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Dr. Francis Collins

Preventing Glaucoma Vision Loss with ‘Big Data’

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Credit: University of California San Diego

Each morning, more than 2 million Americans start their rise-and-shine routine by remembering to take their eye drops. The drops treat their open-angle glaucoma, the most-common form of the disease, caused by obstructed drainage of fluid where the eye’s cornea and iris meet. The slow drainage increases fluid pressure at the front of the eye. Meanwhile, at the back of the eye, fluid pushes on the optic nerve, causing its bundled fibers to fray and leading to gradual loss of side vision.

For many, the eye drops help to lower intraocular pressure and prevent vision loss. But for others, the drops aren’t sufficient and their intraocular pressure remains high. Such people will need next-level care, possibly including eye surgery, to reopen the clogged drainage ducts and slow this disease that disproportionately affects older adults and African Americans over age 40.

Sally Baxter
Credit: University of California San Diego

Sally Baxter, a physician-scientist with expertise in ophthalmology at the University of California, San Diego (UCSD), wants to learn how to predict who is at greatest risk for serious vision loss from open-angle and other forms of glaucoma. That way, they can receive more aggressive early care to protect their vision from this second-leading cause of blindness in the U.S..

To pursue this challenging research goal, Baxter has received a 2020 NIH Director’s Early Independence Award. Her research will build on the clinical observation that people with glaucoma frequently battle other chronic health problems, such as high blood pressure, diabetes, and heart disease. To learn more about how these and other chronic health conditions might influence glaucoma outcomes, Baxter has begun mining a rich source of data: electronic health records (EHRs).

In an earlier study of patients at UCSD, Baxter showed that EHR data helped to predict which people would need glaucoma surgery within the next six months [1]. The finding suggested that the EHR, especially information on a patient’s blood pressure and medications, could predict the risk for worsening glaucoma.

In her NIH-supported work, she’s already extended this earlier “Big Data” finding by analyzing data from more than 1,200 people with glaucoma who participate in NIH’s All of Us Research Program [2]. With consent from the participants, Baxter used their EHRs to train a computer to find telltale patterns within the data and then predict with 80 to 99 percent accuracy who would later require eye surgery.

The findings confirm that machine learning approaches and EHR data can indeed help in managing people with glaucoma. That’s true even when the EHR data don’t contain any information specific to a person’s eye health.

In fact, the work of Baxter and other groups have pointed to an especially important role for blood pressure in shaping glaucoma outcomes. Hoping to explore this lead further with the support of her Early Independence Award, Baxter also will enroll patients in a study to test whether blood-pressure monitoring smart watches can add important predictive information on glaucoma progression. By combining round-the-clock blood pressure data with EHR data, she hopes to predict glaucoma progression with even greater precision. She’s also exploring innovative ways to track whether people with glaucoma use their eye drops as prescribed, which is another important predictor of the risk of irreversible vision loss [3].

Glaucoma research continues to undergo great progress. This progress ranges from basic research to the development of new treatments and high-resolution imaging technologies to improve diagnostics. But Baxter’s quest to develop practical clinical tools hold great promise, too, and hopefully will help one day to protect the vision of millions of people with glaucoma around the world.

References:

[1] Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records. Baxter SL, Marks C, Kuo TT, Ohno-Machado L, Weinreb RN. Am J Ophthalmol. 2019 Dec; 208:30-40.

[2] Predictive analytics for glaucoma using data from the All of Us Research Program. Baxter SL, Saseendrakumar BR, Paul P, Kim J, Bonomi L, Kuo TT, Loperena R, Ratsimbazafy F, Boerwinkle E, Cicek M, Clark CR, Cohn E, Gebo K, Mayo K, Mockrin S, Schully SD, Ramirez A, Ohno-Machado L; All of Us Research Program Investigators. Am J Ophthalmol. 2021 Jul;227:74-86.

