Object To explore the antitumor mechanism of Stellera chamaejasme Linn.(SC).Methods SC containing-serum(SCCS)was derived from mice pretreated with different doses of SC.Cultured human leukemia HL-60 and human gastric adenocarcinoma SGC-7901 cells were used.Inhibition of proliferation was measured using MTT assay.Morphological assessment of apoptosis was performed with fluorescence microscope.DNA fragmentation was assessed by agarose gel electrophoresis and flow cytometry.Expression of bcl-2 protein was measured with immunohistochemistry.Results Exposure of exponentially growing HL-60 cells to mice serum containing 10% SC(pretreated with SC3,6, and 12 g/kg)for 48h resulted in growth inhibition in a dose-dependent manner.Typical morphological changes of apoptosis and DNA fragmentation in HL-60 cells were induced."Apobodies'in the apoptotic cells were observed,'ladder"pattern of agarose gel electrophoresis of DNA from 11.7% to 57.4%.Treatment with SC containing serum decreased the percentage of SGC-7901 cell of bcl-2 protein positive expression from 78.3% to 32.9%.Conclusion SC could induce apoptosis of HL-60 cells and decrease the expression of bcl-2 protein of gastric adenocarcinoma SGC-7901 cells.

The network teaching platform of ophthalmology of Qingdao University , as the basis construction of the national key discipline , forms a perfect and complete set of teaching system with the aid of part of course information, part of network teaching resources and part of answer and interaction. The network teaching platform focuses on the construction of network teaching resources and answer and interaction. In the college teaching of ophthalmology, through building clinical teaching resource database and discussing on network platform, we carry out interactive and discussion-based teaching, and students can prepare before class and discuss after class. The application of network teaching platform of ophthal-mology in classroom teaching and teaching feedback can improve the teaching methods , deepen the teach-ing content, implement the sharing of teaching resources, and lay a solid foundation for ophthalmology teaching reform.

Objective To evaluate the feasibility of utilizing dose-volume histogram (DVH) prediction models of organs at risk (OARs) to deliver automatic treatment planning of prostate cancer.Methods The training set included 30 cases randomly selected from a database of 42 cases of prostate cancer receiving treatment planning.The bladder and rectum were divided into sub-volumes (Ai) of 3 mm in layer thickness according to the spatial distance from the boundary of planning target volume (PTV).A skewed normal Gaussian function was adopted to fit the differential DVH of Ai,and a precise mathematical model was built after optimization.Using the embedded C++ subroutine of Pinnacle scripa,ahe volume of each Ai of the remaining validation set for 12 patients was obtained to predict the DVH parameters of these OARa,ahich were used as the objective functions to create personalized Pinnacle script.Finalla,automatic plans were generated using the script.The dosimetric differences among the original clinical plannina,aredicted value and the automatic treatment planning were statistically compared with paired t-test.Results DVH residual analysis demonstrated that predictive volume fraction of the bladder and rectum above 6 000 cGy were lower than those of the original clinical planning.The automatic treatment planning significantly reduced the V70,V60,V50 of the bladder and the V70 and V60 of the rectum than the original clinical planning (all P＜0.05),the coverage and conformal index (CI) of PTV remained unchangea,and the homogeneity index (HI) was slightly decreased with no statistical significance (P＞ 0.05).Conclusion The automatic treatment planning of the prostate cancer based on the DVH prediction models can reduce the irradiation dose of OARs and improve the treatment planning efficiency.

Objective: To explore the relationship between smoking and hyperuricemia in Chinese residents. Methods: Based on data from the China Health and Nutrition Survey (CHNS), residents with blood samples provided in the 2009 round (including information of socio-demographic factors, lifestyle behaviors, medical history, and laboratory examinations etc.) were selected as the participants in the current analysis. Unconditional logistic regression models were utilized to compute the ORs and corresponding 95%CIs for assessing the relationship between smoking and hyperuricemia. Results: Among the 8 785 subjects, 1 435 had hyperuricemia with a prevalence rate of 16.3%, consisting of 886 men and 549 women with prevalence rates of 21.6% (886/4 110) and 11.7% (549/4 675) , respectively. Compared with never smokers, the adjusted OR (95%CI) for hyperuricemia was 0.83 (0.70-0.98) among current smokers, 0.77 (0.63-0.94) among current smokers with 20-39 years of smoking, and 0.79 (0.65-0.97) among current smokers with 11-20 cigarettes per day. When stratified by gender and compared with non-smoker, the adjusted OR (95%CI) for hyperuricemia among current smokers compared with never smokers was 0.83 (0.70-0.98) among men, while no significant association was found in female current smokers (OR=0.73, 95%CI: 0.42-1.26, P=0.260). Conclusion In Chinese residents, there is an inverse association between smoking and hyperuricemia prevalence, and this association may be related to duration and intensity of smoking among current smokers. The findings need to be validated in large prospective cohort studies.

PURPOSE: The significance of neuroendocrine differentiation (NED) in gastric carcinoma (GC) is controversial, leading to ambiguous concepts in traditional classifications. This study aimed to determine the prognostic threshold of meaningful NED in GC and clarify its unclear features in existing classifications. MATERIALS AND METHODS: Immunohistochemical staining for synaptophysin, chromogranin A, and neural cell adhesion molecule was performed for 945 GC specimens. Survival analysis was performed using the log-rank test and univariate/multivariate models with percentages of NED (PNED) and demographic and clinicopathological parameters. RESULTS: In total, 275 (29.1%) cases were immunoreactive to at least 1 neuroendocrine (NE) marker. GC-NED was more common in the upper third of the stomach. PNED, and Borrmann's classification and tumor, lymph node, metastasis stages were independent prognostic factors. The cutoff PNED was 10%, beyond which patients had significantly worse outcomes, although the risk did not increase with higher PNED. Tumors with ≥10% NED tended to manifest as Borrmann type III lesion with mixed/diffuse morphology and poorer histological differentiation; the NE components in this population mainly grew in insulae/nests, which differed from the predominant growth pattern (glandular/acinar) in GC with <10% NED. CONCLUSIONS: GC with ≥10% NED should be classified as a distinct subtype because of its worse prognosis, and more attention should be paid to the necessity of additional therapeutics for NE components.
Adenocarcinoma ; Chromogranin A ; Classification ; Humans ; Immunohistochemistry ; Lymph Nodes ; Neoplasm Metastasis ; Neural Cell Adhesion Molecules ; Prognosis ; Stomach ; Stomach Neoplasms ; Synaptophysin

Objective:To develop a deep learning model for predicting three-dimensional (3D) voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT).Methods:A total of 110 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 90 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net was constructed to predict 3D dose distributions. Three types of 3D matrices from CT images, structure sets and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of IMRT dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: the average dose prediction bias and mean absolute errors (MAEs)of all voxels within the body, the dice similarity coefficients (DSCs), Hausdorff distance(HD 95) and mean surface distance (MSD) of different isodose surfaces were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; the dosimetric index (DI) including homogeneity index, conformity index, V50, V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired-samples t test. Results:For the 20 testing cases, the average prediction bias ranged from -2.12% to 2.88%, and the MAEs varied from 2.55% to 5.75%. The DSCs value was above 0.9 for all isodose surfaces, the average MSD ranged from 0.21 cm to 0.45 cm, and the average HD 95 varied from 0.61 cm to 1.54 cm. There was no statistically significant difference for all DIs, except for bladder Dmean. Conclusions:This study developed a deep learning model based on 3D U-Res-Net by considering beam configurations input and achieved an accurate 3D voxel-wise dose prediction for rectal cancer treated by IMRT.