Neurological diseases have high morbidity and disability rates， posing a severe threat to human health. Cli nical manifestations include motor， sensory， cognitive and conscious disorders. Chaihu jia longgu muli decoction is derived from Treatise on Febrile Diseases ， with the effects of harmonizing Shaoyang， activating Yang and clearing heat， and tranquilizing the mind. This paper systematically reviews the research progress in clinical application and mechanism of Chaihu jia longgu muli decoction in the field of neurological diseases. It has been found that the decoction shows favorable efficacy in various neurological diseases such as insomnia， depression， epilepsy， vertigo， migraine and vascular dementia. The specific mechanisms are related to regulating neurotransmitter levels， repairing neuronal function， alleviating neuroinflammation， improving mitochondrial dysfunction and regulating intestinal flora. In the future， standardized prospective follow-up cohorts should be established， and core outcome indicators should be clearly defined to strengthen the evidence base. Furthermore， multidisciplinary research should be leveraged to expand the therapeutic value of Chaihu jia longgu muli decoction in the management of neurological diseases.

ObjectiveTo construct and validate a risk assessment model for post-transplant diabetes mellitus （PTDM） using multiple machine learning algorithms， and to realize the early identification of PTDM. MethodsA retrospective analysis was performed for the clinical data of the patients who underwent allogeneic liver transplantation in The First Hospital of Shanxi Medical University from April 1， 2020 to December 31， 2024， and they were randomly divided into a training set and a validation set at a ratio of 7∶3. The LASSO regression analysis combined with 5-fold cross-validation was used for feature selection. Seven machine learning models were developed in the training set， i.e.， logistic regression （LR）， decision tree （DT）， Naive Bayes （NB）， random forest （RF）， K-nearest neighbor （KNN）， extreme gradient boosting （XGBoost）， and adaptive boosting （AdaBoost）. In the validation set， various methods were used to assess the predictive performance of each model， such as accuracy， precision， recall rate， specificity， F1 score， area under the receiver operating characteristic curve （AUROC）， and area under the precision-recall curve （AUPRC）. The Brier score and decision curve analysis were used to assess the calibration and clinical practicability of the models， and the SHAP method was used to analyze feature importance. The independent-samples t test or the Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. ResultsA total of 135 liver transplant recipients were enrolled， among whom 26 developed PTDM， and there were 94 patients in the training set and 41 in the validation set. Feature extraction and screening identified 7 key features of sex， overweight or obesity， anhepatic phase， time of operation， length of hospital stay， early postoperative hypomagnesemia， and impaired fasting glucose （IFG）. In the validation set， the XGBoost model showed the best predictive performance， with an AUROC of 0.907 （95% confidence interval ［CI］： 0.807 — 0.989）， an AUPRC of 0.649 （95%CI： 0.339 — 0.955）， an accuracy of 0.878， a precision of 0.667， a recall rate of 0.750， an F1-score of 0.706， a specificity of 0.909， and a Brier score of 0.104. The decision curve analysis showed that when the threshold probability was below 0.667， application of the XGBoost model in clinical decision-making provided relatively high net benefit. The SHAP analysis showed that the length of hospital stay ranked first in terms of feature importance， followed by time of operation， overweight or obesity， sex， IFG， anhepatic phase， and early postoperative hypomagnesemia. ConclusionThe risk assessment model for PTDM in liver transplant recipients based on XGBoost algorithm has excellent performance and can effectively identify high-risk individuals； however， multicenter large-sample data are needed for further validation.

