AIM-AHEAD
A Look Back at Science’s 2022 Breakthroughs
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Happy New Year! I hope everyone finished 2022 with plenty to celebrate, whether it was completing a degree or certification, earning a promotion, attaining a physical fitness goal, or publishing a hard-fought scientific discovery.
If the latter, you are in good company. Last year produced some dazzling discoveries, and the news and editorial staff at the journal Science kept a watchful eye on the most high-impact advances of 2022. In December, the journal released its list of the top 10 advances across the sciences, from astronomy to zoology. In case you missed it, Science selected NASA’s James Webb Space Telescope (JWST) as the 2022 Breakthrough of the Year [1].
This unique space telescope took 20 years to complete, but it has turned out to be time well spent. Positioned 1.5-million-kilometers from Earth, the JWST and its unprecedented high-resolution images of space have unveiled the universe anew for astronomers and wowed millions across the globe checking in online. The telescope’s image stream, beyond its sheer beauty, will advance study of the early Universe, allowing astronomers to discover distant galaxies, explore the early formation of stars, and investigate the possibility of life on other planets.
While the biomedical sciences didn’t take home the top prize, they were well represented among Science’s runner-up breakthroughs. Some of these biomedical top contenders also have benefited, directly or indirectly, from NIH efforts and support. Let’s take a look:
RSV vaccines nearing the finish line: It’s been one of those challenging research marathons. But scientists last year started down the homestretch with the first safe-and-effective vaccine for respiratory syncytial virus (RSV), a leading cause of severe respiratory illness in the very young and the old.
In August, the company Pfizer presented evidence that its experimental RSV vaccine candidate offered protection for those age 60 and up. Later, they showed that the same vaccine, when administered to pregnant women, helped to protect their infants against RSV for six months after birth. Meanwhile, in October, the company GSK announced encouraging results from its late-stage phase III trial of an RSV vaccine in older adults.
As Science noted, the latest clinical progress also shows the power of basic science. For example, researchers have been working with chemically inactivated versions of the virus to develop the vaccine. But these versions have a key viral surface protein that changes its shape after fusing with a cell to start an infection. In this configuration, the protein elicits only weak levels of needed protective antibodies.
Back in 2013, Barney Graham, then with NIH’s National Institute of Allergy and Infectious Diseases (NIAID), and colleagues, solved the problem [2]. Graham’s NIH team discovered a way to lock the protein into its original prefusion state, which the immune system can better detect. This triggers higher levels of potent antibodies, and the discovery kept the science—and the marathon—moving forward.
These latest clinical advances come as RSV and other respiratory viruses, including SARS-CoV-2, the cause of COVID-19, are sending an alarming number of young children to the hospital. The hope is that researchers will cross the finish line this year or next, and we’ll have the first approved RSV vaccine.
Virus fingered as cause of multiple sclerosis: Researchers have long thought that multiple sclerosis, or MS, has a viral cause. Pointing to the right virus with the required high degree of certainty has been the challenge, slowing progress on the treatment front for those in need. As published in Science last January, Alberto Ascherio, Harvard T.H. Chan School of Public Health, Boston, and colleagues produced the strongest evidence yet that MS is caused by the Epstein-Barr virus (EBV), a herpesvirus also known for causing infectious mononucleosis [3].
The link between EBV and MS had long been suspected. But it was difficult to confirm because EBV infections are so widespread, and MS is so disproportionately rare. In the recent study, the NIH-supported researchers collected blood samples every other year from more than 10 million young adults in the U.S. military, including nearly 1,000 who were diagnosed with MS during their service. The evidence showed that the risk of an MS diagnosis increased 32-fold after EBV infection, but it held steady following infection with any other virus. Levels in blood serum of a biomarker for MS neurodegeneration also went up only after an EBV infection, suggesting that the viral illness is a leading cause for MS.
