Interview with Dr. Jacob Scott

Dr. Jacob Scott is a radiation oncologist and physician-scientist at the Cleveland Clinic, specializing in sarcoma and connective tissue cancers. He holds an M.D. from Case Western Reserve University and a DPhil in mathematical biology from the University of Oxford. As Associate Director for Data Sciences at the Case Comprehensive Cancer Center, Dr. Scott leads research that integrates mathematical modeling with clinical oncology to better understand and combat cancer resistance mechanisms.

Isaac:

Hi everyone and welcome back to High School Mathematical Oncology. I'm Isaac Jung, a high school senior working in Dr. Russell Rockne's lab at City of Hope. This channel is all about helping students like me dive into the field of mathematical oncology by talking to the people actually shaping the field.

Today I'm super excited to be speaking with Dr. Jacob Scott. He's a physician-scientist in the Department of Radiation Oncology at the Cleveland Clinic and an associate professor at Case Western Reserve University School of Medicine. He's a leader in the use of mathematical models to study cancer evolution, resistance, and treatments. And his journey to this field is pretty unique. Dr. Scott, thank you so much for joining us today.

Dr. Scott:

My pleasure. Please call me Jake.

Isaac:

Of course. Okay, so let's start from the beginning. You've had a really unique path from nuclear submarines and physics teaching to becoming a physician-scientist in oncology. Can you share a bit about your journey and what drew you to combine medicine with math and modeling?

Dr. Scott:

Yeah, sure. I think I didn't have like a grand plan to start out with, so let's start there. I think when I was in high school, when I was maybe your age—what year are you?

Isaac:

A senior.

Dr. Scott:

Yeah, I was really interested in physics and engineering and my entire worldview was: I want to go to the Naval Academy. I want to serve in the Navy. I like physics. I like Dungeons and Dragons. That was about all I knew about life. I played sports and did other stuff too, but I had no idea about medicine or mathematical modeling or oncology or any of these things.

And so I went to college. I enjoyed physics. I graduated, joined the Navy—which is a requirement after the Naval Academy—and spent time on a submarine, learning about how the engineering plant works, how this big, beautiful, complex system that is a submarine runs: weapons, power, all this.

But then when I was getting out—so five years to pay back for your college—I was trying to figure out what to do next. And I didn't really know what to do. My dad was a high school English teacher. My mom was a perennial gardener. And so there wasn't anyone in my family who'd done science or medicine or engineering, and I didn't really know where to go next.

So I kind of applied broadly. I got into both physics PhD programs and med school and kind of chose medicine as a lark. It seemed like a good idea. It seemed like a good job after you got done with school. I had just gotten married, wanted to have kids. It made sense, and I got some advice from friends who were physicians to give it a try.

So I did. I came here to Cleveland—actually right across the street from where I am now—to medical school and really, really loved it. But as I finished medicine or medical school, it kind of left me wanting a little bit more. I think in medicine in particular, there's not a lot of what I call rigorous understanding of how this big system works.

And when I say rigorous in this sense, I mean mathematical. You know, in a submarine, you can write down governing equations for all the systems. And you can a little bit in medicine, but a lot less. And so it was a little frustrating.

I ended up choosing radiation oncology, which is the kind of medicine I practice, because I really liked the radiation part. I liked the certainty of the physics that was behind it. But as I did my training—in oncology, after four years of med school, you have to do five years of oncology training—I was a little frustrated. I had the certainty of the physics, and I knew exactly what I was delivering radiation-wise to patients. I was confident in that because there are governing first principles that have equations behind them.

But then the outcomes weren't certain. I could do the same thing to two patients, exactly the same thing, and I was certain that it was exactly the same thing—and two very different things would happen. Because there's all this uncertainty in the biology.

And that drove me back once more to the classroom and drove me to want to do a PhD in applied math—and specifically mathematical biology. It was a desire to continually do better and better for my patients and do better and better to understand what was going on inside them that drove that once more, going back to the drawing board.

Then finishing that PhD—I think I was 35 when I started—I started a laboratory here doing math. The PhD was in mathematical oncology and applied math. And starting a lab up made sense to me for the first time. I think 40 years old was my first job, but for the first time it made sense to me what I was doing. And that’s how I got here. That was nine years ago.

