Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

OBJECTIVE To investigate the mechanism by which Shenfukang capsule （SFK） ameliorates renal interstitial fibrosis （RIF） by regulating macrophage-fibroblast crosstalk via the signal transducer and activator of transcription 3 （STAT3）/Yes-associated protein （YAP） pathway. METHODS RAW264.7 macrophages were induced to polarize with transforming growth factor-β 1 （TGF-β 1 ）. Subsequently， a non-contact co-culture system of macrophages with renal fibroblasts NRK-49F， as well as a non-contact co-culture system of macrophages with YAP-knockdown renal fibroblasts， were established. Cells were treated with low， medium， and high concentrations （4， 8， 16 μg/mL） of SFK， as well as a STAT3 inhibitor （STAT3-I，1.64 μg/mL） and losartan potassium tablets （positive control， 0.28 μg/mL）， for 48 h. After intervention， the protein and mRNA expression levels of CD86， CD163， and STAT3 in macrophages， as well as CD86， F4/80， and STAT3 mRNA， were detected. In co-cultured renal fibroblasts， the protein and mRNA expression levels of α -smooth muscle actin （ α -SMA）， Vimentin， collagen type Ⅰ （Col-Ⅰ）， matrix metalloproteinase-1 （MMP-1）， and YAP were detected. In co-cultured YAP-knockdown renal fibroblasts， the protein and mRNA expression levels of α -SMA， Vimentin， and MMP-1 were also detected. RESULTS Following TGF-β 1 induction， the protein expression levels of CD86， CD163， and STAT3， as well as the mRNA expression levels of CD86， F4/80， and STAT3 in macrophages were significantly increased （ P ＜0.05）. The conditioned medium from polarized macrophages activated renal fibroblasts， as evidenced by significantly increased protein and mRNA expression levels of α -SMA， Vimentin， Col-Ⅰ， and YAP， and significantly decreased protein and mRNA expression levels of MMP-1 in renal fibroblasts （ P ＜0.05）. Treatment with SFK and STAT3-I reversed the changes in the above indicators， with the medium concentration SFK group showing stronge r effects on some indicators than the low and high concentration groups. In the non-contact co-culture experiment of macrophages and YAP-knockdown renal fibroblasts， there were no statistically significant differences in the protein and mRNA expression of α-SMA， Vimentin， and MMP-1 among the groups. CONCLUSIONS SFK can inhibit macrophage-renal fibroblast crosstalk by blocking the STAT3/YAP pathway， thereby delaying the progression of RIF.

OBJECTIVE To explore the influencing factors of Shenfukang capsule in the treatment of chronic kidney disease （CKD） and construct a nomogram prediction model for evaluating its therapeutic efficacy. METHODS CKD patients who were hospitalized from July 2019 to August 2022 in the First Affiliated Hospital of Guangxi Medical University and treated with Shenfukang capsule were selected as study subjects. Clinical data of the patients were collected from the hospital’s electronic medical record system， and they were divided into an effective group and an ineffective group based on treatment outcomes. Lasso-Logistic multivariate regression analysis was used to screen the influencing factors of the efficacy of Shenfukang capsule. Using the effectiveness of Shenfukang capsule treatment as the prediction outcome and the screened influencing factors as predictor variables， a nomogram prediction model was constructed using R software. All patients were randomly divided into a training cohort and a validation cohort. The discriminative ability， calibration， and clinical net benefit of the model were evaluated using the receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis， respectively. RESULTS Lasso-Logistic regression analysis revealed that concurrent diabetes mellitus， decreased levels of blood urea nitrogen， triglycerides， and serum phosphorus， as well as prolonged prothrombin time and elevated apolipoprotein AⅠ levels， were influencing factors for reduced efficacy of Shenfukang capsule in CKD treatment. The evaluation results of the nomogram prediction model showed that the area under curve values were 0.745 and 0.797 in the training and validation cohorts， respectively， which were close and both exceeded 0.70， indicating stable predictive performance. The calibration curves demonstrated good agreement in both datasets， suggesting satisfactory calibration performance. The clinical decision curves were positioned above the two extreme reference lines， indicating high clinical benefit of the model. CONCLUSIONS Diabetes mellitus， blood urea nitrogen， triglycerides， apolipoprotein AⅠ， serum phosphorus， and prothrombin time were factors associated with the efficacy of Shenfukang capsule in CKD patients. The nomogram prediction model established in this study may provide a basis for rational clinical application of Shenfukang capsule and for improving its therapeutic efficacy in CKD.

OBJECTIVE To explore the influencing factors of Shenfukang capsule in the treatment of chronic kidney disease （CKD） and construct a nomogram prediction model for evaluating its therapeutic efficacy. METHODS CKD patients who were hospitalized from July 2019 to August 2022 in the First Affiliated Hospital of Guangxi Medical University and treated with Shenfukang capsule were selected as study subjects. Clinical data of the patients were collected from the hospital’s electronic medical record system， and they were divided into an effective group and an ineffective group based on treatment outcomes. Lasso-Logistic multivariate regression analysis was used to screen the influencing factors of the efficacy of Shenfukang capsule. Using the effectiveness of Shenfukang capsule treatment as the prediction outcome and the screened influencing factors as predictor variables， a nomogram prediction model was constructed using R software. All patients were randomly divided into a training cohort and a validation cohort. The discriminative ability， calibration， and clinical net benefit of the model were evaluated using the receiver operating characteristic （ROC） curve， calibration curve， and decision curve analysis， respectively. RESULTS Lasso-Logistic regression analysis revealed that concurrent diabetes mellitus， decreased levels of blood urea nitrogen， triglycerides， and serum phosphorus， as well as prolonged prothrombin time and elevated apolipoprotein AⅠ levels， were influencing factors for reduced efficacy of Shenfukang capsule in CKD treatment. The evaluation results of the nomogram prediction model showed that the area under curve values were 0.745 and 0.797 in the training and validation cohorts， respectively， which were close and both exceeded 0.70， indicating stable predictive performance. The calibration curves demonstrated good agreement in both datasets， suggesting satisfactory calibration performance. The clinical decision curves were positioned above the two extreme reference lines， indicating high clinical benefit of the model. CONCLUSIONS Diabetes mellitus， blood urea nitrogen， triglycerides， apolipoprotein AⅠ， serum phosphorus， and prothrombin time were factors associated with the efficacy of Shenfukang capsule in CKD patients. The nomogram prediction model established in this study may provide a basis for rational clinical application of Shenfukang capsule and for improving its therapeutic efficacy in CKD.

Objective To explore the dietary nutrition status and nutritional intervention strategy of patients with Alzheimer’s disease (AD). Methods Among the 1 332 patients with AD diagnosed at Xijing Hospital from January 2021 to December 2023 were enrolled as the study subjects. The dietary intake data of patients were collected through questionnaire surveys and dietary reviews. During the study period, 30 patients did not complete the intervention due to withdrawal or loss of follow-up. Based on the actual number of people who completed the intervention, AD patients were randomly divided into intervention group (n=651, individualized nutritional intervention strategy) and control group (n=651, routine nutritional intervention), and both groups were intervened for 3 months. The cognitive function (MMSE score and MoCA score), nutritional status (MNA scale, NRS-2002 scale), and quality of life (GQOL-74) of the two groups of AD patients were compared to evaluate the effectiveness of the intervention strategies. Results A total of 1 332 questionnaires were distributed, and 1 302 valid questionnaires were finally recovered, with an effective recovery rate of 97.75% (1 302/1 332). The survey results showed that there were no statistical differences in baseline characteristics and dietary nutrition status between the two groups of AD patients before intervention (P>0.05). After nutritional intervention, the cognitive function, quality of life, and nutritional status of patients in the intervention group were significantly improved. The MMSE score, MoCA score, MNA score, and GQOL-74 score of the intervention group were significantly higher than those of the control group, while the NRS-2002 score was lower than that of the control group (P<0.05). Conclusion Nutritional intervention strategy has a significant effect on improving nutritional status, cognitive function, and quality of life of AD patients.

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

AIM:To construct a network calcula-tor based on machine learning(ML)models to pre-dict the risk of delayed awakening from anaesthesia in breast cancer(BC)patients.METHODS:A total of 435 BC patients surgically treated at our hospital from January 2023 to June 2024 were selected.The Boruta algorithm was used to screen for important characteristic variables for the risk of delayed awak-ening from anaesthesia.All patients were randomly assigned to a training set(n=261)and a test set(n=174)based on a 3:2 ratio and nine ML models were constructed and trained.Nine ML models were evaluated on the basis of receiver operating charac-teristic(ROC)curves for a random sample of 10 sub-jects and the clinical utility of the models was as-sessed using decision curve analysis.Combined with SHapley Additive exPlanations(SHAP)bar graphs,summary graphs and force diagrams additional in-terpretation and visualization of the ML model.Con-struction of a network calculator for predicting the risk of delayed awakening from anesthesia in BC pa-tients using the R package.RESULTS:Of the 435 BC patients,25.1%experienced delayed awakening from anesthesia.Boruta algorithm screened seven feature variables.The ROC curve shows that the XG-Boost model has the highest area under the curve(AUC)for 10 random samples among the 9 ML mod-els,and the decision curve shows that the XGBoost model has a significant clinical net benefit.The SHAP bar graph shows the importance of ASA classi-fication,surgery time,anesthesia time,intraopera-tive blood loss,propofol,preoperative anemia,and intraoperative hypothermia,and the SHAP summa-ry graph reflects the distribution of the ranges of in-fluence of the seven important characteristic vari-ables,which are"separated at the ends."The SHAP force diagram visualization XGBoost model predict-ed the risk of delayed awakening from anesthesia for individual patients with a predictive value of 0.998 for patients with delayed awakening from an-esthesia and 0.008 91 for patients without delayed awakening from anesthesia.A web-based calculator(https://xz-nomogram.shinyapps.io/DE_web/)based on an interpretable XGBoost model effective-ly predicts the risk of delayed awakening from anes-thesia in BC patients.CONCLUSION:ASA classifica-tion,surgery time,propofol,intraoperative blood loss,anaesthesia time,preoperative anaemia and intraoperative hypothermia are important charac-teristic variables for the risk of delayed awakening from anaesthesia in BC patients.The network calcu-lator based on the interpretable XGBoost model can accurately and quickly quantify the risk of de-layed awakening from anaesthesia,which can help clinicians to effectively adjust the treatment strate-gy and better improve the prognosis of patients.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

