Objective To explore the epidemic conditions of rheumatic fever (RF) with a simple method at some regions.Methods Antistreptococcal DNase B test microtiter method was adopted to detect human sera antibody in natural populations who are responsible to RF.All 216 schoolchildren aged 10～12 years old were chose by the random cluster sampling.The schoolchildren′s serum antibody against DNase B in each season from september 1988 to August 1989 was detected,and compared with antistreptolysin “O” (ASO);furthermore,5 different regional schoolchildren′s serum antibody to DNase B was tested, and 100 schoolchildren aged 10～13 years old were chose in every region.Meanwhile,the RF incidence of 5～18 year schoolchildren was investigated for 4 years,and approximately 364 915 person times were inquired.All the results of the anti DNase B and ASO were compared with the schoolchildren′s RF incidence.Results ① The levels of the anti DNase B were higher during fall,winter and spring seasons,and lower in summer,which were coincided with the RF incidence,and linear relation was very good ( r =0 913, P

Objective To analyze the differences of hospitalization mortality of coronary stenting in patients with coronary heart disease between the elderly (≥65 years old ) and non elderly. Methods Totally 3252 patients who underwent coronary stenting in Guangdong Provincial People’s Hospital were studied retrospectively. They were divided into elderly group (≥65 years old, n=1344) and non elderly group (

AIM: To screen and identify the differentially expressed genes in lymphocytes of patients with unstable angina in order to find the molecular mechanism of unstable angina. METHODS: Suppression subtractive hybridizations (SSH) and dot blot hybridizations were performed to screen the relatively differentially expressed genes in lymphocyte RNA between the patients with unstable angina pectoris and stable angina pectoris. The obtained expressed sequence tags (ESTs) were used as probes to perform Reverse Northern blot with forward and reverse suppression products. And the positive ESTs were performed RNA slot hybridization with unstable and stable angina group. The obtained ESTs were sequenced and analyzed using BLAST (nr) at NCBI. RESULTS: Three up-regulated ESTs in the unstable angina group, and one down-regulated EST in the stable angina group were obtained. All of them are sequences of known genes. CONCLUSION: All these ESTs may be associated with the unstablization of plaque of coronary artery in patients with unstable angina.

Objective:To propose a method of image similarity measurement based on structure information and intuitionistic fuzzy set and measure the similarity between CT image and CBCT image of radiotherapy plan positioning, aiming to objectively measure the setup errors.Methods:A total of four pre-registration images of a nasopharyngeal carcinoma patient on the cross-sectional and sagittal planes and a pelvic tumor patient on the cross-sectional and coronal planes were randomly selected. Five methods were used to quantify the setup errors, including correlation coefficient, mean square error, image joint entropy, mutual information and similarity measure method.Results:All five methods could describe the deviation to a certain extent. Compared with other methods, the similarity measure method showed a stronger upward trend with the increase of errors. After normalization, the results of five types of error increase on the cross-sectional plane of the nasopharyngeal carcinoma patient were 0.553, 0.683, 1.055, 1.995, 5.151, and 1.171, 1.618, 1.962, 1.790, 3.572 on the sagittal plane, respectively. The results of other methods were between 0 and 2 after normalization, and the results of different errors of the same method slightly changed. In addition, the method was more sensitive to the soft tissue errors.Conclusions:The image similarity measurement method based on structure information and intuitionistic fuzzy set is more consistent with human eye perception than the existing evaluation methods. The errors between bone markers and soft tissues can be objectively quantified to certain extent. The soft tissue deviation reflected by the setup errors is of significance for individualized precision radiotherapy.

