Grandparents For Vaccines

For the last six months, I’ve been the Michigan Representative of the volunteer group Grandparents For Vaccines. Our group’s mission is to ensure America’s grandchildren have their best start in life without the threat of vaccine-preventable diseases. We do this by sharing the stories of people who have lived during the time before vaccines were common. 

Are Electromagnetic Fields
Making Me Ill?

There’s a link between being a coauthor of the textbook Intermediate Physics for Medicine and Biology and volunteering for Grandparents For Vaccines. In IPMB, Russ Hobbie and I discuss the misconceptions associated with electromagnetic fields, such as the debunked claims that 60-Hz powerline fields cause leukemia and radiofrequency fields emitted by cell phones cause brain cancer. I explored these topics further in my popular science book Are Electromagnetic Fields Making Me Ill? A tremendous amount of misinformation and many conspiracy theories are associated with these issues. After the rise of the Make America Healthy Again movement, I noticed similar misinformation and conspiracy theories associated with the opposition to vaccines. Naturally I was attracted to groups advocating for vaccines, especially vaccines for children. In addition, last August I became a first-time grandfather. So Grandparents For Vaccines seemed like a perfect fit for me.

Want to learn more about Grandparents For Vaccines? This week I had an essay published by Your Neighborhood Scientist. This nonprofit organization works to make science accessible, understandable, and human-centered. It strives to explain why science is important to communities and why we should support science. Boy, do we need more of that. I thank the founder and executive director of Your Neighborhood Scientist, Audrey Drotos, for publishing my essay and am grateful to the two editors who helped me write it: Trinity Pirrone and Kate Giffin. You can read the essay here.

Another place to learn about Grandparents For Vaccines is Kristen Panthagani's post in her Substack account You Can Know Things. As time goes by, I appreciate more and more the importance of science communicators like Panthagani, Drotos, and others. 

During the Lincoln-Douglas debates, Abraham Lincoln said

In this age, in this country, public sentiment is everything. With it, nothing can fail; against it, nothing can succeed. Whoever molds public sentiment goes deeper than he who enacts statutes, or pronounces judicial decisions.

I believe this holds true for the role of science in America today. We cannot defeat the forces of antiscience by legislation or lawsuits. Our only hope is to convince the public of the value of science. 

The main thing Grandparents For Vaccines does is collect videos of people (mostly, but not exclusively, grandparents) telling stories about their experiences with vaccine-preventable illnesses. If you want to hear some of these inspiring stories, you can find them on the Grandparents For Vaccines YouTube channel. I link to several of these stories below, and others can be found in my Your Neighborhood Scientist essay. If you have such a story of your own, please consider sharing it with us.

 Christine from North Carolina talks about getting the polio vaccine as a child.

Teri from Oregon tells her story about vaccines. The irrepressible Teri Mills, a retired nurse, recruited and trained me as the Michigan Rep for Grandparents For Vaccines.

 DeeDee from Colorado is another nurse who understands the importance of vaccines.

Kathryn from Virginia describes polio pioneers during the first polio vaccine clinical trial.

 Renowned vaccine scientist Paul Offit describes a polio unit in the 1950s.

Arthur Lavin is the founder of Grandparents For Vaccines.

 This is the worst of this bunch of videos, recorded by an odd guy with poor public speaking skills. I include it to show that even if your story isn’t the most inspiring or articulate one, it’s still worth telling.

James Keener (1946–2026)

Mathematical Physiology,
by Keener and Sneyd.

James Keener died in April. He was a biomathematician who worked at the University of Utah. In Chapter 10 of Intermediate Physics for Medicine and Biology, Russ Hobbie and I reference the second edition of his wonderful, highly-cited, two-volume textbook with James Sneyd, Mathematical Physiology (a third edition came out last year). 

I’ve been following Keener’s work since I was a graduate student. His study of reentry induction in a sheet of anisotropic cardiac tissue influenced my own work significantly (J. Math. Biol., Volume 26, Pages 41–56, 1988). He and I were both were interested in the bidomain model of the heart, a mathematical description of the electrical properties of cardiac tissue. I mentioned him in my brief history of the bidomain model because of his article in a special issue of the journal Chaos.

