The Benign Prostatic Hyperplasia and Its Aetiologies

This study aimed at investigating the Benign Prostatic Hyperplasia and Its Aetiologies, therefore th prostatic hyperplasia predominantly involves the stromal compartment of the gland and affects more than 70% of men of 70 years or older with or without obstructive symptoms of benign prostatic hyperplasia. A consensus view is emerging concerning the factors and control systems that modulate cell proliferation and connective tissue biology in the prostate. The purpose of this review is to discuss some of the recent work contributing to the latter in the context of the aetiology of benign prostatic hyperplasia. The current study also reviews the most important findings regarding the key mechanisms involved in the pathophysiology of BPH. The study concluded that although the pathogenesis of BPH is not yet fully understood, several mechanisms seem to be involved in the development and progression of the disease. These mainly include systemic and local hormonal and vascular alterations as well as prostatic inflammation that would stimulate cellular proliferation

Role of L- glutamine and crizanlizumab in sickle cell anaemia painful crisis reduction

BackgroundPatients with sickle cell disease, frequently ‎ suffer from intense painful episodes. Till recently hydroxyurea was the only available medical therapy that approved for reduction of painful episodes.AimsTo summarize the available data from randomized controlled trials that aim to evaluate the efficacy of newly approved L-‎glutamine‎ (alters redox state of red blood cells ‎‎[RBCs]) ‎and ‎crizanlizumab (‎(anti-P-selectin)‎)‎ ‎on vaso-occlusive episodes in Sickle cell disease ‎ patients.Methods PubMed, ‎Google Scholar, and EBSCO ‎ databases were ‎‎systematically search for relevant articles. The terms ‎ ‎ ‎ L-glutamine, sickle cell disease, sickle cell ‎anaemia,‎ ‎‎crizanlizumab ‎and vaso-occlusive episodes‎ were used.Results Out of Four-hundred seventy-two records, only three fulfilled the inclusion criteria. Two trials were aimed to evaluate the efficacy of L-glutamine therapy on the frequency of painful crises in sickle cell anaemia patients. Both studies showed that L-glutamine therapy significantly reduce the frequency of VOEs. Only one trial examined the ability of crizanlizumab on VOEs reduction, and showed crizanlizumab successful reduce the occurrence of VOEs.‎ConclusionNewer agent ‎with different mechanism of action, such as ‎L-glutamine, ‎and crizanlizumab may consider if ‎hydroxyurea not effective or not ‎tolerable

Cross-domain recommender systems using deep neural networks

Doctor of PhilosophyDepartment of Computer ScienceDoina CarageaWith the continuous growth in the number of products available in e-commerce applications and information items available on the Internet, the task of associating users with a small list of personalized items, extracted from a large and diverse pool of items, is clearly beyond human ability, a problem known as Information Overload. Indeed, the task (or ability) of automatically, quickly, and accurately recommending appropriate items to users has become an essential part in determining the success of almost all e-commerce businesses and online service providers. Recommender Systems (RS) aim to exploit users' historical interaction records, to capture users' preferences, and accordingly identify items that may be of interest to particular users. Recommender systems have become an integral part of our lives. People interact with recommender systems on a daily basis for leisure activities, such as finding a movie to watch, a friend to follow, etc., or for professional activities, such as finding a topic to study, finding co-authors to collaborate with, etc. Furthermore, online service providers rely heavily on recommender systems to increase their revenue, to diversify their item recommendations (and indirectly sales), and ultimately to keep their customers satisfied and engaged. Without recommender systems, it seems impossible for online businesses to survive in this competitive world-market. Most available recommender systems are focused on single-domain recommendation tasks (e.g., the task of recommending books to particular users, based on the book history of a large number of users). However, for users with a limited behavior history (i.e., a small number of interactions), single-domain recommender systems perform poorly, suffering from the data sparsity and cold start problems. Recent developments in the general area of transfer learning have led to Cross-Domain Recommender Systems, which exploit user behaviour information from other source domains and transfer this information to a target domain. This knowledge transfer helps alleviate the sparsity issue and produce more accurate recommendations in the target domain. This is possible because users generally interact with multiple, related domains in their daily lives. In this dissertation, I focus on cross-domain recommender system approaches, which employ state-of-the-art deep neural networks, to tackle the data sparsity problem in a target domain. The source domains used to gain additional information in the cross-domain setting are selected to have user overlap with the target domain. Specifically, I propose three novel cross-domain recommendation models, which leverage state-of-the-art single-task recommendation models. The first proposed cross-domain model is a non-sequential recommender system (i.e., the order of the items in a user history is not considered), and it uses a neural collaborative filtering approach to performs knowledge transfer between the source and target at the embedding layer level. The second proposed approach is focused on sequential recommendations, and uses the successful self-attention mechanism to identify useful item preferences in both source and target domains. It also uses the early fusion technique to combine a pre-learned global source representation of a user with a target representation of the user. Finally, the third proposed cross-domain model uses one or more source domains to obtain users' general preferences, while the target domain is used to extract users' current preferences. The two types of preferences, general and target-specific preferences expressed as representational vectors, are then fused together to achieve knowledge transfer across domains and improve the recommendation accuracy in the target domain, especially when the target domain faces the sparsity problem. Public cross-domain datasets are employed to evaluate our proposed models in different settings relevant to target sparsity. Experimental results show that our proposed models increase the recommendation accuracy in the target domain, outperforming existing state-of-the-art recommender system models

