Jordan University of Science and Technology

About: Jordan University of Science and Technology is a education organization based out in Irbid, Irbid, Jordan. It is known for research contribution in the topics: Population & Medicine. The organization has 7582 authors who have published 13166 publications receiving 298158 citations. The organization is also known as: JUST.

Epoetin alfa improves quality of life in patients with cancer

Abstract: BACKGROUND: Anemia in patients with cancer causes fatigue, weakness, and impaired concentration, negatively impacting quality of life (QOL). In clinical trials involving patients with cancer who had varied characteristics, it has been shown that epoetin alfa treatment increased hemoglobin levels and improved QOL. A systematic review and metaanalysis of data from those trials was conducted to summarize existing knowledge on the role of epoetin alfa in improving QOL for anemic patients with cancer. METHODS: The Cochrane Library and other data bases were searched for published and unpublished, randomized/controlled and single-arm studies that included > or = 20 patients with cancer per arm, epoetin alfa treatment, and QOL assessment by Cancer Linear Assessment Score (CLAS), Functional Assessment of Cancer Therapy (FACT) scale, Eastern Cooperative Oncology Group (ECOG) scale, and/or Medical Outcomes Study Short-Form 36 (SF-36) scale. RESULTS: Among 11,459 patients from 23 trials, epoetin alfa and control cohorts were indistinguishable (with regard to demographic, clinical, QOL variables) at baseline. Epoetin alfa improved CLAS (20-25%), FACT-Fatigue (17%), and FACT-Anemia (12%) scores (P = 0.05). ECOG scores worsened for control cohorts (P = 0.05); epoetin alfa cohorts remained unchanged. Four of the SF-36 subscales, Physical Function, Role Physical, Vitality, and Social Function, improved with epoetin alfa (P = 0.05). Results adjusted for confounding factors remained consistent.

The prevalence and risk factors of peripheral neuropathy among patients with type 2 diabetes mellitus; the case of Jordan

Abstract: Peripheral neuropathy is one of the most common microvascular complication of diabetes mellitus. This study is conducted to determine the prevalence of diabetic peripheral neuropathy (DPN) and its associated factors among patients with type 2 diabetes mellitus in Jordan. A cross-sectional study was conducted at the National Center for Diabetes, Endocrinology and Genetics, Jordan. A total of 1003 patients with type 2 diabetes were recruited. Data were collected from participants during a face-to-face structured interview. DPN was assessed using the translated version of Michigan Neuropathy Screening Instrument (MNSI). The overall prevalence of DPN based on MNSI was 39.5%. The most frequently reported symptoms were numbness (32.3%) and pain with walking (29.7%), while the least reported symptoms were the history of amputation (1.3%) and loss of sensation in legs/feet while walking (3.8%). Logistic regression analysis revealed that unemployment, cardiovascular disease, dyslipidemia, diabetic retinopathy and long standing DM (diabetes of ≥ 5 years) were significantly associated with DPN. Peripheral Neuropathy is highly prevalent among Jordanian patients with type 2 diabetes mellitus. DPN was significantly associated with duration of DM, dyslipidemia, diabetic retinopathy, cardiovascular disease, and unemployment. Early detection and appropriate intervention are mandatory among high-risk groups.

A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques

Abstract: With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, Amazon, etc. Hence, the collaborative filtering recommendation algorithms are highly valuable and play a vital role at the success of such businesses in reaching out to new users and promoting their services and products. With the aim of improving the recommendation performance of such an algorithm, this paper proposes a new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. The ?-means algorithm and Singular Value Decomposition (SVD) are both used to cluster similar users and reduce the dimensionality. It proposes and evaluates an effective two stage recommender system that can generate accurate and highly efficient recommendations. The experimental results show that this new method significantly improves the performance of the recommendation systems.