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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.