ≥ 250 mL/week on elevated systolic and diastolic blood pressure were 0.29 and 0.44 (both P <0.05). Conclusions High uric acid level is a risk factor for elevated blood pressure among children and adolescents, with a predominant effect on systolic blood pressure. High uric acid level and SSB intake synergistically promote the increase of blood pressure of children and adolescents.

OBJECTIVE To formulate the Expert Consensus on the Management of Radiopharmaceuticals in Medical Institutions （hereinafter referred to as “the Consensus”）， focusing on key aspects such as the procurement of radiopharmaceuticals， the management of self-made radiopharmaceutical preparations， standardized clinical application， and patient medication safety management， so as to provide a reference for improving the whole-process management of radiopharmaceuticals in medical institutions.METHODS The Consensus was initiated by the Chinese Society of Nuclear Medicine， the Clinical Pharmacy Professional Committee of the China Anti-Cancer Association， and the Committee of Integrative Oncology Pharmacy of the China Anti-Cancer Association. Organized by the consensus drafting group more than 90 experts from the fields of nuclear medicine， nuclear pharmacy， and pharmacy science across China were involved. The Consensus was developed through systematic review of relevant laws， regulations， policy documents， and existing experience in radiopharmaceutical management， followed by extensive discussion and deliberation to finalize the consensus. RESULTS &CONCLUSIONS The Consensus establishes 10 key points for whole-process management of radiopharmaceuticals in medical institutions， covering organizational management and personnel requirements， procurement， preparation， acceptance， storage， risk prevention and control during use， radiation protection， and waste disposal. The Consensus provides a reference for improving radiopharmaceutical management in medical institutions and for ensuring their efficacy and safety in clinical application.

ObjectiveTo propose an X-Gait motor rehabilitation assessment system based on a perception-cognition decoupling architecture to jointly achieve high-accuracy gait recognition and clinical interpretability for low clinical credibility and opaque decision-making workflows in existing intelligent gait assessment models. MethodsIn the perception layer, YOLOv11-Pose and PoseFormerV2 were integrated for 2D-to-3D human pose reconstruction. A long short-term memory network was adopted for temporal classification of normal, abnormal and pathological gaits. The layer-wise relevance propagation algorithm was used to identify discriminative joints and automatically quantify biomechanical indices including trunk forward inclination angle, stance phase percentage and stride symmetry. In the cognition layer, rehabilitation-oriented prompt engineering and prior knowledge graphs were combined to drive large language models, converting quantitative biomechanical measurements into clinically reasonable structured reports. Eight subjects were enrolled for bedside clinical validation to evaluate classification performance and clinical consistency. ResultsThe overall gait classification accuracy of X-Gait reached 94.1%. The 2D pose detection achieved 100% valid detection rate with an average confidence of 0.819. The mean per-joint position error of 3D pose reconstruction was 105.97 mm. In clinical validation of eight cases, outputs generated by explainable artificial intelligence combined with large language models showed high consistency with manual assessments from experienced rehabilitation physicians. ConclusionThe proposed X-Gait system based on perception-cognition decoupling realizes high-precision gait identification and clinically interpretable outputs.

Objective: To investigate the incidence and independent risk factors of adverse events (AEs) associated with therapeutic plasma exchange (TPE) in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT), and to provide evidence to support the safe use of TPE in clinical practice. Methods: This retrospective study included 167 allo-HSCT recipients who underwent a total of 385 TPE procedures between March 2022 and December 2023. Patients were divided into two groups according to the occurrence of TPE-related AEs. Baseline characteristics, plasma exchange parameters, and clinical features were collected. Univariate analysis and multivariable Firth logistic regression were performed to identify independent risk factors for AEs. Results: A total of 385 TPE procedures were performed in 167 allo-HSCT patients, and 33 patients (19.8%) experienced TPE-related AEs. Among the 33 patients who experienced AEs, allergic reactions were the most common (n=18, 54.5%), followed by blood pressure fluctuations (n=11, 33.3%) and hypocalcemia (n=4, 12.1%). Most AEs were classified as grade II (n=23, 69.7%), and no grade IV (fatal) events were observed. Univariate analysis demonstrated a significant difference in the incidence of AEs among different hematocrit (HCT) categories (<20%, 20%-30%, and>30%) (Fisher's exact test, P=0.022). Although the overall difference was statistically significant, none of the pairwise comparisons remained significant after Bonferroni correction (adjusted P>0.0167). Multivariable Firth logistic regression identified higher HCT levels, analyzed as a continuous variable (OR=1.07, 95% CI: 1.01-1.13, P=0.020), and TPE performed for cytokine release syndrome (CRS) (OR=9.21, 95% CI: 1.25-78.00, P=0.030) as potential independent risk factors for TPE-related AEs. Receiver operating characteristic (ROC) curve analysis showed that HCT had an AUC of 0.67 (95% CI: 0.57-0.77) for predicting AEs, with an optimal cutoff value of 24.5%, a sensitivity of 60.6%, and a specificity of 75.4%. Using this cutoff, the AEs rate was significantly higher in patients with HCT≥24.5% than in those with HCT<24.5% (37.7% vs 11.4%, P<0.001). Conclusion: A-mong allo-HSCT patients receiving TPE, allergic reactions were the predominant AEs. Higher HCT levels and TPE performed for CRS may be associated with an increased risk of AEs. HCT≥24.5% may help identify patients requiring intensified monitoring, although its discriminatory ability is moderate (AUC=0.67) and it should not be used as a standalone criterion for clinical decision-making. Findings regarding the CRS subgroup require validation in larger cohorts. Optimizing extracorporeal circuit priming and strengthening peri-procedural monitoring may help improve the safety of TPE.

