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34 mins
ILP Video

MIT Startup Exchange Introduction with Lightning Talks

Richie Bavasso, nQ Medical
Flanigon, Honeycomb Bio
Charles Barr, High Q Imaging
Xinjie (Jeff) Zhang, , Novarials
Andy Vidan, Composable Analytics
Andrew Warren,Glympse Bio 
Clifford Reid, Travera
Charles Fracchia, BioBright
MIT Startup Exchange actively promotes collaboration and partnerships between MIT-connected startups and industry. Qualified startups are those founded and/or led by MIT faculty, staff, or alumni, or are based on MIT-licensed technology. Industry participants are principally members of MIT’s Industrial Liaison Program (ILP).

MIT Startup Exchange maintains a propriety database of over 1,500 MIT-connected startups with roots across MIT departments, labs and centers; it hosts a robust schedule of startup workshops and showcases, and facilitates networking and introductions between startups and corporate executives.

STEX25 is a startup accelerator within MIT Startup Exchange, featuring 25 “industry ready” startups that have proven to be exceptional with early use cases, clients, demos, or partnerships, and are poised for significant growth. STEX25 startups receive promotion, travel, and advisory support, and are prioritized for meetings with ILP’s 230 member companies.

MIT Startup Exchange and ILP are integrated programs of MIT Corporate Relations.
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36 mins
ILP Video

Engineering the Nanoparticle Corona for Sensors at New Biological Interfaces

Michael Strano
Professor of Chemical Engineering
MIT Department of Chemical Engineering
Our lab at MIT has been interested in how the nanoparticle corona – the region of adsorbed molecules surrounding the particle surface - can be engineered for molecular recognition. We have recently introduced a method we call CoPhMoRe or Corona Phase Molecular Recognition for discovering synthetic, heteropolymer corona phases that form molecular recognition sites at the nanoparticle interface, selected from a heteropolymer library. We show that certain synthetic heteropolymers , once constrained onto a single-walled carbon nanotube by chemical adsorption, also form a new corona phase that exhibits highly selective recognition for specific molecules. We have a growing list of biomolecules that we can detect using this approach including riboflavin, L-thyroxine, dopamine, nitric oxide, sugar alcohols, estradiol, as well as proteins such as fibrinogen. The results have significant potential in light of the fact that nanoparticles such as single walled carbon nanotubes can be interfaced to biological systems at the sub-cellular level, with unprecedented sensitivity. Several recent demonstrates indicate that spatial and temporal information on cellular chemical signaling can be obtained using arrays of such sensors. Other examples including sensor tattoos for mice, stable for more than 400 days in-vivo, will be shown. Lastly, I will highlight recent advances to control the trafficking and localization of nanoparticle systems in living plants using a mechanism that we call Lipid Exchange Envelope Penetration (LEEP). We demonstrate a living plant, interfaced with multiple nanoparticle types that can detect explosives, ATP and dopamine within or from outside the plant, and communicate this information to a user’s cell phone. Engineering the nanoparticle corona in this way offers significant potential to translate sensor technology to previously inaccessible environments.
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36 mins
ILP Video

Artificial Intelligence for Medical Images

Pratik Shah
Co Principal Investigator/Research Scientist, Camera Culture
MIT Media Lab
Advances in optics, biological sensing, medical imaging technologies, high throughput genetic sequencing is leading to massive datasets, which need to be analyzed. However, current Artificial Intelligence algorithms usually require 1000?s of examples of well-annotated datasets for high accuracy classification. Fluorescent biomarkers are important indicators of disease such as oral cancer, but imaging them can require specialized and often-expensive devices. Medical images, if diagnosed early with biomarker images and expert knowledge, can be valuable to prevent occurrences of serious systemic illnesses. In this lecture, we will discuss two convolutional neural network classifiers trained with disease signatures and fluorescent biomarker images to identify biomarkers in white light images as a per-pixel binary classification task. Once trained, the classifiers predict the location and intensity of fluorescent biomarkers in white light images without requiring specialized biomarker imaging devices or expert intervention. This generalized approach can be useful in other domains where diagnostic biomarker predicting can augment expert knowledge using standard white light images.
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27 mins
ILP Video

Making Invisible Obvious: Computational Analysis of Medical Images

Polina Golland
Professor of Electrical Engineering and Computer Science
MIT Department of Electrical Engineering and Computer Science
Polina Golland will discuss her group's research in computational analysis of MRI scans that aims to provide accurate measurements of healthy anatomy and physiology, and biomarkers of pathology. Applications range from fetal development to aging brain.
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30 mins
ILP Video

It?s a Small World: The Power of Miniaturization in Cancer Diagnostics and Beyond

Tarek Fadel
Assistant Director, Marble Center for Cancer Nanomedicine
MIT Koch Institute for Integrative Cancer Research
Early and accurate detection of cancer represents an enormous opportunity for sensing technologies to impact patients' lives. I will discuss several examples of diagnostic technologies developed in the Bhatia lab that employ nanosensors to detect tumors using a simple urine test for readout. This platform technology uses nanosensors to detect enzyme activity associated with cancer invasion, and generate bar-coded reporters that can be detected by multiplexed mass spectrometry or antibody-based methods such as lateral flow assays. I will close the presentation with an introduction to the Marble Center for Cancer Nanomedicine, a new growing resource for the nanomedicine community.
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