AIMC Topic: Biosensing Techniques

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Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients.

Scientific reports
Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learnin...

Recent Advancements and Future Prospects on E-Nose Sensors Technology and Machine Learning Approaches for Non-Invasive Diabetes Diagnosis: A Review.

IEEE reviews in biomedical engineering
Diabetes mellitus, commonly measured through an invasive process which although is accurate, has manifold drawbacks especially when multiple reading are required at regular intervals. Accordingly, there is a need to develop a dependable non-invasive ...

Porphyrin-based covalent organic framework as bioplatfrom for detection of vascular endothelial growth factor 165 through fluorescence resonance energy transfer.

Talanta
A fluorescent aptasensor based on porphyrin-based covalent organic framework (p-COF) and carbon dots (CDs) was constructed for detecting vascular endothelial growth factor 165 (VEGF) and for imaging of the breast cancer cell line Michigan cancer foun...

Systematic Review on Which Analytics and Learning Methodologies Are Applied in Primary and Secondary Education in the Learning of Robotics Sensors.

Sensors (Basel, Switzerland)
Robotics technology has become increasingly common both for businesses and for private citizens. Primary and secondary schools, as a mirror of societal evolution, have increasingly integrated science, technology, engineering and math concepts into th...

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning.

Biosensors
Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence s...

Advancing Biosensors with Machine Learning.

ACS sensors
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially d...

High-Performance Organic Electrochemical Transistors with Nanoscale Channel Length and Their Application to Artificial Synapse.

ACS applied materials & interfaces
Organic electrochemical transistors (OECTs) have attracted considerable interests for various applications ranging from biosensors to digital logic circuits and artificial synapses. However, the majority of reported OECTs utilize large channel length...

Machine Learning to Improve the Sensing of Biomolecules by Conical Track-Etched Nanopore.

Biosensors
Single nanopore is a powerful platform to detect, discriminate and identify biomacromolecules. Among the different devices, the conical nanopores obtained by the track-etched technique on a polymer film are stable and easy to functionalize. However, ...

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Nature communications
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c...