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Stain-Free, Rapid, and Quantitative Viral Plaque Assay Using Deep Learning and Holography (Case No. 2022-326)

Intro Sentence: UCLA researchers in the Department of Electrical and Computer Engineering have developed a rapid and stain-free quantitative assay using lens-free holography and deep learning to efficiently and cost-effectively determine the presence of viral plaque-forming units (PFUs) in samples. Background: A broad range of viruses have caused...

Published: 10/17/2024 | Inventor(s): Aydogan Ozcan, Yuzhu Li, Tairan Liu

Keywords(s): Antiviral Drug, Artifical Intelligence (Machine Learning, Data Mining), Assay, Bioassay, Computer Aided Learning, Diagnostic Markers & Platforms, Diagnostic Platform Technologies (E.G. Microfluidics), Diagnostic Test, Digital Holography, Electrical, Electrical Brain Stimulation, Electrical Breakdown, Electrical Engineering, Electrical Impedance, Electrical Load, Electrical Load Equation Of State, Electrical Resistance And Conductance, Electrical Resistivity And Conductivity, Holography, Immunoassay, Immunoassay Sense (Molecular Biology), Lentivirus Viral Vector, Machine Learning, Machine Learning Autonomous Car Gradient Descent, Machine Learning Pain Management, Machine Learning Particulates Global Climate Model, Network Analysis (Electrical Circuits), Perceptual Learning, Plasmid Trabecular Meshwork Aqueous Humour Viral Vector, Targets And Assays, Transcutaneous Electrical Nerve Stimulation, Transfection Viral Vector, Unsupervised Learning , Viability Assay, Viral Delivery Systems, Viral Envelope, Viral Load

Category(s): Diagnostic Markers, Diagnostic Markers > Targets And Assays, Electrical, Life Science Research Tools, Life Science Research Tools > Research Methods, Life Science Research Tools > Other Reagents, Software & Algorithms > Artificial Intelligence & Machine Learning

2022-258 Deep Learning-Enabled Detection and Classification of Bacterial Colonies Using a Thin Film Transistor (TFT) Image Sensor

Summary: UCLA Researchers in the Department of Electrical and Computer Engineering have developed a thin-film transistor-based image sensor that can quantify and identify bacterial colony forming units (CFUs) with high accuracy using a deep-learning algorithm. Background: Bacterial infections are a leading cause of death every year in both developed...

Published: 7/19/2023 | Inventor(s): Aydogan Ozcan, Yuzhu Li, Tairan Liu

Keywords(s): Antibacterial, Artificial Neural Network, Bacteria, Holography, Machine Learning, Magnetic Resonance Imaging Carotid Artery Stenosis, Thin-Film Transistor, Transistor

Category(s): Software & Algorithms, Life Science Research Tools, Life Science Research Tools > Screening Libraries, Materials, Materials > Nanotechnology, Materials > Semiconducting Materials, Life Science Research Tools > Cell Counting And Imaging