Further evidence came last year from a discovery published in the journal Nature by William Robinson, Stanford University School of Medicine, Stanford, CA, and colleagues. The NIH-supported team found a close resemblance between an EBV protein and one made in the healthy brain and spinal cord [4]. The findings suggest an EBV infection may produce antibodies that mistakenly attack the protective sheath surrounding our nerve cells. Indeed, the study showed that up to one in four people with MS had antibodies that bind both proteins.
This groundbreaking research suggests that an EBV vaccine and/or antiviral drugs that thwart this infection might ultimately prevent or perhaps even cure MS. Of note, NIAID launched last May an early-stage clinical trial for an experimental EBV vaccine at the NIH Clinical Center, Bethesda, MD.
AI Gets Creative: Science’s 2021 Breakthrough of the Year was AI-powered predictions of protein structure. In 2022, AI returned to take another well-deserved bow. This time, Science singled out AI’s now rapidly accelerating entry into once uniquely human attributes, such as artistic expression and scientific discovery.
On the scientific discovery side, Science singled out AI’s continued progress in getting creative with the design of novel proteins for vaccines and myriad other uses. One technique, called “hallucination,” generates new proteins from scratch. Researchers input random amino acid sequences into the computer, and it randomly and continuously mutates them into sequences that other AI tools are confident will fold into stable proteins. This greatly simplifies the process of protein design and frees researchers to focus their efforts on creating a protein with a desired function.
AI research now engages scientists around world, including hundreds of NIH grantees. Taking a broader view of AI, NIH recently launched the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program. It will help to create greater diversity within the field, which is a must. A lack of diversity could perpetuate harmful biases in how AI is used, how algorithms are developed and trained, and how findings are interpreted to avoid health disparities and inequities for underrepresented communities.
And there you have it, some of the 2022 breakthroughs from Science‘s news and editorial staff. Of course, the highlighted biomedical breakthroughs don’t capture the full picture of research progress. There were many other milestone papers published in 2022 that researchers worldwide will build upon in the months and years ahead to make further progress in their disciplines and, for some, draw the attention of Science’s news and editorial staff. Here’s to another productive year in biomedical research, which the blog will continue to feature and share with you as it unfolds in 2023.
References:
[1] 2022 Breakthrough of the Year. Science. Dec 15, 2022.
[2] Structure of RSV fusion glycoprotein trimer bound to a prefusion-specific neutralizing antibody. McLellan JS, Chen M, Leung S, Kwong PD, Graham BS, et al. Science. 2013 May 31;340(6136):1113-1117.
[3] Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis. Bjornevik K, Cortese M, Healy BC, Kuhle J, Mina MJ, Leng Y, Elledge SJ, Niebuhr DW, Scher AI, Munger KL, Ascherio A. Science. 2022 Jan 21;375(6578):296-301.
[4] Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM. Lanz TV, Brewer RC, Steinman L, Robinson WH, et al. Nature. 2022 Mar;603(7900):321-327.
Links:
Respiratory Syncytial Virus (RSV) (National Institute of Allergy and Infectious Diseases/NIH)
Multiple Sclerosis (National Institute of Neurological Disorders and Stroke/NIH)
Barney Graham (Morehouse School of Medicine, Atlanta)
Alberto Ascherio (Harvard T.H. Chan School of Public Health, Boston)
Robinson Lab (Stanford Medicine, Stanford, CA)
James Webb Space Telescope (Goddard Space Flight Center/NASA, Greenbelt, MD)
Artificial Intelligence Getting Smarter! Innovations from the Vision Field
Posted on by Michael F. Chiang, M.D., National Eye Institute
One of many health risks premature infants face is retinopathy of prematurity (ROP), a leading cause of childhood blindness worldwide. ROP causes abnormal blood vessel growth in the light-sensing eye tissue called the retina. Left untreated, ROP can lead to lead to scarring, retinal detachment, and blindness. It’s the disease that caused singer and songwriter Stevie Wonder to lose his vision.