Isaac:

Yeah, that's a really cool story. Actually, I'd like to go back to what you were saying about how you do the same treatment for two different patients and there'd be drastically different results.

So you've done a lot of pioneering work showing how radiation therapy can be personalized using tumor biology and modeling. Why is the traditional one-size-fits-all approach to dosing limited, and how does math move us forward toward more personalized treatments?

Dr. Scott:

I think you hit on the central tenet here, which is that, you know, patients are different, tumors are different. There's this idea of heterogeneity between them.

Our field—radiation oncology—has embraced that in terms of anatomical differences and differences between one tumor type and another. So we treat prostate cancer differently than we treat lung cancer, for example. But at the level of intra-tumor-type heterogeneity—so patient to patient to patient, lung cancer to lung cancer to lung cancer—we have not yet adopted a differential approach based on tumor genomics, for example. That's just one of many ways you could treat differently.

Why that's the case, I'm not 100% sure. But I do know that the personalized medicine revolution occurred 20 years ago and our field is still catching up. Some of the work we’ve done has tried to link genomics to actionable mathematical models. That’s really been a central tenet—maybe a quarter of our lab's work has been focused around that.

So I think it's a combination of actually quite simple mathematical models, together with our understanding of tumor genomics and biology, to try to differentiate patients who are more or less sensitive to radiation so we can treat them appropriately.

Isaac:

Yeah, it's a really powerful way of rethinking treatments—like moving away and trying to make it catch up to personalized medicine using math.

Could you share a real-world example where mathematical modeling was used to improve a treatment plan for a patient or change a medical decision?

Dr. Scott:

That's a tough one. I think that mathematical oncology is still working towards that—with a few exceptions.

There are some adaptive therapy mathematical models, which are based on ecological control, in which patients are being treated with different doses or different timings of chemotherapies. Those are based on some mathematical formalisms called evolutionary game theory.

There are certainly a ton of examples in medical oncology where drug choice is governed by computational—not quite models, but at least computational reasoning—where the genome has been understood through those lenses. So the existence of certain mutations has been found to mean you're more sensitive to one treatment or another.

In radiation oncology, we're really close. We're just now accruing our first clinical trials. Patients have been treated, but the numbers are low—it’s only a handful so far. So I think the change is really on the horizon, and that’s pretty exciting.

Isaac:

Yeah, that’s really cool. You mentioned personalized medicine has been around for about 20 years, right?

Dr. Scott:

Yeah, that’s about right.

Isaac:

But mathematical oncology as a field is much newer—like, around 12 years old?

Dr. Scott:

Yeah, roughly 12 years now.

I think we're really close. There are clinical trials now—and I should say that there were some clinical trials at Dana-Farber in lung cancer a few years ago. They looked at scheduling of treatment. Maybe it’s the same drug, but you give it at a different tempo or different frequency based on biomarkers of response. And those have been put into mathematical models. But it's still not normal. It's still, I’d say, experimental.

Isaac:

You've also described cancer as sort of an evolutionary disease, constantly adapting to treatments. What does this mean and how would that change the way we think about treating cancer?

Dr. Scott:

Yeah, great question. I think that evolution is maybe the most interesting thing to study—not even cancer evolution, just evolution.

If there's anything true about life, it’s that it evolves. We can even remember back to Jurassic Park—“Life will find a way.” And tumors are the same. They're not their own life forms, but they're living things in a way.

And so I think the minute you reframe tumors to be evolving systems, a few things happen. One, it becomes relatively obvious why our treatments eventually all fail—because evolution is just an incredible search algorithm.

That's... and it's... you have 10 to the 12th cells that have very large complex genomes that are constantly messing around with their message. And that's a lot of individual agents seeking for a solution.

You think about the world where we have agentic processes that are autonomously looking for solutions. You now have 10 to the 12th things—cells—running a powerful search algorithm. Guess what? You're going to find a solution.

And so from my perspective, it's sort of obvious, once you frame it that way, that all of our treatments will eventually get beaten by the system.

So what we're trying to do is sort of reframe our thinking to study that system and ask: Can we mess with the evolutionary process itself in a way that either breaks it, prevents it from functioning, or slows it down—or something like that?

Isaac:

Yeah, as you said, like eventually every treatment will... like the cancer will find a way around it. So I guess, what are some methods that can be used to help anticipate or slow down this resistance to treatment?

