AIMC Topic: Lab-On-A-Chip Devices

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Application of NIRs coupled with PLS and ANN modelling to predict average droplet size in oil-in-water emulsions prepared with different microfluidic devices.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this study, the potential of microfluidic systems with different microchannel geometries (microchannel with teardrop micromixers and microchannel with swirl micromixers) for the preparation of oil-in-water (O/W) emulsions using two different emuls...

Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia.

Scientific reports
Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role...

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach.

Computers in biology and medicine
The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a metho...

Cheetah: A Computational Toolkit for Cybergenetic Control.

ACS synthetic biology
Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups ...

Metachronal μ-Cilia for On-Chip Integrated Pumps and Climbing Robots.

ACS applied materials & interfaces
Biological cilia often perform metachronal motion, that is, neighboring cilia move out of phase creating a travelling wave, which enables highly efficient fluid pumping and body locomotion. Current methods for creating metachronal artificial cilia su...

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.

PloS one
Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. He...

Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules.

Advanced materials (Deerfield Beach, Fla.)
Insights into the fascinating molecular world of biological processes are crucial for understanding diseases, developing diagnostics, and effective therapeutics. These processes are complex as they involve interactions between four major classes of b...

Magnetically actuated intelligent hydrogel-based child-parent microrobots for targeted drug delivery.

Journal of materials chemistry. B
Small intestine-targeted drug delivery by oral administration has aroused the growing interest of researchers. In this work, the child-parent microrobot (CPM) as a vehicle protects the child microrobots (CMs) under a gastric acid environment and rele...

Machine learning enables design automation of microfluidic flow-focusing droplet generation.

Nature communications
Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive un...