WEBVTT

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I think truth is good for kids.

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We're so busy lying, we don't even recognize the truth no more in society.

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We want everybody to feel good.

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That's not the way life is.

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No way Daniel, please tell me that's not true.

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Oh my gosh, Daniel.

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Now I have to watch after this.

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Darn it.

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Darn it, darn it, darn it.

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We knew it was going to happen.

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We knew that was going to happen, Daniel.

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I got to say it.

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I already said it.

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I'm just surprised that it's McCurnan and Rixie are going to be on

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the road and at the same time.

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What else did we expect?

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This is so crazy.

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I feel so nervous.

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Like what in the world, man?

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Well, I appreciate you pausing that.

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What must be a very riveting discussion.

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I'm hoping that I'll find something, a nugget of something in that one.

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Welcome, ladies and gentlemen, to being home biological.

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If you've been following me for a while, you're here at the top of the wave.

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If you are a skilled TV watcher in need of rescue, you might need it down below.

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This is the stream where we stay focused on the biology.

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We don't take the bait on television and we love our neighbors.

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What in the hell just happened there?

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Did I just delete a whole bunch of stuff?

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Well, that's perfect.

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Well, there you go.

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I'm just, I don't know exactly what happened, but I don't actually think this is such a bad place to start.

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Let me just switch over here.

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And welcome to the show.

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This is Giga Home Biological.

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I think I actually have to adjust my second camera, so give me a second here.

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Let me see if I can focus that quick somewhere on my head.

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That should work.

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Okay, ladies and gentlemen, thank you very much for joining me.

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This is Giga Home Biological.

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We have been working on trying to unravel the teamwork case scenario.

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The operation that I think is most responsible for getting the most of us on board with the Scooby-Doo mystery solving exercise with the really bamboozling ourselves into thinking there was a mystery to solve and bad guys to catch.

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And I think Team Worst Case Scenarios really, really intimately responsible for this.

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And I've been trying to emphasize that Team Worst Case Scenarios, I've identified them, will not talk about the idea in general that augmenting the immune system with intramuscular injection is a pretty dumb idea given the biology that we know of the immune system.

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They're not really seeming to want to touch the idea that transfection in healthy humans is criminally negligent.

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And what I mean by that is that anybody that worked in academic science or pharmaceutical sciences or basic molecular biological sciences and has used transfection either in their research or in their academic pursuits or even just understood what it was.

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And had any kind of professional qualification under the rubric biologist, they should have done known better.

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They should have known that transfection was wholly inappropriate for healthy humans and the fact that they didn't speak out about it, the fact that they didn't speak out immediately about it.

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But instead, we're speaking out about the spike protein or the fear and cleavage site or the homology with the AIDS GP120 protein.

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Any of these things were extemporaneous to the main issue, which is that they were about to transfect than they have, transfected millions of healthy humans.

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Viruses aren't patterned, integrity is just a way for us to address this, this, you know, the muddying of the water using there are no viruses, kind of an argument.

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And so while I think the team worst case scenario discussion is pretty important, I will, what's that?

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Why would that be? Oh, I put the wrong thing in, that's what I did, let's go like this and then that, and then that, and then that, maybe that works, there we go.

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I like the team worst case scenario discussion, I apologize for this IT problems here, I don't know what's going on with the amateur hour, it also looks like I have my F-stop a little open to wide.

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The team worst case scenario is very interesting, but I'm more interested today in the seating of the gain of function narrative with scientific programs and peer reviewed publications for years before the pandemic and who I want to look into particularly is Mark Lipstitch.

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And the reason why I chose him is just because he seems to be the guy who was supposed to play the role of, we should shut all this down for about five or six or eight years before the pandemic.

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He was making a kind of weak argument for the idea that gain of function is very dangerous and we have no reason of doing it.

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And then sort of a limp-risted sort of way and didn't really put any gusto behind it and as a, has a Harvard professor going around the world making sort of your name for yourself under the pretense of speaking out against gain of function virology, I think you're going to find this talk very insightful.

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I have sped it up.

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And what that means is I think I'm still kind of out of focus, but I have sped it up so it he's actually speaking even slower than what he is now speaking at 1.5 speed.

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And what I want to do is just go through this talk and see if we can identify some of the mythology.

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This is 2008, I believe, let me just escape out of here out of the full screen quick and see if.

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Oh yeah, there we go, 2014. Thanks for the invitation and the chance to speak to set.

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And so you can see the Broad Institute is listed here. You know that the Broad Institute, I believe, is the one that Eric Lander is the head of.

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I believe it's also where Lena Chan was appointed after her.

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After her data set was published in May of 2020 regard to the differential between the amino acid to amino acid or, or, yeah, I mean, as a nucleotides.

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I think it was nucleotide changes in the sequences that were recorded during the SARS 1 outbreak versus the sequences that were recorded during the first few months of the SARS 2 outbreak.

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And it seems despite the fact that there were very many more sequences available and very many more patients available, very many more chains of infection that the rate of change that we saw with SARS-CoV-2 in the first six months of the pandemic or first three months of the pandemic was very different

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than the first three or four months of the SARS 1 pandemic.

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And so she was making the argument in this preprint that this was some evidence perhaps of laboratory origin or laboratory stabilization because it didn't evolve that much relative to the first SARS, which was argued to be a more obvious zoonosis.

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I don't want to argue about either of those two theories with regard to SARS 1. I just want to point out that the Broad Institute is connected to this whole narrative of gain of function going back many years before the pandemic.

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And so here's an example of it. And we're going to have a debate about pandemic pathogenic pathogen creation here.

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A diverse and large audience on this topic. I've been interested in this question of creation of potential pandemic pathogens, which I'll define in a minute for a couple of years, starting with an undergraduate lecture I gave in a Harvard class a couple years ago.

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And when I gave the title of the debate, it's slightly disingenuous in the sense that I'm going to give you more of one side of the debate than the other.

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And I think it's an important debate for scientists in all sorts of allied fields, not just virology, but or epidemiology, but all the fields around it within biology and beyond to to think about all of us, many of us who work with dangerous chemicals or dangerous pathogens.

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Do so with the knowledge that we are putting ourselves at some risk in the service of doing good science and our society funds and supports that work, despite the risk to some some people because we see the benefits of science.

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So, we make this sense. We come to the sense that there's a risk benefit balance between the risks, like the risk that led to the death of the researcher in San Francisco a couple of years ago working on a meningococcus from a laboratory acquired infection, that such or the deaths in other settings from chemical exposures.

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We know that those happen and we know that that's and we accept that because we think science has benefits. And the argument.

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So both of those were not viral. One was a chemical he said and one was meningococcus just pointing it out here we're still not talking about virology still not giving examples of really pandemic or pandemic potential being created.

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Just the idea is it and what I want to emphasize is the assumptions that he opens with right the assumptions that they are opening with not debating whether any of these biological phenomenon exist but working under the assumption that they exist and working under the assumption that

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we can use various synthetic or artificial means to get there or the deaths in other settings from chemical exposures.

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And we know that that's and we accept that because we think science has benefits.

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Exactly what solar flare our fire nine said we accept that we accept that happens. And so already we are he is engaged in an enchantment here.

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Already engaged in and setting the presuppositions inside of these people's head framing the debate is what you might call it from a from an outsider's perspective but what he's really doing here is subtly establishing a mythology

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upon about which we're now going to discuss and so the whole discussion will be based on the consensus that this discussion is real and that this discussion is valid and it's based on the fact that we accept these risks exist these events occur.

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The argument that I want to make in a nutshell today is that we have a new type of experimentation where the risks have grown to be not just to the individual investigators or to a few people around them but the risk of generating a new pandemic of a highly virulent infectious agent.

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And that the benefits of that experimentation have not been well defended and in my argument are essentially small compared appropriately to alternatives.

