from the world's big
A Great Discovery, 25 Years in the Understanding
Carol W. Greider is the Daniel Nathans Professor & Director of Molecular Biology & Genetics at Johns Hopkins University. Her research on telomerase (an enzyme she helped discover) and telomere function won her a 2009 Nobel Prize in Medicine. Prior to joining the Johns Hopkins faculty, she obtained a Ph.D. in Molecular Biology from the University of California, Berkeley, in 1997, and was a faculty member at the Cold Spring Harbor Laboratory. She is a member of the National Academy of Sciences and a recipient of the 1998 Gairdner Foundation International Award.
Question: What was the telomere problem and how did you solve it?
Carol Greider: Well, at the basic level it has to do with how cells can divide many times. What was known in the late 1970s was that when you copy a chromosome, the way that the mechanism copies a chromosome, every time a cell divides, the very, very end bit can't be completely copied. And so then the idea was that the chromosomes, or the telomeres, which are the chromosome ends, would get shorter and shorter and shorter every time a cell divides. And that can't happen indefinitely; there has to be some mechanism which will balance that shortening. And that's where the discovery of telomerase comes in. And so for any cell that has to divide many times, they need to have a way to balance so that there's some shortening and some lengthening and some shortening and some lengthening, and an equilibrium is then maintained so that the cells can then go on and divide.
Question: What was your methodology in making the discovery?
Carol Greider: We were very interested in, as I said, telomeres and chromosomes and how they functioned. And so we really went to the source where there are a lot of telomeres. And this was something that Liz Blackburn had done a number of years before, when she had discovered the DNA sequence that telomeres is made up of. And it's a very, very simple repeated DNA sequence that is sort of a monotonous many, many, many repeats. And she had discovered the telomere DNA sequence in this organism called tetrahymena; it's a single-celled organism very much like the paramecium that high school students might go out to a pond and bring back some pond water and see the paramecium floating around. The thing about tetrahymena is that they have 40,000 chromosomes. And so it was a very good source to be able to understand what the ends of the chromosomes were. So when we set out to ask, is there an enzyme that can lengthen the chromosomes to balance the shortening, we went again to tetrahymena, and you can just get these organisms and grow them up in the laboratory. So we would grow up a large batch of the cells, and then you spin the cells down in a centrifuge so you make a very compact collection of them, and then break them open to get the insides of the cells out so that you can understand what is going on inside the cells.
Question: Was the 25-year delay in recognizing your discovery unusual?
Carol Greider: I think it's more that the discovery was made in 1984, and it was clear that it was important at the time. But it was important as a very basic cellular mechanism. And there wasn't a lot of questions; I didn't get a lot of people doubting what the conclusion was. But instead, it wasn't clear what the implications were. In the 25 years that have intervened, my lab and Liz's lab and a number of other labs throughout the world have contributed all kinds of different ideas from different avenues that have made it very clear now what the medical implications are. And so there are clear medical implications that we didn't know at all at the beginning. We were just solving a puzzle because we were curious about how cells worked, although we knew that it was a fundamental mechanism. We weren't just doing experiments just to find out anything, but rather to really understand how a cell works. And the 25 years in between really has allowed it to be clear what the implications of that discovery were.
Recorded November 10th, 2009
Interviewed by Austin Allen
What is telomerase? How was it identified? And why did the discovery take 25 years to win the Nobel? Biologist Carol Greider shares the inside story.
If machines develop consciousness, or if we manage to give it to them, the human-robot dynamic will forever be different.
- 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.
Duke University researchers might have solved a half-century old problem.
- 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.
Duke researchers have developed the first gel-based synthetic cartilage with the strength of the real thing. A quarter-sized disc of the material can withstand the weight of a 100-pound kettlebell without tearing or losing its shape.
Photo: Feichen Yang.<p>That's the word from a team in the Department of Chemistry and Department of Mechanical Engineering and Materials Science at Duke University. Their <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202003451" target="_blank">new paper</a>, published in the journal,<em> Advanced Functional Materials</em>, details this exciting evolution of this frustrating joint.<br></p><p>Researchers have sought materials strong and versatile enough to repair a knee since at least the seventies. This new hydrogel, comprised of three polymers, might be it. When two of the polymers are stretched, a third keeps the entire structure intact. When pulled 100,000 times, the cartilage held up as well as materials used in bone implants. The team also rubbed the hydrogel against natural cartilage a million times and found it to be as wear-resistant as the real thing. </p><p>The hydrogel has the appearance of Jell-O and is comprised of 60 percent water. Co-author, Feichen Yang, <a href="https://today.duke.edu/2020/06/lab-first-cartilage-mimicking-gel-strong-enough-knees" target="_blank">says</a> this network of polymers is particularly durable: "Only this combination of all three components is both flexible and stiff and therefore strong." </p><p> As with any new material, a lot of testing must be conducted. They don't foresee this hydrogel being implanted into human bodies for at least three years. The next step is to test it out in sheep. </p><p>Still, this is an exciting step forward in the rehabilitation of one of our trickiest joints. Given the potential reward, the wait is worth it. </p><p><span></span>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a>, <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank">Facebook</a> and <a href="https://derekberes.substack.com/" target="_blank">Substack</a>. His next book is</em> "<em>Hero's Dose: The Case For Psychedelics in Ritual and Therapy."</em></p>
What would it be like to experience the 4th dimension?
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.
An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.
- 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
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.
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.