Khalifa University

About: Khalifa University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Computer science & Adsorption. The organization has 3752 authors who have published 10909 publications receiving 141629 citations.

NY-ESO-1 Based Immunotherapy of Cancer: Current Perspectives.

Abstract: NY-ESO-1 or New York esophageal squamous cell carcinoma 1 is a well-known cancer-testis antigen (CTAs) with re-expression in numerous cancer types. Its ability to elicit spontaneous humoral and cellular immune responses, together with its restricted expression pattern, have rendered it a good candidate target for cancer immunotherapy. In this review, we provide background information on NY-ESO-1 expression and function in normal and cancerous tissues. Furthermore, NY-ESO-1-specific immune responses have been observed in various cancer types; however, their utility as biomarkers are not well determined. Finally, we describe the immune-based therapeutic options targeting NY-ESO-1 that are currently in clinical trial. We will highlight the recent advancements made in NY-ESO-1 cancer vaccines, adoptive T cell therapy, and combinatorial treatment with checkpoint inhibitors and will discuss the current trends for future NY-ESO-1 based immunotherapy. Cancer treatment has been revolutionized over the last few decades with immunotherapy emerging at the forefront. Immune-based interventions have shown promising results, providing a new treatment avenue for durable clinical responses in various cancer types. The majority of successful immunotherapy studies have been reported in liquid cancers, whereas these approaches have met many challenges in solid cancers. Effective immunotherapy in solid cancers is hampered by the complex, dynamic tumor microenvironment that modulates the extent and phenotype of the antitumor immune response. Furthermore, many solid tumor-associated antigens are not private but can be found in normal somatic tissues, resulting in minor to detrimental off-target toxicities. Therefore, there is an ongoing effort to identify tumor-specific antigens to target using various immune-based modalities. CTAs are considered good candidate targets for immunotherapy as they are characterized by a restricted expression in normal somatic tissues concomitant with a re-expression in solid epithelial cancers. Moreover, several CTAs have been found to induce a spontaneous immune response, NY-ESO-1 being the most immunogenic among the family members. Hence, this review will focus on NY-ESO-1 and discuss the past and current NY-ESO-1 targeted immunotherapeutic strategies.

Cartilage tissue engineering using differentiated and purified induced pluripotent stem cells

Abstract: The development of regenerative therapies for cartilage injury has been greatly aided by recent advances in stem cell biology. Induced pluripotent stem cells (iPSCs) have the potential to provide an abundant cell source for tissue engineering, as well as generating patient-matched in vitro models to study genetic and environmental factors in cartilage repair and osteoarthritis. However, both cell therapy and modeling approaches require a purified and uniformly differentiated cell population to predictably recapitulate the physiological characteristics of cartilage. Here, iPSCs derived from adult mouse fibroblasts were chondrogenically differentiated and purified by type II collagen (Col2)-driven green fluorescent protein (GFP) expression. Col2 and aggrecan gene expression levels were significantly up-regulated in GFP+ cells compared with GFP− cells and decreased with monolayer expansion. An in vitro cartilage defect model was used to demonstrate integrative repair by GFP+ cells seeded in agarose, supporting their potential use in cartilage therapies. In chondrogenic pellet culture, cells synthesized cartilage-specific matrix as indicated by high levels of glycosaminoglycans and type II collagen and low levels of type I and type X collagen. The feasibility of cell expansion after initial differentiation was illustrated by homogenous matrix deposition in pellets from twice-passaged GFP+ cells. Finally, atomic force microscopy analysis showed increased microscale elastic moduli associated with collagen alignment at the periphery of pellets, mimicking zonal variation in native cartilage. This study demonstrates the potential use of iPSCs for cartilage defect repair and for creating tissue models of cartilage that can be matched to specific genetic backgrounds.

Current scenario and challenges in adsorption for water treatment

Abstract: In this opinion paper, the current scenario and the main challenges in adsorption for water treatment are presented shortly. It is expected that this discussion paper will serve as a fast literature directive to support new ideas and novel investigations in the field. A general background about the topic is first presented. Subsequently, some important aspects that are well developed in literature are discussed, including adsorbent materials, adsorption operation mode, modeling, regeneration, and process operation with real samples. In the last section, it has been pointed out what should likely be the next steps required to advance in this knowledge.

Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction

Abstract: Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models.