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But I want to start by the sort of disclaimer that I'm taking one of two easy positions to take. You can either say the risks are small and the benefits are large and then it's easy or you can say the risks are large and the benefits are relatively small which is the position I'm taking and then the decision is pretty clear.

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You should evaluate what I say about risks and what I say about benefits independent of one another and you may agree with me on one and disagree on the other because they're independent questions and I actually find complexity tantalizing and find it a little uncomfortable to have such a simple position which is risks or big benefits or small we should stop.

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So that is my position and you'll see that throughout but you should you should think about each piece separately.

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So in that same vein these views are my own they're not the result of many conversations with colleagues some of them are listed here.

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They're certainly not necessarily the views of the NIH or any part of it which is one of my which is my major funder and some of this much of this material in an earlier version is in a paper in May and plus medicine.

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So the title is the debate over potential pandemic pathogens that's a contentious term exactly what it means or whether it's a good term to use whether it's but what I mean when I say potential pandemic pathogens is is the creation of a strain of virus.

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Basically although it could be another pathogenic species that is transmissible between humans virulent in humans and novel meaning it's not currently found in human populations and there's not protective immunity against it in human populations.

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So there's some right there right.

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Those are already those are already some big presuppositions here so it's transmissible which I guess means what it means that there are some that are and some that aren't I don't know it needs to be virulent so it causes something but then the best one is novel here.

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And they use these terms people think okay so I got to understand these terms and then when I understand these terms I'll understand what these guys are talking about and then by trying to decode what these guys are are sending at us.

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We become enchanted because we accept the understanding the imbibing that's occurring here.

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So just this one slide is making us accept the potential that the idea that there are potential pandemic pathogens which can be novel not currently found in human populations and as he said for which nobody has immunity.

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And that is indeed exactly what the original debate about was in the beginning of 2020.

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And what I came out very early with the idea that well according to I don't know people like Stanley Pearlman the T cell immunity is aimed at conserved epitopes and so we shouldn't really have to worry as much as they're saying and we certainly shouldn't be using the word novel.

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But already in 2014 and it's probably much earlier than this as well this is just the example I chose.

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You can hear people framing the debate in the exact way that it was framed at the beginning of the pandemic which is that this is a novel virus for which everyone has vulnerability or for no one has immunity.

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That's an idea that's a concept just like superheroes are a concept but the idea of an RNA based viral pathogen for which no one has immunity is already a very very large set of assumptions that I'm afraid all of us over the course of our lifetime have been bamboozled into accepting

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the biological fact and phenomenon proven and it's not the case at all.

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I mean are there things at this size scale and in this descriptive pattern of RNA and DNA that the body has to deal with sure I'm sure of it.

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But are they pandemic that's that's that's basically other to be another pathogenetic species that is transmissible between humans virulent in humans and novel meaning it's not currently found in human populations and there's not protective immunity against it in human populations.

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The kinds of experiments that trigger the current discussion mostly almost exclusively have been with influenza virus and the typical setup is to take a virus and influenza virus do a combination of genetic engineering on the RNA sequence of that virus and selection experiments in ferrets usually

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sometimes guinea pigs in which the viruses passage from one from the nose of one guinea pig or ferret to the next.

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And then after several such passages it is allowed to move through airborne droplet transmission from the cage of one ferret to another cage be only airflow and no contact in a setup like the one you see there.

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Throughout the process these viruses are sequenced they're usually quasi species that come out of genetically diverse viruses and so they're deep sequenced and they ask a various phenotypes of these viruses.

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And then that's repeated to enhance the selective pressure and get more and more transmissible viruses.

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And the work was started the controversial work was started with H5N1 avian flu virus.

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And so don't get too excited about this when he says quasi species because quasi species are much more enriching phenomenon in influenza viruses because of the way their genome is segmented.

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There's a lot more opportunity during the packaging process for there to be a diversity of genomic sequences that are assembled.

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And so the quasi species necessarily whatever that phenomenon is in flu viruses is much more diverse and varied relative to that of coronaviruses if you believe the coronavirus cartoon.

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And fortunately I think that coronavirus cartoon is wrong and actually much a large portion of sub genomic RNAs are also being packaged into viral particles which means which explains what Robert Malone said was the vast majority of particles are replication

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and competent it doesn't mean that they can't transfect another cell it doesn't mean that they can't cause viral protein expression but it does mean that they can't transmit an entire genome.

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So they're not infectious from conspecific to conspecific.

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And so they did mention it it's great we know that it's a phenomenon of some sort or another but it has been recently that people that are arguing with us are trying to confound the two.

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So they want you to think that if there's no quasi species then clones aren't really necessary or aren't really needed or don't really represent a significant enrichment step and those are all incorrect.

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And that has nothing to do with whether or not there is a quasi species of X diversity or 10 X diversity.

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And that's again one of these things where they are so desperate for us to have doubt in the biology that we're learning that they try to throw us off by confounding these two phenomenon as being interlinked

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but they don't have anything to do with the what's at the core with infectious clones which is what Cina Babari says in his 2019 paper that he published with Allison Tortura that in situations where infectious material is not available you can use recombinant infectious clones to create viral

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stocks of purities which are not capable of being attained through traditional methods and that's just it that's it.

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And that's why clones are the answer that's why clones are the only real explanation for what occurred and it it is a it is one of the few ways that are obviously present in the literature that such a caper could have been pulled off.

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Now I'm going to tell you that there are a number of ways that this could have been pulled off that are not infectious clones.

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You don't need to have a 30,000 base pair genome inside of a lipid nanoparticle and faithfully packaged and then figure out a way to get that in every one or even in a few people.

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That might have only been to Homish County man and a couple other people that really needed to have the viral culturally culturing work and be robust and produce something that could fool a lot of people at a central laboratory.

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But the reality of this is that a lot of this could have been pulled off by a transfection of bacteria with bacteriophages.

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There's lots of other ways that a toxic protein that could have been detectable by the RNA presence of it could have been placed in the lungs and GI tract of of tens, thousands or hundreds of thousands of people in the United States around the world.

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It had nothing to do with coronaviruses and it would have had nothing to do with infectious clones.

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It would have just had to do with making bacteriophages and packaging whenever you wanted to transfect into them and then transfecting the microbioda present in the gut and present in the lungs.

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Now you have this very similar problem that can also be blamed on the spike protein if that's the transcript you chose to to transfect those bacteria with.

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So here we go. Anyway, we're going to go back to this. This is of course these viral passage experiments that were done in the Netherlands and in Madison, Wisconsin in 2012, something like that, where they purported to do some work with avian flu and a few mutations later

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and they had aerosolized and adapted to ferrets a avian flu. That's what he's talking about right now.

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It has about a 60% case fatality ratio in humans who have been diagnosed with it with the wild flight.

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So these are the two famous studies that were published in 2012. And looking back, it is actually clear that a number of other studies before and now since have also done this kind of work to create a novel virulent transmissible strain.

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And some of them are listed here. Notably, they include a variety of different viral subtypes, including ones that have been seen in humans and ones that have not been seen in humans.

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Ones that have been seen in humans justify the studies in the discussion as if we have to understand whether they're going to become pandemic and the ones that have been seen in humans are justified as they haven't been in humans.

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So we need to study them. So apparently you start with premise A or not A and you get to the conclusion we should do the study, which is a lesson for all of us in grant writing.

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The funders of these experiments to date include governments and the European Union and major foundations.

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The US government and China have been the largest funders I believe, at least in terms of amount of publications that I've been able to find.

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The Gates Foundation did this some early and has at least some members, some high up people in the Gates Foundation have said this will not continue.

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And then on October 17, so a couple of weeks ago, a big event happened, which is that the White House announced a pause on funding of these of these experiments.

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And the statement was that US new US government funding will not be released for data function research projects, recently anticipated to confer attributes influenza MERS or SARS viruses, such that the viruses would be more pathogenic or transmit more readily by the respiratory route.

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And they specifically exclude characterization of testing of naturally occurring isolates, which I think is a very important exclusion.

