BackgroundThe violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance. ObjectiveTo construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention. MethodsA total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value. ResultsLASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168. ConclusionThe SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］

ObjectiveTo investigate the effects of the classical Chinese medicine compound prescription Erjingwan on the inflammatory response and apoptosis of skeletal muscle cells in a mouse model of sarcopenia and decipher the mechanism based on the silent information regulator 1 （SIRT1）/nuclear factor erythroid 2-related factor 2 （Nrf2）/heme oxygenase-1 （HO-1） signaling pathway. MethodsForty C57/BL6 male mice were randomized into a control group， a model group， and groups with different doses of Erjingwan （8，16，32 g·kg^-1）. The mouse model of sarcopenia was established by D-gal-induced skeletal muscle senescence. The body weight and grip strength of mice treated with different doses of Erjingwan were examined to evaluate their physiological functions. Hematoxylin-eosin （HE） staining and Masson staining were used to observe the pathological changes and fibrosis in the skeletal muscle of mice. Enzyme-linked immunosorbent assay （ELISA） was adopted to determine the levels of tumor necrosis factor-α （TNF-α） and interleukin-6 （IL-6） in the serum samples of mice， and biochemical tests were conducted to quantify the levels of superoxide dismutase （SOD）， malondialdehyde （MDA）， and glutathione （GSH） in the serum. The protein and mRNA levels of SIRT1， Nrf2， B-cell lymphoma （Bcl-2）， and Bcl-2-associated X protein （Bax） were determined by Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. ResultsAfter 4 weeks of drug intervention， the model group exhibited significant reductions in body weight and grip strength （P0.01） compared with the control group. Compared with the model group， all doses of Erjingwan increased the body weight in mice at week 8 （P0.01） and grip strength from week 6 （P0.01）. HE staining revealed clear muscle fiber structure in the control group， muscle fiber rupture and atrophy in the model group， and dose-dependent repair of muscle fiber structure in the Erjingwan groups. Masson staining showed minimal collagen fibers and mild fibrosis in the control group， collagen fiber proliferation and severe fibrosis in the model group， and collagen proliferation with dose-dependent inhibition of fibrosis in the Erjingwan groups. ELISA results showed that serum levels of TNF-α and IL-6 were elevated in the model group compared with those in the control group （P0.01）. After intervention， the low-dose Erjingwan group exhibited a decreased TNF-α level （P0.05）， while the medium and high-dose groups showed decreases in both TNF-α and IL-6 levels （P0.01）. Biochemical assays revealed that the model group had decreased SOD and GSH levels （P0.01） and an increased MDA level （P0.01） compared with the control group. The medium and high-dose Erjingwan groups exhibited increases in SOD and GSH levels （P0.01） and decreases in MDA level （P0.01）， compared with the model group. WB and Real-time PCR results showed that compared with the control group， the model group presented down-regulated protein and mRNA levels of SIRT1， Nrf2， HO-1， and Bcl-2 in the muscle tissue （P0.01） and up-regulated protein and mRNA levels of Bax （P0.01）. Compared with the model group， Erjingwan at different doses up-regulated the protein levels of SIRT1， Nrf2， HO-1， and Bcl-2 （P0.01） and down-regulated the protein and mRNA levels of Bax （P0.01） in the muscle tissue. Low-dose Erjingwan elevated the mRNA levels of Nrf2 and HO-1 （P0.05， P0.01）， and medium and high-dose Erjingwan up-regulated the mRNA levels of SIRT1， Nrf2， HO-1， and Bcl-2 （P0.01）. ConclusionErjingwan reduced the content of inflammatory factors in skeletal muscle cells， improved the antioxidant capacity， and attenuated pathological changes and fibrosis in the muscle of the mouse model of sarcopenia by regulating the SIRT1/Nrf2/HO-1 pathway， inflammatory response， and apoptosis network.

