Posted on by Dr. Francis Collins
It is now possible to pull up the design of a guitar on a computer screen and print out its parts on a 3D printer equipped with special metal or plastic “inks.” The same technological ingenuity is also now being applied with bioinks—printable gels containing supportive biomaterials and/or cells—to print out tissue, bone, blood vessels, and, even perhaps one day, viable organs.
While there’s a long way to go until then, a team of researchers has reached an important milestone in bioprinting collagen and other extracellular matrix proteins that undergird every tissue and organ in the body. The researchers have become so adept at it that they now can print biomaterials that mimic the structural, mechanical, and biological properties of real human tissues.
Take a look at the video. It shows a life-size human heart valve that’s been printed with their improved collagen bioink. As fluid passes through the aortic valve in a lab test, its three leaf-like flaps open and close like the real thing. All the while, the soft, flexible valve withstands the intense fluid pressure, which mimics that of blood flowing in and out of a beating heart.
The researchers, led by NIH grantee Adam Feinberg, Carnegie Mellon University, Pittsburgh, PA, did it with their latest version of a 3D bioprinting technique featured on the blog a few years ago. It’s called: Freeform Reversible Embedding of Suspended Hydrogels v.2.0. Or, just FRESH v2.0.
The FRESH system uses a bioink that consists of collagen (or other soft biomaterials) embedded in a thick slurry of gelatin microparticles and water. While a number of technical improvements have been made to FRESH v. 2.0, the big one was getting better at bioprinting collagen.
The secret is to dissolve the collagen bioink in an acid solution. When extruded into a neutral support bath, the change in pH drives the rapid assembly of collagen. The ability to extrude miniscule amounts and move the needle anywhere in 3D space enables them to produce amazingly complex, high-resolution structures, layer by layer. The porous microstructure of the printed collagen also helps for incorporating human cells. When printing is complete, the support bath easily melts away by heating to body temperature.
As described in Science, in addition to the working heart valve, the researchers have printed a small model of a heart ventricle. By combining collagen with cardiac muscle cells, they found they could actually control the organization of muscle tissue within the model heart chamber. The 3D-printed ventricles also showed synchronized muscle contractions, just like you’d expect in a living, beating human heart!
That’s not all. Using MRI images of an adult human heart as a template, the researchers created a complete organ structure including internal valves, large veins, and arteries. Based on the vessels they could see in the MRI, they printed even tinier microvessels and showed that the structure could support blood-like fluid flow.
While the researchers have focused the potential of FRESH v.2.0 printing on a human heart, in principle the technology could be used for many other organ systems. But there are still many challenges to overcome. A major one is the need to generate and incorporate billions of human cells, as would be needed to produce a transplantable human heart or other organ.
Feinberg reports more immediate applications of the technology on the horizon, however. His team is working to apply FRESH v.2.0 for producing child-sized replacement tracheas and precisely printed scaffolds for healing wounded muscle tissue.
Meanwhile, the Feinberg lab generously shares its designs with the scientific community via the NIH 3D Print Exchange. This innovative program is helping to bring more 3D scientific models online and advance the field of bioprinting. So we can expect to read about many more exciting milestones like this one from the Feinberg lab.
 3D bioprinting of collagen to rebuild components of the human heart. Lee A, Hudson AR, Shiwarski DJ, Tashman JW, Hinton TJ, Yerneni S, Bliley JM, Campbell PG, Feinberg AW. Science. 2019 Aug 2;365(6452):482-487.
Tissue Engineering and Regenerative Medicine (National Institute of Biomedical Imaging and Bioengineering/NIH)
Regenerative Biomaterials and Therapeutics Group (Carnegie Mellon University, Pittsburgh, PA)
FluidForm (Acton, MA)
3D Bioprinting Open Source Workshops (Carnegie Mellon)
Video: Adam Feinberg on Tissue Engineering to Treat Human Disease (YouTube)
NIH Support: National Heart, Lung, and Blood Institute; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Common Fund
Posted on by Dr. Francis Collins
Thanks to advances in wearable health technologies, it’s now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. (I’m glad to say I am in normal sinus rhythm.)
For true medical benefit, however, the challenge lies in analyzing the vast amounts of data—often hundreds of hours worth per person—to distinguish reliably between harmless rhythm irregularities and potentially life-threatening problems. Now, NIH-funded researchers have found that artificial intelligence (AI) can help.
