University of Bedfordshire

About: University of Bedfordshire is a education organization based out in Luton, Bedford, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 3860 authors who have published 6079 publications receiving 143448 citations. The organization is also known as: University of Luton.

Health effects of green tea catechins in overweight and obese men: a randomised controlled cross-over trial.

Abstract: Regular consumption of green tea may be cardioprotective. In the present study we investigated the health effects of dietary supplementation with green tea catechins and the potential modifying effect of the catechol-O-methyltransferase (COMT) Val/Met genotype. Subjects (sedentary males, aged 40-69 years, with BMI ≥ 28 and ≤ 38 kg/m(2)) were randomly assigned to consume decaffeinated green tea extract (DGT; 530 mg containing about 400 mg total catechins/capsule, twice daily) and placebo in a complete cross-over design. Ambulatory blood pressure and biomarkers of metabolic function (cholesterol, TAG, glucose and insulin) were measured at weeks 0 and 6. Although a marked increase in the concentration of plasma epigallocatechin gallate (EGCG), urinary epigallocatechin (EGC) and urinary 4'-O-methyl EGC was found after DGT treatment, no effect on blood pressure or biomarkers of metabolic function was observed. However, a period × treatment interaction (P < 0·05) was detected for body-weight change. Despite a similar increase in estimated energy intake during intervention period 1, body weight decreased by 0·64 (sd 2·2) kg and increased by 0·53 (sd 1·9) kg in the DGT and placebo groups, respectively (P = 0·025), suggesting a protective effect of green tea catechins on weight gain. Additionally, the COMT Val/Met genotype influenced urinary accumulation of EGC and 4'-O-methyl EGC (P < 0·01). Mean concentrations were lower in individuals homozygous for the high-activity G-allele, possibly reflecting increased metabolic flux and a more rapid conversion to downstream metabolic species, compared with individuals carrying at least one copy of the low-activity A-allele. Additional studies are needed to confirm these findings and further explore the modifying effect of genotype.

Effective ways to use Internet of Things in the field of medical and smart health care

Abstract: The recent advancements in technology and the availability of the Internet make it possible to connect various devices that can communicate with each other and share data. The Internet of Things (IoT) is a new concept that allows users to connect various sensors and smart devices to collect real-time data from the environment. However, it has been observed that a comprehensive platform is still missing in the e-Health and m-Health architectures to use smartphone sensors to sense and transmit important data related to a patient's health. In this paper, our contribution is twofold. Firstly, we critically evaluate the existing literature, which discusses the effective ways to deploy IoT in the field of medical and smart health care. Secondly, we propose a new semantic model for patients' e-Health. The proposed model named as ‘k-Healthcare’ makes use of 4 layers; the sensor layer, the network layer, the Internet layer and the services layer. All layers cooperate with each other effectively and efficiently to provide a platform for accessing patients' health data using smart phones.