Objective To know the prevalence of chronic obstructive pulmonary disease(COPD) in patients with obstructive sleep apnea-hypopnea syndrome(OSAHS) in Kunming Yunnan Province, and the clinical symptom of OS.Methods Retrospective study of 4 636 cases of patients with snoring, excluding COPD in addition to chronic respiratory disease and assess the patient`s condition.The test included AHI, BMI, Epworth sleepiness scale, lung function.The index of OS was confirmed by AHI>5 times/h and FEV1/FVC<70%.Results During the period of 2006 to 2012, he prevalence of COPD in patients with OSAHS was 10.1% [95% confidence interval (CI) 9.1%-11.1%] in Kunming Yunnan Province.And male`s OS prevalence rate is higher than the female(male 10.2%;female 9.7%).The OS patients` average age was 56.9±14.1, the mean AHI was (47.46±26.79) times/h, and the average FEV1/FVC was 60.09%±23.57%.Polysomnographyresults show that patients suffered obvious hypoxiaat night in both OSAHS group and OS group, but it was more significant in OS group.Pulmonary function test showed that OS patients have more serious chronic obstructive pulmonary disease.Conclusions The prevalence of COPD in OSAHS patients was high in Kunming, Yunnan Province, and the prevalence rate in old group reached more than 24%.In addition, the sympotms of patients with OS were more severe than those with only OSAHS or COPD in lung function and hyoxemia.

Objective: To study a maximum between-cluster variance based on differential search algorithm, and to select the multi-threshold for effectively segmentation of brain magnetic resonance images. Methods: The brain extraction tool(BET) algorithm was used to remove the non-brain tissue part of the original magnetic resonance image. The best-fit with coalescing(BFC) algorithm was used to remove the intensity non-uniformity. The differential search algorithm was used to optimize the maximum between-cluster variance of the image to find the optimal threshold for multi-threshold segmentation of the magnetic resonance image. The method was validated using simulated magnetic resonance(MR) brain image data provided by BrainWeb. Results: For MR images with different noise levels and intensity inhomogeneities, the proposed method was better than FSL, SPM and Brainsuite methods. Conclusions The maximum between-cluster variance based on differential search algorithm has better segmentation accuracy and robustness, especially for cerebrospinal fluid.

Objective To predict the 5-year survival of patients with non-small cell lung cancer (NSCLC) by machine learning, and to improve the prediction efficiency and prediction accuracy. Methods The experiments were performed using NSCLC data from the SEER database. According to the imbalance of patient data, the Borderline-SMOTE method was used for data sampling. The perturbation-based feature selection (PFS) method and decision tree ( DT ) algorithm were used to screen the features and construct the postoperative survival prediction model . Results The patient data was balanced, and seven prognostic variables were screened, including primary site, stage group, surgical primary site, international classification of diseases, race and grade. Compared with LASSO, Tree-based, PFS-SVM and PFS-kNN models, the model constructed using PFS-DT has the best predictive effect. Conclusions The patient survival prediction model based on PFS-DT can effectively improve the accuracy of postoperative survival prediction in patients with NSCLC, and can provide a reference for doctors to provide treatment and improve prognosis.

Objective To investigate the correlation between TRIM29 expression and non-small cell lung cancer (NSCLC) and its different histological types and clinicopathological features. Methods Using computer retrieves data databases such as PubMed, EMbase, China Knowledge Network and Wanfang Data, and collects relevant case-control studies at home and abroad. The time for searching the literature is as of August 1, 2018. Meta-analysis of the relationship between TRIM29 positive expression and clinicopathological features and tissue typing of non-small cell lung cancer was conducted by Review Manager 5.3 software. Results Five studies were included, including 1061 in the NSCLC group and 918 in the control group. Meta-analysis showed statistically significant differences between the expression of TRIM29 between NSCLC group and the control group [OR=18.32, 95％CI (4.62, 72.67), P<0.0001], squamous cell carcinoma and adenocarcinoma group [OR=37.05, 95％CI (2.45, 559.73), P=0.009], NSCLC lymph node metastasis group and NSCLC lymph node without metastasis group [OR=5.62, 95％CI (2.83, 11.17), P<0.00001], NSCLC (Ⅰ+Ⅱ) and (Ⅲ+Ⅳ) [OR=0.18, 95％CI (0.10, 0.32), P<0.00001] and NSCLC well differentiated group and the NSCLC middle and low differentiated group [OR=0.12, 95％CI (0.07, 0.21), P<0.00001], respectively. Conclusions The expression of TRIM29 is significantly correlated with NSCLC and its histological classification, lymph node metastasis, clinical stage and histological grade. This paper has certain help and reference value for the histological and clinical pathological features of NSCLC patients.