Objective To study the effects of intra-bone marrow injection of donor bone marrow cells in combination with low dose radiation on the immunologic reaction of composite tissue allotransplantation.Methods The inbred SD rats were chosen as donors and inbred Wistar rats as recipients. Overall 40 recipients were classified into 4 groups randomly after allogeneic leg transplantation: group A received transplantation only; group B irradiation in the sublethal level (4.5 Gy?2 at a 4-h interval) and fludarabine (50 mg/kg, i.p.); group C, bone marrow cells were directly injected into the intra-bone marrow cavity of the recipients; group D, using a combination of the injection of fludarabine (50 mg/kg, i.p.), irradiation (4.5 Gy?2, at a 4-h interval) and injection of donor bone marrow cells. The rejection of grafts was observed. 120 days after induction of tolerance the mixed lymphocyte reaction (MIR) and skin grafting were examined to confirm tolerance status. To determine graft-versus-host disease (GVHD), rats in tolerance status were also histologically examined. Results As compared with other groups, mean rejection time and mean survival time of limb allografts were prolonged obviously in group D. Donor-specific tolerance was confirmed in all limb allograft recipients in group D by skin grafting and by MLR, and no signs of GVHD were also histologically examined. Conclusion Using a combination of injection of fludarabine, irradiation in the sublethal level and donor bone marrow cells, we have induced donor-specific immunological tolerance in allogeneic limb transplantation in rats without using any immnosuppressants after the operation.

Objective:To establish an analytical method for HPLC fingerprint chromatography of Simiao Junyi ointment to provide basis for the quality control standard. Methods:The separation conditions were established to obtain the HPLC fingerprint chromatog-raphy of the main ingredients in Simiao Junyi ointment. The conditions were as follows:the chromatographic column was Ultimate C18-ODS(250 mm ×4.6 mm,5 μm), the mobile phase was acetonitrile-0.1% phosphate solution, the flow rate was 1.0 ml·min-1, the detection wavelength was 280 nm, and the column temperature was 35℃. The common peaks in the chromatography were analyzed for their belongings. Results:Gradient elution was performed under the above optimal separation conditions, the constituents in Simiao Ju-nyi ointment were separated from each other perfectly, and the optimal fingerprint chromatography was obtained. Though the methodolo-gy examination, the indicators such as precision, stability and repeatability of the method were all promising, and the fingerprint chro-matography could be seen clearly and was easy to be analyzed. The relationships between Simiao Junyi ointment and the common peaks of four medicinal materials in the fingerprint chromatography were preliminary determined, which provided important basis for the quali-ty control of Simiao Junyi ointment. Conclusion:The HPLC fingerprint chromatography of Simiao Junyi ointment can be used as an a-nalysis method for the quality control of Simiao Junyi ointment, which provides reference for the quality control standard for the finished product.

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.

ObjectiveTo systematically review the efficacy of intervention by WeChat on medication compliance， psychotic symptom and recurrence rate of schizophrenic patients in community. MethodsDatabases including PubMed， the Cochrane Library， CBM， CNKI， Wanfang Data and VIP were searched electronically from January 1， 2011 to November 1， 2020 to collect randomized controlled trials （RCTs） about the effects of WeChat intervention on community schizophrenic patients. After two reviewers independently screened literature， extracted data and assessed the risk of bias of included studies， the meta-analysis was performed with Stata 12.0 software. ResultsA total of 381 articles were retrieved and finally 10 RCTs were included， including 1 251 patients with WeChat intervention group 641 cases and routine health education group 610 cases. Meta-analysis showed that compared with the conventional health education group， the WeChat intervention group had higher medication compliance （OR=3.05，95% CI：1.98~4.69，P<0.01）， lower PANSS score （SMD=-1.05，95% CI：-1.46~-0.64，P<0.01） and relapse rate （OR=0.34，95% CI：0.24~0.48，P<0.01）. ConclusionThe interactive intervention based on WeChat platform can effectively improve the medication compliance of patients with schizophrenia in the community， help to reduce the severity of psychotic symptoms and the recurrence rate.