AIMC Topic: Lab-On-A-Chip Devices

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Converging Multidimensional Sensor and Machine Learning Toward High-Throughput and Biorecognition Element-Free Multidetermination of Extracellular Vesicle Biomarkers.

ACS sensors Jul 7, 2020
Extracellular vesicles (EVs) are a frontier class of circulating biomarkers for the diagnosis and prognosis of different diseases. These lipid structures afford various biomarkers such as the concentrations of the EVs () themselves and carried protei...
Biomarkers Neoplasms Extracellular Vesicles Lab-On-A-Chip Devices Humans Machine Learning
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Synapse cell optimization and back-propagation algorithm implementation in a domain wall synapse based crossbar neural network for scalable on-chip learning.

Nanotechnology May 26, 2020
On-chip learning in spin orbit torque driven domain wall synapse based crossbar fully connected neural network (FCNN) has been shown to be extremely efficient in terms of speed and energy, when compared to training on a conventional computing unit or...
Algorithms Deep Learning Pattern Recognition, Automated Neural Networks, Computer Lab-On-A-Chip Devices
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Intelligent classification of platelet aggregates by agonist type.

eLife May 12, 2020
Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological ...
Platelet Aggregation Platelet Activation Humans Neural Networks, Computer Flow Cytometry Thrombosis Lab-On-A-Chip Devices Microfluidic Analytical Techniques
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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...
Single-Cell Analysis Image Processing, Computer-Assisted Computational Biology Software Algorithms Deep Learning Electronic Data Processing Lab-On-A-Chip Devices Escherichia coli Cell Tracking Microscopy, Fluorescence Microfluidics
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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...
Biomechanical Phenomena Microfluidics Erythrocytes Membrane Fluidity Lab-On-A-Chip Devices Blood Circulation Humans Machine Learning
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A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography.

IEEE transactions on biomedical circuits and systems Mar 9, 2020
Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created th...
Signal Processing, Computer-Assisted Lab-On-A-Chip Devices Amplifiers, Electronic Photoplethysmography Monitoring, Physiologic Equipment Design Adult Male Middle Aged Adolescent Wearable Electronic Devices Blood Glucose Female Aged Young Adult Humans Machine Learning Algorithms
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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...
Cell Culture Techniques Lab-On-A-Chip Devices Heart Microfluidics Humans Calibration Robotics Kidney Equipment Design Intestines Skin Brain Liver Blood-Brain Barrier Lung
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Biomimetic smoking robot for in vitro inhalation exposure compatible with microfluidic organ chips.

Nature protocols Jan 10, 2020
Exposure of lung tissues to cigarette smoke is a major cause of human disease and death worldwide. Unfortunately, adequate model systems that can reliably recapitulate disease biogenesis in vitro, including exposure of the human lung airway to fresh ...
Humans Cell Line Robotics Environmental Exposure Inhalation Biomimetics Smoking Lab-On-A-Chip Devices
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CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning.

Lab on a chip Oct 23, 2019
T lymphocytes are a group of cells representing the main effectors of human adaptive immunity. Characterization of the most representative T-lymphocyte subclasses, CD4+ and CD8+, is challenging, but has a significant impact on clinical decisions. Up ...
Lab-On-A-Chip Devices Humans Microfluidic Analytical Techniques CD8-Positive T-Lymphocytes Machine Learning CD4-Positive T-Lymphocytes Light
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A practical guide to intelligent image-activated cell sorting.

Nature protocols Jul 5, 2019
Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell s...
Flow Cytometry Lab-On-A-Chip Devices Cells, Cultured Signal Processing, Computer-Assisted Humans Microalgae Single-Cell Analysis Neural Networks, Computer Image Processing, Computer-Assisted Software
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