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MICHAEL SHORT: All right, guys.
So today I'm not going to be doing most of the talking.
You actually are, because, like I've said,
we've been teaching you all sorts of crazy physics
and radiation biology.
We've taught you how to smell bullshit,
taught you a little bit about how to read papers
and what to look for.
And we're going to spend the second half of today's class
actually doing that.
Well, we're going to have a mini debate on whether or not
hormesis is real.
And you guys are going to spend some time finding
evidence for or against it.
Instead of just me telling you this is what hormesis is
or isn't.
So just to finish up the multicellular effects
from last time, we started talking
about what's called the bystander effect, which says,
if a cell is irradiated, and it dies
or something happens to it, the other cells nearby notice.
And they speed up their metabolism,
their oxidative metabolism, which
can generate some of the same chemical byproducts
as radiolysis does, causing additional cell
damage and mutation.
And there was an interesting--
yeah, I think I left-- we left off here at this study,
where they actually talked about most
of the types of mutations found in the bystander
cells were of different types.
But there were mutations found, in this case,
as a result of what's called oxidative-based damage.
This is oxidative cell metabolism
ramping up and producing more of those metabolic byproducts that
can damage DNA as well.
What we didn't get into is the statistics.
What do the statistics look like for large sample sizes
of people who have been exposed to small amounts of radiation?
I'm going to show you a couple of them.
One of them is the folks within 3 kilometers of the Hiroshima.
So I want you to notice a couple of things.
Here is the dose in gray, maxing out at about two gray.
And in this case this ERR is what's
called Excess Relative Risk.
It's a little different than odds
ratio, where here an excess relative risk of 0
means it's like nothing happened.
So anything above 0 means extra excess relative risk.
So what are some of the features you notice about this data?
What's rather striking about it in your opinion?
Yeah?
Charlie?
AUDIENCE: [INAUDIBLE] so in the [INAUDIBLE]
timeline from [INAUDIBLE] timeline here.
MICHAEL SHORT: This one?
AUDIENCE: Yeah.
MICHAEL SHORT: Oh, yeah, these are the errors.
Yep.
What does it say here?
Is it-- more than one standard error Yeah.
AUDIENCE: There's a lot of variability?
MICHAEL SHORT: Yeah, I mean, look
at the confidence in this data at high doses.
And then while you may say, OK, the amount of relative risk
per amount of radiation increases
with decreasing dose, which is the opposite of what
you might think, our confidence in that number
goes out the window.
Now what do you think of the total number of people that led
to each of these data points?
How many folks do you think were exposed to gray
versus milligray of radiation?
AUDIENCE: A lot less for gray than [INAUDIBLE]..
MICHAEL SHORT: That's right, the sample size.
I thought it was cold and loud in here.
The sample size for the folks in gray is much smaller.
And yet the error bars are much smaller too.
That's not usually the way it goes, is it?
Usually, you think larger sample size, smaller error bars,
unless the effects themselves and confounding variables are
hard to tease out from each other.
If you then look at another set of people,
all of the survivor-- oh. yeah, Charlie?
AUDIENCE: How did they determine the-- the doses [INAUDIBLE]??
MICHAEL SHORT: This would have to be from some estimate.
This would be from models.
It's not like folks had dosimeters everywhere
in Japan in the 1940s.
But this-- these would be estimates
depending on where you lived, let's
say in an urban, suburban, or rural area,
let's see, things like milk intake
right after the bomb, or anything that would have given
you an unusually high amount of radiation,
distance where the winds were going.
This is the best you could do with that data.
And now look at all of the bomb survivors,
including the ones outside 3 kilometer region,
but still got some dose.
What's changed?
AUDIENCE: It seems like they're less likely to get
more risk for less dose.
MICHAEL SHORT: Yeah, the conclusion
is almost flipped for the low dose cases.
If you put them side by side, depending on the folks living
within 3 kilometers of the epicenter of Hiroshima
versus anyone exposed, all the bomb survivors,
you get an almost opposite conclusion for low doses,
despite the numbers being almost,
you know, within each others confidence
intervals for high doses.
So what this tells us is that the effects of high dose
are relatively easy to understand and quite obvious
even with low sample sizes.
What is different between these two data sets?
Well, it's the only difference that's actually listed here.
Distance from the epicenter, right?
So before I tell you what's different,
I want you guys to try to think about what
could be different about the folks living
within 3 kilometers of the epicenter of Hiroshima
versus anyone else in the city or the countryside?
Yeah?
AUDIENCE: Would it be like [INAUDIBLE]??
It seems like a the closer, like, it
would be a lot more instances where you get a higher dose.
So they're underestimating [INAUDIBLE]..
MICHAEL SHORT: Could be, yeah.
It might be harder to figure out exactly how much dose folks had
without necessarily measuring it, right?
But what other major factors or confounding variables
are confusing the data here?
Yeah?
AUDIENCE: Wouldn't a lot of people who lived closer,
like, not inside the radiation, like,
the actual shockwave and heat from the bomb [INAUDIBLE]??
MICHAEL SHORT: So in this case, these are for bomb survivors.
So, yes, that's true.
If you're closer, you get the gamma blast.
You get the pressure wave.
AUDIENCE: But like, even if you survive that, it still like
would affect them in addition to radiation.
Is it counting for people who got injured from that too?
MICHAEL SHORT: It should just account all survivors, yeah.
AUDIENCE: So if they were injured,
that could change how they reacted to the radiation
exposure.
MICHAEL SHORT: Sure.
Absolutely.
And then the other big one is, actually,
someone's kind of mentioned it, but in passing, urban or rural.
The environment that you live in depends on
how quickly, let's say, the ecosystem replenishes or not
if you live in a city or what sort of other toxins
or concentrated sources of radiation
you may be exposed to by living in a city that's
endured a nuclear attack or something else.
It could also depend on the amount of health care
that you're able to receive.
If you show some symptoms of something,
if you live way out in the countryside,
and there weren't a lot of roads,
then maybe you can't get to the best hospital,
or you go to a clinic that we don't know as much.
The point is, there's a lot of confounding variables.
There's a lot more people.
But anything from like lifestyle,
to diet, to relative exposure, think about the differences
in how folks in the city and out in the countryside
may have been exposed to the same dose,
because, again, dose is given in gray, not in sieverts.
That's the best we can estimate.
But would it matter if you were to exposed
to let's say, alpha-particle containing fallout
that you would then ingest versus
exposed to a lot of gamma rays or delayed betas.
It absolutely would.
So the type of radiation and the route of exposure in the organs
that were affected are not accounted for in the study
because, again, the data is in gray.
It's just an estimated joules per kilogram
of radiation exposure, not taking into account the quality
factors for tissue, the quality factors for type of radiation,
the relative exposure, the dose rate,
which we've already talked about.
How much you got as a function of time actually does matter.
So all these things are quite important.
And for all these sorts of studies,
you have to consider the statistics.
So let's now look at a--
I won't say, OK, a cellphone-like study
where one might draw a conclusion if the error
bars weren't drawn.
So based on this, can you say that very low doses
of radiation in this area actually
give you some increased risk of, what do they say,
female breast cancer?
No.
You can't be bold enough to draw a conclusion from the very
low dose region from, let's say, the-- the 1s to 10s
of milligray, that whole region right there that people
are afraid of getting, we don't actually
know if it hurts or it has nothing, or if it helps.
That's a kind of weird thing to think about.
So the question is, what do we do next?
These are the actual recommendations from the ICRP.
And I've highlighted the parts that
are important, in my opinion, for everyone to read.
