University of Jordan

About: University of Jordan is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Population & Medicine. The organization has 7796 authors who have published 13764 publications receiving 213526 citations.

Pomegranate as a source of bioactive constituents: a review on their characterization, properties and applications

Abstract: Increasing awareness about the use of compounds obtained from natural sources exerting health-beneficial properties, including antimicrobial and antioxidant effects, led to increased number of research papers focusing on the study of functional properties of target compounds to be used as functional foods or in preventive medicine. Pomegranate has shown positive health properties due to the presence of bioactive constituents such as polyphenols, tannins, and anthocyanins. Punicalagin is the major antioxidant, abundantly found in pomegranate's peel. Research has shown that pomegranate polyphenols not only have a strong antioxidant capacity but they also inhibit the growth of pathogenic bacteria like V. cholera, P. aeruginosa and S. aureus, B. cereus, E. coli, and S. virulence factor, and inhibits fungi such as A. Ochraceus, and P. citrinum. Compounds of natural origin inhibit the growth of various pathogens by extending the shelf life of foodstuffs and assuring their safety. Therefore, the need to find compounds to be used in combination with antibiotics or as new antimicrobial sources, such as plant extracts. On the basis of the above discussion, this review focuses on the health benefits of pomegranate, by summarizing the current body of research focusing on pomegranate bioactive constituents and their therapeutic potential against some pathogenic microbes.

Skeletal Malocclusion: A Developmental Disorder With a Life-Long Morbidity

Abstract: The likelihood of birth defects in orofacial tissues is high due to the structural and developmental complexity of the face and the susceptibility to intrinsic and extrinsic perturbations. Skeletal malocclusion is caused by the distortion of the proper mandibular and/or maxillary growth during fetal development. Patients with skeletal malocclusion may suffer from dental deformities, bruxism, teeth crowding, trismus, mastication difficulties, breathing obstruction and digestion disturbance if the problem is left untreated. In this review, we focused on skeletal malocclusion that affects 27.9% of the US population with different severity levels. We summarized the prevalence of class I, II and III of malocclusion in different ethnic groups and discussed the most frequent medical disorders associated with skeletal malocclusion. Dental anomalies that lead to malocclusion such as tooth agenesis, crowding, missing teeth and abnormal tooth size are not addressed in this review. We propose a modified version of malocclusion classification for research purposes to exhibit a clear distinction between skeletal vs. dental malocclusion in comparison to Angle’s classification. In addition, we performed a cross-sectional analysis on orthodontic (malocclusion) data through the BigMouth Dental Data Repository to calculate potential association between malocclusion with other medical conditions. In conclusion, this review emphasizes the need to identify genetic and environmental factors that cause or contribute risk to skeletal malocclusion and the possible association with other medical conditions to improve assessment, prognosis and therapeutic approaches. Keyword: Skeletal malocclusion; Micrognathia; Retrognathia; Prognathia; Late-onset diseases

Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering

Abstract: Multi-verse optimizer (MVO) is considered one of the recent metaheuristics. MVO algorithm is inspired from the theory of multi-verse in astrophysics. This chapter discusses the theoretical foundation, operations, and main strengths behind this algorithm. Moreover, a detailed literature review is conducted to discuss several variants of the MVO algorithm. In addition, the main applications of MVO are also thoroughly described. The chapter also investigates the application of the MVO algorithm in tackling data clustering tasks. The proposed algorithm is benchmarked by several datasets, qualitatively and quantitatively. The experimental results show that the proposed MVO-based clustering algorithm outperforms several similar algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA) in terms of clustering purity, clustering homogeneity, and clustering completeness.