The next publication is an exception to my rule of not citing reviews. It appears in a 1998 focus issue of the journal Chaos edited by Art Winfree and dedicated to describing fibrillation in normal ventricular myocardium. It included a review by Brad Roth and Wanda Krassowska (Roth and Krassowska 1998), an analysis of an improved algorithm to solve the bidomain equations by mathematician Jim Keener of the University of Utah and his student Kristina Bogar (Keener and Bogar 1998), and the paper we examine in this section, a review by Natalia Trayanova and her graduate students Kirill Skouibine and Felipe Aguel (Trayanova, Skouibine, and Aguel 1998).

The citation to Keener’s article was

Keener JP, Bogar K (1998) A numerical method for the solution of the bidomain equations in cardiac tissue. Chaos 8:234–241.

I could discuss many of Keener’s other articles. He wrote an excellent review about modeling traveling waves with singular perturbation theory (Physica D: Nonlinear Phenomena, Volume 32, Pages 326–361, 1988) and did some research on ephaptic coupling in cardiac tissue that I wasn’t so keen on (Proc. Natl. Acad. Sci., Volume 107, Pages 20935–20940, 2010). But overall I found his research to be uniformly excellent. I would rank him just behind the late Art Winfree as the best mathematical biologist I have ever known.

It’s been a while since I’ve talked to Jim. He came to Oakland University, where I worked, in 2012 and gave an excellent lecture. I was the host for his trip, and we had many enjoyable hours discussing the heart. To learn more about Keener and his work, read his article “My Career in Mathematical Biology: A Personal Journey.”

I will miss him.  

James Keener, “The Mathematics of Life: Making Diffusion Your Friend”

https://www.youtube.com/watch?v=GVERIzti6IU&t=4s 

Toy Models

Using Math in Physics:
4. Toy Models, by Joe Redish. 

In Intermediate Physics for Medicine and Biology, Russ Hobbie and I introduce many “toy models.” These are simplified models that strip away detail to expose fundamental processes. Why use toy models in biology, which is notoriously complex? To explore this question, I want to focus on an article by the late Joe Redish published in The Physics Teacher: “Using Math in Physics: 4. Toy Models” (Volume 59, Pages 683–688, 2021). The paper is one in a series of articles that Redish wrote about using math in physics, several of which will be cited in the 6th edition of IPMB.

In his introduction, Redish writes

As physicists, we consider our highly simplified models an obvious and natural way to approach physics. Mathematical models of complicated systems can be tricky, so the best way to understand the math is to take the simplest possible example that illustrates a phenomenon, then take it apart and put it back together again, matching the math with physical intuitions and building a mental blend of what the math means physically.

He goes on to say

Simple systems help build understanding: Learning to use this resource effectively to build new understanding is an important step in learning to be an effective scientist.

Toy models help students to learn

to blend physical concepts, knowledge, and intuition with mathematical symbols and processing.

I couldn’t agree more.

One important skill when using a toy model is deciding what to include and what to ignore. Redish addresses this issue:

In choosing a model, we have to decide what phenomena we are trying to describe, how to quantify the quantities involved, and, perhaps most important, what matters and what doesn’t. The world is too complex for us to include everything that’s going on. Deciding what matters and what can be ignored (at least at first) is an essential scientific skill, one that is, unfortunately, rarely taught explicitly even to our physics majors.

Toy models are useful for teaching students how to go back and forth from physics (and biology and medicine) to mathematics. When I was teaching, I noticed that many students understood the physics qualitatively and had good math skills, but had trouble translating between the two. They tend to think of these skill as being separate. Redish says

Once we’ve mapped our physical quantities onto math, we inherit processing tools from mathematics that let us solve problems that we might have difficulty solving. But once we have completed our calculation, we have to interpret the result back in the physics. What did the solution tell us about the physical world? Finally, we have to evaluate that interpretation. Is our model good enough for what we needed to do? Or are there refinements that we have to make, additional factors or effects that we really need to include?

Redish is explicit about why toy models are useful and important. 

We use toy models widely in introductory physics because they support multiple pedagogically valuable developments. 

• Toy models help students build the blend by focusing on the math-physics connection. 
• Toy models are built into most of our problems and can help build physical intuition. 
• Some toy models work way better than we might expect.

