Methods for Increasing Sensitivity and Throughput of Solid-State NMR Spectroscopy of Pharmaceutical Solids

Solid-state nuclear magnetic resonance (SSNMR) spectroscopy has been demonstrated to be a powerful technique for investigating solid dosage formulations. SSNMR has the ability to determine physical form, molecular structure, and dynamics of a pure or formulated active pharmaceutical ingredient (API). To overcome the major shortcomings of SSNMR, acquisition time and sensitivity, a two-sample probe was designed, developed, and tested. The probe allowed for two samples to be acquired simultaneously while being shuttled through the magnet bore with a stepper motor, and exhibited signal to noise ratio (SNR) values comparable to current probes. Pharmaceutical relevance was demonstrated by acquiring spectra of ibuprofen and aspirin simultaneously. 19F SSNMR was used to examine low-level amorphous impurities in crystalline physical mixtures. The model compound was chosen for amorphous form stability, cost, and 19F SSNMR spectral resolution between the crystalline and amorphous solid forms. Triamcinolone was selected for quantitation studies using 19F SSNMR

AMST: Alignment to Median Smoothed Template for Focused Ion Beam Scanning Electron Microscopy Image Stacks

Alignment of stacks of serial images generated by focused ion Beam Scanning electron Microscopy (FIB-SEM) is generally performed using translations only, either through slice-by-slice alignments with SIFT or alignment by template matching. However, limitations of these methods are two-fold: the introduction of a bias along the dataset in the z-direction which seriously alters the morphology of observed organelles and a missing compensation for pixel size variations inherent to the image acquisition itself. These pixel size variations result in local misalignments and jumps of a few nanometers in the image data that can compromise downstream image analysis. We introduce a novel approach which enables affine transformations to overcome local misalignments while avoiding the danger of introducing a scaling, rotation or shearing trend along the dataset. Our method first computes a template dataset with an alignment method restricted to translations only. This pre-aligned dataset is then smoothed selectively along the z-axis with a median filter, creating a template to which the raw data is aligned using affine transformations. Our method was applied to FIB-SEM datasets and showed clear improvement of the alignment along the z-axis resulting in a significantly more accurate automatic boundary segmentation using a convolutional neural network