Dr. Jacob Scott:

That's a tough one. I mean, I think that's the focus of many, many people's careers right now. And so our hypothesis is that the study of the evolutionary process itself can slow that down.

So, can we design treatments with the evolutionary process in mind such that we kind of accept the fact that any treatment's going to fail eventually? And instead of just trying to find— the words we use are “play whack-a-mole,” so wait for it to fail and then change—so a reactionary approach, can we do something that's not reactionary, but that’s sort of taking advantage of it?

And so like playing... the analogy we use is like playing chess, not checkers. Can you really think a few steps ahead? But you can't do that until you understand the rules of the game, right? You can't imagine that someone might move their knight over there if you don't know how a knight moves.

And so I think that it's that process of dissecting the process of evolution—or dissecting the algorithm that is evolution—is I think where my group's work is mostly focused.

Isaac:

Yeah, that's a really cool idea—that cancer cells are... you're playing a game of cancer cells, and you have to figure out the rules of the game in order to actually hopefully beat it.

Dr. Jacob Scott:

Yeah, yeah, exactly.

Isaac:

So your lab has used game theory to model tumors as systems—like competing players with strategies. How does thinking of cancer this way help us design smarter or more adaptive treatments?

Dr. Jacob Scott:

Well, I think it's sort of all bundled up together with the evolutionary argument we just had. I think, you know, if you think about the really big wins with AI and machine learning recently, in scenarios that are similar... chess has long ago been beaten by machine learning algorithms—and recently, Go and a number of other super highly complex, difficult games.

But those are games where the rules are known. And so evolutionary game theory, for example, is one version of a description of the rules. It’s an imperfect simplification, but it's maybe a useful version of the rules.

And so I think anytime we write down a mathematical model, it's basically a description of a rule, right? You say, “Hey, I believe, I hypothesize, that the population behaves in this way.”

So you can say, "What goes up must come down." That's a behavior. But you can also write down Newton's laws and say, “If I know the impulse up and the force of gravity down and the air resistance,” it still implies what goes up must come down—but you now have a rigorous description of that process rather than this more abstract version.

So I think anytime we write down any mathematical model of these processes, what we're really doing is just codifying our hypothesis in a rigorous mathematical language.

And so game theory is just one way you can describe things. It's not right—it's actually definitely wrong—but it might have enough about it that's correct, enough truth in it, that we can move forward.

And so I think that anytime you're doing a mathematical model, it's going to be wrong in some important way. But if there's enough rightness to it that you can have it as useful... that's the classic argument: “All models are wrong, but some are useful.” Then I think that that's sort of the way we think about how the math model can help us.

Isaac:

Yeah, I guess a somewhat accurate comparison might be—you can launch a rocket using Newtonian physics, but for something like satellite navigation, eventually you need to rely on Einstein’s theories, because Newtonian physics starts to break down. Is that right?

Dr. Jacob Scott:

Yeah. Well, you can launch a rocket even without—yeah, I guess. But you could get to the moon even with just Newtonian mechanics, right?

But you're right—once you're moving a satellite and trying to understand how quickly things are... you know, trying to triangulate—you can't have GPS without relativity. But we got to the moon without it.

But that's again, you know, so—you can get further and further with better and better models.

Isaac:

Speaking about those better models—for students who might not understand—could you speak about fitness landscapes and hypercubes?

Dr. Jacob Scott:

Sure. First of all, that's drawn all over my chalkboard over here, so it's dear to my soul—and today.

So hypercubes—or I guess maybe if it's high school students listening, tesseracts like in the Marvel movies, which are just basically cubes of higher dimension than three—are very nice, well-behaved mathematical objects on which you can do algebra, on which you can perform simulations.

They’re objects that behave well, right? They’re the things that we know how the connections work. They're also, for almost 100 years now, the way that we've conceptualized—well, so almost 100 years ago in 1932, the hypercube was first described as a way to understand differences in organisms.

We didn't even know about genes back then, but it used to be called different allelomorphs. So you might remember from middle school or high school biology, the idea of a Punnett square—we have like a rabbit with short ears and long ears—and that's a zero or a one.

And then you have a fast or a slow version of that rabbit. And now you have four types: fast with long ears, fast with short ears, slow with long ears, slow with fast ears. And that makes a square—a square of possible rabbits: one, two, three, four.

