Hegelund McClanahan (pilotduck25)

Metal-enhanced photocatalysis has recently received increasing interest, mainly due to the ability of metal to directly or indirectly degrade pollutants. In this review, we briefly review the recent breakthroughs in metal-enhanced photocatalysis. We discussed the recent progress of surface plasmon resonance (SPR) effect and small size effect of metal nanoparticles on photocatalysis; in particular, we focus on elucidating the mechanism of energy transfer and hot electron injection/transfer effect of metal nanoparticles and clusters while as photocatalysts or as cophotocatalysts. Finally, we discuss the potential applications of metal-enhanced photocatalysis, and we also offer some perspectives for further investigations. To develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac computed tomography (CT) via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging. One hundred patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis planes were manually defined by an imaging expert. A DL model was trained to predict bloodpool segmentations and imaging planes. Deep learning bloodpool segmentations showed close agreement with manual LV [median Dice 0.91, Hausdorff distance (HD) 6.18 mm] and LA (Dice 0.93, HD 7.35 mm) segmentations and a strong correlation with manual ejection fraction (Pearson 0.95 LV, 0.92 LA). Predicted planes had low median location (6.96 mm) and angular orientation (7.96 ° ) errors which were comparable to inter-reader differences ( > 0.71). 84-97% of DL-prescribed LAX planes correctly intersected American Heart Association segments, which was comparable ( > 0.05) to manual slicing. In a test cohort of 144 patients, we evaluated the ability of the DL approach to provide diagnostic imaging planes. Visual scoring by two blinded experts determined ≥94% of DL-predicted planes to be diagnostically adequate. Further, DL-enabled visualization of LV wall motion abnormalities due to CAD and provided reproducible planes upon repeat imaging. A volumetric, DL approach provides multiple chamber segmentations and can re-slice the imaging volume along standardized cardiac imaging planes for reproducible wall motion abnormality and functional assessment. A volumetric, DL approach provides multiple chamber segmentations and can re-slice the imaging volume along standardized cardiac imaging planes for reproducible wall motion abnormality and functional assessment. To extend the benefits of physiologically guided percutaneous coronary intervention to many more patients, angiography-derived, or 'virtual' fractional flow reserve (vFFR) has been developed, in which FFR is computed, based upon the images, instead of being measured invasively. The effect of operator experience with these methods upon vFFR accuracy remains unknown. We investigated variability in vFFR results based upon operator experience with image-based computational modelling techniques. Virtual fractional flow reserve was computed using a proprietary method (VIRTUheart) from the invasive angiograms of patients with coronary artery disease. Each case was processed by an expert (>100 vFFR cases) and a non-expert (<20 vFFR cases) operator and results were compared. The primary outcome was the variability in vFFR between experts and non-experts and the impact this had upon treatment strategy (PCI vs. conservative management). Two hundred and thirty-one vessels (199 patients) were processed. Mean nonlity assurance to ensure reliable, repeatable vFFR results.Elastin (ELN) insufficiency leads to the cardiovascular hallmarks of the contiguous gene deletion disorder, Williams-Beuren syndrome, incl