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Tuesday, June 12th, 2018

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    9:50a
    A new way to mend a broken heart

    After a patient has a heart attack, a cascade of events leading to heart failure begins. Damage to the area in the heart where a blood vessel was blocked leads to scar tissue. In response to scarring, the heart will remodel to compensate. This process often ends in ventricular or valve failure.

    A team of researchers is hoping to halt the progression from heart attack to heart failure with a small device called “Therepi.” The device contains a reservoir that attaches directly to the damaged heart tissue. A refill line connects the reservoir to a port on or under the patient’s skin where therapies can be injected either by the patient or a health care professional.

    A new study published in Nature Biomedical Engineering involving a team of researchers from MIT, Harvard University, Royal College of Surgeons in Ireland, Trinity College Dublin, Advanced Materials and BioEngineering Research (AMBER) Center, and National University of Ireland Galway details how Therepi can be used to restore cardiac function.

    “After a heart attack we could use this device to deliver therapy to prevent a patient from getting heart failure,” explains Ellen Roche, co-first author of the study and assistant professor at MIT’s Department of Mechanical Engineering and Institute for Medical Engineering and Science. “If the patient already has some degree of heart failure, we can use the device to attenuate the progression.”

    Two of the most common systems currently used for delivering therapies to prevent heart failure are inefficient and invasive. In one method, drugs are delivered systemically rather than being administered directly to the site of the damage. The volume of drugs used has to be limited to avoid toxic side effects and often only a small amount reaches the damaged heart tissue. 

    “From a pharmacological point-of-view, it’s a big problem that you’re injecting something that doesn’t stay at the damaged tissue long enough to make a difference,” says William Whyte, co-first author and PhD candidate at Trinity College Dublin and AMBER.

    The alternative method involves an invasive procedure to directly inject therapies into the heart muscle. Since multiple doses are needed, this requires multiple invasive surgeries.

    Therepi addresses the problems with current drug delivery methods by administering localized, non-invasive therapies as many times as needed. The device’s reservoir can be implanted on the heart in just one surgical procedure.

    Localized, bespoke therapies

    The reservoir itself holds amazing potential for drug delivery. Constructed out of a gelatin-based polymer, the reservoir has a half-spherical shape with a flat bottom attached to the diseased tissue. The flat bottom consists of a semi-permeable membrane that can be adjusted to allow more drugs or larger materials to pass directly into the heart tissue.

    “The material we used to construct the reservoir was crucial. We needed it to act like a sponge so it could retain the therapy exactly where you need it,” adds Whyte. “That is difficult to accomplish since the heart is constantly squeezing and moving.”

    The reservoir provides a unique opportunity for administering stem cell therapies. It acts as a cell factory. Rather than pass through the membrane into the heart, the cells stay within the reservoir where they produce paracrine factors that promote healing in the damaged heart tissue.

    In a rat model, the device was shown to be effective in improving cardiac function after a heart attack. The researchers administered multiple doses of cells to a damaged heart throughout a four-week period. They then analyzed the hemodynamic changes in the tissue using a pressure volume catheter and used echocardiography to compare functional changes over time.

    “We saw that the groups that had our device had recovered some heart function,” explains Claudia Varela, a PhD student in the Harvard-MIT Division of Health Sciences and Technology.

    The hearts that received multiple dosages of cells via therapy had more cardiac function than those who received only a single injection or no treatment at all.

    Finding the optimal dose

    Therepi’s capabilities go beyond treating heart disease. Since it provides the opportunity for multiple, localized doses to be delivered, it could be used as a tool to identify the exact dosage appropriate for a host of conditions.

    “We are hoping to use the device itself as a research tool to learn more about the optimal drug loading regime,” says Roche.

    For the first time, researchers could have an opportunity to track multiple refills of localized therapies over time to help identify the best dosing intervals and dose amount.

    “As a pharmacist by training, I’m really excited to start investigating what the best dose is, when is the best time to deliver after a heart attack, and how many doses are needed to achieve the desired therapeutic effect,” adds Whyte.