OBJECTIVE To explore and evaluate the machine learning model for prediction of bacterial bloodstream infections established based on routine test data.METHODS By means of retrospective survey,a total of 5 421 pa-tients who were hospitalized in 3 medical institutions from Jan.2015 to Dec.2022 were recruited as the research subjects,1 914 of whom were assigned as the bloodstream infection group,and 3 507 were assigned as the non-bloodstream infection group.The baseline data including gender and age and the results of routine laboratory tests were collected from the enrolled patients.The 3 types of machine learning algorithms,logistic regression,support vector machine and random forest,were respectively used for the screening of the optimal prediction model;the contribution of feature variables to the predictive capability of the model was interpreted through SHAP.The fea-ture variables of the model were optimized by using recursive feature elimination method,and the predictive effi-ciency of the model was evaluated by the area under the curve(AUC)of receiver operating characteristic(ROC)curves.RESULTS Totally 26 variables involving age,gender and blood routine test indexes were included.The random forest was chosen as the optimal machine learning algorithm for the establishment of prediction model for bloodstream infections,and the accuracy of the model was 0.709,with the AUC 0.706.The result of SHAP ex-planation indicated that the age,hematokrit and erythrocyte volume distribution width-CV had remarkable effect on the model's making right decisions.17 variables of the prediction model showed more remarkable effect than 26 variable on distinguishing from the gram-positive bacteria bloodstream infections from the gram-negative bacteria bloodstream infections,with the AUC 0.715,the sensitivity 0.701,the specificity 0.632.CONCLUSIONS The prediction model that is established based on the blood routine test indexes by machine learning algorithm can pre-dict the bacterial bloodstream infection.Meanwhile,the feature selection strategy can further improve the predic-tive efficiency of the model on basis of lowering the dimensionality.

AIM:To construct a network calcula-tor based on machine learning(ML)models to pre-dict the risk of delayed awakening from anaesthesia in breast cancer(BC)patients.METHODS:A total of 435 BC patients surgically treated at our hospital from January 2023 to June 2024 were selected.The Boruta algorithm was used to screen for important characteristic variables for the risk of delayed awak-ening from anaesthesia.All patients were randomly assigned to a training set(n=261)and a test set(n=174)based on a 3:2 ratio and nine ML models were constructed and trained.Nine ML models were evaluated on the basis of receiver operating charac-teristic(ROC)curves for a random sample of 10 sub-jects and the clinical utility of the models was as-sessed using decision curve analysis.Combined with SHapley Additive exPlanations(SHAP)bar graphs,summary graphs and force diagrams additional in-terpretation and visualization of the ML model.Con-struction of a network calculator for predicting the risk of delayed awakening from anesthesia in BC pa-tients using the R package.RESULTS:Of the 435 BC patients,25.1%experienced delayed awakening from anesthesia.Boruta algorithm screened seven feature variables.The ROC curve shows that the XG-Boost model has the highest area under the curve(AUC)for 10 random samples among the 9 ML mod-els,and the decision curve shows that the XGBoost model has a significant clinical net benefit.The SHAP bar graph shows the importance of ASA classi-fication,surgery time,anesthesia time,intraopera-tive blood loss,propofol,preoperative anemia,and intraoperative hypothermia,and the SHAP summa-ry graph reflects the distribution of the ranges of in-fluence of the seven important characteristic vari-ables,which are"separated at the ends."The SHAP force diagram visualization XGBoost model predict-ed the risk of delayed awakening from anesthesia for individual patients with a predictive value of 0.998 for patients with delayed awakening from an-esthesia and 0.008 91 for patients without delayed awakening from anesthesia.A web-based calculator(https://xz-nomogram.shinyapps.io/DE_web/)based on an interpretable XGBoost model effective-ly predicts the risk of delayed awakening from anes-thesia in BC patients.CONCLUSION:ASA classifica-tion,surgery time,propofol,intraoperative blood loss,anaesthesia time,preoperative anaemia and intraoperative hypothermia are important charac-teristic variables for the risk of delayed awakening from anaesthesia in BC patients.The network calcu-lator based on the interpretable XGBoost model can accurately and quickly quantify the risk of de-layed awakening from anaesthesia,which can help clinicians to effectively adjust the treatment strate-gy and better improve the prognosis of patients.