Objective To develop a sensitive liquid chromatography-tandem mass spectrometric (LC-MS/MS) method for the determination of forsklin in rat plasma.Methods After extraction with methyl tert-butyl ether,chromatographic separation was performed on a C18 column with the mobile phase consisting of water ( 0.1％ formic acid)-acetonitrile in a gradient elution mode.A tandem mass spectrometer equipped with electrospray ionization (ESI) source was used as detector in the positive ion mode.Quantification was performed using multiple reaction monitoring (MRM) with the precursor product combination ions of m/z 411→375.3 and 285→193 for forsklin and diazepam.Results Good linearity was obtained in the 0.5-1000 ng/ml range for the analyte and the analytical method was validated in terms of specificity,precision,accuracy,recovery,stability and matrix effect.These assays gave RSD values always lower than 14.4％ and RE values between -3.5 ％ and 3.8％.In addition,the specificity,extraction recovery,stability and matrix effect were satisfactory.Conclusion Due to its high sensitivity,specificity and simplicity,the method could be used for pharmacokinetic studies of forsklin.

ABSTRACT:In recent years ,there has been high prevalence of murine typhus in Yunnan Province ,People's Republic of China .A large outbreak of murine typhus occurred in Xishuangbanna Prefecture ,Yunnan Province in 2010 .However ,not all cases were confirmed by laboratory assays ;therefore ,field epidemiologic and laboratory investigations of murine typhus in Xishuangbanna Prefecture were conducted in 2011 .Blood samples were collected from clinical diagnostic cases at the acute and convalescence stages of murine typhus in Xishuangbanna Prefecture ,Yunnan Province ,from June to September of 2011 ,and blood and spleen samples were collected from mice sharing the same habitats as the patients .Immunofluorescence assays were used to test for the presence of IgM and IgG antibodies against Rickettsia typhi in sera from patients and mice .Real‐time PCR was used to detect the groEL gene of R .typhi in blood clots from patients at the acute stage and in spleen tissue from mice .A total of 1 157 clinically diagnosed murine typhus cases occurred in Xishuangbanna Prefecture ,Yunnan Province in 2011 ,with an incidence of 102 .10/100 000 .Of these cases ,80 were investigated by laboratory assays and 74 of 80 patients were confirmed to have murine typhus .The coincidence rate between the clinical diagnosis and laboratory detection was 92 .50% .The positivi‐ty rate for IgG antibodies against R .typhi was 14 .0% (14/100) for Rattus f lavipectus ,while the rate by PCR was 9 .0%(9/100) .That laboratory diagnoses confirmed that the severity of the murine typhus outbreak in Xishuangbanna cannot be ig‐nored .The distribution of host animals transmitting R .typhi underscores this conclusion .

Objective:To establish a long-term mortality rate prediction model for patients aged 60 years and over with atrial fibrillation and coronary heart disease using the machine learning method, and identify the corresponding risk factors of mortality.Methods:In this retrospective cohort study, a total of 329(11 cases lost of follow-up)patients with 183 males(55.6%)and 146 females(44.4%), aged(77.8±7.3)years, and 142 patients aged 80 years or older(43.2%)were selected in our hospitals from January 2013 to March 2015.And their clinical data on atrial fibrillation and coronary heart disease were analyzed.They were divided into the death group(151 cases)and the survival group(167 cases)according to the survival outcome.In addition, 60 patients aged 60 years and over admitted to our hospitals from April to July 2015 with atrial fibrillation and coronary heart disease were selected as external data validation set.The clinical data included age, gender, body mass index, diagnosis, co-morbidity, laboratory indicators, electrocardiogram, echocardiogram, treatment data.These patients were followed up for at least 6 years, and the main adverse cardiovascular and cerebrovascular events(MACCE), including death, were recorded.Finally, the data of the enrolled patients were randomly divided into the training set and the test set according to the ratio of 9∶1, Different models were established to predict the long-term mortality of patients with atrial fibrillation and coronary heart disease by machine learning algorithm.The optimal model was established by substituting external data(60 cases)into the model for verification and comparison.The top 20 risk factors for mortality were determined by Shapley additive explanation(SHAP)algorithm.Results:A total of 329 hospitalized patients were included in this study, the overall median follow-up time was 77.0 months(95% CI: 54.0~84.0), 11 cases lost during follow-up(3.3%), and 151 cases died(45.9%). The analysis found that the areas under the ROC curve for a support vector machine(SVM)model, k-Nearest Neighbor(KNN)model, decision tree model, random forest model, ADABoost model, XGBoost model and logistic regression model were 0.76, 0.75, 0.75, 0.91, 0.86, 0.85 and 0.81, respectively.The random forest model had the highest prediction efficiency, with the accuracy of 0.789 and F1 value of 0.806, which was better than the logistic regression model[the Area Under Receiver Operating Characteristic Curve(AUC): 0.91 vs.0.81, P<0.05]. D-dimer, age, number of MACCE, left ventricular ejection fraction, serum albumin level, anemia, New York Heart Association(NYHA)grade, history of old myocardial infarction, estimated glomerular filtration rate(eGFR)and resting heart rate were important risk factors for predicting long-term mortality. Conclusions:The random forest model based on machine learning method can predict the long-term mortality of patients with atrial fibrillation and coronary heart disease aged 60 years and over, have a good identification ability.Its accuracy is higher than that of the traditional Logistic regression model.Reducing the long-term mortality and improving the long-term outcomes can be achieved by intervening on D-dimer levels, correcting hypoproteinemia and anemia, improving cardiac function and controlling resting ventricular rates.