Objective To observe the effect of paraoxonase 1 (PON1) application of the new method of arylesterase activity in patients with coronary heart disease,analysis of paraoxonase 1 (PON1) of the clinical value of arylesterase activity in the new testing method.Methods From January 2014 to January 2016 in our hospital 86 patients with coronary heart disease as the research object,and then select the healthy people at the same time to the hospital physical examination of 50 as the control group to take the research object,spectrophotometric method for the determination of coronary heart disease patients and control subjects serum PON1 arylesterase activity,PON1 arylesterase activity at the same time with statistics the different degree of coronary heart disease,PON1 arylesterase activity between patients with coronary heart disease and control group comparison study and different severity of coronary heart disease patients,the patients with coronary heart disease PON1 aromatic ester enzyme activity,age,gender,BMI,TC,total cholesterol,low density lipoprotein cholesterol LDL-C and glycerin three greases TG included in the analysis of factors of coronary heart disease multiple linear regression equation,to determine the changes of patients with coronary artery disease by PON1 arylesterase activity,to provide a reference for clinical treatment.Results The activity of PON1 in patients with coronary heart disease was significantly lower than that of the control group,and the difference was statistically significant (P<0.05).Single branch lesions in patients with PON1 arylesterase activity was significantly higher than that of double vessel lesions and three lesions were double branch lesions in patients with PON1 arylesterase activity was significantly higher than that in three patients,the differences were statistically significant (P<0.05).According to the multiple linear regression analysis showed that coronary heart disease and the patient′s age,gender,BMI,TC,LDL-C,TG and PON1 arylesterase activity (P<0.05),which was related with age,gender,BMI,TC,LDL-C and TG were positively correlated,negatively correlated with PON1 arylesterase activity.Conclusion The PON1 activity of in patients with coronary heart disease is significantly decreased,and the extent of the disease is more severe,the more obvious the decline of PON1,the activity of PON1 shows a negative correlation with coronary heart disease.

Objective:To study the position of computed tomography(CT)slice of marker points of radiotherapy,which was determined by control scan(CS)method,on the application effect of patients with head and neck tumors who received radiotherapy.Methods:Based on Control Scan(CS)method,a calculator program of mark-point slice position was made,which was an enterprise WeChat program that could be used in calculating position and CT position scan of patients with head and neck tumor(slice thickness was 3mm).A total of 60 patients with head and neck tumor were selected,and the patients who underwent CT positioning scan by using CS method were divided into observation group,and patients who underwent CT position scan by using conventional method were divided into control group,with 30 cases in each group.The number of cases with three metal marker points displayed at the same slice,and the number of slices containing the CT images of marker point between the located CT scan were compared.Results:The number of patients in the observation group and the control group who showed three markers at the same level at the same time were respectively 26 cases and 13 cases,and observation group increased by 13 cases(43.4%)than control group,and the difference was significant(x2=12.382,P<0.05).The number of CT images with only 1 slice of observation group and control group were respectively 4 cases and 0 cases,which increased by 4 cases(13.3%)than the control group,and the difference was significant(x2=2.411,P<0.05).Conclusions:The CT localization scan of radiotherapy,which uses CS to assist patients with head and neck tumor,can precisely calculate and obtain the primary position of target of CT localization scan.It can take the images of 3 mental marker points of patient who receives radiotherapy to occur at the same CT slice as soon as possible,which has better application effect.It can effectively improve the convenience and work effectiveness of radiotherapy.

Objective To delineate the normal stomach and thoracic stomach structure of patients with thoracic and abdominal tumor automatically using the AccuContour software based on deep learning in order to evaluate and compare the results. Methods Thirty-six patients with choracic and abdominal tumors were chosen for this study, and were divided into two groups. Group A included 18 patients with normal stomach, and group B included the other 18 patients undergoing esophageal carcinoma operation with thoracic stomach. The stomach structures were automatically delineated by the AccuContour software in the simulation CT series. Statistical analysis was carried out to data of the differences in volume, position and shape between the automatic and manual delineations, and data of the two kinds of stomach were compared. Results For group A, the differences in volume (ΔV%) between the automatic and manual delineations was (−1.82 ± 9.65)%, the total position difference (ΔL) was (0.51 ± 0.37) cm, the values of dice similarity coefficient (DSC) was 0.89 ± 0.04. There were significant differences in values of ΔV%、ΔL and DSC (P < 0.05). Conclusion The used version of AccuContour software in this study had a satisfactory result of automatic delineation of the normal stomach structure larger than certain volume, but could not delineate the thoracic stomach structures effectively for patients undergoing esophageal carcinoma operation.

Transrectal contrast-enhanced ultrasound (CEUS) is an important examination for rectal tumors. The inhomogeneity of the CEUS images has important clinical significance. However, there is no objective method to evaluate this index. In this study, a method based on gray-level co-occurrence matrix (GLCM) is proposed to extract texture features of images and grade these images according the inhomogeneity. Specific processes include compressing the gray level of the image, calculating the texture statistics of gray level co-occurrence matrix, combining feature selection and principal component analysis (PCA) for dimensionality reduction, and training and validating quadratic discriminant analysis (QDA). After ten cross-validation, the overall accuracy rate of machine classification was 87.01%, and the accuracy of each level was as follows: Grade Ⅰ 52.94%, Grade Ⅱ 96.48% and Grade Ⅲ 92.35% respectively. The proposed method has high accuracy in judging grade Ⅱ and Ⅲ images, which can help to identify the grade of inhomogeneity of contrast-enhanced ultrasound images of rectal tumors, and may be used to assist clinical doctors in judging the grade of inhomogeneity of contrast-enhanced ultrasound of rectal tumors.
Discriminant Analysis ; Humans ; Rectal Neoplasms ; Ultrasonography