The Use of Artificial Neural Networks as a Component of a Cell-based Biosensor Device for the Detection of Pesticides

Ferentinos, Konstantinos P.; Yialouris, Constantine P.; Blouchos, Petros; Moschopoulou, Georgia; Tsourou, Virginia; Kintzios, Spyridon

doi:10.1016/j.proeng.2012.09.313

Cited by 19 publications

(7 citation statements)

References 4 publications

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“…There are many current examples of cell-based transducers in biosensing. One such example is the use of artificial neural networks on an electrode, where the neurons convert an environmental change to electrochemical signal [ 59 ]. Here, neuroblastoma cells were sensitive to specific compounds in pesticides, but similar biosensors could be designed with cells sensitive to different compounds.…”

Section: Transduction and Detection Methods For Biosensingmentioning

confidence: 99%

Recent Advances in Bioprinting and Applications for Biosensing

2014

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Future biosensing applications will require high performance, including real-time monitoring of physiological events, incorporation of biosensors into feedback-based devices, detection of toxins, and advanced diagnostics. Such functionality will necessitate biosensors with increased sensitivity, specificity, and throughput, as well as the ability to simultaneously detect multiple analytes. While these demands have yet to be fully realized, recent advances in biofabrication may allow sensors to achieve the high spatial sensitivity required, and bring us closer to achieving devices with these capabilities. To this end, we review recent advances in biofabrication techniques that may enable cutting-edge biosensors. In particular, we focus on bioprinting techniques (e.g., microcontact printing, inkjet printing, and laser direct-write) that may prove pivotal to biosensor fabrication and scaling. Recent biosensors have employed these fabrication techniques with success, and further development may enable higher performance, including multiplexing multiple analytes or cell types within a single biosensor. We also review recent advances in 3D bioprinting, and explore their potential to create biosensors with live cells encapsulated in 3D microenvironments. Such advances in biofabrication will expand biosensor utility and availability, with impact realized in many interdisciplinary fields, as well as in the clinic.

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Section: Transduction and Detection Methods For Biosensingmentioning

confidence: 99%

Recent Advances in Bioprinting and Applications for Biosensing

2014

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“…ANN systems enable high-quality, quick, and high-capacity detection, claim Ferentinos et al (2012). They have also been used as a potential technique for tracking and rating environmental pollution (Ferentinos et al, 2012).…”

Section: Environmental Monitoring Based On Artificial Neural Network ...mentioning

confidence: 99%

Detection and removal of emerging contaminants from water bodies: A statistical approach

2023

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The integration of mathematical modelling in different scientific domains has increased dramatically in recent years. In general, modelling involves using programming languages, manipulating matrices, designing algorithms, and tracking functions and data to gain new insights and more quantitative and qualitative information about systems. These strategies have motivated researchers to investigate numerous approaches to accurately solve a variety of problems. In this direction, modelling and simulation have been used to create sensitive and focused detection methods for a variety of applications, including environmental control. New pollutants, including pesticides, heavy metals, and medications, are endangering wildlife by poisoning water supplies. As a result, numerous biosensors that use modelling for effective environmental monitoring have been documented in the literature. The most current model-inspired biosensors used for environmental monitoring will be discussed in this review study. Additionally, each analytical biosensor’s capabilities and degree of success will be discussed. Finally, present difficulties in this area will be highlighted.

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“…Moreover, ANN's adoption in biosensing applications can significantly improve disease detection accuracy and reliability as biosensors can produce data in time-series form that can be trained and tested using state-of-the-art NNs [141]. For example, in [142], biosensors generated time-series data to detect pesticides with 85% success rate. In another work, [143], a novel biosensor and ANN-based integrated system detected catechol from water samples with 99.7% accuracy.…”

Section: Integration Of Bio-sensing and Annmentioning

confidence: 99%

Neural Networks for Infectious Diseases Detection: Prospects and Challenges

Azeem¹,

Javaid²,

Fahim³

et al. 2021

Preprint

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Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN's advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

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The Use of Artificial Neural Networks as a Component of a Cell-based Biosensor Device for the Detection of Pesticides

Cited by 19 publications

References 4 publications

Recent Advances in Bioprinting and Applications for Biosensing

Recent Advances in Bioprinting and Applications for Biosensing

Detection and removal of emerging contaminants from water bodies: A statistical approach

Neural Networks for Infectious Diseases Detection: Prospects and Challenges

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