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Background: Multiple genetic changes, availability of cellular nutrients and metabolic alterations play a pivotal role in oncogenesis Aims: We focus on cancer cell's metabolic properties, and we outline the cross talks between cellular oncogenic growth pathways in cancer metabolism. The review also provides a synopsis of the relevant cancer drugs targeting metabolic activities that are at various stages of clinical development. Methods: We review literature published within the last decade to include select articles that have highlighted energy metabolism crucial to the development of cancer phenotypes. Results: Cancer cells maintain their potent metabolism and keep a balanced redox status by enhancing glycolysis and autophagy and rerouting Krebs cycle intermediates and products of β-oxydation. Conclusions: The processes underlying cancer pathogenesis are extremely complex and remain elusive. The new field of systems biology provides a mathematical framework in which these homeostatic dysregulation principles may be examined for better understanding of cancer phenotypes. Knowledge of key players in cancer-related metabolic reprograming may pave the way for new therapeutic metabolism-targeted drugs and ultimately improve patient care.

Obstructive sleep apnea (OSA) is a prevalent and treatable disorder of neurological and medical importance that is traditionally diagnosed through multi-channel laboratory polysomnography(PSG). However, OSA testing is increasingly performed with portable home devices using limited physiological channels. We tested the hypothesis that single channel respiratory effort alone could support automated quantification of apnea and hypopnea events. We developed a respiratory event detection algorithm applied to thoracic strain-belt data from patients with variable degrees of sleep apnea. We optimized parameters on a training set (n=57) and then tested performance on a validation set (n=59). The optimized algorithm correlated significantly with manual scoring in the validation set (R2 = 0.73 for training set, R2 = 0.55 for validation set; p<0.05). For dichotomous classification, the AUC was >0.92 and >0.85 using apnea-hypopnea index cutoff values of 5 and 15, respectively. Our findings demonstrate that manually scored AHI values can be approximated from thoracic movements alone. This finding has potential applications for automating laboratory PSG analysis as well as improving the performance of limited channel home monitors.

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