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Objectives: Nowadays, the market is flooded with combinations of drugs in various dosage forms, but there is a lack of official methods to quantify them. A single dissolution test method for the analysis of combined dosage form is preferred for simplification of quality control testing., Materials and Methods: If the developed dissolution medium mimics the biorelevant and discriminating dissolution procedure for drug products with limited drug aqueous solubility it is a useful tool for qualitative forecasting of the in vivo behavior of formulations., Results: Dissolution profiles were evaluated for atorvastatin and fenofibrate in capsules, using a paddle-type United States Pharmacopeia dissolution apparatus in 900 mL of medium at 50 rpm and 37±0.5°C. The best medium was 900 mL of 0.5% w/v sodium lauryl sulfate. The cumulative % dissolution was more than 85% within 45 min for marketed tablets. The proposed dissolution test conditions have discriminative power, dissimilarity factor (f 1 ) values are low (12-16%), and similarity (f 2 ) factor values are also low (45-48%). Hence the use of 0.5% w/v sodium lauryl sulfate solution is justified., Conclusion: The dissolution method was validated (% relative standard deviation <2). To quantify both drugs simultaneously, a second derivative spectrophotometric method was established (λ max 281 nm and 296 nm, respectively, for atorvastatin and fenofibrate) in acetate buffer, pH 2.8 solution., Competing Interests: Conflict of Interest: No conflict of interest was declared by the authors., (©Copyright 2019 Turk J Pharm Sci, Published by Galenos Publishing House.)

While there has been substantial progress in segmenting natural images, state-of-the-art methods that perform well in such tasks unfortunately tend to underperform when confronted with the different challenges posed by electron microscope (EM) data. For example, in EM imagery of neural tissue, numerous cells and subcellular structures appear within a single image, they exhibit irregular shapes that cannot be easily modeled by standard techniques, and confusing textures clutter the background. We propose a fully automated approach that handles these challenges by using sophisticated cues that capture global shape and texture information, and by learning the specific appearance of object boundaries. We demonstrate that our approach significantly outperforms state-of-the-art techniques and closely matches the performance of human annotators.