Immunoinformatics and Biophysics Approaches to Design a Novel Multi-Epitopes Vaccine Design against Staphylococcus auricularis

Due to the misuse of antibiotics in our daily lives, antimicrobial resistance (AMR) has become a major health problem. Penicillin, the first antibiotic, was used in the 1930s and led to the emergence of AMR. Due to alterations in the microbe’s genome and the evolution of new resistance mechanisms, antibiotics are losing efficacy against microbes. There are high rates of mortality and morbidity due to antibiotic resistance, so addressing this major health issue requires new approaches. Staphylococcus auricularis is a Gram-positive cocci and is capable of causing opportunistic infections and sepsis. S. auricularis is resistant to several antibiotics and does not currently have a licensed vaccine. In this study, we used bacterial pan-genome analysis (BPGA) to study S. auricularis pan-genome and applied a reverse immunology approach to prioritize vaccine targets against S. auricularis. A total of 15,444 core proteins were identified by BPGA analysis, which were then used to identify good vaccine candidates considering potential vaccine filters. Two vaccine candidates were evaluated for epitope prediction including the superoxide dismutase and gamma-glutamyl transferase protein. The epitope prediction phase involved the prediction of a variety of B-Cell and T-cell epitopes, and the epitopes that met certain criteria, such as antigenicity, immunogenicity, non-allergenicity, and non-toxicity were chosen. A multi-epitopes vaccine construct was then constructed from all the predicted epitopes, and a cholera toxin B-subunit adjuvant was also added to increase vaccine antigenicity. Three-dimensional models of the vaccine were used for downward analyses. Using the best-modeled structure, binding potency was tested with MHC-I, MHC-II and TLR-4 immune cells receptors, proving that the vaccine binds strongly with the receptors. Further, molecular dynamics simulations interpreted strong intermolecular binding between the vaccine and receptors and confirmed the vaccine epitopes exposed to the host immune system. The results support that the vaccine candidate may be capable of eliciting a protective immune response against S. auricularis and may be a promising candidate for experimental in vitro and in vivo studies

Converting hotels website visitors into buyers: How online hotel web assurance seals services decrease consumers? concerns and increase online booking intentions

Purpose Despite the increasing utilization of webpages for the purposes of information seeking, customers’ concerns have become a crucial impediment for online shopping. The purpose of this paper is to examine the influence of the effectiveness of web assurance seals services (WASS) and customers’ concerns on customer’s willingness to book hotels through perceived website trust and perceived value. Design/methodology/approach A questionnaire was administrated to measure the study variables. Using partial least squares–structural equation modeling approach to analyze the data collected from 860 users of online hotel websites. Findings The results indicate that WASS influence positively on perceived website trust and negatively on consumers’ concerns. As well as, perceived value and trust play a mediating role in the link between WASS and consumers’ concerns and their intentions. Finally, perceived website trust and perceived value have greater effect on intention to book hotel for low-habit consumers. Research limitations/implications This study ignored the cross-culture issue as it concentrates on the customers from developing countries, so further research may need to compare between two or more than two samples from different societies that could give a significant insights. Second, this study stresses on the WASS to predict customers booking intentions that indicates significant results, so further research may need to examine the role of online reviews as a predictor of customers purchase decision as well. Originality/value To the authors’ best knowledge, this is the first empirical research that investigates and examines the influence of the effectiveness of WASS and consumers’ concerns on consumers’ intentions through perceived value and trust. This research also investigates the moderating role of habit in the link between perceived website, perceived value and consumers’ intentions