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Good, good idea, because those are not nearly so dangerous.

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A little bit of a surprise was the inclusion of MERS and SARS viruses.

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Much of the controversy really had been around influenza.

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And it was a little bit surprising to find MERS and SARS on this list, but it was.

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So it's surprising to find MERS and SARS on this list, according to this guy, which is also very interesting.

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It's also curious to note that this is the start of the Scooby-Doo.

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This is really where they started it, right? One of the ways to make everybody think that gain of function research is dangerous is to ban it.

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I mean, how much easier is it to make something scary than to ban it?

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And not allow it. And then nobody can do it, so you can never confirm whether it's bad or not.

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And everybody just assumes, well, it must be really bad if Obama just said forget it.

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And then they tell you the story of what's bad about it. Well, gain of function is bad.

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Enhanced pathogenicity is bad. We don't want people doing that stuff.

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Oh, wow, that makes sense. Can they do that?

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And so the mystery is starting to be laid right now.

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The foundation of accepting the possibility that this is going on and that it could result in a danger.

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So much of a danger that Obama decided we have to actually, we have to actually stop this.

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I hope you can see how amazing this Scooby-Doo is.

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I hope you can see how enticing it is, how well they did it, how long it's been going on.

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This is 2014.

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But they were. So I want to talk about the risks of such studies,

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the ethics of how to think about such risky studies, the benefits and some alternatives in ways of evaluating alternatives

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or evaluating these experiments in comparison to alternatives.

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So the evaluation of risks came into sharp focus last summer when three incidents in government labs,

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prominent government labs occurred. There was a potential exposure of several dozen CDC employees to anthrax

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due to an inadequate decontamination of a sample. As far as we know, no one was infected.

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But many people were treated prophylactically to prevent infection.

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There was a mix up of a highly virulent flu strain with a less virulent flu strain that was mailed out from the CDC.

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And there was a discovery of smallpox and then it turns out quite a number of other pathogens sort of stuck in a freezer

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that no one had looked in for a long time at an NIH FDA facility.

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This got policymaker's attention. It was actually not really big news if you had been following the pattern of accidents.

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Research comes with risk. Humans are involved. All three of those involve some sort of human error or forgetfulness.

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And in fact, if you look in the literature, there are papers reporting the rate at which such mishaps occur,

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which is remarkably high. It's about twice a week with select agents alone in the United States.

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Twice a week that something reportable with the select agent happens, a loss event or a potential exposure.

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The number of actual infections is much less, but still calculable. And that's the first piece of the input into the risks that I'll talk about.

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So, as you know, politics focuses on stories. It doesn't usually focus on statistics. And this got everyone's attention.

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Those of us who have been concerned about it, tried to use those events to get people's attention by writing articles and making fusses.

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But this is not a one-off story. This was an example of the sort of routine fact that even the best labs make mistakes.

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Now, I think just for a few minutes, I want to slow it down to real time because I think it's really important to hear how a Harvard professor funded by the Center for something, something and other somethings.

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How skilled or not skilled he is as a presenter. Assuming that he's got some practice, assuming that he's got something to say here,

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let's just listen to it at regular speed so that you really get an idea of who and what we're dealing with.

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Because when you listen at one and a half speed, you get the idea maybe that he might be a little more forthright than he is.

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But when you hear him speak at regular speed, I think you might hear somebody who's, is he choosing his words?

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What is he doing?

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That's something we can usually accept and sometimes shouldn't.

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So, the category of creating potential pandemic pathogens or novel strains of influenza, to me is a uniquely dangerous and risky activity compared to some of the other risky activities that we do and should be doing in labs.

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So, I've compared here four different types of activities.

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Work on Ebola virus, for example, in laboratories. That virus, as you know, has high virulence.

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We hope that we can still say it does not have pandemic potential in the sense of global effect of global spread, and I think if it escaped from a laboratory in a developed country, it probably would not be the source of global spread.

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Hopefully, nothing will be.

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But certainly, it has not been considered something that's capable of spreading widely in the developed part of the world.

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And it exists in nature, and so there's an argument that putting it in a high containment lab is not in any sense making it more dangerous than having it out there infecting people.

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And so that's an argument that Fauci made at the beginning of the pandemic or middle of the pandemic or somewhere in 21 or 22 where he said, well, I don't know what everybody's all up in arms about if you have.

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If you have viruses in.

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I keep getting. Oh, man, I need to answer that, but I can't answer it right now. I gotta call back later darn it darn it darn it.

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Shoot, but you can't you can't argue that, but he does. You argue that if you bring a virus from the wild into a laboratory, it's not really that big a deal, but here's where the.

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Here's where that comes in. Here's where the buzzer comes in. Okay.

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When you bring an RNA virus into the laboratory and work on it, chances are pretty good that you're not just using the sample that you got from the bat anus what you're doing actually is you are.

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You are.

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Let me just check this really quick. I'm very sorry. I gotta check this.

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It's not translating. Come on, translate it.

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Okay, I'll look at that later.

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I really think it's important to understand that when you bring an RNA virus into the laboratory that you're not just bringing in the sample in that you got from your bat what you're bringing in is a sequence that you then are going to create a DNA clone up.

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And then you're going to generate infectious RNA from that and then that's where your experiment is going to start.

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And so we're already glossing over the most important aspect of many of these studies, which is that they might find something in the wild they might pretend to find something in the wild they might claim to find something in the wild.

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But in order for them to regularly use it and and and investigate it in a laboratory setting they need to go back to a DNA construct and make infectious RNA from that.

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And you can make all these semantic arguments about when and how they package that virus or whatever but the bottom line is is that in order to bring it back to the laboratory and do anything useful with it, they need to make a clone.

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That is for sure.

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And so it's not as simple as say, well, bringing a wild virus back to the laboratories, no different than bringing back.

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I mean, it's no dangerous than having it in a bad cave. No, because what's in a bad cave is best we can tell is a sequence.

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As best we can tell if we give them all the benefit of the doubt there is that sequence in the lab in that bat.

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Somewhere in that bat there's a whole RNA molecule that's that sequence if we give them everything they say.

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But they can't tell us how much, how many, what the other signals are, what the ratio is between full genomes and other genomes is just crazy.

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And most of these things don't grow. And so the only way to make enough infectious material is to make a clone. It's always the same story.

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And he's not telling it nonetheless, because it's over. And it was already true here.

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If you go to the next category, which is the characterization of natural highly virulent flu viruses.

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Those are highly virulent. They don't have pandemic potential yet because they aren't readily transmissible in people.

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They do exist in nature and in zoonotic infections of people. And we do that at a lower biosafety level, as we should, probably.

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Smallpox is the most highly regulated pathogen at the moment, at least prior to this funding pause.

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Only two labs in the world are allowed to use it.

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And the experiments that they're allowed to do are highly restricted and go through extensive committee approvals.

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That's because it combines high virulence and high transmissibility, pandemic potential, and doesn't any more exist in nature.

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And so, although there's some immunity from prior vaccination and a few natural cases, basically the world is relatively naive to smallpox.

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And that's why it receives such high containment.

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The work that of creating transmissible virulent flu viruses is like smallpox in those three ways, but is done at a level of biosafety intermediate between natural characterization of natural isolates of flu and the work on, say, Ebola or other

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field of viruses.

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So I said accidents happen, and there's a long list of them that are, that I'm starting with the sort of most notorious and severe.

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The outbreak, large outbreak of foot mouth disease that occurred in the UK in the last previous decade was an escape from the perbright biosafety level three agricultural lab.

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And that was a release of SARS from a lab in Beijing in 2004.

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So after the epidemic was over, the natural epidemic was over.

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There's a lot more than that. That's pretty loud. There were seven or seven infections before.

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That's pretty weak.

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And most people who have studied it think that the 1977 re-emergence of H1N1 flu, which had not been present in humans since 1957, when a new strain of flu replaced the currently circulating H1N1.