This article explores the pathogenesis ， evolution and the prevention-treatment strategies of hypertension-related coronary microvascular dysfunction （H-CMD） from the collateral disease theory. Long-term hypertension may lead to coronary microvascular vasomotor imbalance， endothelial injury， vascular remodeling， microthrombosis， reduced perfusion reserve， and microvascular rarefaction， suggesting a progressive pathological process from functional disturbance to structural injury， collateral obstruction， and insufficient repair. Collateral disease theory emphasizes the network-like distribution， gradual branching， and permeating and nourishing functions of collaterals， as well as the concept that prolonged disease enters the collaterals， providing a reference for understanding coronary microcirculatory dysfunction and malnourishment of heart collaterals. Accordingly， the traditional Chinese medicine （TCM） pathogenesis of H-CMD can be summarized into four interconnected stages： Collateral Qi stagnation， scorching injury of collateral vessels， phlegm-stasis obstruction of collaterals， and collateral deficiency with malnourishment. Collateral Qi stagnation is the initiating stage， mainly corresponding to abnormal coronary microvascular vasomotor regulation and endothelial dysfunction. Scorching injury of collateral vessels represents the developmental stage and is closely associated with inflammation， oxidative stress， and endothelial barrier injury after Qi stagnation transforms into fire. Phlegm-stasis obstruction of collaterals represents the prolonged stage and may aggravate vascular wall thickening， luminal stenosis， microthrombosis， and insufficient microcirculatory perfusion. Collateral deficiency with malnourishment is the late-stage outcome， reflecting deficiency of healthy Qi， collateral damage， Qi-blood malnourishment， and weakened repair capacity， mainly manifested as microvascular rarefaction， occlusion， and microcirculatory homeostatic imbalance. Therapeutically， stage-based syndrome differentiation and the combined strategy of dredging and nourishing collaterals should be emphasized. Specifically， promoting Qi movement， resolving stagnation， soothing the liver， and harmonizing collaterals should be adopted in the early stage. Clearing heat， pacifying the liver， calming and protecting collaterals should be adopted in the developmental stage. Resolving phlegm， activating blood， and dredging collaterals should be implemented in the prolonged stage. Supplementing Qi， nourishing blood， tonifying the kidney， regulating balance， strengthening healthy Qi， and nourishing collaterals should be carried out in the late stage. Related formulas and medicinals may act by regulating the nitric oxide/endothelin-1（NO/ET-1） balance， inhibiting inflammation and oxidative stress， improving microcirculatory perfusion， and promoting angiogenesis and endothelial repair. Collateral disease theory may provide a theoretical basis for understanding the dynamic pathogenesis of H-CMD and for stage-based TCM intervention.

Heart failure （HF） is the end-stage of various cardiovascular diseases， with high morbidity and mortality. The existing “new quadruple” standard treatment can hardly reverse the core pathological process of myocardial remodeling， and long-term medication may pose risks of drug resistance and adverse reactions. Therefore， new therapeutic targets and strategies are urgently needed. Metabolic reprogramming is the upstream core mechanism regulating phenotypic polarization and functional activation of macrophages， which drives the occurrence and development of HF through the core axis of metabolic reprogramming-immune imbalance-ventricular remodeling and has become a cutting-edge potential target for the prevention and treatment of HF. Traditional Chinese medicine （TCM） has a long history in the prevention and treatment of HF. A large number of studies in recent years have confirmed that different therapeutic methods of TCM can correct the polarization imbalance， restore the homeostasis of myocardial immune microenvironment， inhibit myocardial injury， and delay the pathological process of HF by targeting and regulating the metabolic reprogramming of glucose， lipids， mitochondria， and amino acids in macrophages. This paper systematically expounds the core pathological mechanism of metabolic reprogramming of macrophages in HF， sorts out the research progress of TCM in preventing and treating HF by intervening this target， analyzes the regulatory rules， summarizes the limitations of existing research， and puts forward future directions. This review aims to provide a systematic theoretical reference and new research ideas for TCM clinical diagnosis and treatment and new drug development of HF.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.