Biosensors For Early Diagnosis Research Articles

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Modeling, Simulation of 3D Label Free Nano Amperometric Biosensor for Early Diagnosis of Cancerous Cells with Sustainable Interfacial Coefficients

I would like to model and simulate 3D label free Nano Amperometric Biosensor for drug delivery and detection of cancerous cells. An Amperometric biosensor is one which has a biological element and a transducer. The analysis to whom the transduction to take place is referred to as analyte. The biological element I use in my work is enzyme, it can be even a living cell, DNA and proteins. Enzyme which I use in my work is Glucose Oxidase (GODx). The analyte primitively used in experimentation is hydrogen peroxide (H2O2) and cancerous blood. The GODx reacts with H2O2 and releases out oxygen, the amount of oxygen is calibrated in terms of current in milli amperes, so the biological change is calibrated terms of electrical parameter and so it is called amperometric biosensor. But in actual practice, the process is reversible, we inhale oxygen and exhale CO2, but a biosensor releases Oxygen out and inhales the analyte decomposition. This process is called Direct Current Cyclic Voltammetry. A potentiometric biosensor calibrates the analytes in terms of potential or voltage.