RESUMEN
This study uses 35Cl and 2H solid-state NMR (SSNMR) spectroscopy and dispersion-corrected plane-wave density functional theory (DFT) calculations to characterize the molecular-level structures and dynamics of hydrates of active pharmaceutical ingredients (APIs). We use 35Cl SSNMR to measure the EFG tensors of the chloride ions to characterize hydrated forms of hydrochloride salts of APIs, along with two corresponding anhydrous forms. DFT calculations are used to refine the crystal structures of the APIs and determine relationships between the 35Cl EFG tensors and the spatial arrangements of proximate hydrogen bonds, which are particularly influenced by interactions with water molecules. We find that the relationship between 35Cl EFG tensors and local hydrogen bonding geometries is complex, but meaningful structure/property relationships can be garnered through use of DFT calculations. Specifically, for every case in which such a comparison could be made, we find that the hydrate has a smaller magnitude of CQ than the corresponding anhydrous form, indicating a chloride ion environment with a ground-state electron density of higher spherical symmetry in the former. Finally, variable-temperature 35Cl and 2H SSNMR experiments on a deuterium-exchanged sample of the API cimetidine hydrochloride monohydrate are used to monitor temperature-dependent influences on the spectra that may arise from motional influences on the 35Cl and 2H EFG tensors. From the 2H SSNMR spectra, we determine that the motions of water molecules are characterized by jump-like motions about their C2 rotational axes that occur on timescales that are unlikely to influence the 35Cl central-transition (+1/2 âï¸ -1/2) powder patterns (this is confirmed by 35Cl SSNMR). Together, these methods show great promise for the future study of APIs in their bulk and dosage forms, especially variable hydrates in which crystallographic water content varies with external conditions such as humidity.
Asunto(s)
Cloruros , Imagen por Resonancia Magnética , Halógenos , Agua , PolvosRESUMEN
PURPOSE: In the context of analyzing neck vascular morphology, this work formulates and compares Mask R-CNN and U-Net-based algorithms to automatically segment the carotid artery (CA) and internal jugular vein (IJV) from transverse neck ultrasound (US). METHODS: US scans of the neck vasculature were collected to produce a dataset of 2439 images and their respective manual segmentations. Fourfold cross-validation was employed to train and evaluate Mask RCNN and U-Net models. The U-Net algorithm includes a post-processing step that selects the largest connected segmentation for each class. A Mask R-CNN-based vascular reconstruction pipeline was validated by performing a surface-to-surface distance comparison between US and CT reconstructions from the same patient. RESULTS: The average CA and IJV Dice scores produced by the Mask R-CNN across the evaluation data from all four sets were [Formula: see text] and [Formula: see text]. The average Dice scores produced by the post-processed U-Net were [Formula: see text] and [Formula: see text], for the CA and IJV, respectively. The reconstruction algorithm utilizing the Mask R-CNN was capable of producing accurate 3D reconstructions with majority of US reconstruction surface points being within 2 mm of the CT equivalent. CONCLUSIONS: On average, the Mask R-CNN produced more accurate vascular segmentations compared to U-Net. The Mask R-CNN models were used to produce 3D reconstructed vasculature with a similar accuracy to that of a manually segmented CT scan. This implementation of the Mask R-CNN network enables automatic analysis of the neck vasculature and facilitates 3D vascular reconstruction.