Study on the mechanism of Bioelectric Recognition Assay: evidence for immobilized cell membrane interactions with viral fragments

Kintzios, Spyridon; Bem, F.; Mangana, Olga; Nomikou, Kyriaki; Markoulatos, Panayotis; Alexandropoulos, N.; Fasseas, Costas; Arakelyan, V.B.; Petrou, A.-L.; Soukouli, Katerina; Moschopoulou, Georgia; Yialouris, Constantine P.; Simonian, Aleksandr

doi:10.1016/j.bios.2004.04.009

Cited by 26 publications

(23 citation statements)

References 16 publications

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“…Preliminary studies [15], [16], [17], [18] have demonstrated the potential application of the method for ultra rapid (1-2 minutes), ultra cheap tests for infectious viruses in humans. Assays have been carried in an entirely crude sample and a high sensitivity of the method (0.1 ng) has been indicated, making it an attractive option for routine sample screening that could help reduce the exceeding use of advanced and costly molecular techniques, such as the reverse transcription polymerase chain reaction (RT-PCR).…”

Section: Bioelectric Recognition Assay (Bera)mentioning

confidence: 99%

A Multisensor Fusion System for the Detection of Plant Viruses by Combining Artificial Neural Networks

Frossyniotis

Anthopoulos

Kintzios

et al. 2006

Artificial Neural Networks – ICANN 2006

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Abstract.Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a multi-net system for the detection of plant viruses, using biosensors. The system is based on the Bioelectric Recognition Assay (BERA) method for the detection of viruses, developed by our team. BERA sensors detect the electric response of culture cells suspended in a gel matrix, as a result to their interaction with virus's cells, rendering thus feasible his identification. Currently this is achieved empirically by examining the biosensor's response data curve. In this paper, we use a combination of specialized Artificial Neural Networks that are trained to recognize plant viruses according to biosensors' responses. Experiments indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel).

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Section: Bioelectric Recognition Assay (Bera)mentioning

confidence: 99%

A Multisensor Fusion System for the Detection of Plant Viruses by Combining Artificial Neural Networks

Frossyniotis

Anthopoulos

Kintzios

et al. 2006

Artificial Neural Networks – ICANN 2006

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“…A dynamic concentration range of 4-800 ng/ml and a 1.3 ng/ml detection limit were observed, and results were observed to be in agreement with the standard ELISA method. The bioelectric recognition assay 120 (BERA) is a CBB method that detects the electric response (membrane potential) of cultured cells suspended in a gel matrix to various ligands that bind to the cell and/or affect its physiology. BERA has been applied for detection of hepatitis C virus 121 in blood samples to rapidly (assay time 3-5 min) and specifically detect at a concentration lower than 100 pg/ml.…”

Section: Infectious Diseasesmentioning

confidence: 99%

Development and Applications of Portable Biosensors

2015

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The significance of microfluidics-based and microelectromechanical systems-based biosensors has been widely acknowledged, and many reviews have explored their potential applications in clinical diagnostics, personalized medicine, global health, drug discovery, food safety, and forensics. Because health care costs are increasing, there is an increasing need to remotely monitor the health condition of patients by point-of-care-testing. The demand for biosensors for detection of biological warfare agents has increased, and research is focused on ways of producing small portable devices that would allow fast, accurate, and on-site detection. In the past decade, the demand for rapid and accurate on-site detection of plant disease diagnosis has increased due to emerging pathogens with resistance to pesticides, increased human mobility, and regulations limiting the application of toxic chemicals to prevent spread of diseases. The portability of biosensors for onsite diagnosis is limited due to various issues, including sample preparation techniques, fluid-handling techniques, the limited lifetime of biological reagents, device packaging, integrating electronics for data collection/analysis, and the requirement of external accessories and power. Many microfluidic, electronic, and biological design strategies, such as handling liquids in biosensors without pumps/valves, the application of droplet-based microfluidics, paper-based microfluidic devices, and wireless networking capabilities for data transmission, are being explored.

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“…(2), whereas the depletion of calcium ions from cells bearing virus-specific antibodies was observed after treatment with either Hepatitis virus B (HBV) or HCV-positive samples [12,36]. An example is the Bioelectric Recognition Assay (BERA) which has been originally developed for the detection of human viruses (HBV, HCV, herpes viruses) and veterinary disease agents (foot and mouth disease virus, prions, blue tongue virus) on the basis of their specific interaction with appropriately immobilized, mammalian cells and the measurement of the change of the electric potential that is caused by the aforementioned interaction [38][39][40]. (3)) [37].…”

Section: The Transduction Of the Signal: Avail-able Optionsmentioning

confidence: 99%