ABSTRACT
Cancer research is an attractive field in molecular biology and medicine. By applying large-scale tools such as advanced genomics and proteomics, cancer diagnosis and treatment have been improved greatly. Cancers of esophagus, gastric, and colon accounted for major health problem globally. Biomarker panel could bring out the accuracy for cancer evaluation tests as it can suggest a group of candidate molecules specified to particular malignancy in a way that distinguishing malignant tumors from benign, differentiating from other diseases, and identifying each stages with high specificity and sensitivity. In this review, a systematic search of unique protein markers reported by several proteomic literatures are classified in their specific cancer type group as novel panels for feasible accurate malignancy diagnosis and treatment. About thousands of introduced proteins were studied; however, a small number of them belonged to a specific kind of malignancy. In conclusion, despite the fact that combinatorial biomarkers appear to be hopeful, more evaluation of them is crucial to achieve the suitable biomarker panel for clinical application. This effort needs more investigations and researches for finding a specific and sensitive panel
ABSTRACT
The seladin-1 [selective Alzheimer disease indicator-1], also known as DHCR24, is a gene found to be down-regulated in brain region affected by Alzheimer disease [AD]. Whereas, hair follicle stem cells [HFSC], which are affected in with neurogenic potential, it might to hypothesize that this multipotent cell compartment is the predominant source of seladin-1. Our aim was to evaluate seladin-1 gene expression in hair follicle stem cells. In this study, bulge area of male Wistar rat HFSC were cultured and then characterized with Seladin-1 immunocytochemistry and flow cytometry on days 8 to 14. Next, 9-11-day cells were evaluated for seladin-1 gene expression by real-time PCR. Our results indicated that expression of the seladin-1 gene [DHCR24] on days 9, 10, and 11 may contribute to the development of HFSC. However, the expression of this gene on day 11 was more than day 10 and on 10th day was more than day 9. Also, we assessed HFSC on day 14 and demonstrated these cells were positive for beta-? tubulin, and seladin-1 was not expressed in this day. HFSC express seladin-1 and this result demonstrates that these cells might be used to cell therapy for AD in future
ABSTRACT
Artificial neural networks are used in many smart apparatus and different fields such as signal processing pattern diagnoses, military systems, medicine, financial systems, and artificial intelligence. In this article using quality of neural networks in optimizing energy cost in moving limb and its effectiveness in organization a cognitive function founded by presenting an algorithm for use in human smart robotics and worldwide research will be described and discussed. Neural system contributes to minimal cost energy in decision-making and command exportation to skeletal muscle and suggested optimal energy cost in skeletal muscle that could be presented in software conformation