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Objective: Developing a simple, rapid, reliable, sensitive, and cost-effective method for prenatal detection of fetomaternal haemorrhage by combining multi-aperture silk membrane with enzyme-linked immunosorbent assay (ELISA), which does not require any complicated instruments and can be visually colored, so as to provide a new method for clinical detection of fetomaternal haemorrhage.Methods: As a carrier, a chemically treated silk membrane was used to immobilize anti-A/anti-B antibody reagent. PBS washed slowly after vertically dropping red blood cells. After adding biotin-labeled anti-A/anti-B antibody reagent, PBS is slowly washed, enzyme-labeled avidin is added, and TMB is used for color development after washing.Results: When there were both anti-A and anti-B fetal erythrocytes in pregnant women’s peripheral blood, the final color was dark brown. When there are no anti-A and anti-B fetal red blood cells in pregnant women’s peripheral blood, the final color development results do not change, which corresponds to the color of chemically treated silk membrane.Conclusion: The new enzyme-linked immunosorbent assay (ELISA) based on a silk membrane can distinguish fetal red blood cells from maternal red blood cells prenatally and can be used for prenatal detection of fetomaternal haemorrhage.

The currently employed red blood cell reagents have a short shelf life. Some hospitals with a small number of specimens will be unable to utilize them within the validity period, resulting in a substantial increase in the purchase price. Therefore, the method of developing long-term red blood cell reagents is a problem worthy of further study. In this experiment, the type and concentration of the red blood cell reagent treatment solution were evaluated based on the red blood cell antigen concentration 24 h after treatment. In addition, the qualified glutaraldehyde/paraformaldehyde reagent was stored for six months, and five red blood cell indices were measured every month. At the same time, the detection indices of treated red blood cell reagents and untreated red blood cell reagents were compared. It was discovered that treated red blood cells containing 0.005% GA and 0.05% PFA were more suitable for the preservation of red blood cells than other treated concentrations, and the preservation time could reach six months. The test tube method (n = 24) and microcolumn gel card (n = 35) were used to determine the accuracy of the treated blood cells containing 0.005% glutaraldehyde +0.05% paraformaldehyde, with an accuracy of 100%. This experiment resulted in the development of a novel reagent for treating red blood cells with glutaraldehyde/paraformaldehyde fixed solution that can effectively prolong its storage time by two to three times that of red blood cell reagents currently on the market.

The development of immunosensing assays for in vitro diagnostics has attracted great attention in recent years. Various substrate materials and immobilization methods of biomolecules were exploited for immunosensors, but their bioactivity and longevity have been facing serious challenges. To address this limitation, we investigated a natural silk cocoon membrane as immunosensing substrate material. By using its intrinsic properties, the target biomolecules were immobilized on the membrane through directional immunoaffinity recognition. The silk cocoon membrane-based immunosensor showed great potential for both qualitative and quantitative immunoassays, through naked-eye observation or analyzing the change in red color intensity, respectively. The immunosensor exhibited significant detection capability for anti-D (titer 1:1024) sensitized red blood cells. The colorimetric responses of concentrations ranged from 1 μg/mL to 1 ng/mL, and the detection limit for anti-D was 3.4 ng/mL. The immunosensor also showed excellent stability for the immobilized antibodies when stored at 4 and 25 °C; the bioactivity remained unchanged or slightly declined within 40 weeks. Even at 37 °C, the bioactivity began to decline after 12 weeks. This current work highlights the potential of using the natural silk cocoon membrane as a substrate for a versatile and thermally stable immunosensing platform for application in immunoassays.

This study presents a portable multichannel microfluidic device for parallel and digital analysis of red cell antigen typing. A zigzag-shaped precise metering channel was designed for the simultaneous aliquoting of samples, which is independent of the volume of the predeposited blood-typing reagents in the reaction chambers. The entire assay protocol can be conducted using a sequential-step spinning protocol, which resembles that of conventional tube tests for blood typing; however, the manual procedure is largely reduced compared to that of conventional systems. After loading the samples, the disc is centrifuged in a defined program with five sequential steps, each of which can be completed in a few seconds. Through step-wise centrifugation, predeposited antibodies react with red blood cells, enabling the parallel identification of multiple red blood cell antigens without cross-contamination in 1 min. This is combined with gentle mixing to rapidly concentrate the agglutinates, making both visual and digital determination of agglutination straightforward. A customized image analysis algorithm for automatically determining the agglutination state was developed to complement this microfluidic system. The acquired image is processed after the test. The blood type is determined using a machine learning algorithm based on a histogram of oriented gradients (HOG) and support vector machines (SVM). This allows digital analysis to mirror the classical laboratory procedure for blood-type determination more accurately. The system was trained using a validated dataset of 150 blood samples, presenting 750 different agglutination patterns. The combination of SVM and HOG achieved 94.10% in the micro-weighted performance evaluation. This integrated microfluidic chip-based platform provides a "sample-in and answer out" demonstration for red blood cell typing, ensuring fast and reliable results because minimum manual steps are involved.

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

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