[3] Smart electronic eyedrop bottle for unobtrusive monitoring of glaucoma medication adherence. Aguilar-Rivera M, Erudaitius DT, Wu VM, Tantiongloc JC, Kang DY, Coleman TP, Baxter SL, Weinreb RN. Sensors (Basel). 2020 Apr 30;20(9):2570.

Links:

Glaucoma (National Eye Institute/NIH)

All of Us Research Program (NIH)

Video: Sally Baxter (All of Us Research Program)

Sally Baxter (University of California San Diego)

Baxter Project Information (NIH RePORTER)

NIH Director’s Early Independence Award (Common Fund)

NIH Support: Common Fund


My Gratitude to the NIH Police

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On September 10, I expressed my gratitude to the men and women of the NIH Police for their service during the pandemic and their heightened efforts over these many months to keep everyone safe on campus. Many officers gathered in NIH’s Wilson Hall for this appreciation event, and I addressed them remotely with my wife Diane Baker by my side. Colleen McGowan (bottom left), director of NIH’s Office of Research Services, emceed the event. Also joining in remotely was Sergeant Alvin Maker (bottom right), NIH’s Community Policing Coordinator. To NIH’s men and women in blue, thank you!

Breakthrough Infections in Vaccinated People Less Likely to Cause ‘Long COVID’

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Long Covid. Two syringes in an arrow pointed down. symptoms of long covid in the background

There’s no question that vaccines are making a tremendous difference in protecting individuals and whole communities against infection and severe illness from SARS-CoV-2, the coronavirus that causes COVID-19. And now, there’s yet another reason to get the vaccine: in the event of a breakthrough infection, people who are fully vaccinated also are substantially less likely to develop Long COVID Syndrome, which causes brain fog, muscle pain, fatigue, and a constellation of other debilitating symptoms that can last for months after recovery from an initial infection.

These important findings published in The Lancet Infectious Diseases are the latest from the COVID Symptom Study [1]. This study allows everyday citizens in the United Kingdom to download a smartphone app and self-report data on their infection, symptoms, and vaccination status over a long period of time.

Previously, the study found that 1 in 20 people in the U.K. who got COVID-19 battled Long COVID symptoms for eight weeks or more. But this work was done before vaccines were widely available. What about the risk among those who got COVID-19 for the first time as a breakthrough infection after receiving a double dose of any of the three COVID-19 vaccines (Pfizer, Moderna, AstraZeneca) authorized for use in the U.K.?

To answer that question, Claire Steves, King’s College, London, and colleagues looked to frequent users of the COVID Symptom Study app on their smartphones. In its new work, Steves’ team was interested in analyzing data submitted by folks who’d logged their symptoms, test results, and vaccination status between December 9, 2020, and July 4, 2021. The team found there were more than 1.2 million adults who’d received a first dose of vaccine and nearly 1 million who were fully vaccinated during this period.

The data show that only 0.2 percent of those who were fully vaccinated later tested positive for COVID-19. While accounting for differences in age, sex, and other risk factors, the researchers found that fully vaccinated individuals who developed breakthrough infections were about half (49 percent) as likely as unvaccinated people to report symptoms of Long COVID Syndrome lasting at least four weeks after infection.

The most common symptoms were similar in vaccinated and unvaccinated adults with COVID-19, and included loss of smell, cough, fever, headaches, and fatigue. However, all of these symptoms were milder and less frequently reported among the vaccinated as compared to the unvaccinated.

Vaccinated people who became infected were also more likely than the unvaccinated to be asymptomatic. And, if they did develop symptoms, they were half as likely to report multiple symptoms in the first week of illness. Another vaccination benefit was that people with a breakthrough infection were about a third as likely to report any severe symptoms. They also were more than 70 percent less likely to require hospitalization.

We still have a lot to learn about Long COVID, and, to get the answers, NIH has launched the RECOVER Initiative. The initiative will study tens of thousands of COVID-19 survivors to understand why many individuals don’t recover as quickly as expected, and what might be the cause, prevention, and treatment for Long COVID.

