Posted on by Lawrence Tabak, D.D.S., Ph.D.
The COVID-19 pandemic continues to present considerable public health challenges in the United States and around the globe. One of the most puzzling is why many people who get over an initial and often relatively mild COVID illness later develop new and potentially debilitating symptoms. These symptoms run the gamut including fatigue, shortness of breath, brain fog, anxiety, and gastrointestinal trouble.
People understandably want answers to help them manage this complex condition referred to as Long COVID syndrome. But because Long COVID is so variable from person to person, it’s extremely difficult to work backwards and determine what these people had in common that might have made them susceptible to Long COVID. The variability also makes it difficult to identify all those who have Long COVID, whether they realize it or not. But a recent study, published in the journal Lancet Digital Health, shows that a well-trained computer and its artificial intelligence can help.
Researchers found that computers, after scanning thousands of electronic health records (EHRs) from people with Long COVID, could reliably make the call. The results, though still preliminary and in need of further validation, point the way to developing a fast, easy-to-use computer algorithm to help determine whether a person with a positive COVID test is likely to battle Long COVID.
In this groundbreaking study, NIH-supported researchers led by Emily Pfaff, University of North Carolina, Chapel Hill, and Melissa Haendel, the University of Colorado Anschutz Medical Campus, Aurora, relied on machine learning. In machine learning, a computer sifts through vast amounts of data to look for patterns. One reason machine learning is so powerful is that it doesn’t require humans to tell the computer which features it should look for. As such, machine learning can pick up on subtle patterns that people would otherwise miss.
In this case, Pfaff, Haendel, and team decided to “train” their computer on EHRs from people who had reported a COVID-19 infection. (The records are de-identified to protect patient privacy.) The researchers found just what they needed in the National COVID Cohort Collaborative (N3C), a national, publicly available data resource sponsored by NIH’s National Center for Advancing Translational Sciences. It is part of NIH’s Researching COVID to Enhance Recovery (RECOVER) initiative, which aims to improve understanding of Long COVID.
The researchers defined a group of more than 1.5 million adults in N3C who either had been diagnosed with COVID-19 or had a record of a positive COVID-19 test at least 90 days prior. Next, they examined common features, including any doctor visits, diagnoses, or medications, from the group’s roughly 100,000 adults.
They fed that EHR data into a computer, along with health information from almost 600 patients who’d been seen at a Long COVID clinic. They developed three machine learning models: one to identify potential long COVID patients across the whole dataset and two others that focused separately on people who had or hadn’t been hospitalized.
All three models proved effective for identifying people with potential Long-COVID. Each of the models had an 85 percent or better discrimination threshold, indicating they are highly accurate. That’s important because, once researchers can identify those with Long COVID in a large database of people such as N3C, they can begin to ask and answer many critical questions about any differences in an individual’s risk factors or treatment that might explain why some get Long COVID and others don’t.
This new study is also an excellent example of N3C’s goal to assemble data from EHRs that enable researchers around the world to get rapid answers and seek effective interventions for COVID-19, including its long-term health effects. It’s also made important progress toward the urgent goal of the RECOVER initiative to identify people with or at risk for Long COVID who may be eligible to participate in clinical trials of promising new treatment approaches.
Long COVID remains a puzzling public health challenge. Another recent NIH study published in the journal Annals of Internal Medicine set out to identify people with symptoms of Long COVID, most of whom had recovered from mild-to-moderate COVID-19 . More than half had signs of Long COVID. But, despite extensive testing, the NIH researchers were unable to pinpoint any underlying cause of the Long COVID symptoms in most cases.
So if you’d like to help researchers solve this puzzle, RECOVER is now enrolling adults and kids—including those who have and have not had COVID—at more than 80 study sites around the country.
 Identifying who has long COVID in the USA: a machine learning approach using N3C data. Pfaff ER, Girvin AT, Bennett TD, Bhatia A, Brooks IM, Deer RR, Dekermanjian JP, Jolley SE, Kahn MG, Kostka K, McMurry JA, Moffitt R, Walden A, Chute CG, Haendel MA; N3C Consortium. Lancet Digit Health. 2022 May 16:S2589-7500(22)00048-6.
