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 confirm trigger items for adverse drug reaction （ADR） induced by bevacizumab， to identify and analyze the occurrence of related ADR， and to establish a predictive model for severe adverse reaction （SAR） caused by this drug. METHODS Based on the global trigger tool （GTT） theory， and referencing the GTT White Paper， drug package inserts and relevant literature， trigger items for bevacizumab-related ADR were confirmed using a single-round Delphi method. Utilizing these established items， electronic medical records of relevant patients at Guilin People’s Hospital from January 2020 to September 2024 were actively screened via the China Hospital Pharmacovigilance System. Pharmacists then identified and tallied the occurrence of bevacizumab-induced ADR. Data from patients with any positive trigger item served as the study subjects （divided into training and test sets at a ratio of 7∶3）， candidate feature variables were selected from 39 related variables using the Boruta algorithm， and the multivariable Logistic regression analysis was performed with the occurrence of SAR as the dependent variable. Based on these candidate features， Logistic Regression， Extreme Gradient Boosting， Light Gradient Boosting Machine， Random Forest， and Categorical Boosting models were constructed. Model performance was evaluated using metrics including the area under the curve （AUC） of receiver operating characteristic curve and recall rate. The Shapley Additive exPlanations （SHAP） method was applied to analyze and interpret the contribution of each variable. A nomogram was constructed based on the optimal model. RESULTS A total of 38 trigger items for active monitoring of bevacizumab-related ADR were determined， comprising 17 laboratory indicators， 13 clinical manifestations， and 8 intervention measures. In total， 483 patients with positive trigger items were included， and 318 patients with bevacizumab-induced ADR were identified， including 83 SARs. The positive predictive values for the trigger items and cases were 43.57% （708/1 625） and 63.84% （318/483）， respectively. Bevacizumab-induced ADR involved 7 systems/organs， with the hematological system being the most frequently involved （64.15%）. The Boruta algorithm selected 7 vari ables： serum potassium， hematocrit， albumin-to-globulin ratio， prealbumin， hypertension history， age and red blood cell count. Multivariable Logistic regression showed that elevated serum potassium levels were associated with a decreased risk of bevacizumab-induced SAR （OR＝0.234， P ＝0.002）， while a history of hypertension （OR＝2.642， P ＝0.006） and increased age （OR＝1.040， P ＝0.025） were associated with an increased risk. The Logistic Regression model demonstrated superior performance with higher AUC， F1 score and recall rate （0.761， 0.447， 0.607）， compared to other models. SHAP evaluation results indicated that variables such as serum potassium， hematocrit， and age ranked highest in importance. CONCLUSIONS Totally 38 trigger entries have been successfully identified for active screening of bevacizumab-related ADR. Elevated serum potassium levels are a protective factor against bevacizumab-induced SAR， whereas the hypertension history and increased age are risk factors. The Logistic Regression model is the optimal predictive model.