I consider the second bullet point to be particularly critical. Students need to gain intuition into how systems behave. They need insight. If they use no math, any insight is totally qualitative. If they use math, they risk missing the insight because they are focused entirely on manipulating the mathematical symbols. In graduate school, I took a course on general relativity. I learned how to do the math well enough to get an A, but I never felt I understood what was happening physically. I would have benefited from some toy models.

Some biologists and medical doctors like to put all possible details into a complicated “black box” computer model. While such an approach has its uses, such as for making numerical predictions to compare to experiments, it provides no insight. (Perhaps the researcher who writes the computer program gains some insight, but the user does not.) Redish says

Many real-world phenomena include lots of competing effects. Making sense of them, figuring out what matters most, and how to approach them can be challenging. Toy models are not just a way of learning to build the blend; they are an analytical tool for approaching a complicated system.

The sixth edition of Intermediate Physics for Medicine and Biology relies even more heavily on toy models than previous editions. If students can gain the intuition from these toy models and can practice building models and analyzing them mathematically, they will be ready to examine even more complicated and diverse biological and medical systems quantitatively.

Elective MRI Screening of the General Public—Buyer Beware

Elective MRI Screening of the 
General Public—Buyer Beware.

I saw an article by Matthew Davenport and Scott Reeder published recently in JAMA (The Journal of the American Medical Association) titled “Elective MRI Screening of the General Public—Buyer Beware.” My initial reaction was “oh no, not more nonsense about the health risks of static magnetic fields!” Fortunately, that’s not what the article is about. No risks from exposure to magnetic fields are mentioned. So, what’s the problem?

Apparently many people are paying for elective whole body magnetic resonance imaging scans, even when not recommended by standard medical practice. In these images, benign growths can look just like small, early-stage tumors; you can’t distinguish them. This leads to additional procedures—such as biopsies, endoscopies, or surgeries—which each carry their own risk. Also, a false positive result can cause anxiety, sleeplessness, and financial strain. When you put all these issues together, elective whole body MRI scans may cause more harm than good, even if the direct risks of having a MRI are nonexistent (take “direct risk” here to mean the risk you are exposed to if you have an MRI scan for free but then the image is accidentally deleted before anyone can look at it).

This may seem an odd topic for a blog about Intermediate Physics for Medicine and Biology. But it reminds us that while physics is important in medicine, other non-technical issues are also critical. You might think that having an image of the inside of the body is always better than not having an image. But apparently it’s not.

Does this mean we should stop striving to develop even better MRI scanners? No! Better scanners should lead to better images and therefore better diagnoses. But we must be careful when interpreting the images (even improved images) especially when screening, where false positives will always be a challenge. As Russ Hobbie and I say in Chapter 5 of IPMB when analyzing the artificial kidney, we must recognize “the distinction between a high-technology treatment [or, in this case, image] and a real conquest [or identification] of a disease.”

IPMB Machete Order

What’s the best order to watch the Star War movies? Aficionados have been arguing about this for years. (In fact, this debate raged before the sequel triology was released. For this post, we’ll pretend the Episodes VII, VIII, and IX don’t exist). Two options are to watch them in the order of their release (starting with Episode IV released in the summer of 1977, between my junior and senior years of high school), or to watch them in order of episode number (starting with Episode I). A third suggestion is to watch them in “Machete Order”:

1. Episode IV, A New Hope
   
2. Episode V, The Empire Strikes Back
3. Episode II, Attack of the Clones 
4. Episode III, Revenge of the Sith 
5. Episode VI, Return of the Jedi
   

 Machete Order on the The Big Bang Theory. 

https://www.youtube.com/watch?v=keSFjjhUyVA

Episode I, The Phantom Menace, is not included; Rod Hilton, the inventor of Machete Order, believed the series was better off without it. Unfortunately, by eliminating Episode I we miss out on most scenes with my favorite character: Jar Jar Binks. 

Jar Jar Binks scenes from Episode I. 

What does all this Star Wars talk have to do with Intermediate Physics for Medicine and Biology? It’s fun to invent a new order of the chapters and sections when studying from IPMB. Below I present my “IPMB Machete Order.” Enjoy. 

First Semester

Chapter 1a: Mechanics. Sections 1.1 and 1.2 are where everyone should start: talking about distance scales and models.

Chapter 2: Exponential Growth and Decay. Chapter 2 introduces some necessary mathematics before diving into the physics.