And the most important one, probably we'll
have to come to terms with some uncertainty
in the amount of damage that little amounts of dose do.
So this is the ICRP saying to the general public,
you guys should chill out.
There's not much we can do about tiny amounts of exposure.
They happen all the time.
You can either worry about it, and get your heart rate up,
and elevate your own blood pressure,
and have a higher chance of dying on your own,
or you can just chill out because there is not
enough evidence to say whether a tiny little amount
of radiation, and we're talking in the milligray or below,
helps, or hurts, or does nothing, which leads me
into the last set of slides for this entire course,
they're not that long because I want you guys to actually
do a lot of the work here, is radiation hormesis, real
or not?
There are plenty of studies pointing one way or the other.
And I want to show you a few of them with some other examples.
The whole idea here is that a little bit of a bad thing
can be a good thing, much like vitamins,
or, let's say, vitamin A in seal livers, a little bit of it
you need.
It's a vital micronutrient.
A whole lot of it can do a whole lot of damage.
You don't usually think of that being the case for radiation.
But some studies may have you believe otherwise
with surprisingly high sample sizes.
So the idea here is that if you've got anything, not just
element and diet, but anything that happens to you,
there's going to be some optimum level where you could
die or have some ill effects if exposed
to too much or too little.
We all know that this happens with high amounts of radiation.
The question is, is that actually happened?
So let's look at some of the data.
In this case, I mentioned selenium and actually
have a fair bit of this data that shows some,
let's say, contradictory results in this case, where
a whole lots of different people were
exposed to a certain amount of selenium accidentally.
I don't think these were any intentional studies.
But some folks received massive doses of selenium
and tried-- folks tried to figure out, well,
what how-- oh, yeah, if you want to see how much they got.
Remember that you want about 5 micrograms per day on average.
That's a pretty crazy amount of selenium
that ended up killing this person in four hours.
But let's look at a sort of medium dose, something way
higher than you would normally get.
Two different studies published in peer-reviewed places--
this one says, "taking mega doses of selenium,"
so enormous doses, "may have acute toxic effects
and showed no decreased incidence
of prostate cancer and increased prostate cancer rates.
35,000 people.
The same supplements greatly reduced
secondary prostate cancer evolution in another study."
Kind of hard to wrap your head around that, right?
Both these studies were done with, I'd say, enough people
and came to absolutely opposite conclusions,
showing that there's definitely other confounding
variables at work here.
So there's kind of two solutions to this problem,
increase your sample size to try to get
a most representative set of the population
or control for other confounding variables.
And then the question is, how do you
model how much is a good thing to go over
what these models mean.
The one that's described right now in the public
is called the linear-no threshold model.
This means that if this axis right here is bad
and this is axis right here is amount
that any amount of radiation is bad for you.
What I think might be a little bit more accurate
is called the linear threshold model.
If you remember from two classes ago,
the ICRP recommends that, I think,
0.01 microsieverts is considered nothing officially.
That would mean there is a threshold below which
we absolutely don't care.
And if there are any ill effects,
they're statistically inseparable from anything else
that would happen.
And that would suggest here this linear threshold model,
where this control line right here would
be the incidence of whatever bad happens in the control
population not exposed to the radiation, the selenium,
the whatever.
There's also a couple of other ones like the hormesis model,
which says that if you get no radiation,
you get the same amount of ill effects as the control group.
If you get a little radiation, you actually
get less ill effects.
In this case, this would be like saying
getting a little bit of radiation to the lungs
could decrease your incidence of lung cancer.
Does anyone believe that idea?
Getting a little bit dose to your lungs
could decrease lung cancer?
OK.
And then you reach some point of crossover point
where, yeah, a lot of this thing becomes bad.
And the question is, is radiation hormetic?
Does this region where things get better actually
lead all the way to x equals 0 as a function of dose?
And I want to skip ahead a little bit
to some of the studies.
No, I don't want to skip ahead.
There are some non hormetic models
that have been proposed in the literature.
It's easy to wrap your head around a linear model, right?
It's just a line.
More is worse.
But the question is, how much?
So folks have proposed things like linear quadratic,
where a little bit of dose is bad.
And then a lot more dose is more bad as a function of dose.
That's actually kind of what we saw in the Hiroshima data.
And I'll show you again in a sec.
So the history of this LNT, or Linear No-Threshold model,
states the following four things--
radiation exposure is harmful.
Well, does anyone disagree with that statement?
I think we all know that even large-- you know,
at least large amounts of radiation exposure is bad.
It's harmful at all exposure levels.
That's the one you have to wonder.
Each increment of exposure adds the overall risk,
saying that it's an always increasing function.
And the rate of accumulation exposure
has no bearing on risk.
The first one's easy.
We know this is true because you expose people
to a lot of radiation, bad things
tend to happen, deterministically.
The second one, we already know is false.
If you look at large sample sets of data, like, the data
we showed before, there's definitely
a non-linear sort of relationship going, where
each incremental amount of exposure
has the same amount of incremental risk.
We know from a lot of studies that's not typically true.
Then the question is, what about these two?
So now it's going to-- we're going to find
and who you some fairly interesting studies.
In this case, leukemia as a function of radiation dose,
what do you guys think about this data set before I
seed any ideas into your heads?
So here is dose and sieverts, not gray.
And here is odds ratio, relative risk of contracting leukemia.
If you were to look at the data points alone,
what would you say?
AUDIENCE: A little bit of dose is good for you.
MICHAEL SHORT: Yeah, you might think that.
But look at all the different types
of models you can draw through the error bars.
As you could draw anything going,
let's say, down and then up.
You could draw a linear no-threshold model,
as long as it got through this line right here
or a linear quadratic model.
So a study like this doesn't quite
give you any sort of measurable conclusion.
A study like this might, especially considering
the number of people involved.
In this case, this is the activity
of radon in air as related to the incidence of lung
cancer per 10,000 people.
Notice the sample size here, 200,000 people
from 1,600 counties that comprise 90% of the population.
Chances are you've then passed the urban-rural divide.
You've then passed any region of the country.
So by including such a gigantic sample size,
you do mostly eliminate the confounding variables.
So, location, you know, house construction,
urban versus rural, age, anything else
are pretty much smeared out in the enormous sample size.
And what do you see here?
AUDIENCE: Looks pretty good for low dose.
MICHAEL SHORT: Yeah, you see a fairly
statistically-significant hormesis
effect, where, you know, the route of exposure
is very well-known.
Everything else seems to be controlled for by--
I mean, we've included something like almost 0.1%
of the US population.
That's not bad.
Other ones for people that get more specific, targeted dose,
in this case, women who received multiple x-rays
to monitor lung collapse during tuberculosis treatment, a group
of people that can be tightly controlled
and followed very well.
These are numbers with one standard deviation.
And that, right there so you can see, is centigray.
So this dose right here is one gray worth of dose.
That's a pretty toasty amount of radiation.
But below that, again, statistically
significant-looking data.
I don't know how many people were in the study
because I didn't extract that information.
But it's something you might be doing in the next half an hour.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Oh, it does.
It says deaths per 10,000 women.
But how many people were in the study?
The question is, what is your sample size?
So like in the last study, it was just 200,000 people
in the samples.
That gives you some pretty good confidence that you've
eliminated confounding results.
So I don't know how many folks get tuberculosis
these days in the US, or whether this was even a US study,
chances are the sample size is smaller.
So than even if the data support your idea of hormesis,
you have to call into question, is
this a large enough, and a representative enough,
sample size to draw any real conclusion?
So then let's keep going.
More data needed.
Evidence for a threshold model.
