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O-Linked β-N-acetylglucosamine (O-GlcNAc) glycosylation is a regulatory post-translational modification occurring on the serine or threonine residues of nucleocytoplasmic proteins. O-GlcNAcylation is dynamically regulated by O-GlcNAc transferase and O-GlcNAcase (OGA), which are responsible for O-GlcNAc addition and removal, respectively. Although O-GlcNAcylation was found to play a significant role in several pathologies such as type II diabetes and neurodegenerative diseases, the role of O-GlcNAcylation in the etiology and progression of cancer remains vague. Here, we followed O-GlcNAcylation and its catalytic machinery in metastatic clones of human colorectal cancer and the effect of OGA knockdown on cellular phenotype and on the transcriptome. The colorectal cancer SW620 metastatic clone exhibited increased O-GlcNAcylation and decreased OGA expression compared with its primary clone, SW480. O-GlcNAcylation elevation in SW620 cells, through RNA interference of OGA, resulted in phenotypic alterations that included acquisition of a fibroblastlike morphology, which coincides with epithelial metastatic progression and growth retardation. Microarray analysis revealed that OGA silencing altered the expression of about 1300 genes, mostly involved in cell movement and growth, and specifically affected metabolic pathways of lipids and carbohydrates. These findings support the involvement of O-GlcNAcylation in various aspects of tumor cell physiology and suggest that this modification may serve as a link between metabolic changes and cancer. [ABSTRACT FROM AUTHOR]

This article describes how denial or non-awareness of one's culture implicitly denies and devalues the understanding of multiculturalism. The author cites how children's hand jives, jump rope songs and circle games are examples of cultural expression. Children learn the songs and rhythms from each other and pass them on adapted to the current times. In this example, children from urban areas in various states knew the same hand jives, while kids from small towns in New England knew a whole different set. The author asserts that White educators in multicultural communities can enhance credibility with an awareness of the depth and breadth of their own cultural underpinnings.

Background: Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of colour fundus photographs. By applying the network to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME) including: micro-aneurysms, haemorrhages, etc., we collect sufficient information to classify patients into R0 (no DR) and R1 or above (DR), as well as M0 (no DME) and M1 (DME). Methods: The AI grading system was trained on public and private screening data to evaluate the presence of DR and DME. The system's core algorithm is a novel deep learning segmentation network (W-net) that locates and segments relevant anatomical features in a retinal image. Both eyes of the patients are graded individually, based on the detected features and classified according to the standard feature-based grading protocol used in the NHS Diabetic Eye Screening Programme. Results: The algorithm performance was evaluated with a series of patient retinal images from routine diabetic eye screenings and achieved state-of-the-art results. It correctly predicted 98% of retinopathy events (95% confidence interval [CI], 97.1-98.8) and 68.9% of maculopathy events (95% CI, 58.1-79.7). Non-disease events prediction rate was 68.6% for retinopathy and 81.3% for maculopathy. Conclusion: This novel deep learning segmentation model trained on a colour fundus photograph data set and tested on patient data from annual diabetic retinopathy screenings can detect and classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without starting a long AI training procedure. The incorporation of the AI grading system can increase the graders' productivity and improve the final outcome of the screening process. Disclosure: O. Katz: Employee; Self; NECAM, Research Support; Self; Northgate Public Services. D. Presil: Employee; Self; NECAM, Research Support; Self; Northgate Public Services. L. Cohen: Employee; Self; NECAM, Research Support; Self; Northgate Public Services. R. Nachmani: Employee; Self; NECAM, Research Support; Self; Northgate Public Services. N. Kirshner: Employee; Self; NEC, Research Support; Self; Northgate Public Service. D. R. Owens: Advisory Panel; Self; 360 Consulting. T. H. Lev: Employee; Self; NECAM (NEC Corporation of America), Research Support; Self; Northgate Public Services. A. Hadad: Consultant; Self; Necam. [ABSTRACT FROM AUTHOR]