Texture Analysis of Dried Droplets for the Quality Control of Medicines

Carreón, Yojana J. P.; Díaz-Hernández, Orlando; Santos, Gerardo J. Escalera; Cipriano-Urbano, Ivan; Solorio-Ordaz, F.; González-Gutiérrez, Jorge; Zenit, Roberto

doi:10.3390/s21124048

Cited by 12 publications

(10 citation statements)

References 85 publications

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“…Third, variants prepared by means of the different mixing procedures share a great diversity of textures. Therefore, the use of traditional techniques such as gray level co-occurrence matrix (GLCM) 49 – 51 , and fractal-based measures 2 can lead to ambiguities in texture differentiation. We have observed that LCFD histograms of DEM patterns are not unique.…”

Section: Discussionmentioning

confidence: 99%

Deep learning applied to analyze patterns from evaporated droplets of Viscum album extracts

Acuna

Terán

Kokornaczyk

et al. 2022

Sci Rep

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This paper introduces a deep learning based methodology for analyzing the self-assembled, fractal-like structures formed in evaporated droplets. To this end, an extensive image database of such structures of the plant extract Viscum album Quercus$$10^{-3}$$ 10 - 3 was used, prepared by three different mixing procedures (turbulent, laminar, and diffusion based). The proposed pattern analysis approach is based on two stages: (1) automatic selection of patches that exhibit rich texture along the database; and (2) clustering of patches in accordance with prevalent texture by means of a Dense Convolutional Neural Network. The fractality of the patterns in each cluster is verified through Local Connected Fractal Dimension histograms. Experiments with Gray-Level Co-Occurrence matrices are performed to determine the benefit of the proposed approach in comparison with well established image analysis techniques. For the investigated plant extract, significant differences were found between the production modalities; whereas the patterns obtained by laminar flow showed the highest fractal structure, the patterns obtained by the application of turbulent mixture exhibited the lowest fractality. Our approach is the first to analyze, at the pure image level, the clustering properties of regions of interest within a database of evaporated droplets. This allows a greater description and differentiation of the patterns formed through different mixing procedures.

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Section: Discussionmentioning

confidence: 99%

Deep learning applied to analyze patterns from evaporated droplets of Viscum album extracts

Acuna

Terán

Kokornaczyk

et al. 2022

Sci Rep

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“…Some previous studies demonstrated that NaCl induces the formation of complex aggregates in alcoholic drinks and medicines [ 56 , 59 ]. Our finding show that NaCl generates complex aggregates that allow one to classify and differentiate among deposit groups.…”

Section: Discussionmentioning

confidence: 99%

“…Entropy is defined from gray level co-occurence matrix. According to [ 56 , 57 ], we calculated the probability of variation between gray level i and j in a displacement distance ( d ) and angle (

) with the matrix element

given by:

…”

Section: Methodsmentioning

confidence: 99%

Patterns in Dried Droplets to Detect Unfolded BSA

Carreón

Gómez-López

Díaz-Hernández

et al. 2022

Sensors

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The morphological analysis of patterns in dried droplets has allowed the generation of efficient techniques for the detection of molecules of medical interest. However, the effectiveness of this method to reveal the coexistence of macromolecules of the same species, but different conformational states, is still unknown. To address this problem, we present an experimental study on pattern formation in dried droplets of bovine serum albumin (BSA), in folded and unfolded conformational states, in saline solution (NaCl). Folded proteins produce a well-defined coffee ring and crystal patterns all over the dry droplet. Depending on the NaCl concentration, the crystals can be small, large, elongated, entangled, or dense. Optical microscopy reveals that the relative concentration of unfolded proteins determines the morphological characteristics of deposits. At a low relative concentration of unfolded proteins (above 2%), small amorphous aggregates emerge in the deposits, while at high concentrations (above 16%), the “eye-like pattern”, a large aggregate surrounded by a uniform coating, is produced. The radial intensity profile, the mean pixel intensity, and the entropy make it possible to characterize the patterns in dried droplets. We prove that it is possible to achieve 100% accuracy in identifying 4% of unfolded BSA contained in a protein solution.

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“…It suppresses the recrystallization and the coffeering effect [176]. In terms of pharmaceutical applications, Carre´on et al [241] studied the quality of different medicinal drugs (methotrexate, ciprofloxacin, clonazepam, and budesonide) as a function of the initial concentration, added NaCl, and substrate temperature.…”

Section: B Current Challenges Of Bio-colloidal Drying Dropletsmentioning

confidence: 99%