Establishment of the prediction models of malignant brain edema after vascular recanalization in anterior circulation acute large vascular occlusion stroke / 国际外科学杂志

Objective:Based on Logistic regression and XGBoost algorithm, the prediction model of malignant brain edema (MBE) after vascular recanalization of anterior circulation acute great vessel occlusive stroke (ALVOS) was constructed, and the prediction performance was compared.Methods:A retrospective selection of 382 patients with anterior circulation ALVOS who underwent early endovascular treatment (EVT) in our hospital from March 2014 to June 2020 and successfully recanalized the occluded blood vessel was selected. The patients were divided into the training group ( n=267) and the test group ( n=115) according to the ratio of 7∶3 by the random number table method. According to whether the patients had MBE after successful recanalization of the occluded blood vessels, the training group was divided into the MBE group ( n=41) and non-MBE group ( n=226). The baseline data, treatment and brain computed tomography perfusion(CTP) results of MBE group and non-MBE group in training group and test group were compared respectively, including age, admission score of National Institutes of Health Stroke Scale (NIHSS), grade of cerebral collateral circulation, cerebral blood volume, and so on. Logistic regression model and XGBoost algorithm model were used to screen the predictors of MBE in ALVOS patients with occluded vessels successfully recanalized, and the discrimination and calibration of the two models were compared. The measurement data conforming to the normal distribution were expressed as mean ± standard deviation ( ± s), and the independent sample t test was used for comparison between the two groups. Non-normally distributed measurement data were represented by M ( Q1, Q3), using independent sample Mann-Whitney U test. The chi-square test was used to compare the count data between groups. Results:There was no significant difference in baseline data, treatment status, and cranial computed tomography perfusion (CTP) imaging results of the training group and the test group ( P>0.05). The age, admission systolic blood pressure, admission NIHSS score, proportion of hypertension, proportion of cerebral collateral circulation 0-2, proportion of thrombus removal times> 3 times, time from onset to recanalization, and cerebral blood volume (CBV) of MBE group were (68.95±8.04) years old, (146.71±22.73) mmHg, 17(13, 21) min, 87.80%, 82.93%, 68.29%, (365.64±87.83) min, (32.56±5.73) mL/100 g, obvious higher than the non-MBE group [(60.27±7.13) years old, (137.92±19.58) mmHg, 14(10, 18) points, 73.01%, 60.62%, 2.65%, (307.59±74.05) min, (27.49±5.46) mL/100 g] ( P<0.05). The results of Logistic regression model showed that age, NIHSS on admission, grade of cerebral collateral circulation, times of thrombectomy and time from onset to recanalization were the predictors of MBE after successful recanalization of occluded vessels after EVT in patients with anterior circulation ALVOS ( P<0.05). The top five important feature scores of XGBoost algorithm model were cerebral collateral circulation classification 34, embolectomy times 27, onset to vascular recanalization time 25, admission NIHSS score 22, age 16.In the training set, the area under the curve of the Logistic regression model was 0.816(95% CI: 0.749-0.883), and the Hosmer-Lemeshow test showed that χ2=1.547, P=0.438. The area under the curve of the XGBoost algorithm model was 0.856(95% CI: 0.799-0.913), and the Hosmer-Lemeshow test showed that χ2=1.021, P=0.998. Conclusion:Logistic regression model and XGBoost algorithm model had similar prediction performance for MBE after successful recanalization of occluded vessels after EVT in patients with anterior circulation ALVOS, and collateral circulation classification, number of thrombolysis, time from onset to recanalization, NIHSS score on admission, and age could be used as predictors.

Prediction model of perioperative risk of deep venous thrombosis in patients with acute multiple knee joint injuries based on Logistic regression and XGBoost algorithm / 国际外科学杂志

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.

Prediction of plasma protein binding rate based on machine learning / 中国药科大学学报

@#Predicting the protein binding rate of drugs in plasma is helpful to us in understanding the pharmacokinetic characteristics of drugs, with much value of reference for early research on drug discovery. In this study, plasma protein binding rate information of 2 452 clinical drugs were collected.Two pieces of software, Molecular Operating Environment (MOE) and Mordred, were used to calculate molecular descriptors, which were used as input features of the model.Extreme gradient boosting (XGBoost) algorithm and random forest (RF) algorithm were then used to build a machine learning model.The results showed that, compared with MOE, the prediction performance of the constructed model was better using the molecular descriptor calculated by Mordred as the input of the model.The prediction performance results of the model constructed using the XGBoost algorithm and the RF algorithm were similar, and the R2 of the optimal model were both 0.715.According to the research results, it can be concluded that the drug plasma protein binding rate is closely related to some physical and chemical properties of the drug molecule, such as water solubility, octanol/water partition coefficient and conjugated double bonds.Using these parameters to predict the plasma protein binding rate of drugs has the advantages of convenience and efficiency, which can provide reference for related pharmacokinetic studies.

Prediction of drug-induced cell viability by SAE-XGBoost algorithm based on LINCS-L1000 perturbation signal / 生物工程学报

Different cell lines have different perturbation signals in response to specific compounds, and it is important to predict cell viability based on these perturbation signals and to uncover the drug sensitivity hidden underneath the phenotype. We developed an SAE-XGBoost cell viability prediction algorithm based on the LINCS-L1000 perturbation signal. By matching and screening three major dataset, LINCS-L1000, CTRP and Achilles, a stacked autoencoder deep neural network was used to extract the gene information. These information were combined with the RW-XGBoost algorithm to predict the cell viability under drug induction, and then to complete drug sensitivity inference on the NCI60 and CCLE datasets. The model achieved good results compared to other methods with a Pearson correlation coefficient of 0.85. It was further validated on an independent dataset, corresponding to a Pearson correlation coefficient of 0.68. The results indicate that the proposed method can help discover novel and effective anti-cancer drugs for precision medicine.