Chapter 10a: Feedback and Control. Much of Sections 10.1–10.7 and 10.11–10.12 can be considered mathematical biology and follows naturally after Chapter 2.

Chapter 1b, Mechanics. Physics almost always starts with mechanics. This ordering respects that tradition. Sections 1.3–1.20 discuss biomechanics and fluid dynamics.

Chapter 13: Sound and Ultrasound. Acoustics can be thought of as part of mechanics. The wave equation is introduced. Ultrasound is used for imaging, but Fourier analysis is not used as extensively as in other imaging techniques, so ultrasonic imaging can be analyzed before the power of Fourier methods are introduced.

Chapter 4: Transport in an Infinite Medium. Diffusion follows naturally from mechanics. The diffusion equation can be compared to and contrasted with the wave equation that will have been studied already.

Chapter 14a: Atoms and Light. Section 14.6 discusses the diffusion approximation of photon transport in a turbid medium. It might be best to study this section right after studying diffusion in general.

Chapter 5: Transport through Neutral Membranes. This chapter applies diffusion to transport through membranes. It also introduces osmosis.

Chapter 14b: Atoms and Light. Russ Hobbie and I don’t develop light as a consequence of Maxwell’s equations, so there’s no reason to delay Sections 14.1-14.5 and 14.7-14.15 and wait to discuss optics after the electricity and magnetism chapters.

Chapter 15: Interactions of Photons and Charged Particles with Matter. After describing infrared, visible, and ultraviolet light in Chapter 14, it is natural to analyze x-rays in Chapter 15.

Chapter 16: Medical Use of X-Rays. Chapter 16 follows naturally after Chapter 15 (the two are almost one long chapter about x-rays).

Chapter 3: Systems of Many Particles. Usually, an introductory physics sequence covers mechanics, heat, and sound in the first semester, and electricity, magnetism, and modern physics in the second. Therefore, Chapter 3 about thermodynamics fits naturally at the end of the first semester. One advantage of this ordering is that diffusion is already familiar, so the heat equation (another name for the diffusion equation) is easier to understand. It’s true that the concept of the Boltzmann factor and absolute temperature are used in earlier chapters. No ordering is perfect. 

 

Second Semester

Chapter 6: Impulses in Nerve and Muscle Cells. Chapter 6 begins a sequence of chapters about electricity and magnetism, the traditional starting point for a second semester of physics.

Chapter 7: The Exterior Potential and the Electrocardiogram. More bioelectricity.

Chapter 10b: Feedback and Control. Sections 10.8–10.9 apply mathematics to the heart. They fit naturally after Chapter 7.

Chapter 9a: Electricity and Magnetism at the Cellular Level. Sections 9.1–9.9 are about electricity, not magnetism, and might best follow the two chapters on bioelectricity.

Chapter 8: Biomagnetism. Magnetism traditionally follows electricity, and we won’t change that order.

Chapter 9b: Electricity and Magnetism at the Cellular Level. The last section, Section 9.10, deals with the effects of weak electric and magnetic fields, and can be covered after the biomagnetism chapter.

Chapter 11: The Method of Least Squares and Signal Analysis. Chapter 11 contains some heavy duty mathematics, including Fourier analysis, so it is appropriate that it comes late in the semester. Yet, the reason we include these topics is because they are essential for several chapters on imaging.

Chapter 12: Images. Chapter 12 follows naturally after Chapter 11. A highlight of the chapter is the analysis of tomography. General properties of two-dimensional images are developed.

Chapter 18: Magnetic Resonance Imaging. Now that you have the full power of Fourier techniques, you can study MRI.

Chapter 17: Nuclear Physics and Nuclear Medicine. Modern physics typically appears near the end of a introductory physics sequence, and we preserve that order. Yet, several of the nuclear medicine imaging techniques, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT), require an understanding of tomographic methods.

The good news is that you don’t have to adopt IPMB Machete Order; you can just follow the order presented in the book. Or, you can make up your own schedule. When I taught Biological Physics and Medical Physics at Oakland University, I followed the order of the chapters in the book, except I skipped Chapter 9 (not enough time) and I moved Chapters 11 and 12 to after Chapter 16 to avoid starting the second semester with a lot of mathematics. If you find a different ordering that works for you, let me know. 

