Metal–organic framework-engineered enzyme/nanozyme composites: Preparation, functionality, and sensing mechanisms

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 496, P. 153884 - 153884

Published: July 9, 2024

Language: Английский

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Integrating machine learning and biosensors in microfluidic devices: A review

Biosensors and Bioelectronics, Journal Year: 2024, Volume and Issue: 263, P. 116632 - 116632

Published: Aug. 3, 2024

Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvironments, however, introduces necessity of locally controlling variables involved phenomena under investigation. For this reason, literature has deeply explored possibility introducing sensing elements investigate physical quantities biochemical concentration inside microfluidic devices. Biosensors, particularly, well known for their high accuracy, selectivity, responsiveness. However, signals could be challenging interpret must carefully analysed carry out correct information. In addition, proper data analysis been demonstrated even increase biosensors' mentioned qualities. To regard, machine learning algorithms undoubtedly among most suitable approaches undertake job, automatically highlighting biosensor signals' characteristics at best. Interestingly, it was also benefit themselves, a new paradigm that is starting name "intelligent microfluidics", ideally closing benefic interaction disciplines. This review aims demonstrate advantages triad microfluidics-biosensors-machine learning, which still little used but great perspective. After briefly describing single entities, different sections will benefits dual interactions, applications where reviewed employed.

Language: Английский

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Recent advances in medical gas sensing with artificial intelligence–enabled technology

Medical Gas Research, Journal Year: 2025, Volume and Issue: 15(2), P. 318 - 326

Published: Jan. 18, 2025

Recent advancements in artificial intelligence–enabled medical gas sensing have led to enhanced accuracy, safety, and efficiency healthcare. Medical gases, including oxygen, nitrous oxide, carbon dioxide, are essential for various treatments but pose health risks if improperly managed. This review highlights the integration of intelligence sensing, enhancing traditional sensors through advanced data processing, pattern recognition, real-time monitoring capabilities. Artificial improves ability detect harmful levels, enabling immediate intervention prevent adverse effects. Moreover, developments nanotechnology resulted materials, such as metal oxides carbon-based nanomaterials, which increase sensitivity selectivity. These innovations, combined with intelligence, support continuous patient predictive diagnostics, paving way future breakthroughs care.

Language: Английский

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Two-dimensional nanomaterials-based optical biosensors empowered by machine learning for intelligent diagnosis

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118162 - 118162

Published: Feb. 1, 2025

Language: Английский

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Artificial intelligence-assisted point-of-care devices for lung cancer

Clinica Chimica Acta, Journal Year: 2025, Volume and Issue: unknown, P. 120191 - 120191

Published: Feb. 1, 2025

Language: Английский

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Plasmonic Ring Resonator-Based Sensors: Design, Performance, and Applications

Plasmonics, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Language: Английский

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Integrating Artificial Intelligence in Nanomembrane Systems for Advanced Water Desalination

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103321 - 103321

Published: Nov. 6, 2024

Language: Английский

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Waves of change: Electrochemical innovations for environmental management and resource recovery from water – A review

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121879 - 121879

Published: July 22, 2024

Language: Английский

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Machine learning-powered wearable interface for distinguishable and predictable sweat sensing

Biosensors and Bioelectronics, Journal Year: 2024, Volume and Issue: 265, P. 116712 - 116712

Published: Aug. 28, 2024

Language: Английский

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Weight Differences-Based Multi-level Signal Profiling for Homogeneous and Ultrasensitive Intelligent Bioassays

ACS Nano, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

Current high-sensitivity immunoassay protocols often involve complex signal generation designs or rely on sophisticated signal-loading and readout devices, making it challenging to strike a balance between sensitivity ease of use. In this study, we propose homogeneous-based intelligent analysis strategy called Mata, which uses weight quantify basic immune signals through subunits. We perform nanomagnetic labeling target capture events micrometer-scale polystyrene subunits, enabling magnetically regulated kinetic expression. Signal subunits are classified the multi-level classifier in synergy with developed deep learning recognition models. Subsequently, quantified achieve ultra-high sensitivity. Mata achieves detection 0.61 pg/mL 20 min for interleukin-6 detection, demonstrating comparable conventional digital immunoassays over 22-fold that chemiluminescence reducing time by more than 70%. The entire process relies homogeneous reaction can be performed using standard bright-field optical imaging. This balances high convenient operation has few hardware requirements, presenting promising solution wide accessibility.

Language: Английский

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