ObjectiveTo investigate the effects of the classical Chinese medicine compound prescription Erjingwan on the inflammatory response and apoptosis of skeletal muscle cells in a mouse model of sarcopenia and decipher the mechanism based on the silent information regulator 1 （SIRT1）/nuclear factor erythroid 2-related factor 2 （Nrf2）/heme oxygenase-1 （HO-1） signaling pathway. MethodsForty C57/BL6 male mice were randomized into a control group， a model group， and groups with different doses of Erjingwan （8，16，32 g·kg^-1）. The mouse model of sarcopenia was established by D-gal-induced skeletal muscle senescence. The body weight and grip strength of mice treated with different doses of Erjingwan were examined to evaluate their physiological functions. Hematoxylin-eosin （HE） staining and Masson staining were used to observe the pathological changes and fibrosis in the skeletal muscle of mice. Enzyme-linked immunosorbent assay （ELISA） was adopted to determine the levels of tumor necrosis factor-α （TNF-α） and interleukin-6 （IL-6） in the serum samples of mice， and biochemical tests were conducted to quantify the levels of superoxide dismutase （SOD）， malondialdehyde （MDA）， and glutathione （GSH） in the serum. The protein and mRNA levels of SIRT1， Nrf2， B-cell lymphoma （Bcl-2）， and Bcl-2-associated X protein （Bax） were determined by Western blot and Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）， respectively. ResultsAfter 4 weeks of drug intervention， the model group exhibited significant reductions in body weight and grip strength （P0.01） compared with the control group. Compared with the model group， all doses of Erjingwan increased the body weight in mice at week 8 （P0.01） and grip strength from week 6 （P0.01）. HE staining revealed clear muscle fiber structure in the control group， muscle fiber rupture and atrophy in the model group， and dose-dependent repair of muscle fiber structure in the Erjingwan groups. Masson staining showed minimal collagen fibers and mild fibrosis in the control group， collagen fiber proliferation and severe fibrosis in the model group， and collagen proliferation with dose-dependent inhibition of fibrosis in the Erjingwan groups. ELISA results showed that serum levels of TNF-α and IL-6 were elevated in the model group compared with those in the control group （P0.01）. After intervention， the low-dose Erjingwan group exhibited a decreased TNF-α level （P0.05）， while the medium and high-dose groups showed decreases in both TNF-α and IL-6 levels （P0.01）. Biochemical assays revealed that the model group had decreased SOD and GSH levels （P0.01） and an increased MDA level （P0.01） compared with the control group. The medium and high-dose Erjingwan groups exhibited increases in SOD and GSH levels （P0.01） and decreases in MDA level （P0.01）， compared with the model group. WB and Real-time PCR results showed that compared with the control group， the model group presented down-regulated protein and mRNA levels of SIRT1， Nrf2， HO-1， and Bcl-2 in the muscle tissue （P0.01） and up-regulated protein and mRNA levels of Bax （P0.01）. Compared with the model group， Erjingwan at different doses up-regulated the protein levels of SIRT1， Nrf2， HO-1， and Bcl-2 （P0.01） and down-regulated the protein and mRNA levels of Bax （P0.01） in the muscle tissue. Low-dose Erjingwan elevated the mRNA levels of Nrf2 and HO-1 （P0.05， P0.01）， and medium and high-dose Erjingwan up-regulated the mRNA levels of SIRT1， Nrf2， HO-1， and Bcl-2 （P0.01）. ConclusionErjingwan reduced the content of inflammatory factors in skeletal muscle cells， improved the antioxidant capacity， and attenuated pathological changes and fibrosis in the muscle of the mouse model of sarcopenia by regulating the SIRT1/Nrf2/HO-1 pathway， inflammatory response， and apoptosis network.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

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.

BACKGROUND:The most common complication of traumatic femoral neck fractures after internal fixation is femoral head necrosis.Currently,many studies have reported on the risk factors that affect the occurrence and development of postoperative femoral head necrosis,but there is still a lack of tools to predict the risk of femoral head necrosis after internal fixation of femoral neck fractures.OBJECTIVE:To develop a predictive model that estimates the risk of femoral head necrosis shortly after patients with femoral neck fractures receive cannulated screw internal fixation.METHODS:A retrospective analysis reviewed clinical records of 172 patients who underwent cannulated screw internal fixation for femoral neck fractures at Department of Orthopedics of Mianyang Central Hospital from January 2013 to June 2023.Patients were categorized into two groups based on the presence or absence of femoral head necrosis within one year post-operation:the necrosis group and the non-necrosis group.Univariate analysis,Lasso regression,and multivariate Logistic regression techniques were employed to identify the determinants of femoral head necrosis.A nomogram prediction model was constructed using R language's"rms"package,version 4.0.The receiver operating characteristic curve was used to evaluate the discriminatory ability of the model.The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model,and the decision curve analysis was used to determine its clinical application benefits.Internal validation of the study was conducted using the Bootstrap method,involving 1 000 repeated samplings.To delve deeper into the primary factors influencing femoral head necrosis post-internal fixation of the femoral neck,this paper employed the SHAP method for data set analysis.RESULTS AND CONCLUSION:(1)The risk factors leading to femoral head necrosis in the short term after cannulated screw fixation of femoral neck fractures include:smoking,diabetes,Garden classification,fracture line location,reduction quality,age,and operation time.(2)The prediction model demonstrated robust performance,evidenced by an area under the curve of 0.940(95％Confidence Interval:0.903 to 0.977),indicating a high level of prediction accuracy.The model achieved a sensitivity of 90.2％and a specificity of 87.6％,indicating that its diagnostic performance was stable.The Hosmer-Lemeshow goodness-of-fit test yielded a chi-square value of 6.593 with a P-value of 0.581,confirming that the model's predictions closely align with the observed outcomes.(3)The calibration curve of the model also performed well,and its overall trend was very close to the ideal curve,further proving the high accuracy of the model.(4)The internal validation was carried out by the Bootstrap method with 1 000 repeated samplings,and the area under the curve of the model internal validation was still as high as 0.939,proving that the model had good stability.(5)Through the decision curve,it is found that within the probability threshold range of 1％to 92％,the model can obtain the maximum net benefit value.(6)The SHAP analysis results show that among the risk factors analyzed in this study,the location of the fracture line serves as the most significant predictor of femoral head necrosis following internal fixation with cannulated screws in femoral neck fractures,and subcapital fractures are extremely prone to femoral head necrosis after surgery.(7)It is concluded that the validated prediction model demonstrates strong discriminative power and reliability,offering practical clinical utility.It serves as a useful reference tool for short-term risk assessment of femoral head necrosis following internal fixation of femoral neck fractures.