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So it then re-emerged in 1977, and the sequence looked like about a 1953 or...

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Okay, it can't take it anymore.

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So the conclusion was that that isolate had been genetically frozen, therefore must have been physically frozen, and they're sort of two hypotheses.

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One is it was in the permafrost in some somebody's dead body, which is not impossible.

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Or the other is that it was in a laboratory freezer in probably China or Russia, and as they were trying to make do experimental work on it, it was released and became the circulating strain until 2009.

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So we had 22 years of global spread of that virus, probably from the lab accident.

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If you look at accidental infections that don't result in onward transmission, there are a bunch more, and this is a list of them.

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I won't go through all of them.

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My favorite is because it illustrates human error is the person who thought they were doing a West Nile experiment in 2003 in Singapore and found out that their bio was contaminated with the SARS virus, unrelated to West Nile, and was infected by that SARS virus.

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So this is a little bit busy slide.

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But if you take those anecdotes and the other anecdotes, and you actually keep track of them carefully, as the CDC does, in a paper published in 2012, over the period of 2004 to 2010, there were at least four laboratory-associated infections in biosafety level three in the United States with select agents.

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It reflects something less than 2000 years, lab years of work, and it's not an exact number because the way the figures are reported, you can't isolate only the biosafety level three lab years, although you can isolate the biosafety level three isolates.

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So this is an optimistic estimate that so far, pandemic potential pathogens transmissible virulent and novel with no immunity for them, right?

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That's his definition, and by starting the whole talk with this idea, he makes everybody accept this premise.

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And so the limited spectrum of debate is already set from the beginning.

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Now he establishes that we already accept that they have aerosolized, they have enriched through passage, and then aerosolized an avian flu in ferrets.

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He makes you accept that. He says a little bit about the quasi-species, which I dropped in there, make sure you remember that that's more important for flu than it is for coronavirus.

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And then pandemic potential pathogen flu studies are listed.

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The funders are discussed that include US and China and the Bill and Melinda Gates Foundation saying it's going to stop.

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And then in 2017, of course, we have, this is not, it should be 14, because 2017 is when they actually ended it so that we were looking at a video in 2014, sorry about smear and my ink there.

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And so that's why we are seeing this, now we have this list of things, which is really an interesting list.

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It's a potential exposure to anthrax, the wrong flu was sent from the CDC, and smallpox was in the wrong freezer or something like that.

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So there were lots of interesting things that he cited as evidence for occurring, and then he goes down and lists all kind of stuff for the lab leaks.

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And so now he's basically established his whole argument already.

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You've accepted that pandemic potential pathogens are real, you've accepted that they can be enriched for in a laboratory.

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You've accepted that they were so dangerous that gain of function research was paused and that Bill Gates said this had to stop.

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And then he gave you a bunch of anecdotes about how often mistakes have been made and it's humans, you know, errors happen all the time, he says.

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And so that's, I think, a very insightful place for us to be because that's, it's really curious.

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It sets up the stage perfectly for the Scooby-Doo to come in a few years.

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.2% risk occurs of a laboratory associated infection per lab year in BSL 3.

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A similar estimate that's a bit higher comes from intramural labs at the NIH, where they measure it by worker years instead of lab years.

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So that's about 1% per full-time worker year. So if someone was in the BSL 3, 2000 hours the whole year, the figures come out to about 1% risk that they get infected.

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And now the idea is that the reason why these things didn't go global is because there were a few bases that were wrong.

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The reason why it didn't go global is because none of these had a fear and cleavage site in them, I guess.

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And if they had those four extra amino acids to make a polybasically recite, any of these could have been the equivalent of a nuclear bomb.

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So those are small, but, and perhaps acceptable if you think the work is important enough, and if the risk is to that person.

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But if we're thinking about a pandemic context, then the risk really has to be inflated to allow for the potential consequences.

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So it's not just the probability of an infection, but it's multiplying that probability times the consequence of a pandemic should that infection occur and lead to a pandemic, so it's a sort of multi-step process.

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So if you break that down, and the probability of a pandemic from one unit of research, which we'll call a laboratory year of research.

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And then you need the probability that if there's a laboratory associated infection, this is a conditional probability, probability that a pandemic results.

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And we've estimated that from Hankel et al. You get a bigger number if you use the NIAID estimates.

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Several groups, including ours, have estimated the probability that if you have a single introduction of a virus with a flu-like reproductive number of epidemiological properties, that it will spread widely.

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And for something like H5N1, where there's no human immunity, if it's transmissible, it would be expected to spread widely with reasonably high probability, not 100% probability because it might just fizzle out.

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It might just be that the first person infected doesn't happen to infect others or is effectively quarantined or whatever.

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The estimates that I did and that Jamie Lloyd Smith did don't take into account those sort of quarantining and countermeasures.

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A more recent estimate from a group in Europe suggests, which includes those countermeasures, suggests that's around 10 or 20% probability that a single laboratory infection would result in extensive global spread.

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So make no mistake about it. What he's discussing here right now are publications which are coming out from him and his students, which describe mathematics, extremely simple mathematics, which purport to estimate the potential

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danger of a pandemic virus release, a flu virus, a novel virus release. This is all just

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back of the napkin calculations. This is all just whiteboard talk. There's no, there's nothing here.

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But this is how pandemic potential was established in the literature.

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By people like this, doing math like this, getting estimates from one paper or another paper.

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Pilot high and deep higher and deeper, ladies and gentlemen, that's all that is being done here.

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Citing his previous work for his new work. And before you know it, there's five papers out that say that pandemic potential is real.

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And that we need to regulate it.

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So if you multiply those together, you've got something like a one into 10,000 to one in 1000 risk per BSL three lab year of gain a function work on flu.

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So that's the probability side. And those numbers should be adjusted. I'm proposing those as strong men that people should encounter and argue with and modify.

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So control measures should help, which are factored into only the final estimate.

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Vaccination and prophylaxis of lab workers should help if they're known to be exposed.

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The enhancement to BSL three plus, which is somewhere intermediate between BSL three and BSL four could help.

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And there are cool molecular strategies of putting in micro RNA tags targets for micro RNA RNAs that are human specific, not targeted by ferrets, which can sort of disable the virus in humans while leaving it pathogenic and ferrets.

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Some people have embraced those enthusiastically. Others of others gain a function PIs have argued that that would undermine their experiments.

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Reasons to increase the estimate is essentially that the US probably has one of the better safety records and that the US is the safety record as published is not very complete.

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Which might mean that we don't have as good a safety record as we think. But in any case, there is global possibilities of these accidents.

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So then the consequence side is the probability the attack rate in a pandemic, if you get one.

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Or also meaning the number of people infected times the risk that they die times the global population.

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Using the last four pandemics, several publications have suggested that the pandemic attack rate is somewhere between a quarter and a third in each of those last four pandemics.

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Case fatality risk, of course, is unknowable for a strain that hasn't been invented.

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So wait, one, one.

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Twenty five to thirty eight percent of the people from the last four pandemics. Is he talking about mayors and SARS two? I thought those were the pandemic. What is he talking about? The flus.

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Seventy seven.

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Seventy seven.

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What 18? I mean, what does he mean?

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Twenty four percent. Think about this number for just a second because it's one of those things where, of course, I want to call B.S. when I hear B.S.

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But I also want to point out that it's not crazy, according to Mark Lipstitch.

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Lipstitch to have a virus escape a laboratory and have between twenty four and thirty eight percent of people on the planet get infected by it.

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That's the case. That's a pretty hot background.

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Every time a coronavirus jumps out of a bat cave between twenty four and thirty eight percent of the population will eventually encounter it.

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That's a pretty hot background, especially since we can't keep track of them since they're so hard to track. That's a pretty hot background.

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I mean, in one hand, he wants to argue that we haven't even been able to keep track of all these things, you know, and then on the other hand, they want you.

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Man, oh man, is this amazing. It is a thick and heavy mythology.

