AIMC Topic: Microfluidics
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Machine learning enables design automation of microfluidic flow-focusing droplet generation.
Nature communications
Jan 4, 2021
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...
Flow driven robotic navigation of microengineered endovascular probes.
Nature communications
Dec 22, 2020
Minimally invasive medical procedures, such as endovascular catheterization, have considerably reduced procedure time and associated complications. However, many regions inside the body, such as in the brain vasculature, still remain inaccessible due...
Microfiber-Shaped Programmable Materials with Stimuli-Responsive Hydrogel.
Soft robotics
Dec 3, 2020
Programmable materials have artificially designed physical shapes responding to external stimuli, as well as high design capability and high flexibility. Here, we propose a microfiber-shaped programmable material with an axial pattern of stimuli-resp...
Intelligent image-based deformation-assisted cell sorting with molecular specificity.
Nature methods
May 25, 2020
Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on...
A high-throughput system combining microfluidic hydrogel droplets with deep learning for screening the antisolvent-crystallization conditions of active pharmaceutical ingredients.
Lab on a chip
May 18, 2020
Crystallization of active pharmaceutical ingredients (APIs) is a crucial process in the pharmaceutical industry due to its great impact in drug efficacy. However, conventional approaches for screening the optimal crystallization conditions of APIs ar...
DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.
PLoS computational biology
Apr 13, 2020
Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image an...
Applications of machine learning for simulations of red blood cells in microfluidic devices.
BMC bioinformatics
Mar 11, 2020
BACKGROUND: For optimization of microfluidic devices for the analysis of blood samples, it is useful to simulate blood cells as elastic objects in flow of blood plasma. In such numerical models, we primarily need to take into consideration the moveme...
Analysis of neurite length of hippocampal neurons cultured into 3D artificial network patterned microfluidic chips.
The International journal of neuroscience
Feb 28, 2020
The study aims to lay a foundational probe for the thorough application microfluidic chips in brain function research with microfluidic chips. Neuron slide culture is a common culture method , and the microfluidic chip with the artificial network pa...
Robotic fluidic coupling and interrogation of multiple vascularized organ chips.
Nature biomedical engineering
Jan 27, 2020
Organ chips can recapitulate organ-level (patho)physiology, yet pharmacokinetic and pharmacodynamic analyses require multi-organ systems linked by vascular perfusion. Here, we describe an 'interrogator' that employs liquid-handling robotics, custom s...
Cancer Modeling-on-a-Chip with Future Artificial Intelligence Integration.
Small (Weinheim an der Bergstrasse, Germany)
Nov 13, 2019
Cancer is one of the leading causes of death worldwide, despite the large efforts to improve the understanding of cancer biology and development of treatments. The attempts to improve cancer treatment are limited by the complexity of the local milieu...
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