H blood group detection by the L-fucose binding lectin of the green marine alga Ulvalactuca

Abstract After mucosal damage or gingival inflammation, complement proteins leak into the oral cavity and mix with salivary proteins such as salivary agglutinin (SAG/gp-340/DMBT1). This protein is encoded by the gene Deleted in Malignant Brain Tumors 1 (DMBT1), and it aggregates bacteria, viruses and fungi, and activates the lectin pathway of the complement system. In the lectin pathway, carbohydrate structures on pathogens or altered self cells are recognized. SAG is highly glycosylated, partly on the basis of the donor’s blood group status. Whereas secretors express Lewis b, Lewis y, and antigens from the ABO-blood group system on SAG, non-secretors do not. Through mannose-binding lectin (MBL) binding and C4 deposition assays, we aimed to identify the chemical structures on SAG that are responsible for complement activation. The complement-activating properties of SAG were completely abolished by oxidation of its carbohydrate moiety. SAG-mediated activation of complement was also inhibited in the presence of saccharides such as fucose and Lewis b carbohydrates, and also after pretreatment with the fucose-binding lectin, Anguilla anguilla agglutinin. Complement activation was significantly (p<0.01) higher in secretors than in non-secretors. Our results suggest that fucose-rich oligosaccharide sidechains, such as Lewis b antigens, are involved in the activation of complement by SAG.

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ABO blood group detection based on image processing technology

2017 2nd International Conference on Image, Vision and Computing (ICIVC) ◽

10.1109/icivc.2017.7984637 ◽

2017 ◽

Author(s):

Yue-fang Dong ◽

Wei-wei Fu ◽

Zhe Zhou ◽

Nian Chen ◽

Min Liu ◽

...

Keyword(s):

Image Processing ◽

Blood Group ◽

Processing Technology ◽

Abo Blood Group ◽

Image Processing Technology ◽

Group Detection

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Pendeteksi Golongan Darah Manusia Berbasis Tensorflow menggunakan ESP32-CAM

ELKOMIKA Jurnal Teknik Energi Elektrik Teknik Telekomunikasi & Teknik Elektronika ◽

10.26760/elkomika.v9i2.359 ◽

2021 ◽

Vol 9 (2) ◽

pp. 359

Author(s):

HUMMAM GHASSAN GHIFARI ◽

DENNY DARLIS ◽

ARIS HARTAMAN

Keyword(s):

Image Processing ◽

Object Detection ◽

Blood Sample ◽

Blood Group ◽

Human Blood ◽

Blood Type ◽

Ip Address ◽

Human Blood Group ◽

Group Detection ◽

The Ideal

ABSTRAKPendeteksian golongan darah dilakukan untuk mengetahui golongan darah yang dimiliki. Hingga saat ini pendeteksian golongan darah masih dilakukan oleh petugas analis kesehatan menggunakan kemampuan mata manusia. Pada penelitian ini dilakukan perancangan alat pendeteksi golongan darah menggunakan ESP32-CAM. Alat ini menggunakan kamera OV2640 untuk menangkap citra, yang diproses menggunakan Tensorflow Object Detection API sebagai framework untuk melatih serta mengolah citra darah. Model latih akan digunakan pada kondisi pendeteksian langsung dan ditampilkan dalam bentuk jendela program golongan darah beserta tingkat akurasinya. Dalam penelitian ini pengujian dilakukan menggunakan 20 dataset dengan jarak pengukuran antara ESP32-CAM dengan citra golongan darah yaitu sejauh 20 cm. Hasil yang didapat selama pengujian mayoritas golongan darah yang dapat terdeteksi adalah golongan darah AB.Kata kunci: ESP32-CAM, Tensorflow, Python, Golongan Darah, Pengolahan Citra ABSTRACTBlood group detection is performed to determine the blood group. Currently, in detecting blood type, it still relies on the ability of the human eyeThis paper presents a human blood group detection device using ESP32-CAM. This tool uses ESP32-CAM to capture images, and the Tensorflow Object Detection API as a framework used to train and process an image. The way this tool works is that the ESP32-CAM will capture an image of the blood sample and then send it via the IP address. Through the IP Address, the python program will access the image, then the image will be processed based on a model that has been previously trained. The results of this processing will be displayed in the form of a window program along with the blood type and level of accuracy. In this study, testing was carried out based on the number of image samples, the number of datasets, and the measurement distance. The ideal measurement distance between the ESP32-CAM and the blood group image is 20 cm long. The results obtained during the testing of the majority of blood groups that can be detected are AB blood group.Keywords: ESP32-CAM, Tensorflow, Python, Blood Type, Image Processing

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Blood Group Detection using Image Processing MATLAB

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36059 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 5006-5013

Author(s):

P. Hansik Sagar

Keyword(s):

Image Processing ◽

Real Time ◽

Blood Group ◽

Human Error ◽

Healthcare Applications ◽

Group Detection ◽

Canny Edge ◽

Time Test ◽

Processing Techniques ◽

Blood Grouping

Blood grouping is one of the common and most essentiality for many of the major healthcare applications. Traditional way to determine the blood group involve human such as trained medical professionals which generally lead to human error. One of the solutions to overcome this issue is to automate and digitize this method. Image processing and computer vision techniques can be used for this purpose. Therefore, in this paper, we investigate the blood group detection using image processing techniques. For this purpose, experiment starts by taking images of sample blood slide as input and convert it into gray scale followed by binarization and canny edge detection. Finally, it decided the agglutination by counting detected edges. Performance of method is tested on real- time blood sample dataset. Experimental results show the accuracy of proposed method is comparable to real- time test.

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