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Insurance Status Helps Explain Racial Disparities in Cancer Diagnosis

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Diverse human hands
Credit: iStock/jmangostock

Women have the best odds of surviving breast cancer if their disease is caught at an early stage, when treatments are most likely to succeed. Major strides have been made in the early detection of breast cancer in recent years. But not all populations have benefited equally, with racial and ethnic minorities still more likely to be diagnosed with later-stage breast cancer than non-Hispanic whites. Given that recent observance of Martin Luther King Day, I thought that it would be particularly appropriate to address a leading example of health disparities.

A new NIH-funded study of more than 175,000 U.S. women diagnosed with breast cancer from 2010-2016 has found that nearly half of the troubling disparity in breast cancer detection can be traced to lack of adequate health insurance. The findings suggest that improving insurance coverage may help to increase early detection and thereby reduce the disproportionate number of breast cancer deaths among minority women.

Naomi Ko, Boston University School of Medicine, has had a long interest in understanding the cancer disparities she witnesses first-hand in her work as a medical oncologist. For the study published in JAMA Oncology, she teamed up with epidemiologist Gregory Calip, University of Illinois Cancer Center, Chicago [1]. Their goal was to get beyond documenting disparities in breast cancer and take advantage of available data to begin to get at why such disparities exist and what to do about them.

Disparities in breast cancer outcomes surely stem from a complicated mix of factors, including socioeconomic factors, culture, diet, stress, environment, and biology. Ko and Calip focused their attention on insurance, thinking of it as a factor that society can collectively modify.

Many earlier studies had shown a link between insurance and cancer outcomes [2]. It also stood to reason that broad differences among racial and ethnic minorities in their access to adequate insurance might drive some of the observed cancer disparities. But, Ko and Calip asked, just how big a factor was it?

To find out, they looked to the NIH’s Surveillance Epidemiology, and End Results (SEER) Program, run by the National Cancer Institute. The SEER Program is an authoritative source of information on cancer incidence and survival in the United States.

The researchers focused their attention on 177,075 women of various races and ethnicities, ages 40 to 64. All had been diagnosed with invasive stage I to III breast cancer between 2010 and 2016.

The researchers found that a higher proportion of women receiving Medicaid or who were uninsured received a diagnosis of advanced stage III breast cancer compared with women with health insurance. Black, American Indian, Alaskan Native, and Hispanic women also had higher odds of receiving a late-stage diagnosis.

Overall, their sophisticated statistical analyses traced up to 47 percent of the racial/ethnic differences in the risk of locally advanced disease to differences in health insurance. Such late-stage diagnoses and the more extensive treatment regimens that go with them are clearly devastating for women with breast cancer and their families. But, the researchers note, they’re also costly for society, due to lost productivity and escalating treatment costs by stage of breast cancer.

These researchers surely aren’t alone in recognizing the benefit of early detection. Last week, an independent panel convened by NIH called for enhanced research to assess and explore how to reduce health disparities that lead to unequal access to health care and clinical services that help prevent disease.


[1] Association of Insurance Status and Racial Disparities With the Detection of Early-Stage Breast Cancer. Ko NY, Hong S, Winn RA, Calip GS. JAMA Oncol. 2020 Jan 9.

[2] The relation between health insurance coverage and clinical outcomes among women with breast cancer. Ayanian JZ, Kohler BA, Abe T, Epstein AM. N Engl J Med. 1993 Jul 29;329(5):326-31.

[3] Cancer Stat Facts: Female Breast Cancer. National Cancer Institute Surveillance, Epidemiology, and End Results Program.


