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Ceres Nanosciences Receives $6.5M NIH Award to Expand Nanotrap® Particle Manufacturing Capacity for COVID-19 Testing

MANASSAS, Va. — September 2, 2020 — Ceres Nanosciences, a privately held company that makes innovative products to improve diagnostic testing, announced it has been awarded a contract from the National Institutes of Health (NIH) Rapid Acceleration of Diagnostics (RADxSM) initiative. The contract, for  $6.5 million, will support an expansion to increase the manufacturing capacity of Ceres’ Nanotrap® Virus Particles for COVID-19 tests. ​ Ceres’ Nanotrap Virus Particles improve diagnostic testing for SARS-CoV-2 by eliminating the need for RNA extraction kits, reducing sample processing time, and improving the sensitivity of the downstream assays in point-of-care systems and in high-throughput laboratory developed tests. ​ The RADx initiative...

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Press Release – July 2, 2020   ​New preprint demonstrates a rapid and versatile method for improving detection of SARS-CoV-2

Ceres Nanosciences and its collaborators at George Mason University have developed a Nanotrap® particle method that significantly improves detection of SARS-CoV-2. In a preprint posted on June 25, 2020, the teams describe how Nanotrap particles enable a rapid method for capturing and concentrating heat-inactivated and infectious SARS-CoV-2.  The simple method described in the preprint takes as little as 10 minutes to prepare SARS-CoV-2 RNA for downstream testing and obviates the need for commercial nucleic acid extraction kits. Using contrived samples with heat-inactivated and infectious SARS-CoV-2, the authors demonstrated significant (5- to 25-fold) improvements in real-time RT-PCR detection of the virus...

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Ceres Nanosciences Receives $225,000 NIH SBIR Phase I Grant to Improve Serum Quality for Cell and Tissue Engineering

MANASSAS, Va. — January 31, 2020 — Ceres Nanosciences, Inc. (Ceres) today announced it has been awarded a $225,000 Phase I SBIR grant from the NIH to apply the Nanotrap® technology for improved quality testing and for the decontamination of animal serum used in cell and tissue culture applications.   According to a 2019 Genetic and Engineering News report, the cell and tissue engineering market generated an estimated $9 billion in product sales in 2017(1).  Because animal serum is a source of many of the required nutrients and growth factors for cell and tissue manufacturing, it is a key supplement added to...

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JF Healthcare’s AI Technology Is First to Beat Radiologists in Stanford Chest X-ray Diagnostic Competition

CONCORD, Mass., Aug. 20, 2019 /PRNewswire/ — JF Healthcare, a medical diagnostic start-up based in Nanchang, China, is the first organization in the world to beat Stanford University radiologists in a competition designed by the Stanford Machine Learning group to compare the capability of artificial intelligence (AI) to human experts in interpreting chest x-rays. The AI team from JF Healthcare recently achieved an average AUC score (a measure of diagnostic accuracy) of 0.926 and is currently ranked No. 1 in the world on Stanford’s CheXpert leaderboard. Significantly, the JF team outperforms all three Stanford radiologists on the test set, demonstrating the...

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CheXpert PR

Background In the United States, about half of all radiographic exams are X-rays, mostly of the chest. In other developing countries around the world, chest X-rays are even more widely used, for example, to detect lung cancer early, stop the spread of tuberculosis, and support the responsible use of antibiotics for pneumonia. Chest X-ray is the “bread and butter” for modern medical imaging to some extent. Because of the critical role of chest X-ray, on January 2019 the Machine Learning (ML) group at Stanford University lead by Dr. Andrew Ng released a large-scale chest X-ray dataset CheXpert for research, which...

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CHINESE AI START-UP BEATS STANFORD TEAM IN X-RAY DIAGNOSTIC COMPETITION

The company’s goal is to make AI-driven x-ray analysis available across significant swathes of China’s rural areas. Jeff Rowe | Aug 23, 2019 11:52 am The Stanford Machine Learning group, based at Stanford University, recently launched a competition designed to compare AI’s capability in interpreting chest x-rays to the capabilities of human experts from Stanford.  And the humans lost. The winner was an AI team from JF Healthcare, a medical diagnostic start-up based in Nanchang, China, which outperformed all three Stanford radiologists involved with an average AUC score (a measure of diagnostic accuracy) of 0.926. According to a JF statement, the test demonstrates...

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