You add a third allele to that—you go from a square to a cube, from a 2-cube to a 3-cube, as we call it. And similarly, you can extend that into n dimensions.

And so you can also represent the whole genome that way. So you can say, “Hey, what if I have a huge long string of ones and zeros,” for example—you can represent it in that way.

And so a fitness landscape is simply a mapping from the possibilities that are represented by one of those cubes. So each corner of those cubes—each corner of the square—is a type of rabbit, the other type of rabbit, a fourth type of rabbit.

You then ask the question, “How fit is each of those types?” And so you go from a representation of the genome that’s like a square—but then each of those corners now has a hill or a height associated with it—a fitness associated with it.

And so now you have sort of a map with hills and valleys. And you can write down equations of motion that follow Darwin's laws that say, “All things must get fitter.” So you write down probability models—probabilistic models—where things go uphill more often than they do other ways.

And it's a really nice way to think about modeling populations. And what I think is really nice about it as well is that, for the most part, you can connect those objects to experimental results.

So I think some of the most beautiful math in the world is abstract and will never be made real. Especially in math bio, we have these abstract versions which might teach us philosophically about scenarios—but maybe aren’t actualizable.

What I like about some of these models is that they are something you can test in a lab or parameterize in the lab. You can go to the laboratory, you can get those four kinds of rabbits, and you can measure how fast they go—or measure what the reproductive rate is.

And the same goes with cancer cells or bacteria or whatever it is you're studying.

Isaac:

Yeah, it sounds like those are some really powerful tools that can be used—like just trying to figure out what conditions make it less likely for cancer cells to survive, or I mean, even like different types of drugs—like which one, order, type of dosage—all of that.

Dr. Jacob Scott:

That's exactly right. So you can follow populations as they move through these large-scale maps and ask how they change as they move.

Isaac:

Yeah. So what are some of the biggest challenges when trying to build a model that can actually predict how cancer progresses or responds to therapy?

Dr. Jacob Scott:

It's all hard. I think the most important thing isn't what model you build. The most important thing is what question you're asking.

And so I would focus back to the question—because you can build a hundred different models in any scenario, but the quality of the model really depends on what the question itself is.

So if you're asking a question about evolutionary flows or treatment optimization or whatever it is, the model you'll build is different. And it also depends on the timescale of the question. It also depends on the length scale of the question—and the resolution of the data, or lack of resolution of the data.

And so I think that the question you're asking should be—you know, how do you ask the right question, or how do you come upon the right thing to study? And that's a tough one. Some of that's more art than science, if you will.

Isaac:

Yeah. I guess I was also wondering about gene expression—such as like non-coding RNA or microRNA—that... like there's a lot of new data about how they might impact... what was it called? Like epigenetics, I think it's called.

How can we use those molecular signals and models to predict how cancer might grow or respond to treatment?

Dr. Jacob Scott:

Yeah, great question. Again, it comes down to the specific question you're asking—because you could have the highest quality data in the world, but if it's not related to your question, who cares? You could give me a perfect map of an entire galaxy far, far away, and it’s never going to help me figure out what my son wants for dinner.

So I think the same thing applies here. Yes, we have amazing data on non-coding RNAs, epigenetics, genome architecture, and chromatin structure. But if you're asking questions about large-scale population dynamics, there might be a disconnect.

Connecting the beautiful, high-resolution molecular data to the large-scale behavior of a tumor the size of your fist is a tough problem. I think mathematical models are uniquely suited to help bridge those scales. Maybe you can write down a model of molecular dynamics within one cell and then scale it up—but at some point, these scale separations become really difficult.

In physics, there's the idea of a grand unified theory—combining quantum mechanics and gravity. But usually, they don’t come together. If you're talking about gravity, you're talking about big things—so quantum effects don’t matter. If you're talking about tiny things, gravity doesn’t matter. So there's this huge separation.

The reality of the universe is that both gravity and quantum mechanics are important—but depending on the question you're asking, you can often ignore one or the other. I think the same logic applies here.

So, is your question about the mechanism of mutation? Genome regulation? Or is it about how a tumor made of 10¹² cells will respond to a drug? That distinction really matters. I’m kind of dodging your question a bit, but it really comes down to understanding the scale your question operates on, and then finding data that directly informs that scale.

You could argue the genome informs all scales—and you're not wrong—but there are so many steps in between that sometimes the fine molecular details don’t directly impact the outcome you're trying to predict.

