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Seth Berkley On The Politics of AIDS

Seth Berkley:  There’s a couple of complicated issues from my perspective and again, I don’t want to speak for the US Government. But from my perspective, the critical issue is your interventions have to be science based. What do I mean by that? You’ve got to follow the data. There’s a lot of theory, there’s a lot of religious beliefs, political beliefs. At the end of the day, if you’re trying to stop an epidemic, you need to ask the question what are the public health drivers of this epidemic and how can we intervene, and that requires having data. You’re doing what we call evidence-based medicine, evidence-based prevention and that would be the core to a strategy for the United States. The US is the leader. Certainly the US is the leader in research. The NIH budgeted $2.9 billion a year for AIDS, is the worlds leader and so a lot of the research is going on here and PEPFAR -- President’s Emergency Plan for AIDS Relief --  has been an extraordinary outpouring of support for these issues. But as you know, there’s been controversy around that on whether the abstinence-only mandates and other things might be tainting some of the incredibly important work that goes there. And I think the challenge there is to make sure that we’re using evidence to define our goals moving forward.

Question: How would you improve PEPFAR?

Seth Berkley:  I think PEPFAR has been an extraordinary addition to the world of trying to get drugs and treatments out there. I think more collaboration with the global fund, which is doing a lot of this work for the rest of the world, making sure that anything we do within PEPFAR has scientific basis for it, making sure that we try to get the best access to inexpensive drugs and diagnostics and prevention strategies out there. Those are all going to be key components to try and make this work. The other thing is, is that at the end of the day, I am an AIDS activities and I obviously want to see this epidemic stopped. That’s what we’re trying to do by creating a better tool, but we’ve got to take this within the overall health system and one of the challenges is, is that we’ve got to think about the delivery not only of AIDS drugs but of other drugs as well. It doesn’t do good if somebody is just treated for AIDS but then doesn’t have access to simple medicines for other conditions. So how we integrate all of this into a better health system for those living in developing countries and developed countries is a critical priority.

Question: Should we be focusing more on diseases that are preventable?

Seth Berkley:  First of all, we should focus on all of the important diseases, so it is an either/nor. We should focus on malaria, TB and HIV but also maternal mortality and other infectious diseases. One of the things you have to do though in a place with limited resources is prioritize based upon the importance of disease with needs. And one of the challenges with HIV has been, up until now we’ve been unable to end the epidemic with the existing preventive strategies. That’s one of the reasons you need better tools. One can make an argument it would be fabulous to have better tools for many different conditions, but if you’ve got good drug treatments, you’ve got good prevention strategies that work, then you can argue that there’s a balance between getting new tools and using existing strategies. For HIV, you’ve got prevention strategies that do something but nowhere have they stopped the epidemic. So I think for HIV, it’s different. We absolutely have to focus on getting better tools but meanwhile, we do have to deal with the people who are sick and dying from this disease as well as continuing to spread it. So it’s getting that balance right; it isn’t an either/or, it really is both.

The U.S. President's Emergency Plan for AIDS Relief (PEPFAR) has helped expand AIDS treatment and prevention services in a number of developing countries and there is even more that can be done.

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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