AIMC Topic: Biosensing Techniques

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A cost-effective approach using generative AI and gamification to enhance biomedical treatment and real-time biosensor monitoring.

Scientific reports
Biosensors are crucial to the diagnosis process since they are designed to detect a specific biological analyte by changing from a biological entity into electrical signals that can be processed for further inspection and analysis. The method provide...

AdapTree: Data-Driven Approach to Assessing Plant Stress Through the AI-Sensor Synergy.

Sensors (Basel, Switzerland)
This study investigates plant stress assessment by integrating advanced sensor technologies and Artificial Intelligence (AI). Multi-sensor data-including electrical impedance spectroscopy, temperature, and humidity-were used to capture plant physiolo...

Machine Learning-Enhanced Dual-Band Plasmonic Sensing for Simultaneous Qualitative and Quantitative Detection of Biomolecules in the Mid-Infrared Region.

Sensors (Basel, Switzerland)
Recently, sensing for biomolecules has become increasingly popular in the fields of environmental monitoring, personal health, and food safety. Plasmonic biosensors have been a powerful tool due to their high sensitivity and label-free operation. How...

Deep learning enabled open-set bacteria recognition using surface-enhanced Raman spectroscopy.

Biosensors & bioelectronics
Accurate bacterial identification is vital in medical and healthcare settings. Traditional methods, though reliable, are often time-consuming, underscoring the need for faster, more efficient alternatives. Deep learning-assisted Surface-enhanced Rama...

Advancing optical nanosensors with artificial intelligence: A powerful tool to identify disease-specific biomarkers in multi-omics profiling.

Talanta
Multi-omics profiling integrates genomic, epigenomic, transcriptomic, and proteomic data, essential for understanding complex health and disease pathways. This review highlights the transformative potential of combining optical nanosensors with artif...

A machine-learning-integrated portable electrochemiluminescence sensing platform for the visualization and high-throughput immunoassays.

Talanta
Electrochemiluminescence (ECL)-based point-of-care testing (POCT) has the potential to facilitate the rapid identification of diseases, offering advantages such as high sensitivity, strong selectivity, and minimal background interference. However, as...

Machine learning-assisted washing-free detection of extracellular vesicles by target recycling amplification based fluorescent aptasensor for accurate diagnosis of gastric cancer.

Talanta
Extracellular vesicles (EVs) are promising non-invasive biomarkers for cancer diagnosis. EVs proteins play a critical role in tumor progress and metastasis. However, accurately and reliably diagnosing cancers is greatly limited by single protein mark...

Point-of-Care Testing: The Convergence of Innovation and Accessibility in Diagnostics.

Analytical chemistry
Over the years, the evolution of point-of-care testing (POCT) has been driven by technological advancements in materials, design, and artificial intelligence, as well as breakthrough developments in wearable technologies. These innovations are shifti...

Noninvasive blood glucose monitoring using a dual band microwave sensor with machine learning.

Scientific reports
The potential for continuous non-invasive blood glucose monitoring has attracted a lot of interest in the field of medical diagnostics. This paper provides a new shape of a dual-band bandpass filter (DBBPF) acting as a microwave transmission line sen...

A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior.

Sensors (Basel, Switzerland)
The dynamic process of eating-including chewing, biting, swallowing, food items, eating time and rate, mass, environment, and other metrics-may characterize behavioral aspects of eating. This article presents a systematic review of the use of sensor ...