Rapid Automated Blood Group Analysis with QCM Biosensors

Berlitz, Patrick; Claussen, Jonathan C.; Döring, Julia; Drechsel, Hartmut; Northoff, Hinnak; Gehring, Frank K.

doi:10.1007/978-3-642-23508-5_270

Cited by 4 publications

(2 citation statements)

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“…The developed QCM sensors were endowed with the ability of recognizing subtle differences in the urine’s biochemistry at minimized noise levels [ 56 ]. In addition, QCM-D was utilized for rapid automated blood group analysis [ 57 ] and for monitoring coagulation homeostasis [ 58 ]. The results demonstrated the suitability of the QCM-D-based Clauss fibrinogen assay (CFA) in comparison with common coagulation reference methods [ 59 ].…”

Section: Sensing Principles Of Qcmmentioning

confidence: 99%

Practical Use of Quartz Crystal Microbalance Monitoring in Cartilage Tissue Engineering

Naranda

Bračič

Vogrin

et al. 2022

JFB

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Quartz crystal microbalance (QCM) is a real-time, nanogram-accurate technique for analyzing various processes on biomaterial surfaces. QCM has proven to be an excellent tool in tissue engineering as it can monitor key parameters in developing cellular scaffolds. This review focuses on the use of QCM in the tissue engineering of cartilage. It begins with a brief discussion of biomaterials and the current state of the art in scaffold development for cartilage tissue engineering, followed by a summary of the potential uses of QCM in cartilage tissue engineering. This includes monitoring interactions with extracellular matrix components, adsorption of proteins onto biomaterials, and biomaterial–cell interactions. In the last part of the review, the material selection problem in tissue engineering is highlighted, emphasizing the importance of surface nanotopography, the role of nanofilms, and utilization of QCM to as a “screening” tool to improve the material selection process. A step-by-step process for scaffold design is proposed, as well as the fabrication of thin nanofilms in a layer-by-layer manner using QCM. Finally, future trends of QCM application as a “screening” method for 3D printing of cellular scaffolds are envisioned.

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Section: Sensing Principles Of Qcmmentioning

confidence: 99%

Practical Use of Quartz Crystal Microbalance Monitoring in Cartilage Tissue Engineering

Naranda

Bračič

Vogrin

et al. 2022

JFB

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“…The work proposed by Berlitz et al (2012) utilized protein A covering of the gold surface of QCM biosensors for the immobilization of antibodies against blood bunch antigens An and B, which allows the recognizable proof of the four principle blood bunches A, B, AB and 0 with two estimations. The Rhesus framework was inspected with various examinations on and the fruitful recognition of Rh-D, Rh-C, Rh-c, Rh-E and Rh-e antigens on human erythrocytes.…”

Section: Literature Reviewmentioning

confidence: 99%

An association between fingerprint patterns with blood group and lifestyle based diseases: a review

Patil

Ingle

2020

Artif Intell Rev

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In the current era of the digital world, the hash of any digital means considered as a footprint or fingerprint of any digital term but from the ancient era, human fingerprint considered as the most trustworthy criteria for identification and it also cannot be changed with time even up to the death of an individual. In the court of law, fingerprint-proof is undeniably the most dependable and acceptable evidence to date. Fingerprint designs are exclusive in each human and the chance of two individuals having identical fingerprints is an exceptional case about one in sixty-four thousand million also the fingerprint minutiae patterns of the undistinguishable twins are different, and the ridge pattern of each fingertip remain unchanged from birth to till death. Fingerprints can be divided into basic four categories i.e. Loop, whorl, arch, and composites, nevertheless, there are more than 100 interleaved ridge and valleys physiognomies, called Galton's details, in a single rolled fingerprint. Due to the immense potential of fingerprints as an effective method of identification, the present research paper tries to investigate the problem of blood group identification and analysis of diseases those arises with aging like hypertension, type 2-diabetes and arthritis from a fingerprint by analyzing their patterns correlation with blood group and age of an individual. The work has been driven by studies of anthropometry, biometric trademark, and pattern recognition proposing that it is possible to predict blood group using fingerprint map reading. Dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. Apart from its use in predicting the diagnosis of disease; dermatoglyphics is also used in forensic medicine in individual identification, physical anthropology, human genetics and medicine. However, the Machine and Deep Learning techniques, if used for fingerprint minutiae patterns to be trained by Neural Network for blood group prediction and classification of common clinical diseases arises with aging based on lifestyle would be an unusual research work.

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