We’re entering uncharted waters in the US government. I don’t think it’s hyperbolic to say there will be new regulations, new laws that we’ve never seen before. While I don’t pretend to understand the new administration in any way, I do expect there will be more chaos at the level of policy-making than we’ve seen in decades and one thing that chaos does is it increases the likelihood of extreme outcomes.
So here is one speculative policy idea:
I think we should trade the 12-year exclusivity period from biologics to small molecule drugs. Continue reading →
You want to know how to drive a scientist crazy? Insist that you believe something that’s not supported by current scientific evidence. Tell her vaccines cause autism, or creationism is just as valid a theory as evolution, or that climate change isn’t really happening, I mean, after all, a monster blizzard hit Washington DC this January! Global warming, pssh…
There’s an old episode of Friends that did a good job of showing how this kind of conversation goes. Phoebe professes not to believe in evolution and Ross, a paleontologist, keeps trying to convince her that evolution is real using scientific evidence and logic. He grows increasingly frustrated and insistent as she continues to deny the basis of his life’s work, finally losing it when she goads him into admitting (like a good scientist) that even theories like evolution are not immune from questioning and testing.
We train scientists to carefully generate, weigh and use evidence. To no one’s surprise, this leads many scientists to generalize and think that in all matters having to do with the physical world we all should and of course will follow the evidence. Yes, sometimes that leads to unpopular ideas, and sometimes the ideas change as the weight of evidence changes. This training can make scientists kind of boring at cocktail parties. Still, the overall scientific process keeps moving forward and it’s because of this reliance on evidence.
But many people (including, at times, even some scientists) don’t always think the same way about things in the physical world. And that’s why I’m pessimistic that CRISPR-Cas technology will peacefully resolve the Genetically Modified Organism (GMO) debate. Continue reading →
I once had an idea for a science fiction story where everyone was paranoid about their genetic information getting out because of a misguided belief that genes equal destiny and that the burden of privacy is all on the individual. People would wear protective suits and carefully guard against leaving any iota of tissue out in public—not a single follicle or skin flake. All to prevent anyone else—potential employers, rivals, even potential lovers—finding out information about their genes.
I planned the story as a satire, taking our current world where Precision Medicine and cheap genome sequencing and not-quite-as-cheap genome interpretation are real things, and extrapolating to an absurdity. I wanted to highlight the kinds of more realistic challenges we might face as we learn more about our genes and face increasing questions about privacy and access to health care services. Of course, I thought this was completely speculative; I’d just be building a straw man story to make a point. I knew something this extreme would never really happen.
One of the neat things about the people in the baseball research community is how willing many of them are to continually question the status quo. Maybe it’s because sabermetrics is itself a relatively new field, and so there’s a humility there. Assumptions always, always need to be questioned.
Case in point: a great post by Ken Arneson entitled “10 things I believe about baseball without evidence.” He uses the latest failure of the Oakland A’s in the recent MLB playoffs to highlight areas of baseball we still don’t understand, and for which we may not even be asking the right questions. Why, for example, haven’t the A’s advanced to the World Series for decades despite fielding good and often great teams? Yes there’s luck and randomness, but at some point the weight of the evidence encourages you to take a second look. Otherwise, you become as dogmatic as those who still point to RBIs as the measure of the quality of a baseball batter. Which they are not.
One of the thought-provoking things Arneson brings up is the question of whether the tools we use shape the way we study phenomena–really, the way we think–and therefore unconsciously limit the kinds of questions we choose to ask. His example is the use of SQL in creating queries and the inherent assumptions of that datatype that objects within a SQL database are individual events with no precedence or dependence upon others. And yet, as he points out, the act of hitting a baseball is an ongoing dialog between pitcher and batter. Prior events, we believe, have a strong influence on the outcome. Arneson draws an analogy to linguistic relativity, the hypothesis that the language a person speaks influences aspects of her cognition.
So let me examine this concept in the context of another area of inquiry–biological research–and ask whether something similar might be affecting (and limiting) the kinds of experiments we do and the questions we ask.
Comment From Bill
St. Louis is being hindered in the stretch drive by some kind of GI bug passing through (so to speak) the team. Reports have as many as 15 guys down with it at once. That seems a lot, but given the way a baseball clubhouse works, my question is why don’t we see more of that? Answering that baseball players are fanatically interested in sanitation and hygiene ain’t gonna cut it, I don’t think…
So this comment caught my eye. Ever since I began following sites like BaseballProspectus.com and Fangraphs.com, and reading things like Moneyball, I’ve found myself thinking about efficiency and unappreciated or unexplored resources in different situations.
I realize this was a throwaway line in a baseball chat. But it piqued my interest because it seems to point out something that’s maybe underappreciated and understudied about how sports teams go about their business–specifically, the kinds of things they do to keep their athletes healthy.
All opinions are my own and do not necessarily reflect those of Novo Nordisk.
A few days ago the New York Times ran a nice article discussing a recent test of whether fecal transplants can be done using a pill format delivery system. The research, reported (and free, no less!) in the Journal of the American Medical Association, was peformed by physicians at Massachusetts General Hospital who had formulated human feces in an encapsulated pill format to see if that would be effective as a kind of fecal transplant. Fecal transplants appear to overcome infections by Clostridiumdifficile in patients. However, the conventional method for providing a fecal transplant is to deliver a liquid slurry either nasopharyngeally or via an enema-like procedure, neither of which is easily scalable. Also, yuck.
The current work, in which 14 of 20 patients responded to initial treatments using the poop pills, and an additional 4 responded the second time around, provided a proof of concept that a frozen, pill format delivery system may be a workable alternative to the current standard.
But as I was reading this article, I was struck by another thought. Are we missing a great opportunity for research into the interplay between the microbiome and human physiology?
All opinions are my own and do not necessarily reflect those of Novo Nordisk.
It’s Fourth of July weekend in Seattle as I write this. Which means it’s overcast. This was predictable, just as it’s predictable that for the two months after July 4th the Pacific Northwest will be beautiful, sunny and warm. Mostly.
Too bad forecasting so many other things–baseball, earthquakes, health outcomes–isn’t nearly as easy. But that doesn’t mean people have given up. There’s a lot to be gained from better forecasting, even if the improvement is just by a little bit.
And so I was eager to see the results from a recent research competition in health forecasting. The challenge, which was organized as a crowdsourcing competition, was to find a classifier for whether and how rheumatoid arthritis (RA) patients will respond to a specific drug treatment. The winning methods are able to predict drug response to a degree significantly better than chance, which is a nice advance over previous research.
And imagine my surprise when I saw that the winning entries also have an algorithmic relationship to tools that have been used for forecasting baseball performance for years.