Cancer Disparities (National Cancer Institute/NIH)

Breast Cancer (National Cancer Institute/NIH)

Naomi Ko (Boston University)

Gregory Calip (University of Illinois Cancer Center, Chicago)

NIH Support: National Center for Advancing Translational Sciences; National Cancer Institute; National Institute on Minority Health and Health Disparities

Artificial Intelligence Speeds Brain Tumor Diagnosis

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Real time diagnostics in the operating room
Caption: Artificial intelligence speeds diagnosis of brain tumors. Top, doctor reviews digitized tumor specimen in operating room; left, the AI program predicts diagnosis; right, surgeons review results in near real-time.
Credit: Joe Hallisy, Michigan Medicine, Ann Arbor

Computers are now being trained to “see” the patterns of disease often hidden in our cells and tissues. Now comes word of yet another remarkable use of computer-generated artificial intelligence (AI): swiftly providing neurosurgeons with valuable, real-time information about what type of brain tumor is present, while the patient is still on the operating table.

This latest advance comes from an NIH-funded clinical trial of 278 patients undergoing brain surgery. The researchers found they could take a small tumor biopsy during surgery, feed it into a trained computer in the operating room, and receive a diagnosis that rivals the accuracy of an expert pathologist.

Traditionally, sending out a biopsy to an expert pathologist and getting back a diagnosis optimally takes about 40 minutes. But the computer can do it in the operating room on average in under 3 minutes. The time saved helps to inform surgeons how to proceed with their delicate surgery and make immediate and potentially life-saving treatment decisions to assist their patients.

As reported in Nature Medicine, researchers led by Daniel Orringer, NYU Langone Health, New York, and Todd Hollon, University of Michigan, Ann Arbor, took advantage of AI and another technological advance called stimulated Raman histology (SRH). The latter is an emerging clinical imaging technique that makes it possible to generate detailed images of a tissue sample without the usual processing steps.

The SRH technique starts off by bouncing laser light rapidly through a tissue sample. This light enables a nearby fiberoptic microscope to capture the cellular and structural details within the sample. Remarkably, it does so by picking up on subtle differences in the way lipids, proteins, and nucleic acids vibrate when exposed to the light.

Then, using a virtual coloring program, the microscope quickly pieces together and colors in the fine structural details, pixel by pixel. The result: a high-resolution, detailed image that you might expect from a pathology lab, minus the staining of cells, mounting of slides, and the other time-consuming processing procedures.

To interpret the SRH images, the researchers turned to computers and machine learning. To teach a computer, it must be fed large datasets of examples in order to learn how to perform a given task. In this case, they used a special class of machine learning called deep neural networks, or deep learning. It’s inspired by the way neural networks in the human brain process information.

In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result, in this case a brain tumor diagnosis.

The team trained the computer to classify tissues samples into one of 13 categories commonly found in a brain tumor sample. Those categories included the most common brain tumors: malignant glioma, lymphoma, metastatic tumors, and meningioma. The training was based on more than 2.5 million labeled images representing samples from 415 patients.

Next, they put the machine to the test. The researchers split each of 278 brain tissue samples into two specimens. One was sent to a conventional pathology lab for prepping and diagnosis. The other was imaged with SRH, and then the trained machine made a diagnosis.

Overall, the machine’s performance was quite impressive, returning the right answer about 95 percent of the time. That’s compared to an accuracy of 94 percent for conventional pathology.

Interestingly, the machine made a correct diagnosis in all 17 cases that a pathologist got wrong. Likewise, the pathologist got the right answer in all 14 cases in which the machine slipped up.

The findings show that the combination of SRH and AI can be used to make real-time predictions of a patient’s brain tumor diagnosis to inform surgical decision-making. That may be especially important in places where expert neuropathologists are hard to find.

Ultimately, the researchers suggest that AI may yield even more useful information about a tumor’s underlying molecular alterations, adding ever greater precision to the diagnosis. Similar approaches are also likely to work in supplying timely information to surgeons operating on patients with other cancers too, including cancers of the skin and breast. The research team has made a brief video to give you a more detailed look at the new automated tissue-to-diagnosis pipeline.


[1] Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM 2nd, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA. Nat Med. 2020 Jan 6.


Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)

New Imaging Technique Allows Quick, Automated Analysis of Brain Tumor Tissue During Surgery (National Institute of Biomedical Imaging and Bioengineering/NIH)

Daniel Orringer (NYU Langone, Perlmutter Cancer Center, New York City)

Todd Hollon (University of Michigan, Ann Arbor)

NIH Support: National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering

Celebrating 2019 Biomedical Breakthroughs

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Science 2019 Biomedical Breakthroughs and a Breakdown

Happy New Year! As we say goodbye to the Teens, let’s take a look back at 2019 and some of the groundbreaking scientific discoveries that closed out this remarkable decade.

Each December, the reporters and editors at the journal Science select their breakthrough of the year, and the choice for 2019 is nothing less than spectacular: An international network of radio astronomers published the first image of a black hole, the long-theorized cosmic singularity where gravity is so strong that even light cannot escape [1]. This one resides in a galaxy 53 million light-years from Earth! (A light-year equals about 6 trillion miles.)

Though the competition was certainly stiff in 2019, the biomedical sciences were well represented among Science’s “runner-up” breakthroughs. They include three breakthroughs that have received NIH support. Let’s take a look at them:

In a first, drug treats most cases of cystic fibrosis: Last October, two international research teams reported the results from phase 3 clinical trials of the triple drug therapy Trikafta to treat cystic fibrosis (CF). Their data showed Trikafta effectively compensates for the effects of a mutation carried by about 90 percent of people born with CF. Upon reviewing these impressive data, the Food and Drug Administration (FDA) approved Trikafta, developed by Vertex Pharmaceuticals.

The approval of Trikafta was a wonderful day for me personally, having co-led the team that isolated the CF gene 30 years ago. A few years later, I wrote a song called “Dare to Dream” imagining that wonderful day when “the story of CF is history.” Though we’ve still got more work to do, we’re getting a lot closer to making that dream come true. Indeed, with the approval of Trikafta, most people with CF have for the first time ever a real chance at managing this genetic disease as a chronic condition over the course of their lives. That’s a tremendous accomplishment considering that few with CF lived beyond their teens as recently as the 1980s.

Such progress has been made possible by decades of work involving a vast number of researchers, many funded by NIH, as well as by more than two decades of visionary and collaborative efforts between the Cystic Fibrosis Foundation and Aurora Biosciences (now, Vertex) that built upon that fundamental knowledge of the responsible gene and its protein product. Not only did this innovative approach serve to accelerate the development of therapies for CF, it established a model that may inform efforts to develop therapies for other rare genetic diseases.

Hope for Ebola patients, at last: It was just six years ago that news of a major Ebola outbreak in West Africa sounded a global health emergency of the highest order. Ebola virus disease was then recognized as an untreatable, rapidly fatal illness for the majority of those who contracted it. Though international control efforts ultimately contained the spread of the virus in West Africa within about two years, over 28,600 cases had been confirmed leading to more than 11,000 deaths—marking the largest known Ebola outbreak in human history. Most recently, another major outbreak continues to wreak havoc in northeastern Democratic Republic of Congo (DRC), where violent civil unrest is greatly challenging public health control efforts.

As troubling as this news remains, 2019 brought a needed breakthrough for the millions of people living in areas susceptible to Ebola outbreaks. A randomized clinical trial in the DRC evaluated four different drugs for treating acutely infected individuals, including an antibody against the virus called mAb114, and a cocktail of anti-Ebola antibodies referred to as REGN-EB3. The trial’s preliminary data showed that about 70 percent of the patients who received either mAb114 or the REGN-EB3 antibody cocktail survived, compared with about half of those given either of the other two medicines.

So compelling were these preliminary results that the trial, co-sponsored by NIH’s National Institute of Allergy and Infectious Diseases (NIAID) and the DRC’s National Institute for Biomedical Research, was halted last August. The results were also promptly made public to help save lives and stem the latest outbreak. All Ebola patients in the DRC treatment centers now are treated with one or the other of these two options. The trial results were recently published.

The NIH-developed mAb114 antibody and the REGN-EB3 cocktail are the first therapeutics to be shown in a scientifically rigorous study to be effective at treating Ebola. This work also demonstrates that ethically sound clinical research can be conducted under difficult conditions in the midst of a disease outbreak. In fact, the halted study was named Pamoja Tulinde Maisha (PALM), which means “together save lives” in Kiswahili.

