Browsing by Subject "Statistical significance"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Open Access Effect of six weeks aerobic training upon blood trace metals levels(2006) Savaş, S.; Şenel, Ö.; Çelikkan, H.; Uǧraş, A.; Aksu, M. L.This study was carried out to investigate the effects of 6-week aerobic exercise program upon blood Zn and Cu levels. There were 12 male university students with an average age of 21.67+/-0.89 years and no regular training habits participated in the study. The participants were subjected three days a week 1 hour a day continuous running program on treadmill with an intensity of 60-70% for a period of six weeks. They were fed with zinc and copper free diet throughout the study and it was made sure that they were not using copper or zinc containing vitamin tablets. The difference between the pre and post study period were found to be statistically significant as regards to both resting and maximal loading conditions (p<0.01). The pre and post training maxVO2 values were also found to be positively correlated with the copper and zinc levels in blood. Both the copper and zinc blood levels were found decreased after the training period p<0.05.Item Open Access Implantable microelectromechanical sensors for diagnostic monitoring and post-surgical prediction of bone fracture healing(John Wiley and Sons Inc., 2015) McGilvray, K. C.; Ünal, E.; Troyer, K. L.; Santoni, B. G.; Palmer, R. H.; Easley, J. T.; Demir, Hilmi Volkan; Puttlitz, C. M.The relationship between modern clinical diagnostic data, such as from radiographs or computed tomography, and the temporal biomechanical integrity of bone fracture healing has not been well-established. A diagnostic tool that could quantitatively describe the biomechanical stability of the fracture site in order to predict the course of healing would represent a paradigm shift in the way fracture healing is evaluated. This paper describes the development and evaluation of a wireless, biocompatible, implantable, microelectromechanical system (bioMEMS) sensor, and its implementation in a large animal (ovine) model, that utilized both normal and delayed healing variants. The in vivo data indicated that the bioMEMS sensor was capable of detecting statistically significant differences (p-value <0.04) between the two fracture healing groups as early as 21 days post-fracture. In addition, post-sacrifice micro-computed tomography, and histology data demonstrated that the two model variants represented significantly different fracture healing outcomes, providing explicit supporting evidence that the sensor has the ability to predict differential healing cascades. These data verify that the bioMEMS sensor can be used as a diagnostic tool for detecting the in vivo course of fracture healing in the acute post-treatment period. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.