Objective:To compare the enrichment effects of ultrafiltration, polyethylene glycol (PEG) precipitation, aluminum salt precipitation, and anionic membrane adsorption-elution on viruses in drinking water.Methods:Using phage MS2 as the target virus, three different concentrations of drinking water samples were prepared, and the samples were enriched by ultrafiltration 1, ultrafiltration 2, PEG precipitation, aluminum salt precipitation, and anionic membrane adsorption-elution method, respectively. Real-time fluorescence quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was used to quantify MS2 nucleic acid in pre and post concentrated samples and the recovery rates of MS2 in samples with high, medium and low concentrations were compared among the five methods.Results:Comparing the MS2 enrichment recovery rates of individual enrichment method in water samples of different concentrations, ultrafiltration method 1, PEG precipitation method, aluminum salt precipitation method, and membrane adsorption-elution method were not affected by the sample concentration, and the differences of the recovery rates for the three concentration water samples among the four methods were not statistically significant ( P>0.05). The MS2 enrichment recovery rates of the five enrichment methods were significantly different in all concentration samples ( P<0.05). The recovery rates of ultrafiltration method 1 were higher in all three concentration samples, followed by aluminum salt precipitation and anionic membrane adsorption-elution, PEG precipitation were higher in high concentration samples, but lower in low and medium concentration samples, and the recovery rates of ultrafiltration method 2 were the lowest in all three concentration samples. Comparing the Ct values of MS2 in the enriched samples by five methods, the Ct values of ultrafiltration method 1 were the smallest in the three concentration water samples. There was no statistically significant difference in MS2 Ct values among the five enrichment methods in the medium and high concentration water samples ( P>0.05). In low concentration simulated water samples, only the difference of MS2 Ct value between ultrafiltration method 1 and ultrafiltration method 2 was statistically significant ( Z=16.000, P=0.016). Conclusions:Considering the operation simplicity, operation time and virus recovery rate after enrichment, ultrafiltration was the most effective method for virus enrichment in drinking water.

Objective To investigate the influencing factors of the course of perioperative prophylactic antibiotics in patients undergoing total hip arthroplasty,and explore management strategies for enhancing perioperative prophylactic medication administration.Methods The clinical data for patients undergoing total hip arthroplasty at Affiliated Hospital of Chengdu University from January,2020 to September,2024 were retrospectively collected.Patients were divided into a 24 h group and a 48-72 h group based on the duration of prophylactic antibacterial therapy.The general characteristics,surgical-related indicators,preoperative and postoperative laboratory test results,and surgical outcome measures between the two groups of patients were compared.Multivariate Logistic regression analysis was performed to identify influencing factors associated with prolonged duration of prophylactic antibacterial therapy.Results A total of 126 patients who underwent total hip arthroplasty were enrolled,including 74 cases in the 24 h group and 52 cases in the 48-72 h group.Univariate analysis results showed that there were statistically significant differences in the following indicators between the two groups:surgical cause,surgical duration,intraoperative blood loss,drainage duration of plasma drainage tubes,preoperative white blood cell count,and preoperative neutrophil count(P<0.05).The results of multivariate Logistic regression analysis showed that the reason for surgery and the duration of plasma drain tube drainage were the influencing factors of antimicrobial treatment course for total hip arthroplasty(P<0.05).The results of receiver operating characteristic curve analysis showed that the prediction model(constructed based on the drainage time of plasma drainage tube)for prophylactic antimicrobial treatment course to 48-72 h was 0.721.When the drainage time of plasma drainage tubes was≥40.56 h,the risk of requiring prophylactic antimicrobial therapy for an extended course of 48-72 h increased significantly.Conclusion The patient's surgical reason and the duration of plasma drain drainage may be related to the prophylactic antimicrobial course of more than 24 hours.Clinical pharmacists may utilize this parameter as a patient-specific characteristic,with the support of information systems,the hierarchical patient management can be implemented,thereby enhancing the effectiveness of medication surveillance and progressively elevating the 24 h discontinuation rate of perioperative prophylactic antibiotics.

OBJECTIVE To establish and verify the risk prediction models for postoperative multidrug-resistant or-ganisms(MDROs)infections in the liver transplantation patients based on the machine learning algorithms so as to provide bases for identification of the population at high risk of postoperative MDROs infections.METHODS The liver transplantation patients who were retrospectively collected from intensive care Ⅳ database(MIMIC-Ⅳ)and eICU collaborative research database(eICU)were recruited as the research subjects,meanwhile,the patients who underwent liver transplantation in the First Hospital of Shanxi Medical University from Jan.2021 to Jul.2024 were assigned as the external verification group.The variables were selected by Lasso regression,and the models were established based on 7 types of machine learning algorithms such as extreme gradient boosting algorithm and random forest.The predictive performances of the models were evaluated by comparing the areas under receiver operating characteristic(ROC)curves and the accuracy,the characteristic variables were interpreted by Shapley additive explanations(SHAP),and the risk prediction calculator was established.RESULTS A total of 637 pa-tients were finally enrolled in the study,and the incidence of postoperative MDROs infections was 35.79％.Total-ly 15 variables were finally selected for construction of the model.The area under the receiver operating character-istic curve of XGBoost model was 0.82 for the internal test set,0.78 for the external test set;the predictive per-formance of XGBoost model was better than that of the rest of 6 models.SHAP algorithm indicated that the top 5 important predictive factors were as follows:hepatic encephalopathy,length of intensive care unit(ICU)stay,albumin,model of end-stage liver disease(MELD)and total length of hospital stay.CONCLUSION The risk pre-diction models that are established based on the machine learning algorithms have remarkable effect on prediction of the postoperative MDROs infections and can accurately identify the liver transplantation patients at high risk of postoperative MDROs infections,which may provide guidance for the identification of high-risk population and the development of prevention and treatment measures for infections.