Oh My! I Was Wrong

Recently, there has been a brouhaha about “Ghost Murmur,” a proposed way to detect the magnetic field of a human heart from miles away. I was interviewed about Ghost Murmur by Deni Béchard, a reporter for Scientific American. He wrote¹

Bradley Roth, a physicist at Oakland University and author of the 2023 review Biomagnetism: The First Sixty Years, agrees. “People have been measuring the magnetic field of the heart for 60 years, and usually it’s done in a lab with shielding, and it’s done just a few centimeters or a couple inches from the heart, and even then you can barely record it.” A helicopter-borne version, he says, “would be not just a small advance, but it’d be a revolutionary advance from the state of the art.”

I stand by that statement, as written. In fact, I’m probably even more skeptical of the reality of Ghost Murmur now than when I spoke to Béchard. I confess, however, that I was wrong about one thing. I based my thinking on the magnetic field falling off as from a current dipole, so with the inverse square of the distance. Current dipoles are the usual way to model biomagnetic fields. I had seen other people online saying that the magnetic field falls off as from a magnetic dipole, so inverse cube, but I presumptuously assumed they were confusing a current dipole and a magnetic dipole.

Then I got to wondering if I was right. I know that for a current dipole in an unbounded conductor, the fall off is indeed one over distance squared. But how about for a bounded conductor, like the human body? Does that change things?

Being a physicist, my first impulse was to model the human body as a sphere. Anyone familiar with biomagnetism knows that a radial dipole in a sphere produces no magnetic field outside it. But how does the magnetic field of a tangential dipole in a sphere fall off with distance?

Homework Problem 21 in Chapter 8 of Intermediate Physics for Medicine and Biology provides the answer. It is a closed form expression for the magnetic field of a dipole in a sphere (originally derived by Jukka Sarvas).² The expression is sort of strange-looking, but it’s exactly what we need.

 

Let’s assume p = p x̂, r = r ẑ, and r₀ = r₀ ẑ, implying that a tangential dipole lies a distance r₀ from the sphere center along the z axis, and the magnetic field is measured a distance r from the sphere center also along the z axis, where r > r₀ ( r₀ is inside the sphere and r is outside). The homework problem defines a = r – r₀. I will take the limit as r >>> r₀. So a = r. In that case F = r (r² + r² – r₀ r) which approaches 2r³. The gradient of F becomes 6r². So the expression for the magnetic field falls with distance as 1/F (first term) or as r∇F/F² (second term). In both cases, the falloff is proportional to the inverse cube.

Ah Ha! I was wrong to say the magnetic field of a current dipole in a conducting sphere falls off as the inverse square. It is the inverse cube. The effect of the sphere boundary changes things from 1/r² to 1/r³. In fact, Flavio Grynszpan and David Geselowitz³ define a magnetic dipole m that is related to the electric, or current, dipole p for the case of a spherical conductor. So the folks who modeled the heart as a magnetic dipole knew what they were talking about. Apparently I didn’t. But at least I learned something, which is always a good thing.

What does this mean for Ghost Murmur? It means I was wrong in saying the magnetic field measured 60 km from the heart is one trillion (10¹²) times smaller than the magnetic field measured 60 mm from the heart. The field would be more like one quintillion (10¹⁸) times smaller. Yikes! 

 

 References

¹https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/ 

²Sarvas J (1987) Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. Phys. Med. Biol. 32:11–22. 

³Grynszpan F, Geselowitz DB (1973) Model studies of the magnetocardiogram. Biophys. J. 13:911–925.

Evangelista Torricelli and the Torr

Evangelista Torricelli. 

In Chapter 1 of Intermediate Physics for Medicine and Biology, Russ Hobbie and I discuss the units of pressure.

The SI unit of pressure is N m^–2 or pascal (Pa). The density is expressed in kg m^–3, so that ρg has units of N m^–3 and ρgz is in N m^–2. Pressures are often given as equivalent values of z in some substance, for example, in millimeters of mercury (torr) or centimeters of water.

Metric units are usually named after famous scientists. So who is Torr? Actually, the unit is a shortened version of the name of Italian physicist Evangelista Torricelli (1608–1647).

