I think that HIV is probably the best model or example of translation in this respect. So the product Giovanni and I have is one where we are actually tracking patient for three years. And what we are trying to do, there are markers such as intermedia thickness which are traditionally used in a research setting to say how or whether or not a person is developing cardiovascular disease. It's a good indication of cardiovascular risk. And so we are comparing these bio-mechanical parameters to that and also other serum-based bio-markers. So we're actually actively recruiting patients, following these patients, we can be basically taking images of these patients over four times over this four-year period. So that's for that study. Now for the pancreatic cancer one, we are using not patients directly, but what we are using is patient tumors. So we're taking patient tumors, we're implanting these in mice, we're looking at how radiotherapy, immuno-therapy, how those different therapies improve drug delivery and whether or not we can see those differences. Let's take a best case scenario. So let's say we develop these techniques, it turns out to be great in terms of the mice studies and we do a pilot study or clinical trials and it turned out to be great. Now if that works out, what we could do is we probably would collaborate or get an ultrasound manufacturer involved. And the techniques that we have there is a different version or I would say a slimmed down version on a lot of the scanners. But this will allow the ultrasound manufacturers such as GE, Siemens, etcetera, Phillips to incorporate our techniques and then it will allow clinicians to have a tool which they can actually use. Now, what they could do, for example, if they go back to cardiovascular disease, Framingham risk factor is one factor that people use. Now I don't think we will replace it, but we could enhance it. So you would add this as another factor which they could use to assess cardiovascular risk not only in HIV but in anyone who have cardiovascular disease. Whereas pancreatic cancer research, what we could do is implement that and put it in the endoscopic platform, and then it would allow the clinician to assess on a patient-by-patient individually how the different therapies are working, and so that would allow them to select the appropriate therapy for a given patient. Pancreatic cancer is hard and sad for a number of reasons. One, the disease is aggressive. Two, a lot of time when you detect it, it's pretty advanced. But there are few cases when you can detect it and it hasn't metastasized. And so this is useful with understanding even for that small number of patients, I think about 10 percent of patients who they'll find will have that, is understanding why can't we treat it? Why if you do surgical resection, it still doesn't improve survival? You're starting to answering the whys. And for the HIV, it's a similar thing. Maybe we'll understand why does HIV age the vasculature of people? So it's trying to understand a mechanism that produces these undesirable effects. So this is where I think that, and understanding that will ultimately lead to better improvement. One thing I've been thinking about is, and we hear this in the media, in the university, is big data. How does that fit into what I do and how do you define big data? Now I think when I'm on grant review panels and when I talk to everyone, initially, you're skeptical, but it's here. So one of the things I'm thinking about that I'm actually beginning to pursue that or try to understand that area is how models based on lot of data, how that can improve a lot of the techniques that we develop. When I was a student, they had this thing called computational-aided diagnosis. So, radiologist probably won't like me saying this. This is where you get a computer to make a lot of decision about tumor classifications, which I think these machine learning algorithms could potentially be used to do. So that's one area. I'm pretty excited about the pancreatic cancer research and it's an interesting research space, and I like it for multiple reasons. So I'm a physicist by training and so now I'm learning biology. And it's bringing biology, bringing physics, bringing in all these different elements together to understand and try to solve a problem. And it's always interesting when you look at that disease because they spent a lot of money from NIH, and there's a lot of proposals and the improvement is not a lot. So, it's probably one of the most important areas for me to pursue for the next five years. I hope you enjoyed those amazing stories from Dr. Dorsey and Dr. Doyley. They're doing some incredible, innovative and amazing work. Now, this kind of amazing work and this kind of innovation is being done all across the CTSA program. And in recognition of this, the National Center for Advancing Translational Science has established the Trial Innovation Network. This is a new collaborative initiative within the CTSA program and has three organizational components. The first is the CTSA program hubs, all of those centers distributed across the country, universities and research institutes, collaborating together to create a translational research highway. The second component are the Trial Innovation Centers. These are centers that advance and bring to the forefront and move, through this translational process, the kinds of innovative discoveries and methods that Dr. Dorsey and Dr. Doyley have shown you. And the last part is the recruitment innovation center. Of course, to test these therapies and these discoveries, we need to recruit volunteers. In recognition of how important this is, NCATS has established recruitment innovation centers that have developed and are implementing the best practices for involving people in clinical research and helping us speed discovery to the clinic. As part of the Trial Innovation Network, these hubs work together synergistically. Remember before, we discussed that partnership and collaboration and team science is really the essence of translational research. This structure, at the highest level, emphasizes that kind of collaboration across the translational spectrum. Thank you for joining us for this segment of introduction to translational science, translation to patients. We look forward to seeing you in the next segment, segment four, translation to practice.
