Method of evaluation of process of red blood cell sedimentation based on photometry of droplet samples
The paper focuses on research aimed at creating and testing a new approach to evaluate the processes of aggregation and sedimentation of red blood cells for purpose of its use in clinical laboratory diagnostics. The proposed method is based on photometric analysis of blood sample formed as a sessile drop. The results of clinical approbation of this method are given in the paper. Analysis of the processes occurring in the sample in the form of sessile drop during the process of blood cells sedimentation is described. The results of experimental studies to evaluate the effect of the droplet sample focusing properties on light radiation transmittance are presented. It is shown that this method significantly reduces the sample volume and provides sufficiently high sensitivity to the studied processes.
Convolutional neural network-based automatic classification for incomplete antibody reaction intensity in solid phase anti-human globulin test image
The precise classification of incomplete antibody reaction intensity (IARI) in hydrogel chromatography medium high density medium solid-phase Coombs test is essential for haemolytic disease screening. However, an automatic and contactless method is required for accurate classification of IARI. Here, we present a deep ensemble learning model that integrates five different convolutional neural networks into a single model for IARI classification. A dataset, including 1628 IARI images and corresponding labels of IARI categories ((-), (1 +), (2 +), (3 +), and (4 +)), was used. We trained our model using 1302 IARIs and validated its performance using 326 IARIs. The proposed model achieved 100%, 99.4%, 99.4%, 100%, and 100% accuracies in the ( −), (1 +), (2 +), (3 +), and (4 +) categories, respectively. The results were compared with those of manual classification by immunologists (average accuracy: 99.8% vs. 88.3%, p  < 0.01). Following model assistance, all three immunologists achieved increased accuracy (average accuracy: + 6.1%), with the average accuracy of junior immunologists maximum increasing by 11.3%. The time required for model classification was 0.094 s·image –1 , whereas that required manually was 5.528 s·image –1 . The proposed model can thus substantially improve the accuracy and efficiency of IARI classification and facilitate the automation of haemolytic disease screening equipment. Graphical abstract
Measuring Sheet Resistances of Dielectrics Using Co‐Planar Hydrogel Electrochemical Cells with Practical Applications to Characterize the Protective Quality of Paints on Sculptures
Electrical properties of thin dielectric films at the solid-liquid phase boundary are an important performance characteristic of many devices, coatings and sensors. In this paper, co-polymeric hydrogels of polyacrylic acid co-sulfonic acid, swollen with a salt solution to act as the solid electrolyte, were used to assess interfaces using electrochemical impedance spectroscopy (EIS) in a co-planar geometry. Silane-modified glasses were characterized by the co-planar hydrogel EIS cell and found to be distinguishable based on their surface monolayer chemistry. EIS measurements were also made on primed and painted metal substrates, using both test panels and an outdoor sculpture, Tony Smith's Stinger. The panels were then exposed to accelerated and outdoor weathering and showed degradation on the surface paint layers, which was observable electrochemically using EIS and confirmed visually by SEM. Electrochemical spectral features were compared between data from a standard paint-test cell versus this co-planar hydrogel cell; both cell types were able to measure coating capacitance, providing useful information about the condition of the bulk coating. Yet, sheet resistance (Rs) was a spectral feature seen only by the co-planar hydrogel cell. Thus, it can be concluded that the use of such co-planar hydrogel cells can provide an earlier warning sign to coating degradation and such cells provide a new type of spectral information that is not assessable by the standard geometry.