RESUMO
Objective:Based on Logistic regression and XGBoost algorithm, the prediction model of perioperative risk of deep venous thrombosis in patients with acute multiple knee joint injuries was constructed, and the prediction performance was compared.Methods:A total of 120 patients with acute multiple injuries around the knee treated in the Department of Orthopaedic Trauma, Guangzhou Panyu District Central Hospital from January 2017 to June 2020 were retrospectively selected. According to the proportion of 7∶3, the patients were randomly divided into training set ( n=84) and test set ( n=36). The prediction models of Logistic regression and XGBoost algorithm were constructed by training set data, to screen the predictors of perioperative deep venous thrombosis in patients with acute multiple injury around knee joint, and the prediction effect of the model was evaluated by test set data. The measurement data conforming to the normal distribution were expressed as mean±standard deviation ( Mean± SD), and the independent t-test was used for comparison between groups; the measurement data of non-normal distribution were expressed as the median (interquartile range) [ M( P25, P75)], the independent sample Mann-Whitney U test was used for comparison between groups; the Chi-square test was used for comparison of enumeration data between groups. Results:The results of Logistic regression model showed that age, hypertension, coronary heart disease, time from injury to operation, D-dimer at 1 day after operation and multiple injuries were predictive factors for perioperative deep venous thrombosis in patients with acute multiple injuries around the knee joint. The top five important feature scores of XGBoost algorithm model were combined multiple injuries (35 points), time from injury to operation (28 points), age (24 points), coronary heart disease (21 points) and D-dimer 1 day after operation (16 points). In the training set, the area under the curve of the Logistic regression model was 0.805 (95% CI: 0.637-0.912), and χ2=1.436, P=0.329 for Hosmer and Lemeshow test. The area under the curve of the XGBoost algorithm model was 0.847(95% CI: 0.651-0.920), and χ2=1.103, P=0.976 for Hosmer and Lemeshow test. Conclusion:Logistic regression model and XGBoost algorithm model are similar in predicting perioperative deep venous thrombosis in patients with acute multiple injuries around the knee, and multiple injuries, time from injury to operation, age, coronary heart disease and D-dimer 1 day after operation can be used as predictive factors.
RESUMO
A simple and sensitive method was developed for the detection of mercury ions with quartz crystal microbalance(QCM), based on the specific thymine(T)-Hg2+-T structure and gold nanoparticles. The gold nanoparticles were prepared by the citrate reduction method. In a self-assembled way, the particle surface was modified with the probe oligonucleotides which were partially replaced by 6-Mercaptohexan-1-ol to reduce the steric hindrance of hybridization. The sensitivity was optimized by the oligonucleotide with a strand length of 9bp and a T number of 7. The linear range was 5.0-100 nmol/L with a detection limit of 2.0 nmol/L. And Ca2+, Mg2+ and other metal ions had no significant interferences. This method was successfully applied for the detection of Hg2+in environmental water samples, with RSD less than 2.9% and recoveries of 97.3%-101.2%.