Objective:To investigate the efficacy and safety of allopurinol in stable coronary heart disease patients with asymptomatic hyperuricemia (AH).Methods:Sixty stable coronary heart disease with AH patients admitted to the Changsha Third Hospital from January 2022 to December 2023 were selected. Patients were randomly divided into an observation group (allopurinol treatment group) and a control group (placebo group). The results of tablet exercise tests, treatment efficacy, blood uric acid levels, liver and kidney function indicators, and incidence of adverse events were compare and analyzed between two groups of patients.Results:The observation group had better exercise termination time, maximum ST descent time, and ST recovery time than the control group (all P<0.05). The total effective rate of the observation group after 6 weeks of treatment was significantly higher than that of the control group, and the difference was statistically significant (χ 2=5.455, P=0.02). There was no statistically significant difference in blood uric acid levels and liver and kidney function indicators between the two groups before treatment (all P>0.05). After treatment, both groups showed significant improvement in blood uric acid levels and liver and kidney function indicators compared to before treatment (all P<0.05). The levels of blood uric acid and liver and kidney function indicators in the observation group were significantly better than those in the control group after treatment (all P<0.05). The incidence of adverse events in the observation group during treatment was lower than that in the control group (χ 2=5.192, P=0.023). Conclusions:Allopurinol has a certain therapeutic effect on stable coronary heart disease with asymptomatic hyperuricemia, which helps patients enhance their physical activity and reduce the incidence of cardiovascular events.

AIM:This study aims to investigate the sensitizing effects of lycorine(Lyc)in combination with cisplatin(Cis)on human osteosarcoma cells and to explore the underlying mechanisms of action.METHODS:Human osteosarcoma cell lines MG63,HOS,and cisplatin-resistant HOS/DDP cells were utilized to evaluate the effects of Lyc and cisplatin,both alone and in combination,on cell viability using the CCK8 assay.The clonogenic assay was performed to assess cell proliferation capacity,while the scratch assay evaluated the drugs' effects on cell migration.Reactive oxygen species(ROS)levels were measured using a ROS assay kit,and changes in intracellular glutathione(GSH)levels were assessed with a GSH/oxidized glutathione(GSSG)assay kit.The mitochondrial membrane potential was analyzed via JC-1 staining to determine the drugs' effects on mitochondrial function.Intracellular iron(Ⅱ)content changes were detected us-ing FerrOrange,a fluorescence probe,and cellular malondialdehyde(MDA)levels were measured using an MDA assay kit.RT-qPCR was employed to evaluate the expression levels of key genes related to ferroptosis,and Western blot analysis was conducted to detect changes in the protein expression levels of Yes-associated protein 1(YAP1),acyl-CoA synthetase long-chain family member 4(ACSL4),glutathione peroxidase 4(GPX4),heme oxygenase-1(HO-1),and transferrin re-ceptor(TFRC).RESULTS:Both Lyc and cisplatin effectively inhibited the proliferation of human osteosarcoma cells.Notably,the combination of Lyc and cisplatin led to a more substantial reduction in cell viability,proliferation,and migra-tion abilities in MG63,HOS,and HOS/DDP cells compared to cisplatin alone.Additionally,this combination significant-ly increased ROS levels while decreasing GSH content,indicating mitochondrial damage and elevated iron(Ⅱ)and MDA levels.RT-qPCR results revealed that the combination treatment more significantly downregulated ferroptosis-promoting genes and upregulated ferroptosis-inhibiting genes compared to cisplatin treatment alone(P<0.05).Western blot results showed a slight decrease in GPX4 protein expression following Lyc and cisplatin treatment,while expression levels of YAP1,TFRC,ACSL4,and HO-1 were significantly increased(P<0.05).CONCLUSION:Lyc enhances the sensitivi-ty of MG63,HOS,and HOS/DDP cells to cisplatin by promoting ferroptosis through the YAP1/TFRC signaling pathway.

