Objective To investigate the relationship between the expression of TGF ? receptor Ⅰ(RⅠ)and apoptosis in gastric carcinoma and precancerous lesions,and their effects in the development of gastric carcinoma.Methods The expressions of TGF ?RⅠ in 103 cases,including CSG(30 cases),IM(30 cases),Dys(18 cases)and GAC(25 cases)were detected by immunohistochemical techniques(SP),apoptosis cells were examined by terminal deoxynucleotidyl transferase(TdT) mediated dUTP nick end labelling(TUNEL).Results Both the expressions of TGF ?RⅠ and apoptosis indexes(AI:percentage of TUNEL positive cells)showed negative correlation with the degree of gastric mucosa lesions from CSG,IM,Dys to GAC(r=-0\^7272,P

Objective To study the relationships among the expression of inhibitors of DNA binding 1 (ID-1) , Ki-67 and Bcl-2 in esophageal squamous cell carcinoma (ESCC) ,and to investigate the potential role of ID-1 in the carcinogenesis of ESCC. Methods One hundred and eighteen cases of surgical resected ESCC specimens and 20 cases of normal tissues ( sampled far from the tumors, as control) were involved. Immunohistochemical technique was applied to detect the expression of ID-1, Ki-67 and Bcl-2. Results The positivity and staining intensity of ID-1 , Ki-67 and Bcl-2 in ESCC were higher than those in normal tissues. Positive immunological reactions of ID-1, Ki-67 and Bcl-2 were found in 86.44% (102/118) , 81.36% (96/118) and 59. 32% (70/118) cases of examined tumor samples, respectively. The expression of ID-1 and Bcl-2 were positively correlated with the histological grades, while the Ki-67 expression showed negative correlation with differentiation degree. No relationship was found among age, sex, lymph node metastasis and the expression of ID-1, Ki-67 and Bcl-2 in ESCC tissues. Conclusion ID-1 expression may be participated in the regulation of apoptosis in ESCC cells, but may not be considered as a biomarker for evaluation of ESCC metastasis.

In four patients with chronic pancreatitis from two hereditary pancreatitis (HP) families and 63 normal controls, five exons of cationic trypsinogen gene (PRSS1) were amplified by PCR and it's products were analyzed by sequencing, related clinical data were also collected. All the four patients were found mutations in the PRSS1 gene but their clinical feature is absolutely different. Six patients with diabetes mellitus were found in pedigree No. 1, it's members show pancreatitis symptom later, at about 29, the tumor markers (CA19-9, CA72-4) is obviously higher than the patients in pedigree No. 2, two patients with chronic pancreatitis in pedigree No. 2, show symptom earlier without diabetes mellitus, their clinical characterization are different too. The number of CD4+T cell/CD8+T is very low in Ⅲ 8, but Ⅲ 7 is normal, and the level of anti-HBs of Ⅲ 8 is variable in the course of pancreatitis, but the phenomenon was not found in Ⅲ 7. In their PRSS1 gene two guanosine (G) to adenosine (A) mutations were found in PRSS1 exon 3 of pedigree No. 1, one was detected at 336 basyl, the other mutation occurs at 361 basyl. The results of the mutations were Lys →Lys and Ala →Thr. While thymine (T) to adenosine (A) and (guanosine) G→(adenosine) A mutation in PRSS1 exon 3 was detected in the other patient of pedigree No. 2 (Ⅲ 8). One was 361 basyl, the other at 415 basyl. While c.415 T→A was not found in the proband of pedigree No. 2 PRSS1 gene (Ⅲ 7). All of the mutations were heterozygous mutation, that is to say all of the trypsinogen were wild type and mutant type concomitance, the normal and abnormal pathway of active trypsinogen exist partially. At the same time, the mutations of SPINK1 were not observed. Compared with the documents and registration of NCBI, it can be concluded that PRSS1 gene had many kinds of mutations in hereditary pancreatitis, the heterozygous mutations (c.336 G→A, c.415 T→A) were the novel mutations and related with clinical phenotype. What's more, it's the first time that the multisite heterozygous mutations of PRSS1 gene were reported. The presence of the mutations in four patients with chronic pancreatitis, it's absence in their relatives and the strong evolutionary conservation of the mutation, all indicate that the trypsinogen mutation is associated with hereditary pancreatitis and for the first time raises the question whether a gain or a loss of trypsin function participates in the onset of Chinese pancreatitis.

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.