BACKGROUND:The most common complication of traumatic femoral neck fractures after internal fixation is femoral head necrosis.Currently,many studies have reported on the risk factors that affect the occurrence and development of postoperative femoral head necrosis,but there is still a lack of tools to predict the risk of femoral head necrosis after internal fixation of femoral neck fractures.OBJECTIVE:To develop a predictive model that estimates the risk of femoral head necrosis shortly after patients with femoral neck fractures receive cannulated screw internal fixation.METHODS:A retrospective analysis reviewed clinical records of 172 patients who underwent cannulated screw internal fixation for femoral neck fractures at Department of Orthopedics of Mianyang Central Hospital from January 2013 to June 2023.Patients were categorized into two groups based on the presence or absence of femoral head necrosis within one year post-operation:the necrosis group and the non-necrosis group.Univariate analysis,Lasso regression,and multivariate Logistic regression techniques were employed to identify the determinants of femoral head necrosis.A nomogram prediction model was constructed using R language's"rms"package,version 4.0.The receiver operating characteristic curve was used to evaluate the discriminatory ability of the model.The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model,and the decision curve analysis was used to determine its clinical application benefits.Internal validation of the study was conducted using the Bootstrap method,involving 1 000 repeated samplings.To delve deeper into the primary factors influencing femoral head necrosis post-internal fixation of the femoral neck,this paper employed the SHAP method for data set analysis.RESULTS AND CONCLUSION:(1)The risk factors leading to femoral head necrosis in the short term after cannulated screw fixation of femoral neck fractures include:smoking,diabetes,Garden classification,fracture line location,reduction quality,age,and operation time.(2)The prediction model demonstrated robust performance,evidenced by an area under the curve of 0.940(95％Confidence Interval:0.903 to 0.977),indicating a high level of prediction accuracy.The model achieved a sensitivity of 90.2％and a specificity of 87.6％,indicating that its diagnostic performance was stable.The Hosmer-Lemeshow goodness-of-fit test yielded a chi-square value of 6.593 with a P-value of 0.581,confirming that the model's predictions closely align with the observed outcomes.(3)The calibration curve of the model also performed well,and its overall trend was very close to the ideal curve,further proving the high accuracy of the model.(4)The internal validation was carried out by the Bootstrap method with 1 000 repeated samplings,and the area under the curve of the model internal validation was still as high as 0.939,proving that the model had good stability.(5)Through the decision curve,it is found that within the probability threshold range of 1％to 92％,the model can obtain the maximum net benefit value.(6)The SHAP analysis results show that among the risk factors analyzed in this study,the location of the fracture line serves as the most significant predictor of femoral head necrosis following internal fixation with cannulated screws in femoral neck fractures,and subcapital fractures are extremely prone to femoral head necrosis after surgery.(7)It is concluded that the validated prediction model demonstrates strong discriminative power and reliability,offering practical clinical utility.It serves as a useful reference tool for short-term risk assessment of femoral head necrosis following internal fixation of femoral neck fractures.

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.

Triptolide （TP）， the core active component of the traditional Chinese medicine Tripterygium wilfordii ， exhibits remarkable pharmacological activities including anti-inflammatory， immunosuppressive and anti-tumor effects， and holds broad application prospects in the treatment of major diseases such as autoimmune diseases and malignant tumors. However， TP has a narrow therapeutic window and causes multi-organ toxicities including liver， kidney and reproductive toxicities， which severely restrict its safe clinical application and new drug development. Therefore， toxicity reduction and efficacy enhancement has become a core scientific problem urgently to be solved in this field. This paper systematically reviews the four core strategies for TP toxicity reduction and efficacy enhancement， including structural modification， dosage form improvement， herbal compatibility， and external therapies of traditional Chinese medicine. Among them， structural modification optimizes the toxic and efficacy characteristics of TP from the molecular structure level， with typica l derivatives including （5 R ）-5-hydroxy triptolide， ZT01， PG490-88， etc. Dosage form modification achieves toxicity reduction and efficacy enhancement via targeted and sustained-controlled drug release of diverse delivery systems. It includes triptolide preparations such as nanoparticles， liposomes， microemulsion gels and liquid crystals， possessing favorable clinical transformation potential. The herbal compatibility and external therapies of traditional Chinese medicine conform to the holistic view of traditional Chinese medicine and have a profound clinical application foundation， but their mechanisms of action are insufficiently elucidated， and they lack unified standardized specifications and high-quality evidence-based proof. In the future， we should rely on multi-omics technology to elucidate the toxic and efficacy mechanisms， integrate technologies to optimize preparations， improve the evaluation system and promote clinical transformation.