Diagnostic tests based on pattern formation in drying body fluids – A mapping review

Kokornaczyk, Maria Olga; Bodrova, Natalia Borisovna; Baumgartner, Stephan

doi:10.1016/j.colsurfb.2021.112092

Cited by 15 publications

(18 citation statements)

References 51 publications

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“…As resulted out of three recent review articles concerning DEM applied as an analytical tool in pharmaceutics 10 and medical diagnostics 11 , 56 the main shortcoming and limitation of the method is the often exclusively applied visual pattern evaluation that can be subjective and thus a possible source of bias. The development and implementation of computerized image evaluation techniques that can be used complementary or alternatively to the visual evaluation is therefore of great importance for ensuring an objective DEM pattern evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…One possible way to obtain such patterns is the Droplet Evaporation Method (DEM), based on the self-assembly of particles suspended in fluids in nano and microstructures during evaporation. DEM has found application in several fields: inkjet printing 3 , manufacturing of new electronic and optical materials including thin films and coatings 4 , 5 , drug discovery 6 , as also quality analysis 2 , 7 – 10 and medical diagnostics 1 , 11 – 13 . The method has also been successfully applied to exhibit coupled physical mechanisms in blood 14 , protein-protein interactions 15 or to study the morphology associated with polymers 16 and liquid crystals 17 .…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

Acuna

Terán

Kokornaczyk

et al. 2022

Sci Rep

Self Cite

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“…, drying rate, nature of the liquid–air–solid contact line, the substrate, and the interactions among the dispersed particles. Thus, a seemingly simple process of droplet drying is a much more complex phenomenon in reality whether it is (i) contact-free drying as in the case of spray drying − or (ii) drying of a droplet on a substrate. − Comprehending the nature of the deposited patterns on a porous substrate − is of special interest in several applications, including inkjet printing and disease diagnosis through pattern recognition. − Interesting patterns , may manifest during the drying of colloidal droplets on the substrate depending on the physicochemical environments. These patterns may be either (i) uniform, , (ii) centrally peaked, , or (iii) centrally dipped. , Further, the structural correlation among the colloidal particles during drying is primarily driven by evaporative assembly and is also of fundamental interest.…”

Section: Introductionmentioning

confidence: 99%

Pattern of an Evaporated Colloidal Droplet on a Porous Membrane Dictated by Competitive Processes of Flow and Absorption

et al. 2022

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Understanding the deposition pattern formed by an evaporated colloidal drop is of fundamental and technological interest. Such an evaporative process is important in various applications starting from inkjet printing to disease diagnosis. In this work, it is shown that the deposit pattern on a porous membrane can be tuned by varying the colloidal viscosity and membrane pore size. We have used small-angle X-ray scattering (SAXS) in scanning mode for profiling of deposit morphology and also for estimation of the interparticle correlation. It is demonstrated that low viscosity and small pore size favor a centrally dipped pattern owing to the coffee ring effect, which can be modified to a contrasting centrally peaked pattern by increasing the viscosity and pore size. To comprehend the experimental observations, a computer model has been developed using a continuity equation that well corroborates the experimental observations on the final deposited pattern and also provides the time evolution of the pattern. The work provides a way to tune the pattern of colloidal stain on a porous substrate by controlling flow and absorption.

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“…In research, the analysis of blood drops has been proposed as a promising method for detecting biochemical characteristics that depend on the state of the person, for instance before and after exhaustive exercise [9], a bacterial infection [10], or thalassemia in children [11]. Some other bodily fluids investigated with this method include saliva, cervical mucus, urine, serum, and plasma with the final goal of diagnosing cancer, inflammations, infections, or other diseases [12].…”

Section: Introductionmentioning

confidence: 99%

Drying Patterns of Cerebrospinal Fluid as Indicator for Alzheimer’s Disease by a Machine Learning Framework

Arévalo

O’Brien

Антонова

et al. 2022

J. Phys.: Conf. Ser.

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Cerebrospinal fluid contains specific biomarkers of Alzheimer’s disease that include amyloid beta peptides and tau proteins. In this work, we present for the first time possible evidence that the formation of the constituents of cerebrospinal fluid during drying is related with Alzheimer’s. We use machine learning to examine optical microscope images of dried cerebrospinal fluid patterns from patients with Alzheimer’s and healthy controls to create a diagnostic model. To analyze the images, the histogram of oriented gradients is used as a feature descriptor. Each image is mapped into the corresponding feature space, and principal component analysis is applied for dimensionality reduction. A machine-learning prediction model with a sensitivity of 82% was built. These promising preliminary results show great potential for new rapid and low-cost diagnostic pathways in the detection of Alzheimer’s disease.

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Diagnostic tests based on pattern formation in drying body fluids – A mapping review

Cited by 15 publications

References 51 publications

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

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

Pattern of an Evaporated Colloidal Droplet on a Porous Membrane Dictated by Competitive Processes of Flow and Absorption

Drying Patterns of Cerebrospinal Fluid as Indicator for Alzheimer’s Disease by a Machine Learning Framework

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