Lanthanide-doped upconversion nanocrystals (UCNCs) have recently found great potential in the applications of near-infrared bioimaging and nonlinear optoelectronic devices due to their tunable spectral characteristics and excellent photostability. In particular, their near-infrared excitati ... more
Deep learning enables early detection and classification of live bacteria using holography
Development of an AI-powered smart imaging system for early-detection and classification of live bacteria in water samples
Hongda Wang, Hatice Ceylan Koydemir, Yunzhe Qiu, Bijie Bai, Yibo Zhang, Yiyin Jin, Sabiha Tok, Enis Cagatay Yilmaz, Esin Gumustekin, Yilin Luo, Yair Rivenson, Aydogan Ozcan
Waterborne diseases affect more than 2 billion people worldwide, causing substantial economic burden. For example, the treatment of waterborne diseases costs more than $2 billion annually in the United States alone, with 90 million cases recorded per year. Among waterborne pathogen-related problems, one of the most common public health concerns is the presence of total coliform bacteria and Escherichia coli (E. coli) in drinking water, which indicates fecal contamination. Traditional culture-based bacteria detection methods often take 24-48 hours, followed by visual inspection and colony counting by an expert, according to the United States Environmental Protection Agency (EPA) guidelines. Alternatively, molecular detection methods based on, for example, the amplification of nucleic acids, can reduce the detection time to a few hours, but they generally lack the sensitivity for detecting bacteria at very low concentrations, and are not capable of differentiating between live and dead microorganisms. Furthermore, there is no EPA-approved nucleic acid-based method for detecting coliform bacteria in water samples.
Therefore, there is an urgent need for an automated method that can achieve rapid and high-throughput bacterial colony detection with high sensitivity to provide a powerful alternative to the currently available EPA-approved gold-standard methods that take at least 24 hours and require an expert for colony counting.
In a new paper published in Light: Science & Applications, a team of scientists, led by Professor Aydogan Ozcan from the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), USA, and co-workers have developed an AI-powered smart imaging system for early-detection and classification of live bacteria in water samples. Based on holography, they designed a highly sensitive and high-throughput imaging system, which continuously captures microscopic images of a whole culture plate, where bacteria grow, to rapidly detect colony growth by analyzing these time-lapse images with a deep neural network. Following the detection of each colony growth, a second neural network is used to classify the type of bacteria.
The efficacy of this unique platform was demonstrated by performing early detection and classification of three types of bacteria, i.e., E. coli, Klebsiella aerogenes (K. aerogenes), and Klebsiella pneumoniae (K. pneumoniae), and the UCLA researchers achieved a limit-of-detection of 1 colony forming bacterium per 1 Liter of water sample under 9 hours of total test time, demonstrating a time saving of more than 12 hours for bacteria detection as compared to the gold-standard EPA methods. These results highlight the transformative potential of this AI-powered holographic imaging platform, which not only enables highly sensitive, rapid and cost-effective detection of live bacteria, but also provides a powerful and versatile tool for microbiology research.
- Escherichia coli
- bacteria detection
Recently, a new polarization-dipole azimuth-based super-resolution technique has been proposed by a group of researchers in Peking University (China), Tsinghua University (China), and University of Technology Sydney (Australia). It not only provides a new dimension for super-resolution, but ... more
As power plants and energy stores, mitochondria are essential components of almost all cells in plants, fungi and animals. Until now, it has been assumed that these functions underlie a static structure of mitochondrial membranes. Researchers at the Heinrich Heine University Düsseldorf (HHU ... more
UCLA biochemists have achieved a first in biology: viewing at near-atomic detail the smallest protein ever seen by the technique whose development won its creators the 2017 Nobel Prize in chemistry. That technique, called cryo-electron microscopy, enables scientists to see large biomolecule ... more
UCLA bioengineering professor Ali Khademhosseini has led the development of a tissue-based soft robot that mimics the biomechanics of a stingray. The new technology could lead to advances in bio-inspired robotics, regenerative medicine and medical diagnostics. The simple body design of stin ... more
- 1Detect neurodegenerative diseases such as Alzheimer's by a simple eye scan?
- 2Fluorescence microscopy at highest spatial and temporal resolution
- 3The Mechanics of the Immune System
- 4Resolve Biosciences Launches New Era in Single-Cell Spatial Analysis
- 5Quick look under the skin
- 6New ion trap to create the world's most accurate mass spectrometer
- 7How does your computer smell?
- 8Clocking electron movements inside an atom
- 9Sartorius closes 2020 with strong growth
- 10A clear path to better insights into biomolecules
- Identifying activity origin of single-atom catalyst through atom-by-atom counting
- Probing molecular orientation by polarization-selective transient absorption spectroscopy
- Integrated approach streamlines genome sequencing to advance single-cell technology
- Decoding electron dynamics
- Single-cell test can reveal precisely how drugs kill cancer cells