This is probably the most boring-looking graph
that actually gives you some idea of,
should there be a threshold for how much radiation
is a bad thing?
In this case, it's very careful data.
It's a very carefully-controlled data set, lung cancer
death from radon in miners.
And folks that are going down underground probably
have a higher incidence of lung cancer
overall from all the horrible stuff
they're exposed to, whether it's coal or, you know,
if you're mining gypsum.
Oh, there's lots of nasty stuff down there.
But there is an additional amount
of deaths responsible from radon.
Here's your relative list risk level of 1
and up to 10 picocuries per liter,
which was around the maximum of the last study.
It's as boring as it gets, which helps refute
the idea of a linear no-threshold model,
because if there was a linear no-threshold model,
this dose versus risk would be reliably and significantly
going up.
So there's data out there to support this.
And even-- even better ones, lung cancer deaths
from radon in homes.
The study was careful to look at.
If you look at the legend here, these
are different cities ranging from Shenyang in China,
to Winnipeg in Canada, to New Jersey, which is apparently
a city, to places in Finland, Sweden, and Stockholm,
which are somehow different places.
Yeah.
So when you see a study like this where they actually
control and check to make sure they're
not getting any single locality as
an unrepresentative measurement, and the data just
looked like a crowd--
a cloud along relative risk equals 1,
this either refutes the idea that there is no threshold
or supports the idea that there's
got to be some threshold lying beyond 10 picocuries per liter.
So, again, to me, it supports the ICRP's recommendation
of chill out.
You're going to have a little bit of radon in your basement.
But pretty big studies, and quite a lot of them,
show that a little bit isn't going to add any risk to you.
So if you're worried about risk, they're
statistically is none based on quite a few of these studies.
And in order to enable you to find these studies on your own,
I wanted to go through five minutes of where to look.
And the answer is not Google because Google is not very
good at finding every study.
It also picks up a whole lot of garbage
that's not peer reviewed because it just scrabbles the internet,
you know?
That's what it does really well.
Instead, I want us to take the next half hour,
split into teams for and against hormesis,
and try and find studies that confirm
or refute the idea that radiation hormesis is
an actual effect.
So how many of you have some sort of computer device
with you here?
Good.
Enough so that there is equal amount in each group.
I'd like to switch now to my own browser.
And I want to show you guys the Web of Science.
Web of-- yeah, [INAUDIBLE] I use Pine on my phone.
It's much better science.
So if you just Google search Web of Science, and you're at MIT,
it will recognize your certificates
and send you into the actual best scientific paper indexing
thing out there.
AUDIENCE: Better than Google Scholar?
MICHAEL SHORT: Oh, my god, it's better than Google Scholar.
Yeah.
If you think you've found everything
by looking at Google Scholar, you're only fooling yourself.
You're not fooling anybody else.
It's getting better.
But it doesn't find anything.
And Google Scholar is really good at finding
things that aren't peer reviewed,
self-published stuff, things on archive, things
that you can't trust because they
haven't passed the muster of the scientific community.
So instead, let's say you would just
do a simple search for radiation hormesis.
You can all do this.
Don't worry.
I'm not showing you how to search.
I'm showing you some of the other features
of Web of Science.
And you end up with 534 papers.
You can, let's say, sort by number of times cited,
which may or may not be a factor in how trustworthy the data is.
It might just correlate with the age of the paper.
It might also be controversial.
So if people cite it as an example of what to do wrong,
it might be highly cited.
You know, people have made tenure cases and like careers
on papers that ended up being wrong.
And all you see is 10,000 citations saying this person
is an idiot.
If the committee val-- you know, judging you for a promotion
doesn't read that far into it, they're
like, oh, my god, 10,000 citations, right?
Boom!
Tenure, that's all you have to do.
I think I have it a little tougher.
The important part is while with a title like that, oh, man,
the more-- the real fun part though is you
can see who has cited this paper.
So if you want to then go see, why has this paper been cited
260 times, you can instantly see all the titles, and years,
and number of additional citations of the papers
that have cited it.
So this is how you get started with a real research, research.
Yeah, that's what I meant to say,
is starting from a paper and a tool like Web of Science,
you can go forward and backward in citation time,
backward in time to see what evidence this paper used
to make their claims, forward in time to see what
other people thought about it.
So who wants to be for hormesis?
All right, everyone, all you guys on one side of the room,
all you guys, other guys on the other side of the room.
And I'd like you guys to try to find
the most convincing studies that you can
to prove the other side wrong.
I suggest using Web Science, not Google Scholar.
It's pretty easy to figure out how to learn how to use.
And let's see what conclusion we come to.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Yep, hormesis by the wall--
yeah, anti-hormesis by the window.
There we go.
And I'm going to hide this because I don't want to give
anyone an unfair advantage.
AUDIENCE: So [INAUDIBLE].
SARAH: So this is a graph showing the immune response
in the cells of mice showing that after they were given
doses from 0 to 2 gray, or 0 to 7 on the right,
the response of the immune system.
So at the lower doses below like 0.5 gray, which is in the range
that we're looking at, well, the immune system in the mice
had a stronger response at low doses of radiation
and then very quickly tapered off,
supporting the claim the low doses are good for mice.
[LAUGHTER]
MICHAEL SHORT: [INAUDIBLE]
SARAH: I have another graph too.
MICHAEL SHORT: So this percentage change in response,
I'm assuming 100 years is no dose.
OK.
SARAH: Yes.
So at higher doses, the response of the immune system
was suppressed, which follows with what all the other studies
show about giving doses in excess of like 1 gray to cells.
MICHAEL SHORT: Cool.
So anti-hormesis group.
SARAH: Oh, I have another graph, but--
MICHAEL SHORT: Oh, you do?
SARAH: Yeah.
MICHAEL SHORT: Oh, I wasn't going to call them out.
I was going to have them criticize what's up here.
SARAH: Oh, no.
I have another graph.
MICHAEL SHORT: [INAUDIBLE] next.
SARAH: I have two of the same ones.
No, I have another one somewhere.
I'll find it in a sec.
This one.
All right, so this one is incidences
of lung cancer based on mean radon level
and corrected for smoking.
So you can't say that it was just from people smoking.
So for radon levels up to 7 picocuries per liter,
the incidence of fatal lung cancer
actually decreased as you had more radon.
MICHAEL SHORT: Oh.
AUDIENCE:
SARAH: Yes.
MICHAEL SHORT: Anything else you guys
want to show before we let the anti-hormesis folks poke at it?
SARAH: That's what I got.
MICHAEL SHORT: OK.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: What are your thoughts?
AUDIENCE: OK, could you go back to the last one.
SARAH: I will try, yes.
AUDIENCE: Do you have any other [INAUDIBLE]..
AUDIENCE: [INAUDIBLE] response.
AUDIENCE: So-- so a mouse is twice--
almost twice as effective at fending off disease?
OK, I-- I am not a mouse biologist,
but the smell test makes me think that--
that perplexed me.
And I guess you didn't do studies [INAUDIBLE]..
SARAH: I am not personally offended by this.
So you're good.
AUDIENCE: Enormous-- enormous change.
And if radiation hormesis has such a strong effect
on these mice, then why isn't it something everywhere as a thing
now.
Like, if radiation-- if hormesis is responsible for 80%
[? movement ?] in mice, [INAUDIBLE] like where--
SARAH: I don't know that it was improvement.
I think it was just in the amount of response they saw.
I don't know if that means it's--
well, that doesn't always mean it was
effective at doing something.
Right.
MICHAEL SHORT: [INAUDIBLE] you guys have comments too?