Isaac:

That makes a lot of sense. Different questions live on different scales.

Dr. Jacob Scott:

Exactly. And that makes it harder—or even impossible—to generalize across those scales right now.

Isaac:

You've mentioned in your work that it's really important to balance rigorous science with the urgent needs of patients. How do you manage that—especially when complex models give you results that don’t make intuitive sense?

Dr. Jacob Scott:

That’s a great question. I’ll try to answer it—and also touch on something related.

So, for example, this morning I was in clinic from 8 to noon, taking care of several cancer patients with a range of issues. And honestly, there was no mathematical modeling involved in most of that. Well, actually, radiation planning does use mathematical models, but I didn’t use any models of biology to guide my decisions.

Instead, I relied on well-established standards of care—things based on clinical trials and evidence-based medicine. That’s my clinical hat. I’m board-certified, licensed, and required to do the right thing based on current evidence—even though I know it won’t work for everyone.

Then I walk over to this building, my lab, and I put on my scientist hat. Now I’m supposed to question everything, poke holes in everything, and doubt the very foundations of what we do clinically.

So it’s a bit of a mental split. In the clinic, I must be certain and act confidently, because patients depend on that. But as a scientist, I have to embrace uncertainty and chase the unknown.

We know the standard of care doesn’t work for everyone—and that’s why we do research. But trying to understand every mechanistic detail for every individual patient is impossible right now.

So it’s a balance between delivering the best care today and working toward a better tomorrow.

Isaac:

Yeah, that's really interesting.

AI and machine learning—how do you see them impacting the field of mathematical modeling, both as tools and in terms of connecting to other disciplines?

Dr. Jacob Scott:

I think they’re already touching every facet of our lives.

One of the things AI and ML are really good at is handling large, sparse, heterogeneous datasets. That’s incredibly helpful—because one of the big challenges is that we have all this data, but much of it wasn’t collected purposefully.

Take electronic medical records, for example. I work at Cleveland Clinic, and we have a massive amount of data on patients. But that data wasn’t gathered for a specific hypothesis—it’s just the result of people showing up to appointments, missing others, getting admitted for unrelated issues—it’s messy. It’s real life.

Machine learning is helping us make sense of that mess. I wouldn’t say it gives us "understanding," exactly, but it helps us organize and interpret the data in a more useful way.

In mathematical oncology specifically, it’s also helping us connect models to real-world data. I can write a mathematical model and get precise answers—or distributions if it’s stochastic—but how do I relate that to noisy, real-life data? AI is helping us bridge that gap.

It’s also changing how people code, and the kinds of technical skills we need. It’s already impacting medical education. I just saw that one of the new large language models got 100% on the medical board exams.

So now we have to ask: if a computer can do that, should students still be memorizing all this information? Or should they be taught how to use these tools, so they can focus on higher-level thinking?

That’s an open question, and we’re figuring it out in real time.

Isaac:

Yeah, AI is definitely going to have a huge impact—especially for students watching this.

Dr. Jacob Scott:

Absolutely. I’d bet every single student watching this video has already used AI today.

Isaac:

Probably. I used it a bit to prepare—I asked it how to phrase certain questions or whether an idea made sense.

Dr. Jacob Scott:

Exactly. It’s a great sounding board. A place to test ideas, brainstorm, and explore. And it's private—for now, at least. It's an amazing tool, and we’re all still learning how best to use it. It's exciting.

Isaac:

A lot of students watching this—myself included—are really excited about working at the intersection of math and medicine. But we’re also trying to figure out what that path actually looks like. From your perspective, what are the most important skills or areas we should start learning now?

Dr. Jacob Scott:

Great question. And honestly, if you asked me six months ago—or six months from now—I might give a different answer, because AI is changing things so rapidly.

That said, I’m a bit biased. My undergrad degree was in physics, and I think that helped me tremendously.

What is physics, really? It’s the study of the natural world, and its language is math. That prepared me to approach the human condition—something in the natural world—with a mathematical mindset.

And I still believe that’s valuable. Even if ChatGPT can do your homework for you, it's not about getting the answer—it’s about learning how to think. So I would say a strong foundation in the core STEM subjects—math, physics, chemistry, biology—is still incredibly important.

