Stream.Transcripts/twitch/1943638441,1943718655 (2023-10-05) - Throwback Thursday Immunology 101-2020 -- 5 Oct 2023/1943638441,1943718655 (2023-10-05) - Throwback Thursday Immunology 101-2020 -- 5 Oct 2023 -- Gigaohm Biological High Resistance Low Noise Information Brief.vtt

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But if he could work for a bit of work for a little bit of work for a bit of work for a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of an a little bit of a little bit of a little bit of a little bit of a little bit of a little bit of the more coming into the little bit of a little bit of a little bit of a little bit of a little bit

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People everywhere are starting to listen to him.

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It's embarrassing.

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But there's still one thing on solve.

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What happened to the real mummy?

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Zoynk's the mama mama mama mama mama mama mama mama.

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Oh, beah!

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I found the mummy.

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Well, gang, I guess that wraps up the mystery and the mummy, too.

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Oh, we're here!

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Why would you understand that you were in the city of the age?

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Yes, two days ago.

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What sites did you see?

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We're here!

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Think of a bear and a tummy.

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Good!

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You don't understand, Mr. Brent.

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The box of holy weapons, please.

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Why would you understand?

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Why would you understand?

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Why would you understand that you were in the city of the age?

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Yes, two days ago.

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What sites did you see?

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Good evening, ladies and gentlemen.

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Welcome to a much wider debate about the possibilities that have brought us here.

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We understand very well here at Giga Olvajigova.

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It is a matter of what is perceived to be true.

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That's when we're trying to break.

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Because we don't want to mislead the young.

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We want the young to grow up knowing the truth.

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We want the young to grow up being protected by the trauma.

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We don't want our young people to grow up as slaves.

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And so we are trying to show everyone in our family, everyone in our neighborhood.

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That the brick wall at the back of the theater is visible.

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They've already started to move the tables and chairs out of the way.

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The scenery is being cleared.

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The lights in the theater might even start to come on pretty soon, so you better get ready.

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And Jessica Hawke would say that the lights in the theater are already on.

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I think Mark Housatonic, Mark Koolak would say that the lights in the theater have been on for quite some time.

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And I'm here to say that at least it took me, you know, if you don't have anything to be ashamed of with me

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because the lights only, I just only noticed, I only opened my eyes recently.

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So I got nothing but humbleness coming to this angle.

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Because I just recently figured out that it's possible that it's all wise.

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Turtles all the way down as some people might say.

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Good evening, ladies and gentlemen.

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This is Giga Ohm Biological High Resistance Low Noise Information Brief, brought to you by a biologist.

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It's starting to get a little spooky because I'm in here so often and so regularly.

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Kind of feels like I never leave this room anymore.

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But I do think we're moving the ball forward and I do think that this is worthwhile.

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And so thanks for joining me.

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This is the never ending daily report about how not to take the bait on TV and social media.

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And this is the daily report about the Scooby Doo.

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That is the mystery that we've all been tricked into solving.

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And thereby sort of kind of brainwashing ourselves into thinking something that is true that is not.

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And because all that matters is what is perceived to be true, this is very dangerous.

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Us being misled to solve a mystery by many of these players on this screen.

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And I think that's really what we've got to be careful of because if we're not careful of it,

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we will follow one of these people right to our and our grandchildren's doom.

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And those are pretty big words, you know, but I think it was Rochelle Walensky who said that she was having this feeling of impending doom.

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And I don't think it's so much impending as intended.

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And it's all based around this mythology of a magic RNA molecule that when properly ordained with the right sub-sequences,

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it is capable of circling the globe and doing more damage than a nuclear weapon.

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And yeah, I just don't think that biology is there to support this narrative.

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But what is there is a illusion of consensus that it's possible.

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Just like there is an illusion of consensus that vaccination is just obviously the best medical advancement in the history of modern man.

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And in reality, I think we're going to find in the coming years a new consensus about the idea that intramuscular injection was about as dumb as some of the more sort of barbaric medical practices that we've been guilty of.

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Or, you know, we've ventured through on our way to objectively starting to understand our physiology and how we might augment it.

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And transfection is not immunization.

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I guess I'm going to turn down the wind outside and then think it was going to get all windy out, but it's not raining on my mic.

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And so these are two equivalent statements.

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This one is more pertinent for the pandemic.

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This one is more pertinent for the coming years and being able to succinctly say it when need be.

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So this is the ever morphing little people map.

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You can think of the little tile puzzles that you get at dime stores and you used to get as a kid.

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Every time you move one of these pieces, then other pieces move around with it.

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We've been trying to figure out how these people are connected.

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Why these people would be, let's say, prematurely or almost artificially connected to one another in a way that cannot be spontaneous.

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There's no reason, for example, for a horse farmer, 20 year plus career vaccine technology broker and consultant to randomly come on the stream of someone who was a fugitive of January 6.

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And someone who has been posing as a medical doctor since the beginning of the pandemic without a medical degree.

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So that coupled with the fact that one of his most stalwart supporters of this fake doctor is none of the George Webb, somebody who has enticed a lot of people to believe that he's an intrepid reporter interested in the truth.

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And meanwhile, George Webb is the, you know, sort of arch nemesis of Robert Malone and Robert Malone, although it's his arch nemesis has no problem sharing all of the websites and media appearances of George Webb with his millions of followers.

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And it's interesting because, very early on in 2021, there was a guy named, Herod Vundenbosch, who started to come out and talk about the possible generation of variants by a non sterilizing vaccine.

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And I followed him very closely for a very long time.

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And it never became really obvious to me until this year that Robert Malone and Herod Vundenbosch had worked together for at least a year at Solve on a flu vaccine in 2008 or 2009.

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Which is a little, let's say, strange in light of all that we know about how this movement has evolved and all that we know about their opinions about the faith and the faith being that there's a novel virus that we had to do something like shut down the world.

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That the mRNA probably saved millions of people, even if we rushed it and gain a function is real, the likely source of this pandemic and it even if it wasn't, it surely will be the next one.

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Interestingly, we made some very good progress when we were able to be present when Denny and Peter met on the virtual set of the the council fire of Giga on biological I hope on Monday we're going to have another council fire.

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I think some real progress was made here because Denny is is commanded as enough command over his data to adequately explain it and defend it.

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And I think that Peter had enough pushback legitimate pushback from Denny that he maybe considered the proportionality of how he's attributed his understanding of what happened in 2020 and 2021.

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And I think it's very exciting if he could come to the realization that some of the assumptions that he has made may be overblown and the role of a virus versus the role of mistreating people who were sick with it.

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Or sick at all could actually be something that could be a revelation for him. Remember that he came out very early and was involved very early.

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And so it's possible that he was in enough meetings and had enough phone calls that he could be convinced of the severity of the impending doom much in the same way that somebody who streamed or watched Kevin McCarran McCarran stream in March and April of

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or February and March of 2020 could have easily come to the conclusion that wow a realistic possibility is 1 billion people dead.

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If he had if Peter McCullough had other people talking to him very early on at the beginning of the pandemic pushing that as a possibility, maybe even with more credentials than Kevin McCarran if you could imagine,

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then it's very easy to imagine how Peter McCullough is still trapped within that narrative that they themselves trapped him in.

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And so he needs a lifeline like everybody else. I don't.

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I just don't see a bad guy in Peter McCullough. I see some I see someone with who maybe even is afraid at this point and because he knows exactly what we're up against and so he's got as much courage as anyone on this battlefield because he's been here for three years

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and thank you so much Peter if you're watching this for being on the show. And Denny, thank you very much for for for being here to talk to you.

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As you know, we also did a show about Phillip Buckholtz who, you know, quite predictably has now blocked me on Twitter.

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We can think about this pretty succinctly because again, he's a guy who was involved in testing from the very beginning.

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He has a vested interest in in the narrative as it stands, the existence of a novel virus, the need to test for it, the effectiveness of testing for it.

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And he came out against the PCR tests as if and his final little caveat was that in the hands of a competent individual PCR is highly accurate.

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Excuse my language, but no shit Sherlock. That's not the point. The point is that PCR can also be wildly abused as a commercial product in order to make millions of dollars.

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And I think we're going to find that there's going to be a lawsuit or two in the next couple of years is going to expose the fraud of the PCR.

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We'll see where this goes, but there's no way that they're going to get away with this. I just can't imagine.

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But again, this isn't just par for the courts. We are figuring this stuff out. You see, all these people reveal themselves for who they are.

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If they can't just say you're mistaken or put me on mute.

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That's what we're dealing with there. They are trying very hard to disconnect from interacting with people who go against the faith.

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It's very much like if you can imagine a Christian who was told that Jesus is a myth that you might say at some point, well, if that's what you believe, then I won't try to convert you anymore and we'll just agree to disagree.

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And that's kind of where he is, except he's talking about biology, which should be sort of easy to explain and obvious to debate.

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But they're not interested in that. Just like Kevin McCarron is not interested in it. They're not really interested in a legitimate discussion about what they really know, like certainty, and what they're estimating, and what they hope is true, and what they have no idea.

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And this is how they work. They tell you that PCR works great in the hands of a competent person. It's amazingly accurate. That's totally true.

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But that doesn't in any way explain how 250 different commercial products could have been brought to market under EUA in America to PCR test for the coronavirus.

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That doesn't say that 250 groups of competent individuals made products. There's nothing about that that's guaranteed just because this guy over here, Philip, tweets that in the competent hands or in competent hands PCR works.

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In competent hands, an automobile is very safe. In competent hands, a skateboard is like an extension of your body and you can do pirouettes on one wheel.

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In competent hands, a pistol is a, are you kidding me? And so this is where we are. This is the state of the debate.

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Where as we've quoted many times in the beginning before, Thomas Sol has said, it's very easy to believe anything you want.

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If you attribute bad motives to the people that you that have views opposite that you and don't ever think about the views that they have.

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Of course, I'm butchering that quote, but you know what I mean.

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And so rather than have a discussion, they just block.

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And there's just no way you can defend the PCR test from the beginning to the end of the pandemic is being.

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Objective fair, unmotivated by conflict of interest and un abused by the sister. You just cannot make that argument. That's impossible.

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And yet in a very succinct, ridiculously over the top certain tweet, he did that. And that's why he blocked me.

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So you see where we're going here. These people will continue to double down.

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And so if you want to know what they've done, you've got to constantly remind yourself of where we've been.

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I keep using this analogy that you're in a really nice luxury, fast car with a great stereo.

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And whether you're a man or a woman or whatever, the driver is very attractive and a very good storyteller and driving with one hand.

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And so you're very engrossed by the driver and by the car and the luxury.

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And because the car is so luxurious, you don't hear anything. You don't feel the road. You're just almost like you're in a starship.

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And so even though you're going so fast through this city and you think you're seeing the city, actually, you're missing all the garbage.

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You're missing all the crime. You're missing all the homelessness. You're missing all the graffiti.

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It's just like this magical wonderland of lights and shining and sparkling.

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And what looked to be like just people. And it's super cool.

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And the music's playing and the car smells new and the story is great. And you're engrossed in the ride.

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And that's what happened at the beginning of the pandemic, except instead of being a luxury automobile, it was stay at home, be afraid, don't know anything.

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And listen to a bunch of people on TV all say the same thing.

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And so it's important to go back and look at what they were saying and listen to what they were saying because if they were saying the faith from the very beginning, then we know we were set up.

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If they were saying we have no idea about this virus, we have no idea whether our immune system can cope with this virus.

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Then we know they're full of it. If they say that, well, we have to measure everything because maybe antibodies are the most important thing or maybe it's something else.

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Then we know they're full of it because we knew already for coronavirus and it's for SARS virus and MERS virus that it's all T cells.

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And that yes, you can do it with antibodies or at least it appears that antibodies can be made.

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But there were previous studies where mice without B cells can still survive SARS.

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Are you kidding me? We knew this before the pandemic started.

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I didn't because I had to learn it in 2020, but by the end of 2020, I damn well knew it.

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And so if he recites the idea that we have to learn all this stuff, figure all this stuff out and measure it as quick as possible because we know nothing.

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You'll see where we are.

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And now listen carefully to the tone of his voice listen carefully to if he is enthusiastically telling you this stuff or if you think is something else going on because for me.

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I remember this and I haven't seen it yet since I watched it way back when.

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But I remember this is already triggering me because it was like either you're too calm in this very uncertain and haphazard time or you are full of it and you know that what you're saying is like is like too basic to be real.

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And so therefore you feel very comfortable saying it's almost like saying well, you know, if you were a.

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I don't know a professional.

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You're a professional something I don't know what I'm just trying to think about your professional football player, but you're in a park.

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And you don't want everybody to know how good of a football player you are so you say yeah I played football in high school you know I kind of like it.

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And so you set their expectations really low so that no matter what you find it's brilliant no matter what you find you're defining who you are at that moment.

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This guy who knows all kinds of stuff about immunology works at one of the neatest independent not for profit immunology institutes in the world is going to try and pretend that he has no real idea whether or not we can make memory to this coronavirus how we will do

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what cell types might be most important and whether or not antibodies are useful and to what target they would be useful for it's ridiculous.

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But if he does that then every single paper he writes is is gold.

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If he started out while we expect a B and C and D.

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Then he it's like you you tell all the Marvel story at once and then you don't have five movies you only have one.

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So they're all interested in the idea of it being novel because they can all get in on this bonanza of discovery.

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Using all the same measurements.

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It's ridiculous way to hear it.

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Today about these responses in in COVID-19.