AUDIENCE: Additionally, that's like an extremely small
of a dose for such a massive response
in like a field that is so based on probability.
Like, how can something like the dose range
that small have that much of an impact on mice?
SARAH: Well, from 0 to half a gray is pretty significant.
AUDIENCE: But [INAUDIBLE]
SARAH: [INAUDIBLE]
AUDIENCE: --before you get to the 0.6 gray.
AUDIENCE: You're also only looking
at the cells from [INAUDIBLE] it seems like.
And it like looked varied depending
on the kind of tissue.
So you can't do it for overall.
MICHAEL SHORT: OK, I want to hear
from the pro-hormesis team.
What makes your-- what makes your legs a little shaky trying
to stand and hold this up?
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Aha.
SARAH: Didn't read the study.
[LAUGHTER]
MICHAEL SHORT: I like this--
I like this idea that, yeah, you're
only looking at one type of cell, which may or may
respond differently to different types of radiation.
There are no error bars.
SARAH: No, not even a whole mouse either.
AUDIENCE: [INAUDIBLE] in the mouse.
MICHAEL SHORT: Oh, oh to trigger an immune response.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: It's like-- there are--
there's other cells nearby.
But they're like, oh, you're not my cell.
I'm going to [INAUDIBLE].
AUDIENCE: [INAUDIBLE] mice.
MICHAEL SHORT: Yeah.
So that's-- that's a valid point.
But, yeah, did it say in the study how many?
SARAH: Again, did not read the study.
[LAUGHTER]
Read the conclusion.
MICHAEL SHORT: The data alone, just taken it at face value,
make it look like hormesis is a definite thing, Yeah, Kristin?
AUDIENCE: I'm saying if there is [INAUDIBLE]..
MICHAEL SHORT: Yeah.
SARAH: True.
Nine mice cell samples.
MICHAEL SHORT: Let's go to the other study.
SARAH: All right, the-- the lung one?
MICHAEL SHORT: Yeah, it seems to be
more controlled and more legit.
SARAH: Yeah.
This one has error bars.
MICHAEL SHORT: Yeah, 1 has error bars, 2, corrected for smoking.
So let's see what the caption says.
Lung cancer fatality rates compared with mean radon levels
in the US.
SARAH: And for multiple counties because it
talks about counties plural.
So--
MICHAEL SHORT: So multiple counties
helped control for single localities, or--
AUDIENCE: So the 0 level there is theoretical.
So the data that you have down here,
like, we don't know what actually happens [INAUDIBLE]..
SARAH: Past what?
AUDIENCE: Like-- like below 1, the mean radon levels
because everyone is exposed to radon.
SARAH: Well, it says average residential level of 1.7.
So I think that means maybe some people have less, maybe
some people have more.
I don't know what the minimum radon level is.
MICHAEL SHORT: It's not going to be 0.
SARAH: It's not 0.
MICHAEL SHORT: Yeah, no one gets 0
unless you live in a vacuum chamber.
SARAH: I don't know what kind of scale that's on.
AUDIENCE: Me too.
MICHAEL SHORT: Yeah.
Cool, yeah.
So this-- this is fairly convincing.
If the point here was to show there
is the theory of linear no threshold,
and here's what's an actual data with error bars shows.
It does a pretty good job in saying,
the theory is not right, in this case.
Can you say that in all cases?
It's hard to tell.
In the first study you found that was on the cellular level.
Maybe the multicellular level--
multicellular level, certainly not the organism level,
like we said, how many mice.
This is just parts of mice.
Just--
SARAH: It could be the same mouse.
MICHAEL SHORT: Some cells-- yeah.
This one is definitely at the organism level.
It's for-- for gross amounts of exposure, how many of them
resulted in increased incidence of lung cancer?
The answer is pretty much none.
They all showed a statistically-significant
decrease, which is pretty interesting.
So thanks a lot.
Sarah.
And the whole team.
Now one of you guys come up and find [INAUDIBLE]..
SARAH: Carrying the team.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: So who wants to come up?
Or does no one [INAUDIBLE]?
SARAH: Let's throw down, right?
Fixing to scrap.
MICHAEL SHORT: OK, you can just pull it out.
SARAH: OK, Are you sure?
MICHAEL SHORT: Yeah.
SARAH: OK.
I don't want to break things.
MICHAEL SHORT: No, pulling it out's fine.
If you jam it in, you can bend the pins.
And that's happened here before.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Yeah, if you want to take a minute to send
each other the links, go ahead.
No, I like this, though, is you can--
you can find a graph that supports something.
And you can cite it in a paper.
And you can get that paper published.
But looking more carefully at the data
does sometimes call things into question.
AUDIENCE: Just like [INAUDIBLE].
MICHAEL SHORT: Like, I think you guys found
a good example of that mouse cell study
that looks like it supports hormesis,
but you can't say so for sure.
Make sure no one's waiting for their room.
No one's kicking us out.
AUDIENCE: Have we got a paper that I found here
but we can't open up on there.
MICHAEL SHORT: Interesting.
Can you send me the link?
AUDIENCE: [INAUDIBLE]
AUDIENCE: Wait, that wasn't an option.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Yeah.
I mean, we can continue this.
There's-- we're not-- since we're not going to the reactor
since that valve was broken, let's keep it up.
AUDIENCE: Hey, [INAUDIBLE] workbook
and [INAUDIBLE] put it in the log book.
AUDIENCE: That's your fault.
AUDIENCE: [INAUDIBLE]
AUDIENCE: I wasn't even [INAUDIBLE]..
AUDIENCE: [INAUDIBLE]
Email us by name.
AUDIENCE: [INAUDIBLE]
AUDIENCE: It's not over yet.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Yeah, actually, I like this.
This will be a good--
quite a good use of recitation.
I'll keep my email open in case folks want
to send things to present.
AUDIENCE: That's the whole title.
GUEST SPEAKER: So one-- one of the main problems
that we had with the hormesis effect
was that all of the studies that we've seen
seem to cover a large scope of like tissues,
different effects, and all sorts of things,
like, yeah, there's a lot of studies.
There's a lot of trends.
But, like, the things in particular
that they're studying are all over the place.
And a lot of the--
a lot of the research done, like these studies
here, are not actually meant to study hormesis.
It's kind of like recycled data that's
used from some other study.
And they're kind of like pulling from multiple sources, which
increases the uncertainty.
Then, additionally, we have conflicting
epidemiological evidence of low dosages.
So we're, in one instance, you may see a reduction
in breast cancer mortality.
You'll see excess thyroid cancer in children, other, which is--
MICHAEL SHORT: That's the same study that was just shown,
the Cohen 1995 residential radon study.
GUEST SPEAKER: Yeah.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: [INAUDIBLE]
[LAUGHTER]
GUEST SPEAKER: And so I think--
we're not-- I don't think we're trying to disqualify hormesis
as, like, completely wrong.
I think one of the biggest issues
that we're taking with it is that it's a small effect,
if anything.
It's something that we really don't know about.
It's hard to quantify.
And it's, at the end of the day, really just not worth it, not
worth looking into because of all of the variable--
variables that go into it.
And the effects that, like, we just don't know about.
We don't understand it.
So, yeah, fire away.
MICHAEL SHORT: That's a a great viewpoint, actually.
Yeah, Monica?
AUDIENCE: [INAUDIBLE]
OK, so it says support for radiation hormesis [INAUDIBLE]
cell in animal studies, OK?
And then it cites an example.
Can you tell me how that, like, you know,
supports what you're saying?
AUDIENCE: Can you just highlight the part?
MICHAEL SHORT: Oh, right-- right up here.