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Yeah, at the moment, there's debate about it, but I think the strong evidence is that wild type H5N1 bird flu is really sixty percent lethal.

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If you get it, it's not that there are a bunch of mild cases that are being missed. People have looked and not found such cases.

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It could go down. It seems to have gone down in the strains that were modified in the in the gain of function experiments.

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So maybe sixty percent, hopefully sixty percent would not be maintained, but we don't know.

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And then the global population, I think, is the one number we can all agree on roughly.

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So when you multiply those three numbers together, the consequence of a pandemic with a really violent strain is two million to one point four billion fatalities.

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Gaining of function is the worst case scenario, one billion dead.

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Wow. I guess Kevin McCarran, when he said that a billion people might die at the beginning of the pandemic, he wasn't so far off.

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I mean, the worst case scenario is one point four billion fatalities.

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That's pretty remarkable.

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That's a big number, to say the least. To put it in context, one percent is half of the 1918 strain and about about a hundred times that of the 2009 pandemic.

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So this is not a typical pandemic. Most pandemics we experience in our lifetimes, which we'll probably experience a few more, should not be at this level.

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But using the data we have available on the strain, we have to consider scenarios that are perhaps worse than will occur.

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So that's the other piece of the equation. And that also could be modified, so the virulence could be reduced.

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On the other hand, there will be a lot of, if there is a laboratory accident, a laboratory accident that leads to a pandemic, we're all in big trouble as scientists.

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Our credibility is badly harmed. Schools will be closed. All sorts of things will happen that are not counted in those mortality costs.

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How did he know schools will be closed?

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Probability times consequence.

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As people do for other dangerous activities that have a small risk of a bad consequence, like nuclear energy, or in fact other kinds of scientific experiments like the building of the Large Hadron Collider, people made estimates like this, you can get an expected value.

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So that expected value is a little hard to think about for people who don't think about these things.

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It's probably nothing bad will happen. Small probability, something very bad will happen.

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And one way to combine those probabilities is just to multiply the probability about the consequence.

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And you get somewhere between 2000 and a million plus fatalities per year of experimentation.

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So again, again, what is he saying? He is saying that the background is hot as fire.

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He's saying that for every lab year of BSL three work that we do on these viruses, we are getting this many fatalities, which means there's a lot of people that are getting exposed in developing immunity and surviving without even knowing it.

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Just thinking it's a cold or a respiratory disease or a bad flu.

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If you believe the stories that he's telling right now, I'm not saying I do, but it's extraordinary that it seems as though people listen to these talks and don't listen to them for what they say, but try to take what they say and fit it into their pre-existing model of what's going on.

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Because if you actually took what he says at face value, he's telling you that there is a huge background of viruses in circulation right now that we can barely keep track of coming out of laboratories all around the world every year and he's given you 10 minutes of evidence for it.

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Along with a bunch of off the cuff back of the napkin calculations, it suggests crazy numbers of viruses are circulating every year.

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On top of the natural ones, laboratory created ones because of this work.

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And so when we turn the PCR testing on in 2020, we turned on something that wasn't on a silent background.

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How could it possibly be out of silent background of his, you know, estimate is this?

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Do you see the absolute contradiction that's being delayed out here?

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They're telling you the truth right now.

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It's a background signal that they've been characterizing for decades, and now they've tested for it and told you that it indicates a pandemic and they can do it whenever they want to.

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If you don't want to find other ones, the reason I go through them in rather excruciating detail is that none of the consideration up to this time has used any numbers.

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Risk assessment in this context has been a bunch of people sitting in a room and saying, yeah, I think that lab's pretty safe.

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And that's not acceptable when you have these kinds of numbers available and on their face, they look unusually bad.

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So I'm not wedded to any of these numbers, pick your own well-supported numbers.

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But if these numbers are within a factor of 1,000 of being right, then we should think really hard.

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I mean, say they're 1,000-fold overstated and it's actually two fatalities per VSL-3 lab year, we would not be happy running a VSL-3 lab that we thought was going to kill two people in the same year.

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That would not be a kind of trade-off we would take happily in society, or as the director of a VSL-3 lab.

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So these are presented for you to think about and to challenge.

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It's worth noting that this work is happening in at least six countries.

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There is global variation in lab standards and in the enforcement of those standards.

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And I think what we do in the consideration during this funding pause will be watched very closely by other countries.

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So in thinking about these experiments, it raises ethical questions because people's lives are being put at risk.

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There's a benefit that's seen as a reason to put those lives at risk.

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And we need some principle on which to weigh those.

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And again, I'm not going to try to.

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Now, independent of what you think right now, I just want to point out logically and objectively how far away this bullet point is from questioning the fidelity of RNA virology, the fidelity of RNA viruses and pandemic potential.

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How far away is that statement right there from questioning the real risk of pandemic potential?

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It is actually three or four steps away from questioning.

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It's three or four assumptions away from questioning it.

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Such risks, the risks imply that they exist.

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They're non zero.

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And are not justified unless they're necessary to life saving discoveries.

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And so now here we are again in this gray area where we're supposed to ask the legitimate question.

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Yeah, but aren't there.

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There might be really useful things we could do with this.

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Maybe there is a debate to be had here.

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Maybe this limited spectrum of debate is something that I should participate in or learn the nuances of.

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And as long as you dive deep into this with the help of Mark Lipsich, you're never ever going to round about to the real questioning the presuppositions of this whole talk.

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It's extraordinary.

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He's not especially powerful wizard of enchantment, but it's the it is the effectiveness of the enchantment, not the effectiveness of the spellcaster in this case.

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But imagine how effective and you've seen it with James Giordano, how effective this kind of enchantment is given a skilled spellcaster.

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This is the best way to weigh them, but I think it's one way to weigh them that that's worthy of some thought.

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As all of you who've done human subjects training, though, the probably most influential document in bioethics is the Nuremberg code.

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It does not apply to these data function experiments.

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It is about experiments on identified human subjects.

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It says, if you're going to put human life at risk in an experiment to say test the efficacy of a drug or a vaccine, it should satisfy certain criteria, two of which I've listed here.

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The experiment should be such as the fruitful results for the good of society, unpercurable by other approaches.

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And that the humanitarian importance of the problem to be solved should be enough to justify the risk.

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Holy crap. Do you see what I see?

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Now, I'm not an expert on the Nuremberg code, and it says two and six here.

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But if you just look at two and six, it looks like they can justify the extremely speedy rollout of transfections based on the idea that it was good for society and unpercurable

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by any other means of study or methods.

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And because the humanitarian importance of the problem was important and solved by the experiment, we might as well do it.

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It sounds like to me, the Nuremberg code leaves the door wide open.

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And now I would need to investigate this further.

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I would encourage you all to investigate this further.

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But imagine that we've been encouraged for quite some time now, many years, in fact, to consider the Nuremberg code.

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And the book to throw at these people, the book by which to prosecute them, that something about the informed consent in the Nuremberg code was violated or something like that.

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But look at this, the good of society, unpercurable by methods or other methods.

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I mean, that's a completely, it's an argument that could be made for mRNA.

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That they couldn't make this many doses of any other vaccine this fast.

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And that the humanitarian importance of the problem to be solved by the experiment was so great.

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Don't you see how beautifully this all intermeshes together?

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I mean, could you have scripted a pre pandemic warm up discussion any better than this?

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So it says, if you're going to put you and you at risk by being subjects in my clinic, if I'm going to put you at risk because you're subjects in my clinical trial, it better be the case that I've thought of all the other alternatives and concluded there's not one to get the same benefit.

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And that the benefit is big enough to justify the risk.

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So, as I said, that code does not apply to this.

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But I think it suggests that we share some moral intuitions about what it's okay to do and what it's not okay to do to put people at risk.

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And I would say that putting people at risk who are not even aware that they're being put at risk entails at least some of the same obligations.

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A remarkable statement that I only learned about about maybe a year and a half ago, but all of our leading scientific bodies have signed up to is this statement.

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Scientists have an obligation to do no harm and they should always take into consideration a reasonably foreseeable consequences of their own activities.

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Doing no harm is actually a really high bar and I'm not sure I agree with that. I'm not sure. I mean, we do.

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I mean, couldn't you at least argue that all adults have the obligation to live their life like this kind of an obligation to do no harm and you should always take into consider the reasonable foreseeable consequences of your own activities.

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Is he seriously going to disagree with this basic premise of operation.

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What in the hell is going on here.

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Scientists have an obligation to do no harm and they should always take into consideration the reasonably foreseeable consequences of their own activities.

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Doing no harm is actually a really high bar and I'm not sure I agree with that. I'm not sure. I mean, we do harm every time we get on a plane to go to a conference, but we, we, you know, no harm is a very, very high bar. But nonetheless, that is the, that is the statement that our science has signed up to.

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And I think it's worth at least considering.

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When thinking about the ethics of this, I think the response on the other side, which is an understandable one is how can you stop good science from going on.

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You should not be censoring or prohibiting any science because all science is valuable.

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And that has a certain plausibility until you serve on a study section, or even worse, submit something to a study section and get it back with the 11th percentile.

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Most good science that could yield benefits for society is not done.

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It's not done mostly because we don't have the money to do it.

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Or we don't believe that our society has the money to do it.

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Or because the reviewers believe the benefits are too small.

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I'm sorry. I didn't understand what you say. I think it's worth at least considering.

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Sorry. I apologize.

57:47.000 --> 57:54.000
When thinking about the ethics of this, I think the response on the other side, which is an understandable one is how can you stop good science from going on.

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You should not be censoring or prohibiting any science because all science is valuable.

57:57.000 --> 58:05.000
And that has a certain plausibility until you serve on a study section, or even worse, submit something to a study section and get it back with the 11th percentile.

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Most good science that could yield benefits for society is not done.

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It's not done mostly because we don't have the money to do it.

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Or we don't believe that our society has the money to do it.

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Or because the reviewers believe the benefits are too small.

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Or that there are better ways to do the science that the reviewer thought of and you didn't.

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We don't do certain science because IRB is prohibited.

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We don't do experiments to figure out people's cold tolerance by immersing them in ice water the way it was done in the past because that's considered bad unethical and allowable.

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We don't do certain experiments on animals that would be interesting to learn from because the harm to the animals is too great.

58:35.000 --> 58:41.000
And we don't do, for example, smallpox experiments that might be interesting, some smallpox experiments because the risk of biosafety is too great.

58:41.000 --> 58:49.000
So this is not a, this funding pause, which by the way applies to roughly two dozen projects in the four or so billion dollar NIAID budget.

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It applies to 24 projects or so.

58:51.000 --> 58:54.000
This is not a sort of chop at the heart of science.

58:54.000 --> 59:00.000
This is a reprioritizing and saying this kind of science is too risky, at least for the moment, and we need to think about it before we do it.

59:00.000 --> 59:03.000
So what about the benefits?

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Obviously, this work has been done by people who believe for, in some cases, good reasons that it's beneficial for the progress of science and also for public health.

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And I think there's some merit in that and I would agree that there are some benefits.

59:15.000 --> 59:22.000
I think most of those benefits can be achieved by other means that do not involve creating these potential pandemic pathogens and that will be my last section.

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I want to talk about the benefits because I think, again, we all do the work we do because we see benefits and we all justify it in terms of what it can do for society, not only for pure knowledge.

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Arguably, the first gain of function experiment in current terms was this experiment in 2005 where a group at CDC and NIH and elsewhere reconstructed the 1918 pandemic flu virus.

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There was a lot of discussion about whether that was a good idea.

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There was some discussion about whether there was a good idea, none of it quantitative as far as I'm aware.

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And the paper was published and they analyzed the reasons why it was so virulent and the sequence of it and all sorts of other things.

01:00:01.000 --> 01:00:08.000
And then about a year ago, the authors of this paper published an article describing all the public health things benefits that could not have been possible without this.

01:00:08.000 --> 01:00:10.000
I don't think any of those are true.

01:00:10.000 --> 01:00:16.000
It's not that this was not useful. It's that there are lots of public health benefits that we got from our general knowledge of H1N1 flu.

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None of them required making the virus.

01:00:18.000 --> 01:00:21.000
We didn't get any new antivirals or vaccines based on this work.

01:00:21.000 --> 01:00:30.000
In the 2009 pandemic, for example, there was a question about whether people who were old enough to have seen this virus would be protected by their antibodies against the then new 2009 virus.

01:00:30.000 --> 01:00:33.000
And that was an important hypothesis to test. It was tested. The answer was yes.

01:00:33.000 --> 01:00:36.000
And that's claimed as a success for reconstructing this virus.

01:00:36.000 --> 01:00:40.000
It's not. It's a success for thinking about this virus. You didn't even need the sequence for that.

01:00:40.000 --> 01:00:42.000
You could use the sequence for other things.

01:00:42.000 --> 01:00:48.000
But there has been a tendency, I would argue, among the proponents of this kind of work to overstate the benefits and especially the essential benefits.

01:00:48.000 --> 01:00:51.000
The benefits for which their work began to function work is essential.

01:00:51.000 --> 01:00:56.000
And so I think that's a worthwhile thing to think about in evaluating other claims of benefits.

01:00:57.000 --> 01:01:10.000
The largest benefit that's been claimed recently for the recent gain of function experiments is that if we know what to look for out there when we do birds when we do surveillance of flu and birds, we can call the birds that have dangerous looking viruses.

01:01:10.000 --> 01:01:14.000
We can prepare vaccine stocks against pre pre pandemic vaccine stocks.

01:01:14.000 --> 01:01:21.000
So these are strains that aren't infecting any people are only infecting a handful of people, but might someday become pandemic and we can stockpile vaccines as we do right now for H5N1.

01:01:22.000 --> 01:01:28.000
The US has about 100 million or so doses of H5N1 vaccine, even though there are no cases of that virus in the United States.

01:01:28.000 --> 01:01:37.000
So it's a reasonable idea that we would use our surveillance, use what we learn about transmissibility to prioritize viruses for such countermeasures.

01:01:37.000 --> 01:01:45.000
The problem with that is that in the last five years, there were about 1600 outbreaks of flocks that were infected with highly pathogenic avian flu.

01:01:46.000 --> 01:01:56.000
And there were about 1600 sequences of one virus or one part of a virus deposited in each of two databases, some of them overlapping, but let's call it two sequences per flock that was known in the world to have these.

01:01:56.000 --> 01:02:07.000
So we are not getting a comprehensive picture of flu virus given that each flock as many birds, each bird has many virus sequences and that we're getting to per flock on average.

01:02:08.000 --> 01:02:19.000
We're taking roughly almost a year before the general public gets to see these sequences. Now, the people in WHO get to see it sooner than that, but this is not a sort of high speed endeavor of find the virus, take an action, analyze it, take an action.

01:02:19.000 --> 01:02:22.000
We can't do that. We don't have that kind of surveillance in place.

01:02:22.000 --> 01:02:36.000
I think maybe the most telling example, though, is what we've done in response to H7N9, which some of you will remember, most of you will remember is a strain of virus, flu virus that has been problematic the last two winters, zoonautically infecting humans in China.

01:02:36.000 --> 01:02:48.000
And then a few other places. Without any gain of function data, without any knowing of the sequences in the gain of function experiments, we know that H7N9 is as concerning as any virus could be before it becomes pandemic.

01:02:48.000 --> 01:02:52.000
There have been four pretty well documented cases of human to human transmission.

01:02:52.000 --> 01:03:06.000
If you look at the sequence and of the phenotypes of the viruses isolated from the people, not the ducks, but the people who get it from the animals, those viruses show human receptor binding phenotypes, which is one of the most important phenotypes for transmission.

