Biomedical research models in the science of fracture healing - Pitfalls & promises.

Development of intervention strategies to stimulate fracture healing has long been a focus of musculoskeletal research. Considerable investment in empirical research has led to the discovery of several genes and signaling pathways that are involved in skeletal development and regeneration. However, there are currently very few biologic interventions that can efficiently be used to enhance fracture healing in clinical practice. This translational barrier is due in part to experimental barriers to mechanism discovery. Animal models, biomechanical models, finite element models, and mathematical models are a few examples of models that aid in the discovery of mechanisms. Understanding the advantages, limitations, and specialized uses of each model type is critical to our ability to interpret mechanistic insights from such research and to help bridge the translation gap between pre-clinical research and clinical practice. In this review, we look at specific modeling methods used in the study of the fracture healing mechanism. We also discuss the strength and limitations to translation of each method, hopefully leading to a better understanding of how we can use models to advance the study of fracture healing.

T Wave Alternans in high arrhythmic risk patients: analysis in time and frequency domains: a pilot study.

BACKGROUND: T wave alternans (TA) is a repolarisation phenomenon manifesting as a microvolt beat to beat change in the amplitude of the T wave and ST segment. TA has been shown to be a predictor of arrhythmic risk in unselected myocardial infarction populations. TA has not been used to differentiate risk within the ischaemic cardiomyopathy population. METHODS: The subjects investigated comprised, Group 1: 7 stable patients with remote (>20 months) extensive myocardial scarring and no arrhythmic events (NYHA 3 and 4). Group2: 9 post infarction patients with malignant arrhythmia and implantable defibrillator. During breath holding, 20 continuous QRST complexes from each patients X, Y and Z leads were digitally recorded. Time domain, resultant absolute difference vectors (ATA), were calculated for alternate resultant T wave sequences. Group differences between the magnitude and temporal distribution of mean ATAs and their spectral and cross-spectral analysis were compared. RESULTS: Group 1 v Group 2 mean ATAs were 10.7 (7.17) v 11.7 (8.48) respectively, not significant. Each group had a homogenous temporal distribution of ATAs. Both group's largest mean ATA frequency components were between 0 to 25 Hz, the largest ATA component being at the DC frequency. Cross spectral analysis showed no significant differences in group ATA frequency content. CONCLUSION: The frequency content and microvolt magnitude of T wave alternans was not significantly different in these two groups. The specificity of T wave alternans for differentiating arrhythmic risk in post infarction scarring and heart failure needs investigation.