https://www.selleckchem.com/products/phycocyanobilin.html 80 and specificity of 0.54 in the test group; the AUC was 0.730 (95% confidence interval, 0.699-0.761). The AUC for the algorithm was higher than that for the Diamond-Forrester model (0.730 vs. 0.623, P < 0.001) and the CAD consortium clinical score (0.730 vs. 0.652, P < 0.001). Our results suggested that a deep learning algorithm based on facial photos can assist in CAD detection in this Chinese cohort. This technique may hold promise for pre-test CAD probability assessment in outpatient clinics or CAD screening in community. Further studies to develop a clinical available tool are warranted. Our results suggested that a deep learning algorithm based on facial photos can assist in CAD detection in this Chinese cohort. This technique may hold promise for pre-test CAD probability assessment in outpatient clinics or CAD screening in community. Further studies to develop a clinical available tool are warranted.Nanoscale carbon black as virtually pure elemental carbon can deposit deep in the lungs and cause pulmonary injury. Airway remodeling assessed using computed tomography (CT) correlates well with spirometry in patients with obstructive lung diseases. Structural airway changes caused by carbon black exposure remain unknown. Wall and lumen areas of sixth and ninth generations of airways in 4 lobes were quantified using end-inhalation CT scans in 58 current carbon black packers (CBPs) and 95 non-CBPs. Carbon content in airway macrophage (CCAM) in sputum was quantified to assess the dose-response. Environmental monitoring and CCAM showed a much higher level of elemental carbon exposure in CBPs, which was associated with higher wall area and lower lumen area with no change in total airway area for either airway generation. This suggested small airway wall thickening is a major feature of airway remodeling in CBPs. When compared with wall or lumen areas, wall area percent (WA%) was not affected by subject charact