To top off the life-saving progress in 2019, the FDA just approved the first vaccine for Ebola. Called Ervebo (earlier rVSV-ZEBOV), this single-dose injectable vaccine is a non-infectious version of an animal virus that has been genetically engineered to carry a segment of a gene from the Zaire species of the Ebola virus—the virus responsible for the current DRC outbreak and the West Africa outbreak. Because the vaccine does not contain the whole Zaire virus, it can’t cause Ebola. Results from a large study in Guinea conducted by the WHO indicated that the vaccine offered substantial protection against Ebola virus disease. Ervebo, produced by Merck, has already been given to over 259,000 individuals as part of the response to the DRC outbreak. The NIH has supported numerous clinical trials of the vaccine, including an ongoing study in West Africa.

Microbes combat malnourishment: Researchers discovered a few years ago that abnormal microbial communities, or microbiomes, in the intestine appear to contribute to childhood malnutrition. An NIH-supported research team followed up on this lead with a study of kids in Bangladesh, and it published last July its groundbreaking finding: that foods formulated to repair the “gut microbiome” helped malnourished kids rebuild their health. The researchers were able to identify a network of 15 bacterial species that consistently interact in the gut microbiomes of Bangladeshi children. In this month-long study, this bacterial network helped the researchers characterize a child’s microbiome and/or its relative state of repair.

But a month isn’t long enough to determine how the new foods would help children grow and recover. The researchers are conducting a similar study that is much longer and larger. Globally, malnutrition affects an estimated 238 million children under the age 5, stunting their normal growth, compromising their health, and limiting their mental development. The hope is that these new foods and others adapted for use around the world soon will help many more kids grow up to be healthy adults.

Measles Resurgent: The staff at Science also listed their less-encouraging 2019 Breakdowns of the Year, and unfortunately the biomedical sciences made the cut with the return of measles in the U.S. Prior to 1963, when the measles vaccine was developed, 3 to 4 million Americans were sickened by measles each year. Each year about 500 children would die from measles, and many more would suffer lifelong complications. As more people were vaccinated, the incidence of measles plummeted. By the year 2000, the disease was even declared eliminated from the U.S.

But, as more parents have chosen not to vaccinate their children, driven by the now debunked claim that vaccines are connected to autism, measles has made a very preventable comeback. Last October, the Centers for Disease Control and Prevention (CDC) reported an estimated 1,250 measles cases in the United States at that point in 2019, surpassing the total number of cases reported annually in each of the past 25 years.

The good news is those numbers can be reduced if more people get the vaccine, which has been shown repeatedly in many large and rigorous studies to be safe and effective. The CDC recommends that children should receive their first dose by 12 to 15 months of age and a second dose between the ages of 4 and 6. Older people who’ve been vaccinated or have had the measles previously should consider being re-vaccinated, especially if they live in places with low vaccination rates or will be traveling to countries where measles are endemic.

Despite this public health breakdown, 2019 closed out a memorable decade of scientific discovery. The Twenties will build on discoveries made during the Teens and bring us even closer to an era of precision medicine to improve the lives of millions of Americans. So, onward to 2020—and happy New Year!


[1] 2019 Breakthrough of the Year. Science, December 19, 2019.

NIH Support: These breakthroughs represent the culmination of years of research involving many investigators and the support of multiple NIH institutes.

Aging Research: Blood Proteins Show Your Age

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Blood Test for Aging
Credit: Adapted from iStock/jarun011

How can you tell how old someone is? Of course, you could scan their driver’s license or look for signs of facial wrinkles and gray hair. But, as researchers just found in a new study, you also could get pretty close to the answer by doing a blood test.

That may seem surprising. But in a recent study in Nature Medicine, an NIH-funded research team was able to gauge a person’s age quite reliably by analyzing a blood sample for levels of a few hundred proteins. The results offer important new insights into what happens as we age. For example, the team suggests that the biological aging process isn’t steady and appears to accelerate periodically—with the greatest bursts coming, on average, around ages 34, 60, and 78.