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And yeah, and I'm going to keep it fairly simple for for a broader audience and talk you through it here.

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This is work done by my group and Alex studies group at the low light Institute for immunology. I assume you can see my screen.

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Perfectly.

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So LGI is basically a freestanding we're nonprofit and we're basically a freestanding immunology department on the UCSD campus.

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And we just recently published this paper and then a talk is through that work.

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So there are major there's been major knowledge gaps in understanding and unity to to the SARS-CoV-2.

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And this is resulted in a lot of fear and a lot of worry because of these unknowns.

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And big question.

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So I mean, he's preaching the preaching the faith here right now, right? It's the unknowns. We don't know anything. It's a novel virus. It's just like, wow.

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I mean, everybody's vulnerable. We know nothing.

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So it's a real it's a real it's caused a lot of fear.

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He says.

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He doesn't really sound like he believes it, but that's what he says.

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I mean, what what choices of words are those? Why you're an immunologist, you're not a government agent or, you know, a bureaucrat.

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So why in the world would he have this as an introduction, especially at this COVID-19 virtual? Oh, so it was going to thousands of.

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Oh, okay. Now I get it.

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I mean, it's just to to SARS-CoV-2.

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And this is a result. And this is resulted in a lot of fear and a lot of worry because of these unknowns.

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And big questions have been, you know, how much of an adaptive immune response is there to COVID-19?

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How long does immunological memory last? And what kind of immunity is important against COVID-19?

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And these have important implications for the pandemic itself as well as vaccine design.

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And from a vaccine design perspective,

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the normal way people approach vaccine development is you,

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you're largely trying to mimic, you frequently find mimic natural immunity.

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And so if the human immune system normally deals well with a given pathogen, it's a good candidate for a vaccine.

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And COVID-19 fits that criteria.

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It's an acute infection that results in those people suggesting that people make a sufficient and protective adaptive immune response to it.

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And a question is, what are the components of that immunity? How big are they?

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And so listen to how crafty that is. He says that if the human immune response is pretty robust,

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then it's a good candidate for a vaccine, but that's actually totally not true.

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And that's really bizarre, actually, because if we make a robust immune response or an immune response,

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which results in acute infection resolving in most humans,

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that means that our immune response doesn't need augmenting.

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And that's a really extraordinary, I mean, I don't know what that is,

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but that's like a real contradiction almost in the same two sentences if you think about it.

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Because now he's not talking about whether it's good for you to get vaccinated.

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He's talking about whether it's an easy target. Why is it an easy target?

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Is it an easy target because everybody's going to live anyway?

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Is it an easy target because there isn't much disease to measure and so it'll be hard to measure the effectiveness of your vaccine?

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Could that be something to do with this? Because measles is a set of symptoms that doesn't show up if you are vaccinated with an effective immunization.

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And I would like to say that better. If you are immunized with an effective immunization methodology,

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then the symptomology of measles won't show up in your lifetime.

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That's how it should work in an ideal cartoon.

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So what he says here is that if it's an illness whose infection and symptomology resolve in the vast majority of humans,

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then it's a good candidate for vaccines.

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And how could people potentially apply that to vaccine development?

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Your adaptive immune system really falls into three general categories, antibodies, helper T-cells, and killer T-cells.

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So think about the inversion here in case you don't forget about where you've forgotten what we learned already three years ago.

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It's dendritic cells, T-cells, and then B-cells.

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And in fact, the T-cells split up and take some of these cytotoxic T-cells back to the infection even as another group of T-cells is looking for B-cells to assist.

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So it's really, as you can see from the very beginning in May of 2020, there is a concerted effort across the board for these immunology experts to invert the actual immunological response.

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They are changing the way we think and making sure, sorry, I'm bumping the table,

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making sure that everybody thinks of antibodies as one of the primary immune responses and seroprevalent antibodies.

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Maybe that's not the right way to say it, but antibodies in terms of seroprevalence has being an adequate and reasonable defense against a respiratory virus.

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It's extraordinary, and I know that there are lots of physiological cartoons where circulating antibodies can help with viremia, but nevertheless, just see that we are not teaching anybody useful immunology here.

31:50.000 --> 32:01.000
And therefore, what is he doing? He is laying down immunomethology. There's no other way to see it. This is promoting the vaccine without even existing it.

32:02.000 --> 32:18.000
Antibodies are important and almost all currently licensed human vaccines and certainly neutralizing antibodies against COVID-19 reported and infected individuals.

32:19.000 --> 32:28.000
Helper T cells are critical for antibody responses. You're not going to get any neutralizing antibodies against COVID-19 without the helper T cells.

32:28.000 --> 32:45.000
And so knowing, are these actually responding? Most people is important. And just relating back to classic SARS, CD4 T cells were shown by Perlman and others that the CD4 T cells can also actually protect independently of antibodies.

32:46.000 --> 33:07.000
There he said, Perlman and others, and you've looked at immunology lectures from me for already three years where I regularly cite Perlman's early papers from 2002, three, four, and five, where he does various manipulations of mice exposed to a version of SARS or another.

33:07.000 --> 33:20.000
I don't remember, but I think it is a version of SARS and the mice that have T cells are able to clear mice that have T cells are able to clear the virus.

33:20.000 --> 33:29.000
And he's able to demonstrate that those T cells are aimed at the end protein and they're aimed at the ORF1A, the open reading frame 1A.

33:30.000 --> 33:38.000
And so these were already studies that were done when in the early 2000s, we're talking about 20 years ago.

33:38.000 --> 33:44.000
And Stanley Perlman is one of the experts on the WHO Committee for Coronavirus in the pandemic.

33:45.000 --> 33:53.000
And Stanley Perlman knows darn well that the, and everybody who works on the immune system should darn well know.

33:53.000 --> 33:59.000
He just said it backwards, where he said you're not going to have any antibodies if you don't have CD4 T helper cells.

33:59.000 --> 34:01.000
That's true.

34:01.000 --> 34:03.000
That's very true.

34:03.000 --> 34:05.000
But then why do you mention the antibodies first?

34:05.000 --> 34:17.000
You said already in the earlier segment, like five seconds before that patients who have had severe COVID also have antibodies to the virus.

34:17.000 --> 34:27.000
Then you know that T cells have been activated and they necessarily have not been activated to the same epitopes because you know that's what leak recognition is all about.

34:28.000 --> 34:45.000
So it's extraordinary that people who know immunology 101 and 201 and 301 are unable to, you know, keep it at the 30,000 foot level without just throwing everything out the window and not really adequately explaining the basics.

34:46.000 --> 34:48.000
They've inverted this.

34:48.000 --> 35:14.000
There's no reason for antibodies to be the first thing to talk about in killer T cells to be the last thing to talk about unless you want to obfuscate the fact that previous T cell immunity to conserved regions of conserved proteins in this genome will be present

35:14.000 --> 35:17.000
in almost everyone alive.

35:17.000 --> 35:24.000
If their immunology is true, you can't have it both ways.

35:24.000 --> 35:28.000
Either immunology and virology works the way they say it does.

35:28.000 --> 35:35.000
And we have previous immunity encoded in T cells, both helper and cytotoxic.

35:36.000 --> 35:45.000
Or, or, it doesn't work that way and then they have to stop arguing about antibodies as well.

35:45.000 --> 35:47.000
You can't have it both ways.

35:47.000 --> 36:04.000
It's really very extraordinary how they are able to get away with this with a whole nation full of people, a world full of people that know the immunology, the illusion of consensus is created just because no one can ask questions and no one will.

36:05.000 --> 36:08.000
Oops.

36:08.000 --> 36:12.000
And in SARS mouse.

36:12.000 --> 36:23.000
And in last, but not least, the killer T cells, you know, antibodies can take care of virus outside of cells, but you really need the killer T cells to take care of virus inside of cells.

36:23.000 --> 36:28.000
And it's been shown many different ways that the CDA is important in a variety of bio infections.

36:29.000 --> 36:40.000
And for both of the T cells, there are, there's now an anecdotal publication out of Italy that two people who are a gamma globulinemic.

36:40.000 --> 36:41.000
Okay.

36:41.000 --> 36:44.000
So no, no B cells, no ability to make an antibody response.

36:44.000 --> 36:49.000
Those individuals survived COVID-19 suggesting that T cells.

36:49.000 --> 36:50.000
There you go.

36:50.000 --> 36:52.000
So it's not only mice.

36:53.000 --> 36:57.000
It's a gamma globulin, globulinic.

36:57.000 --> 37:01.000
A gamma globulinic individuals.

37:01.000 --> 37:03.000
There are people who can't make.

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I can't make antibodies.

37:05.000 --> 37:07.000
And they're surviving COVID-19.

37:07.000 --> 37:09.000
That's pretty strange, isn't it?

37:09.000 --> 37:10.000
No.

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I couldn't resist.

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That's just crazy.

37:18.000 --> 37:25.000
A successful and dominant role in protection from this disease, at least in some individuals.

37:25.000 --> 37:35.000
So we wanted to try and establish a benchmark of measurements of adaptive immune responses.

37:35.000 --> 37:46.000
And this is harder for T cells than for antibodies because what T cells recognize are epitopes, small peptide epitopes that could come from anywhere within the virus.

37:46.000 --> 37:48.000
They vary from person to person.

37:48.000 --> 37:55.000
And of course the assays involved have to use live cells from infected individuals.

37:55.000 --> 38:00.000
Alex SETI here at LGI, my collaborator.

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He's probably the world expert, the world's top expert on predicting and mapping T cells, epitopes over the years.

38:08.000 --> 38:21.000
And so we focused on trying to collect appropriate patient samples and get the samples to him to look for epitope specific responses.

38:21.000 --> 38:24.000
And then we looked at the T cell biology together.

38:24.000 --> 38:33.000
So for this study as a starting point, we considered several key parameters.

38:33.000 --> 38:39.000
One was we wanted to measure immune responses in average COVID-19 cases.

38:39.000 --> 38:43.000
And the average COVID-19 case does not require hospitalization.

38:43.000 --> 38:52.000
And so we focused on non-hospitalized people who had essentially flu-like symptoms, had self-limiting disease.

38:52.000 --> 38:57.000
And then we measured their immune response after symptoms were gone.

38:57.000 --> 39:02.000
We're there memory cells when we look at convalescent cases.

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And we think that's a key benchmark to establish for what a normal immune response looks like to COVID-19.

39:09.000 --> 39:17.000
Our preference with T cell assays is to start with cytokine agnostic assays because in a new disease like COVID-19,

39:17.000 --> 39:22.000
it's not clear which cytokine responses may be dominant if at all.

39:22.000 --> 39:31.000
So being agnostic regarding that gives us a better assessment of what the overall magnitude of the C4 responses are.

39:31.000 --> 39:40.000
And then third, we wanted to use those predicted epitopes, but really full genome spanning peptide pools so that we could know that we were seeing the full picture,

39:40.000 --> 39:44.000
and not just some unknown piece of the picture.

39:44.000 --> 39:49.000
Okay, so with those in mind, we proceeded with the study.

39:50.000 --> 39:59.000
We started by going about 20 patients who again were non-hospitalized, had COVID-19 disease and resolved.

39:59.000 --> 40:05.000
All of these individuals were PCR-conterm positive in the acute phase.

40:05.000 --> 40:18.000
And we saw that they all indeed seroconverted and made anti-RBT IgG responses and the majority made IgM and IgA responses as well.

40:18.000 --> 40:21.000
In terms of the T cell assays that we...

40:21.000 --> 40:31.000
I'm not sure if I'm honest with you how specific the IgM and the IgA responses would need to be in order for this signal to be here.

40:31.000 --> 40:42.000
In other words, coronavirus infection in general may raise the antibody levels for this level of specificity depending on how they tested it out.

40:42.000 --> 40:46.000
How specific is their test for the receptor binding domain?

40:46.000 --> 40:52.000
I assume it's pretty specific because that's a relatively small region.

40:52.000 --> 40:56.000
I don't know, this is pretty convincing, right?

40:56.000 --> 41:06.000
It looks like the negatives are really negative and the people that get COVID are developing this antibody, which is specific for...

41:06.000 --> 41:16.000
I'm not sure how they do IgM, IgA, IgG, but we could probably look that up.

41:16.000 --> 41:19.000
I'm sure there's a pretty solid way of doing that.

41:19.000 --> 41:35.000
So, you know, this seems to be evidence for at least something RNA-wise or something transfection-wise that is causing this signal to show up in these people after they're infected.

41:35.000 --> 41:38.000
It's curious. I don't know what to say other than it's there. I'm not denying it.

41:38.000 --> 41:41.000
It's nonsense as well.

41:41.000 --> 41:50.000
In terms of the T cell assays that we use, a number of labs have started using these approaches of being cytokine agnostic.

41:50.000 --> 41:59.000
T cells respond to antigen by making a variety of different cytokines, but also by expressing various surface activation markers.

41:59.000 --> 42:04.000
So, we refer to these as AMSA activation induced marker assays.

42:04.000 --> 42:11.000
And we found TfH cells are the type of CD4 T cell that are required to help B cells.

42:11.000 --> 42:17.000
And those T cells are notoriously stingy cytokine producers.

42:17.000 --> 42:28.000
So, we found, for example, that cytokine assay is 10 to miss 98% of that category of CD4 T cells, and that category of CD4 T cells is essential for antibody responses.

42:28.000 --> 42:34.000
So, be careful that realize that all of this data is produced from papers before the pandemic.

42:34.000 --> 42:40.000
So, we're not looking at SARS-CoV-2 responses here. We're looking at other activation responses.

