INTRODUCTION Coronary artery disease (CAD) is emerging as the biggest killer of the 21st century. A number of theories have been postulated to explain the aetiology of atherosclerosis. The present study attempts to elucidate the interaction, if any, between inflammation, oxidative stress and dyslipidaemia in CAD. METHODS A total of 753 patients undergoing angiography were evaluated and 476 we...

Background: Falls amongst elderly people are often associated with fractures. Training of balance and physical performance can reduce fall risk; however, it remains a challenge to identify individuals at increased risk of falling to whom this training should be offered. It is believed that fall risk can be assessed by testing balance performance. In this study a test battery of physiological pa...

Given the limited pool of donor organs, accurate predictions of survival on the wait list and post-transplantation are crucial for cardiac transplantation decisions and policy. However, current clinical risk scores do not yield accurate predictions. We develop a new methodology (ToPs – Trees of Predictors) built on the principle that specific predictors should be used for specific clusters with...

Research in oncology quality of care and health outcomes has been limited by the difficulty of identifying cancer stage in health care claims data. Using linked cancer registry and Medicare claims data, we develop a tool for classifying lung cancer patients receiving chemotherapy into early vs. late stage cancer by (i) deploying ensemble machine learning for prediction, (ii) establishing a set ...

It is highly necessary to identify low versus high risk thymic epithelial tumors (TETs) before operation to guide optimal treatment strategies. Current CT diagnostic parameters could not effectively achieve this goal. We evaluated three parameters of CT scan in a cohort of 216 TETs patients. Parameters of contrast enhancement, risk of aggressiveness, and nodule with fibrous septum were evaluate...

Background. Little data exists regarding injury risk factors for professional football players. Athletes with poor dynamic balance or asymmetrical strength and flexibility (i.e. poor fundamental movement patterns) are more likely to be injured. The patterns of the Functional Movement ScreenTM (FMS) place the athlete in positions where range of motion, stabilization, and balance deficits may be ...