OBJECTIVE To establish and verify the risk prediction models for postoperative multidrug-resistant or-ganisms(MDROs)infections in the liver transplantation patients based on the machine learning algorithms so as to provide bases for identification of the population at high risk of postoperative MDROs infections.METHODS The liver transplantation patients who were retrospectively collected from intensive care Ⅳ database(MIMIC-Ⅳ)and eICU collaborative research database(eICU)were recruited as the research subjects,meanwhile,the patients who underwent liver transplantation in the First Hospital of Shanxi Medical University from Jan.2021 to Jul.2024 were assigned as the external verification group.The variables were selected by Lasso regression,and the models were established based on 7 types of machine learning algorithms such as extreme gradient boosting algorithm and random forest.The predictive performances of the models were evaluated by comparing the areas under receiver operating characteristic(ROC)curves and the accuracy,the characteristic variables were interpreted by Shapley additive explanations(SHAP),and the risk prediction calculator was established.RESULTS A total of 637 pa-tients were finally enrolled in the study,and the incidence of postoperative MDROs infections was 35.79％.Total-ly 15 variables were finally selected for construction of the model.The area under the receiver operating character-istic curve of XGBoost model was 0.82 for the internal test set,0.78 for the external test set;the predictive per-formance of XGBoost model was better than that of the rest of 6 models.SHAP algorithm indicated that the top 5 important predictive factors were as follows:hepatic encephalopathy,length of intensive care unit(ICU)stay,albumin,model of end-stage liver disease(MELD)and total length of hospital stay.CONCLUSION The risk pre-diction models that are established based on the machine learning algorithms have remarkable effect on prediction of the postoperative MDROs infections and can accurately identify the liver transplantation patients at high risk of postoperative MDROs infections,which may provide guidance for the identification of high-risk population and the development of prevention and treatment measures for infections.

Objective:To investigate the efficacy and safety of allopurinol in stable coronary heart disease patients with asymptomatic hyperuricemia (AH).Methods:Sixty stable coronary heart disease with AH patients admitted to the Changsha Third Hospital from January 2022 to December 2023 were selected. Patients were randomly divided into an observation group (allopurinol treatment group) and a control group (placebo group). The results of tablet exercise tests, treatment efficacy, blood uric acid levels, liver and kidney function indicators, and incidence of adverse events were compare and analyzed between two groups of patients.Results:The observation group had better exercise termination time, maximum ST descent time, and ST recovery time than the control group (all P<0.05). The total effective rate of the observation group after 6 weeks of treatment was significantly higher than that of the control group, and the difference was statistically significant (χ 2=5.455, P=0.02). There was no statistically significant difference in blood uric acid levels and liver and kidney function indicators between the two groups before treatment (all P>0.05). After treatment, both groups showed significant improvement in blood uric acid levels and liver and kidney function indicators compared to before treatment (all P<0.05). The levels of blood uric acid and liver and kidney function indicators in the observation group were significantly better than those in the control group after treatment (all P<0.05). The incidence of adverse events in the observation group during treatment was lower than that in the control group (χ 2=5.192, P=0.023). Conclusions:Allopurinol has a certain therapeutic effect on stable coronary heart disease with asymptomatic hyperuricemia, which helps patients enhance their physical activity and reduce the incidence of cardiovascular events.

Moyamoya disease (MMD) is widely recognized as an important cause of ischemic stroke and hemorrhagic stroke. Cerebral revascularization is the standard treatment for preventing stroke recurrence in patients with MMD. However, cerebral hyperperfusion syndrome (CHS) is one of the main complications of cerebral revascularization in patients with MMD. This article reviews the predictive factors for postoperative CHS in patients with MMD undergoing cerebral revascularization.