These findings indicate that it may be possible one day to devise a blood test to identify individuals who are aging faster biologically than others. Such folks might be at risk earlier in life for cardiovascular problems, Alzheimer’s disease, osteoarthritis, and other age-related health issues.

What’s more, this work raises hope for interventions that may slow down the “proteomic clock” and perhaps help to keep people biologically younger than their chronological age. Such a scenario might sound like pure fantasy, but this same group of researchers showed a few years ago that it’s indeed possible to rejuvenate an older mouse by infusing blood from a much younger mouse.

Those and other earlier findings from the lab of Tony Wyss-Coray, Stanford School of Medicine, Palo Alto, CA, raised the tantalizing possibility that certain substances in young blood can revitalize the aging brain and other parts of the body. In search of additional clues in the new study, the Wyss-Coray team tracked how the protein composition of blood changes as people age.

To find those clues, they isolated plasma from more than 4,200 healthy individuals between ages 18 and 95. The researchers then used data from more than half of the participants to assemble a “proteomic clock” of aging.  Within certain limits, the clock could accurately predict the chronological age of the study’s remaining 1,446 participants. The best predictions relied on just 373 of the clock’s almost 3,000 proteins.

As further validation, the clock also reliably predicted the correct chronological age of four groups of people not in the study. Interestingly, it was possible to make a decent age prediction based on just nine of the clock’s most informative proteins.

The findings show that telltale proteomic changes arise with age, and they likely have important and as-yet unknown health implications. After all, those proteins found circulating in the bloodstream come not just from blood cells but also from cells throughout the body. Intriguingly, the researchers report that people who appeared biologically younger than their actual chronological age based on their blood proteins also performed better on cognitive and physical tests.

Most of us view aging as a gradual, linear process. However, the protein evidence suggests that, biologically, aging follows a more complex pattern. Some proteins did gradually tick up or down over time in an almost linear fashion. But the levels of many other proteins rose or fell more markedly over time. For instance, one neural protein in the blood stayed constant until around age 60, when its levels spiked. Why that is so remains to be determined.

As noted, the researchers found evidence that the aging process includes a series of three bursts. Wyss-Coray said he found it especially interesting that the first burst happens in early mid-life, around age 34, well before common signs of aging and its associated health problems would manifest.

It’s also well known that men and women age differently, and this study adds to that evidence. About two-thirds of the proteins that changed with age also differed between the sexes. However, because the effect of aging on the most important proteins of the clock is much stronger than the differences in gender, the proteomic clock still could accurately predict the ages in all people.

Overall, the findings show that protein substances in blood can serve as a useful measure of a person’s chronological and biological age and—together with Wyss-Coray’s earlier studies—that substances in blood may play an active role in the aging process. Wyss-Coray reports that his team continues to dig deeper into its data, hoping to learn more about the origins of particular proteins in the bloodstream, what they mean for our health, and how to potentially turn back the proteomic clock.


[1] Undulating changes in human plasma proteome profiles across the lifespan. Lehallier B, Gate D, Schaum N, Nanasi T, Lee SE, Yousef H, Moran Losada P, Berdnik D, Keller A, Verghese J, Sathyan S, Franceschi C, Milman S, Barzilai N, Wyss-Coray T. Nat Med. 2019 Dec;25(12):1843-1850. 


What Do We Know About Healthy Aging? (National Institute on Aging/NIH)

Cognitive Health (NIA)

Wyss-Coray Lab (Stanford University, Palo Alto, CA)

NIH Support: National Institute on Aging

Why When You Eat Might Be as Important as What You Eat

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Fasting and eating schedule
Adapted from Wilkinson MJ, Cell Metab, 2019

About 1 in 3 American adults have metabolic syndrome, a group of early warning signs for increased risk of type 2 diabetes, heart disease, and stroke. To help avoid such health problems, these folks are often advised to pay close attention to the amount and type of foods they eat. And now it seems there may be something else to watch: how food intake is spaced over a 24-hour period.

