Objective To compare coronary risk factors and disease prevalence among Indians, Pakistanis, and Bangladeshis, and in all South Asians (these three groups together) with Europeans. Results There were differences in social and economic circumstances, lifestyles, anthropometric measures and disease both between Indians, Pakistanis, and Bangladeshis and between all South Asians and Europeans. Bangladeshis and Pakistanis were the poorest groups. For most risk factors, the Bangladeshis (particularly men) fared the worst: smoking was most common (57%) in that group, and Bangladeshis had the highest concentrations of triglycerides (2.04 mmol/l) and fasting blood glucose (6.6 mmol/l) and the lowest concentration of high density lipoprotein cholesterol (0.97 mmol/l). Blood pressure, however, was lowest in Bangladeshis. Bangladeshis were the shortest (men 164 cm tall v 170 cm for Indians and 174 cm for Europeans). A higher proportion of Pakistani and Bangladeshi men had diabetes (22.4% and 26.6% respectively) than Indians (15.2%). Comparisons of all South Asians with Europeans hid some important differences, but South Asians were still disadvantaged in a wide range of risk factors. Findings in women were similar. Conclusion Risk of coronary heart disease is not uniform among South Asians, and there are important differences between Indians, Pakistanis, and Bangladeshis for many coronary risk factors. The belief that, except for insulin resistance, South Asians have lower levels of coronary risk factors than Europeans is incorrect, and may have arisen from combining ethnic subgroups and examining a narrow range of factors.

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1. Free radicals generated by ferric nitrilotriacetate (FeNTA) can activate osteoclastic activity and this is associated with elevation of the bone resorbing cytokines interleukin (IL)-1 and IL-6. In the present study, we investigated the effects of 2 mg/kg FeNTA (2 mg iron/kg) on the levels of serum IL-1 and IL-6 with or without supplementation with a palm oil tocotrienol mixture or alpha-tocopherol acetate in Wistar rats. 2. The FeNTA was found to elevate levels of IL-1 and IL-6. Only the palm oil tocotrienol mixture at doses of 60 and 100 mg/kg was able to prevent FeNTA-induced increases in IL-1 (P < 0.01). Both the palm oil tocotrienol mixture and alpha-tocopherol acetate, at doses of 30, 60 and 100 mg/kg, were able to reduce FeNTA-induced increases in IL-6 (P < 0.05). Therefore, the palm oil tocotrienol mixture was better than pure alpha-tocopherol acetate in protecting bone against FeNTA (free radical)-induced elevation of bone-resorbing cytokines. 3. Supplementation with the palm oil tocotrienol mixture or alpha-tocopherol acetate at 100 mg/kg restored the reduction in serum osteocalcin levels due to ageing, as seen in the saline (control) group (P< 0.05). All doses of the palm oil tocotrienol mixture decreased urine deoxypyridinoline cross-link (DPD) significantly compared with the control group, whereas a trend for decreased urine DPD was only seen for doses of 60 mg/kg onwards of alpha-tocopherol acetate (P < 0.05). 4. Bone histomorphometric analyses have shown that FeNTA injections significantly lowered mean osteoblast number (P < 0.001) and the bone formation rate (P < 0.001), but raised osteoclast number (P < 0.05) and the ratio of eroded surface/bone surface (P < 0.001) compared with the saline (control) group. Supplementation with 100 mg/kg palm oil tocotrienol mixture was able to prevent all these FeNTA-induced changes, but a similar dose of alpha-tocopherol acetate was found to be effective only for mean osteoclast number. Injections of FeNTA were also shown to reduce trabecular bone volume (P < 0.001) and trabecular thickness (P < 0.05), whereas only supplementation with 100 mg/kg palm oil tocotrienol mixture was able to prevent these FeNTA-induced changes.

Diabetic Foot Ulcers (DFU) detection using computerized methods is an emerging research area with the evolution of machine learning algorithms. However, existing research focuses on detecting and segmenting the ulcers. According to DFU medical classification systems, i.e. University of Texas Classification and SINBAD Classification, the presence of infection (bacteria in the wound) and ischaemia (inadequate blood supply) has important clinical implication for DFU assessment, which were used to predict the risk of amputation. In this work, we propose a new dataset and novel techniques to identify the presence of infection and ischaemia. We introduce a very comprehensive DFU dataset with ground truth labels of ischaemia and infection cases. For hand-crafted machine learning approach, we propose new feature descriptor, namely Superpixel Color Descriptor. Then, we used Ensemble Convolutional Neural Network (CNN) model for more effective recognition of ischaemia and infection. The novelty lies in our proposed natural dataaugmentation method, which clearly identifies the region of interest on foot images and focuses on finding the salient features existing in this area. Fi- * Corresponding author: Tel.: +44 161 247 1503;Email address: M.Yap@mmu.ac.uk (Moi Hoon Yap ) nally, we evaluate the performance of our proposed techniques on binary classification, i.e. ischaemia versus non-ischaemia and infection versus noninfection. Overall, our proposed method performs better in the classification of ischaemia than infection. We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks than hand-crafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification.