Objective:To study the radiation dose rate around patients underwent renal dynamic imaging of SPECT with 99Tcm-DTPA, so as to provide the basis for ensuring the radiation safety of the surrounding environment and the general public.Methods: 109 patients who underwent renal dynamic imaging were enrolled in this study, and the radiation dose rates of different time and different distance after 99Tcm-DTPA was injected patients were measured. And then, the changes of dose rate following time and distance were analyzed. Results: At the 0.5h, 1h, 2h, 3h, 6h, and 7h after radiopharmaceuticals were injected patients underwent renal dynamic imaging, the radiation dose rates were 0.76-8.86 μSv/h, 0.68-7.27 μSv/h, 0.57-4.52 μSv/h, 0.56-3.90 μSv/h, 0.23-2.07 μSv/h and 0.21-1.05 μSv/h, respectively, between 0.5 m and 4 m away from patients. At 24h after radiopharmaceuticals were injected, all of radiation dose rates at different distances around patients were same with background values.Conclusion: The radiation dose rates around patients underwent renal dynamic imaging will rapidly decrease with the increasing of time and distance. At 24h after radiopharmaceuticals were injected, all of radiation dose rates at different distances away from patients were same with background values.

Objective To evaluate the effect of relaxation training on sudden deafness patients with mild or moderate grade.Methods 78 patients were divided into the observation group (38 cases) and the control group (40 cases).Besides usual care and nursing,the observation group was treated with relaxation training daily in the morning and evening.The pure tone audiometry scores before and after nursing intervention and treatment was compared between two groups.Results There was significant difference in threshold level between two groups.Conclusions Relaxation training was an effective nursing intervention for sudden deafness patients with mild-to-moderate grade.

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.

Objective To study the radiation level in the surrounding of patients undergoing 99Tcm-MDP SPECT bone imaging and associated influencing factors and provide experimental data for the radiation safety of personnel around the patients.Methods A total of 367 patients undergoing whole-body bone imaging in Cancer Hospital,Chinese Academy of Medical Sciences were investigated to measure the ambient dose equivalent rate around the patients at different time and different distances from the patients,analyze the variations of ambient dose equivalent rate with time and distances,estimate the dose level at different distances around the patients,and evaluate the radiation dose to personnel around the patients.Results The ambient dose equivalent rate around the patient decreased exponentially with time;the effective half-life of 99Tcm in patients' body increased with time.The ambient dose equivalent rate varied by power function with increasing distance within 4 meters from the patient with the mean power value of -1.45.The radiation levels were 238.3 μSv at 0.5 m,99.7 μSv at 1 m,and 61.8 μSv at 1.5 m from the patient undergoing 99Tcm injection to its vanishing in patients' body.At different time points,the radiation doses at 0.5 m off the patient for 10 min were:9.9,3.0,1.9 μSv at 0,3 and 6 h,respectively.Conclusions The ambient dose equivalent rate around the patients undergoing 99Tcm-MDP SPECT bone imaging decrease rapidly with increasing time and distance.The patients can cause a certain degree of exposures to the surrounding personnel,but the exposure level is far lower than the relevant national standards.It is suggested that the patients should not receive other types of diagnosis and treatments spending lot of time and in close contact with medical staff while bone imaging being conducted on the same day.