Objective:To analyze the public demands for information about congenital birth defects in “Baidu zhidao” based on word frequency retrieval.Methods:Based on discussion between obstetrics and gynecology experts and epidemiological experts, the key words related to congenital birth defects were determined and the search strategy was formulated. Python 2.7 was used for web crawler search. Questions related to congenital birth defects were obtained on the “Baidu zhidao” platform, and then the R 4.0.2 software was used to process the data, complete the semantic analysis of keywords and statistical analysis of word frequency, and draw word cloud graph and polar chart to describe the key results.Results:A total of 16668 non-repetitive questions were retrieved from “Baidu zhidao” platform, and the frequency of semantic words was 15 371. Among them, 35.02% were the names and symptoms of congenital birth defects. In addition, the frequency of congenital heart disease was the highest (26.09%). The results of subject analysis of key words of birth defects showed that the average word frequency of diagnosis and treatment semantic words (49.55) was significantly higher than that of etiology and prevention semantic words (12.47). In addition, the key words of examination, cause, treatment, development and heredity were more frequently used in the semantic words related to the seven types of systemic malformations.Conclusion:The public in China has a high demand for information on congenital birth defect related diseases, and their causes, prevention and treatment, especially congenital heart disease.

Objective: To perform a retrospective analysis of the prognosis and influence factors of radiotherapy concurrent with che-motherapy and adjuvant temozolomide therapy in adult patients with high-grade brainstem glioma. Methods: Twenty-nine patients with pathological diagnosis of high-grade glioma (World Health Organization [WHO] Ⅲ and Ⅳ) from June 2012 to December 2013 were eligible for inclusion in the analysis. Demographic and clinical characteristics including age, gender, the time from morbidity to operation, the size of the lesion, the method of operation, the Karnofsky Performance Status (KPS) score, and the pathological grade were examined. The significance of related prognostic factors was evaluated via univariate and multivariate Logistic regression analy-sis. A P-value of<0.05 was considered to be statistically significant. Results: The median overall survival (OS) was 11.5 months. Univari-ate analysis showed that low WHO grade index was associated with better outcome (P<0.05). Multivariate analysis suggested that high KPS score (>60) and low WHO grade were associated with better survival. Conclusions: In this study, low pathological grade and high KPS score were independently associated with better survival among patients with high-grade brainstem glioma.

Objective:To construct and validate a prediction model combined machine learning with imaging omics characteristics in differentiating anaplastic glioma from glioblastoma.Methods:Imaging data of 241 patients with anaplastic glioma or glioblastoma, confirmed by pathology in our hospital from August 2005 to August 2012, were retrospectively collected. These patients were divided into a training group ( n=140) and a verification group ( n=101) according to random number table method. MRIcron software was used to delineate tumor boundaries of patients from the training group on preoperative T1 enhanced MR imaging. The regions of interest (ROIs) were outlined on preoperative T1 enhanced MR imaging, and the radiomic features were extracted from ROIs by Matlab software. Least absolute shrinkage and selection operator (LASSO) regression model was used to screen the features, and then, the selected features were used to construct the prediction model by support vector machine (SVM) classifier. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of the model. Results:In these 241 patients, 101 were with anaplastic glioma and 140 were with glioblastoma confirmed by pathology. In the training group and validation group, there was statistical difference in age between patients with anaplastic glioma and glioblastoma ( P<0.05); there was no significant difference in gender distribution, tumor location, and percentages of tumor necrosis or edema between patients with anaplastic glioma and glioblastoma ( P>0.05). Totally, 431 radiomic features were extracted; 11 radiomic features were screened by LASSO regression model and the prediction model was established. The AUC of ROC curve was 0.942 and 0.875, respectively, in the training group and validation group. Conclusion:The prediction model combined machine learning and imaging omics characteristics can effectively discriminate anaplastic glioma from glioblastoma.

OBJECTIVETo study transforming growth factor-beta1 (TGF-beta1) autoproduction in keloid fibroblasts and the regulation effect of blocking TGF-beta intracellular signaling on rhTGF-beta1 autoproduction.

METHODSKeloid fibroblasts cultured in vitro were treated with either rhTGF-beta1 (5 ng/ml) or recombinant adenovirus containing a truncated type II TGF-beta receptor gene (50 pfu/cell). Their effects of regulating gene expression of TGF-beta1 and its receptor I and II were observed with Northern blot.

RESULTSrhTGF-beta1 up-regulated the gene expression of TGF-beta1 and receptor I, but not receptor II. Over-expression of the truncated receptor II down-regulated the gene expression of TGF-beta1 and its receptor I, but not receptor II.

CONCLUSIONSTGF-beta1 autoproduction was observed in keloid fibroblasts. Over-expression of the truncated TGFbeta receptor II decreased TGF-beta1 autoproduction via blocking TGF-beta receptor signaling.

Activin Receptors, Type I ; biosynthesis ; pharmacology ; Cells, Cultured ; Down-Regulation ; Fibroblasts ; drug effects ; metabolism ; Gene Expression ; Humans ; Keloid ; metabolism ; Protein-Serine-Threonine Kinases ; RNA, Messenger ; genetics ; metabolism ; Receptors, Transforming Growth Factor beta ; biosynthesis ; metabolism ; Sensitivity and Specificity ; Signal Transduction ; Trans-Activators ; metabolism ; Up-Regulation