RESUMEN
The colorless solution of furan-2-yl bis(indolyl)methane (BIM) is newly revealed to work as a palladium (Pd2+) ion-selective chromogenic agent by turning orange. 5-(N-Methyl-N-phenyl-aminomethyl)-furan-2-yl BIM could be synthesized from 5-chloromethylfurfural as a biorenewable feedstock via one-pot and double functionalization, and a mixture of its solution and Pd2+ ions showed the highest absorbance at 465 nm in UV-Vis analysis. On the other hand, other metal ions (Cu2+, Cr2+, Cr3+, Fe2+, Fe3+, Ni2+, Zn2+, In2+, Pt2+, or Ce3+) exhibited no response.
RESUMEN
Although deuterium incorporation into pharmaceutical drugs is an attractive way to expand drug modalities, their physicochemical properties have not been sufficiently examined. This study focuses on examining the changes in physicochemical properties between flurbiprofen (FP) and flurbiprofen-d8 (FP-d8), which was successfully prepared by direct and multiple H/D exchange reactions at the eight aromatic C-H bonds of FP. Although the effect of deuterium incorporation was not observed between the crystal structures of FP and FP-d8, the melting point and heat of fusion of FP-d8 were lower than those of FP. Additionally, the solubility of FP-d8 increased by 2-fold compared to that of FP. Calculation of the interaction energy between FP/FP-d8 and water molecules using the multi-component density functional theory method resulted in increased solubility of FP-d8. These novel and valuable findings regarding the changes in physicochemical properties triggered by deuterium incorporation can contribute to the further development of deuterated drugs.
RESUMEN
Purpose: The necessity of image retakes is initially determined on a preview monitor equipped with an operating system; therefore, some image blurring is only noticed later, on a high-resolution monitor. The purpose of this study is to investigate blur detection performance on radiographs via a deep learning approach compared with human observers. Approach: A total of 99 radiographs (blurry 57, nonblurry 42) were independently observed and rated by six observers using preview and diagnostic liquid crystal displays (LCDs). The deep convolution neural network (DCNN) was trained and tested using ninefold cross-validation. The average areas under the ROC curves (AUCs) were calculated for each observer with LCDs and by stand-alone DCNN for each test session and then statistically tested using a 95% confidence interval. Results: The average AUCs were 0.955 for stand-alone DCNN and 0.827 and 0.947 for human observers using preview and diagnostic LCDs, respectively. The DCNN revealed a high performance for image motion blur on digital radiographs (sensitivity 94.8%, specificity 96.8%, and accuracy 95.6%), along with the capability to detect a slight motion blur that was overlooked by human observers with a preview LCD. There were no cases of motion blur overlooked by the stand-alone DCNN, of which some were incorrectly recognized as nonblurry by human observers. Conclusions: The deep learning-based approach was capable of distinguishing slight motion blur that was unnoticeable on a preview LCD, and thus, is expected to aid the human visual system for detecting blurred images in the initial review of digital radiographs.