42:40.000 --> 42:48.000
And he's trying to make the argument that follicular T helper cells are...

42:48.000 --> 42:55.000
And so, he's looking for vaccine-specific CD4 T cells. So, he's looking at a pertussis vaccine.

42:55.000 --> 43:03.000
And so, that's an interesting, how do you say it? It's an interesting angle for him to have come from before the pandemic to now.

43:03.000 --> 43:09.000
So, now you can start to better understand his angle. You can better understand his history.

43:09.000 --> 43:18.000
You can better understand why he's talked the way he's talked from the very beginning here with this emphasis on why we need to know these unknowns.

43:18.000 --> 43:25.000
Not because it's a novel virus, but because the vaccine that we're going to make for it is going to need to know this stuff.

43:25.000 --> 43:31.000
Because since most people live through this, it's a great vaccine candidate.

43:31.000 --> 43:44.000
This is in May of 2020, when they had shut down schools and made our kids miserable, made our households miserable,

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told us we couldn't see our grandparents.

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They put sand in skate parks and closed playgrounds, all at this time.

43:58.000 --> 44:08.000
When this guy is saying that there's a new disease that we know nothing about, that we're just learning about, T cells are important.

44:09.000 --> 44:17.000
And we're going to have a vaccine because, I mean, most people survived, so it's a great candidate.

44:17.000 --> 44:28.000
I was studying the context of pertussis vaccine that we got a much better picture of what the vaccine elicited responses were with this type of assay.

44:28.000 --> 44:37.000
We applied that assay to people infected with SARS-CoV-2, and this is what that type of raw data looked like.

44:37.000 --> 44:41.000
And the summary is really shown here.

44:41.000 --> 44:47.000
So why not show us the raw data a little bit longer instead of going to the cartoon?

44:47.000 --> 45:00.000
So these are cells and they're scatter plots, and they're very difficult to read, but the idea would be that anything in this square is going to be significant.

45:00.000 --> 45:08.000
So as there are more that are moving into the square here that's drawn with these little lines here, these are the ones that are OK.

45:08.000 --> 45:14.000
They're really activated, CD137, you see?

45:14.000 --> 45:24.000
So the assay is supposed to show CD4 cells that are being activated by a recent exposure to an infection.

45:24.000 --> 45:28.000
And the activation, there's two markers here that they're looking at.

45:28.000 --> 45:33.000
It's not super complicated, but how many T cells do they have that are not activated?

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You've got to think very carefully about what this assay can and can't tell us as he goes to the cartoon.

45:39.000 --> 45:43.000
And this is what that type of raw data looked like.

45:43.000 --> 45:46.000
And that's not raw data.

45:46.000 --> 45:50.000
This is already summary data and measurements, right?

45:50.000 --> 45:54.000
This is not the raw data. I don't know why they say that.

45:54.000 --> 45:57.000
The summary is really shown here.

45:57.000 --> 46:00.000
If it was a summary, there would be numbers, but there's no summary.

46:00.000 --> 46:04.000
It's like 100% and 70% really.

46:04.000 --> 46:05.000
What did you have?

46:05.000 --> 46:09.000
N equals 20? Like what's going on here?

46:09.000 --> 46:19.000
That and 100% of people we saw IgG, 80% IgA, and 100% of the people had virus-specific CD4 T cell responses.

46:19.000 --> 46:24.000
And 70% at the technical CD8 T cell responses in the convalescence phase.

46:24.000 --> 46:28.000
But they didn't show. I don't think specific responses.

46:28.000 --> 46:32.000
We showed general activation markers.

46:32.000 --> 46:40.000
So he just said that there were specific T cell responses, but were there?

46:40.000 --> 46:48.000
No, see, he's doing this activation marker thing.

46:48.000 --> 46:52.000
There's no, yes, I understand that they were exposed to COVID-19,

46:52.000 --> 46:59.000
but it doesn't show that this response is specific for an epitope in COVID-19.

46:59.000 --> 47:01.000
It doesn't show that at all.

47:01.000 --> 47:08.000
So he completely said that incorrectly. He should have said that and we showed that T cells are activated after.

47:08.000 --> 47:13.000
But he can't say that they're specific for the virus. That's ridiculous. Listen.

47:13.000 --> 47:27.000
And the summary is really shown here that 100% of people we saw IgG, 80% IgA, and 100% of the people had virus-specific CD4 T cell response.

47:27.000 --> 47:30.000
Notice that he doesn't mention IgM. Why doesn't he mention IgM?

47:30.000 --> 47:38.000
Because if he's really making the argument that it was IgM, then the IgM should have converted to IgG.

47:38.000 --> 47:42.000
Because that's how that class switching works.

47:42.000 --> 47:47.000
But he's not making that argument. He's afraid to make that argument because if IgG comes up first,

47:47.000 --> 47:51.000
once the answer, ladies and gentlemen, it's a memory response.

47:51.000 --> 47:55.000
And that's what he doesn't want you to think and doesn't want you to realize.

47:55.000 --> 48:03.000
And once we get that paper from Japan, it clearly looks at the time course of infection.

48:03.000 --> 48:12.000
And the rise phase of the antibodies that are present in circulation, the rise phase that hits first is IgG,

48:12.000 --> 48:14.000
which means it's a memory response.

48:14.000 --> 48:23.000
And that's why he's not talking about Figure 1, which he showed us earlier, where there was IgM and IgA and IgG.

48:23.000 --> 48:29.000
He can kind of make the story here with IgA and ignore the IgM. But why is he doing that?

48:29.000 --> 48:39.000
Because if he doesn't tell you that the IgM to IgG was a class switch, temporarily separate from one another,

48:39.000 --> 48:45.000
he's telling you that the IgG came up at the same time, which makes it a memory response.

48:45.000 --> 48:52.000
That means overlapping memory with previous viruses, previous proteins.

48:53.000 --> 49:00.000
It can even be just a previous glycoproteins for which that antibody is sufficiently specific.

49:00.000 --> 49:08.000
It doesn't need to be hyper specific for the receptor binding domain of one virus in order to work.

49:08.000 --> 49:14.000
Can you imagine the fact that the more general the wrench is, the more useful the tool is?

49:15.000 --> 49:22.000
So your immune system is more likely to make an antibody that fits a lot of epitopes that are related to the same general epitope

49:22.000 --> 49:25.000
than it would to try to make a perfect fit for one.

49:28.000 --> 49:34.000
They have pruned our imagination into almost unrecognizable.

49:34.000 --> 49:39.000
It's just disturbing.

49:39.000 --> 49:48.000
If we just thought about the sacred biology, the point at which the complicated part starts

49:48.000 --> 49:56.000
and imagine it as complicated rather than stupid, simple, dumb, simple, then we would get somewhere.

49:57.000 --> 50:04.000
This is the kind of limited imagination on purpose that we have been subjected to for three years.

50:04.000 --> 50:14.000
This is not someone trying to share with you the beauty and complexity of the pattern integrity that is you.

50:14.000 --> 50:20.000
This is a person that's trying to share with you their dumb, simple explanation and sales pitch

50:21.000 --> 50:28.000
for why a vaccine is a pretty good idea for a disease that the vast majority of people survive.

50:28.000 --> 50:35.000
And usually don't even notice that they've had it according to this mythology.

50:35.000 --> 50:47.000
This is May 2020 when they were putting sand in skate parks when they were closing parks and playgrounds

50:47.000 --> 50:51.000
when they were telling people to wear masks outside.

50:53.000 --> 50:58.000
Ladies and gentlemen, do you see it, what they did to us, they lied.

50:58.000 --> 51:03.000
They got a lot of people to lie about this and they told them it was a national security priority.

51:03.000 --> 51:10.000
And so even if we're a little exaggerating, there's no room for skepticism.

51:10.000 --> 51:16.000
Because if we have skepticism, we could lose the plot.

51:16.000 --> 51:18.000
Do you understand Shane?

51:18.000 --> 51:25.000
If you can't come to and bring this to bear, then we're going to find somebody else who can.

51:25.000 --> 51:28.000
But we need people on board, Shane.

51:28.000 --> 51:30.000
Do you understand?

51:30.000 --> 51:33.000
There can be no dissent.

51:33.000 --> 51:38.000
We need people fully on board with how this vaccine works.

51:38.000 --> 51:44.000
We need people fully on board with the correlates of immunity that we're going to sell in six months.

51:44.000 --> 51:49.000
And so we need you to go out there and sell this model of the immune system.

51:49.000 --> 51:51.000
I know you like T-cells, man.

51:51.000 --> 51:52.000
Come on, Shane.

51:52.000 --> 51:53.000
We know you like T-cells.

51:53.000 --> 51:55.000
We know that eventually we're going to have to look at them.

51:55.000 --> 51:58.000
So we got your back.

51:58.000 --> 52:01.000
But for now we need you to push.

52:01.000 --> 52:03.000
We don't know what's going on.

52:03.000 --> 52:04.000
It's novel.

52:04.000 --> 52:05.000
Everything's novel.

52:05.000 --> 52:07.000
So we need emergency use authorization.

52:07.000 --> 52:09.000
We need an emergency declared.

52:09.000 --> 52:10.000
Of course we did.

52:10.000 --> 52:12.000
Everything's novel.

52:12.000 --> 52:13.000
Unknown.

52:14.000 --> 52:19.000
Shane, if you help us out, you're there was going to be a place on the arc for you.

52:19.000 --> 52:20.000
It's awesome, man.

52:20.000 --> 52:22.000
Things I can do it.

52:22.000 --> 52:23.000
Here's your script.

52:23.000 --> 52:24.000
Oh, wait.

52:24.000 --> 52:25.000
I have a script.

52:25.000 --> 52:26.000
Yep.

52:26.000 --> 52:27.000
Here's your script.

52:27.000 --> 52:28.000
Fonts is 70% undetectable.

52:28.000 --> 52:32.000
CPA T cell responses in the compilescence phase.

52:32.000 --> 52:36.000
And overall, we consider that good news.

52:36.000 --> 52:41.000
You know, when this is the first study reporting.

52:41.000 --> 52:51.000
And it's in specific measurements of antibiotic BCD-4 and CD-8s to this virus as well as on top of that doing so in non-hospitalized patients.

52:51.000 --> 52:57.000
And generally the outcomes were that it's antiviral immunity that matches expectations.

52:57.000 --> 53:09.000
This is largely what we expect to see when a person was exposed to a new respiratory viral infection of that sort of magnitude.

53:09.000 --> 53:15.000
And this knowledge can now be applied to other aspects of the disease.

53:15.000 --> 53:20.000
In addition to those measurements, the measurements did correlate an important way between them.

53:20.000 --> 53:29.000
So, for example, the magnitude of the spike specific CD-4 T cell response correlated with the spike RVD domain, IgG.

53:29.000 --> 53:31.000
We did not show that data.

53:31.000 --> 53:32.000
I did not see the data.

53:32.000 --> 53:35.000
I didn't see this data of the spike.

53:35.000 --> 53:36.000
Where are we out here?

53:36.000 --> 53:38.000
This is 922.

53:38.000 --> 53:44.000
I didn't see any spike specific T cell responses shown here.

53:44.000 --> 53:45.000
This is antibodies.

53:45.000 --> 53:58.000
Here's the IgM I was talking about that he's a very robust signal, but he didn't say anything about it because if he does, he's got to explain how they can be in time with one another rather than sequential.

53:59.000 --> 54:06.000
This is the T cell receptor dependent activation induced marker assay.

54:06.000 --> 54:10.000
And this is to retest this vaccine.

54:10.000 --> 54:14.000
And then we go here.

54:14.000 --> 54:16.000
Oh, there's spike.

54:16.000 --> 54:17.000
Okay.

54:17.000 --> 54:18.000
All right.

54:18.000 --> 54:21.000
There was spike in this figure.

54:21.000 --> 54:22.000
Okay.

54:22.000 --> 54:23.000
So I'm wrong.

54:23.000 --> 54:24.000
I don't mind being wrong.

54:24.000 --> 54:26.000
I was, I guess they tested for something.

54:26.000 --> 54:29.000
The only question is what health specific was that test, right?

54:29.000 --> 54:33.000
So anyway, I just wanted to make sure I was not.

54:33.000 --> 54:34.000
So it is true.

54:34.000 --> 54:35.000
Okay.

54:35.000 --> 54:41.000
That the T cell help to the B cells was intact and proper.

54:41.000 --> 54:46.000
And in fact, that that general topic is a specialty of my lab.

54:46.000 --> 54:54.000
We helped establish TfH cells as the key type of CD4s that are required for for B cell help.

54:54.000 --> 55:10.000
And one of the things that's evolved is our understanding of those TfH cells, the T cells that help the antibodies over the past 10 years now from really those cells being being defined as such.

55:10.000 --> 55:12.000
And these cells are important.

55:12.000 --> 55:27.000
This is an animation summarizing the advancement in knowledge of this field over the past 10 years that the TfH cells are required very early in infection for interacting with the B cells to stimulate the B cells to proliferate and divide.

55:27.000 --> 55:40.000
And then they are essential in the dermal centers, which is where the cells undergo mutation and proliferation and evolution to generate high affinity antibodies.

55:40.000 --> 55:50.000
And so without TfH cells, you don't have dermal centers and generally fail to get high affinity antibodies and memory.

55:50.000 --> 55:56.000
So, again, in the first past that news is good.

55:56.000 --> 56:03.000
We then looked at the CD4 responses to every single orph in the genome.

