OBJECTIVE:To find out a new purifying process of Vernonia Anthelmintica Willd by clarifying agent.METHO-DS:Using orthogonal design L16(45),influcing factors including clarifing agent on extraction process were investigated by detecting the content of total flavone ingredients.RESULTS:Different ratios of concentration volume had significant influence on the content of total flavone ingredients(P

OBJECTIVE:To investigate the effects of extracts of different constitutes of Vernonia Anthelmintica Willd different on the serum levels of Cu and Zn in mice METHODS:Different groups of mice were given separately with (B) 1/10,(C)1/30,(C′)1/50 doses of LD50 of extracts of different constitutes of Vernonia Anthelmintica Willd for 7 days,and the levels of Cu and Zn in serum were determined RESULTS:The levels of Cu and Zn in serum were different in different groups of mice(P

OBJECTIVE:To observe the superiority of countercurrent chromatography(CCC)in isolating active componets of botanicals.METHODS:By pharmacodynamic test,active components of herbs,isolated by CCC,were screened.RESULTS:An active part of herb was found and a component with a purity of95%was obtained.CONCLUSION:CCC is a rapid,effective and unabsorpting isolation technology compared with other methods in developing herbal researches.

Objective:To analyze the situation of therapeutic drug monitoring ( TDM) for 3 new antiepileptic drugs including leve-tiracetam, lamotrigine and oxcarbazepine in our hospital to provide reference for the individualized drug treatment for the children with epilepsy. Methods:The basic information, dose and TDM data of levetiracetam, lamotrigine and oxcarbazepine of the children with ep-ilepsy during 2015 and 2016 were collected and statistically analyzed in our hospital. Results: Without obvious difference in mean dose, the average plasma concentrations of oxcarbazepine in 3-6-year-old,6-10-year-old and 10-18-year-old children were significantly higher than those in less than 3-year-old children(P<0. 05). The mean dose and the average plasma concentrations of lamotrigine in children at different ages had no statistical significance(P>0. 05). The average plasma concentration of levetiracetam in less than 3-year-old children was significantly higher than that in the other children (P<0. 05). The effective rate of clinical treatment within dif-ferent plasma concentration ranges had statistical significance(P<0. 05). Conclusion: There are inter-individual differences in the plasma concentrations of antiepileptic drugs among the children with epilepsy at different ages. So the plasma concentration of antiepi-leptic drugs should be monitored regularly in order to ensure the safety and effectiveness of medication for children.

Objective:To analyze the situation of therapeutic drug monitoring ( TDM) for 3 new antiepileptic drugs including leve-tiracetam, lamotrigine and oxcarbazepine in our hospital to provide reference for the individualized drug treatment for the children with epilepsy. Methods:The basic information, dose and TDM data of levetiracetam, lamotrigine and oxcarbazepine of the children with ep-ilepsy during 2015 and 2016 were collected and statistically analyzed in our hospital. Results: Without obvious difference in mean dose, the average plasma concentrations of oxcarbazepine in 3-6-year-old,6-10-year-old and 10-18-year-old children were significantly higher than those in less than 3-year-old children(P<0. 05). The mean dose and the average plasma concentrations of lamotrigine in children at different ages had no statistical significance(P>0. 05). The average plasma concentration of levetiracetam in less than 3-year-old children was significantly higher than that in the other children (P<0. 05). The effective rate of clinical treatment within dif-ferent plasma concentration ranges had statistical significance(P<0. 05). Conclusion: There are inter-individual differences in the plasma concentrations of antiepileptic drugs among the children with epilepsy at different ages. So the plasma concentration of antiepi-leptic drugs should be monitored regularly in order to ensure the safety and effectiveness of medication for children.

OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in breast cancer， and provide a scientific reference for clinical healthcare workers in selecting or developing effective predictive models. METHODS A systematic search was conducted for studies on predictive models of the risk of chemotherapy-induced myelosuppression in breast cancer across the CNKI， VIP， Wanfang， PubMed， Web of Science， Cochrane Library， Embase， and Scopus databases， with a time frame of the establishment of the database to May 7， 2024. Literature was independently screened by 2 investigators， data were extracted according to critical appraisal and data extraction for systematic reviews of predictive model studies， and the risk of bias evaluation tool for predictive model studies was used to analyze the risk of bias and applicability of the included studies. RESULTS There were totally 7 studies， comprising 12 models. Among them， 11 models indicated an area under the subject operating characteristic curve of 0.600-0.908； 2 models indicated calibration. The common predictor variables of the included models were age， pre-chemotherapy neutrophil count， pre-chemotherapy lymphocyte count， and pre-chemotherapy albumin. The overall risk of bias of the 7 studies was high， which was mainly attributed to the flaws in the study design， insufficient sample sizes， inappropriate treatment of variables， non-reporting of missing data， and the lack of indicators for the assessment of the models， but the applicability was good. CONCLUSIONS The predictive performance of risk predictive models for chemotherapy-induced myelosuppression in breast cancer remains to be further enhanced， and the overall risk of model bias is high. Future studies should follow the specifications of model development and reporting， then combine machine learning algorithms to develop risk predictive models with good predictive performance， high stability， and low risk of bias， so as to provide a decision-making basis for the clinic.

OBJECTIVE To construct a prediction model for the efficacy of serotonin-norepinephrine reuptake inhibitor （SNRI） in inpatients with moderate and severe depression by using a machine learning method. METHODS The case records of inpatients with moderate and severe depression treated with SNRI antidepressants were collected from a third-grade class-A hospital in Xinjiang from January 2022 to October 2024； those patients were divided into effective group and ineffective group based on the Hamilton depression scale-24 score reduction rate. After screening the characteristic variables related to the therapeutic efficacy of SNRI drugs through LASSO regression， five prediction models including support vector machine， k-nearest neighbor， random forest， lightweight gradient boosting machine and extreme gradient boosting were constructed using the training set. Bayesian optimization was used to adjust the hyperparameters of these models. The performance of the models was evaluated in the validation set to select the optimal model. The Shapley additive explanations method was used to perform explainable analysis on the best model. RESULTS The medical records from 355 hospitalized patients with moderate and severe depression were collected， comprising 285 cases in the effective group and 70 cases in the ineffective group， resulting in an overall therapeutic response rate of 80.28%. After feature variable screening， five characteristic variables for therapeutic efficacy were obtained， including Hamilton anxiety scale， blood urea nitrogen， combination of anti-anxiety drugs， drinking history， and first onset of the disease. Compared with other models， the random forest model performed the best. The area under the receiver operating characteristic curve was 0.85， the area under the precision-recall curve was 0.87， the accuracy was 0.74， and the recall rate value was 0.75. CONCLUSIONS The random forest model constructed based on five characteristic variables demonstrates potential for predicting the therapeutic efficacy of SNRI antidepressants in hospitalized patients with moderate and severe depression.