 A longitudinal study of COVID-19 sequelae and immunity: baseline findings. Sneller MC, Liang CJ, Marques AR, Chung JY, Shanbhag SM, Fontana JR, Raza H, Okeke O, Dewar RL, Higgins BP, Tolstenko K, Kwan RW, Gittens KR, Seamon CA, McCormack G, Shaw JS, Okpali GM, Law M, Trihemasava K, Kennedy BD, Shi V, Justement JS, Buckner CM, Blazkova J, Moir S, Chun TW, Lane HC. Ann Intern Med. 2022 May 24:M21-4905.
COVID-19 Research (NIH)
National COVID Cohort Collaborative (N3C) (National Center for Advancing Translational Sciences/NIH)
Emily Pfaff (University of North Carolina, Chapel Hill)
Melissa Haendel (University of Colorado, Aurora)
NIH Support: National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases
As COVID-19 rapidly expanded throughout the world in April 2020, many in the biomedical technology community voiced significant concerns about the lack of available diagnostic tests. At that time, testing for SARS-CoV-2, the coronavirus that causes COVID-19, was conducted exclusively in clinical laboratories by order of a health-care provider. “Over the counter” (OTC) tests did not exist, and low complexity point of care (POC) platforms were rare. Fewer than 8 million tests were performed in the U.S. that month, and it was clear that we needed a radical transformation to make tests faster and more accessible.
By February 2022, driven by the Omicron variant surge, U.S. capacity had increased to a new record of more than 1.2 billion tests in a single month. Remarkably, the overwhelming majority of these—more than 85 percent—were “rapid tests” conducted in home and POC settings.
The story behind this practice-changing, “test-at-home” transformation is deeply rooted in technologic and manufacturing innovation. The NIH’s National Institute of Biomedical Imaging and Bioengineering (NIBIB), working collaboratively with multiple partners across NIH, government, academia, and the private sector, has been privileged to play a leading role in this effort via the Rapid Acceleration of Diagnostics (RADx®) initiative. On this two-year anniversary of RADx, we take a brief look back at its formation, impact, and potential for future growth.
On April 24, 2020, Congress recognized that testing was an urgent national need and appropriated $1.5 billion to NIH via an emergency supplement . The goal was to substantially increase the number, type, and availability of diagnostic tests in only five to six months. Since the “normal” commercialization cycle for this type of diagnostic technology is typically more than five years, we needed an entirely new approach . . . fast.
The RADx initiative was launched just five days after that challenging Congressional directive . Four NIH RADx programs were eventually created to support technology development and delivery, with the goal of matching test performance with community needs .The first two programs, RADx Tech and RADx Advanced Technology Platforms (ATP), were developed by NIBIB and focused on innovation for rapidly creating, scaling up, and deploying new technologies.
RADx Tech is built around NIBIB’s Point of Care Technologies Research Network (POCTRN) and includes core activities for technology review, test validation, clinical studies, regulatory authorization, and test deployment. Overall, the RADx Tech network includes approximately 900 participants from government, academia, and the private sector with unique capabilities and resources designed to decrease inherent risk and guide technologies from design and development to fully disseminated commercial products.
At the core of RADx Tech operations is the “innovation funnel” rapid review process, popularized as a shark tank . A total of 824 complete applications were submitted during two open calls in a four-month period, beginning April 2020 and during a one-month period in June 2021. Forty-seven projects received phase 1 funding to validate and lower the inherent risk of developing these technologies. Meanwhile, 50 companies received phase 2 contracts to support FDA authorization studies and manufacturing expansion 
Beyond test development, RADx Tech has evolved to become a key contributor to the U.S. COVID-19 response. The RADx Independent Test Assessment Program (ITAP) was launched in October 2021 to accelerate regulatory authorization of new tests as a joint effort with the Food and Drug Administration (FDA) . The ITAP acquires analytical and clinical performance data and works closely with FDA and manufacturers to shave weeks to months off the time it normally takes to receive Emergency Use Authorization (EUA).