And that won’t change. Learning how to think critically is always going to be a valuable skill. Like I said earlier, the one thing that ChatGPT and other large language models can’t quite do yet is ask truly great questions.

That’s still our domain. Now, on the physician side, there's also empathy—talking to patients, supporting them, performing surgeries and procedures. But on the scientific side, that creative spark—that ability to ask sharp, insightful questions—is where we as humans still bring something unique.

So I would encourage students to focus on sharpening that "question-asking" skill. And I think the best way to do that is by diving deep into the basic sciences. They’ll always be foundational.

But beyond that, I’d say this: I have high school–aged kids, and I truly believe the most important thing anyone can do is chase what they’re passionate about. If something excites you—if it gets you out of bed in the morning—go after it. And if that passion changes, don’t be afraid to change course.

One of the saddest things I see is when someone says, “I’ve always wanted to be a craniofacial surgeon,” and they hold onto that dream simply because it’s what they said at 12—even if, by 18 or 22, they’re drawn to something completely different, like veterinary medicine, chemistry, or even poetry.

The ability to grow and change is one of the most beautiful things about being human. So pursue what excites you—because the world is going to look very different when you're 30 than it does when you're 20, or even today.

Isaac:

Yeah, especially with how fast everything is changing. Thank you for that advice—it really resonates.

You've also said that science isn’t just about equations and facts, but about the ideas and ethics behind it. What should young scientists keep in mind as they grow in their careers?

Dr. Jacob Scott:

First and foremost: if you compromise your scientific ethics, you have nothing left. So keep your integrity close. Once you lose that, it’s very hard to move forward.

I still believe—and I admit I’m biased as a scientist—that there’s something noble about the pursuit of truth. That’s really what we’re doing. Just like theologians, philosophers, or artists, we’re all seeking truth in our own way. All of these disciplines are creative, and they all take a lot out of you—but they also give a lot back.

They also come with a lot of rejection. And imposter syndrome is very real. I still feel it every day. By the metrics, I’m "successful"—I’m a full professor, I just got promoted last year—but I still look around and think, Am I supposed to be here? Do I really know what I’m talking about? How can I keep up with all these brilliant young minds?

My papers still get rejected. My ideas still get dismissed. That’s just part of the process. So you really need a strong internal compass—knowing what you're doing, and why. Because any creative or meaningful pursuit will be difficult. People resist new ideas. If you suggest a different way of understanding the world, expect to be laughed at—until, maybe, you’re proven right. And then suddenly everyone acts like it was obvious all along.

That’s just the natural cycle of innovation in any field—artistic or scientific.

Isaac:

Yeah, that's really great advice. There’s always going to be doubt and challenges, but if you're doing something meaningful, it's worth sticking with it.

Dr. Jacob Scott:

Exactly. If it’s meaningful, it’s going to be hard. That doesn’t mean you shouldn’t do it—but it does mean you’ll need support. So make sure you have friends. Stay true to yourself. And keep going.

Isaac:

One last question. What advice would you give to a student interested in STEM—maybe not in mathematical modeling specifically, but in science more broadly?

Dr. Jacob Scott:

Beyond what I’ve already said, maybe I’ll just try to sum things up.

Don’t be afraid to question the status quo. I teach a class in medical school every year, and here’s something to think about: everything I teach in that class was unknown when I sat in that same lecture as a student.

So just remember—many of the “truths” you’re learning today will be wrong in the future. That’s especially true in challenging scientific fields. And it’s your job—your generation’s job—to prove me wrong, just like it was my job to question and improve on what the generation before me believed.

Yes, we stand on the shoulders of giants. But sometimes, those giants are wrong. In the 1700s, the medical field believed human health was governed by four bodily humors—blood, bile, and two others. That was the correct answer on the boards back then! Today, we laugh at that.

So one day, people will laugh at the things I’ve discovered. And that’s how it should be. So keep asking questions. Keep pushing boundaries.

Isaac:

Dr. Scott, thank you so much for speaking with me today. This has been such an eye-opening conversation—not just about cancer and math, but also about how we ask questions and how we grow as scientists and thinkers.

To all the students watching, we’ll link Dr. Scott’s lab and publications in the description if you’d like to explore more.

Thanks again, Dr. Scott—your time and insights meant a lot.

Dr. Jacob Scott:

Thanks for the interest, Isaac. And take care, everybody.