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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.)

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

It is becoming increasingly clear that mitochondria play an important role in neural function. Recent studies show mitochondrial morphology to be crucial to cellular physiology and synaptic function and a link between mitochondrial defects and neuro-degenerative diseases is strongly suspected. Electron microscopy (EM), with its very high resolution in all three directions, is one of the key tools to look more closely into these issues but the huge amounts of data it produces make automated analysis necessary. State-of-the-art computer vision algorithms designed to operate on natural 2-D images tend to perform poorly when applied to EM data for a number of reasons. First, the sheer size of a typical EM volume renders most modern segmentation schemes intractable. Furthermore, most approaches ignore important shape cues, relying only on local statistics that easily become confused when confronted with noise and textures inherent in the data. Finally, the conventional assumption that strong image gradients always correspond to object boundaries is violated by the clutter of distracting membranes. In this work, we propose an automated graph partitioning scheme that addresses these issues. It reduces the computational complexity by operating on supervoxels instead of voxels, incorporates shape features capable of describing the 3-D shape of the target objects, and learns to recognize the distinctive appearance of true boundaries. Our experiments demonstrate that our approach is able to segment mitochondria at a performance level close to that of a human annotator, and outperforms a state-of-the-art 3-D segmentation technique.

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.