The RADx Tech program also created a Variant Task Force to monitor the performance of tests against each new coronavirus “variant of concern” that emerges. This helps to ensure that marketed tests continue to remain effective. Other innovative RADx Tech projects include Say Yes! Covid Test, the first online free OTC test distribution program, and Project Rosa, which conducts real-time variant tracking across the country .
RADx Tech, by any measure, has exceeded even the most-optimistic expectations. In two years, RADx Tech-supported companies have received 44 EUAs and added approximately 2 billion tests and test products to the U.S. capacity. These remarkable numbers have steadily increased from more than16 million tests in September 2020, just five months after the program was established .
RADx Tech has also made significant contributions to the distribution of 1 billion free OTC tests via the government site, COVID.gov/tests. It has also provided critical guidance on serial testing and variants that have improved test performance and changed regulatory practice [9,10]. In addition, the RADx Mobile Application Reporting System (RADx MARS) reduces barriers to test reporting and test-to-treat strategies’ The latter offers immediate treatment options via telehealth or a POC location whenever a positive test result is reported. Finally, the When to Test website provides critical guidance on when and how to test for individuals, groups, and communities.
As we look to the future, RADx Tech has enormous potential to impact the U.S. response to other pathogens, diseases, and future pandemics. Major challenges going forward include improving home tests to work as well as lab platforms and building digital health networks for capturing and reporting test results to public health officials .
A recent editorial published in the journal Nature Biotechnology noted, “RADx has spawned a phalanx of diagnostic products to market in just 12 months. Its long-term impact on point of care, at-home, and population testing may be even more profound .” We are now poised to advance a new wave of precision medicine that’s led by innovative diagnostic technologies. It represents a unique opportunity to emerge stronger from the pandemic and achieve long-term impact.
 NIH mobilizes national innovation initiative for COVID-19 diagnostics, NIH news release, April 29, 2020.
 Rapid scaling up of Covid-19 diagnostic testing in the United States—The NIH RADx Initiative. Tromberg BJ, Schwetz TA, Pérez-Stable EJ, Hodes RJ, Woychik RP, Bright RA, Fleurence RL, Collins FS. N Engl J Med. 2020 Sep 10;383(11):1071-1077.
 We need more covid-19 tests. We propose a ‘shark tank’ to get us there. Alexander L. and Blunt R., Washington Post, April 20, 2020.
 RADx® Tech/ATP dashboard, National Institute of Biomedical Imaging and Bioengineering, NIH.
 New HHS actions add to Biden Administration efforts to increase access to easy-to-use over-the-counter COVID-19 tests. U.S. Department of Health and Human Services Press Office, October 25, 2021.
 A method for variant agnostic detection of SARS-CoV-2, rapid monitoring of circulating variants, detection of mutations of biological significance, and early detection of emergent variants such as Omicron. Lai E, et al. medRxiV preprint, January 9, 2022.
 Longitudinal assessment of diagnostic test performance over the course of acute SARS-CoV-2 infection. Smith RL, et al. J Infect Dis. 2021 Sep 17;224(6):976-982.
 Comparison of rapid antigen tests’ performance between Delta (B.1.61.7; AY.X) and Omicron (B.1.1.529; BA1) variants of SARS-CoV-2: Secondary analysis from a serial home self-testing study. Soni A, et al. MedRxiv preprint, March 2, 2022.
 Reporting COVID-19 self-test results: The next frontier. Health Affairs, Juluru K., et al. Health Affairs, February 11, 2022.
 Radical solutions. Nat Biotechnol. 2021 Apr;39(4):391.
Get Free At-Home COVID Tests (COVID.gov)
When to Test (Consortia for Improving Medicine with Innovation & Technology, Boston)
RADx Programs (NIH)
RADx® Tech and ATP Programs (National Institute of Biomedical Imaging and Biomedical Engineering/NIH)
[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the eighth in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.]
Posted on by Lawrence Tabak, D.D.S., Ph.D.
More than 215 million people in the United States are now fully vaccinated against the SARS-CoV-2 virus responsible for COVID-19 . More than 40 percent—more than 94 million people—also have rolled up their sleeves for an additional, booster dose. Now, an NIH-funded study exploring how mRNA vaccines are performing over time comes as a reminder of just how important it will be to keep those COVID-19 vaccines up to date as coronavirus variants continue to circulate.
