Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolution with minimal equipment cost. In low-dose CBCT environments, metallic inserts such as implants, crowns, and dental fillings cause severe artifacts, which result in a significant loss of morphological structures of teeth in reconstructed images. Such metal artifacts prevent accurate 3D bone-teeth-jaw modeling for diagnosis and treatment planning.

Enhanced intestinal lymphatic absorption of saquinavir through supersaturated self-microemulsifying drug delivery systems.

The therapeutic potential of saquinavir, a specific inhibitor of human immunodeficiency virus (HIV)-1 and HIV-2 protease enzymes, has been largely limited because of a low solubility and consequnt low bioavailability. Thus, we aimed to design a supersaturated self-microemulsifying drug delivery system (S-SMEDDS) that can maintain a high concentration of saquinavir in gastro-intestinal fluid thorugh inhibiting the drug precipitation to enhance the lymphatic transport of saquinavir and to increase the bioavailability of saquinavir considerably. Solubilizing capacity of different oils, surfactants, and cosurfactants for saquinavir was evaluated to select optimal ingredients for preparation of SMEDDS.