AUDIENCE: OK.
GUEST SPEAKER: We haven't seen it in humans.
AUDIENCE: Well, often, biological studies
are done on rats because they have similar effects to humans.
But it's a lifespan of, like, 1/10 a human's lifespan.
So, biologically, that's accepted.
GUEST SPEAKER: Medicine also is not
accepted until it works on humans, not on animals.
AUDIENCE: [INAUDIBLE]
GUEST SPEAKER: So we can cure cancer in rats all day.
But, like, if it doesn't work in like the human body,
then it just--
we still don't use it, like, it needs
to clear the hurdle of human usefulness
before we actually use it.
MICHAEL SHORT: Let's actually look at this paragraph.
They relate to carcinogensis in different tissues
and the dose-response relationships [INAUDIBLE]..
AUDIENCE: So there's a line that says
the evidence for hormesis in these studies
is not compelling since the data may also be also
be reasonably interpreted to support no radiogenic effect
in the low dose range.
MICHAEL SHORT: Oh, that's interesting.
Now, how would one interpret-- because you showed the Cohen
data.
So how would one interpret that to mean no effect?
I'm trying now determine in this--
are the claims of this paper that you've been [INAUDIBLE]??
And this brings up, actually, another point.
They do agree that there's been hundreds of cell and animal
studies.
They cite three human studies.
So since we have the time, you guys
may want to look for more than three human studies, done
at the time of this writing.
It's not fair to take ones that were done afterwards.
AUDIENCE: [INAUDIBLE]
GUEST SPEAKER: What?
Let's find out.
AUDIENCE: After 2000.
MICHAEL SHORT: It might say at the bottom of the first page.
AUDIENCE: Oh, wait, in the-- in the [INAUDIBLE]..
MICHAEL SHORT: 2000, yep.
Yeah.
So if you want to refute that point,
you may want to find more human studies pre 2000.
It wouldn't be fair to do otherwise.
But, actually, I liked what you said.
So what you're proposing--
if there's a mostly blank board, is
that most people should adopt the model that
looks something like this.
This is the axis of how much bad or that 0.
And this is dose in gray.
And whether your model does this, or this, or this,
it sounds to me like you are defining a--
like you're defining a kill zone.
[INAUDIBLE] maybe the--
GUEST SPEAKER: Yes.
MICHAEL SHORT: The point isn't whether or not hormesis exists.
The effect may be so small that who cares.
But the bigger discussion is how much is that, not
is a little bit good.
Is that what you're getting at?
GUEST SPEAKER: Yeah, the like, maybe it does look like this.
But the dip is small, really not that
different from the linear threshold model, we noticed.
MICHAEL SHORT: Oh, so in addition
to being a basic science question,
could the issue of hormesis almost
be a sidetrack in getting proper radiation policy through?
That's a point I hadn't heard made before,
but I quite like it.
Because it's not like you're going
to recommend everyone smokes three cigarettes a day
or, you know, everyone gets blasted
by little bit of radiation once a year as part of a treatment.
I don't think anyone would buy that.
Even if it did help, I don't think anybody would emotionally
buy that.
But by focusing on--
you know, that-- there's a nice expression
is the most important thing is make the most important thing
the most important thing.
It means don't lose sight of the overall goal, which
is if you're making policy on how much radiation
exposure you're allowed, do you focus
on saying, a little bit is actually good,
or do you focus on saying, here's the amount that's bad?
And anything below that, we shouldn't
be regulating or overregulating because there's no evidence
to say whether it's good or bad outside the kill zone.
I quite like that point, actually.
It means that the supporters of radiation
should chill out as well.
Cool, all right, so any other studies you want to point out?
GUEST SPEAKER: We had a couple of abstracts.
MICHAEL SHORT: Yeah, let's see.
GUEST SPEAKER: But I don't--
I'm not sure.
AUDIENCE: [INAUDIBLE]
GUEST SPEAKER: OK.
AUDIENCE: Some of the other ones don't compare hormetic models.
But they look at--
they say [INAUDIBLE].
It's like--
GUEST SPEAKER: Do you want to come up?
AUDIENCE: Yeah, this one says [INAUDIBLE]..
GUEST SPEAKER: All right.
AUDIENCE: [INAUDIBLE]
AUDIENCE: It basically compares threshold models
with no-threshold models in [INAUDIBLE]..
AUDIENCE: [INAUDIBLE]
So perhaps hormetic is still better for you,
but they-- the [INAUDIBLE] was good enough with [INAUDIBLE]..
MICHAEL SHORT: So what they're saying is the--
the choice of model really doesn't
matter, as long as it fits through the data
that we've got.
And it seems to be, again, what happens in the low-dose regime
is less important, right?
AUDIENCE: And it will-- they were satisfied when
it fell from the [INAUDIBLE].
MICHAEL SHORT: So they're saying the best estimate of this--
interesting.
AUDIENCE: They prefer no threshold [INAUDIBLE]..
MICHAEL SHORT: That's funny.
"If a risk model with a threshold is assumed,
the best estimate is below 0 sieverts.
But then how is their confidence interval from--
oh, less than 0 to 0.13.
They don't quantify how much lower
it goes because a negative dose doesn't make sense.
No.
So, yeah, it's a strong conclusion.
But it looks-- looks fairly well supported
to say that we can't say with those confidence intervals
that they give if there is or isn't a threshold.
Interesting.
What do you guys think of this?
So what would you delve into the study
to try to agree with or refute this claim?
AUDIENCE: They use a linear quadratic model only,
it looks like.
So they're not considering any of the other proposed
models, which is a little--
maybe not sketchy, but it just seems
like it'd be very easy to consider other models
and why didn't they do that.
MICHAEL SHORT: Sure.
You know, what no study has gotten into yet is,
what's the mechanism of, let's say, ill effect acceleration.
This is something that, at least at the grad school level,
we try to hammer to everyone constantly
is not just what's the data, but what's the mechanism.
What's the reason for an acceleration of ill effects?
So if you guys had to think with increasing radiation exposure,
let's say we wanted this linear quadratic model idea, what
could be some reasons or mechanisms for an increased
amount of risk per unit dose as the dose gets higher?
Yeah?
AUDIENCE: Well, your body [INAUDIBLE]..
But then-- so at some-- you get more dose--
you get more dosing [INAUDIBLE].
It just keep fixing itself.
And once you get past a certain point,
then it can't [? fix itself ?] [? fast enough. ?] The
additional damage keeps snowballing events.
And they're giving it more damage
to curb more radiation because you would run out of--
of various [INAUDIBLE].
MICHAEL SHORT: Sure.
Works for me.
Yeah, I like that-- the idea there
was that you've got some capacity to deal
with damage from radiation.
And then once you exceed that capacity, you don't also--
with a higher dose, you don't also
ramp up your capacity to deal with that dose.
So in the linear region, let's say,
you're somewhat absorbing the additional ill effects of dose
by capacity to repair DNA or repair cells.
Then once you exceed that threshold,
you're beyond that point.
So that could be a plausible mechanism
for why there could be a linear quadratic model that could
be tested, certainly with single cell or multi cell studies,
like these-- these radiation microbeams or, you know,
injecting something that would be absorbed
by one cell [INAUDIBLE] irradiated,
and seeing what the ones nearby do.
So you could count that as number
of mutations, number of cell deaths,
anything, something that could be quantitatively tested.
So that's pretty cool.
I actually quite like this study.
It's awfully hard to poke a hole in--
in the logic used here.
The claims aren't outrageous.
They're saying, this is what the data is saying.
