InSilixa Inc. and DNA Software Inc. Enter into Agreement
News Mar 16, 2016
InSilixa Inc. and DNA Software Inc. have announced a joint agreement to develop Infectious disease assays specifically for InSilixa’s sample-to-answer CMOS biochip platform. InSilixa’s proprietary sample-to-answer CMOS biochip technology enables rapid detection, quantification, and genotyping of pathogens (viruses and bacteria) in clinical samples and the simultaneous identification of their drug resistance profiles using a highly-multiplexed targeted mutation detection technique.
The first generation of InSilixa’s products will focus on infectious diseases MDx applications in near-patient and point-of-care (POC) settings, including the comprehensive analysis of seasonal respiratory infection outbreaks, rapid detection of MDR bacteria (“super bugs”) in urgent care settings and the detection, quantification and comprehensive drug resistance genotyping of the human immunodeficiency virus (HIV) in blood samples from patients with HIV/AIDS.
The multiplexed PCR designs provided by DNAS Software will leverage their algorithms for signature sequence identification, ThermoBLASTTM for scanning oligonucleotides against collections of genomic sequences, and their PCR design software. “We are very excited to work with Dr. Santa Lucia and his industry leading team at DNA Software to realize the full potential of our unique platform” said Dr. Arjang Hassibi, CEO of InSilixa Inc.
“The agreement between InSIlixa and DNAS is a tremendous complement of technologies that will result in multiplexed detection of a wide variety of pathogens with outstanding sensitivity and specificity” said Dr. John SantaLucia, CEO of DNA Software, Inc.
Computer bits are binary, with a value of 0 or 1. By contrast, neurons in the brain can have all kinds of different internal states, depending on the input that they received. This allows the brain to process information in a more energy-efficient manner than a computer. A new study hopes to bring the two closer together.
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.
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