The deposit patterns derived from droplet evaporation allow current development of medical tests and new strategies for diagnostic in patients. For such purpose, the development and implementation of algorithms capable of characterizing and differentiating deposits are crucial elements. We report the study of deposit patterns formed by the droplet evaporation of binary mixtures of proteins containing NaCl. Optical microscopy reveals aggregates such as tip arrow-shaped, dendritic and semi-rosette patterns, needle-like and scalloped lines structures, as well as star-like and prism-shaped salt crystals. We use the first-order statistics (FOS) and gray level co-occurrence matrix (GLCM) to characterize the complex texture of deposit patterns. Three significant findings arise from this analysis: first, the FOS and GLCM parameters structurally characterize protein deposits. Secondly, they conform to simple exponential laws that change as a function of the NaCl concentration. Finally, the parameters are capable of revealing the different structural changes that occur during the droplet evaporation.
The quality control of medicines guarantees the effectiveness of treatments for diseases. We explore the use of texture analysis of patterns in dried droplets as a tool to readily detect both impurities and changes in drug concentration. Four types of medicines associated with different routes of administration were analyzed: Methotrexate, Ciprofloxacin, Clonazepam, and Budesonide. We use NaCl and a hot substrate at 63 ∘C to promote aggregate formation and to reduce droplet drying time. Depending on the medicine, optical microscopy reveals different complex aggregates such as circular to oval splatters, fern-like islands, crown shapes, crown needle-like and bump-like patterns as well as dendritic branched and star-like crystals. We use some physical features of the stains (as the stain diameter and superficial area) and gray level co-occurrence matrix (GLCM) to characterize patterns of dried droplets. Finally, we show that structural analysis of stains can achieve 95% accuracy in identifying medicines with 30% water dilution, while it achieves 99% accuracy in detecting drugs with 10% other substances.
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