Objective To evaluate the value of serum Mac-2 binding protein (M2BP) for assessment of the severity of schisto somiasis-induced liver fibrosis, so as to provide insights into non-invasive diagnosis and disease surveillance of liver fibrosis caused by schistosomiasis. Methods A total of 234 individuals with a history of Schistosoma japonicum infection were sampled from Xinhua Village, Lushan City, Jiangxi Province from 2019 to 2020, and 234 serum samples were collected from all participants. All participants received B-ultrasound examinations of the liver. Serum samples were categorized into four groups (grades 0, Ⅰ, Ⅱ and Ⅲ schistosomiasis-induced liver fibrosis groups) according to B-ultrasound examination results, and then, each group was randomly divided into a receiver operating characteristic (ROC) curve group and an efficacy assessment group at a ratio of 7∶3. Serum M2BP concentration was measured in four groups using the enzyme-linked immunosorbent assay (ELISA), and differences in serum M2BP concentrations were compared with analysis of variance and Spearman correlation analysis. Serum M2BP concentration was subjected to ROC curve analysis among individuals with different grades of schistosomiasis-induced liver fibrosis in the ROC curve group to determine the optimal diagnostic threshold of M2BP concentration at different fibrosis grades, and the area under the ROC curve (AUC) was calculated to evaluate the diagnostic performance. The diagnostic accuracy was verified by comparing the accordance rate and Kappa consistency test in the efficacy assessment group. Results Among 234 serum samples, there were 79 samples with grade 0 schistosomiasis-induced liver fibrosis, 87 samples with Grade Ⅰ, 46 samples with Grade Ⅱ and 22 samples with Grade Ⅲ according to the B-ultrasound examinations. The mean serum M2BP concentrations were (0.40 ± 0.31) [95% confidence interval (CI): (0.33, 0.47)], (0.64 ± 0.48) [95% CI: (0.53, 0.74)], (1.76 ± 0.58) [95% CI: (1.59, 1.93)] μg/mL and (2.56 ± 0.93) [95% CI: (2.14, 2.97)] μg/mL in the four groups, respectively (F = 150.796, P < 0.001), and the severity of schistosomiasis-induced liver fibrosis significantly positively correlated with serum M2BP concentration (rs = 0.715, P < 0. 001). The sample sizes of grades 0, Ⅰ, Ⅱ and Ⅲ schistosomiasis-induced liver fibrosis sera were randomly allocated as follows: 55 versus 24, 61 versus 26, 32 versus 14, and 15 versus 7 in the ROC curve and efficacy assessment groups, respectively, and the serum M2BP concentrations were (0.39 ± 0.29) μg/mL and (0.42 ± 0.36) μg/mL (F = 0.196, P > 0.05), (0.59 ± 0.47) μg/mL and (0.75 ± 0.51) μg/mL (F = 1.967, P > 0.05), (1.73 ± 0.59) μg/mL and (1.85 ± 0.57) μg/mL (F = 0.417, P > 0.05), and (2.46 ± 0.64) μg/mL and (2.76 ± 1.41) μg/mL (F = 0.491, P > 0.05), respectively. ROC curve analysis showed that the optimal diagnostic thresholds of serum M2BP concentration were 0.347 86 μg/mL (AUC = 0.635, P < 0.05), 1.188 83 μg/mL (AUC = 0.938, P < 0.000 1) and 2.021 21 μg/mL (AUC = 0.821, P < 0.000 1) for grade Ⅰ, Ⅱ and Ⅲ schistosomiasis-induced liver fibrosis. In addition, the accordance rates between the optimal diagnostic threshold of serum M2BP and B-ultrasound examinations for predicting grade Ⅰ, Ⅱ and Ⅲ schistosomiasis-induceed liver fibrosis were 69.23%, 85.71% and 71.43% (χ2 = 1.340, P > 0.05), and the overall Kappa consistency test showed moderate consistency [Kappa = 0.608, 95% CI: (0.428, 0.788); Z = 6.609, P < 0.000 1]. Conclusions Serum M2BP may serve as a potential biomarker for assessing moderate to advanced schistosomiasis-induced liver fibrosis; however, its diagnostic value for early-stage schistosomiasis-induced liver fibrosis remains limited.

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.

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.

Objective: To analyze the association between intraoperative blood transfusion and adverse perinatal outcomes(a composite of maternal death, hepatic encephalopathy, etc) in patients with acute fatty liver of pregnancy (AFLP), identify independent risk factors for these outcomes, and develop and validate a risk prediction model for early identification of high-risk patients. Methods: Clinical data of 56 AFLP patients admitted to our hospital from January 2008 to January 2025 were retrospectively analyzed. The chi-square test was used to compare the incidence of adverse perinatal outcomes between the transfusion group and the non-transfusion group. Univariate and multivariate Firth-penalized logistic regression analyses were performed to identify independent predictors of adverse outcomes. Based on the identified independent risk factors, a combined prediction model was developed. Internal validation was performed using the Bootstrap method (1 000 resamplings) to assess the model′s generalizability. Model performance was evaluated using receiver operating characteristic(ROC) curves and calibration curves. Finally, a nomogram for predicting adverse perinatal outcomes in AFLP was constructed. Results: Overall, 57.1%(32/56) of the patients received blood product transfusion during the termination of pregnancy. Multivariate Firth-penalized logistic regression analysis showed that, given the limited sample size, intraoperative blood transfusion had no independent statistical effect on adverse outcomes (OR=0.812, 95%CI:0.133-3.698, P=0.797). Mediation analysis also revealed no significant indirect effect. The independent risk factors were decreased preoperative fibrinogen (OR=14.062, 95%CI:2.389-126.656, P=0.003), pregnancy with infection (OR= 4.536, 95%CI:1.143-22.107, P=0.031), and cesarean section (OR=8.691, 95%CI:1.321-90.081, P=0.023). The combined prediction model achieved an AUC of 0.881 (95% CI:0.793-0.969, P<0.001), indicating good discrimination. At the optimal cut-off value, the sensitivity was 65.6%, specificity 95.8%, positive predictive value 95.5%, and negative predictive value 67.6%. Internal validation by the Bootstrap method showed that the predictive model had good discrimination and no obvious overfitting. The calibration curve demonstrated that the model′s predicted risk was consistent with the actual observed risk. Based on this, an AFLP perinatal adverse outcome nomogram model was constructed. Conclusion: Under the limited sample size of this study, no independent statistical effect of intraoperative blood product infusion on adverse perinatal outcomes in AFLP patients was detected. A multivariate Firth-penalized logistic regression model incorporating decreased preoperative fibrinogen, pregnancy with infection, and cesarean delivery demonstrated good discrimination and calibration. The derived nomogram may serve as an exploratory tool for early risk stratification and proactive intervention.

Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

Objective To investigate the expression characteristics of the transcription factor E2F2 in clear cell renal cell carcinoma (ccRCC), its impact on patient prognosis, and its potential role in the tumor immune microenvironment, with validation using clinical samples and functional assays. Methods RNA sequencing data and clinical information of ccRCC patients were obtained from the TCGA database; GSE53757 and GSE66272 datasets were downloaded from the GEO database as external validation cohorts. The expression difference of E2F2 between ccRCC tissue and normal tissue was compared, and the relationship between E2F2 expression and clinical pathological characteristics was analyzed. Kaplan-Meier (KM) survival analysis with log-rank test was performed to evaluate the effects of E2F2 on overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). The prognostic efficacy of E2F2 in predicting 1-, 3- and 5-year survival was evaluated using receiver operating characteristic (ROC) curve. GSEA was performed to identify E2F2-related signaling pathways, and the ESTIMATE and CIBERSORT algorithms were used to assess the relationship between E2F2 expression level and tumor immune cell infiltration and immune microenvironment. Twenty pairs of surgically resected and pathologically confirmed ccRCC tissues and matched adjacent non-tumor tissues were collected from our hospital, and immunohistochemistry (IHC) was performed to detect the protein expression of E2F2. Human ccRCC cell line 786-O was used for functional assays;shRNA was used to knock down E2F2 expression (constructing stable shE2F2#1 and shE2F2#2 cell lines), and qRT-PCR and Western blot were performed to verify knockdown efficiency, followed by MTT, colony formation, and Transwell migration assays to evaluate the effects of E2F2 on the biological behaviors of ccRCC cells. Results E2F2 was significantly upregulated in ccRCC tissues (the same results in the verification set). Higher E2F2 expression was associated with advanced histological grade, clinical stage and higher TNM classification (P＜0.05). KM analysis showed that patients in the E2F2 high-expression group had worse OS, PFS, and DSS (P=0.019, 0.036, ＜0.001); the ROC curves showed area under the curve (AUC) of E2F2 of predicting 1-, 3- and 5- year survival of ccRCC patients were 0.844, 0.851 and 0.815, respectively. GSEA revealed that the E2F2 low-expression group was enriched in multiple metabolism-related pathways, whereas the E2F2 high-expression group was associated with immune-related pathways. Immune analysis demonstrated that high E2F2 expression was associated with increased immune scores, increased proportion of immune cells, higher tumor mutation burden, and potentially stronger immunotherapy response. IHC results showed that the E2F2 immunoreactivity score in ccRCC tissues was significantly higher than that in adjacent non-tumor tissues (P＜0.05). Functional assays indicated that E2F2 knockdown significantly inhibited the proliferation, colony formation, and migration of ccRCC cells. Conclusion E2F2 is highly expressed in ccRCC and may be involved in tumorigenesis and progression through modulation of the immune microenvironment. Its expression level is closely associated with patient prognosis, and E2F2 has the potential to serve as a prognostic biomarker and immunotherapeutic target in ccRCC.

Macrophages are innate immune cells connected with the development of inflammation. Retinoic acid has previously been proved to have anti-inflammatory and anti-arthritic properties. However, the exact mechanism through which retinoic acid modulates arthritis remains unclear. This study aimed to investigate whether retinoic acid ameliorates rheumatoid arthritis by modulating macrophage polarization. This study used retinoic acid to treat mice with adjuvant arthritis and evaluated anti-inflammatory effects by arthritis score, thermal nociceptive sensitization test, histopathologic examination and immunofluorescence assays. In addition, its specific anti-arthritic mechanism was investigated by flow cytometry, cell transfection and inflammatory signaling pathway assays in RAW264.7 macrophages in vitro. Retinoic acid significantly relieved joint pain and attenuated inflammatory cell infiltration in mice. Furthermore, this treatment modulated peritoneal macrophage polarization, increased levels of arginase 1, as well as decreased inducible nitric oxide synthase expression. In vitro, we verified that retinoic acid promotes macrophage transition from the M1 to M2 type by upregulating mitogen-activated protein kinase (MAPK) phosphatase 1 (MKP-1) expression and inhibiting P38, JNK and ERK phosphorylation in lipopolysaccharide-stimulated RAW264.7 cells. Notably, the therapeutic effects of retinoic acid were inhibited by MKP-1 knockdown. Retinoic acid exerts a significant therapeutic effect on adjuvant arthritis in mice by regulating macrophage polarization through the MKP-1/MAPK pathway, and play an important role in the treatment of rheumatic diseases.