A fully automated artificial intelligence (AI)-based multispectral absorbance imaging system effectively classified function and potency of induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE) from patients with age-related macular degeneration (AMD). The finding from the system could be applied to assessing future cellular therapies, according to research presented at the 2018 ARVO annual meeting. The software, which uses convolutional neural network (CNN) deep learning algorithms, effectively evaluated release criterion for the iPSC-RPE cell-based therapy in a standard, reproducible, and cost-effective fashion. The AI-based analysis was as specific and sensitive as traditional molecular and physiological assays, without the need for human intervention. "Cells can be classified with high accuracy using nothing but absorbance images," wrote lead investigator Nathan Hotaling and colleagues from the National Institutes of Health in their poster.
Bracco Diagnostics Inc., the U.S. subsidiary of Bracco Imaging S.p.A., a leading global company in the diagnostic imaging business, announced the results of an experimental artificial intelligence (AI) study of two gadolinium-based contrast agents (GBCAs) which found that ProHance (Gadoteridol) Injection, 279.3 mg/mL and Gadavist provided similar degree and pattern of contrast enhancement in brain magnetic resonance imaging (MRI) of patients with glioblastoma multiforme (GBM) previously enrolled in a large scale, multicenter, randomized, double blinded controlled clinical study (the TRUTH study).1 Full study results will be presented at the Radiological Society of North America (RSNA) Annual Meeting on Wednesday, December 4, in Chicago, IL. GBCAs are widely used imaging agents with a favorable safety profile. While recent research has shown that the gadolinium from these agents may remain in the body for months to years after injection,2 the American College of Radiology and the Food and Drug Administration agree that there are no known adverse clinical consequences associated with gadolinium retention in the brain based on the available data.3,4 Nevertheless, some practitioners have concerns, and questions have been raised over whether using a GBCA that retains less would come with a tradeoff in the effectiveness of the contrast enhancement. The purpose of this study was to use AI to determine the effectiveness of standard concentration ProHance (0.5mmol/ml) compared to double concentration Gadavist (1.0 mmol/ml), since animal studies have shown that Gadavist retains two to seven times more in the brain versus ProHance, at up to 4 weeks after injection5-6.
Technique key to scale up manufacturing of therapies from induced pluripotent stem cells. Researchers used artificial intelligence (AI) to evaluate stem cell-derived "patches" of retinal pigment epithelium (RPE) tissue for implanting into the eyes of patients with age-related macular degeneration (AMD), a leading cause of blindness. The proof-of-principle study helps pave the way for AI-based quality control of therapeutic cells and tissues. The method was developed by researchers at the National Eye Institute (NEI) and the National Institute of Standards and Technology (NIST) and is described in a report appearing online today in the Journal of Clinical Investigation. NEI is part of the National Institutes of Health.
Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text.
A team of scientists led by researchers at the University of Georgia Center for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks. In the research, published in the January 2019 issue of Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than a thousand genomes to predict the animal sources, especially livestock, of Salmonella Typhimurium. Deng, an assistant professor of food microbiology at the center, and Shaokang Zhang, a postdoctoral associate with the center, led the project, which also included experts from the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Minnesota Department of Health and the Translational Genomics Research Institute. According to the Foodborne Disease Outbreak Surveillance System, close to 3,000 outbreaks of foodborne illness were reported in the U.S. from 2009 to 2015. Of those, 900 -- or 30 percent -- were caused by different serotypes of Salmonella, including Typhimurium, Deng said.