AIMC Topic: Microfluidic Analytical Techniques

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TimePAD─Unveiling Temporal Sequence ELISA Signal by Deep Learning for Rapid Readout and Improved Accuracy in a Microfluidic Paper-Based Analytical Platform.

Analytical chemistry
The integration of paper-based microfluidics with deep learning represents a pivotal trend in enhancing diagnostic capabilities. This paper introduces a new approach to improve the performance of a paper-based microfluidic enzyme-linked immunosorbent...

Image-based fuzzy logic control for pressure-driven droplet microfluidics as autosampler for multimodal imaging microscopy.

Lab on a chip
Here we present a highly customisable image-based fuzzy logic control (FLC) method for pressure-driven droplet microfluidics. The system is designed to position droplets of different sizes in microfluidic chips of varying channel size in the centre o...

Machine Learning-Enhanced Predictive Modeling for Arbitrary Deterministic Lateral Displacement Design and Test.

IEEE transactions on nanobioscience
The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfl...

Transformative laboratory medicine enabled by microfluidic automation and artificial intelligence.

Biosensors & bioelectronics
Laboratory medicine provides pivotal medical information through analyses of body fluids and tissues, and thus, it is essential for diagnosis of diseases as well as monitoring of disease progression. Despite its universal importance, the field is cur...

A Single-Cell Interrogation System from Scratch: Microfluidics and Deep Learning.

The journal of physical chemistry. B
Live-cell imaging using fluorescence microscopy enables researchers to study cellular processes in unprecedented detail. These techniques are becoming increasingly popular among microbiologists. The emergence of microfluidics and deep learning has si...

Microfluidic Optical Aptasensor for Small Molecules Based on Analyte-Tuned Growth of Gold Nanoseeds and Machine Learning-Enhanced Spectrum Analysis: Rapid Detection of Mycotoxins.

ACS sensors
Natural toxins, mainly small molecules, are a category of chemical hazards in agri-food systems that pose threats to both public health and food security. Current standard methods for monitoring these toxins, predominantly based on liquid chromatogra...

High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System.

ACS nano
CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput,...

Integrating machine learning and biosensors in microfluidic devices: A review.

Biosensors & bioelectronics
Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research to Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvir...

Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.

Lab on a chip
Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic plat...

GNN-Based Concentration Prediction With Variable Input Flow Rates for Microfluidic Mixers.

IEEE transactions on biomedical circuits and systems
Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation a...