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...

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...

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...

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...

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...


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...

1 2 3 4
GreyBird Ventures

is named after the arctic tern, a bird that migrates 71,000 km annually. As they can live for 30 years, this is the lifetime equivalent of three trips to the moon and back. Not bad for a 100 gram bird. We strive to emulate its size to performance ratio as well as its global reach.


For additional information, please contact: