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Speaker 1: Emily. One of the things that is really interesting about

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Speaker 1: the mRNA vaccines, at least is that people have really

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Speaker 1: different responses to them. So I want to know if

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Speaker 1: you're one of the lucky ones who gets an mRNA

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Speaker 1: vaccine for COVID or whatever and like goes about their day,

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Speaker 1: or if you're the type like me whose body seems

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Speaker 1: to think they're under assault from some kind of alien chemical.

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Speaker 2: Yeah.

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Speaker 3: I was in bed for a day after my early

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Speaker 3: COVID vaccines, and I am not a person who spends

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Speaker 3: time in bed, so I had a pretty extreme reaction,

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Speaker 3: I think, like, but my husband had like nothing.

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Speaker 1: Yeah, yeah, I'm so jealous. You know, I don't track

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Speaker 1: things very much, like I have a fitness watch or

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Speaker 1: whatever that I never look at, but I definitely noticed

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Speaker 1: my sleep score went from like, you know, ninety to

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Speaker 1: twenty or something like. It was insane the night after

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Speaker 1: I got one of these shots.

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Speaker 3: So the other person who has no I know who

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Speaker 3: I had no reaction to this is my father. My

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Speaker 3: father has gotten a COVID booster every year. He must

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Speaker 3: have gotten fifty COVID vaccines by now, and he has

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Speaker 3: never had any reaction to them. And I wonder if

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Speaker 3: like people who have a better immune system have more

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Speaker 3: of a reaction. That's how I'm explaining the difference of

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Speaker 3: me and me and my husband. Yeah, validate me.

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Speaker 1: I had thought the same thing because I wanted to

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Speaker 1: be like, ooh, I'm I'm so sweaty. I this must

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Speaker 1: this must be good. And I actually did dig into

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Speaker 1: the data a little bit and honestly, like, there's not

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Speaker 1: that much correlation between your symptoms post vaccine and your

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Speaker 1: antibody levels or anything like that.

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Speaker 3: SOO okay, well that's true.

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Speaker 1: But that is just scratching the surface of this amazing

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Speaker 1: molecule called RNA, which I'm so excited to talk to

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Speaker 1: you about today.

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Speaker 3: I can't wait. I'm Emily Astro, I'm an economist and

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Speaker 3: a data x.

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Speaker 1: And I'm Perry Wilson. I'm a medical doctor.

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Speaker 3: It's Thursday, July second, twenty twenty six. And this is wellness.

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Speaker 1: Actually, because you're getting a staggering amount of health and

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Speaker 1: wellness information nowadays from every source imaginable, and some of

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Speaker 1: it is awesome.

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Speaker 3: And some of it is well actually both Fortunately we

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Speaker 3: are both people who know how to read studies, how

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Speaker 3: to parse the data, and can tell you what's worth

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Speaker 3: thinking about and what you can safely ignore.

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Speaker 1: But before we dig in, a note that this podcast

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Speaker 1: is for educational purposes and should not be construed as

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Speaker 1: medical advice. We don't know your unique situation, so talk

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Speaker 1: to your doctor for personal health decisions.

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Speaker 3: This week we're asking what's the deal with RNA? Harry

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Speaker 3: and I will give the official smasher pass, and then

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Speaker 3: we'll get to your question of the week. But first,

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Speaker 3: let's do the health news roundup after the break, and

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Speaker 3: now for the health news of the week. First up, Perry,

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Speaker 3: the FDA has dropped their enforcement action against Whoop for

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Speaker 3: its blood pressure feature. I have a Whoop. I have

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Speaker 3: never used it to record my blood pressure, but I'm

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Speaker 3: curious what is going on here and how do you

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Speaker 3: feel about it?

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Speaker 1: As a real doctor, So always good to talk about WOOP.

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Speaker 1: I know that you're a fan, And as I've disclosed before,

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Speaker 1: Whoop actually funded a study from our lab a while ago,

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Speaker 1: So bit of a conflict of interest to disclose that

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Speaker 1: said blood pressure measurement using things that don't squeeze your

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Speaker 1: arm or your wrist is sort of fraught. Like there

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Speaker 1: are a lot of devices on the market that promise

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Speaker 1: to do this like non squeezy things, and watches are

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Speaker 1: clearly one of them. The data on their accuracy is

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Speaker 1: generally not great. But that's not what got Woop and trouble.

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Speaker 1: What got Whoop in trouble is that you cannot diagnose

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Speaker 1: a disease if the if you haven't gotten FDA clearance,

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Speaker 1: and because they said your blood pressure was in the

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Speaker 1: red zone, if it was greater than systolic, greater than

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Speaker 1: one hundred and forty or whatever, the FDA was like,

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Speaker 1: that's hypertension. You're diagnosing a disease. That's a no no.

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Speaker 1: What I love is how Weop got around this, which

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Speaker 1: is that they I don't know if you saw this

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Speaker 1: on your whoop, if your woop was like equipped with

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Speaker 1: the blood pressure thing or not. But the graphic had

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Speaker 1: like originally was like green, yellow, red, So there were

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Speaker 1: three zones, right, and the red zone was hypertension. That's

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Speaker 1: no go. So what they did they changed it to

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Speaker 1: like a gradient, so it like changes from green to

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Speaker 1: yellow to red, but without a defined cup point. So

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Speaker 1: now they're not diagnosing a disease anymore. It's just like

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Speaker 1: a wellness Like how good you like? Qualitative action thing

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Speaker 1: and uh, and so the FDA has dropped its concerns.

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Speaker 3: What I find so interesting about that is, in general,

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Speaker 3: these cutoffs are always very stupid, Like.

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Speaker 1: We should always are arbitrary. Yeah, we should always.

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Speaker 3: Be thinking about things as a continuum. So as a

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Speaker 3: reporting mechanism, that's great, of course, as a way to

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Speaker 3: find your blood pressure things, as you point out, things

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Speaker 3: that live on your wrist are not as good as

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Speaker 3: things that squeeze your arm, which is actually how we

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Speaker 3: measure that.

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Speaker 1: Can I like make Emily Oster mad for a second,

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Speaker 1: because I was reviewing some of the literature on these

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Speaker 1: cuffless blood pressure machines and the ones that really do

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Speaker 1: our measured blood pressure, like pass through FDA clearance, the

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Speaker 1: whole deal, and one of the studies I saw that

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Speaker 1: god FDA clearance, this is the design, Emily, tell me

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Speaker 1: what the problem is here. You come to the mall,

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Speaker 1: you sit down, they measure your blood pressure. You sit down,

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Speaker 1: They measure a blood pressure with a cuff. Okay, that's

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Speaker 1: the gold standard.

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Speaker 3: Okay.

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Speaker 1: Then they put on their little device on your wrist

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Speaker 1: and they calibrate it okay to the measured blood pressure. Okay.

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Speaker 1: So they're like Okay, your blood pressures one three, five

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Speaker 1: or seventy five. We're calibrating this thing to risk. Okay,

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Speaker 1: now you go and walk around the mall for a while.

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Speaker 1: Come back an hour later. They met your blood pressure

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Speaker 1: with the cuff thing again and they see what the

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Speaker 1: watch reports and they say, oh, look they're very similar,

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Speaker 1: Like the watch is very similar to the blood pressure cuff,

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Speaker 1: which Emily, Yeah, what's the problem here.

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Speaker 3: There are a lot of problems. There's a lot of problems.

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Speaker 3: What's the problem you most want to identify.

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Speaker 1: I want to say that if my watch did nothing

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Speaker 1: more than just report out whatever the original value you

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Speaker 1: calibrated it to was, it would do great because most great,

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Speaker 1: most people's blood pressure doesn't change that much over an

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Speaker 1: hour when they're sitting down, right, Like, this is not

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Speaker 1: a test. You could just have an algorithm that's like

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Speaker 1: I always just say one three, five or seventy five

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Speaker 1: or whatever you calibrated me to originally.

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Speaker 3: Totally it would be just as It's just as good

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Speaker 3: basically as an algorithm that's like we took the blood

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Speaker 3: pressure measurement and then we just said that was your

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Speaker 3: blood pressure, yes, and then it's about right on average

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Speaker 3: it's going to be right. It's going to be right

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Speaker 3: on average most.

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Speaker 1: Of the time. So anyway, I'm suspicious these cuff lists

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Speaker 1: blood pressure cuffs. Moving on, Emily, there is some concerning

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Speaker 1: news about baby formula. When isn't there news about baby formula?

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Speaker 1: But this time it's botulism. I guess what's going on?

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Speaker 3: Yeah, So in the past year there have been two

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Speaker 3: outbreaks of botulism in baby formula. There was one last

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Speaker 3: fall in like October, in a formula called buy Heart.

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Speaker 3: A bunch of babies got sick, like over fifty babies.

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Speaker 3: None of them died, thank goodness. Botulism is treatable, but

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Speaker 3: it can be quite serious. I think some of these

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Speaker 3: babies have d long term consequences. This was the first

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Speaker 3: time there was ever a bochelism outbreak a baby formula.

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Speaker 3: Boulism gets in because if bacteria gets in and it

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Speaker 3: produces these botulism spores, it is not something we have

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Speaker 3: traditionally tested for in formula because we haven't seen it,

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Speaker 3: and so you know, this was a surprise. And then

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Speaker 3: a few weeks ago there was another bochulism case in

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Speaker 3: a different formula. Another three cases identified and I think

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Speaker 3: It really caused a lot of people to say, you know,

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Speaker 3: oh my god, is there just botulism in all formulas.

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Speaker 3: It's an issue with baby formula that it just gets

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Speaker 3: botulism more than we thought, and we should be sort

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Speaker 3: of worried about all of this. The thing that came

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Speaker 3: out towards the end of last week and over the

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Speaker 3: weekend is that, in fact, this is basically the same outbreak.

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Speaker 3: So both of these companies were using the same provider

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Speaker 3: of the organic whole milk that they were using the

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Speaker 3: same milk provider, the same drying plant. That was not

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Speaker 3: obvious from earlier reporting. Let's put aside why, but it

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Speaker 3: does seem like, actually, there is like one thing that

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Speaker 3: happened in one plant in Nevada where some botulism got

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Speaker 3: into this powdered organic milk, and both companies used it.

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Speaker 3: So on the one hand, I don't know how reassuring

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Speaker 3: it is, but I think it's a little bit reassuring

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Speaker 3: to suggest that in fact, this is like one specific

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Speaker 3: thing that happened.

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Speaker 2: We still need more regulatory oversight, but.

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Speaker 1: Yeah, that's super concerning. Botulism causes like a flaccid paralysis

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Speaker 1: in kids. You know, it's botox, right, Like, this is

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Speaker 1: the toxin that botulism secretes is botox. And that's fine

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Speaker 1: if you're injecting into tiny amounts into small muscles in

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Speaker 1: your face and you're an adult. It's not great if

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Speaker 1: it's getting systemic in your body.

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Speaker 3: So that's that. Okay, Let's turn to something a little

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Speaker 3: uh weirder, which is that stat News has been reporting

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Speaker 3: that there was a request by the White House for

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Speaker 3: compassionate use of Eli Lilly's new weight loss drug, which

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Speaker 3: is called retruit to Tide.

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Speaker 1: Is I'm saying that retat true Tide, which.

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Speaker 3: Is a very it's a GLP one but reportedly in

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Speaker 3: the trials like enormously effective, superpotent. It has not yet

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Speaker 3: been approved, but things that are not yet approved can

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Speaker 3: be requested for compassionate use in some circumstances. This would

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Speaker 3: be an unusual circum stance. But the stat news is

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Speaker 3: speculating that the patient was Trump. Yeah, wtf, Perry, I

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Speaker 3: can't even understand what's going on here.

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Speaker 1: Okay, Yeah, for people not in this space, like, let

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Speaker 1: me first of all say that stat news is no rag, like,

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Speaker 1: this is a really.

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Speaker 2: Excellent not Bob's speculative blog.

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Speaker 1: I mean, it's like a real, real news organization with

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Speaker 1: real reporters. So the facts of the case that we

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Speaker 1: have is that there's been one approval for compassionate use

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Speaker 1: for retatritide, and the reason that there's only one is because, like,

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Speaker 1: why would a GLP one be used for compassionate use,

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Speaker 1: Like that's not you know, usually these are drugs like

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Speaker 1: for end of life, like late stage cancers, you know,

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Speaker 1: like last ditch, hail married types of things. Okay, fine,

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Speaker 1: we know it was done in April for a seventy

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Speaker 1: nine year old patient. We know that. I'm not sure

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Speaker 1: it was the White House, but there were top health

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Speaker 1: officials involved, including a senior clinician at the NIA whose

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Speaker 1: name is Ranganath Muniyappa, who requested the drug to treat whoever.

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Speaker 1: This patient was for refractory obesity with obstructive sleep APNE

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Speaker 1: and pulmonary hypertension, which is a complication of severe obesity,

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Speaker 1: and that the patient had previously tried to zeppetite, which

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Speaker 1: is munjaro so it was like and it had been ineffective.

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Speaker 1: So okay, fine, there are lots of seventy nine year

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Speaker 1: olds in April in Washington, DC, I like what I'm

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Speaker 1: just could it be Trump?

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Speaker 4: Sure?

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Speaker 1: But like what it strikes me and I don't want

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Speaker 1: to get conspiratorial that they must have some other sourcing

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Speaker 1: here than just like oh, a seventy nine year old man,

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Speaker 1: and like Trump seems like he's overweight, Like right.

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Speaker 3: I agree, there's something going on behind this that we

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Speaker 3: don't understand, and I don't know how much we will learn,

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Speaker 3: although I will say that there are people in the Senate.

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Speaker 3: Maggie Hassen in particular, is like trying to get our

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Speaker 3: fea junior to like revee who this.

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Speaker 2: Person is that got this special access.

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Speaker 3: Yeah, I don't know. Like everything in this space, there

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Speaker 3: are so many like messed up incentives and distrust and

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Speaker 3: anger on all sides. It's very difficult to understand what

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Speaker 3: is actually going on.

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Speaker 1: And honestly, I couldn't even parse this right. Like, I

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Speaker 1: guess if Trump wants to take a GLP one, great

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Speaker 1: for Trump. If it were Trump, the only scandal kind

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Speaker 1: of would be I mean sure, I guess it's like

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Speaker 1: pulling the levers of power to you know, get something

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Speaker 1: special for yourself. But a lot of people.

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Speaker 3: Are getting from like Tom Hounty pharmacies and child already anyway, So.

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Speaker 1: Exactly, I think more it would be that they made

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Speaker 1: such a big show of releasing his quote unquote health

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Speaker 1: records and was like the healthiest person in the world.

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Speaker 1: So if it turns out that he has severe obstructive

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Speaker 1: sleep apnea that's led to pulmonary hypertension, you sort of

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Speaker 1: want to know problem why that wasn't reported.

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Speaker 3: But anyway, we'll keep you updated on this really really

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Speaker 3: weird story as we come. And that is it for

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Speaker 3: the Health News of the week. After the break, What's

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Speaker 3: the deal with RNA? All right, we're back and we're

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Speaker 3: going to talk about what is the deal with RNA?

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Speaker 3: And before we get into it, before we start talking

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Speaker 3: about vaccines and controversies and Jeffrey epscene and other important

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Speaker 3: topics in this space, I think it's important to return

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Speaker 3: to high school biology. I'm very up to date on

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Speaker 3: this because I have a person who just finished high

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Speaker 3: school biology, so I basically know everything. But I think

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Speaker 3: we want to set the stage here for people and

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Speaker 3: explain what is RNA and when I say m RNA

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Speaker 3: in particular, which is what a lot of this conversation

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Speaker 3: is going to focus on. What are we talking about So,

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Speaker 3: although I have recently finished a course in ninth grade biology, Perry,

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Speaker 3: I'm going to turn this over to you.

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Speaker 2: As a person who is a scientist.

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Speaker 1: Sure, So, there are three elements that we need to

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Speaker 1: know here, DNA, RNA, and protein. DNA gets transcribed into rna,

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Speaker 1: RNA gets translated into protein. Let me explain what those

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Speaker 1: words mean. So DNA lives in the nucleus of the cell.

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Speaker 1: And think of DNA as like a big cookbook, Like

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Speaker 1: it's got a recipe in there for every single thing

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Speaker 1: that any cell could make, like a really good cookbook, Okay,

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Speaker 1: And it's all there, and it's bound really nicely, you know,

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Speaker 1: leather bindings. It smells of rich mahogany, and it's very protected.

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Speaker 1: The nucleus is like the holiest of holies of the cells.

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Speaker 1: It's very difficult to get things in and out of

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Speaker 1: the nucleus. They need special signaling and all kinds of stuff.

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Speaker 1: So that's DNA. You don't want to mess with that,

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Speaker 1: right we know if you mess with DNA too much

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Speaker 1: and get cancer or other problems. So that's kept very sacred.

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Speaker 1: But then you've got to get the recipes out to

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Speaker 1: the cell to tell the factories of the cell, like

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Speaker 1: what to build today, right, I'm a you know, if

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Speaker 1: you're a white blood cell and you need to make

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Speaker 1: antibodies or whatever, it is, your job is to make

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Speaker 1: so that DNA gets transcribed into RNA. So DNA, you'll remember,

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Speaker 1: has four bases, like four letters. Essentially, that is the

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Speaker 1: genetic code. And so just like AGCD like whatever, in

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Speaker 1: different combination of those letters, that's what makes you human.

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Speaker 1: And it's billions of billions and billions of base pairs long.

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Speaker 1: And mRNA also has four letters, although one of them

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Speaker 1: is different than the DNA letters doesn't even matter. But RNA, now,

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Speaker 1: which is a single helix as opposed to DNA's double helix,

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Speaker 1: is allowed to exit the nucleus. It special permission. It

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Speaker 1: leaves the nucleus and goes into the cytoplasm of the cell.

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Speaker 1: In the cytoplasm of the cell, RNA docks with these factories,

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Speaker 1: which are called ribosomes, which reads that code, and every

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Speaker 1: three letters of code corresponds to one amino acid. If

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Speaker 1: you go back to our protein episode, you will remember

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Speaker 1: that proteins are just long chain of amino acids, and

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Speaker 1: there's something like twenty two amino acids. So we've got

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Speaker 1: to change these four letters in various combinations into twenty

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Speaker 1: two amino acids plus codes that say stop, you know,

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Speaker 1: stop working and you're done and things like that. And

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Speaker 1: this is like, like we know what all this is.

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Speaker 1: It's kind of amazing, Like we know that if you

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Speaker 1: have you know, AAA, like that corresponds to this specific

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Speaker 1: amino acid, and if you have ACA, and it responds

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Speaker 1: to this specific amino acid, and so that code gets

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Speaker 1: translated into a chain of amino acids, and those amino

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Speaker 1: acid chains grow and then fold and become a big

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Speaker 1: sticky glob. That's what we call a protein, and that's

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Speaker 1: how cells work.

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Speaker 3: I just want to pause here because I think when

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Speaker 3: we talk about this biology, like it is easy to

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Speaker 3: just listen and be like, okay, you're right. Like they

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Speaker 3: come out and they turn it in and it turns

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Speaker 3: all the amino acids insurance into protein. But I would

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Speaker 3: like us to retain in this conversation a sense of wonder, Yeah,

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Speaker 3: which is like how amazing is that that? Like we

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Speaker 3: have these things And then the RNA comes out and

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Speaker 3: it like just clicks along and it makes all the

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Speaker 3: little legos and it puts them together, like holy, and

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Speaker 3: then it becomes all the things you need and it

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Speaker 3: turns into these protein and it makes your body. Like

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Speaker 3: that is really really really incredibly cool.

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Speaker 1: It's just really really cool.

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Speaker 3: It's just cool.

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Speaker 1: It's it's super cool. And what's I think additionally fascinating

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Speaker 1: to me is that proteins are so complicated, right, Like

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Speaker 1: there's a million proteins, like proteins that make your hair,

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Speaker 1: and proteins that you know, make lungs, you know, mucus,

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Speaker 1: and like all these different kinds of proteins and you know,

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Speaker 1: in millions fascinating combinations, and all of it can be

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Speaker 1: represented by just like a single chain of these four

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Speaker 1: letters in a very specific order. That is very important.

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Speaker 1: It's it's amazing.

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Speaker 3: It's really cool. Okay, So when we talk about m RNA,

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Speaker 3: in what way is that different from the RNA or

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Speaker 3: that is the RNA.

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Speaker 1: So mRNA is a subtype of RNA. So RNA is

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Speaker 1: any of these single helix molecules that have these four

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Speaker 1: unique base pairs in a chain. And mRNA is certainly

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Speaker 1: the one that you're learning about in high school biology.

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Speaker 1: It stands for messenger RNA. It is the message coming

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Speaker 1: from the DNA in the nucleus out into the cytoplasm,

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Speaker 1: and like, here's what you need to do, sell like,

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Speaker 1: please produce this protein for me.

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Speaker 3: I think, in your in your like beautiful cookbook example,

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Speaker 3: this is a recipe. So it is if we took

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Speaker 3: we opened up the cookbook, we copied like xerox one

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Speaker 3: of the pages and sent that recipe out in the

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Speaker 3: cell and said, okay, now you know you make like

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Speaker 3: chickpeas and tomatoes sheet pan meal. And then the thing

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Speaker 3: puts together the thing into the chickp and tomato sheet

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Speaker 3: pan meal protein and folds it up and sends it

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Speaker 3: to make your hair or whatever. But and then, and

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Speaker 3: I think importantly, after that recipe is made and the

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Speaker 3: chickpea protein leaves, the page just goes away. It just

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Speaker 3: gets trashed.

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Speaker 1: Your cells are absolutely lousy with substances that break down RNA.

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Speaker 1: When I did I'm a clinical researcher now, so I

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Speaker 1: work with humans, which which is much more fun than

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Speaker 1: being in a wet lab. But when I did work

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Speaker 1: in a wet lab way back in college and stuff

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Speaker 1: and was working with RNA, it was like DNA. I'll

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Speaker 1: just say it's super hardy, right, you can, like you

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Speaker 1: can extract it from amber and clone dinosaurs like you

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Speaker 1: can do less.

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Speaker 3: And actually, okay, just people that doesn't actually work, don't

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Speaker 3: try it.

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Speaker 2: Don't try it.

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Speaker 1: I mean it goes poorly, It does go poorly. But

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Speaker 1: no DNA you know, can last for centuries whatever. It's

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Speaker 1: super easy, doesn't matter RNA. When we were working with RNA,

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Speaker 1: it was like, okay, clean everything off the off the

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Speaker 1: bench top, and like wipe it down with all these

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Speaker 1: special things, and like don't breathe on it. Wear a

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Speaker 1: mask because you have RNAs is in your breath and

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Speaker 1: your sweat and everything you touch. It's just like so ephemeral.

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Speaker 1: And that's really important actually because you know, to stick

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Speaker 1: with our recipe analogy. It's like you photocopy, you know,

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Speaker 1: something from your cookbook for a plate of brownies, and

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Speaker 1: like you want that to be done right. You don't

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Speaker 1: want the dumb factory to just be like brownies, brownies

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Speaker 1: bodies bound patties forever. It has to degrade and it

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Speaker 1: does not to mention, there are a bunch of pathogens

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Speaker 1: viruses where RNA is their nuclear material. It's the same

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Speaker 1: RNA that we have, like we're all earthlings. We all

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Speaker 1: have the same code, which is kind of weird and

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Speaker 1: cool in and of itself, but we've evolved to fight

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Speaker 1: those things off. An RNA your body sees in general

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Speaker 1: as a hostile force, which means when you inject RNA

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Speaker 1: into someone, your body responds generally quite robustly.

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Speaker 3: Okay, this is great background. Now I want to talk

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Speaker 3: about why this is such an interesting path for potential

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Speaker 3: doing various health related things in your body. And I

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Speaker 3: think in some sense it's quite simple, which is the

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Speaker 3: proteins that you make are doing like everything, that's all.

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Speaker 3: The huge share of what your body is doing is

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Speaker 3: being You are your proteins, right, you are your proteins.

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Speaker 3: And so let's say I wanted to do something different.

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Speaker 3: I wanted to make a more of something or less

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Speaker 3: of something, or something new that I wanted to introduce

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Speaker 3: the idea in principle that I could give me something injected,

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Speaker 3: like in some way treat a person and tell their

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Speaker 3: body make this protein you weren't making before feels incredibly powerful,

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Speaker 3: or we'll get to this maybe at the end, like

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Speaker 3: don't make this protein anymore. Right, So there's the sort

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Speaker 3: of control of your protein idea here, which feels like

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Speaker 3: conceptually in some ways very straightforward, and most of the

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Speaker 3: issues that people encounter. The reason that mRNA is a

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Speaker 3: relatively new technology is your body like does not want

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Speaker 3: you to do that. Like it is a very very

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Speaker 3: poor idea for your body to just be like, give

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Speaker 3: me some RNA, I'll make whatever. Like your body's like no, no, no.

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Speaker 3: I came up with the plan. I had these things.

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Speaker 3: I was going to make these proteins. I'm not going

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Speaker 3: to make the other protein. You know, I'm not going

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Speaker 3: to make other proteins that you don't.

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Speaker 1: I mean, that's like viruses. Whole deal is they're like, hey, man,

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Speaker 1: here's some here's some RNA, Like, can you make more

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Speaker 1: of me?

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Speaker 5: Please?

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Speaker 1: And in general, your body doesn't want to do that,

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Speaker 1: and the successful viruses obviously I've figured out ways to

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Speaker 1: get around that, and now and now so of weight.

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Speaker 3: But evolving in yeah, but evolving in a world full

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Speaker 3: of viruses, we are like we have developed a lot

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Speaker 3: of ways to not allow our external RNA forces to

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Speaker 3: control our cells. And so most of the research that

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Speaker 3: allowed the creation of the mRNA vaccines and many of

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Speaker 3: the other things that are now potential are things that

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Speaker 3: allow us to better control how our body deals with proteins.

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Speaker 1: Yeah, yeah, and we don't have to go into too

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Speaker 1: much detail about like how we fixed the RNA the

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Speaker 1: central RNA problem. But there are two Nobel prizes here,

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Speaker 1: which I always feel like if there's some Nobel prizes,

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Speaker 1: we should at least give a hat tip for two technologies.

436
00:23:10,040 --> 00:23:13,200
Speaker 3: I don't have any. So yeah, neither of us have any.

437
00:23:13,200 --> 00:23:15,640
Speaker 1: So let's talk about not yet, Emily.

438
00:23:15,320 --> 00:23:15,800
Speaker 4: Not yet.

439
00:23:17,080 --> 00:23:20,399
Speaker 1: So the one that I think most people will have

440
00:23:20,440 --> 00:23:22,600
Speaker 1: heard of because it is directly relevant to the COVID

441
00:23:22,680 --> 00:23:27,320
Speaker 1: vaccine was Catalan Kirico and her colleague Drew Weissman, who

442
00:23:27,400 --> 00:23:30,199
Speaker 1: got the Nobel Prize in twenty twenty three. They figured

443
00:23:30,200 --> 00:23:34,280
Speaker 1: out that swapping one of these letters uridine in RNA

444
00:23:34,560 --> 00:23:38,200
Speaker 1: for a very slightly modified chemical called pseudo uridine, it

445
00:23:38,240 --> 00:23:43,840
Speaker 1: looks almost the same, eliminates a large proportion of that

446
00:23:44,320 --> 00:23:49,400
Speaker 1: intense immune response to RNA. So without that, like any

447
00:23:49,520 --> 00:23:52,399
Speaker 1: RNA that comes in your body, unless it's packaged in

448
00:23:52,440 --> 00:23:54,840
Speaker 1: a virus or something that's like has its own techniques

449
00:23:54,880 --> 00:23:58,240
Speaker 1: to avoid detection, is just getting chewed up like before

450
00:23:58,240 --> 00:24:01,719
Speaker 1: it hits the floor. So major advance there. That was,

451
00:24:01,960 --> 00:24:03,760
Speaker 1: by the way, they got the prize in twenty twenty three,

452
00:24:03,760 --> 00:24:05,640
Speaker 1: but they developed that in two thousand and five, which

453
00:24:05,720 --> 00:24:09,440
Speaker 1: is really the start of human mRNA technology. There's another

454
00:24:09,480 --> 00:24:12,960
Speaker 1: Nobel prize in this area that will go that attaches

455
00:24:13,000 --> 00:24:16,200
Speaker 1: to inhibitory RNAs, which you had alluded to the idea

456
00:24:16,240 --> 00:24:18,720
Speaker 1: of like telling your cells not to do something, which

457
00:24:18,760 --> 00:24:21,200
Speaker 1: is even more mind blowing to some extent, which we'll

458
00:24:21,200 --> 00:24:21,919
Speaker 1: get to you later on.

459
00:24:22,440 --> 00:24:25,399
Speaker 3: The second innovation that has been incredibly important here is

460
00:24:25,440 --> 00:24:28,199
Speaker 3: how you get the m RNA into the cell. The

461
00:24:28,240 --> 00:24:33,680
Speaker 3: cell does not want you to be putting mRNA into it,

462
00:24:33,880 --> 00:24:37,639
Speaker 3: and they basically figure it out how to like surround

463
00:24:37,680 --> 00:24:42,080
Speaker 3: it with a lipid that allows it to be coming

464
00:24:42,080 --> 00:24:44,600
Speaker 3: into the cell, and then the lipid goes away and

465
00:24:44,680 --> 00:24:47,720
Speaker 3: they delivered the mRNA to the cell.

466
00:24:48,600 --> 00:24:51,159
Speaker 1: Yeah, so it was a long time coming. It was

467
00:24:51,200 --> 00:24:53,639
Speaker 1: an idea, Like, this wasn't a crazy idea. People have

468
00:24:53,720 --> 00:24:55,840
Speaker 1: talked about this for decades and decades and decades. It

469
00:24:55,920 --> 00:24:57,840
Speaker 1: just took some technology to make it feasible.

470
00:24:58,480 --> 00:25:02,520
Speaker 3: But once it was feasible, this is incredibly cool and

471
00:25:02,560 --> 00:25:05,879
Speaker 3: I think changer, game changing, and I think it's worth

472
00:25:06,160 --> 00:25:11,000
Speaker 3: like talking about it in the context of the covid vaccine,

473
00:25:11,040 --> 00:25:15,320
Speaker 3: So just the covid vaccine, the mRNA covid vaccines, these

474
00:25:15,320 --> 00:25:19,160
Speaker 3: are the Maderna and Pfizer vaccines work in a very

475
00:25:19,200 --> 00:25:26,399
Speaker 3: specific way. They introduce mRNA, which codes for a spike

476
00:25:26,440 --> 00:25:31,080
Speaker 3: protein on the covid virus, and that is a particular

477
00:25:31,080 --> 00:25:34,000
Speaker 3: protein on the outside of the virus. And what this

478
00:25:34,080 --> 00:25:36,880
Speaker 3: does is it tells your body to make this protein.

479
00:25:37,480 --> 00:25:40,919
Speaker 3: And then because this protein is not a protein that

480
00:25:40,960 --> 00:25:44,240
Speaker 3: you've identified, your body makes antibodies to that protein. And

481
00:25:44,400 --> 00:25:47,960
Speaker 3: then if you are have the actual covid virus arrives,

482
00:25:48,000 --> 00:25:51,240
Speaker 3: you already have antibodies to this. And this is actually

483
00:25:51,359 --> 00:25:55,119
Speaker 3: very different than traditional vaccines because a traditional vaccine would

484
00:25:55,560 --> 00:25:58,639
Speaker 3: introduce either a killed form of the virus in which

485
00:25:58,840 --> 00:26:04,240
Speaker 3: you would then create antibodies to that virus structure overall,

486
00:26:05,280 --> 00:26:08,560
Speaker 3: or there are some protein based vaccines which would actually

487
00:26:08,560 --> 00:26:13,560
Speaker 3: introduce the protein completely. But this is harnessing your body's

488
00:26:13,640 --> 00:26:19,919
Speaker 3: own cells to make the effectively make the vaccine. So

489
00:26:19,920 --> 00:26:25,199
Speaker 3: it's like incredibly cool and it is incredibly fast. So

490
00:26:25,359 --> 00:26:31,400
Speaker 3: a typical traditional vaccine development takes what Perry like ten

491
00:26:31,520 --> 00:26:36,880
Speaker 3: or fifteen years on January tenth of twenty twenty, they

492
00:26:36,960 --> 00:26:40,840
Speaker 3: publish the first stars Kobe two genome online, like somebody

493
00:26:40,880 --> 00:26:42,760
Speaker 3: had sequenced the genome of this virus, which is the

494
00:26:42,760 --> 00:26:44,560
Speaker 3: thing we can do, and they put it up online.

495
00:26:44,920 --> 00:26:47,919
Speaker 1: Remember, yes, cast your mind back to January ten, twenty twenty.

496
00:26:48,200 --> 00:26:50,080
Speaker 3: You didn't know about coviding. You didn't know about you know,

497
00:26:50,200 --> 00:26:51,000
Speaker 3: we're not thinking about it.

498
00:26:51,119 --> 00:26:56,440
Speaker 1: Was like a China thing. It's March where we get lockdown, right, So, okay, January,

499
00:26:56,480 --> 00:26:57,480
Speaker 1: we've got the January ten.

500
00:26:58,760 --> 00:27:05,879
Speaker 3: On January thirteenth of twenty twenty, they had the sequence

501
00:27:05,920 --> 00:27:09,560
Speaker 3: locked in to produce the vaccine. They could effectively print

502
00:27:09,560 --> 00:27:13,639
Speaker 3: the vaccine three days after they had this. That is

503
00:27:13,880 --> 00:27:18,919
Speaker 3: absolutely insane, bananas. The first human phase one injections of

504
00:27:18,960 --> 00:27:25,840
Speaker 3: this were March sixteenth, twenty twenty, which is basically the

505
00:27:25,920 --> 00:27:28,080
Speaker 3: day that we locked down. So it was like the

506
00:27:28,119 --> 00:27:30,280
Speaker 3: first day we had lockdowns, we already had We were

507
00:27:30,320 --> 00:27:32,280
Speaker 3: already not only do we have the vaccine, we were

508
00:27:32,320 --> 00:27:33,240
Speaker 3: putting it in people.

509
00:27:33,840 --> 00:27:34,040
Speaker 2: Yep.

510
00:27:34,280 --> 00:27:36,800
Speaker 3: And then it was December when there was an emergency

511
00:27:36,960 --> 00:27:39,240
Speaker 3: use authorization issued at the end of you know, so

512
00:27:39,560 --> 00:27:43,159
Speaker 3: almost all of that time, the vast, vast majority of

513
00:27:43,200 --> 00:27:47,159
Speaker 3: the time was just doing trials, which of course is

514
00:27:47,760 --> 00:27:51,639
Speaker 3: you can't really make any faster because that's how trials work.

515
00:27:51,680 --> 00:27:54,480
Speaker 1: They Involvedeah, you got to recruit people, you give some place,

516
00:27:54,800 --> 00:27:55,560
Speaker 1: some get the vaccine.

517
00:27:55,640 --> 00:27:55,840
Speaker 5: Yep.

518
00:27:56,040 --> 00:28:02,680
Speaker 3: It's just like it's just absolutely like it's astonishing, Yeah,

519
00:28:02,680 --> 00:28:03,560
Speaker 3: it is.

520
00:28:03,920 --> 00:28:04,159
Speaker 5: You know.

521
00:28:04,680 --> 00:28:07,840
Speaker 1: I think one of you alluded to the fact that, Okay,

522
00:28:07,880 --> 00:28:10,600
Speaker 1: the code that was being used that they had three

523
00:28:10,680 --> 00:28:13,280
Speaker 1: days to take the entire genome of SARS CoV two

524
00:28:13,280 --> 00:28:15,920
Speaker 1: and decide which component of that they wanted to put

525
00:28:15,920 --> 00:28:18,120
Speaker 1: in the vaccine, and they chose the spike protein, which

526
00:28:18,119 --> 00:28:21,600
Speaker 1: is appropriate because that is in SARS viruses. That's the

527
00:28:21,640 --> 00:28:24,160
Speaker 1: thing your body generates antibodies too. We already know that's

528
00:28:24,240 --> 00:28:26,480
Speaker 1: like the thing it likes to fight against. So great

529
00:28:26,520 --> 00:28:30,520
Speaker 1: spike protein. You could ask, okay, why not just produce

530
00:28:30,520 --> 00:28:33,560
Speaker 1: a bunch of spike protein and inject people with that

531
00:28:33,720 --> 00:28:38,040
Speaker 1: instead of mRNA. And the reason is that proteins are sticky, gross,

532
00:28:38,040 --> 00:28:40,680
Speaker 1: complicated things that have to fold in a very particular

533
00:28:40,720 --> 00:28:45,560
Speaker 1: way to work and require these incredibly sensitive conditions, and

534
00:28:45,680 --> 00:28:48,360
Speaker 1: are chains of twenty two amino acids, and all of

535
00:28:48,360 --> 00:28:51,320
Speaker 1: those are difficult to work with and mRNA is just

536
00:28:51,440 --> 00:28:54,200
Speaker 1: four letters, and you said, you know, could be printed,

537
00:28:54,720 --> 00:28:58,760
Speaker 1: and yeah, I mean that's it's it's literally like writing

538
00:28:58,800 --> 00:29:02,920
Speaker 1: a computer program. Let me give you a way to

539
00:29:02,960 --> 00:29:06,280
Speaker 1: put this in context. So, like if you think of

540
00:29:06,320 --> 00:29:10,440
Speaker 1: the flu vaccine, which is a vaccine we produce every year. Obviously,

541
00:29:11,000 --> 00:29:14,280
Speaker 1: the flu vaccine, the way that works is you infect

542
00:29:14,400 --> 00:29:18,600
Speaker 1: fertilized chicken eggs, so that you give the poor embryonic

543
00:29:18,680 --> 00:29:21,560
Speaker 1: chickens the flu and they get a bunch of flu

544
00:29:21,640 --> 00:29:24,360
Speaker 1: virus in the chicken eggs, and then you crack the

545
00:29:24,440 --> 00:29:26,960
Speaker 1: chicken eggs and blend them up and then you inactivate

546
00:29:26,960 --> 00:29:29,240
Speaker 1: the flu virus. So you kill the flu virus in

547
00:29:29,280 --> 00:29:32,680
Speaker 1: all those chicken eggs, and that's what you get injected

548
00:29:32,840 --> 00:29:35,880
Speaker 1: into you. And so you need yeah, five hundred million

549
00:29:35,920 --> 00:29:41,920
Speaker 1: eggs a year to do that for an mr ANDA vaccine.

550
00:29:42,960 --> 00:29:46,680
Speaker 1: If you have a ten liter bioreactor, so ten liters

551
00:29:47,200 --> 00:29:51,000
Speaker 1: is so five of those coke bottles. Okay, a ten

552
00:29:51,040 --> 00:29:55,240
Speaker 1: liter bioreactor can make a few million doses a day

553
00:29:55,840 --> 00:30:00,239
Speaker 1: of mRNA vaccine. There is absolutely no contestan We can

554
00:30:00,280 --> 00:30:02,600
Speaker 1: talk about efficacy and all that stuff, but like in

555
00:30:02,720 --> 00:30:07,240
Speaker 1: terms of sheer, scaleability and rapidity. That is what makes

556
00:30:07,360 --> 00:30:09,320
Speaker 1: MR and A really really special.

557
00:30:09,600 --> 00:30:12,560
Speaker 2: Yeah, it is really really cool.

558
00:30:13,360 --> 00:30:15,520
Speaker 3: And I think printing is the analogy that these guys,

559
00:30:15,560 --> 00:30:17,240
Speaker 3: when you talk to these guys about making the printing

560
00:30:17,720 --> 00:30:19,280
Speaker 3: is the analogy to sort of tell the thing, Okay,

561
00:30:19,280 --> 00:30:22,440
Speaker 3: make this one and then like it bakes this one

562
00:30:22,440 --> 00:30:24,280
Speaker 3: in the bio reactor and then you have and then

563
00:30:24,320 --> 00:30:25,080
Speaker 3: you have it and.

564
00:30:25,040 --> 00:30:28,160
Speaker 1: Then you have it. And what's weirder is it actually worked.

565
00:30:28,840 --> 00:30:33,120
Speaker 1: Like I you know, those initial randomized trials that came

566
00:30:33,160 --> 00:30:36,080
Speaker 1: out for the initial version of the vaccine, the vaccine

567
00:30:36,080 --> 00:30:40,400
Speaker 1: efficacy against infection was above ninety percent, which was was

568
00:30:40,400 --> 00:30:43,959
Speaker 1: was nuts. Now did that persist over time? It definitely

569
00:30:44,040 --> 00:30:47,680
Speaker 1: did not. The virus mutates, we have to keep up

570
00:30:47,720 --> 00:30:51,120
Speaker 1: with various strains, the spike protein changes. There's a lot

571
00:30:51,160 --> 00:30:55,360
Speaker 1: of issues with that, as there are with all vaccines,

572
00:30:55,520 --> 00:30:57,600
Speaker 1: and COVID is one that has been tricky to.

573
00:30:57,640 --> 00:30:58,040
Speaker 5: Keep up with.

574
00:30:58,080 --> 00:31:02,920
Speaker 1: But I will say the most the latest studies, including

575
00:31:03,160 --> 00:31:08,000
Speaker 1: that that our friend doctor j. Bodicharia suppressed from CDC,

576
00:31:08,120 --> 00:31:13,600
Speaker 1: still demonstrates moderate efficacy against hospitization and severe illness.

577
00:31:13,720 --> 00:31:16,280
Speaker 3: And in the first I mean again, I think it's

578
00:31:16,320 --> 00:31:20,000
Speaker 3: like to return to both the wonder and also the

579
00:31:20,040 --> 00:31:23,920
Speaker 3: moments early in COVID, like the protective like this saved

580
00:31:24,320 --> 00:31:28,120
Speaker 3: literally millions of people, and having the vaccine in December

581
00:31:28,720 --> 00:31:33,360
Speaker 3: rather than in you know, December of twenty thirty or whenever, Like,

582
00:31:33,400 --> 00:31:35,680
Speaker 3: it's just that that is millions and millions and millions

583
00:31:35,720 --> 00:31:37,880
Speaker 3: of people who did not die, and that is just

584
00:31:38,800 --> 00:31:40,680
Speaker 3: really yeah important.

585
00:31:41,600 --> 00:31:44,360
Speaker 1: It's really important also to point out the difference between

586
00:31:44,800 --> 00:31:48,200
Speaker 1: an mRNA vaccine, which is causing yourself to generate one protein,

587
00:31:48,280 --> 00:31:50,920
Speaker 1: and most vaccines like the flu vaccine and even some

588
00:31:50,960 --> 00:31:53,600
Speaker 1: of the non mRNA based COVID vaccines, which use the

589
00:31:53,640 --> 00:31:59,520
Speaker 1: whole virus. You know, COVID has twenty seven twenty nine

590
00:31:59,520 --> 00:32:02,959
Speaker 1: proteins in it, so COVID's RNA codes for twenty nine

591
00:32:03,000 --> 00:32:05,160
Speaker 1: different proteins. And the way it works when you get

592
00:32:05,160 --> 00:32:08,600
Speaker 1: an infection is it matches onto your cell and it

593
00:32:08,640 --> 00:32:11,280
Speaker 1: injects all its RNA into your cell, and your cell

594
00:32:11,320 --> 00:32:13,800
Speaker 1: starts making those twenty nine proteins, and those twenty nine

595
00:32:13,800 --> 00:32:16,680
Speaker 1: proteins come together and create a new COVID virus, and

596
00:32:16,720 --> 00:32:19,120
Speaker 1: your cell gets filled up with those COVID viruses until

597
00:32:19,120 --> 00:32:21,360
Speaker 1: it bursts and spreads it throughout the rest of your

598
00:32:21,360 --> 00:32:23,480
Speaker 1: body until your immune system can get a hold of it.

599
00:32:23,600 --> 00:32:27,440
Speaker 1: And so you know, when we think about vaccination, there's

600
00:32:27,480 --> 00:32:30,880
Speaker 1: a real argument that mRNA is kind of the cleanest

601
00:32:31,160 --> 00:32:33,920
Speaker 1: form of vaccination, Like you're exposing your body to more

602
00:32:34,040 --> 00:32:37,040
Speaker 1: or less the minimal amount of substance that you need

603
00:32:37,080 --> 00:32:39,880
Speaker 1: to generate an immune response as opposed to like the

604
00:32:40,040 --> 00:32:41,680
Speaker 1: entire virus. For what it's worth.

605
00:32:42,760 --> 00:32:45,400
Speaker 3: So I will say again, I think this is like,

606
00:32:45,720 --> 00:32:48,760
Speaker 3: there is so much wonder here, and I think that

607
00:32:49,000 --> 00:32:53,280
Speaker 3: when people were first encountering this, that wonder was much

608
00:32:53,320 --> 00:32:56,840
Speaker 3: more dominant than it has become over time. And we're

609
00:32:56,840 --> 00:32:59,680
Speaker 3: going to talk about like some of the conspiracy theories

610
00:32:59,680 --> 00:33:02,400
Speaker 3: in some of the stuff that sort of came out

611
00:33:02,520 --> 00:33:07,440
Speaker 3: of all of this crazy discussion, but it is also

612
00:33:07,560 --> 00:33:10,720
Speaker 3: worth before we get into that, talking about whether there

613
00:33:10,760 --> 00:33:14,960
Speaker 3: are well documented, non conspiratorial risks that are associated with

614
00:33:14,960 --> 00:33:18,880
Speaker 3: this particular approach to vaccines as opposed to these other approaches.

615
00:33:19,120 --> 00:33:22,680
Speaker 3: I think the one that is a real risk that

616
00:33:22,800 --> 00:33:27,520
Speaker 3: is worth discussing is the risk of myocarditis, particularly in

617
00:33:28,560 --> 00:33:33,280
Speaker 3: young men. Yeah, yeah, that's real, So let's talk about

618
00:33:33,320 --> 00:33:35,960
Speaker 3: that before we Let's start with that, because I do

619
00:33:36,000 --> 00:33:38,200
Speaker 3: think people should hear that piece of it, and then

620
00:33:38,240 --> 00:33:40,760
Speaker 3: we'll talk about the rest of the stuff, which is

621
00:33:40,880 --> 00:33:42,840
Speaker 3: much less, much less well supported.

622
00:33:43,080 --> 00:33:47,400
Speaker 1: Yes, Okay, So myocarditis is inflammation of heart muscle cells,

623
00:33:47,600 --> 00:33:51,080
Speaker 1: and it can obviously be caused by a wide variety

624
00:33:51,120 --> 00:33:54,960
Speaker 1: of things. There are autoimmune forms of myocarditis, there's infectious

625
00:33:55,040 --> 00:33:58,440
Speaker 1: forms of myocarditis. Covid itself can cause milcarditis. But there

626
00:33:58,600 --> 00:34:02,640
Speaker 1: is vaccine associated my Cardnite is associated with the mRNA vaccines.

627
00:34:03,960 --> 00:34:10,400
Speaker 1: The cause is a little bit unclear. The thought, the

628
00:34:10,680 --> 00:34:15,480
Speaker 1: sort of leading hypothesis is that the spike protein bears

629
00:34:15,640 --> 00:34:20,200
Speaker 1: some similarity to a protein that lives on heart cells.

630
00:34:20,520 --> 00:34:22,680
Speaker 1: It's like a potassium channel on the heart cells, you know,

631
00:34:22,719 --> 00:34:25,359
Speaker 1: they sort of they're like they just happen to kind

632
00:34:25,400 --> 00:34:27,920
Speaker 1: of have similar shapes, and so in some people, if

633
00:34:27,960 --> 00:34:33,239
Speaker 1: you're generating antibodies against spike protein, you those antibodies might

634
00:34:33,280 --> 00:34:36,319
Speaker 1: cross react with some of the heart cells. It's quite

635
00:34:36,400 --> 00:34:41,720
Speaker 1: clear that estrogen is protective against this, and that testosterone

636
00:34:42,600 --> 00:34:45,200
Speaker 1: makes this worse, which is one of the reasons I

637
00:34:45,200 --> 00:34:48,399
Speaker 1: think we see a higher risk in young men than

638
00:34:48,600 --> 00:34:52,239
Speaker 1: in other populations. So there's clearly a risk, right so

639
00:34:52,280 --> 00:34:55,319
Speaker 1: the question, as you know, will always come up with

640
00:34:55,360 --> 00:34:59,440
Speaker 1: every medical decision that anyone ever makes, is the benefit

641
00:34:59,480 --> 00:34:59,960
Speaker 1: worth the risk?

642
00:35:00,520 --> 00:35:03,439
Speaker 3: Yeah, and I think you know, here if we kind

643
00:35:03,440 --> 00:35:06,840
Speaker 3: of look at the numbers, you know, in a million,

644
00:35:07,280 --> 00:35:10,120
Speaker 3: these estimates suggests in a million vaccines, you'd expect something

645
00:35:10,239 --> 00:35:15,640
Speaker 3: like forty myocarditis cases. But you know, the number of

646
00:35:15,680 --> 00:35:19,000
Speaker 3: hospitalizations and and you know, potential serious illness that might

647
00:35:19,040 --> 00:35:22,120
Speaker 3: be prevented by that is actually quite significantly in excessive.

648
00:35:22,160 --> 00:35:25,120
Speaker 3: That and that's particularly was particularly true early on, in

649
00:35:25,160 --> 00:35:28,480
Speaker 3: the early on in the pandemic. You know, we we

650
00:35:28,560 --> 00:35:31,799
Speaker 3: have arrived at a place where COVID boosters have become

651
00:35:31,840 --> 00:35:35,640
Speaker 3: a much less common thing for healthy adults and for kids.

652
00:35:35,680 --> 00:35:38,560
Speaker 3: And so at this point, most people with teenage boys

653
00:35:38,600 --> 00:35:41,680
Speaker 3: are not getting them an additional COVID booster, and the

654
00:35:41,719 --> 00:35:45,440
Speaker 3: groups that are having the most significantly should be engaging

655
00:35:45,440 --> 00:35:47,480
Speaker 3: with the boosters. I think at this point, in my view,

656
00:35:47,520 --> 00:35:50,680
Speaker 3: are older adults for whom myocarditis is actually not a

657
00:35:50,680 --> 00:35:54,200
Speaker 3: significant not a significant risk, perhaps because their testosterone is

658
00:35:55,160 --> 00:35:56,400
Speaker 3: not so good anymore.

659
00:35:56,920 --> 00:35:58,480
Speaker 1: Yeah, that would be really interesting to see if the

660
00:35:58,480 --> 00:36:01,680
Speaker 1: people now that testosterone self limitation is having its moment like,

661
00:36:01,920 --> 00:36:04,839
Speaker 1: what's going to happen. There's going to be an interaction. Yeah,

662
00:36:04,880 --> 00:36:06,560
Speaker 1: I mean it's not I don't want to say that

663
00:36:06,600 --> 00:36:08,759
Speaker 1: there's a recommendation for teenage boys not to get the

664
00:36:08,760 --> 00:36:09,560
Speaker 1: COVID vaccine.

665
00:36:09,600 --> 00:36:09,920
Speaker 5: It is.

666
00:36:10,480 --> 00:36:13,080
Speaker 1: It is worth having a discussion with a healthcare provider

667
00:36:13,120 --> 00:36:15,080
Speaker 1: about the risks and benefits because it's going to differ

668
00:36:15,120 --> 00:36:17,760
Speaker 1: a bit based on your own susceptibilities.

669
00:36:18,520 --> 00:36:18,799
Speaker 4: You know.

670
00:36:19,239 --> 00:36:22,440
Speaker 1: The thing we don't know, I think is that in

671
00:36:22,480 --> 00:36:24,440
Speaker 1: the initial phase of the pandemic, many of us had

672
00:36:24,480 --> 00:36:29,640
Speaker 1: never been exposed to spike protein at all. Right, it

673
00:36:29,680 --> 00:36:32,399
Speaker 1: was brand new. It was the novel coronavirus and cove

674
00:36:32,520 --> 00:36:35,160
Speaker 1: if you remember when it just got started. Now we're

675
00:36:35,160 --> 00:36:38,120
Speaker 1: in an era where basically everyone has been infected at

676
00:36:38,200 --> 00:36:43,320
Speaker 1: least once. Many people have you been vaccinated, if not once,

677
00:36:43,880 --> 00:36:48,400
Speaker 1: multiple times, and so there is obviously we haven't reached

678
00:36:48,440 --> 00:36:51,280
Speaker 1: a state of herd immunity because people are still getting infected,

679
00:36:51,320 --> 00:36:55,320
Speaker 1: but the severity of infections have decreased even among people

680
00:36:55,680 --> 00:37:01,040
Speaker 1: who weren't vaccinated. You know, back in the original days.

681
00:37:01,160 --> 00:37:05,279
Speaker 1: I remember, so I was on service in April of

682
00:37:05,320 --> 00:37:08,759
Speaker 1: twenty twenty, and I instead of being a kidney doctor

683
00:37:08,800 --> 00:37:11,200
Speaker 1: I got plucked out to be a COVID doctor because

684
00:37:11,200 --> 00:37:14,160
Speaker 1: that's what was happening to everyone. And I remember this, like,

685
00:37:14,200 --> 00:37:18,080
Speaker 1: thirty five year old guy totally healthy coming in with

686
00:37:18,120 --> 00:37:21,120
Speaker 1: COVID was on cardiopulmonary bypass because that's how bad it was.

687
00:37:21,280 --> 00:37:24,279
Speaker 1: And these things do still happen with COVID, but it's

688
00:37:24,320 --> 00:37:26,719
Speaker 1: not the same, and in part it's not the same

689
00:37:26,719 --> 00:37:30,319
Speaker 1: because we do we have been exposed before, and you know,

690
00:37:30,360 --> 00:37:33,440
Speaker 1: there's just a fundamental difference between being exposed to a virus,

691
00:37:33,480 --> 00:37:36,440
Speaker 1: even a mutated form that you've been exposed to previously,

692
00:37:36,920 --> 00:37:39,880
Speaker 1: and one that is your immune system is completely unprepared for.

693
00:37:39,960 --> 00:37:42,680
Speaker 1: So there's some really interesting questions about how to proceed

694
00:37:42,680 --> 00:37:47,880
Speaker 1: with vaccination. I certainly agree that the benefits outweigh the

695
00:37:47,960 --> 00:37:51,279
Speaker 1: risk for the vast majority of people. My only sort

696
00:37:51,280 --> 00:37:54,319
Speaker 1: of question is in this young male group who tend

697
00:37:54,320 --> 00:37:57,120
Speaker 1: to do pretty well with the virus if they get it.

698
00:37:57,800 --> 00:37:58,080
Speaker 2: Yeah.

699
00:37:58,160 --> 00:38:01,960
Speaker 3: So I think one really important point is, regardless of

700
00:38:02,000 --> 00:38:05,520
Speaker 3: how you feel about the COVID vaccine, at this point,

701
00:38:05,560 --> 00:38:06,680
Speaker 3: you know, I think there are people here are going

702
00:38:06,719 --> 00:38:09,200
Speaker 3: to be like, look, I've had COVID six times, I'm

703
00:38:09,200 --> 00:38:11,399
Speaker 3: not getting any more boosters. I'm not boostering my kids.

704
00:38:11,440 --> 00:38:13,920
Speaker 3: It's like just if I look out at the world,

705
00:38:14,800 --> 00:38:17,440
Speaker 3: that is where a large share of people are. But

706
00:38:17,600 --> 00:38:24,160
Speaker 3: this conversation about mRNA is still incredibly important because there

707
00:38:24,160 --> 00:38:28,200
Speaker 3: are many other things, other vaccines that we might want

708
00:38:28,200 --> 00:38:31,920
Speaker 3: to use mRNA for, other things like cancer treatments, which

709
00:38:31,960 --> 00:38:34,520
Speaker 3: we'll talk about in a second. And so yeah, I

710
00:38:34,560 --> 00:38:37,000
Speaker 3: actually I want to make sure that we're we're sort

711
00:38:37,000 --> 00:38:40,200
Speaker 3: of stepped out of the particular COVID COVID thing and

712
00:38:40,480 --> 00:38:44,200
Speaker 3: keep thinking about the mRNA the potential of this technology.

713
00:38:44,320 --> 00:38:48,400
Speaker 3: So in leading into that, it is probably worth debunking

714
00:38:48,440 --> 00:38:49,920
Speaker 3: some of the conspiracy.

715
00:38:49,400 --> 00:38:52,960
Speaker 2: Theories that I think really arose around COVID.

716
00:38:53,040 --> 00:38:55,440
Speaker 3: So look, let me just the way I would like

717
00:38:55,520 --> 00:38:56,880
Speaker 3: to do this is I'm just going to give you

718
00:38:56,920 --> 00:38:59,880
Speaker 3: the theory, and you're going to tell me yes or no,

719
00:39:00,640 --> 00:39:04,960
Speaker 3: science or vibes on this theory. Okay, okay, true or false? Perry,

720
00:39:06,239 --> 00:39:10,560
Speaker 3: mRNA changes your DNA false.

721
00:39:10,640 --> 00:39:13,200
Speaker 1: I think this is okay, wait, I'm sorry you said

722
00:39:13,239 --> 00:39:16,399
Speaker 1: one thing. I think this is the uber conspiracy theory.

723
00:39:16,440 --> 00:39:19,160
Speaker 1: I think part of the reasons, aside from the fact

724
00:39:19,160 --> 00:39:21,480
Speaker 1: that COVID was just like we all had free time

725
00:39:21,520 --> 00:39:24,520
Speaker 1: on our hand to bake conspiracy theories. And that's where

726
00:39:24,600 --> 00:39:27,160
Speaker 1: mRNA got started. I think because it sounds like DNA,

727
00:39:27,640 --> 00:39:30,640
Speaker 1: and that sounds like gene editing, and like it just

728
00:39:30,880 --> 00:39:33,560
Speaker 1: it fosters this. And I just want to reiterate that

729
00:39:34,760 --> 00:39:36,719
Speaker 1: the nucleus of the cell where the DNA lives is

730
00:39:36,719 --> 00:39:42,520
Speaker 1: this highly highly protected inner sanctum. It is very difficult

731
00:39:42,880 --> 00:39:46,840
Speaker 1: for RNA to get into the nucleus. That requires special codes,

732
00:39:46,920 --> 00:39:52,880
Speaker 1: special transporters, and even then it's extraordinarily hard for RNA

733
00:39:53,000 --> 00:39:59,000
Speaker 1: to get reverse transcribed to DNA and get put into DNA. Remember,

734
00:39:59,120 --> 00:40:03,319
Speaker 1: there's a whole set of viruses that their trick is

735
00:40:03,320 --> 00:40:06,840
Speaker 1: that they bring with them reverse transcriptase to make RNA

736
00:40:06,880 --> 00:40:10,160
Speaker 1: go into DNA instead of vice versa, HIV being the

737
00:40:10,760 --> 00:40:14,759
Speaker 1: best example of this. Retroviruses like that, like this is

738
00:40:14,760 --> 00:40:16,320
Speaker 1: their trick. It's like, oh, we're going to get the

739
00:40:16,400 --> 00:40:18,880
Speaker 1: RNA into your DNA. They had to evolve an entire

740
00:40:19,000 --> 00:40:24,920
Speaker 1: system to do that. RNA itself is like deliberately excluded

741
00:40:25,480 --> 00:40:27,279
Speaker 1: from the nucleus and the DNA.

742
00:40:27,760 --> 00:40:30,920
Speaker 3: Okay, great, we also have a couple of clips, so

743
00:40:30,960 --> 00:40:32,719
Speaker 3: that's like the uber thing. Then there are some more

744
00:40:32,760 --> 00:40:34,719
Speaker 3: specific things. So we're going to play these We're gonna

745
00:40:34,719 --> 00:40:35,360
Speaker 3: play these clips.

746
00:40:35,440 --> 00:40:37,239
Speaker 5: So where was walking around with a jab that got

747
00:40:37,239 --> 00:40:38,400
Speaker 5: a little bit of Epstein in them?

748
00:40:38,480 --> 00:40:39,120
Speaker 6: No way.

749
00:40:39,280 --> 00:40:43,080
Speaker 5: Epstein's main funder of the mRNA him Bill Gates Fauci,

750
00:40:43,200 --> 00:40:44,799
Speaker 5: And what he did was he put a little bit

751
00:40:44,840 --> 00:40:47,320
Speaker 5: of his own gena code in each one of the jabs.

752
00:40:47,440 --> 00:40:49,719
Speaker 5: But yeah, how did he pull that off? It's all

753
00:40:49,719 --> 00:40:52,320
Speaker 5: in the files. Man. He was doing experiments and funding

754
00:40:52,400 --> 00:40:55,759
Speaker 5: that chrisper and that mRNA technology back in the day.

755
00:40:55,840 --> 00:40:57,839
Speaker 5: And he said in the same document that he wanted

756
00:40:57,880 --> 00:41:00,000
Speaker 5: to have his code in all of it. So anytime

757
00:41:00,040 --> 00:41:02,239
Speaker 5: with anybody in jets, they've got a piece of him

758
00:41:02,239 --> 00:41:04,320
Speaker 5: inside of them. Is that his consciousness being transferred to

759
00:41:04,360 --> 00:41:06,600
Speaker 5: what exactly just a little code? It's a little ego thing.

760
00:41:06,680 --> 00:41:08,400
Speaker 5: You know, how much of an effect is it going

761
00:41:08,440 --> 00:41:11,160
Speaker 5: to have on a person who knows But for him consciously,

762
00:41:11,160 --> 00:41:14,040
Speaker 5: it's like I'm in everybody, every single person's out here,

763
00:41:14,160 --> 00:41:16,600
Speaker 5: I'm inside of it. Off the population, right sick?

764
00:41:16,880 --> 00:41:22,640
Speaker 3: Okay, Perry, what about that one.

765
00:41:21,480 --> 00:41:27,759
Speaker 1: DNA, Epstein's DNA in the RNA vaccines. You know, Epstein's

766
00:41:27,840 --> 00:41:31,040
Speaker 1: DNA is in a lot of places but not there,

767
00:41:31,400 --> 00:41:31,960
Speaker 1: not there.

768
00:41:34,160 --> 00:41:38,279
Speaker 3: Yeah, we're gonna leave that one. Uh, and then we

769
00:41:38,360 --> 00:41:41,560
Speaker 3: have this deep population.

770
00:41:42,960 --> 00:41:44,239
Speaker 2: Well I'm just going to play it for you.

771
00:41:44,600 --> 00:41:48,600
Speaker 6: The mRNA depopulation technology that was in the COVID vaccines

772
00:41:48,800 --> 00:41:51,880
Speaker 6: is now in our food supply. And who has this

773
00:41:51,960 --> 00:41:55,120
Speaker 6: been sponsored by? This is sponsored by yours only, Bill Gates.

774
00:41:55,239 --> 00:41:58,840
Speaker 6: Bill Gates funded toront of Biosciences is now moving to

775
00:41:59,080 --> 00:42:03,640
Speaker 6: reprogram life itself. They are planning to spray synthetic mr

776
00:42:03,760 --> 00:42:07,360
Speaker 6: and A pesticides on your crops, on your vegetables, on

777
00:42:07,400 --> 00:42:10,359
Speaker 6: your fruits, on your meat, on everything in the food chain.

778
00:42:10,480 --> 00:42:13,319
Speaker 6: As revealed by doctor Mike Eden, who has been vice

779
00:42:13,320 --> 00:42:16,759
Speaker 6: president of Pizer for sixteen years, that this mrnay technology

780
00:42:17,000 --> 00:42:19,640
Speaker 6: is depopulation technology and they have been doing this for

781
00:42:19,680 --> 00:42:21,760
Speaker 6: a long time now. They have been trying to sneak

782
00:42:21,800 --> 00:42:24,440
Speaker 6: it into our food supply. And finally he wants that

783
00:42:24,480 --> 00:42:28,440
Speaker 6: this ammerine technology is intentionally manufactured to cause infertility and

784
00:42:28,480 --> 00:42:30,919
Speaker 6: to kill human beings. Now, this is not something new

785
00:42:31,000 --> 00:42:33,080
Speaker 6: if you look at it, depopulation technology.

786
00:42:34,200 --> 00:42:36,040
Speaker 3: Okay, it's in our food supply.

787
00:42:37,360 --> 00:42:42,120
Speaker 1: What, dude, what I want to ask some of the

788
00:42:42,160 --> 00:42:44,880
Speaker 1: conspiracy do you remember early on, They were like, everyone

789
00:42:44,880 --> 00:42:46,320
Speaker 1: who's getting one of these shots is going to be

790
00:42:46,360 --> 00:42:49,759
Speaker 1: dead within thirty days, and like, why does it?

791
00:42:51,400 --> 00:42:51,759
Speaker 4: Do you know?

792
00:42:52,239 --> 00:42:52,479
Speaker 3: Check?

793
00:42:52,719 --> 00:42:55,160
Speaker 1: Hey, I'm still here.

794
00:42:55,560 --> 00:42:59,640
Speaker 3: Yeah, So look, I mean I think the best this

795
00:42:59,760 --> 00:43:02,960
Speaker 3: is now the universe of conspiracy theories about mRNA. There

796
00:43:03,040 --> 00:43:06,359
Speaker 3: are a million crazy things. Either. The best defense about

797
00:43:06,360 --> 00:43:09,440
Speaker 3: them from an individual standpoint is just understanding the basic

798
00:43:09,480 --> 00:43:12,120
Speaker 3: biology of what this is and what it does, which

799
00:43:12,680 --> 00:43:16,480
Speaker 3: demystifies the whole situation. So if you are still the

800
00:43:16,520 --> 00:43:19,000
Speaker 3: next time you hear one of these crazy conspiracy things,

801
00:43:19,080 --> 00:43:20,560
Speaker 3: just go back and listen to like the first six

802
00:43:20,600 --> 00:43:23,680
Speaker 3: minutes of this deep dive and make yourself feel better.

803
00:43:24,600 --> 00:43:29,160
Speaker 3: And that is very important because there are some incredibly

804
00:43:29,239 --> 00:43:32,240
Speaker 3: cool possible things that may come down the line with mRNA.

805
00:43:32,480 --> 00:43:37,520
Speaker 3: The most interesting, well, the one we are furthest along on,

806
00:43:37,800 --> 00:43:40,759
Speaker 3: I would say right now is the idea that this

807
00:43:40,840 --> 00:43:47,120
Speaker 3: may actually be a cancer vaccine. So that is incredibly cool,

808
00:43:47,400 --> 00:43:50,000
Speaker 3: and I think the basic idea, and then maybe you

809
00:43:50,000 --> 00:43:52,359
Speaker 3: can tell us how far we have gotten along with this,

810
00:43:52,680 --> 00:43:57,000
Speaker 3: is that if I have a tumor and I biopsy

811
00:43:57,120 --> 00:44:01,319
Speaker 3: the tumor, that tumor may have specific antigen sort of

812
00:44:01,320 --> 00:44:07,360
Speaker 3: specific characteristics that I could in principle have antibodies to fight,

813
00:44:07,920 --> 00:44:11,480
Speaker 3: and if I could make a vaccine that would effectively

814
00:44:11,640 --> 00:44:17,080
Speaker 3: create antibodies to your particular tumor, that may actually be

815
00:44:17,160 --> 00:44:18,360
Speaker 3: a path to treatment.

816
00:44:18,480 --> 00:44:21,520
Speaker 2: And because mRNA is perfect.

817
00:44:21,239 --> 00:44:24,240
Speaker 1: But it takes ten to fifteen years to make a vaccine, Emily,

818
00:44:24,239 --> 00:44:25,360
Speaker 1: you don't have that kind of time.

819
00:44:25,560 --> 00:44:28,200
Speaker 3: And because mRNA is programmable and we can do it

820
00:44:28,480 --> 00:44:30,959
Speaker 3: in a short period of time, it may actually be

821
00:44:31,600 --> 00:44:34,960
Speaker 3: feasible at an individual level in this kind of personalized

822
00:44:34,960 --> 00:44:39,560
Speaker 3: medicine way that really hasn't been possible with our existing technology.

823
00:44:39,800 --> 00:44:41,920
Speaker 1: Yeah, I mean this is where we get to the

824
00:44:41,960 --> 00:44:46,160
Speaker 1: future and why RNA's so much more than COVID vaccines

825
00:44:46,239 --> 00:44:49,160
Speaker 1: or flu vaccines and things that are getting developed. So, yeah,

826
00:44:49,160 --> 00:44:52,080
Speaker 1: this is real. I'll give you a study that appeared

827
00:44:52,080 --> 00:44:55,680
Speaker 1: in the Lancet in twenty twenty four. So this was

828
00:44:55,719 --> 00:44:58,480
Speaker 1: a trial one hundred and fifty seven patients. They had

829
00:44:59,320 --> 00:45:03,360
Speaker 1: relatively high stage stage three B to four cutaneous melanoma,

830
00:45:03,360 --> 00:45:07,400
Speaker 1: so skin cancer, melanoma, skin cancer, pretty serious. They were

831
00:45:07,480 --> 00:45:10,760
Speaker 1: randomized to get pemberlysium AB, which is that immune therapy

832
00:45:10,800 --> 00:45:14,440
Speaker 1: that's really transformed melanoma over the past decade. That's what

833
00:45:14,480 --> 00:45:17,200
Speaker 1: like Jimmy Carter got, which kept him alive for so long.

834
00:45:17,719 --> 00:45:21,480
Speaker 1: Great drug that was the standard. Or they got pemberlelysm

835
00:45:21,480 --> 00:45:25,600
Speaker 1: ab plus an mRNA vaccine and this was like a

836
00:45:25,719 --> 00:45:30,200
Speaker 1: custom printed mRNA vaccine for their specific tumor. So they

837
00:45:30,320 --> 00:45:34,000
Speaker 1: took the biopsy, they figured out what proteins were different

838
00:45:34,520 --> 00:45:37,640
Speaker 1: than their normal cells, and they made an mRNA for them.

839
00:45:37,680 --> 00:45:39,319
Speaker 1: And again you can do this in like a matter

840
00:45:39,360 --> 00:45:44,080
Speaker 1: of weeks, which is just insane. So they gave the

841
00:45:44,120 --> 00:45:49,280
Speaker 1: patients there. The primary outcome was recurrence of melanoma or death.

842
00:45:49,920 --> 00:45:52,880
Speaker 1: Forty percent of people in the pemberlysim mab alone group,

843
00:45:52,960 --> 00:45:55,600
Speaker 1: that's the Jimmy Carter standard of care, forty percent had

844
00:45:55,600 --> 00:45:59,160
Speaker 1: recurrence of melanoma or death. That's actually a good outcome

845
00:45:59,200 --> 00:46:03,520
Speaker 1: for this stage melanoma. It's obviously advanced melanoma is very serious,

846
00:46:05,160 --> 00:46:08,400
Speaker 1: versus twenty two percent in the combo group. So it

847
00:46:08,560 --> 00:46:13,040
Speaker 1: essentially cut in half the new melanoma and death rate

848
00:46:13,320 --> 00:46:16,799
Speaker 1: in this disease. And it's totally personalized medicine. This is

849
00:46:16,920 --> 00:46:20,719
Speaker 1: the real promise of RNA technology.

850
00:46:21,239 --> 00:46:23,480
Speaker 3: Yeah, and I mean it feels like there are many

851
00:46:23,600 --> 00:46:26,480
Speaker 3: kinds of cancers for which this would be potentially would

852
00:46:26,480 --> 00:46:27,040
Speaker 3: be every.

853
00:46:27,120 --> 00:46:30,320
Speaker 1: Every kind of cancer. I mean, like I challenge every

854
00:46:30,360 --> 00:46:34,440
Speaker 1: cancer has mutations compared to your ordinary cells otherwise or

855
00:46:34,480 --> 00:46:37,200
Speaker 1: otherwise it would be your ordinary cell. So there's always something,

856
00:46:37,560 --> 00:46:41,520
Speaker 1: you know, whether it's targetable with antibodies is just about

857
00:46:41,520 --> 00:46:44,160
Speaker 1: revving up your immune system. That's why pemberlesum mab works.

