Heme oxygenase-1 regulates cell proliferation via carbon monoxide-mediated inhibition of T-type Ca2+ channels

Induction of the antioxidant enzyme heme oxygenase-1 (HO-1) affords cellular protection and suppresses proliferation of vascular smooth muscle cells (VSMCs) associated with a variety of pathological cardiovascular conditions including myocardial infarction and vascular injury. However, the underlying mechanisms are not fully understood. Over-expression of Cav3.2 T-type Ca2+ channels in HEK293 cells raised basal [Ca2+]i and increased proliferation as compared with non-transfected cells. Proliferation and [Ca2+]i levels were reduced to levels seen in non-transfected cells either by induction of HO-1 or exposure of cells to the HO-1 product, carbon monoxide (CO) (applied as the CO releasing molecule, CORM-3). In the aortic VSMC line A7r5, proliferation was also inhibited by induction of HO-1 or by exposure of cells to CO, and patch-clamp recordings indicated that CO inhibited T-type (as well as L-type) Ca2+ currents in these cells. Finally, in human saphenous vein smooth muscle cells, proliferation was reduced by T-type channel inhibition or by HO-1 induction or CO exposure. The effects of T-type channel blockade and HO-1 induction were non-additive. Collectively, these data indicate that HO-1 regulates proliferation via CO-mediated inhibition of T-type Ca2+ channels. This signalling pathway provides a novel means by which proliferation of VSMCs (and other cells) may be regulated therapeutically.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.