In the meantime, these latest findings offer the encouraging news that help is already here in the form of vaccines, which provide a very effective way to protect against COVID-19 and greatly reduce the odds of Long COVID if you do get sick. So, if you haven’t done so already, make a plan to protect your own health and help end this pandemic by getting yourself fully vaccinated. Vaccines are free and available near to you—just go to vaccines.gov or text your zip code to 438829.

Reference:

[1] Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study. Antonelli M, Penfold RS, Merino J, Sudre CH, Molteni E, Berry S, Canas LS, Graham MS, Klaser K, Modat M, Murray B, Kerfoot E, Chen L, Deng J, Österdahl MF, Cheetham NJ, Drew DA, Nguyen LH, Pujol JC, Hu C, Selvachandran S, Polidori L, May A, Wolf J, Chan AT, Hammers A, Duncan EL, Spector TD, Ourselin S, Steves CJ. Lancet Infect Dis. 2021 Sep 1:S1473-3099(21)00460-6.

Links:

COVID-19 Research (NIH)

Claire Steves (King’s College London, United Kingdom)

COVID Symptom Study


COVID-19 Infected Many More Americans in 2020 than Official Tallies Show

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Map of U.S.. Counties showing varying levels of COVID-19 infection
Caption: Percentage of people in communities across the United States infected by the novel coronavirus that causes COVID-19 as of December 2020. Credit: Pei S, Nature, 2021.

At the end of last year, you may recall hearing news reports that the number of COVID-19 cases in the United States had topped 20 million. While that number came as truly sobering news, it also likely was an underestimate. Many cases went undetected due to limited testing early in the year and a large number of infections that produced mild or no symptoms.

Now, a recent article published in Nature offers a more-comprehensive estimate that puts the true number of infections by the end of 2020 at more than 100 million [1]. That’s equal to just under a third of the U.S. population of 328 million. This revised number shows just how rapidly this novel coronavirus spread through the country last year. It also brings home just how timely the vaccines have been—and continue to be in 2021—to protect our nation’s health in this time of pandemic.

The work comes from NIH grantee Jeffrey Shaman, Sen Pei, and colleagues, Columbia University, New York. As shown above in the map, the researchers estimated the percentage of people who had been infected with SARS-CoV-2, the novel coronavirus that causes COVID-19, in communities across the country through December 2020.

To generate this map, they started with existing national data on the number of coronavirus cases (both detected and undetected) in 3,142 U.S. counties and major metropolitan areas. They then factored in data from the Centers for Disease Control and Prevention (CDC) on the number of people who tested positive for antibodies against SARS-CoV-2. These CDC data are useful for picking up on past infections, including those that went undetected.

From these data, the researchers calculated that only about 11 percent of all COVID-19 cases were confirmed by a positive test result in March 2020. By the end of the year, with testing improvements and heightened public awareness of COVID-19, the ascertainment rate (the number of infections that were known versus unknown) rose to about 25 percent on average. This measure also varied a lot across the country. For instance, the ascertainment rates in Miami and Phoenix were higher than the national average, while rates in New York City, Los Angeles, and Chicago were lower than average.

How many people were potentially walking around with a contagious SARS-CoV-2 infection? The model helps to answer this, too. On December 31, 2020, the researchers estimate that 0.77 percent of the U.S. population had a contagious infection. That’s about 1 in every 130 people on average. In some places, it was much higher. In Los Angeles, for example, nearly 1 in 40 (or 2.42 percent) had a SARS-CoV-2 infection as they rang in the New Year.

Over the course of the year, the fatality rate associated with COVID-19 dropped, at least in part due to earlier diagnosis and advances in treatment. The fatality rate went from 0.77 percent in April to 0.31 percent in December. While this is great news, it still shows that COVID-19 remains much more dangerous than seasonal influenza (which has a fatality rate of 0.08 percent).