858
00:46:44,160 --> 00:46:46,080
Speaker 1: But now we can say, like, not only just rev

859
00:46:46,160 --> 00:46:48,880
Speaker 1: up the immune system on the whole, we can be

860
00:46:48,920 --> 00:46:52,400
Speaker 1: like and specifically for cells that you know, look like

861
00:46:52,440 --> 00:46:53,080
Speaker 1: this so cool?

862
00:46:53,200 --> 00:46:56,080
Speaker 3: And I think you know, part of what makes this

863
00:46:56,200 --> 00:47:00,200
Speaker 3: kind of treatment so exciting is that many of are

864
00:47:00,239 --> 00:47:03,120
Speaker 3: existing A huge sure of our existing cancer treatments. Basically,

865
00:47:03,200 --> 00:47:06,680
Speaker 3: most of chemotherapy works by killing, you know, all of

866
00:47:06,719 --> 00:47:10,359
Speaker 3: your fast like targeting flies on you half fast they

867
00:47:10,360 --> 00:47:12,080
Speaker 3: produce and so, and that's why you lose your hair,

868
00:47:12,200 --> 00:47:15,480
Speaker 3: you lose your fingernails, your people have gut issues, all

869
00:47:15,560 --> 00:47:18,120
Speaker 3: kinds of other stuff. If you can find a way

870
00:47:18,160 --> 00:47:21,479
Speaker 3: to target only the cancer things, that's obviously a path

871
00:47:21,680 --> 00:47:24,640
Speaker 3: to a much lower side effect, much more effective treatment.

872
00:47:25,080 --> 00:47:28,160
Speaker 3: Were there significant either side effects or downsides in that

873
00:47:28,280 --> 00:47:29,160
Speaker 3: Lancet trial.

874
00:47:29,000 --> 00:47:32,440
Speaker 1: Not really, I mean the side effects were associated primarily

875
00:47:32,440 --> 00:47:35,799
Speaker 1: with pemberlesim mab. You get like autoimmune types of conditions

876
00:47:35,840 --> 00:47:39,200
Speaker 1: in other organ systems. But the vaccine related side effects

877
00:47:39,239 --> 00:47:41,560
Speaker 1: were what we've come to expect from RNA vaccines, which

878
00:47:41,600 --> 00:47:43,000
Speaker 1: is like, you don't feel great for a day or so,

879
00:47:43,160 --> 00:47:46,440
Speaker 1: but you're probably willing to accept that in this scenario

880
00:47:47,239 --> 00:47:47,680
Speaker 1: for sure.

881
00:47:48,000 --> 00:47:50,480
Speaker 3: Okay, so before we go, let's talk about one other

882
00:47:50,840 --> 00:47:54,640
Speaker 3: like promised thing, which is SI rna.

883
00:47:55,000 --> 00:47:57,439
Speaker 1: This is so sci fi, very sci fi.

884
00:47:58,160 --> 00:48:01,719
Speaker 3: So we've been talking a lot about about RNA treatments

885
00:48:01,719 --> 00:48:04,880
Speaker 3: that tell your body, okay, make more of this protein.

886
00:48:06,360 --> 00:48:09,440
Speaker 3: But the idea here is to make less of a protein.

887
00:48:09,840 --> 00:48:13,319
Speaker 1: It's so cool. Yeah, So I guess what you need

888
00:48:13,360 --> 00:48:16,279
Speaker 1: to know about SI rna is that your body has

889
00:48:16,320 --> 00:48:21,160
Speaker 1: a built in mechanism, multiple built in mechanisms to degrade mRNA. Remember,

890
00:48:21,160 --> 00:48:22,960
Speaker 1: we don't want to keep making the brownies over and

891
00:48:22,960 --> 00:48:24,640
Speaker 1: over and over again. So you've got all sorts of

892
00:48:24,640 --> 00:48:27,600
Speaker 1: proteins and enzymes and things that are out there being

893
00:48:27,640 --> 00:48:30,080
Speaker 1: like degrade mRNA, like don't let it be used too

894
00:48:30,120 --> 00:48:33,120
Speaker 1: many times, which is important and good, and we want

895
00:48:33,120 --> 00:48:37,880
Speaker 1: that one of the mechanisms and the Andrew fire and

896
00:48:37,960 --> 00:48:40,000
Speaker 1: Craig Mellow got the Nobel Prize in two thousand and

897
00:48:40,040 --> 00:48:44,040
Speaker 1: six for discovering this mechanism. Is there's a protein in

898
00:48:44,080 --> 00:48:47,040
Speaker 1: your cell that targets mRNAs and choose them up. That's

899
00:48:47,080 --> 00:48:50,160
Speaker 1: its job. It's a garbage, garbage eating protein. But the

900
00:48:50,200 --> 00:48:55,919
Speaker 1: protein itself has a binding pocket for RNA and that

901
00:48:56,160 --> 00:49:00,760
Speaker 1: RNA tells the protein what RNA to look for and digest.

902
00:49:00,920 --> 00:49:04,840
Speaker 1: So like it's a programmable it like the protein is

903
00:49:04,920 --> 00:49:09,480
Speaker 1: programmable to look and target for specific mRNAs, which like

904
00:49:09,560 --> 00:49:12,240
Speaker 1: why we have something like that, I mean, evolution, it's amazing,

905
00:49:12,239 --> 00:49:15,080
Speaker 1: it's unbelievable, but we can exploit that. And that's what

906
00:49:15,200 --> 00:49:18,359
Speaker 1: sRNAs are. They are these custom designed RNAs that bind

907
00:49:18,360 --> 00:49:21,320
Speaker 1: to this special protein and say, look for this mRNA

908
00:49:21,360 --> 00:49:23,560
Speaker 1: and chew it up. And what that means is that

909
00:49:23,680 --> 00:49:28,040
Speaker 1: if there's a protein you don't like, you can tell cells, hey,

910
00:49:28,920 --> 00:49:32,040
Speaker 1: chew up. Any any copy of that recipe, any brownie

911
00:49:32,040 --> 00:49:33,719
Speaker 1: recipes you see, chew them up. I don't even want

912
00:49:33,719 --> 00:49:35,200
Speaker 1: to see it get made, right, I don't want a

913
00:49:35,239 --> 00:49:38,000
Speaker 1: single brownie coming out of this cell. And what this

914
00:49:38,080 --> 00:49:38,640
Speaker 1: lets you do.

915
00:49:39,280 --> 00:49:42,280
Speaker 2: See totally totally bananas.

916
00:49:42,040 --> 00:49:48,400
Speaker 1: Is totally bananas, is give someone an injection that treats

917
00:49:48,440 --> 00:49:52,719
Speaker 1: them for months at a time, six months at a time.

918
00:49:53,239 --> 00:49:58,920
Speaker 1: For example, there's a drug called enclysseran which targets a

919
00:49:59,239 --> 00:50:02,120
Speaker 1: protein called pa CSK nine. So you inject this drug,

920
00:50:02,160 --> 00:50:04,680
Speaker 1: it's an sRNA drug. It makes your liver cell not

921
00:50:04,760 --> 00:50:09,200
Speaker 1: produce this PCSK nine protein that's a major component of

922
00:50:09,200 --> 00:50:12,239
Speaker 1: the cholesterol synthesis cascade. There are other drugs that you

923
00:50:12,280 --> 00:50:17,239
Speaker 1: take once a day that target PCSK nine, but this

924
00:50:17,360 --> 00:50:21,160
Speaker 1: drug you get injected once and for six months. In

925
00:50:21,200 --> 00:50:26,399
Speaker 1: this trial, the LDL level was fifty percent lower in

926
00:50:26,440 --> 00:50:29,880
Speaker 1: the treatment group than the placebo group. One injection six

927
00:50:29,920 --> 00:50:33,839
Speaker 1: months long, and everyone was on max dose statins. Every

928
00:50:33,880 --> 00:50:36,160
Speaker 1: single person the trials on max dose statins. And then

929
00:50:36,200 --> 00:50:40,359
Speaker 1: you get one drug. There's another. There's another drug called zilbsaran.

930
00:50:40,800 --> 00:50:43,319
Speaker 1: This was in the New England Journal twenty twenty three,

931
00:50:43,440 --> 00:50:47,520
Speaker 1: which is a twice yearly blood pressure medication. So it

932
00:50:47,560 --> 00:50:50,560
Speaker 1: targets a protein called angiate tensinogen which raises your blood pressure.

933
00:50:50,760 --> 00:50:53,439
Speaker 1: Basically suppresses that protein for six months at a time,

934
00:50:53,880 --> 00:50:57,200
Speaker 1: and on average, compared to placebo people had a fifteen

935
00:50:57,239 --> 00:51:00,760
Speaker 1: to twenty millimeter of mercury drop in system blood pressure.

936
00:51:00,760 --> 00:51:03,480
Speaker 1: That's the number on top one injection six months of

937
00:51:03,480 --> 00:51:06,080
Speaker 1: blood pressure control. Now, as a doctor, I'm like, that

938
00:51:06,120 --> 00:51:07,520
Speaker 1: makes me a little nervous because like what if you

939
00:51:07,520 --> 00:51:09,279
Speaker 1: get sick and have diarrhea and stuff, like maybe you

940
00:51:09,280 --> 00:51:11,520
Speaker 1: want a little blood pressure. I don't know, cholesterol I

941
00:51:11,520 --> 00:51:15,120
Speaker 1: feel better about. But like, from a technological standpoint, there's

942
00:51:15,160 --> 00:51:18,640
Speaker 1: a future out there where instead of every morning you

943
00:51:18,680 --> 00:51:21,200
Speaker 1: wake up and brush your teeth and take your six pills,

944
00:51:21,880 --> 00:51:24,799
Speaker 1: you like go twice a year and you get a

945
00:51:24,800 --> 00:51:28,440
Speaker 1: couple of injections and you're good to go. Absolutely nuts.

946
00:51:28,760 --> 00:51:34,160
Speaker 3: That is totally, just completely insane. So you talked about

947
00:51:34,480 --> 00:51:40,080
Speaker 3: blood pressure. This is potentially like an incredibly rich technology.

948
00:51:40,080 --> 00:51:41,520
Speaker 3: So early in my career I worked a lot of

949
00:51:41,600 --> 00:51:44,399
Speaker 3: hunting dis disease. I was doing a lot of work

950
00:51:44,440 --> 00:51:47,920
Speaker 3: with people. Huntingdons is a degenerative neurological disorder. It's a

951
00:51:48,360 --> 00:51:54,640
Speaker 3: genetically caused is totally totally devastating, runs through families and

952
00:51:55,080 --> 00:51:58,440
Speaker 3: there within the last year or two there has been

953
00:51:58,480 --> 00:52:02,480
Speaker 3: probably the most significant step forward in treatment of this disease,

954
00:52:02,520 --> 00:52:06,880
Speaker 3: which relies on this kind of technology effectively limiting production

955
00:52:07,080 --> 00:52:09,320
Speaker 3: of this protein. This is a disease where you produce

956
00:52:09,360 --> 00:52:11,160
Speaker 3: too much of this protein and that leads to to

957
00:52:11,320 --> 00:52:15,280
Speaker 3: generation and so again, turning off the potential for producing

958
00:52:15,280 --> 00:52:19,560
Speaker 3: that protein has slowed disease progression like seventy five percent

959
00:52:19,600 --> 00:52:23,640
Speaker 3: over several years in indosing. So it's just just a

960
00:52:23,760 --> 00:52:27,720
Speaker 3: technology with unbelievable amounts of potential going forward.

961
00:52:27,960 --> 00:52:30,160
Speaker 1: Yeah, it's so exciting. It's good that people are starting

962
00:52:30,200 --> 00:52:32,799
Speaker 1: to learn about it, and hopefully learning about it in

963
00:52:32,840 --> 00:52:35,480
Speaker 1: a broader way than what you might see on social media.

964
00:52:36,360 --> 00:52:40,560
Speaker 3: Totally okay. My goal in this episode, I will be

965
00:52:40,719 --> 00:52:43,000
Speaker 3: just totally upfront, was to deliver a sense of wonder.

