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Background: Early prostate cancer antigen (EPCA), a nuclear matrix protein, has recently been recommended as a hopeful biomarker for early prostate carcinogenesis. Objectives:To evaluate the validity of serum early prostate cancer antigen (EPCA) in the early detection of prostate cancer (PCa) and the characteristics of Sudanese patients diagnosed with prostate cancer. Method:In this study, seventy men were considered as a case subject, who were diagnosed as cancer prostate at Gezira Hospital for Renal Disease and Surgery (GHRDS), Sudan during the period February 2018 to July 2019. Randomly selected sixty patients of BPH patients and forty-five apparently healthy men as controls group. EPCA, and PSA estimations were performed from serum samples using the principle of Enzyme Linked Immunosorbent Assay (ELISA). Results:There was significant association between age, family history, residence, unhealthy habits, education, BMI, occupations and prostate cancer, results also revealed a significant elevation between the means of the serum levels of both early prostate cancer antigen and prostate specific antigen of the patients with prostate cancer when compared with the control groups. EPCA biomarker offered the best performance statically (P= 0.00) and highest specificity (81%) and sensitivity (94%) in prostate cancer detection in Sudanese males over PSA biomarker. Conclusions: Cases of studied prostate cancer linked to many risk factors. Serum levels of early prostate cancer antigen and prostate specific antigen were significantly increased in patients with prostate cancer, however, our data proposed that EPCA has higher power statistical value, so could be utilized as a prostate cancer specific and sensitive biomarker for early detection of prostate cancer.

The use of modern educational technology methods has become an important area of research in order to support learning as well as collaboration. This is especially evident with the rise of internet and web 2.0 platforms that have transformed users’ role from mere content consumers to fully content consumers-producers. Furthermore, people engaged in collaborative learning capitalize on one another’s resources and skills, unlike individual learning. This paper proceeds with a categorization of the main tools and functions that characterize the personalization learning aspect, in order to discuss their trade-offs with collaborative learning systems. It proposes a framework of a personalized information research (IR) within a collaborative learning system, incorporating the characterization of the research type carried by the query, as well as modeling and constructing semantic users’ profiles. We use the context of the user query into a prediction mechanism of the search type, based on a previous identification of users’ levels and interests. The paper is concluded by presenting experiment results, revealing that the use of the subject ontology extension approach satisfyingly contributes to improvement in the accuracy of system recommendations.