If you change the model, you can or not have a threshold
and still get an acceptable fit.
Can we actually look in the study itself?
One thing I want to know is, what sort of--
do they do meta analysis, or did they--
yeah, so this was on the Japanese atomic bomb survivors.
So did they analyze previous data,
or did they get their own.
And then if so, what was the sample size?
Somewhere it'll be, like, yeah, [INAUDIBLE]..
So where [INAUDIBLE].
GUEST SPEAKER: Where am I--
where should I be looking for this--
MICHAEL SHORT: Probably further down
in any sort of methodology section--
materials and methods, here we go.
OK, here it is, 86,500 something survivors.
Oh, yes, with lots of follow up.
AUDIENCE: But how are you able to determine the dose?
Like--
MICHAEL SHORT: That is a good question.
AUDIENCE: Because especially for--
if we're looking like low dose, and you're estimating,
it's very easy to, like, estimate wrong, or, like,
because then-- then it calls into question you have--
[INAUDIBLE] modeling they're using.
MICHAEL SHORT: Mhm.
So that's a great question is, how
do they know what those people die?
So how would we go about trying to trace that?
This is when you dig back in time.
They reference this, the data appears et al,
whatever, whatever.
So if you can go to Web of Science,
pull up this Pierce et al Web paper.
Look at cited references.
Yeah, right there.
And look for that 1996 Pierce study.
Let's see if it has it.
You can just like control F for Pierce, and we'll find it.
Pierce and [INAUDIBLE].
Yeah, 1996, that's the one.
GUEST SPEAKER: Where?
Which one?
This one?
MICHAEL SHORT: [INAUDIBLE].
This is the 1996 one.
Yep.
So let's see if we can trace this back
and find out how they estimated the dose of these folks.
GUEST SPEAKER: So I just go to full text?
MICHAEL SHORT: Yeah.
AUDIENCE: How [INAUDIBLE].
MICHAEL SHORT: OK.
So interesting, this LLS cohort.
So there was some life span study,
which was also referred to actually in the lecture notes
as one of the original studies, says,
who met certain conditions concerning adequate follow up.
Although estimates of the--
OK, I want to see the next page.
Although we estimate-- that might
be what we're looking for.
Number of survivors, let's see.
AUDIENCE: It's 92%.
MICHAEL SHORT: OK, here we go, materials and methods.
The portion of the LSS cohort used here
includes the same number of survivors
for whom dose estimates are currently available,
et cetera, with estimated doses greater than 5 millisieverts is
[INAUDIBLE].
Table 1 summarizes the exposure distribution.
So let's go find table 1 and see where the data came from.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: So it turns out that this is specifically--
DS-86 weighted colon dose in sieverts.
Interesting.
AUDIENCE: It [INAUDIBLE].
So how did they get that?
MICHAEL SHORT: I don't know.
But it sounds like we need to find this LSS, this Just LSS.
So let's look at the things that this paper cites.
Find this LSS.
So I'm walking-- what I'm doing here is walking you through how
to do your own research.
And if someone comes to you with some internet emotional
argument of, this and that about radiation is wrong,
instead of yelling back louder, which
means you lost the argument, you hit the books.
And this is how you do the research.
AUDIENCE: LSS-85, does that mean it was [INAUDIBLE]..
MICHAEL SHORT: Probably.
Version of-- title not available.
I hope it's not that one.
Can you search for LSS?
Nothing?
So let's go back to the paper and find
what citation that was.
If you go up a little bit, I think there was like a sup--
a superscript up to the last page, I'm sorry.
There was a superscript on LSS stuff.
AUDIENCE: So general documentation
of the selection of LSS cohorts [INAUDIBLE]..
MICHAEL SHORT: Thank you.
All right, let's find references 9 and 10 in the--
yeah, [INAUDIBLE].
AUDIENCE: Can you click one of the References tab?
MICHAEL SHORT: Oh, yeah, up there, References.
Awesome!
9 and 10, OK.
Let's find them.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: So let me show you
quickly how to use Web of Science
to get what you're looking for if I could jump on?
GUEST SPEAKER: [INAUDIBLE] up here?
MICHAEL SHORT: You don't have to, yeah.
But thank you for being up here for so long and running this.
So we're looking for--
where was-- the article was here.
Went into references.
I guess that was like the last--
I don't want to close all your tabs.
Here we go.
So GW, is that Beebe and Usagawa.
So we'll go to Web of Science, look for authors,
any paper with those authors.
So you can do a more advanced search.
This is where things get really interesting and specific.
So ditch the topic.
Search by Beebe and add a field, Usagawa.
And then anything with these two folks
in the author field that is indexed by Web of Science
will pop up.
Nothing.
Did I spell anything wrong?
Usagawa, of course.
That's unfortunate.
Last thing to try is Add Wild Cards.
Interesting.
This is actually one place where I would use Google
to find a specific report.
So because you're not looking to survey a field that's
out there, but you're looking for any document
that you can confirm is that document.
Let's head there.
Oh, it looks like Stanford's got it.
That's something that references it.
So at this point, we've hit the maximum
that we can do on the computer.
But if you finally want to trace back
to see how were the Hiroshima data acquired,
take these citations, bring it to one of the MIT
librarians like Christ Sherratt is our nuclear librarian.
AUDIENCE: He's a nuclear librarian?
MICHAEL SHORT: And we have a nuclear librarian, yeah.
MIT libraries is pretty awesome.
So when you're looking for anything here
in terms of research or whatever,
there's actually someone whose job
it is to help you find nuclear documents.
And chances are, this is a pretty big one.
So I wouldn't be surprised if we have
a physical or electronic copy.
So we're now like one degree of separation
away from finding the original Hiroshima
data, where we can find out how did they estimate that dose.
So I think this is fairly--
hopefully, this is fairly instructive
to show you how do you go about getting the facts to prove
or disprove something, knowing the-- not just the physics
that you know, but how to go out and find that stuff.
Now, I did see a bunch of sources
from the pro hormesis team.
You still want me to show them?
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: OK.
Thanks.
All right, you just want to hold this up while your--
let's go to your sources.
OK, here we go.
AUDIENCE: All right.
MICHAEL SHORT: So walk us through what you found.
GUEST SPEAKER: I just need to open them up.
AUDIENCE: Go through them all, or--
MICHAEL SHORT: Yeah, let's do them all.
GUEST SPEAKER: There's not too much.
Kind of-- OK, so, I unfortunately
was not able to find like too many pretty graphs, or data,
or anything of the sort.
But if you look up, what did I search for this?
I think I just looked up radiation hormesis.
And this is one of the articles that turned up.
And it seems to be pretty well cited.
You can see it's been cited 184 times.
And kind of the quick look through the citations,
from what I saw, seemed to be in support of it.
And if you actually look at the abstract itself, where is it?
AUDIENCE: [INAUDIBLE]
GUEST SPEAKER: Yeah, well-- the last sentence
is pretty excellent.
"This is consistent with data both from animal studies
and human epidemiological observations
on low-dose induced cancer.
The linear no-threshold hypothesis
should be abandoned and should-- and be replaced
by a hypothesis that is scientifically justified
and causes less unreasonable fear
and unnecessary expenditure."
MICHAEL SHORT: You know what?
I want to see what are the human epidemiological observations
that they cite.
GUEST SPEAKER: Yeah, so unfortunately, the MIT
libraries does not have an electronic copy
of this article.
And I wasn't able to find one.
But going through some of the citations for it--
MICHAEL SHORT: Before you do, could you
go back to the article?
GUEST SPEAKER: Sure.
MICHAEL SHORT: I want to point something out.
