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
Effect of Aeration and Agitation Regimes on Lipase Production by Newly Isolated Rhodotorula mucilaginosa–MTCC 8737 in Stirred Tank Reactor Using Molasses as Sole Production Medium
The influence of media and process parameters (aeration and agitation) on fermentation broth rheology and biomass formation has been studied in 1.5-l stirred tank reactor for lipase production using Rhodotorula mucilaginosa MTCC 8737. Molasses, as sole production medium, is used for lipase production by varying aeration (1, 2, and 3 vvm) and agitation speeds (100, 200, and 300 rpm). Maximum lipase activity of 72 U/ml was obtained during 96 h of fermentation at 2 vvm, 200 rpm, pH 7, and 25 ± 2°C temperature. Lipase production kinetics with respect to dry cell weight of biomass showed YP/S of 25.71 U/mg, specific product formation of 10.9 U/mg DC, and YX/S 2.35 mg/mg. Maximum lipase activity (MC 2) of 56 U/ml was observed at 1% molasses, and a further increase in the molasses concentration of (%) 1.5 and 2 inhibited the product formation of lipase with 15 and 8.5 U/ml, respectively. The production kinetics of molasses media showed YP/X was 14 U/mg DC, YP/S 16 U/mg, and YX/S 1.14 mg/mg during 96 h of bioreactor operation. The kLa values for all batches (MC 1–MC 4) at 96 h of fermentation were 32, 28, 21, and 19/h, and the |oxygen transfer rate were 54.4, 56, 35.7, and 17.29 mg/l h, respectively. Increase in molasses concentration resulted in decreased lipase activity by increase in viscosity of the fermentation broth.