A powerful computer “studied” more than 90,000 EKG recordings, from which it “learned” to recognize patterns, form rules, and apply them accurately to future EKG readings. The computer became so “smart” that it could classify 10 different types of irregular heart rhythms, including atrial fibrillation (AFib). In fact, after just seven months of training, the computer-devised algorithm was as good—and in some cases even better than—cardiology experts at making the correct diagnostic call.
EKG tests measure electrical impulses in the heart, which signal the heart muscle to contract and pump blood to the rest of the body. The precise, wave-like features of the electrical impulses allow doctors to determine whether a person’s heart is beating normally.
For example, in people with AFib, the heart’s upper chambers (the atria) contract rapidly and unpredictably, causing the ventricles (the main heart muscle) to contract irregularly rather than in a steady rhythm. This is an important arrhythmia to detect, even if it may only be present occasionally over many days of monitoring. That’s not always easy to do with current methods.
Here’s where the team, led by computer scientists Awni Hannun and Andrew Ng, Stanford University, Palo Alto, CA, saw an AI opportunity. As published in Nature Medicine, the Stanford team started by assembling a large EKG dataset from more than 53,000 people . The data included various forms of arrhythmia and normal heart rhythms from people who had worn the FDA-approved Zio patch for about two weeks.
The Zio patch is a 2-by-5-inch adhesive patch, worn much like a bandage, on the upper left side of the chest. It’s water resistant and can be kept on around the clock while a person sleeps, exercises, or takes a shower. The wireless patch continuously monitors heart rhythms, storing EKG data for later analysis.
The Stanford researchers looked to machine learning to process all the EKG data. In machine learning, computers rely on large datasets of examples in order to learn how to perform a given task. The accuracy improves as the machine “sees” more data.
But the team’s real interest was in utilizing a special class of machine learning called deep neural networks, or deep learning. Deep learning is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.
In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened. The network is typically composed of multiple information-processing layers, which operate on the data and compute increasingly complex and abstract representations.
Those data reach the final output layer, which acts as a classifier, assigning each bit of data to a particular category or, in the case of the EKG readings, a diagnosis. In this way, computers can learn to analyze and sort highly complex data using both more obvious and hidden features.
Ultimately, the computer in the new study could differentiate between EKG readings representing 10 different arrhythmias as well as a normal heart rhythm. It could also tell the difference between irregular heart rhythms and background “noise” caused by interference of one kind or another, such as a jostled or disconnected Zio patch.
For validation, the computer attempted to assign a diagnosis to the EKG readings of 328 additional patients. Independently, several expert cardiologists also read those EKGs and reached a consensus diagnosis for each patient. In almost all cases, the computer’s diagnosis agreed with the consensus of the cardiologists. The computer also made its calls much faster.
Next, the researchers compared the computer’s diagnoses to those of six individual cardiologists who weren’t part of the original consensus committee. And, the results show that the computer actually outperformed these experienced cardiologists!
The findings suggest that artificial intelligence can be used to improve the accuracy and efficiency of EKG readings. In fact, Hannun reports that iRhythm Technologies, maker of the Zio patch, has already incorporated the algorithm into the interpretation now being used to analyze data from real patients.
As impressive as this is, we are surely just at the beginning of AI applications to health and health care. In recognition of the opportunities ahead, NIH has recently launched a working group on AI to explore ways to make the best use of existing data, and harness the potential of artificial intelligence and machine learning to advance biomedical research and the practice of medicine.
Meanwhile, more and more impressive NIH-supported research featuring AI is being published. In my next blog, I’ll highlight a recent paper that uses AI to make a real difference for cervical cancer, particularly in low resource settings.
 Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY.
Nat Med. 2019 Jan;25(1):65-69.
Arrhythmia (National Heart, Lung, and Blood Institute/NIH)
Andrew Ng (Palo Alto, CA)
NIH Support: National Heart, Lung, and Blood Institute
Posted on by Dr. Francis Collins
Researchers have learned in recent years how to grow miniature human hearts in a dish. These “organoids” beat like the real thing and have allowed researchers to model many key aspects of how the heart works. What’s been really tough to model in a dish is how stresses on hearts that are genetically abnormal, such as in inherited familial cardiomyopathies, put people at greater risk for cardiac problems.
Enter the lab-grown human cardiac tissue pictured above. This healthy tissue comprised of the heart’s muscle cells, or cardiomyocytes (green, nuclei in red), was derived from induced pluripotent stem (iPS) cells. These cells are derived from adult skin or blood cells that are genetically reprogrammed to have the potential to develop into many different types of cells, including cardiomyocytes.