In a three-month pilot study, NIH-funded researchers found that when individuals with metabolic syndrome consumed all of their usual daily diet within 10 hours—rather than a more customary span of about 14 hours—their early warning signs improved. Not only was a longer stretch of daily fasting associated with moderate weight loss, in some cases, it was also tied to lower blood pressure, lower blood glucose levels, and other improvements in metabolic syndrome.

The study, published in Cell Metabolism, is the result of a joint effort by Satchidananda Panda, Salk Institute for Biological Sciences, La Jolla, CA, and Pam R. Taub, University of California, San Diego [1]. It was inspired by Panda’s earlier mouse studies involving an emerging dietary intervention, called time-restricted eating (TRE), which attempts to establish a consistent daily cycle of feeding and fasting to create more stable rhythms for the body’s own biological clock [2, 3].

But would observations in mice hold true for humans? To find out, Panda joined forces with Taub, a cardiologist and physician-scientist. The researchers enlisted 19 men and women with metabolic syndrome, defined as having three or more of five specific risk factors: high fasting blood glucose, high blood pressure, high triglyceride levels, low “good” cholesterol, and/or extra abdominal fat. Most participants were obese and taking at least one medication to help manage their metabolic risk factors.

In the study, participants followed one rule: eat anything that you want, just do so over a 10-hour period of your own choosing. So, for the next three months, these folks logged their eating times and tracked their sleep using a special phone app created by the research team. They also wore activity and glucose monitors.

By the pilot study’s end, participants following the 10-hour limitation had lost on average 3 percent of their weight and about 3 percent of their abdominal fat. They also lowered their cholesterol and blood pressure. Although this study did not find 10-hour TRE significantly reduced blood glucose levels in all participants, those with elevated fasting blood glucose did have improvement. In addition, participants reported other lifestyle improvements, including better sleep.

The participants generally saw their metabolic health improve without skipping meals. Most chose to delay breakfast, waiting about two hours after they got up in the morning. They also ate dinner earlier, about three hours before going to bed—and then did no late night snacking.

After the study, more than two-thirds reported that they stuck with the 10-hour eating plan at least part-time for up to a year. Some participants were able to cut back or stop taking cholesterol and/or blood-pressure-lowering medications.

Following up on the findings of this small study, Taub will launch a larger NIH-supported clinical trial involving 100 people with metabolic syndrome. Panda is now exploring in greater detail the underlying biology of the metabolic benefits observed in the mice following TRE.

For people looking to improve their metabolic health, it’s a good idea to consult with a doctor before making significant changes to one’s eating habits. But the initial data from this study indicate that, in addition to exercising and limiting portion size, it might also pay to watch the clock.


[1] Ten-hour time-restricted eating reduces weight, blood pressure, and atherogenic lipids in patients with metabolic syndrome. Wilkinson MJ, Manoogian ENC, Zadourian A, Lo H, Fakhouri S, Shoghi A, Wang X, Fleisher JG, Panda S, Taub PR. Cell Metab. 2019 Jan 7; 31: 1-13. Epub 2019 Dec 5.

[2] Time-restricted feeding without reducing caloric intake prevents metabolic diseases in mice fed a high-fat diet. Hatori M, Vollmers C, Zarrinpar A, DiTacchio L, Bushong EA, Gill S, Leblanc M, Chaix A, Joens M, Fitzpatrick JA, Ellisman MH, Panda S. Cell Metab. 2012 Jun 6;15(6):848-60.

[3] Time-restricted feeding is a preventative and therapeutic intervention against diverse nutritional challenges. Chaix A, Zarrinpar A, Miu P, Panda S. Cell Metab. 2014 Dec 2;20(6):991-1005.


Metabolic Syndrome (National Heart, Lung, and Blood Institute/NIH)

Obesity (National Institute of Diabetes and Digestive and Kidney Diseases/NIH)

Body Weight Planner (NIDDK/NIH)

Satchidananda Panda (Salk Institute for Biological Sciences, La Jolla, CA)

Taub Research Group (University of California, San Diego)

NIH Support: National Institute of Diabetes and Digestive and Kidney Diseases

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