The prevalence of chronic disease, in particular diabetes and complications, arising in the UAE are alarmingly high. According to the 2017 report from the International Diabetes Federation (IDF), 11% of the population in the Middle East and North Africa (MENA) region had type 2 diabetes. Disturbingly, the prevalence is higher in the UAE population, with 19.3% of those between the ages of 20 and 79 with type 2 diabetes1. A number of major co-morbidities are linked to diabetes and of these, cardiovascular disease is the highest cause of mortality in the UAE at 34.9% of all deaths recorded in the UAE in 2015 (source: Health Authority of Abu Dhabi, 2016). In this project, we propose a study to evaluate the proactive approach of P4 Medicine. Specifically, the project will take the holistic approach of analyzing the whole genome sequences, phenome and clinical data, the microbiome, as well as activity measurements of at least 100 Emiratis who are at risk of cardiac heart disease (CHD), toward positively impacting on the health status of these individuals.

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Harnessing adult stem cells for regenerative medicine (RM) is a global initiative that holds great promise in saving daily lives. This field is evermore needed in the Gulf Region, particularly the United Arab Emirates (UAE), due to high road-related injuries. According to the World Health Organization, road accidents in the UAE are the number one cause of death in children and the second in adults. Among the many health adverse-effects of road collisions, skin injuries are by far the most prevalent. Skin is a very regenerative organ as it is composed of many stem cell (SC) populations residing at different niches with diverse capacity of regeneration. However, when extensively damaged the inherent process of regeneration is hampered. One method to externally support skin regeneration after severe damage is by epidermal SC transplants.

Utilizing epidermal SCs for regeneration requires full understanding of their biology during self-renewal and differentiation. Self-renewal is a process whereby SCs maintain their pool by dividing and inhibiting differentiation. Differentiation is, however, when SCs divide to give rise to a specialized cell. By comprehending these states, we would be able to overcome the current limitations of growing epidermal SCs in culture. Previous attempts to successfully propagate epidermal SCs required unknown factors derived from bovine serum and mouse feeder cells. Recently, efforts to use chemically defined serum-free conditions to culture epidermal SCs have been successful, albeit not without problems. Indeed, when tested chemically, cultured epidermal SCs fail to form skin. Here, we propose to overcome the limitation of epidermal SCs grown in chemically defined conditions by using our recent discovered RNA pathway controlling epidermal SC differentiation. Enhancing skin SC research would position Abu Dhabi in the forefront of skin healthcare; hence, achieving its strategic goal in becoming a knowledge-based economy.

The post Using the Epitranscriptome of Non-coding RNAs to Improve Human Epidermal Stem Cell Therapeutics appeared first on ���ճԹ�.

There is a dearth of genome information from subjects of Arabian ancestry, and this study was conceived to improve on this unacceptable position, particularly among the local ethnic groups of the United Arab Emirates.

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As the lymph node (LN) is the integrating center for immune cells, whereby the body invokes immune responses against foreign substances, it is an ideal site for the study of drug interaction with biological components. We have developed a novel microfluidic platform replicating the LN microenvironment, called LN-on-Chip, to facilitate biological investigations of immune cellular kinetics, cell-cell interactions, cell-drug interactions, and sampling. We recreated the biological scaffold and reintroduced the cellular residents in an in vivo-like distribution into the device. We showed that the developed LN-on-Chip incorporates key features of the native human LN, namely the compartmentalization of immune cells within distinct structural domains and the replication of lymphatic fluid flow pattern supports 3D cell culture in biomimetic matrices, and sustains high rates of cell viability over the typical timeframe of immunotoxicity experiments. Further, we demonstrated enhanced proliferation of immune cell coculture in a low-flow perfusion LN system and observed immune cellular interactions. The ultimate goal of this platform is to enable investigations into the effects of pharmaceutics to downstream immunology in more physiologically relevant microenvironments, thus, contributing to increased safety, lowered cost, and shorter cycles for drug development.

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Caps-Sim will be made up of four main components: i) haptic station; ii) vision station; iii) control station; and iv) human machine interface (HMI). The haptic station provides the operator with the force information from virtual or remote environments, and is also used to read the operator’s hand motion. In order to provide image and force feedback, the vision station is used to capture images and generate 3D mapping and computer “virtual interaction” forces. The capsule movements are controlled using the control station. The HMI runs a simulation engine where the capsule device and its main components are modeled and visualized. The magnetic actuation of the capsule is performed in the HMI simulation engine. The HMI also has a display unit where the colon images are visualized and rendered to the user using a 3D virtual reality glass. The proposed version of Caps-Sim provides a simulator to develop and test endoscopic capsules main functions: i) capsule tele-operation; ii) haptic feedback for capsule navigation; iii) 3D virtual reality navigation; and iv) region of interest (ROI) tracking. The proposed Caps-Sim is modular and can be applied to any capsule type or any other robotic endoscopes regardless of its underlying technology.

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The proposed Care4MyHeart platform will help reduce patients’ CVD risk by gradually establishing a behavior change in their everyday living routine. It will utilize advanced machine learning and modeling techniques to provide gender- and age-specific CVD exercise programs and an autonomous helper-agent, providing informed feedback to the patient and the healthcare provider, establishing a collaborative patient-professional partnership. Overall outcome will be a ‘co-production of health’ business model from a multi-stakeholder eco-system, towards the integration of Care4MyHeart into healthcare systems across Arab countries and internationally. Care4MyHeart will be realized via motion capture, exercise evaluation, physiological and lifestyle monitoring, exercise gaming, home-based human-computer interfacing, multi-parametric data modeling, and advanced decision support systems. The overall concept and system are easily transferable to address other diseases/conditions (e.g., diabetes, osteoporosis, obesity), providing market opportunities for the commercialization of Care4MyHeart beyond CVD, thus aligning with the major targets of the Abu Dhabi 2030 healthcare plan. Care4MyHeart will be realized in collaboration with the Cleveland Clinic AD and it is expected to establish Khalifa University as an internationally recognized center of excellence in personalized healthcare, generate intellectual property, and expose graduate and undergraduate students to the state-of-the-art in signal processing-, biomedical engineering- and healthcare-related research.

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Computational tools for predicting guide efficacy are used when designing genome editing experiments. The accuracy of available tools for predicting the activity of guide sequences is low and this delays the progress of genome editing experiments.

In this proposal, our goal is to shorten the time needed to carry successful genome editing experiments by developing better tools for predicting guide-sequence activity. Results obtained using the new prediction algorithms will also be used to gain insights into the factors influencing the efficiency of different guide sequences. Modern tools based on resent innovations in artificial intelligence and computer vision research will be utilized to develop the proposed tools.

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