Objective:To propose a machine learning-based markerless beam′s eye view (BEV) tumor tracking algorithm that can be applied to low-quality megavolt (MV) images with multileaf collimator (MLC)-induced occlusion and non-rigid deformation.Methods:This study processed the registration of MV images using the window template matching method and end-to-end unsupervised network Voxelmorph and verified the accuracy of the tumor tracking algorithm using dynamic chest models. Phantom QA plans were executed after the treatment offset was manually set on the accelerator, and 682 electronic portal imaging device (EPID) images obtained during the treatment were collected as fixed images. Moreover, the digitally reconstructed radiography (DRR) images corresponding to the portal angles in the planning system were collected as floating images for the study of target volume tracking. In addition, 533 pairs of EPID and DRR images of 21 lung tumor patients treated with radiotherapy were collected to conduct the study of tumor tracking and provide quantitative result of changes in tumor locations during the treatment. Image similarity was used for third-party validation of the algorithm.Results:The algorithm could process images with different degrees (10%-80%) of data missing and performed well in non-rigid registration of images with data missing. As shown by the phantom verification, 86.8% and 80% of the tracking errors were less than 3 mm and less than 2 mm, respectively, and the normalized mutual information (NMI) varied from 1.18 ± 0.02 to 1.20 ± 0.02 after registration ( t = -6.78, P = 0.001). The tumor motion of the clinical cases was dominated by translation, with an average displacement of 3.78 mm and a maximum displacement of 7.46 mm. The registration result of the cases showed the presence of non-rigid deformations, and the corresponding NMI varied from 1.21 ± 0.03 before registration to 1.22 ± 0.03 after registration ( t = -2.91, P = 0.001). Conclusions:The tumor tracking algorithm proposed in this study has reliable tracking accuracy and high robustness and can be used for non-invasive and real-time tumor tracking requiring no additional equipment and radiation dose.

Objective:To evaluate the feasibility of predicting lung cancer target position by online optical surface motion monitoring.Methods:CT images obtained in different ways of stereotactic body radiotherapy (SBRT) plans from 16 lung cancer cases were selected for experimental simulation. The planned CT and the original target position were taken as the reference, and the 10 phases of CT in four dimension CT and each cone beam (CBCT) were taken as the floating objects, on which the floating target location was delineated. The binocular visual surface imaging method was used to obtain point cloud data of reference and floating image body surface, while the point cloud feature information was extracted for comparison. Based on the random forest algorithm, the feature information difference and the corresponding target area position difference were fitted, and an online prediction model of the target area position was constructed.Results:The model had a high prediction success rate for the target position. The variance explainded and root mean squared error ( RMSE) of left-right, superior-inferior, anterior-posterior directions were 99.76%, 99.25%, 99.58%, and 0.0447 mm, 0.0837 mm, 0.0616 mm, respectively. Conclusion:The online monitoring of lung SBRT target position proposed in this study is feasible, which can provide reference for online monitoring and verification of target position and dose evaluation in clinical radiotherapy.

Objective:To propose a markerless beam's eye view (BEV) tumor tracking algorithm, which can be applied to megavolt (MV) images with poor image quality, multi-leaf collimator (MLC) occlusion and non-rigid deformation.Methods:Window template matching, image structure transformation and demons non-rigid registration method were used to solve the registration problem in MV images. The quality assurance (QA) plan was generated in the phantom and executed after manually setting the treatment offset on the accelerator, and 682 electronic portal imaging device (EPID) images in the treatment process were collected as fixed images. Meanwhile, the digitally reconstructured radiograph (DRR) images corresponding to the field angle in the planning system were collected as floating images to verify the accuracy of the algorithm. In addition, a total of 533 images were collected from 21 cases of lung tumor treatment data for tumor tracking study, providing quantitative results of tumor location changes during treatment. Image similarity was used for third-party verification of tracking results.Results:The algorithm could cope with different degrees (10%-80%) of image missing. In the phantom verification, 86.8% of the tracking errors were less than 3 mm, and 80% were less than 2 mm. Normalized mutual information (NMI) varied from 1.182±0.026 to 1.202±0.027 ( P<0.005) before and after registration and the change of Hausdorff distance (HD) was from 57.767±6.474 to 56.664±6.733 ( P<0.005). The case results were predominantly translational (-6.0 mm to 6.2 mm), but non-rigid deformation still existed. NMI varied from 1.216±0.031 to 1.225±0.031 ( P<0.005) before and after registration and the change of HD was from 46.384±7.698 to 45.691±8.089 ( P<0.005). Conclusions:The proposed algorithm can cope with different degrees of image missing and performs well in non-rigid registration with data missing images which can be applied in different radiotherapy technologies. It provides a reference idea for processing MV images with multi-modality, partial data and poor image quality.