GUEST SPEAKER: Yes.
MICHAEL SHORT: Can you tell if this was peer reviewed?
GUEST SPEAKER: I do not know how to do that.
MICHAEL SHORT: It appears to be a conference.
GUEST SPEAKER: OK.
MICHAEL SHORT: Not all conferences require peer review
in order to present the papers.
So while conference proceedings will typically
be published as a record of what happened at the conference,
we don't know if this one was peer reviewed and checked
for facts by an independent party.
Could you go up a little bit, and maybe there'll
be some information on that?
Oh, it did go in the British Journal of Radiology.
OK, that's a good sign.
So conference proceedings, you don't know.
But in order to publish something in a journal,
you do because then in order to get in the journal,
things have to be peer reviewed to meet the journal standards,
regardless of whether they came from a conference or just
a regular submission.
So, OK, that's good to see.
So, now, what else you got?
GUEST SPEAKER: And then one of the key sentences
that I found right here, adaptive protection
causes DNA damage prevention, and repair, and immune system
or immune stimulation.
It develops with a delay of hours,
may last for days to months, decreases steadily
at doses above about 100 milligray to 200
milligray and is not observed anymore
after acute exposures of more than about 500 milligray.
That's all pretty interesting.
Like I said, unfortunately, I couldn't find the actual paper.
So you can't really delve into some of those claims.
But I tried to look at some of the citations that
delved into them.
And this is where my presentation gets a little bit
shakier because I'm not particularly
good at parsing some of this complex stuff very quickly.
MICHAEL SHORT: Let's do it together.
GUEST SPEAKER: All right.
[INAUDIBLE]
MICHAEL SHORT: If you could click Download Full Text
in PDF, it'll just be bigger.
GUEST SPEAKER: OK.
MICHAEL SHORT: There we go.
GUEST SPEAKER: So it seemed to me
this one was more looking through the statistics
of various studies.
I'm not entirely sure.
But I think the conclusion--
[INAUDIBLE]
There we go.
So the very last paragraph, "the present practice
assumes linearity in assessing risk from even the lowest dose
exposure of complex tissue to ionizing radiation.
By applying this type of risk assessment
to radiation protection of exposed workers
and the public alike, society may gain a questionable benefit
at unavoidably substantial cost.
Research on the p values given above
may eventually reveal the true risk,
which appears to be inaccessible by epidemiological studies
alone.
MICHAEL SHORT: So what are they going
on claiming [INAUDIBLE] versus not being willing to claim it?
GUEST SPEAKER: So it seems like they're
saying that at the current, there's not really a problem--
a statistically valid assertion of
the linear no-threshold model and that the benefits
to society gained from that are not worth the cost to society
from that assumption.
MICHAEL SHORT: So what sort of costs
do you think society incurs by adapting
a linear no-threshold dose risk model?
GUEST SPEAKER: I mean, it could pose unnecessary regulations
on like nuclear power, which could
be arguably better for society.
MICHAEL SHORT: Sure.
Nuclear power plants emit radiation, fact,
to use the old cell phone methodology.
There's always going to be some very small amount of tritium
released.
The question is, does it matter?
And if legislation is made to say absolutely no tritium
release is allowed, well, you're not
going be allowed to run a nuclear plant.
That's not the question we should be asking.
The question we should be asking is, how much is harmful?
So I think that's what this study is really getting at
is I'm glad to see someone say, you may have a benefit.
But the cost is not worth the benefit.
Like I-- I had a multiple of the same arguments
with different people when they were complaining, well,
how dare would you expose me to any amount of radiation
at any risk that I can't control.
I used to protest outside Draper Labs
for 30 years protesting nuclear power.
I was like, OK, how did you get there?
They were like, oh, I drove.
What?
In a car?
Do you even know the risks per mile of getting on the road,
let alone in Cambridge specifically?
No?
Well, I was like, you should really consider
where you put your effort?
It's-- again, it's emotions versus numbers.
I'm going to go with numbers because I
tend to make bad decisions when I follow my emotions,
as do most people because most decisions are
more complex than fight or flight nowadays.
Yeah?
AUDIENCE: So a lot of the discussion
just seems to be around like expanding [INAUDIBLE]..
But a lot of the arguments don't seem
to like really [INAUDIBLE].
But, yeah, like there's a certain extent,
like, oh, you will see [INAUDIBLE]..
MICHAEL SHORT: Yeah.
AUDIENCE: [INAUDIBLE] are doing the same.
MICHAEL SHORT: You make a great point.
That's why I like your-- your chosen idea so much
is, well, you didn't say chosen.
That's what I-- yeah.
Yeah, the question we should be asking ourself
is not what is the dose-risk relationship, but when should
we actually care.
It's like both sets of studies have kind of
come to the conclusion that, nah, right?
AUDIENCE: [INAUDIBLE] dose doesn't really matter.
GUEST SPEAKER: Yeah, and then I found this last one
is a little bit more assertive.
It's kind of just hitting the same nail
on kind of the elimination of the linear no-threshold model.
But then it does go on to make some more powerful claim right
here.
"These data are examined within the context
of low-dose radiation induction of cellular signaling
that may stimulate cellular protection
systems over hours to weeks against accumulation
of DNA damage."
MICHAEL SHORT: Was this the paper cited
in the other one that actually said hours two weeks?
GUEST SPEAKER: I believe so, yeah.
MICHAEL SHORT: OK, cool.
GUEST SPEAKER: And then we can actually--
MICHAEL SHORT: [INAUDIBLE] this one?
GUEST SPEAKER: Yes.
We can look up the full text on Google Scholar.
MICHAEL SHORT: That's OK.
When you know what you're looking for, you can verify it.
That's-- that's a useful thing for Google is like to find
known content.
But if you're trying to survey a field in Google, no.
GUEST SPEAKER: That's not what I wanted.
MICHAEL SHORT: Not yet.
I'm sure-- I'm sure they're working on it.
But they're not Web of Science yet.
GUEST SPEAKER: All right.
AUDIENCE: [INAUDIBLE]
GUEST SPEAKER: Does anybody see a Get The Full Paper button?
Oh, wait, right here, right?
MICHAEL SHORT: Yep.
That's it.
GUEST SPEAKER: OK.
Sign in?
MICHAEL SHORT: Sounds like we don't subscribe to this.
GUEST SPEAKER: Oh, I was able to get to it somehow.
Well, yeah.
AUDIENCE: I have another article supporting this claim, though.
MICHAEL SHORT: OK.
GUEST SPEAKER: But this one--
AUDIENCE: Submit it, or bring yours up, or whatever.
GUEST SPEAKER: And then this one--
this one just had some nice data.
If I'm going to summarize, it had--
it was looking at the amount of DNA damage instances
compared normal background dose to like very, very low dose.
And the very, very low dose was significantly less
than the normal background dose.
So that just kind of shows that like
very low levels of radiation are like no worse for you than just
background dose, which is interesting.
MICHAEL SHORT: Cool.
GUEST SPEAKER: Yeah.
MICHAEL SHORT: I also want to make sure,
do you guys have more articles you want to show?
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: If you want to send it to me,
I'll put it up here.
GUEST SPEAKER: All right, I minimized because I didn't just
want to leave your email.
MICHAEL SHORT: Oh, I don't care.
There's nothing--
GUEST SPEAKER: OK.
MICHAEL SHORT: I'll bring it back up.
So that's all the ones you sent?
Cool.
Actually, this one-- this debate is turning out
a whole lot more interesting than previously because,
well, because you're thinking.
It's actually really nice to see this.