Torricelli was born in Rome. He eventually succeeded Galileo as the chair of mathematics at the University of Pisa. Torricelli’s work focused on the concept of pressure. Galileo suggested he study the rise and fall of water in a tube. Torricelli put water—and eventually mercury—in a tube closed at the bottom, turned the tube up-side-down, and let the liquid run out until it’s level stopped falling. There was an empty space at the upper closed end: one of the first vacuums ever created. German scientist Otto von Guericke subsequently used a pump to create much stronger vacuums, and brilliantly demonstrated the enormous pressure of air.

Torricelli’s work ultimately led to his invention of the barometer, which could be used to measure air pressure. Using a barometer similar to Torricelli’s design, French physicist Blaise Pascal measured the air pressure at different elevations (recording the pressure at the base and near the top of a mountain), proving that we live under a mountain of air. To honor these groundbreaking ideas, the two common units for pressure are the pascal and the torr. The barometer and the thermometer (a rudimentary version of which Galileo invented) paved the way for much future research. For instance, using the barometer the chemist Robert Boyle was able to relate volume and pressure in a gas: Boyle’s law.

Torricelli also worked on optics, including lens design. Another of his interests was mathematics, and his calculations of areas and volumes was a precursor to integral calculus. For instance, he discovered Torricelli’s trumpet, which is found by rotating the curve y = 1/x about the x axis. It has finite volume but infinite surface area. Unfortunately, his promising career was cut short. He died when only 39, probably of typhoid fever. 

Ultimately, Torricelli’s discovery led to measurement of a person’s blood pressure, one of the most common diagnostic procedures in medicine. When blood pressure is recorded as 126/64, both the systolic pressure (measured when the heart contracts, 126) and diastolic pressure (measured when the heart is relaxed, 64) are given in millimeters of mercury, which is equivalent to the torr. So, almost every medical exam honors the work of Evangelista Torricelli.

Barometers: The Surprising Science Story.

 https://www.youtube.com/watch?v=fXQVgCpHoRk

Henry Oldenburg, Inventor of Peer Review

Henry Oldenburg.

I’ve had many research articles published in scientific journals, and each one of them was peer reviewed. This means the journal editor sent my manuscript to two or three experts to read it, comment on it, correct it, and judge it. I can’t say I loved having anonymous reviewers criticize my work, but the process did improve my papers. I’ve also reviewed hundreds of submitted manuscripts for journals, and those poor authors had to suffer my wrath. Interestingly, in my experience books undergo much less peer review than journal articles. None of the editions of Intermediate Physics for Medicine and Biology that I was a coauthor on underwent any peer review. Perhaps Russ Hobbie’s first edition did; I don’t know.

How did all this reviewing get started? With any complicated development, it’s dangerous to point to one person as the inventor. Nevertheless, I’ll go out on a limb: The person who introduced peer review into science was Henry Oldenburg (1619–1677).

Oldenburg was born in Germany, but immigrated to England during the Interregnum: the time between the execution of king Charles I and the restoration of his son Charles II. Oldenburg was a friend of author John Milton and chemist Robert Boyle. When the Royal Society of London was founded in 1660, Oldenburg became its first secretary and was made the founding editor of the Philosophical Transactions of the Royal Society. He began the practice of sending manuscripts to other scientists to evaluate their quality. This process of peer review was crucial for science back in the 17th century and continues to be essential for science in the 21st century. The lack of peer review for many pseudoscientific ideas being promoted today (climate change denial, vaccine hesitancy, etc.) is causing all sorts of problems for our modern society.

Oldenburg did much more than establish peer review. He was, in many ways, the organizer of modern science. I’ve never managed to master any language other than English, so I’m particularly impressed that Oldenburg knew German, English, Dutch, French, Italian, and Latin. The Royal Society wisely put him in charge of foreign correspondence. Antonie van Leeuwenhoek—a scientist from the Netherlands known as the father of microscopy—would send Oldenburg rambling letters in Dutch describing his observations. Oldenburg translated and edited them, and published them in the Philosophical Transactions, making van Leeuwenhoek famous. Oldenburg also corresponded with Italian biologist Marcello Malpighi, the discoverer of capillaries, and Danish geologist Nicolas Steno, the founder of stratigraphy. Malpighi and Steno both published in Latin, the language of science at that time, so most scientists could read their work, but Oldenburg did translate their ideas into English, making them accessible to a wider society. Dutch physicist Christiaan Huygens wrote letters in French to Oldenburg, who translated and published them. This list of scientists sounds like a Who’s Who of the scientific revolution.