56:03.000 --> 56:09.000
This requires much larger amounts of blood and a lot of peptide synthesis.

56:09.000 --> 56:16.000
But Alex said it really felt like this was important, particularly for the first view of the virus to know.

56:16.000 --> 56:26.000
And so here you have ORF1A. This would be where the poly protein that becomes the RNA dependent RNA polymerase is found.

56:26.000 --> 56:30.000
Here you have T cells responses to that.

56:30.000 --> 56:38.000
And many other places along here, ORF1 is right here, ORF1A.

56:38.000 --> 56:43.000
ORF3 and 4, I'm not sure which ones those are, but that one looks like they're very nicely represented.

56:43.000 --> 56:47.000
And then here, of course, you have M and N.

56:47.000 --> 56:51.000
Both of these proteins being fairly conserved across coronaviruses.

56:51.000 --> 56:54.000
They're both N is bigger than M.

56:54.000 --> 56:57.000
And the E protein, I thought, would be better represented.

56:57.000 --> 56:58.000
It's not.

56:58.000 --> 57:05.000
But the membrane protein and the N protein, which is the protein around which the RNA is coiled.

57:05.000 --> 57:07.000
And then you have the S protein.

57:07.000 --> 57:09.000
So that's interesting.

57:09.000 --> 57:13.000
The question is how specific is that S protein for this coronavirus?

57:13.000 --> 57:19.000
There's another coronavirus that we have in circulation on Earth that also binds the ACE2 receptor.

57:19.000 --> 57:34.000
And so that could easily mean that our overlapping T cell memory and B cell memory to that coronavirus and its receptor binding domain could overlap greatly with this one.

57:34.000 --> 57:39.000
And that means that we have memory B cells that we're ready to go and memory T cells that we're ready to go.

57:39.000 --> 57:47.000
And that would be also probably explained why this response that they're measuring in their own methodologies is so strong.

57:47.000 --> 57:51.000
It's not showing you the novel response to a novel coronavirus.

57:51.000 --> 58:03.000
It's showing you an augmented general response to a general coronavirus and what needs to be cleaned up if a general coronavirus infects your virus.

58:03.000 --> 58:14.000
You have a lot of this S protein and a lot of this N protein around because of the way that sloppy viral replication takes place.

58:14.000 --> 58:16.000
It's a lot of protein production.

58:16.000 --> 58:31.000
And so you make antibodies to those immunogenic proteins that tend to go into circulation just by their abundance relative to the number of subgenomic RNAs that are made for all of those.

58:31.000 --> 58:34.000
That scales pretty well here.

58:34.000 --> 58:35.000
So why not?

58:35.000 --> 58:57.000
If there's an N protein in every coronavirus that anyone has ever found, if there's an S protein in every coronavirus that anyone's ever found, if one of the four endemic coronaviruses binds ACE2, if there's an M protein in every coronavirus that's ever been found, then how do we really know these are specific responses?

58:57.000 --> 58:59.000
We don't.

58:59.000 --> 59:07.000
But they're selling that to you because they're working under the assumption that these are novel responses to a novel virus.

59:07.000 --> 59:15.000
They are not under any obligation to prove to you that these are specific signals and they haven't tried to.

59:15.000 --> 59:18.000
The control is nothing.

59:18.000 --> 59:24.000
The control aren't the same proteins from a related coronavirus.

59:24.000 --> 59:26.000
That wouldn't work.

59:26.000 --> 59:29.000
Do you see my point?

59:29.000 --> 59:45.000
You are relying on the diagnosis of the PCR however they chose these people and you are relying on their measurements that they themselves use to define the existence or non-existence of an infection.

59:45.000 --> 59:53.000
Never knowing if these are not people pre-imposed, this is unexposed and exposed.

59:53.000 --> 59:58.000
They don't have measurements from these people before and after if they did, then it would be a different graph, right?

59:58.000 --> 01:00:04.000
Because those with memory responses would be there already.

01:00:04.000 --> 01:00:10.000
That's how spectacular this is.

01:00:10.000 --> 01:00:19.000
And so we're not saying that the unexposed aren't unexposed and the COVID-19 people aren't, are exposed, aren't exposed.

01:00:19.000 --> 01:00:26.000
I'm just suggesting to you that the fidelity of these signals is being exaggerated.

01:00:26.000 --> 01:00:39.000
The specificity of these signals is being exaggerated and they have never been since the very beginning under any obligation to exhaustively show that a novel coronavirus

01:00:39.000 --> 01:00:42.000
is circulating from person to person.

01:00:42.000 --> 01:00:50.000
They've never had to deal with that part of the equation because we started from the very beginning with that assumption

01:00:50.000 --> 01:00:58.000
because we've all assumed that that's possible since the Simpsons said it's possible and since the Planet of the Apes said it's possible

01:00:58.000 --> 01:01:03.000
and the X-Files said it's possible.

01:01:03.000 --> 01:01:15.000
And countless other mythologies have told us that that's possible including 60 minutes in Peter Dasek.

01:01:15.000 --> 01:01:27.000
And this illusion of consensus created over the last decades in pop culture, media, books and on television and now on social media

01:01:27.000 --> 01:01:39.000
has made it almost unnecessary for him to show anything of a scientific basis for a novel disease because everybody assumes that a novel disease is a novel disease

01:01:39.000 --> 01:01:43.000
that's like saying you saw a bird outside of course.

01:01:43.000 --> 01:01:48.000
How many times have I heard that there's going to be a novel virus? I'm not surprised there's one.

01:01:48.000 --> 01:01:55.000
That's the conclusion that they're working under right now. That's the conclusion that he's pushing on all of these people

01:01:55.000 --> 01:01:59.000
and all the people that are watching if they're not objecting. What is it?

01:01:59.000 --> 01:02:02.000
It's an illusion of consensus.

01:02:02.000 --> 01:02:10.000
These people in a Zoom meeting of a thousand are less likely to speak up than in a Zoom meeting of five.

01:02:10.000 --> 01:02:17.000
And so how many of these experts are going to feel an expert enough to go hey Shane, you know this is all great and everything

01:02:17.000 --> 01:02:20.000
but how do we know that our previous immunity wouldn't have helped us?

01:02:20.000 --> 01:02:34.000
How do we know that this isn't a memory response or building on a memory response rather than a novel assembly of new memory cells for this novel disease you keep saying about?

01:02:34.000 --> 01:02:42.000
If there's a thousand people in this from all over the United States, nobody is going to have the guts to speak up.

01:02:42.000 --> 01:02:45.000
That's exactly what they wanted.

01:02:45.000 --> 01:02:48.000
An illusion of consensus.

01:02:48.000 --> 01:02:52.000
What parts of the virus are really being targeted?

01:02:52.000 --> 01:03:01.000
And at first glance it was a little tough to understand the data and then we reorganized the proteins in order of predicted protein abundance

01:03:01.000 --> 01:03:06.000
based on coronavirus subgenomic RNA expression.

01:03:06.000 --> 01:03:14.000
And indeed the proteins that are most highly expressed or predicted to be most highly expressed by the virus were indeed the proteins

01:03:14.000 --> 01:03:22.000
that are most highly recognized by CD4 T cell responses from COVID-19 patients, including MNS.

01:03:22.000 --> 01:03:29.000
And largely there's a relationship between predicted protein expression and the magnitude of the CD4 T cell response.

01:03:29.000 --> 01:03:48.000
So we're not looking at cytotoxic T cells, we have no idea if cytotoxic T cells would be aimed at RF1A, but there are plenty of other papers that we've looked at in past streams that do show that T cells aimed at RF1AB and those proteins

01:03:48.000 --> 01:03:58.000
and those epitopes are the ones that are activated first and that people that have sufficient quantities of cells with that specificity

01:03:58.000 --> 01:04:02.000
never have any problem with SARS infection.

01:04:02.000 --> 01:04:06.000
And those papers were out there.

01:04:06.000 --> 01:04:11.000
I don't know if that immunology is right or not, we'd have to go back and look and we will.

01:04:11.000 --> 01:04:18.000
But I know we covered them and they're on our list and we just have to go back and open those slide decks.

01:04:18.000 --> 01:04:39.000
But there was without a question, a parallel here in the sense of a purposeful, slow, careful exploration of the cave, rather than a purposeful leading us out of the cave.

01:04:39.000 --> 01:04:44.000
This is part of the Scooby-Doo.

01:04:44.000 --> 01:04:49.000
This is the academic side of it in May 27, 2020.

01:04:49.000 --> 01:04:52.000
This is two of those different viral proteins.

01:04:52.000 --> 01:05:03.000
This also separately corroborated that the epitope prediction approaches that the SETA lab used to boil this down into a smaller, easier to run assay was also successful.

01:05:03.000 --> 01:05:12.000
So we detected responses for sure in 21 out of the 25 different proteins.

01:05:12.000 --> 01:05:22.000
So we felt like all of that was key knowledge and important to get out there for people to get a sense of T&B cell responses to this virus.

01:05:22.000 --> 01:05:40.000
But a second, really, half of the story has been that when we looked at unexposed healthy individuals, we found SARS-2 reactive T-cells already in 50% of unexposed normal healthy donors.

01:05:40.000 --> 01:05:43.000
And this was a surprise to us.

01:05:43.000 --> 01:05:49.000
We checked this data many different ways to convince our sounds.

01:05:49.000 --> 01:05:57.000
And for us, a key aspect of this was that the blood samples used for these experiments were collected between 2015 and 2018.

01:05:57.000 --> 01:06:09.000
So there's no way any of these people have seen SARS-2 before, and yet they have memory T-cells capable of recognizing multiple different SARS-2 antigens.

01:06:09.000 --> 01:06:13.000
And so we predict, but did not directly show.

01:06:13.000 --> 01:06:28.000
One of the things to keep in mind here with these funny percentages here, it's kind of comical. They can't possibly sample all the T-cells in somebody's body.

01:06:29.000 --> 01:06:36.000
What percentage of T-cells are they realistically sampling in an experiment like this?

01:06:36.000 --> 01:06:45.000
And then to talk as though, well, you know, we sampled the T-cells, and the sample that we show lets us know that this, what?

01:06:45.000 --> 01:06:54.000
Isn't it a random sample of the total T-cells in the body, and so that random sample doesn't need to be representative at all?

01:06:54.000 --> 01:06:57.000
Why would you expect it to be?

01:06:57.000 --> 01:07:04.000
Especially when there are resident T-cells around the lungs and there are resident T-cells at all the barriers.

01:07:04.000 --> 01:07:11.000
The T-cells aren't all waiting in the lymph node for the bat signal.

01:07:11.000 --> 01:07:21.000
Once T-cells become memory T-cells, they go different places, but they don't necessarily go back to a lymph node to wait.

01:07:21.000 --> 01:07:34.000
So this is quite extraordinary, because again, although he is admitting that there's previous immunity and he's reluctantly admitting that they don't know how this could be, except obviously we know how it could be.

01:07:34.000 --> 01:07:45.000
There's homology, RNA viruses aren't some kind of special thing where everything's different and everything's different and all things need a new solution.

01:07:45.000 --> 01:07:49.000
If that was the case, that would be ridiculous.

01:07:49.000 --> 01:07:57.000
Aberrant, foreign RNA in DNA is aberrant, foreign RNA in DNA, and the body has many ways of dealing with it.

01:07:57.000 --> 01:08:03.000
We can bet our butts that there are probably ways that it deals with it we haven't discovered yet.

01:08:03.000 --> 01:08:10.000
So there's no way for us to get so arrogant as to think that we have some kind of accurate readout of what's going on here.

01:08:10.000 --> 01:08:23.000
We can make some measurements, but if your model of what these measurements mean doesn't make accurate predictions about measurements in the future, then you're full of crap.

01:08:23.000 --> 01:08:35.000
And that's what's annoying about this, is that if they included this data as relevant to the previous data, then the previous data would be data minus this.

01:08:36.000 --> 01:08:43.000
They would look for antibodies too, do you think they're going to look for antibodies in these unexposed normal healthy donors?

01:08:43.000 --> 01:08:46.000
Do you think they'll look for antibodies that overlap?

01:08:46.000 --> 01:08:51.000
That these would be, these are presumably memory T cells that people have generated.

01:08:51.000 --> 01:08:56.000
Of course, they're not going to look for antibodies because they're not there, but the B cells would be.

01:08:56.000 --> 01:09:03.000
And these T cells, because they're unexposed, the rest of them might be dormant.

01:09:03.000 --> 01:09:06.000
The rest of some of them, this is the whole point.

01:09:06.000 --> 01:09:12.000
These are blood samples collected before the pandemic.

01:09:12.000 --> 01:09:15.000
Do you see?

01:09:16.000 --> 01:09:32.000
Overlapping previous immunity from other RNA viruses that use an RNA-dependent RNA polymerase or an N protein or something similar or a fusion protein like the spike or something similar or glycol proteins.

01:09:33.000 --> 01:09:36.000
We have antibodies and we have T cell memory to those.

01:09:36.000 --> 01:09:43.000
And there are motifs that are shared across these entities because these mechanisms are conserved.

01:09:46.000 --> 01:09:54.000
And so the whole point of this exercise was to create the illusion that there was something that could be novel.

01:09:55.000 --> 01:10:00.000
You know, like there's a brand new story out there that no one's ever told before.

01:10:00.000 --> 01:10:06.000
And it's got a wizard and an elf and a princess that needs rescuing.

01:10:06.000 --> 01:10:12.000
And there's this guy in armor who's like a knight and then there's this really bad guy.

01:10:14.000 --> 01:10:21.000
Well, there is no new story with that premise because there's a million stories like that.

01:10:21.000 --> 01:10:23.000
No, no, it's a brand new one.

01:10:23.000 --> 01:10:26.000
You won't have any idea what's going to happen.

01:10:26.000 --> 01:10:30.000
And you can't imagine the kinds of things that would go on.

01:10:30.000 --> 01:10:40.000
There's no way you can imagine it because it's a brand new story about castles and wizards and dragons and princesses that need rescuing.

01:10:40.000 --> 01:10:42.000
It's a brand new story.

01:10:42.000 --> 01:10:45.000
Okay, so it has a princess that needs rescuing.

01:10:45.000 --> 01:10:47.000
Yes, it has wizards, yes.

01:10:47.000 --> 01:10:49.000
And as dragons, yes.

01:10:50.000 --> 01:10:51.000
Interesting.

01:10:51.000 --> 01:10:54.000
So that it's kind of the same as like these other books, right?

01:10:54.000 --> 01:10:56.000
No, no, no, it's completely different.

01:10:57.000 --> 01:10:59.000
We have to make a whole new bookshelf for it.

01:10:59.000 --> 01:11:11.000
I mean, honestly, I think we're going to need to open a whole new wing of the English department just for this book and everything that comes after it.

01:11:12.000 --> 01:11:16.000
Because it's going to establish a new genre of fantasy fiction.

01:11:19.000 --> 01:11:25.000
That's the kind of story they're telling from a biological perspective that this is a new, but we just don't know nothing.

01:11:25.000 --> 01:11:31.000
I mean, I've been studying the immune system my whole career and this is just nuts.

01:11:37.000 --> 01:11:48.000
During their previous exposures to common cold coronaviruses and that there's variation in either the magnitude of these responses or which common cold coronaviruses people have seen recently,

01:11:48.000 --> 01:11:56.000
resulting in variable amounts of pre-existing cross-reactive T sounds capable of recognizing SARS-2.

01:11:56.000 --> 01:12:07.000
And feeling colleagues in Germany have also seen similar things specifically for S.

01:12:07.000 --> 01:12:14.000
So there are multiple labs reporting that there are SARS-2 cross-reactive T sounds in unexposed individuals.

01:12:14.000 --> 01:12:17.000
And I'll give you this as a sense and we can potentially come back to it.

01:12:17.000 --> 01:12:24.000
This is actually the CDC data of quote unquote, common cold coronavirus circulation in the...

01:12:24.000 --> 01:12:28.000
I'm going to get it wrong, but I think it's HKU1. I don't know. We've got to look it up.

01:12:28.000 --> 01:12:32.000
Which one of those do you know the chat? Who's going to win the prize?

01:12:32.000 --> 01:12:38.000
Which one of those is an ACE2 binding coronavirus?

01:12:39.000 --> 01:12:44.000
Of course he's not going to show us this for the... this is what?

01:12:44.000 --> 01:12:52.000
Percent positive from a little tiny study somewhere, but there's people testing positive. It's not people dying.

01:12:52.000 --> 01:12:57.000
And it's before the pandemic. It's weird, right?

01:12:58.000 --> 01:13:00.000
In the U.S.

01:13:00.000 --> 01:13:04.000
Actually 43 in the window is just describing.

01:13:04.000 --> 01:13:13.000
And you can actually see all four of those viruses are present and they're present in different ways, at different times, in a complicated pattern.

01:13:13.000 --> 01:13:17.000
This is the team of people who worked on this project.

01:13:17.000 --> 01:13:21.000
Lots of team work. First author was Alba.

01:13:21.000 --> 01:13:28.000
One senior author is Alex and myself with a big clinical and technical team.

01:13:28.000 --> 01:13:35.000
And so I'll open it up to Q&A, but we'd end with, you know, what's next?

01:13:35.000 --> 01:13:49.000
We're doing our best to work with vaccine developers to apply this knowledge for best practices to measure CD4 and CD8 T cell responses in COVID-19 vaccines.

01:13:49.000 --> 01:13:56.000
And epitope mapping and measure immune responses in different COVID-19 disease severity.

01:13:56.000 --> 01:14:01.000
As I said, this was specifically to set a benchmark looking at average cases.

01:14:01.000 --> 01:14:11.000
Now we in many other labs can make use of this knowledge when looking at hospitalized patients with different disease severity in presentations.

01:14:11.000 --> 01:14:16.000
We certainly think T cell considerations can be important for vaccine design.

01:14:16.000 --> 01:14:21.000
We do think that our data is good news for S specific vaccine designs.

01:14:21.000 --> 01:14:24.000
Almost all of the vaccine candidates out there are basically S only.

01:14:24.000 --> 01:14:33.000
And so if it turned out in our studies that your average COVID-19 person really didn't make a CD4 T cell response to S or that it looked really strange.

01:14:33.000 --> 01:14:40.000
That would be very bad news for vaccine strategies, but we saw that S is actually well represented in an average COVID-19 case.

01:14:40.000 --> 01:14:43.000
And so that's positive news for most of the vaccine strategies.

01:14:43.000 --> 01:14:49.000
And finally, we think these pre-existing cross-reactive T cells and people are very intriguing.

01:14:49.000 --> 01:14:55.000
And we think it may be important for vaccine trials to measure pre-existing.

01:14:55.000 --> 01:15:00.000
Oh, you're about to say something very bad, naughty, naughty.

01:15:00.000 --> 01:15:04.000
These pre-existing cross-reactive T cells and people are very intriguing.

01:15:04.000 --> 01:15:12.000
And we think it may be important for vaccine trials to measure pre-existing memory that can cross-reactive T cells too,

01:15:12.000 --> 01:15:17.000
because it could potentially be a big distinguish of differential outcomes in those trials.

01:15:17.000 --> 01:15:24.000
A simple speculation would be that if a person already has memory CD4s that can recognize the virus,

01:15:24.000 --> 01:15:31.000
they would be more likely to make a faster, bigger, better antibody response to a given candidate vaccine.

01:15:31.000 --> 01:15:35.000
That remains to be determined, but can only be determined if you actually...

01:15:35.000 --> 01:15:42.000
Actually, if you have previous memory to, let's say, a fusion protein from another coronavirus,

01:15:42.000 --> 01:15:53.000
there's potential that you will be building on a memory and you will produce a robust IgG response from the very beginning.

01:15:53.000 --> 01:15:58.000
That's the crazy part about this that they don't really go into, which I find very strange.

01:15:58.000 --> 01:16:06.000
It really shouldn't be that way unless we're exposed to something we've already seen.

01:16:06.000 --> 01:16:11.000
And I think that's part of this cover-up, the idea that, you know, these unexposed things aren't specific,

01:16:11.000 --> 01:16:18.000
but they're specific because coronaviruses can't change everything as best as their cartoon works.

01:16:18.000 --> 01:16:26.000
If you accept their cartoon virology at face value, then they have to know that.

01:16:26.000 --> 01:16:30.000
They know that's true, but, you know, that's where we're at.

01:16:30.000 --> 01:16:32.000
Measure the pre-existing T cells.

01:16:32.000 --> 01:16:35.000
Yeah, I will stop there and take questions.

01:16:35.000 --> 01:16:38.000
Jane, thank you for that wonderful talk.

01:16:38.000 --> 01:16:41.000
I have two related questions.

01:16:41.000 --> 01:16:45.000
Did you compare symptomatic and asymptomatic individuals?

01:16:45.000 --> 01:16:55.000
And do you think the pre-existing T cells have any implication for why so many people are asymptomatic but carriers?

01:16:55.000 --> 01:16:59.000
Yeah, great questions.

01:16:59.000 --> 01:17:03.000
And, yeah, and I meant to address both of those directly.

01:17:03.000 --> 01:17:07.000
So thank you for reminding me of those.

01:17:07.000 --> 01:17:12.000
All of the cases, all of the patients that we looked at were symptomatic.

01:17:12.000 --> 01:17:21.000
And my understanding is that it can be a pretty wide range of what people might define as asymptomatic,

01:17:22.000 --> 01:17:28.000
but these people were PCR confirmed positive and they had symptoms.

01:17:28.000 --> 01:17:38.000
We're definitely curious to see what would such responses look like in truly asymptomatic individuals.

01:17:38.000 --> 01:17:40.000
We don't currently have such samples.

01:17:40.000 --> 01:17:48.000
If anybody has such samples and would like to work with us, that would be great fun or we can provide the peptides for people to do it themselves

01:17:48.000 --> 01:17:56.000
because I definitely think asymptomatic individuals are very interesting and not well understood.

01:17:56.000 --> 01:17:58.000
The second question was...

01:17:58.000 --> 01:18:07.000
I mean, the only way to take that is that this is perpetuating the idea that asymptomatic is a thing.

01:18:07.000 --> 01:18:11.000
And I think we can be fairly confident that it's not.

01:18:12.000 --> 01:18:19.000
If asymptomatic is a thing, then it's more like a toxin and not like a virus because then a toxin,

01:18:19.000 --> 01:18:28.000
you can imagine getting exposed to a sort of sub-toxic level where you have chemical exposure,

01:18:28.000 --> 01:18:33.000
but you don't show the signs and symptoms of the chemical exposure, that's possible.

01:18:33.000 --> 01:18:39.000
But from a biological perspective asymptomatic infection,

01:18:39.000 --> 01:18:47.000
so that means that the virus is replicating, but your body does nothing and then you can spread it that way, that's absolutely absurd.

01:18:47.000 --> 01:18:56.000
And clearly this is another thing that they wanted to get in there and he says he's apologizing for not upset it.

01:18:56.000 --> 01:18:59.000
The guy is almost like he's a handler.

01:18:59.000 --> 01:19:04.000
He's got the guts to ask a question, the question he asks is right on narrative.

01:19:04.000 --> 01:19:06.000
What about the asymptomatic people?

01:19:07.000 --> 01:19:14.000
The pre-existing cross-reactive T cells and unexposed individuals and what we're seeing at about 50% of people,

01:19:14.000 --> 01:19:25.000
I specifically refer to how that might affect responses in vaccine trials, but absolutely the same speculation applies to COVID-19 disease severity itself.

01:19:25.000 --> 01:19:35.000
I think the simplest speculation would be that memory CD4 T cells aren't going to completely protect an individual,

01:19:35.000 --> 01:19:52.000
keep them from being infected, but it's I think quite plausible based on animal models of other diseases and the known role in at least the mouse model of memory CD4 is providing protective immunity from SARS,

01:19:52.000 --> 01:20:04.000
that having cross-reactive memory T cells from other bioinfections would essentially shift the pathogenesis curve for a given person with COVID-19.

01:20:04.000 --> 01:20:10.000
So instead of getting moderate disease, you maybe now get mild disease or instead of getting a lethal infection,

01:20:10.000 --> 01:20:14.000
you now get a severe or moderate.

01:20:14.000 --> 01:20:22.000
I'm always saying that if you have previous T cell immunity that overlaps with other coronaviruses, your whole disease course will shift.

01:20:22.000 --> 01:20:24.000
Well, no kidding.

01:20:24.000 --> 01:20:26.000
No kidding.

01:20:26.000 --> 01:20:36.000
And you didn't expect that as the preeminent immunology expert that you were before the pandemic, you didn't expect that to be the case.

01:20:36.000 --> 01:20:43.000
Given what we know about the homology between coronaviruses, given what we know about the desire to have a pan-coronavirus PCR test,

01:20:43.000 --> 01:20:56.000
given what we know about how easy it is to identify a whole bunch of different coronaviruses by the same 350 base pair long segment of the RNA dependent RNA polymerase gene.

01:20:56.000 --> 01:21:02.000
Are you kidding me?

01:21:02.000 --> 01:21:08.000
That's how disingenuous this is, ladies and gentlemen, most of these people might not know just because they choose not to.

01:21:08.000 --> 01:21:10.000
It's a look away kind of thing.

01:21:10.000 --> 01:21:14.000
I'm going to stay in my lane. I'm going to be my expert in my own little thing.

01:21:14.000 --> 01:21:18.000
And if this gets out of my control, I got nothing to do with it.

01:21:18.000 --> 01:21:22.000
And I'm not talking about if the gain of function gets out of control.

01:21:22.000 --> 01:21:34.000
I'm talking about if the bullshit narrative, if the exaggeration of the science, the exaggeration of the potential for disaster gets out of hand.

01:21:34.000 --> 01:21:39.000
People are going to help me because it's going to help my research, it's going to help my career.

01:21:39.000 --> 01:21:44.000
And I'm inserted as an expert from the very beginning.

01:21:44.000 --> 01:21:47.000
My star has just begun to rise.

01:21:47.000 --> 01:21:58.000
And I'm not going to have to say this too many more times before the vaccine will come out, and there will be all kinds of other papers for me to do.

01:21:58.000 --> 01:22:13.000
And so this is a self-fulfilling prophecy in a way. We don't know. Now we know. We don't know. Now we know. I'm the expert because I figured out what we didn't know.

01:22:13.000 --> 01:22:16.000
It's like shooting fish in a barrel.

01:22:17.000 --> 01:22:23.000
I think that's a really intriguing and obvious speculation to make.

01:22:23.000 --> 01:22:38.000
And it is going to be challenging to directly address because you really need to be able to measure such T cells before an infection and after an infection.

01:22:38.000 --> 01:22:47.000
We're certainly exploring if there are clever ways we can interpret data just from people who have already.

01:22:47.000 --> 01:22:58.000
And the other thing that goes along with what he just beautifully said, which is you need to measure T cells before and after, just like you need to measure viral prevalence in the world before and after.

01:22:58.000 --> 01:23:06.000
Otherwise saying there's a pandemic while you roll out a new test is ridiculous.

01:23:06.000 --> 01:23:16.000
And so in a lot of ways he's admitting in a lot of ways he's talking very unsure and it doesn't feel like he's having a good time here.

01:23:16.000 --> 01:23:23.000
And that's because he's not really telling the truth. He's following the directives of whoever handled him.

01:23:23.000 --> 01:23:34.000
He's following the directions of whoever told him that what we need to do for this national security issue is to make sure there is no descending opinions.

01:23:34.000 --> 01:23:45.000
So keep it general enough. Keep it high level enough so that there's no arguing with you. You're the authority. You're going to establish these things.

01:23:45.000 --> 01:23:55.000
And so we're not going to question the novelty of the virus. We're just going to be curious about whether previous responses can be helpful in some people.

01:23:55.000 --> 01:24:00.000
Understand Shane.

01:24:00.000 --> 01:24:04.000
They had the disease.

01:24:04.000 --> 01:24:10.000
But otherwise it's going to take some serious longitudinal studies to get at that. But yeah, exactly right.

01:24:10.000 --> 01:24:13.000
Serious longitudinal studies. There's also the.

01:24:13.000 --> 01:24:17.000
I'm going to need millions of dollars to do that.

01:24:17.000 --> 01:24:34.000
The glass has empty speculation that it's possible that some preexisting T cells could be bad predictors. You know, there could be some negative aspect of those T cells that would affect disease progression, which would also be important to understand in the future.

01:24:34.000 --> 01:24:36.000
And we have a number of questions.

01:24:36.000 --> 01:24:40.000
Felippo, if you can unmute yourself, you can ask your question.

01:24:40.000 --> 01:24:52.000
Yeah, Andy, thank you. Just related to this discussion up to now, we've seen, you know, we've in the New York area, there's two aspects of this one we're seeing in the hardest hit areas.

01:24:52.000 --> 01:24:56.000
Only, you know, if you go measure antibodies randomly.

01:24:56.000 --> 01:25:01.000
Only about 20% have it's never goes above 20%.

01:25:01.000 --> 01:25:07.000
So the antibodies in New York City never go above 20%. But I thought there was spread in April.

01:25:07.000 --> 01:25:11.000
I thought in March and in April, there was this huge.

01:25:11.000 --> 01:25:14.000
Bomb of of spread.

01:25:14.000 --> 01:25:24.000
And that this virus was going everywhere. But now they're in May, the end of May in 2020. And when they sample around New York, it's only 20% of people have antibodies.

01:25:25.000 --> 01:25:39.000
And that's already taking for granted the idea that whatever antibodies they say are specific for this virus are really specific for this virus, which is a huge gigantic assumption without a control.

01:25:40.000 --> 01:25:52.000
They have no impetus. There's no drive. There's no quest to prove to you. It's novel. If they say it tests for this virus, you believed it.

01:25:52.000 --> 01:25:59.000
So did I. So does Shane.

01:26:00.000 --> 01:26:08.000
In this cartoon down here, he's saying common cold viruses question mark. Do you see it?

01:26:08.000 --> 01:26:20.000
He hasn't done any research to find out what it is about these coronaviruses that makes a homologous immune response detectable in people who aren't exposed to the new one.

01:26:20.000 --> 01:26:28.000
Why doesn't he want to talk about that? Why doesn't he want to investigate that? Why isn't that one of the things that he says he's going to follow up on?

01:26:29.000 --> 01:26:36.000
Because if he does that, then you the whole illusion of the novelty of this virus will go away.

01:26:36.000 --> 01:26:46.000
The whole illusion that their PCR test can separate these epitopes from the new epitopes will go away.

01:26:46.000 --> 01:26:52.000
Ladies and gentlemen, you can see it, right? It took three years, but we can see it now.

01:26:53.000 --> 01:27:00.000
And I'll get to the second part of the question is that we've seen a lot of more anecdotal evidence of maybe people living together.

01:27:00.000 --> 01:27:07.000
One gets sick, one develops antibodies, the other person doesn't get sick and doesn't develop antibodies.

01:27:07.000 --> 01:27:14.000
Could the 50%, could your 50% be explaining this?

01:27:14.000 --> 01:27:22.000
I mean, could it be giving sufficient immunity that people actually don't get traditionally infected and don't develop IgGs?

01:27:22.000 --> 01:27:27.000
Traditionally infected means IgGs? No.

01:27:27.000 --> 01:27:37.000
We started this talk by citing Perlman's work that showed that traditionally infected cells are animals clear the virus with T cells.

01:27:37.000 --> 01:27:40.000
The T cells are what matter.

01:27:40.000 --> 01:27:49.000
He just started the talk by telling you that people that can't make antibodies survive COVID.

01:27:49.000 --> 01:27:58.000
So the traditional kind of infection you're talking about doesn't exist except for in the cartoon land of public health,

01:27:58.000 --> 01:28:04.000
where respiratory infections produce antibodies, where vaccines produce antibodies, therefore they're the same.

01:28:04.000 --> 01:28:09.000
And vaccines are safer than infections, so it's an easy choice.

01:28:09.000 --> 01:28:14.000
The traditional model of infection is a cartoon of bullshit.

01:28:14.000 --> 01:28:16.000
That's the thing.

01:28:17.000 --> 01:28:24.000
It is a dumb, simple model of something that can't happen.

01:28:24.000 --> 01:28:27.000
Sorry for the swear words.

01:28:27.000 --> 01:28:30.000
It's definitely hypothetically possible.

01:28:30.000 --> 01:28:37.000
So we've tried to be clear to people.

01:28:37.000 --> 01:28:39.000
They didn't study that we've done.

01:28:39.000 --> 01:28:44.000
We have no data that directly speaks to immunity.

01:28:44.000 --> 01:28:49.000
Obviously what we've done is we've measured T cells, are they present or absent?

01:28:49.000 --> 01:29:00.000
And what we're seeing is that there are T cells that are present and unexposed people that can't recognize this virus.

01:29:00.000 --> 01:29:02.000
I was informed.

01:29:02.000 --> 01:29:05.000
He's got real trouble saying that, doesn't he?

01:29:05.000 --> 01:29:13.000
He's got a real hard time saying that there are T cells in unexposed individuals that are capable of recognizing this virus.

01:29:13.000 --> 01:29:16.000
And that means that there's memory response.

01:29:16.000 --> 01:29:21.000
That means that we don't need to be vaccinated because we don't know how to augment that.

01:29:21.000 --> 01:29:24.000
Holy cow.

01:29:24.000 --> 01:29:28.000
This virus. I was informed.

01:29:28.000 --> 01:29:35.000
Yesterday that one of the biggest papers in France.

01:29:36.000 --> 01:29:43.000
We reported that we had proven that 50% of people were going to be fine.

01:29:43.000 --> 01:29:48.000
I was like, Oh, God, that is not at all. What we said.

01:29:48.000 --> 01:29:53.000
But it is.

01:29:53.000 --> 01:29:59.000
50% of the people are going to be fine. We already know that way more than 50% of the people are going to be fine.

01:30:00.000 --> 01:30:06.000
What Shane has shown us is a mechanism by which we can explain how everybody's going to be fine.

01:30:06.000 --> 01:30:11.000
Because overlapping immunity to other RNA viruses is sufficient.

01:30:11.000 --> 01:30:16.000
And we need to boost our immune system. We need to boost our health. We need to take care of our nutrition.

01:30:16.000 --> 01:30:21.000
We need to do a lot of other things to avoid inflammation.

01:30:21.000 --> 01:30:27.000
We need to use antibiotics when we get a secondary pneumonia, which we didn't do.

01:30:27.000 --> 01:30:31.000
And in May, we weren't doing it at all.

01:30:31.000 --> 01:30:37.000
So were these people with secondary pneumonia? No, probably not. They had mild infection of something, I guess.

01:30:37.000 --> 01:30:41.000
Some kind of respiratory information.

01:30:41.000 --> 01:30:46.000
But it's extraordinary how they talk around this. It's, it's incredible. May 2020.

01:30:46.000 --> 01:30:52.000
I agree that it's conceivable that that such T sounds.

01:30:52.000 --> 01:30:54.000
Could provide.

01:30:54.000 --> 01:31:00.000
He's trying not to say what the biology really is. He's trying to leave the door open for not knowing.

01:31:00.000 --> 01:31:04.000
He's trying to leave the door open for more papers to be done.

01:31:04.000 --> 01:31:08.000
Trying to leave the door open for more investigation.

01:31:08.000 --> 01:31:12.000
It's really awful if you hear it.

01:31:12.000 --> 01:31:16.000
He knows the right answer. He knew the right answer before he started doing the experiments.

01:31:16.000 --> 01:31:23.000
But he started out by saying we don't know anything. This knowledge gap is really making people frightened.

01:31:23.000 --> 01:31:32.000
And so I'm going to come to the rescue and slowly but surely brick by brick give you the knowledge that you need to survive.

01:31:32.000 --> 01:31:38.000
Even though all the previous immunology that we know seems to tell us we're going to be okay.

01:31:38.000 --> 01:31:42.000
It gives us a lot of reason to believe that we already know why.

01:31:42.000 --> 01:31:47.000
Stanley Perlman's work being some of the best from the early 2000s.

01:31:47.000 --> 01:31:57.000
Sited by this very guy at the start of this talk. But still talking like 20 years ago we didn't do those experiments.

01:31:57.000 --> 01:32:07.000
Immunity at a level that it could reduce an average symptomatic infection to become asymptomatic.

01:32:07.000 --> 01:32:10.000
That is definitely hypothetically possible.

01:32:10.000 --> 01:32:20.000
And in our minds the best ways to address that are we've really got to get before and after measurements.

01:32:20.000 --> 01:32:28.000
Or we have to be able to identify epitopes that can distinguish cross-reactive from de novo responses.

01:32:28.000 --> 01:32:33.000
And so we're certainly hard at work to try and generate both of those.

01:32:33.000 --> 01:32:46.000
And reaching out to people to ask do people have longitudinal studies right of groups of people where perhaps we can capture people before and after COVID-19 and measure the T cells that are present.

01:32:46.000 --> 01:32:58.000
Those are the ways to actually get at that question we think it's incredibly intriguing and obviously I would love for that speculation to be true.

01:32:58.000 --> 01:33:04.000
But there's really a lot more data that would have to be generated to validate that.

01:33:04.000 --> 01:33:20.000
Laura if you unmute yourself you can. Thanks I hope am I unmuted. Yes. Great. So similar related questions you effectively have a cohort of individuals if you can identify those patients who tested positively in your T cell assay.

01:33:20.000 --> 01:33:30.000
Can you contact them and survey them by email to see what was their rate at which they got ill with COVID-19.

01:33:30.000 --> 01:33:34.000
So that is if whether you can unveil all that patient data.

01:33:34.000 --> 01:33:55.000
And the second is you know in the hypothetical case where let's say this assay is more informative than the ubiquitous IgG assays that you know everybody is relying on these days because so far you demonstrate correlation if they're positive for reactive T cells they're also positive in IgG.

01:33:55.000 --> 01:34:10.000
But of course we want to know for those individuals like those autoimmune or those that lack IgGs that also don't get sick if this is in fact more informative what would it take to make it a widespread test.

01:34:10.000 --> 01:34:14.000
Yeah really good questions for the first one.

01:34:14.000 --> 01:34:28.000
We can and are following up with with the people that we already enrolled but it's a numbers game okay so I mean in California there aren't there aren't that many cases and so you know we think to do a longitudinal study.

01:34:28.000 --> 01:34:43.000
So what we need in the range of like you know 400 people that we would enroll before infection and then see at some point in time to maybe 10% of those become infected.

01:34:43.000 --> 01:34:48.000
Those would be the numbers that we would expect to have to run.

01:34:48.000 --> 01:34:57.000
You also have the difficulty that there's nobody now knows whether or not they've been exposed you only know if you have confirmed exposure.

01:34:57.000 --> 01:35:02.000
So it's actually more useful to look at all the pre 2020 samples.

01:35:02.000 --> 01:35:13.000
And then reach out and then certain and simply survey those individuals by email have their physicians reach out to them to see did they contact COVID-19 or not.

01:35:13.000 --> 01:35:16.000
And see then how that rate compares to averages.

01:35:16.000 --> 01:35:31.000
Agreed and it becomes a numbers game you know can you get enough of those enough of those samples and enough enough follow but I agree that's that's absolutely in an approach and we're trying to do that.

01:35:31.000 --> 01:35:37.000
I just don't know that we have the numbers for it for the

01:35:37.000 --> 01:35:41.000
developing into a widespread test. Right. Thank you.

01:35:41.000 --> 01:35:45.000
I think widespread the key there.

01:35:45.000 --> 01:35:50.000
T cell assays are intrinsically more challenging than antibody assays because they involve.

01:35:50.000 --> 01:35:57.000
Don't forget there's a there's a twin interview with Christian Anderson where they talk about his early career.

01:35:57.000 --> 01:36:03.000
And his early career involved being a cellular immunologist and working on T cells.

01:36:03.000 --> 01:36:13.000
In England and becoming so frustrated with it that he decided to go into infectious disease instead T cells are hard to work on because T cells are everywhere and nowhere.

01:36:13.000 --> 01:36:29.000
Because they're autonomous actors in your immune system symphony that don't do what they're told or go where they're told but follow complex concentration gradients and activation cycles.

01:36:29.000 --> 01:36:38.000
And so getting at that response is like trying to I don't know.

01:36:38.000 --> 01:36:48.000
It's trying to keep track of some kind of ecological thing like a like a squirrel with only one measuring device like a like a camera.

01:36:48.000 --> 01:36:54.000
And then thinking that you're going to get an accurate sampling of all the squirrels in the in the neighborhood.

01:36:54.000 --> 01:37:06.000
But all you've got is a camera on your back porch no bait or even if you have bait you think you're going to see what the squirrels are doing and all the squirrels are going to come to your house.

01:37:06.000 --> 01:37:15.000
And so they take a sample from somebody's blood and now already what do you have you got a you know you got the park in your neighborhood.

01:37:16.000 --> 01:37:28.000
And so if there's a there's a whole bunch of black squirrels in Bethel Park am I going to find them if I sample from the basketball court and the playground down the street guaranteed.

01:37:28.000 --> 01:37:35.000
Would I get an accurate sample of how many black squirrels there are in the neighborhood if I did that over and over and over again.

01:37:35.000 --> 01:37:37.000
Of course I wouldn't.

01:37:37.000 --> 01:37:40.000
There's no reason to expect that I would.

01:37:40.000 --> 01:37:54.000
And so if they sample T cells from a patient do you think they're getting an accurate representation of the distribution of T cell specificity across this genome of course they're not.

01:37:54.000 --> 01:38:02.000
That's what they're talking about when they say T cell assays are notoriously difficult T cells are notoriously difficult to study period.

01:38:02.000 --> 01:38:06.000
There are no assays.

01:38:06.000 --> 01:38:13.000
The only time they get a really robust signal and where there are abundance of T cells that recognize a particular target that's the whole point.

01:38:13.000 --> 01:38:18.000
Otherwise how can you possibly resolve these signals.

01:38:18.000 --> 01:38:24.000
How many helper T cells do you really need for a response we don't know that answer.

01:38:25.000 --> 01:38:32.000
So he's walking on eggshells here trying to keep the biggest and broadest barn doors open for possibility.

01:38:32.000 --> 01:38:49.000
So he's got a lifetime of work ahead of him even if he knows that none of the assays that he's currently using can ever as adequately answer these questions because the model that they use don't make accurate doesn't make accurate predictions about measurements in the future.

01:38:49.000 --> 01:38:55.000
I think both cells and cellular readouts are more challenging.

01:38:55.000 --> 01:39:02.000
Certainly the tuberculosis quantifera and assay is widely used.

01:39:02.000 --> 01:39:04.000
It definitely works.

01:39:04.000 --> 01:39:11.000
And so conceivably a similar type assay could be developed for.

01:39:11.000 --> 01:39:17.000
For stars to specific T cells using using mapped epitopes that may be.

01:39:17.000 --> 01:39:25.000
So here he seems to be implying that they're not that aim methodology that he used was not spike specific.

01:39:25.000 --> 01:39:28.000
So if we go back to that, where are we now here?

01:39:28.000 --> 01:39:32.000
I just want to make sure you know what I'm talking about 2628.

01:39:32.000 --> 01:39:42.000
If we go back here to this aim experiment that he did, which was right.

01:39:42.000 --> 01:39:46.000
I thought it was at like nine.

01:39:46.000 --> 01:39:49.000
See first he shows antibodies.

01:39:49.000 --> 01:39:55.000
And then he shows the T cell activation with regard to the pertussis vaccine.

01:39:55.000 --> 01:40:00.000
And it's antigen specific.

01:40:00.000 --> 01:40:03.000
What is this?

01:40:03.000 --> 01:40:11.000
A germanol centered T follicle helper cells something like that are very stingy cytokine producers yada yada yada.

01:40:11.000 --> 01:40:16.000
And then right after this he shows this graph where.

01:40:16.000 --> 01:40:19.000
Sorry right before that.

01:40:19.000 --> 01:40:23.000
He shows this graph where it seems to be spike and non spike.

01:40:24.000 --> 01:40:30.000
But I don't know for sure.

01:40:30.000 --> 01:40:36.000
What this means spike T cells there aren't that many spike T cells it might just be that.

01:40:36.000 --> 01:40:44.000
The the epitope that they chose from the spike protein has this much overlap and that they pull a lot of T cells without.

01:40:45.000 --> 01:40:55.000
These are these are all T cells without that specificity the vast majority of them are piling up there only a few of them are here that looks like a lot though.

01:40:55.000 --> 01:41:01.000
And so again I don't know how much this is verified I don't know how these essays work so I'm kind of.

01:41:01.000 --> 01:41:06.000
You know critiquing something that I can't fully understand but.

01:41:06.000 --> 01:41:12.000
I do know that they're probably exaggerating the specificity of this spike specific signal.

01:41:12.000 --> 01:41:19.000
And they are definitely exaggerating the specificity of this T cell activated signal.

01:41:19.000 --> 01:41:33.000
Again because why because when he did the non infected individuals 50% of them had cells had T cell responses very similar to this with the same specificity for spike.

01:41:33.000 --> 01:41:44.000
So if that's the case then in this case you would have expected at least 50% of these signals to be there before they were exposed to SARS-CoV-2.

01:41:44.000 --> 01:41:59.000
And if there are signals that are sub threshold that are also overlapping then in these other people then all of these signals and potential could be based on previous immunity to other fusion proteins from other RNA viruses.

01:41:59.000 --> 01:42:09.000
And they make no effort to distinguish on purpose because we're all agreed that the assumption is that these are new novel viruses so we don't need to make that.

01:42:09.000 --> 01:42:11.000
We don't have to prove that to you.

01:42:11.000 --> 01:42:16.000
The whole point is it's novel from the beginning so why do we have to prove that?

01:42:16.000 --> 01:42:19.000
That's the enchantment.

01:42:19.000 --> 01:42:23.000
It's not the droids you're looking for kind of thing.

01:42:23.000 --> 01:42:28.000
It's 2622 is that what I said?

01:42:28.000 --> 01:42:32.000
I don't know what type assay could be developed.

01:42:32.000 --> 01:42:36.000
That flows cytometry assays that are not appropriate for what?

01:42:36.000 --> 01:42:40.000
They're not appropriate for commercial products.

01:42:40.000 --> 01:42:43.000
Make no mistake about it ladies and gentlemen.

01:42:43.000 --> 01:42:45.000
That's what he's saying here.

01:42:45.000 --> 01:42:52.000
T cell assays in order to be even remotely specific cannot be made into commercial products.

01:42:53.000 --> 01:42:57.000
You heard it here first or at least you're hearing it here again.

01:42:57.000 --> 01:43:01.000
That's definitely what he's saying here and not so many words.

01:43:01.000 --> 01:43:03.000
But that's why.

01:43:03.000 --> 01:43:07.000
Because you don't want to admit that the reason why we test for anybody is because we can.

01:43:07.000 --> 01:43:11.000
And the reason why we don't test for T cells is because we can't.

01:43:11.000 --> 01:43:15.000
We can't monetize that.

01:43:16.000 --> 01:43:21.000
So do you need a blood draw of a substantial sample to pursue and lengthy your time of course.

01:43:21.000 --> 01:43:23.000
For those that.

01:43:23.000 --> 01:43:25.000
Yes.

01:43:25.000 --> 01:43:26.000
I mean.

01:43:26.000 --> 01:43:27.000
Yeah.

01:43:27.000 --> 01:43:28.000
Take it from there.

01:43:28.000 --> 01:43:29.000
Yeah.

01:43:29.000 --> 01:43:30.000
You've been salted.

01:43:30.000 --> 01:43:32.000
You can unmute yourself.

01:43:32.000 --> 01:43:33.000
Yep.

01:43:33.000 --> 01:43:34.000
You're all set.

01:43:34.000 --> 01:43:35.000
Yeah.

01:43:35.000 --> 01:43:36.000
Shane.

01:43:36.000 --> 01:43:37.000
Very exciting talk.

01:43:37.000 --> 01:43:44.000
Can you tell us that now that you are deciphering which epitopes are featured by.

01:43:44.000 --> 01:43:47.000
Which HLA's in particular patients.

01:43:47.000 --> 01:43:53.000
Is that going to allow you or is it worth it to use this for individualized treatments?

01:43:53.000 --> 01:43:55.000
Say individualized vaccines.

01:43:55.000 --> 01:43:57.000
Individualized vaccines.

01:43:57.000 --> 01:44:00.000
He's mentioning individualized vaccines.

01:44:00.000 --> 01:44:06.000
Can you use something like tetramers in order to determine tetramers.

01:44:07.000 --> 01:44:08.000
Aptamers.

01:44:08.000 --> 01:44:09.000
Aptamers.

01:44:09.000 --> 01:44:13.000
That's what they can use to stimulate the immune system.

01:44:13.000 --> 01:44:15.000
And they already knew that for a long time.

01:44:15.000 --> 01:44:18.000
A very short amino acid chain.

01:44:18.000 --> 01:44:23.500
Attached to another immunogenic molecule is the perfect way to stimulate the immune system

01:44:23.500 --> 01:44:24.500
to an epitope.

01:44:24.500 --> 01:44:26.000
They knew that already.

01:44:26.000 --> 01:44:28.000
That's what he's talking about.

01:44:28.000 --> 01:44:34.000
So can we make that little short epitope specific for each individual in their vaccine?

01:44:34.000 --> 01:44:36.000
Can we make individualized vaccines?

01:44:36.000 --> 01:44:38.000
Mark, did you hear it?

01:44:38.000 --> 01:44:40.000
Did you hear him say it?

01:44:40.000 --> 01:44:42.000
Patients.

01:44:42.000 --> 01:44:47.000
Is that going to allow you or is it worth it to use this for individualized treatments?

01:44:47.000 --> 01:44:50.000
Say individualized vaccines with epitopes.

01:44:50.000 --> 01:44:54.000
They're particularly good for people with a particular HLA.

01:44:54.000 --> 01:45:03.000
Or can you use something like tetramers in order to determine where people are in their response

01:45:03.000 --> 01:45:09.000
and better treat them as individuals based on their HLA sequences?

01:45:09.000 --> 01:45:10.000
Yeah.

01:45:10.000 --> 01:45:11.000
Yeah.

01:45:11.000 --> 01:45:12.000
Great.

01:45:12.000 --> 01:45:13.000
Great questions.

01:45:13.000 --> 01:45:21.000
Certainly the SETI lab is actively mapping epitopes because there is a lot of value in knowing.

01:45:21.000 --> 01:45:27.000
So DC sign that is supposed to be in or near the receptor binding domain of this spike protein.

01:45:27.000 --> 01:45:36.000
DC sign is a HLA, a generic HLA, stimulating epitope, supposedly.

01:45:36.000 --> 01:45:40.000
That's the whole reason why that's an exciting thing to talk about.

01:45:40.000 --> 01:45:45.000
I haven't really gone beyond what I've seen jickey leaks in other people cover that that

01:45:45.000 --> 01:45:48.000
supposedly is in the spike.

01:45:48.000 --> 01:45:53.000
But if it's there, there would be no biological reason to expect it to remain there for a very

01:45:53.000 --> 01:45:57.000
long time because the spike changes quite rapidly.

01:45:57.000 --> 01:46:02.000
And nobody's been paying any attention to it's coming and going in different variants.

01:46:02.000 --> 01:46:06.000
Even the dissident people who say that it's important.

01:46:06.000 --> 01:46:09.000
And so that leads me to believe that it's a red herring.

01:46:09.000 --> 01:46:14.000
However, if it's there, it would be there in the sequence because they wanted it to be there

01:46:14.000 --> 01:46:20.000
in the spike that they were going to transfect people with in order to make sure that it would

01:46:21.000 --> 01:46:28.000
be immunogenic enough to generate a robust antibody response that they could predict and detect.

01:46:28.000 --> 01:46:33.000
That's the worst case scenario in my humble opinion is that they lied about the virus

01:46:33.000 --> 01:46:40.000
and released a clone that had a spike protein that has already a well tested immunogen protein.

01:46:40.000 --> 01:46:46.000
So that when they decided in 45 minutes to use the spike protein to code on optimize it

01:46:46.000 --> 01:46:53.000
and change it's uracils to uracils to pseudo uridine, they knew already that they had a sequence

01:46:53.000 --> 01:46:57.000
in there that was going to guarantee a robust antibody response.

01:46:57.000 --> 01:47:07.000
And maybe even a somewhat homologous one or homogenous one across people because more people

01:47:07.000 --> 01:47:13.000
would be apt to respond to this section of the protein because of this unique stimulating epitope.

01:47:13.000 --> 01:47:20.000
It's an interesting scary story, but it remains the same scary story in regards to the virology.

01:47:20.000 --> 01:47:27.000
You don't put that into a virus and then the virus itself is able to do things that other

01:47:27.000 --> 01:47:30.000
RNA viruses couldn't do.

01:47:30.000 --> 01:47:36.000
You can't go from, you know, a small firework to a nuclear bomb because you changed a few

01:47:36.000 --> 01:47:38.000
letters in the code. That doesn't work.

01:47:38.000 --> 01:47:44.000
But you could change a few letters in that protein's code and then make it a more effective

01:47:44.000 --> 01:47:48.000
immunogen in the context of transfection.

01:47:48.000 --> 01:47:53.000
And if that was possible and that was done, then it would be very much to your advantage.

01:47:53.000 --> 01:47:58.000
If you're going to lie about a virus, you might as well lie about a virus that has a useful epitope in it,

01:47:58.000 --> 01:48:04.000
that you're sure will make your countermeasure effective and you're sure will make your roll out

01:48:04.000 --> 01:48:10.000
of a never-before-intested genetic vaccine technology will look super effective

01:48:10.000 --> 01:48:15.000
across any number of measuring correlates.

01:48:15.000 --> 01:48:20.000
That's the worst-case scenario. The other one is they just lied about everything.

01:48:20.000 --> 01:48:24.000
But if there is some biology and there is some mechanism behind the lies,

01:48:24.000 --> 01:48:31.000
this is likely a possible candidate and here they are talking about HLA subtypes

01:48:31.000 --> 01:48:36.000
and screening for those epitopes, which the HLA subtypes are most likely to choose,

01:48:36.000 --> 01:48:41.000
which is kind of along the same lines of a ethnically or racially targeted virus,

01:48:41.000 --> 01:48:45.000
but not really the same, but kind of.

01:48:45.000 --> 01:48:51.000
It's definitely the same receptor set and the same story about how the receptor set is determined

01:48:51.000 --> 01:48:58.000
and what your epitopes are going to be, which is what he's explained before.

01:48:58.000 --> 01:49:01.000
The C-cell memory is unique for everyone.

01:49:01.000 --> 01:49:06.000
That's what makes it pretty hard for an RNA virus to do a nuclear bomb-style three-year circulation

01:49:06.000 --> 01:49:09.000
of the world in rainbow colors.

01:49:09.000 --> 01:49:15.000
Cool epitopes. Certainly on the class one side,

01:49:15.000 --> 01:49:18.000
there's...

01:49:18.000 --> 01:49:23.000
the epitopes can be predicted quite well and are recurrent and so making tetramararies

01:49:23.000 --> 01:49:27.000
or other reagents to more finely follow the responses as a value.

01:49:28.000 --> 01:49:32.000
That's a much harder sell on the class two side.

01:49:32.000 --> 01:49:42.000
There's so much binding diversity and lack of great predictability for any given epitope

01:49:42.000 --> 01:49:53.000
and any given person that I would say we were struck in a positive way by how broad the response is,

01:49:53.000 --> 01:50:01.000
even in this relatively small group of donors across quite a few different proteins of COVID-19

01:50:01.000 --> 01:50:03.000
of SARS-2.

01:50:03.000 --> 01:50:12.000
And so finding a quote-unquote sort of magic epitope isn't needed and also that any given epitope,

01:50:12.000 --> 01:50:21.000
even if somebody has a given HLA for class two, there's a good chance they wouldn't respond to that specific epitope

01:50:21.000 --> 01:50:25.000
and so it was good to see that there were broad responses across them.

01:50:25.000 --> 01:50:34.000
We're certainly exploring that and particularly Alex's group is identifying as many epitopes as they possibly can

01:50:34.000 --> 01:50:40.000
in order to try and get a sense of this with certainly a lot of interest in S,

01:50:40.000 --> 01:50:44.000
predominantly from the vaccine side with all of these vaccines focused on S,

01:50:44.000 --> 01:50:50.000
we really want to know if there was anything odd about any particular epitopes.

01:50:50.000 --> 01:50:59.000
So in May of 2020, they are already making vaccines aimed at the spike.

01:50:59.000 --> 01:51:13.000
You see, that's extraordinary, really, because there was no evidence in New York City or elsewhere that there was huge spread.

01:51:13.000 --> 01:51:15.000
They were lying about it.

01:51:15.000 --> 01:51:23.000
They were doing everything they possibly could to maximize the illusion that this spread was happening.

01:51:23.000 --> 01:51:26.000
But now in retrospect, we know that that was an exaggeration.

01:51:26.000 --> 01:51:27.000
We know that they were lying.

01:51:27.000 --> 01:51:34.000
We know that they were pushing the worst case scenario in front and behind the scenes.

01:51:34.000 --> 01:51:40.000
And so now with 2020 hindsight, we look back and we can see very clearly that we were bamboozled.

01:51:40.000 --> 01:51:51.000
We were lied to by people high and low, social media, zoom meetings, TV news, the whole nine yards.

01:51:51.000 --> 01:52:01.000
And the people that weren't lying were fooled, were coerced, or were too dumb to know,

01:52:01.000 --> 01:52:06.000
or were following the look away doctrine, which is, you know, what you don't see, it doesn't happen.

01:52:07.000 --> 01:52:10.000
What happens in Vegas stays in Vegas.

01:52:10.000 --> 01:52:13.000
You got to break some eggs to make an omelet.

01:52:13.000 --> 01:52:17.000
Whatever your rationale phrase is.

01:52:17.000 --> 01:52:24.000
A moment our perspective is there's a broad number of epitopes and it's probably best to focus on that breath.

01:52:24.000 --> 01:52:25.000
But a very good question.

01:52:25.000 --> 01:52:27.000
Great. Thank you very much.

01:52:27.000 --> 01:52:30.000
Rodney, you can unmute yourself.

01:52:30.000 --> 01:52:31.000
Yes.

01:52:31.000 --> 01:52:34.000
Thanks, Shane. That was really very interesting.

01:52:34.000 --> 01:52:41.000
So, so Andy and Laura, both of my questions, but I had a third one.

01:52:41.000 --> 01:52:53.000
And the third one is, have you thought about looking at populations like in Hong Kong or in China to see what their CD fours look like,

01:52:53.000 --> 01:52:59.000
especially given that they may have been exposed to stars like viruses in advance.

01:52:59.000 --> 01:53:03.000
And maybe that's why things are not as bad there as they are.

01:53:03.000 --> 01:53:14.000
Yeah, absolutely. I mean, that's a very interesting question and a very interesting speculation.

01:53:14.000 --> 01:53:22.000
You know, like just looking at the US, right, we were wondering, well, okay, just in hypothetical fantasy land.

01:53:22.000 --> 01:53:29.000
What if it's one particular coronavirus that's particularly positively correlated or negative or maybe in different regions of the country, right?

01:53:29.000 --> 01:53:38.000
These are circulating to different degrees. At least in the US, the coronavirus data is actually that for that period of time,

01:53:38.000 --> 01:53:42.000
the CDC divides the country into these four regions.

01:53:42.000 --> 01:53:49.000
And while there are fine scale differences, right, in the epidemiology there, not a lot.

01:53:49.000 --> 01:53:55.000
It was mostly the same, right? Same viruses peaked in each region in the same year.

01:53:55.000 --> 01:54:05.000
So at least in the US, that doesn't look to be a regionalism. It doesn't look to be a distinguish or per se.

01:54:05.000 --> 01:54:14.000
But yes, outside of the US, I fully agree. We've talked with at least one group in Hong Kong that said,

01:54:14.000 --> 01:54:22.000
hey, your data look great. We'd love to look at these coronaviruses and others to see, you know,

01:54:22.000 --> 01:54:32.000
do the T cells, do people have different T cell reactivities to different coronaviruses or coronaviruses that aren't so widely spread?

01:54:32.000 --> 01:54:39.000
And do people write in Hong Kong? Well, it's somebody else's data. I'm not going to talk about it.

01:54:39.000 --> 01:54:45.000
So exactly right. Very good question. And we know at least some people are starting to explore.

01:54:45.000 --> 01:54:55.000
The simplest approach is actually a combined serological T cell approach. So, for example, just running serology to see if you have 100 people,

01:54:55.000 --> 01:55:01.000
are they all positive for all four common cold coronaviruses or does it does it stratify in different ways?

01:55:01.000 --> 01:55:12.000
And then can you relate that to the SARS-2 reactive T cells for some more exotic coronavirus?

01:55:12.000 --> 01:55:22.000
Oh, wow. So let's just get rid of this. Oops. Sorry about that. And let's bring this back up.

01:55:22.000 --> 01:55:39.000
So this was throwback Thursday with a talk by Sean Crotty from May 27, 2020. And I hope it illustrated to you how the the immunomythology 101 was laid down from the very beginning and how everybody was committed to this lie very early.

01:55:39.000 --> 01:55:50.000
This inversion, this making everything dumb simple so that people would accept antibodies as a correlative immunity, the only one we really have.

01:55:50.000 --> 01:55:57.000
And because we know nothing is the best we got, and we're going to design vaccines based on it.

01:55:57.000 --> 01:56:06.000
And of course, that's what this is all about, right? A friend of mine pointed out that a lot of people win their Nobel Prize for work they did like 10 years earlier,

01:56:06.000 --> 01:56:16.000
or even 20 years earlier. And these guys are winning a Nobel Prize for work they apparently did like last year, which is extraordinary.

01:56:16.000 --> 01:56:31.000
It may even be the case that this sets a legal precedence of sort that makes it very hard for anybody to be blamed for anything going wrong because if you try to sue Pfizer beyond tech,

01:56:31.000 --> 01:56:43.000
Pfizer or beyond tech will say, but what are you talking about? We did the best we could. We did the state of the art. We did what we use the Nobel Prize winning methodology.

01:56:43.000 --> 01:57:00.000
So what do you mean? What do we do wrong? So this could be part of a covert and overt, you know, kind of campaign behind the scenes and and in front of the cameras to legitimize

01:57:00.000 --> 01:57:16.000
the whole methodology as we've talked about many times, of course, before we think that that's definitely the case, but also just the whole the whole what was done, how it was done, how it was accomplished.

01:57:17.000 --> 01:57:30.000
All of these things are trying to overwrite the noise that's there, try to make it seem like it's really clean and really simple, just like we listened to his lecture.

01:57:30.000 --> 01:57:39.000
We heard Mark's little short from yesterday, where his lecture where he's just talking about how you know our RNA and lipids, it's all the same.

01:57:39.000 --> 01:57:55.000
It's much better than monoclonal antibodies or the potential to be much better than monoclonal antibodies. And so we're going to explore that video in in depth, I think tomorrow that Mark hinted was very good and I think it's going to be spectacular.

01:57:55.000 --> 01:57:58.000
Thanks very much for joining me.

01:57:58.000 --> 01:58:10.000
And this has been giggle and biological where we believe that the who led us to believe there was a pandemic with the cooperation of all governments around the world that were coerced or on board.

01:58:10.000 --> 01:58:13.000
We used a test that wasn't specific.

01:58:13.000 --> 01:58:20.000
We said that something was novel when it wasn't when we had previous immunity that even stray shame karate at a hard time hiding.

01:58:20.000 --> 01:58:33.000
They're lying about this immunomethologies because they have every intention of using this immunomethology to mislead our children and to take away their sovereignty over their bodies and the bodies of their children.

01:58:33.000 --> 01:58:48.000
The reason why they want to do that is because they would like that data, whether or not they can do anything useful to it, they have convinced each other as peers as rulers that that data is theirs, and that they that we belong to them.

01:58:48.000 --> 01:58:57.000
And as their subjects, we should give their data to them so that they can extend their lifetimes, they can do all the transgenic magic to themselves.

01:58:57.000 --> 01:59:15.000
And that's also the sort of evil overlord mythology that they would like us to believe so that it paralyzes us so that it keeps us on our couch and keeps us clicking on the next YouTube video instead of actually making a phone call or testifying in court or filing suit

01:59:15.000 --> 01:59:19.000
or not wearing or doing what you're told when you're told to do it.

01:59:19.000 --> 01:59:23.000
So thanks for waking up. Thanks for being here with me tonight.

01:59:23.000 --> 01:59:33.000
If you want to follow what I'm doing or help me out at all, it's at gingolmbiological.com or you can scroll down to the bottom and you can become a supporter at a couple different levels.

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02:00:13.000 --> 02:00:16.000
This has been gingolmbiological.

02:00:16.000 --> 02:00:31.000
Make sure you remember that intramuscular injection of any combination of substances with the intent of augmenting the immune system is dumb and you can say that shortly for the current iteration by saying transfection is not immunization.

02:00:31.000 --> 02:00:44.000
Stop all transfections in humans because they are trying to eliminate the control group by any means necessary. This has been gingolmbiological.

02:00:44.000 --> 02:00:51.000
Hey, high-resistance, low-noise information brief brought to you by a biologist. Thanks very much for coming. Thanks for being here tonight.

02:00:51.000 --> 02:00:58.000
I hope it was a little bit enjoyable and, like I say, sleep well and I will see you guys again tomorrow.

02:01:04.000 --> 02:01:12.000
Lots of people here tonight. At some point we had 115 people, so the people that are here now are obviously the hard cores.

02:01:12.000 --> 02:01:18.000
But man, oh man, we're doing better and better every night. Please keep sharing the stream if you can.

02:01:18.000 --> 02:01:25.000
And yeah, let's just keep moving forward, keep pushing the ball forward.

02:01:25.000 --> 02:01:36.000
Don't forget to check out Mark Cusatonic on YouTube and watch all the work that he's doing because, yeah, there aren't very many people to follow.

02:01:36.000 --> 02:01:47.000
Jessica Hockett, Nick Hudson, Dr. Thomas Binder from Switzerland, Denny Rangula from Canada.

02:01:47.000 --> 02:01:51.000
Yeah, there's some good ones out there, but you've got to be careful. Thanks, guys.

02:01:53.000 --> 02:01:55.000
Whoa.

02:01:55.000 --> 02:01:59.000
That should have been like this. That would have been like this.

02:02:36.000 --> 02:02:38.000
Thank you.