Now, effective treatments are available—if the disease is diagnosed early and accurately. Advancements in neonatal care have led to the survival of extremely premature infants, who are at highest risk for severe ROP. Despite major advancements in diagnosis and treatment, tragically, about 600 infants in the U.S. still go blind each year from ROP. This disease is difficult to diagnose and manage, even for the most experienced ophthalmologists. And the challenges are much worse in remote corners of the world that have limited access to ophthalmic and neonatal care.
Artificial intelligence (AI) is helping bridge these gaps. Prior to my tenure as National Eye Institute (NEI) director, I helped develop a system called i-ROP Deep Learning (i-ROP DL), which automates the identification of ROP. In essence, we trained a computer to identify subtle abnormalities in retinal blood vessels from thousands of images of premature infant retinas. Strikingly, the i-ROP DL artificial intelligence system outperformed even international ROP experts [1]. This has enormous potential to improve the quality and delivery of eye care to premature infants worldwide.
Of course, the promise of medical artificial intelligence extends far beyond ROP. In 2018, the FDA approved the first autonomous AI-based diagnostic tool in any field of medicine [2]. Called IDx-DR, the system streamlines screening for diabetic retinopathy (DR), and its results require no interpretation by a doctor. DR occurs when blood vessels in the retina grow irregularly, bleed, and potentially cause blindness. About 34 million people in the U.S. have diabetes, and each is at risk for DR.
As with ROP, early diagnosis and intervention is crucial to preventing vision loss to DR. The American Diabetes Association recommends people with diabetes see an eye care provider annually to have their retinas examined for signs of DR. Yet fewer than 50 percent of Americans with diabetes receive these annual eye exams.
The IDx-DR system was conceived by Michael Abramoff, an ophthalmologist and AI expert at the University of Iowa, Iowa City. With NEI funding, Abramoff used deep learning to design a system for use in a primary-care medical setting. A technician with minimal ophthalmology training can use the IDx-DR system to scan a patient’s retinas and get results indicating whether a patient should be sent to an eye specialist for follow-up evaluation or to return for another scan in 12 months.
Many other methodological innovations in AI have occurred in ophthalmology. That’s because imaging is so crucial to disease diagnosis and clinical outcome data are so readily available. As a result, AI-based diagnostic systems are in development for many other eye diseases, including cataract, age-related macular degeneration (AMD), and glaucoma.
Rapid advances in AI are occurring in other medical fields, such as radiology, cardiology, and dermatology. But disease diagnosis is just one of many applications for AI. Neurobiologists are using AI to answer questions about retinal and brain circuitry, disease modeling, microsurgical devices, and drug discovery.
If it sounds too good to be true, it may be. There’s a lot of work that remains to be done. Significant challenges to AI utilization in science and medicine persist. For example, researchers from the University of Washington, Seattle, last year tested seven AI-based screening algorithms that were designed to detect DR. They found under real-world conditions that only one outperformed human screeners [3]. A key problem is these AI algorithms need to be trained with more diverse images and data, including a wider range of races, ethnicities, and populations—as well as different types of cameras.
How do we address these gaps in knowledge? We’ll need larger datasets, a collaborative culture of sharing data and software libraries, broader validation studies, and algorithms to address health inequities and to avoid bias. The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) project and NIH’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program project will be major steps toward addressing those gaps.
So, yes—AI is getting smarter. But harnessing its full power will rely on scientists and clinicians getting smarter, too.
References:
[1] Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. JAMA Ophthalmol. 2018 Jul 1;136(7):803-810.
[2] FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Food and Drug Administration. April 11, 2018.
[3] Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, Gencarella MD, Gee H, Maa AY, Cockerham GC, Lynch M, Boyko EJ. Diabetes Care. 2021 May;44(5):1168-1175.
Links:
Retinopathy of Prematurity (National Eye Institute/NIH)
Diabetic Eye Disease (NEI)
Michael Abramoff (University of Iowa, Iowa City)
Bridge to Artificial Intelligence (Common Fund/NIH)
[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the second in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]