01:03:06.000 --> 01:03:17.000
And if you put those viruses from people into guinea pigs or ferrets, they transmit, so they've gained ferret transmission, which argues, by the way, that ferret transmission and easy human to human transmission are maybe not the same thing.

01:03:17.000 --> 01:03:27.000
But all those danger signs, none of which require gain of function studies, should have said, this is a high priority virus. And if there were actions to be triggered, those should have been triggered by these four cases.

01:03:27.000 --> 01:03:33.000
Despite that, we have allowed zoonautically transmission to continue. We let the bird markets have the bird markets closed briefly in China.

01:03:33.000 --> 01:03:39.000
And this is a year old slide, but still true. The last winter, there were more cases and there were more cases expected this winter.

01:03:40.000 --> 01:03:48.000
So, I don't think it's the case that when the world sees a high-risk flu virus, we drop everything and prioritize countermeasures. We go about our business to a large extent.

01:03:50.000 --> 01:03:58.000
The other argument that's been made more in the past and now seems to be resurfacing is that we need to know this for vaccine design. We can design better vaccines if we know what makes a flu virus transmissible.

01:03:58.000 --> 01:04:06.000
I find that not totally implausible because more science is difficult. It's probably helpful. But we don't know the molecular basis of transmissibility in any pathogen.

01:04:06.000 --> 01:04:11.000
And we have about three dozen good vaccines that work pretty well. They have a flu vaccine that doesn't work very well.

01:04:11.000 --> 01:04:17.000
But none of those do we know, you know, Jenner didn't know the DNA sequence of a smallpox and did a pretty good job.

01:04:17.000 --> 01:04:24.000
And in fact, what you look for is things that are highly antigenic, things that are safe, and not particularly things that are transmissible.

01:04:24.000 --> 01:04:29.000
And that's not just my view. The former head of work vaccines has made the same point in a letter to science.

01:04:31.000 --> 01:04:42.000
I think the most interesting question scientifically, and actually the subject of a meeting that I'm going to try to organize, if enough flu virologists are speaking to me in a few months, is that transmission is a complicated phenotype.

01:04:42.000 --> 01:04:46.000
And we wrote a piece that just came out in Eli for a couple of weeks ago to try to make this point.

01:04:46.000 --> 01:04:52.000
So decisions about what we should do depend on our level of overall concern about a particular virus.

01:04:52.000 --> 01:04:57.000
So this is about pandemic risk assessment. If you see a virus, is it a bad one that we should worry about or just one of the many that we shouldn't?

01:04:57.000 --> 01:05:01.000
And that level of concern depends on its transmissibility and the severity of infection.

01:05:01.000 --> 01:05:03.000
Whoa, those are zooming in.

01:05:03.000 --> 01:05:15.000
High level epidemiologic phenotypes depend on a bunch of biological traits, the pH and temperature tolerance, virus morphology, whether it binds human or bird receptors, whether it's stable at the right temperatures, whether it's drug resistant, et cetera.

01:05:15.000 --> 01:05:27.000
And in my humble opinion, these are some of the most diabolical pieces of the enchantment, these kinds of visual aids that are supposed to give you an idea of what he's thinking about what he's talking about.

01:05:27.000 --> 01:05:44.000
All these things do is bamboozle you into accepting the presuppositions of this whole diagram, which is that there's pandemic potential out there, that it's accessible in a laboratory that it could potentially kill between 400 million and 1.4 billion people.

01:05:45.000 --> 01:05:59.000
That's what this is all about. This is the idea. This is the enchantment. This is where they what they've done. And it's gone. It goes back very far in terms of their making this idea plausible.

01:06:00.000 --> 01:06:18.000
And if you see it for what it is, then you see a big part of how we got here and why at the drop of a hat at the start of the pandemic, people were willing to go full bore all in for their careers for the comfort for the fame for the fortune.

01:06:18.000 --> 01:06:24.000
Because this was going to happen. They had this already lined up. This was going to happen.

01:06:25.000 --> 01:06:36.000
And a lot of these people, I think, have been aware that this was going to happen, that this shift was going to happen. And you're not going to be able to just shift the entire vaccine industry on a whim.

01:06:36.000 --> 01:06:50.000
You're not going to be able to shift from one methodological and technological basis for these for these countermeasures to another without a real, a real good chaos created.

01:06:51.000 --> 01:06:59.000
You can't make a new order by rearranging an old order. First, you have to wreck it. Then you can rearrange it.

01:06:59.000 --> 01:07:08.000
And they needed as much chaos and as much wrecking as possible at the start of this pandemic so that they could rearrange the ownership structure of America.

01:07:08.000 --> 01:07:18.000
And that's what they're doing right now. That's what's happening behind the scenes in the banks and with the corporations and with real estate. That's what's all going on right now.

01:07:20.000 --> 01:07:30.000
And this illusion is at the heart of it. This debate is at the heart of it. You got to ask yourself, was the receptor binding domain specificity sufficient to cause a pandemic?

01:07:30.000 --> 01:07:39.000
Is there subunit stability? Should we look for drug resistance? What about pathophysiology? Is it causing cardiac problems?

01:07:39.000 --> 01:07:48.000
These are questions they want you to be willing to look in the literature to learn, study and investigate, write a grand proposal about this.

01:07:48.000 --> 01:07:58.000
And every time you do any one of those things, you accept the presuppositions of the debate, which is that there is pandemic potential in nature and even more in laboratories.

01:07:58.000 --> 01:08:08.000
A sequence in ways that are really very poorly understood at the moment. We know some mutations in the sequence that seem to be associated with certain phenotypes, but that is not 100% association.

01:08:08.000 --> 01:08:21.000
And when you take some of the most famous mutations that are supposed to have a certain phenotype, you can find a counter example. For example, this is a favorite of many virologists as a mutation in the basic polymerase subunit.

01:08:21.000 --> 01:08:26.000
And it's because in many backgrounds, it increases virulence and transmissibility of the virus.

01:08:27.000 --> 01:08:40.000
The very group that started this whole controversy, the Fourier group in the Netherlands, published a paper saying that in the pandemic strain, where it was identified that they had this mutation in some strains, and people worried about it, they showed that, in fact, it changed neither virulence nor transmissibility.

01:08:40.000 --> 01:08:45.000
There are a lot of other examples. And we should try to understand this better. And there are certainly many important scientific questions.

01:08:45.000 --> 01:08:50.000
But the idea that we have an immediate application, I think, is an exaggeration.

01:08:51.000 --> 01:09:06.000
So this is kind of a flowchart of the potential benefits, as people have suggested them, and I think I'll skip it because it takes a long time. But essentially, this epidemic, this problem with epistasis and uncertain genotype phenotype mapping, sort of at the root of all of the issues with

01:09:06.000 --> 01:09:09.000
claiming benefits, and then there's some more specific benefits, some more specific concerns that I've made.

01:09:09.000 --> 01:09:27.000
So that's really interesting, because what he's talking about there is that the potential interaction of genotypes of the virus, genotypes of the host, have some interference there with regard to what the immune response is, and how to augment it, and where the

01:09:28.000 --> 01:09:40.000
host goes, and where this goes, and the specificity of it, and so whether or not a vaccine or an antibody will work well, and a given host might depend on this relationship. It's interesting.

01:09:40.000 --> 01:09:47.000
It's interesting about the other two benefits that are claimed.

01:09:47.000 --> 01:09:50.000
So the last thing I want to talk about is alternatives.

01:09:50.000 --> 01:10:00.000
The basic argument I want to make is that there are more mechanistic approaches and safe ones to understanding how flu adapts and transmits in humans, and that these can better serve the public health goals of defeating flu.

01:10:00.000 --> 01:10:07.000
And then secondly, that we don't have to understand our enemy to defeat it. The army is not full of anthropologists. There are a few, but we don't have to understand our enemy to defeat it.

01:10:07.000 --> 01:10:13.000
We can make a good vaccine and do it fast, then it doesn't really matter how flu transmits if we can do something to stop it.

