Abstract. The purpose of this study is to enhance the dissolution rate of prednisone by co-grinding with Neusilin to form a complex that can be incorporated into a mini-tablet formulation for pediatrics. Prednisone-Neusilin complex was co-grinded at various ratios (1:1, 1:3, 1:5, and 1:7). The physicochemical properties of the complex were characterized by various analytical techniques including: differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD), scanning electron microscope (SEM), particle size, surface area, solubility, and dissolution rate. The co-grinded prednisone-Neusilin complex (1:7) was blended with other excipients and was formulated into a 2-mm diameter mini-tablet. The minitablets were further evaluated for thickness, weight, content uniformity, and dissolution rate. To improve taste masking and stability, mini-tablets were coated by dip coating with Eudragit® EPO solution. DSC and XRPD results showed that prednisone was transformed from crystalline state into amorphous state after co-grinding with Neusilin. Particle size, surface area, and SEM results confirmed that prednisone was adsorbed to Neusilin's surface. Co-grinded prednisone-Neusilin complex (1:7) had a solubility of 0.24 mg/ mL and 90% dissolved within 20 min as compared to crystalline prednisone which had a solubility of 0.117 mg/mL and 30% dissolved within 20 min. The mini-tablets containing co-grinded prednisoneNeusilin complex (1:7) exhibited acceptable physicochemical and mechanical properties including dissolution rate enhancement. These mini-tablets were successfully dip coated in Eudragit® EPO solution to mask the taste of the drug during swallowing. This work illustrates the potential use of co-grinded prednisone-Neusilin to enhance solubility and dissolution rate as well as incorporation into a mini-tablet formulation for pediatric use.
Measuring oil concentration in the aquatic environment is essential for determining the potential exposure, risk, or injury for oil spill response and natural resource damage assessment. Conventional analytical chemistry methods require samples to be collected in the field, shipped, and processed in the laboratory, which is also rather time-consuming, laborious, and costly. For rapid field response immediately after a spill, there is a need to estimate oil concentration in near real time. To make the oil analysis more portable, fast, and cost effective, we developed a plug-and-play device and a deep learning model to assess oil levels in water using fluorescent images of water samples. We constructed a 3D-printed device to collect fluorescent images of solvent-extracted water samples using an iPhone. We prepared approximately 1,300 samples of oil at different concentrations to train and test the deep learning model. The model comprises a convolutional neural network and a novel module of histogram bottleneck block with an attention mechanism to exploit the spectral features found in low-contrast images. This model predicts the oil concentration in weight per volume based on fluorescence image. We devised a confidence interval estimator by combining gradient boosting and polymodal regressor to provide a confidence assessment of our results. Our model achieved sufficient accuracy to predict oil levels for most environmental applications. We plan to improve the device and iPhone application as a near-real-time tool for oil spill responders to measure oil in water.
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