The results, published in the journal Science Translational Medicine, show that people who received two doses of either the Pfizer or Moderna COVID-19 mRNA vaccines did generate needed virus-neutralizing antibodies . But levels of those antibodies dropped considerably after six months, suggesting declining immunity over time.
The data also reveal that study participants had much reduced protection against newer SARS-CoV-2 variants, including Delta and Omicron. While antibody protection remained stronger in people who’d also had a breakthrough infection, even that didn’t appear to offer much protection against infection by the Omicron variant.
The new study comes from a team led by Shan-Lu Liu at The Ohio State University, Columbus. They wanted to explore how well vaccine-acquired immune protection holds up over time, especially in light of newly arising SARS-CoV-2 variants.
This is an important issue going forward because mRNA vaccines train the immune system to produce antibodies against the spike proteins that crown the surface of the SARS-CoV-2 coronavirus. These new variants often have mutated, or slightly changed, spike proteins compared to the original one the immune system has been trained to detect, potentially dampening the immune response.
In the study, the team collected serum samples from 48 fully vaccinated health care workers at four key time points: 1) before vaccination, 2) three weeks after the first dose, 3) one month after the second dose, and 4) six months after the second dose.
They then tested the ability of antibodies in those samples to neutralize spike proteins as a correlate for how well a vaccine works to prevent infection. The spike proteins represented five major SARS-CoV-2 variants. The variants included D614G, which arose very soon after the coronavirus first was identified in Wuhan and quickly took over, as well as Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529).
The researchers explored in the lab how neutralizing antibodies within those serum samples reacted to SARS-CoV-2 pseudoviruses representing each of the five variants. SARS-CoV-2 pseudoviruses are harmless viruses engineered, in this case, to bear coronavirus spike proteins on their surfaces. Because they don’t replicate, they are safe to study without specially designed biosafety facilities.
At any of the four time points, antibodies showed a minimal ability to neutralize the Omicron spike protein, which harbors about 30 mutations. These findings are consistent with an earlier study showing a significant decline in neutralizing antibodies against Omicron in people who’ve received the initial series of two shots, with improved neutralizing ability following an additional booster dose.
The neutralizing ability of antibodies against all other spike variants showed a dramatic decline from 1 to 6 months after the second dose. While there was a marked decline over time after both vaccines, samples from health care workers who’d received the Moderna vaccine showed about twice the neutralizing ability of those who’d received the Pfizer vaccine. The data also suggests greater immune protection in fully vaccinated healthcare workers who’d had a breakthrough infection with SARS-CoV-2.
In addition to recommending full vaccination for all eligible individuals, the Centers for Disease Control and Prevention (CDC) now recommends everyone 12 years and up should get a booster dose of either the Pfizer or Moderna vaccines at least five months after completing the primary series of two shots . Those who’ve received the Johnson & Johnson vaccine should get a booster at least two months after receiving the initial dose.
While plenty of questions about the durability of COVID-19 immunity over time remain, it’s clear that the rapid deployment of multiple vaccines over the course of this pandemic already has saved many lives and kept many more people out of the hospital. As the Omicron threat subsides and we start to look forward to better days ahead, it will remain critical for researchers and policymakers to continually evaluate and revise vaccination strategies and recommendations, to keep our defenses up as this virus continues to evolve.
 COVID-19 vaccinations in the United States. Centers for Disease Control and Prevention. February 27, 2022.
 Neutralizing antibody responses elicited by SARS-CoV-2 mRNA vaccination wane over time and are boosted by breakthrough infection. Evans JP, Zeng C, Carlin C, Lozanski G, Saif LJ, Oltz EM, Gumina RJ, Liu SL. Sci Transl Med. 2022 Feb 15:eabn8057.
 COVID-19 vaccine booster shots. Centers for Disease Control and Prevention. Feb 2, 2022.
COVID-19 Research (NIH)
Shan-Lu Liu (The Ohio State University, Columbus)
NIH Support: National Institute of Allergy and Infectious Diseases; National Cancer Institute; National Heart, Lung, and Blood Institute; Eunice Kennedy Shriver National Institute of Child Health and Human Development
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