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How the Recession Has Affected Medical Research

Question: How has the recession affected the NIH's funding priorities?

Francis Collins: Having the economy struggling at the level that it is and having rising concerns, understandably so, about the federal deficit has certainly had an impact on funding for biomedical research through the National Institutes of Health.  When you look at the support we’ve had, we are grateful that even in tough economic times there has been a willingness to try to keep up at least with what we were doing before.  But we haven’t gained much in terms of buying power over the last 10 years, we’ve been pretty much flat—even though the dollars being put in medical research have gone up a big, inflation has eroded that.  The one exception was a $10 billion dollar increment as part of the Recovery Act, which needed to be spent in two years and which was invested in ways that I think are truly exciting. But science doesn’t operate on two-year cycles, so now that the Recovery Act money is running out, we are facing what could be very lean times indeed for medical research.  

That forces us to be even more specific about how we set priorities. It forces us to say, we can’t do everything.  It forces us in some instances to close down programs that have been reasonably productive, but compared to what we’d like to do now in terms of new an innovative projects aren’t quite as compelling as if we had unlimited resources.  It makes the job of a science manager a lot tougher, but is the reality of what we are currently living with.

Question:
Does the tight budget make it less likely that unorthodox or creative studies will get funding?

Francis Collins:
I think there’s been a lot of concern that when budgets get tight, the peer review process can tend to be a little more conservative.  And budgets are tight right now.  If you send a grant to the NIH that’s got your best ideas in it, the chances that you’ll get funded is less than 20%.  In some of our institutes, it’s down around 10%. That’s a terrible stress on the system. That means that investigators are having to write and rewrite grants over and over again in order to just keep their labs going.  That means that reviewers who come to look at those grants spend time going into the details of a big pile of exciting applications knowing that probably they’re only going to be able to fund a small number.  

And if you were a reviewer, and you’re looking at a pile of grants and amongst them are some very solid applications from very well-established investigators who have a really amazing track record.  And then there’s another pile of new investigators you haven’t heard of who are just getting into the scientific arena, and don’t have as much preliminary data and haven’t published as much.  There is a tendency, I think, to go with the proven entities and that may mean you are missing out on the innovative stuff from the new investigators.  

To try to counter that, NIH has established a number of programs which can only be applied to if you have a slightly wacky idea.  So those include things like the Pioneer Awards, and the New Innovator Awards, and the Transformative R01 Awards.  All of those have a pretty high bar for innovation and a pretty limited requirement for preliminary data.  And they are some of the most exciting science that we are currently supporting, but it’s a small fraction of the total.  But it is an effort to try to avoid the conservatism that might otherwise become more prominent in times of difficult budget support.

Recorded September 13, 2010
Interviewed by David Hirschman

Having a smaller budget is forcing the NIH to be even more specific about how it sets priorities, and, in some instances, to close down productive programs.

Does conscious AI deserve rights?

If machines develop consciousness, or if we manage to give it to them, the human-robot dynamic will forever be different.

Videos
  • Does AI—and, more specifically, conscious AI—deserve moral rights? In this thought exploration, evolutionary biologist Richard Dawkins, ethics and tech professor Joanna Bryson, philosopher and cognitive scientist Susan Schneider, physicist Max Tegmark, philosopher Peter Singer, and bioethicist Glenn Cohen all weigh in on the question of AI rights.
  • Given the grave tragedy of slavery throughout human history, philosophers and technologists must answer this question ahead of technological development to avoid humanity creating a slave class of conscious beings.
  • One potential safeguard against that? Regulation. Once we define the context in which AI requires rights, the simplest solution may be to not build that thing.

A new hydrogel might be strong enough for knee replacements

Duke University researchers might have solved a half-century old problem.

Photo by Alexander Hassenstein/Getty Images
Technology & Innovation
  • Duke University researchers created a hydrogel that appears to be as strong and flexible as human cartilage.
  • The blend of three polymers provides enough flexibility and durability to mimic the knee.
  • The next step is to test this hydrogel in sheep; human use can take at least three years.
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Hints of the 4th dimension have been detected by physicists

What would it be like to experience the 4th dimension?

Two different experiments show hints of a 4th spatial dimension. Credit: Zilberberg Group / ETH Zürich
Technology & Innovation

Physicists have understood at least theoretically, that there may be higher dimensions, besides our normal three. The first clue came in 1905 when Einstein developed his theory of special relativity. Of course, by dimensions we’re talking about length, width, and height. Generally speaking, when we talk about a fourth dimension, it’s considered space-time. But here, physicists mean a spatial dimension beyond the normal three, not a parallel universe, as such dimensions are mistaken for in popular sci-fi shows.

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Predicting PTSD symptoms becomes possible with a new test

An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.

Image source: camillo jimenez/Unsplash
Technology & Innovation
  • 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
  • Early treatment is available but there's been no way to tell who needs it.
  • Using clinical data already being collected, machine learning can identify who's at risk.

The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.

In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.

That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.

70 data points and machine learning

nurse wrapping patient's arm

Image source: Creators Collective/Unsplash

Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:

"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."

The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.

Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."

Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.

Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.

On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.

Going forward

person leaning their head on another's shoulder

Image source: Külli Kittus/Unsplash

Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."

"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.

The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.

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