    While the team has been focusing on how Therepi can mitigate the effects of heart disease, the device could be used in other parts of the body. By optimizing the design and adjusting the materials used to construct the reservoir, Therepi could be used for a wide range of diseases and health problems.

    “The device is really a platform that can be tailored to different organ systems and different conditions,” says Varela. “It’s just a great example of how intersectional research looking at both devices and biological therapies can help us come up with new ways to treat disease.”  

    1:30p
    Artificial intelligence senses people through walls

    X-ray vision has long seemed like a far-fetched sci-fi fantasy, but over the last decade a team led by Professor Dina Katabi from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has continually gotten us closer to seeing through walls.

    Their latest project, “RF-Pose,” uses artificial intelligence (AI) to teach wireless devices to sense people’s postures and movement, even from the other side of a wall.

    The researchers use a neural network to analyze radio signals that bounce off people’s bodies, and can then create a dynamic stick figure that walks, stops, sits, and moves its limbs as the person performs those actions.

    The team says that RF-Pose could be used to monitor diseases like Parkinson’s, multiple sclerosis (MS), and muscular dystrophy, providing a better understanding of disease progression and allowing doctors to adjust medications accordingly. It could also help elderly people live more independently, while providing the added security of monitoring for falls, injuries and changes in activity patterns. The team is currently working with doctors to explore RF-Pose’s applications in health care.

    All data the team collected has subjects' consent and is anonymized and encrypted to protect user privacy. For future real-world applications, they plans to implement a “consent mechanism” in which the person who installs the device is cued to do a specific set of movements in order for it to begin to monitor the environment.

    “We’ve seen that monitoring patients’ walking speed and ability to do basic activities on their own gives health care providers a window into their lives that they didn’t have before, which could be meaningful for a whole range of diseases,” says Katabi, who co-wrote a new paper about the project. “A key advantage of our approach is that patients do not have to wear sensors or remember to charge their devices.”

    Besides health care, the team says that RF-Pose could also be used for new classes of video games where players move around the house, or even in search-and-rescue missions to help locate survivors.

    Katabi co-wrote the new paper with PhD student and lead author Mingmin Zhao, MIT Professor Antonio Torralba, postdoc Mohammad Abu Alsheikh, graduate student Tianhong Li, and PhD students Yonglong Tian and Hang Zhao. They will present it later this month at the Conference on Computer Vision and Pattern Recognition (CVPR) in Salt Lake City, Utah.

    One challenge the researchers had to address is that most neural networks are trained using data labeled by hand. A neural network trained to identify cats, for example, requires that people look at a big dataset of images and label each one as either “cat” or “not cat.” Radio signals, meanwhile, can’t be easily labeled by humans.

    To address this, the researchers collected examples using both their wireless device and a camera. They gathered thousands of images of people doing activities like walking, talking, sitting, opening doors and waiting for elevators.

    They then used these images from the camera to extract the stick figures, which they showed to the neural network along with the corresponding radio signal. This combination of examples enabled the system to learn the association between the radio signal and the stick figures of the people in the scene.

    Post-training, RF-Pose was able to estimate a person’s posture and movements without cameras, using only the wireless reflections that bounce off people’s bodies.

    Since cameras can’t see through walls, the network was never explicitly trained on data from the other side of a wall – which is what made it particularly surprising to the MIT team that the network could generalize its knowledge to be able to handle through-wall movement.

    “If you think of the computer vision system as the teacher, this is a truly fascinating example of the student outperforming the teacher,” says Torralba.

    Besides sensing movement, the authors also showed that they could use wireless signals to accurately identify somebody 83 percent of the time out of a line-up of 100 individuals. This ability could be particularly useful for the application of search-and-rescue operations, when it may be helpful to know the identity of specific people.

    For this paper, the model outputs a 2-D stick figure, but the team is also working to create 3-D representations that would be able to reflect even smaller micromovements. For example, it might be able to see if an older person’s hands are shaking regularly enough that they may want to get a check-up.

    “By using this combination of visual data and AI to see through walls, we can enable better scene understanding and smarter environments to live safer, more productive lives,” says Zhao.

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