And this is the--
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: I'm not surprised.
Don't worry.
It's just pleasant to have a debate about something
controversial with a whole group of people
who are thinking and researching rather than shouting
and like throwing plates.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Oh, no, if you want throw a chair,
but I might throw one back.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: I wonder if anyone's gone out recently
and has come up with all of the pro and anti hormesis studies
and actually written a paper that says,
that's not the point, because, really, what we're
getting-- huh?
AUDIENCE: You could write that.
MICHAEL SHORT: No, I think you could write that paper now.
AUDIENCE: Well, oh.
MICHAEL SHORT: It would make for a pretty cool undergrad thesis,
actually.
Yeah?
Maybe I can tell you a little bit
about what an undergrad thesis actually
entails because the seniors are all asking.
But it's good for you to know ahead of time.
So the main requirement for an undergrad thesis
is it's got to be your work.
That doesn't mean you have to have
collected the data yourself, like done an experiment.
But it has to be some original thought, or idea,
or accumulation of yours.
So trying to settle this debate and trying to figure out what
would be a proposed chill region to say,
forget the linear threshold or no threshold.
That's for the basic scientists.
If you are a government and want to legislate something that
actually captures should people be afraid or not,
defining that region would be a pretty cool study to do
in the meta-analysis of lots of other studies,
tracing back how worthy--
I mean, a lot of people refer to the Hiroshima data
set because that's about the biggest one we have.
In addition to folks with radon or folks that smoke,
they were all exposed to the same thing
in the relatively same area.
So it's a good control group of people.
But how was-- how were those doses estimated?
You have to dig that up.
And the act of digging that up and then recasting
all of these new studies in the basis of everything
we've learned since would make for a pretty cool
undergrad thesis topic.
So as undergrad chair, I wouldn't say no to that.
Threshold and other departures from linear quadratic curvature
in the same data set appears to--
is it the LSS data set?
Let's try to get the full text.
Awesome!
I think it's looking good.
Great!
Now I've seen that name before.
Interesting.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Interesting.
They propose another model called
a power of dose, a power law.
And they say, depending on this--
there's little evidence that it's
statistically different from one which
is a what do they call one linear threshold
quadratic threshold or linear quadratic threshold, OK?
So, again, it seems to be yet another paper saying,
I don't think it matters.
Statistics says it doesn't matter.
You could fit any model to this data.
Let's get to the methods.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Interesting.
So dose response for all non-cancer mortality
in the atomic bomb survivors.
So, also, in this case, it's mortalities not
caused by cancer.
AUDIENCE: Like, caused by radiation disease?
Or is that caused by [INAUDIBLE]??
MICHAEL SHORT: So this would be--
I think what they're getting at is is there a response,
or is there a change in the amount of mortality
not due to cancer and the--
the--
AUDIENCE: Health benefits other than decreasing risk of cancer.
MICHAEL SHORT: Or in this case, health detriments, right?
Because in this-- you know, it never goes negative.
You can't really tell in some cases.
Let's see.
Yeah, quite hard to tell, especially considering.
And so at the low doses, what would you guys
say for the low dose data?
AUDIENCE: That doesn't matter.
MICHAEL SHORT: I see a pretty well-defined chill zone
right there, right?
AUDIENCE: Chill zone?
MICHAEL SHORT: We're definitely still
in the chill zone at 0.4 sieverts of colon dose.
And that's a pretty hefty amount of dose.
You know, we're talking eight or nine times the allowed amount
that you're able to get in a year from occupational safety
limits.
Once the doses get higher, things
seem to get a little more deterministic or statistically
significant.
But, yeah, look at all the different models.
The linear threshold, quadratic threshold,
linear quadratic threshold, power of dose
all goes straight through not just like in the error bars,
but almost straight through most of the data points,
except for the really far away ones.
So this is a pretty neat study, showing,
like, hey, the relationship does not
appear to matter for doses of consequence.
I would call 2 sieverts a dose of consequence
based on our earlier discussion of biological effects.
Luckily, it doesn't go much farther than that.
You don't want a lot of people to have
received doses beyond 10 gray.
But this is pretty compelling to me
to say, like, we can argue about what the real model is
and what the underlying mechanism is, but is
this a question we really should be asking ourselves
when the total risk--
let's say, when the total risk to an organism
reaches about 100%, once you reach a a dose where it doesn't
even matter, then is this a question
that we should really be debating in the public sphere?
I love the outcome of this particular debate.
Lots of statistics, don't have time to parse.
Is there anything else, Chris, that you wanted
to highlight in this study?
AUDIENCE: This appears to [INAUDIBLE] comments
on Professor Donald Pierce on [INAUDIBLE]..
MICHAEL SHORT: Oh, OK, well--
AUDIENCE: Do you think it could be the same Pierce?
MICHAEL SHORT: Maybe.
It was a UK Pierce, I think.
That's pretty cool.
So anyone else have any other papers
they want to show for or against or for our sort
of collective new conclusion?
Which is that we should just relax.
Cool.
Well, that went-- yeah?
Charlie?
AUDIENCE: I just had had a question, like,
what would be like a posed use of radiation
hormesis [INAUDIBLE]?
[INAUDIBLE]
MICHAEL SHORT: So let's say you could
prove beyond a shadow of a doubt that a little bit of radiation
exposure was a good thing.
You might then prescribe radiation treatments
in order to reap the benefits.
I don't think there's been a single study that
shows that there's like deterministic benefits
from irradiating people.
Some of the studies show that folks
that have gotten exposed via various routes
do show a lower incidence of cancer.
So you could almost think of it like a vitamin, not
an injectable vitamin.
But-- so back-- there are lots of pictures online
and stories of way up in the north in Russia
and northern countries that expose you
to ultraviolet radiation to stimulate
the production of vitamin D in your skin cells
because in the absence of an ingestible source of vitamin D,
you make it naturally, but not when there's eternal darkness.
So they'd actually have kids stand in front of a UV lamp,
which does have ill effects.
That can cause also skin cancers,
but the benefits of the organism in generating
vitamin D that you need for health are greater.
So that might be an example.
These-- these sorts of ideas are not that far fetched.
If you put little kids in front of UV lamps,
which you know can do bad things,
but also does more good things, then who's to say it
shouldn't happen for radiation?
Well, no one's to say yet because we
have no real conclusive proof that it is helpful.
But that was the-- yeah?
AUDIENCE: Have there been any mechanisms that [INAUDIBLE]??
MICHAEL SHORT: You mean in-- for radiation
or for something else?
AUDIENCE: For radiation.
MICHAEL SHORT: The mechanisms of-- so that one study that
Chris showed that--
what was the idea?
That-- [INAUDIBLE].
The first one that you showed, the mouse one,
and then the one that Chris mentioned
where a little bit of radiation dose
stimulated the immune system.
That might be a potential good thing,
where the damage or death of a few cells
may stimulate the nearby ones to ramp up an immune response,
thus snuffing out any other infection or problem that's
coming up.
That could be a use.
But we have to be proved with much more confidence
than anything I've seen today.
So that's a good question.
Yeah, like how would you use it?
Use it like a vitamin, like a UV lamp, like a SAD lamp.
Although, I don't think SAD lamps
do anything bad, the Seasonal Affective Disorder,
the most unfortunate acronym in the world.
Yeah.
AUDIENCE: [INAUDIBLE]
MICHAEL SHORT: Yes.
I don't know if that would be easy to swallow.
Yeah.
Cool.
All right, any other thoughts from this exercise?
I think I'll do more interactive classes like this.
It's good to hear you guys talk for a change.
Cool.
OK.