فهرست مطالب asma dabiri

Aim: Due to limited information on these genes and to a better understanding of common biomarkers associated with cancer of the digestive tract routes, we aim to evaluated expression level of Olfactomedin4 (OLFM4) and (pro)collagen11A1/COL11A1 genes in people with gastric cancer in Iran.
Gastric cancer is one of the main cause of cancer death. The early prognosis of gastric cancer is still a matter of debate. Human olfactomedin4 (OLFM4) is a glycoprotein that generally known as the antiapoptotic protein. (pro) collagen11A1/COL11A1 codes for the alpha-1 subunit of type XI collagen which exists in extracellular minor fibrillar collagen. In most cases, OLFM4 and COL11A1 are found to be up-regulated in many types of human cancers including gastric cancer.
35 tissue samples were collected including 25 sample of patients with intestinal gastric cancer and 10 healthy controls. Expression level of OLFM4 and COL11A1 genes identified by using RGQ software. For analysis of real time-PCR products, RotorGene Q series software was used.
Our finding showed that expression level of OLFM4 was significantly upregulated and COL11A1 did not show any significant difference in expression level in Iranian population with gastric cancer samples compared with those in normal samples.
The results recommend that expression profiling of OLFM4 can be used for diagnosis of gastric cancer, and OLFM4 seems to be used as a biomarker for the diagnosis of gastric cancer. Regarding to our result, unlike some studies, COL11A1 did not show any significant difference between normal and tumor tissue which could explain ethological role in distribution of gastric cancer.

This study investigated textual and inter-textual reading of a group of Iranian EFL undergraduates’ careful English reading types. In this research, Khalifa and Weir’s (2009) reading framework was used to propose a more inclusive aspect of a careful reading framework and the reading construct for instructional and assessment goals. The participants of this study were B.A. students of English Translation at Shiraz Payame Noor University. To obtain the required data, a questionnaire and a careful reading test along with reading journals, interviews, and retrospective verbal protocols were used. The findings revealed that careful reading at the sentential and textual levels were seen to be practiced frequently by the participants. However, reading purposes and cognitive processes requiring integrating information from different texts, reading critically to establish and evaluate the authors’ position on a particular topic, building links across texts, judging the relatedness of texts, evaluating the writer’s ideas and comparing viewpoints were not seen as prevalent emerged reading patterns. The participants performed differently on tasks measuring different types of careful reading at different levels in a descending order of difficulty. In doing the tasks, although no statistically significant difference was found between the performance of males and females, they performed differently regarding their age and educational level differences. Subsequently, based on the outcomes, in the proposed careful reading framework, some new variables such as educational level, age, documents knowledge, better understanding and careful reading at multiple text level structures were added to Khalifa and Weir’s (2009) reading framework.

In this study combined Fuzzy Logic and AHP method is used to present a suitable algorithm for industrial technology developments. For this purpose, the technology development base of different groups was determined. Then with regard to professional committee and presentation of information about capabilities of Fars province, areas in technology developments and ranking were examined with the usage of Expert Choice Software. Finally, with aligning results from Fuzzy Logic method and AHP, priorities in technology developments were determined. On this basis, the determined development priorities in Fars industry were Electronics and Electrics industries ranked as 0.56 as the first priorities, Chemical industries as 0.53, food industries as 0.48, Mining industries as 0.42, Metal and Machine industries as 0.38 and finally, Medicine industries as 0.34 in the next priorities.