ABSTRACT
Objective:To evaluate the effects of different concentrations of ropivacaine on the growth and migration of lung cancer cells.Methods:Human lung adenocarcinoma cell strain A549 cells and human lung squamous cell strain H520 cells were divided into 4 groups ( n=24 each) using a random number table method: control group (group C) and different concentrations of ropivacaine groups (Ⅰ-Ⅲ groups). Cells were commonly cultured in group C. Ropivacaine 3, 5 and 7 mmol/L were added and then the cells were cultured in Ⅰ-Ⅲ groups, respectively. The cell survival rate was determined using the CCK-8 method at 24, 48 and 72 h of treatment (T 1-3). The cell cycle and apoptosis were detected at T 1 using flow cytometry. The expression of Cyclin D1, cyclin-dependent kinase 4 (CDK4), cleaved poly (ADP-ribose) polymerase-1 (PARP-1) and cleaved caspase-3 was detected using Western blot. Wound healing assay was used to measure cell migration distance. The activities of RhoA and Rac1 were detected by microplate spectrophotometry. Results:The cell viability of A549 and H520 cells sequentially decreased at T 1-3, the proportion of G0/G1 phase and apoptosis sequentially increased, the expression of Cyclin D1 and CDK4 was down-regulated sequentially at T 1, the expression of cleaved PARP-1 and cleaved caspase-3 was up-regulated sequentially, and the cell migration distance, RhoA, and Rac1 activity decreased sequentially in C, Ⅰ, Ⅱ and Ⅲ groups ( P<0.05). Conclusions:Ropivacaine can inhibit the growth and migration ability of lung cancer cells in a concentration-dependent manner, which is related to induction of cell cycle arrest and apoptosis.
ABSTRACT
OBJECTIVE: To evaluate the application of decision tree method and Logistic regression in the prediction of acute myocardial infarction (AMI) events. METHODS: The clinical data of 295 patients, who underwent coronary angiography due to angina or chest pain with unidentified causes in Zhejiang provincial People's Hospital during October 2018 and April 2019, were retrospectively analyzed. Fifty five patients were identified as AMI. Logistic regression and decision tree methods were performed to establish predictive models for the occurrence of AMI, respectively; and the models created by decision tree analysis were divided into Logistic regression-independent model (Tree 1) and Logistic regression-dependent model (Tree 2). The performance of Logistic regression and decision tree models were compared using the area under the receiver operating characteristic (ROC) curve. RESULTS Logistic regression analysis showed that history of coronary artery disease, multi-vessel coronary artery disease, statin use and apolipoprotein (ApoA1) level were independent influencing factors of AMI events (all P<0.05). Logistic regression-independent decision tree model (Tree 1) showed that multi-vessel coronary artery disease was the root node, and history of coronary artery disease, ApoA1 level (the cutoff value:1.314 g/L) and anti-platelet drug use were descendant nodes. In Logistic regression-dependent decision tree model (Tree 2), multi-vessel coronary artery disease was still the root node, but only followed by two descendant nodes including history of coronary artery disease and ApoA1 level. The area under the curve (AUC) of ROC of Logistic regression model was 0.826, and AUCs of decision tree models were 0.765 and 0.726, respectively. AUC of Logistic regression model was significantly higher than that of Tree 2 (95% CI=0.041-0.145, Z=3.534, P<0.001), but was not higher than that of Tree 1 (95% CI=-0.014-0.121, Z=-1.173, P>0.05). CONCLUSIONS The predictive value for AMI event was comparable between Logistic regression-independent decision tree model and Logistic regression model, implying the data mining methods are feasible and effective in AMI prevention and control.