966
00:52:43,080 --> 00:52:47,640
Speaker 3: I mean, I think these technologies are incredibly exciting and

967
00:52:48,239 --> 00:52:51,840
Speaker 3: I really hope we do not stop researching them because

968
00:52:51,960 --> 00:52:55,080
Speaker 3: people did not like the COVID vaccine, which feels like

969
00:52:55,719 --> 00:52:59,080
Speaker 3: really throwing away you know, what is potentially one of

970
00:52:59,080 --> 00:53:04,240
Speaker 3: the most significant innovations of the past many decades because

971
00:53:04,280 --> 00:53:07,560
Speaker 3: you didn't like one specific you know, kind of lockdown

972
00:53:07,640 --> 00:53:12,640
Speaker 3: or whatever. So more mRNA research would be great. I

973
00:53:12,680 --> 00:53:13,840
Speaker 3: think we could smash your pass.

974
00:53:14,160 --> 00:53:14,680
Speaker 1: Let's do it.

975
00:53:14,800 --> 00:53:17,719
Speaker 3: Let me guess, Perry, are you a smasher pass on

976
00:53:18,400 --> 00:53:19,480
Speaker 3: RNA technologies?

977
00:53:19,960 --> 00:53:25,120
Speaker 1: I am a huge smash on RNA technologies. These will

978
00:53:25,160 --> 00:53:29,800
Speaker 1: transform medicine. Mark my words, it's a major paradigm shift. Emily,

979
00:53:30,040 --> 00:53:30,920
Speaker 1: smash your pass.

980
00:53:31,160 --> 00:53:34,919
Speaker 3: I'm also smash on these. I am incredibly excited about them,

981
00:53:34,920 --> 00:53:38,040
Speaker 3: and I just I just really hope we keep pushing

982
00:53:38,040 --> 00:53:41,600
Speaker 3: this stuff forward. All Right, that's it for RNA your

983
00:53:41,600 --> 00:53:43,600
Speaker 3: mailback question of the Week after the break.

984
00:53:49,320 --> 00:53:52,000
Speaker 4: Hi, Emily and Perry. This is Melissa from the Seattle

985
00:53:52,080 --> 00:53:55,240
Speaker 4: area and am listening to your sleep episode and I

986
00:53:55,320 --> 00:53:57,840
Speaker 4: have a couple questions that maybe you can diagnose me

987
00:53:57,880 --> 00:54:01,080
Speaker 4: and figure out if I'm okay. The first is that

988
00:54:02,320 --> 00:54:05,080
Speaker 4: I believe that since I was in middle school or

989
00:54:05,160 --> 00:54:07,640
Speaker 4: high school, maybe a few times a year I'll have

990
00:54:08,040 --> 00:54:12,120
Speaker 4: a really bad dream where I am being either chased,

991
00:54:12,160 --> 00:54:14,239
Speaker 4: where someone is hurting me, and I'm aware that it's

992
00:54:14,280 --> 00:54:16,200
Speaker 4: a dream, but in my dream or in real life,

993
00:54:16,239 --> 00:54:18,480
Speaker 4: I guess I can't breathe, and the only way that

994
00:54:18,600 --> 00:54:22,800
Speaker 4: I wake up is from screaming. And I'm a side sleeper,

995
00:54:22,880 --> 00:54:25,520
Speaker 4: and if I fall back asleep in the same side,

996
00:54:25,520 --> 00:54:28,640
Speaker 4: the dream immediately comes back, or if I flee my body,

997
00:54:28,680 --> 00:54:32,360
Speaker 4: it goes away. But again, I can't breathe in my sleep,

998
00:54:32,360 --> 00:54:35,160
Speaker 4: and the only way for me to wake up from

999
00:54:35,160 --> 00:54:36,799
Speaker 4: the stream that I know is a dream but it's

1000
00:54:36,880 --> 00:54:40,319
Speaker 4: really scary is from screaming. And I think my twin

1001
00:54:40,360 --> 00:54:43,640
Speaker 4: sister sometimes has the same thing. Any idea with that is.

1002
00:54:44,600 --> 00:54:49,480
Speaker 4: Another thing that has happened a few times is that

1003
00:54:50,080 --> 00:54:53,520
Speaker 4: I have bit my husband on the arm in the

1004
00:54:53,520 --> 00:54:55,759
Speaker 4: middle of my sleep because I'm having a dream that

1005
00:54:55,880 --> 00:54:58,480
Speaker 4: I need to bite into something harder. And I also

1006
00:54:58,520 --> 00:55:00,319
Speaker 4: do the same thing with my three year old sun

1007
00:55:00,320 --> 00:55:02,080
Speaker 4: when we were sharing a bit on vacation. I bit

1008
00:55:02,120 --> 00:55:07,160
Speaker 4: his finger and luckily you woke up before anything bad happened.

1009
00:55:07,560 --> 00:55:11,239
Speaker 4: But yeah, can you diagnose me? What's going on?

1010
00:55:11,600 --> 00:55:11,880
Speaker 5: Thanks?

1011
00:55:11,920 --> 00:55:12,200
Speaker 4: Guys?

1012
00:55:12,800 --> 00:55:13,280
Speaker 2: Oh Man?

1013
00:55:14,040 --> 00:55:16,880
Speaker 3: Okay, Perry, what do you think?

1014
00:55:17,880 --> 00:55:20,799
Speaker 1: Yeah? Okay, So, first of all, really sorry you're going

1015
00:55:20,840 --> 00:55:25,560
Speaker 1: through this. This sounds both scary, you know, and if

1016
00:55:25,640 --> 00:55:28,160
Speaker 1: not outright traumatizing. I mean, I do need to say,

1017
00:55:28,760 --> 00:55:32,600
Speaker 1: because I'm a medical doctor that like I can't despite

1018
00:55:32,640 --> 00:55:35,439
Speaker 1: being asked, I can't diagnose you from AFAR. I don't

1019
00:55:35,760 --> 00:55:39,880
Speaker 1: know your medical history or anything like that. But I

1020
00:55:39,920 --> 00:55:42,120
Speaker 1: appreciate that you heard some things on the sleep episode

1021
00:55:42,239 --> 00:55:47,480
Speaker 1: that seem to be resonating. I agree that you know

1022
00:55:47,480 --> 00:55:51,239
Speaker 1: there's some stuff that you mentioned that sounds like it's

1023
00:55:51,239 --> 00:55:55,840
Speaker 1: worth discussing with a sleep medicine doctor. In particular, the

1024
00:55:56,160 --> 00:55:58,319
Speaker 1: like acting out of dreams that we talked about in

1025
00:55:58,360 --> 00:56:02,239
Speaker 1: the episode can be associated with a condition called REM

1026
00:56:02,320 --> 00:56:06,960
Speaker 1: behavioral disorder our AM behavioral disorder that the real terrors

1027
00:56:07,040 --> 00:56:10,080
Speaker 1: that are happening could sound a little bit like night terrors.

1028
00:56:10,440 --> 00:56:14,680
Speaker 1: There is a really clear test for this, so there's

1029
00:56:14,880 --> 00:56:18,759
Speaker 1: a special kind of sleep study called video polysomnography, so

1030
00:56:18,800 --> 00:56:21,640
Speaker 1: it's not only your conventional sleep study that would detect

1031
00:56:21,680 --> 00:56:25,320
Speaker 1: things like sleep apnea. You mentioned something about position being important,

1032
00:56:25,360 --> 00:56:27,560
Speaker 1: so that also leads to the question of sleep apna.

1033
00:56:27,600 --> 00:56:30,640
Speaker 1: But a video polysymnography records you during sleep, and this

1034
00:56:30,760 --> 00:56:36,560
Speaker 1: is critical for REM sleep disturbances because the eg on

1035
00:56:36,560 --> 00:56:38,839
Speaker 1: your brain knows you're in REM and if you listen

1036
00:56:38,840 --> 00:56:41,160
Speaker 1: to our sleep episode, remember you're supposed to be paralyzed.

1037
00:56:41,520 --> 00:56:44,280
Speaker 1: When you're in REM sleeps, you don't act out your dreams,

1038
00:56:44,320 --> 00:56:46,560
Speaker 1: and so the video can then be correlated with that

1039
00:56:46,640 --> 00:56:49,880
Speaker 1: to see if paralysis is working as intended. So this

1040
00:56:50,000 --> 00:56:53,280
Speaker 1: is definitely something to check out, especially if you've hurt

1041
00:56:53,440 --> 00:56:56,440
Speaker 1: or come close to hurting a loved one who shares

1042
00:56:56,480 --> 00:56:57,000
Speaker 1: the bed with you.

1043
00:56:57,880 --> 00:57:00,520
Speaker 3: Yeah, And I would just let me just say one

1044
00:57:00,560 --> 00:57:03,600
Speaker 3: thing about this and all these sleep questions, which is like,

1045
00:57:03,760 --> 00:57:05,680
Speaker 3: if there is an issue in your sleep that is

1046
00:57:05,719 --> 00:57:08,800
Speaker 3: affecting that is affecting your life, you should try to

1047
00:57:08,800 --> 00:57:10,680
Speaker 3: get help with it. I think actually sleep is a

1048
00:57:10,719 --> 00:57:14,520
Speaker 3: place where people underinvest in trying to figure out what

1049
00:57:14,600 --> 00:57:16,439
Speaker 3: the help would be, in part because it's not so clear,

1050
00:57:16,560 --> 00:57:18,040
Speaker 3: like well, what are you going to do? And I

1051
00:57:18,080 --> 00:57:19,960
Speaker 3: think the answer and that answer made it very clear,

1052
00:57:20,040 --> 00:57:22,240
Speaker 3: is like, here's the thing. You probably didn't know about this,

1053
00:57:22,400 --> 00:57:24,800
Speaker 3: but here's a thing that you would do in this situation.

1054
00:57:24,920 --> 00:57:26,800
Speaker 3: If you go to someone and say, you know, I'm

1055
00:57:26,840 --> 00:57:29,440
Speaker 3: having this problem with my sleep, here is the issue.

1056
00:57:29,680 --> 00:57:32,640
Speaker 3: It is really affecting my life. There is probably something

1057
00:57:33,080 --> 00:57:35,640
Speaker 3: that can at least help you figure out more about

1058
00:57:35,680 --> 00:57:38,680
Speaker 3: what could be done or what's going on. So don't suffer,

1059
00:57:39,240 --> 00:57:40,400
Speaker 3: don't suffer in silence.

1060
00:57:40,480 --> 00:57:45,720
Speaker 1: Yeah, don't mess with sleep. Well, that's it for us today.

1061
00:57:46,000 --> 00:57:48,320
Speaker 1: Stick with us next week when we'll ask what's the

1062
00:57:48,400 --> 00:57:50,000
Speaker 1: deal with cortisol?

1063
00:57:51,760 --> 00:57:56,160
Speaker 3: Wellness Actually is produced in association with iHeartMedia. Our senior

1064
00:57:56,200 --> 00:57:59,960
Speaker 3: producer is Tamar Avishai, our executive producer at iHeart is

1065
00:58:00,040 --> 00:58:03,560
Speaker 3: Jennifer Bassett. Our theme music is by Eric Deutsch and

1066
00:58:03,640 --> 00:58:05,720
Speaker 3: our content is for educational purposes only.

1067
00:58:06,400 --> 00:58:08,680
Speaker 1: If you like the show, help other people find us,

1068
00:58:09,000 --> 00:58:11,760
Speaker 1: leave a rating and review on Apple Podcasts or your

1069
00:58:11,880 --> 00:58:14,600
Speaker 1: podcatcher of choice, and help us spread the word about

1070
00:58:14,640 --> 00:58:17,680
Speaker 1: the show. You can follow us on Instagram at Wellness

1071
00:58:17,720 --> 00:58:20,800
Speaker 1: Actually pod and don't forget We want to hear from you.

1072
00:58:21,320 --> 00:58:23,840
Speaker 1: Head over to Wellness Actually dot fm and leave us

1073
00:58:23,840 --> 00:58:26,440
Speaker 1: a question for our mailbag or suggest a topic for

1074
00:58:26,480 --> 00:58:27,120
Speaker 1: a future show.

1075
00:58:27,920 --> 00:58:29,760
Speaker 3: We'll let the influencers have the last word.

1076
00:58:30,760 --> 00:58:37,479
Speaker 4: Radio. These are to days. This is the long distance call.

1077
00:58:39,120 --> 00:58:43,000
Speaker 1: The way the camera follows us, sloan mode, the way

1078
00:58:43,040 --> 00:58:43,680
Speaker 1: we look too