Oldenburg didn’t write to just foreign scientists. He had an extensive correspondence with Englishmen Isaac Newton, Robert Hooke, Robert Boyle, John Flamsteed, Edmond Halley, and Christopher Wren. Oldenburg wasn’t a great scientist himself, but he comes across as a central facilitator of 17th century science. I wonder what the scientific revolution would have looked like without him?

I love both science and writing. I wonder, sometimes, if Oldenburg might have had the best job in the world. He got to learn about the work of many famous scientists, and furthermore was able to influence the presentation of their results. Newton and Hooke were vastly better scientists, but they don’t come across as being happy. Oldenburg seems happy. I think I would have rather had Oldenburg’s job. I woulda hada lotta fun.

 Henry Oldenburg as a translator.

 https://www.youtube.com/watch?v=rOiDLoCmB5U

Digital Twin-Guided Ablation for Ventricular Tachycardia

Russ Hobbie and I discuss the electrical behavior of the heart in Chapter 7 of Intermediate Physics for Medicine and Biology. We focus a lot on cardiac arrhythmias and devices such as pacemakers and defibrillators used to treat those arrhythmias. One researcher that Russ and I cite during this discussion is Natalia Trayanova.

This week, Trayanova is in the news because of her recent study published in the New England Journal of Medicine: “Digital Twin-Guided Ablation for Ventricular Tachycardia” (Volume 394, Pages 1345–1347, 2026). Ablation is a way of intentionally destroying a small region of tissue, usually by burning it. Often an arrhythmia can be prevented if just the right spot in the heart is ablated. The trick is finding that spot. Trayanova’s team created computer simulations that are specific for each person’s own heart: a digital twin. If someone had a heart attack that killed the tissue in a certain region of the heart, that damage is included in the twin. If a patient’s heart has grown and remodeled because it had to pump harder than normal (perhaps the heart wall thickened), those changes are included in the twin. They next run their computer model over and over, destroying different regions of tissue until they find the location that stops the arrhythmia. Then they tell the surgeon where to ablate.

This sounds great in theory, but does it work in practice? The recent New England Journal of Medicine publication reports the results of the first clinical trial.

We conducted the TWIN-VT study... a clinical study performed under a Food and Drug Administration (FDA) investigational device exemption... to prospectively test the ability of the heart digital twin to guide ischemic VT [ventricular tachycardia, a type of heart arrhythmia] ablation procedures. The FDA limited the study to one institution in the United States [Johns Hopkins University, where Trayanova works] and 10 participants.

What were the results?

In all 10 participants, no VT was inducible at the end of the procedure. No periprocedural complications occurred.

In other words, all the patients got rid of their arrhythmia and there were no complications. Wow! This is better than traditional ablation without a digital twin. Granted, the study had only ten patients, so it should be considered a preliminary result, not a definitive conclusion. But still, Wow!

This study has implications for Intermediate Physics for Medicine and Biology. Trayanova was trained in physics, and then later changed her research area to biomedical engineering and medicine. Her work suggests that basic engineering principles, computational methods, and physics training (all topics stressed in IPMB) can impact—dare I say “revolutionize”—modern medicine. As you will hear in Trayanova’s TED talk below, sometimes physics ideas are not immediately embraced by medical doctors, but the effort to introduce the rigor of physics into medicine is worth the effort. 

Well done, Natalia. 

Your Personal Digital Heart, Natalia Trayanova, TEDxJHU

https://www.youtube.com/watch?v=wSDMPxGGy3A 

Interview with Natalia Trayanova on DoctorPodcasts hosted by Robert Cykiert, M.D.

https://www.youtube.com/watch?v=STeJylvPQzk 

Digital Twins Improve Patient Outcomes

https://www.youtube.com/watch?v=uRstW1eiSrM

Ghost Murmur

This story isn't worth a whole post, but the claim of "ghost murmur" technology used to save an American pilot in Iran was obviously made by someone who had not studied Intermediate Physics for Medicine and Biology.

See my quote it this recent Scientific American article:  https://www.scientificamerican.com/article/what-is-the-quantum-ghost-murmur-purportedly-used-in-iran-scientists/