Today, the landscape has changed considerably. Vaccines are now widely available, giving many more people immune protection without ever having to get infected. And yet, the rise of the Delta and other variants means that breakthrough infections and reinfections—which the researchers didn’t account for in their model—have become a much bigger concern.

Looking ahead to the end of 2021, Americans must continue to do everything they can to protect their communities from the spread of this terrible virus. That means getting vaccinated if you haven’t already, staying home and getting tested if you’ve got symptoms or know of an exposure, and taking other measures to keep yourself and your loved ones safe and well. These measures we take now will influence the infection rates and susceptibility to SARS-CoV-2 in our communities going forward. That will determine what the map of SARS-CoV-2 infections will look like in 2021 and beyond and, ultimately, how soon we can finally put this pandemic behind us.

Reference:

[1] Burden and characteristics of COVID-19 in the United States during 2020. Pei S, Yamana TK, Kandula S, Galanti M, Shaman J. Nature. 2021 Aug 26.

Links:

COVID-19 Research (NIH)

Sen Pei (Columbia University, New York)

Jeffrey Shaman (Columbia University, New York)


The Amazing Brain: Motor Neurons of the Cervical Spine

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Today, you may have opened a jar, done an upper body workout, played a guitar or a piano, texted a friend, or maybe even jotted down a grocery list longhand. All of these “skilled” arm, wrist, and hand movements are made possible by the bundled nerves, or circuits, running through a part of the central nervous system in the neck area called the cervical spine.

This video, which combines sophisticated imaging and computation with animation, shows the density of three types of nerve cells in the mouse cervical spine. There are the V1 interneurons (red), which sit between sensory and motor neurons; motor neurons associated with controlling the movement of the bicep (blue); and motor neurons associated with controlling the tricep (green).

At 4 seconds, the 3D animation morphs to show all the colors and cells intermixed as they are naturally in the cervical spine. At 8 seconds, the animation highlights the density of these three cells types. Notice in the bottom left corner, a light icon appears indicating the different imaging perspectives. What’s unique here is the frontal, or rostral, view of the cervical spine. The cervical spine is typically imaged from a lateral, or side, perspective.

Starting at 16 seconds, the animation highlights the location and density of each of the individual neurons. For the grand finale, viewers zoom off on a brief fly-through of the cervical spine and a flurry of reds, blues, and greens.

The video comes from Jamie Anne Mortel, a research assistant in the lab of Samuel Pfaff, Salk Institute, La Jolla, CA. Mortel is part of a team supported by the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative that’s developing a comprehensive atlas of the circuitry within the cervical spine that controls how mice control their forelimb movements, such as reaching and grasping.

This basic research will provide a better understanding of how the mammalian brain and spinal cord work together to produce movement. More than that, this research may provide valuable clues into better treating paralysis to arms, wrists, and/or hands caused by neurological diseases and spinal cord injuries.

As a part of this project, the Pfaff lab has been busy developing a software tool to take their imaging data from different parts of the cervical spine and present it in 3D. Mortel, who likes to make cute cartoon animations in her spare time, noticed that the software lacked animation capability. So she took the initiative and spent the next three weeks working after hours to produce this video—her first attempt at scientific animation. No doubt she must have been using a lot of wrist and hand movements!

With a positive response from her Salk labmates, Mortel decided to enter her scientific animation debut in the 2021 Show Us BRAINs! Photo and Video Contest. To her great surprise and delight, Mortel won third place in the video competition. Congratulations, and continued success for you and the team in producing this much-needed atlas to define the circuitry underlying skilled arm, wrist, and hand movements.

Links:

Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)

Spinal Cord Injury Information Page (National Institute of Neurological Disorders and Stroke/NIH)

Samuel Pfaff (Salk Institute, La Jolla, CA)

Show Us Your BRAINs! Photo and Video Contest (Brain Initiative/NIH)

NIH Support: National Institute of Neurological Disorders and Stroke


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