| The Advancements of Microfluidic Technologies with Artificial Intelligence in the Detection of Water-Borne Pathogens |
| Paper ID : 1014-NFOS |
| Authors |
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Reem Hatem Elhady *1, May Emad Eldin Mostafa1, Salma Ahmed1, Alaa Gamal1, Fayz Nasr Eldin1, Farah Kamel1, Soha A Abdel-Gawad2, Heba M Hamama3 1Bionanotechnology Department, Faculty of Nanotechnology for Postgraduate Studies, Cairo University, El Sheikh Zayed Campus, El Shiekh Zayed, Giza, 12588, Egypt 2Bionanotechnology Department, Faculty of Nanotechnology for Postgraduate Studies, Cairo University, El Sheikh Zayed Campus, El Shiekh Zayed, Giza, 12588, Egypt Chemistry Department, Faculty of Science, Cairo University, Giza, Egypt. 3Bionanotechnology Department, Faculty of Nanotechnology for Postgraduate Studies, Cairo University, El Sheikh Zayed Campus, El Shiekh Zayed, Giza, 12588, Egypt Entomology Department, Faculty of Science, Cairo University, Giza, Egypt. |
| Abstract |
| Detection of water-borne pathogens is time-consuming, costly, and tiresome work for identifying the causes of diseases from water-contaminated sources, like rivers and lakes. Water contamination is a source of the dissemination of many pathogens and diseases like salmonellosis, typhoid, cholera, and hepatitis. Microfluidic chip technologies have been used in research and as a successful market for water purification systems. It can be used as an accurate and precise detection system of those water-borne pathogens. The combination of these technologies with nanoparticles, functionalized with bio-receptors, can enable as much more specific, sensitive, and reliable detection systems. Especially with the benefits of manufacturing microfluidic chips allowing rapid, on-site, and portable detection systems. Although microfluidic chips integrated with nanomaterials can be a beneficial factor in detecting, monitoring, and analyzing populations of bacterial pathogens, it is not fully reliable for constant and real-time monitoring. The use of Artificial Intelligence (AI) models, algorithms, and neural networks can aid in the use of providing constant and real-time monitoring of the data. The use of both advanced and reliable technology systems can aid in the prediction and understanding of environmental samples at a deeper scale. It can help in monitoring the populations of pathogens, forecasting and detecting outbreaks, introducing possible unidentified or known pathogens, and detecting and identifying hotspots of existing microbial communities. The present study highlights the integration of AI in the design, fabrication, and specification of microfluidic chips for producing advanced sensing technology. Also, the future of integrating nanotechnology within this system is discussed. |
| Keywords |
| Microfluidic chips, Water-purification, Artificial intelligence, Bio-receptors, Micro-organisms, Water Pollution. |
| Status: Accepted |