NOV 02, 2016 01:30 PM PDT

A Platform to Digitize Biology: A Potential Pathway to Exponential Medicine

C.E. CREDITS: P.A.C.E. CE | Florida CE
  • Chair, Department Biomaterials, New York University College of Dentistry and Bioengineering Institute & Chief Scientific Officer, Founder of SensoDx
      Dr. John T. McDevitt is a pioneer in the development of the Programmable Bio-Nano-Chip (p-BNC) sensor systems. This universal mHealth platform technology has a capacity to digitize biological signatures for a broad range of key health conditions. These powerful mini-detection ensembles with multiplexed and multi-class (cellular, genomic, proteomic) capabilities have been developed and validated in the context of cancer diagnostics, cardiovascular disease, saliva-based diagnostics, infectious diseases, drugs of abuse detection and cell imaging systems. Further, over the past 5 years the McDevitt laboratory has secured an in-depth experience with management and execution of international, multi-site clinical studies that have resulted in the collection of unique databases using these p-BNC tools. These chip-based sensors have laid the foundation for the efficient collection of first-in-kind wellness and disease signatures for the areas of adjunctive oral cancer tests as well as for cardiac wellness profiles. McDevitt has displayed a strong track record of translating essential bioscience discoveries into real-world clinical practice. He serves as the Scientific Founder and Chief Scientific Officer for SensoDx, LLC. a diagnostic company committed to development of affordable medical microdevice technologies. Dr. McDevitt currently serves as the Chair for the Biomaterials Department at New York University's College of Dentistry. McDevitt and his team over the past decade have raised over $25M in Federal and Foundation support. McDevitt and his team have written more than 200 peer-reviewed scientific manuscripts and have contributed to more than 100 patents and patent applications. In addition to the "2016 AACC Wallace H. Coulter Lectureship Award", this work was recognized with the "Best of What's New Award" in the Medical Device Category for 2008 by Popular Science as well as for the "Best Scientific Advances Award" in 1998 by the Science Coalition.


    Today there are about 7B mobile phone worldwide and about 50,000 mobile health applications actively changing the landscape of how healthcare will be delivered in the next years. While numerous physical Micro-Electro-Mechanical Systems (MEMS)-based sensors are already integrated into smart phones, the linkage to bio-MEMS sensors remains largely untapped. The use of biomarkers has become increasingly intrinsic to the practice of medicine and clinical decision-making. Indeed, up to 70 percent of current medical decisions are made using diagnostic tests performed in traditional health care settings, using phlebotomists, remote laboratories, and delayed reporting. This inefficient flow of diagnostic information stifles arrival of exponential medicine. Likewise, for patients to actively manage their own wellness, we must surmount this gap.

    To help overcome this significant barrier, the McDevitt laboratory has recently developed the Programmable Bio-Nano-Chip (p-BNC) system. This diagnostic technology combines unique chem- and bio-sensing capabilities with powerful machine learning algorithms yielding a platform to digitize biology that is capable of generating intuitive single-valued indices across several major diseases. These biomarker-driven modalities have the potential to capture diseases early before the spiral out of control. This lecture will summarize the use of these new diagnostic tools in the context of multi-parameter measurements for the areas of oral cancer adjunctive tests as well as for risk profiling of patients with cardiac heart disease. (Recent awards for the technology include: “Best Scientific Advances Award” by the Science Coalition, “Best of What's New Award” by Popular Science, “AACC Wallace H. Coulter Lectureship Award-2016”).

    Learning Objectives:

    • Upon completion of this session, participants will acquire an understanding of the capabilities of a new diagnostic platform that has embedded artificial intelligence. 
    • Lecture participants will be exposed to new multi-parameter measurements that can be used as adjunctive tests to aid in the diagnosis of potentially malignant oral lesions.
    • Lecture participants will be exposed to new multi-parameter measurements that can be used to define the risk level of patients with heart disease.

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