Objective To investigate the impacts of midazolam on the proliferation,migration and invasion of breast cancer MDA-MD-231 cells and its regulation on cyclic adenosine monophosphate(cAMP)/protein kinase A(PKA)/cAMP response element binding protein(CREB)signaling pathway.Methods MDA-MD-231 cells were cultured in vitro and grouped into control group,midazolam L group,midazolam M group,midazolam H group,and midazolam H+Sp-cAMPS group.Midazolam L group,midazolam M group,midazolam H group were treated with 5μmol/L,10μmol/L,and 20μmol/L of midazolam,respectively,midazolam H+Sp-cAMPS group was added with 20μmol/L of midazolam+10μmol/L of Sp-cAMPS 3-(4,5)-dimethylthiahiazo(-z-y1)-3,5-di-phenytetrazoliumromide(MTT)assay was applied to detect cell proliferation;The migration ability was detected by cell scratch test;Transwell test was applied to determine the invasion ability of cells;Cell apoptosis was detected by flow cytometry;The expression level of cAMP was detected by enzyme-linked immunosorbent assay(ELISA);Western blot was applied to verify the expression of phosphorylated(p-)PKA/PKA,p-CREB/CREB protein.Results Compared with control group,the cells in midazolam L group,midazolam M group,midazolam H group showed apoptosis,the apoptosis rate was obviously increased,the cell proliferation,migration and invasion abilities and the expression levels of cAMP,p-PKA/PKA,p-CREB/CREB proteins were obviously decreased(P<0.05);Compared with midazolam H group,midazolam H+Sp-cAMPS group had good cell growth,obviously reduced apoptosis rate,and obviously increased cell proliferation,migration and invasion abilities,and the expression levels of cAMP,p-PKA/PKA,p-CREB/CREB proteins(P<0.05).Conclusion Midazolam may inhibit the proliferation,migration and invasion of breast cancer cells by inhibiting the activation of cAMP/PKA/CREB signaling pathway.

Objective:To investigate the correlation between neutrophil/lymphocyte ratio (NLR) combined with low-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and severity of coronary lesions in patients with acute coronary syndrome (ACS).Methods:Patients who were diagnosed with ACS due to chest pain and received emergency coronary angiography in the First Affiliated Hospital of University of Science and Technology of China and the Affiliated Hospital of Anhui Medical University from January 2017 to June 2020 were enrolled in the final analysis. The data of gender, age, body mass index (BMI), past history, emergency blood routine indicators [neutrophil (NEU), lymphocyte (LYM), monocyte (MON), eosinophil (EOS), basophil (BAS), red blood cell (RBC), mean corpuscular volume (MCV), blood red cell distribution width (RDW), mean platelet volume (MPV), platelet volume distribution width (PDW)], blood lipid index [triglyceride (TG), total cholesterol (TC), HDL-C, LDL-C, very low-density lipoprotein cholesterol (VLDL-C)], and coronary angiography were collected. The results of coronary angiography were evaluated by the Gensini score. According to the Gensini score, the patients were divided into the control group (Gensini score = 0, 55 cases) and the study group (Gensini score > 0, 889 cases), and then the patients in the study group were divided into the low-Gensini-score group (Gensini score < 66, 419 cases) and the high-Gensini-score group (Gensini score ≥ 66, 470 cases). The differences in the general baseline data of the four groups were compared, and the correlation between the statistically significant data and the Gensini score was linearly analyzed, and then the combined diagnostic factors (NLR combined with LDL-C/HDL-C ratio) were obtained by Logistic regression analysis. The receiver operator characteristic curve (ROC curve) was used to evaluate the predictive value of NLR combined with LDL-C/HDL-C ratio in predicting the severity of coronary artery lesions in patients with ACS. Finally, multivariate linear regression analysis was used to establish the predictive model between NLR combined with LDL-C/HDL-C ratio and Gensini score.Results:A total of 944 patients were finally included. The differences in gender, age, BMI, hypertension, diabetes, smoking history, NEU, LYM, MON, EOS, RDW, TC, HDL-C, LDL-C, NLR, LDL-C/HDL-C ratio between the control group and the study group were statistically significant. The differences in BMI, hypertension, diabetes, smoking history, NEU, LYM, MON, EOS, TG, TC, HDL-C, LDL-C, NLR and LDL-C/HDL-C ratio between the low-Gensini-score group and the high-Gensini-score group were statistically significant. Linear regression analysis showed that compared with other indicators, the correlation between NLR, LDL-C/HDL-C ratio and Gensini score was stronger in the study group ( r values were 0.634 and 0.663, respectively, both P < 0.05). Binary Logistic regression analysis of the indicators related to Gensini score showed that NEU, LYM, HDL-C and LDL-C were independent risk factors for coronary stenosis in patients with ACS [odds ratio ( OR) were 0.189, 10.309, 13.993, 0.251, 95% confidence intervals (95% CI) were 0.114-0.313, 4.679-22.714, 3.402-57.559, 0.121-0.519, respectively, all P < 0.05]. ROC curve analysis showed that NLR combined with LDL-C/HDL-C ratio had higher predictive value in predicting the severity of coronary lesions in ACS patients [area under the ROC curve (AUC) was 0.952, 95% CI was 0.93-0.969], when the cutoff value was -3.152, the sensitivity was 98.20%, and the specificity was 81.60%. According to the results of multivariate linear regression analysis, the prediction model between NLR, LDL-C/HDL-C ratio and Gensini score was established, and the formula was Gensini score = -7.772+15.675×LDL-C/HDL-C ratio+8.288×NLR ( R2 = 0.862). Conclusion:There is a significant correlation between emergency NLR combined with LDL-C/HDL-C ratio and Gensini score in patients with ACS at admission, which has a certain predictive value for the severity of coronary artery stenosis in patients with ACS, and can be used as a predictor for evaluating the severity of coronary artery disease.

Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.