01:10:13.000 --> 01:10:18.000
So developing better measures to prevent and treat it is possible, even if we give up on the science.

01:10:18.000 --> 01:10:30.000
So that's a very strange set of words to put together an honor as a Harvard professor. Say it again, we don't have to understand the mechanism.

01:10:30.000 --> 01:10:34.000
And we can still prevent transmission with a vaccine. How does that work?

01:10:34.000 --> 01:10:39.000
And do it fast, then doesn't really matter how flu transmits if we can do something to stop it.

01:10:39.000 --> 01:10:46.000
So developing better measures to prevent and treat it is possible, even if we give up on the science of transmission as interesting as it is.

01:10:46.000 --> 01:10:51.000
So we don't know how transmission occurs in flu.

01:10:51.000 --> 01:11:00.000
That's weird to me. I don't really understand what's going on there.

01:11:00.000 --> 01:11:03.000
The wrong way to frame the debate, viral bombs.

01:11:03.000 --> 01:11:08.000
I think the wrong way to think about the gain of function question is, should we do this work or not?

01:11:08.000 --> 01:11:15.000
Because there are benefits to doing the work. And if the choice is just not doing it and not doing anything else, then it's a hard question what we should do.

01:11:15.000 --> 01:11:23.000
But that's not actually the alternative. The real, we have a budget for the National Institutes of Health and for others who are concerned about this, CDC and others.

01:11:23.000 --> 01:11:26.000
And we can spend money on one thing or we can spend money on other things.

01:11:26.000 --> 01:11:38.000
And I would argue, and we make this claims much more extensively in universal vaccine for the wind there. That's been, this has been a silly idea for many, many, many years.

01:11:38.000 --> 01:11:47.000
I published paper so I won't belabor it. I would argue that the only category that has high risk to life, only category of experiments are the two dozen or so projects that create potential pandemic pathogens.

01:11:47.000 --> 01:11:53.000
There are also relatively low throughput and relatively ungeneralizable in the sense that only a very small number of virus strains can be identified.

01:11:53.000 --> 01:12:01.000
The sample sizes of ferrets are in the range from two to eight. So the statistics are unsupported, I would say, in most of these experiments.

01:12:01.000 --> 01:12:07.000
Alternatives including work include working with pieces of the virus or defective viruses in vitro, which by definition can't infect anyone.

01:12:07.000 --> 01:12:13.000
They have higher throughput because they're cheaper. We can look at natural bird and compare them to human strains and try to see from their sequences what's happened.

01:12:13.000 --> 01:12:25.000
So use the experiments that have been done in nature. And then we can do other things that are not about learning how flu transmits, but are very useful, like developing a universal vaccine, which could be used to treat to prevent a wide variety of strains.

01:12:25.000 --> 01:12:33.000
And so again here, you're, you're supposed to accept this presupposition that a universal vaccine is a legitimate goal.

01:12:34.000 --> 01:12:36.000
Just a matter of time.

01:12:36.000 --> 01:12:46.000
Like using retroviruses to fix childhood diseases in every hospital in America. This is the kind of nonsense we're at right now. This is where we are.

01:12:46.000 --> 01:12:55.000
And this is how we got here because this has already been the discussion topic at meetings for nearly a decade.

01:12:56.000 --> 01:13:00.000
And people have been actively engaging in this debate.

01:13:00.000 --> 01:13:06.000
Having meetings about it, having dinners, discussing about it, writing papers.

01:13:06.000 --> 01:13:09.000
Not just the, not just the bad ones.

01:13:09.000 --> 01:13:15.000
We can work on the technological aspects of accelerating vaccine production, which has been done, for example, by Novartis.

01:13:15.000 --> 01:13:21.000
A few about a year ago, they published a paper showing the creation of a vaccine seed stock in five days once they had a sequence.

01:13:21.000 --> 01:13:26.000
We can work on host targeted therapeutics, all sorts of other things that are not about understanding the virus, but about killing it.

01:13:26.000 --> 01:13:32.000
So how should we think about these alternatives? This is a cartoon version of how we think we should think about it.

01:13:32.000 --> 01:13:35.000
Essentially, the whole talk has been cartoons.

01:13:35.000 --> 01:13:40.000
We have a portfolio of things that we do to try to stop flu, and we should make that portfolio large.

01:13:40.000 --> 01:13:43.000
And we do to some extent, which should probably be larger.

01:13:43.000 --> 01:13:50.000
And on one hand, we can consider a portfolio that has gained a function and other work on flu biology and other work that's not flu biology, but is toward the same goal.

01:13:50.000 --> 01:14:03.000
And we should compare that against the same money spent on alternative approaches to flu, biology, alternative mechanisms that are not flu biology and not gain a function, but taking into account the opportunity costs that these relatively expensive gain of function experiments have.

01:14:03.000 --> 01:14:11.000
We should weigh those benefits and say and ask whether expanding these other things could get some of the many of the same benefits as gain of function, at least in public health arena.

01:14:11.000 --> 01:14:14.000
It would not get exactly the same scientific results.

01:14:14.000 --> 01:14:16.000
And we should ask which of these we favor.

01:14:17.000 --> 01:14:24.000
And on the side favoring alternatives, we should place the risk of gain of function, which, if it's anything like what I described, is a very heavy weight.

01:14:24.000 --> 01:14:30.000
On the other side, we could put the risks of the alternative approaches, but those risks number in the very, very small to zero range.

01:14:30.000 --> 01:14:35.000
So it's so there's not much weight on that side.

01:14:35.000 --> 01:14:43.000
I think when you do that, if you believe actually either the risk or the benefit part of what I said, you might end up favoring the use of alternatives instead of gain of function.

01:14:43.000 --> 01:14:47.000
If you believe both, then you, I think, really have to favor the alternatives.

01:14:47.000 --> 01:14:53.000
And if you believe neither, then you should conclude that we really should continue gain of function.

01:14:53.000 --> 01:14:55.000
This is a public discussion. It will be had over the next.

01:14:55.000 --> 01:14:59.000
Oh, my gosh. Look at the bottom of this thing.

01:14:59.000 --> 01:15:09.000
One of these organizations is called Midas models of infectious disease agent study.

01:15:09.000 --> 01:15:16.000
And then center for communicable disease dynamics.

01:15:16.000 --> 01:15:36.000
I can't. I can't. I can't. I gotta show you this one thing. I just want you to see this one thing. I know this is going to be annoying. This is the end of his talk. So who cares if we see the last five minutes of his talk.

01:15:37.000 --> 01:15:41.000
Wait until you see what I've got to show you just before I leave.

01:15:41.000 --> 01:15:49.000
Okay, I'm going to put another window. I'm going to open this one and I'm going to look for what's his name.

01:15:52.000 --> 01:15:57.000
I just got to go into my history and find it because it'll be easier that way.

01:16:06.000 --> 01:16:09.000
It should be an evolution list. No.

01:16:09.000 --> 01:16:12.000
Darn it. Oh, yeah, there.

01:16:12.000 --> 01:16:24.000
So this is as far as I know, this is the guy that Mark Lipstitch did his study with before he became who he is.

01:16:24.000 --> 01:16:30.000
And so this is a models interference and algorithms meeting. This is with Martin Noak of the Broad Institute again.

01:16:30.000 --> 01:16:37.000
Hello. Can you see me have the Broad Institute again? And I'm just going to play the first few minutes of this. Hopefully it will be.

01:16:37.000 --> 01:16:43.000
I got to go on high speed. He is from somewhere else other than the United States. He's got a little bit of an accent, but you'll like it.

01:16:43.000 --> 01:16:49.000
And I just want you to understand that this is considered very high level science.

01:16:49.000 --> 01:16:58.000
What he's about to explain evolutionary dynamics and high level bio computational biology, which is basically what those other two.

01:16:58.000 --> 01:17:07.000
These two things that we were looking at the bottom here, models of infectious disease agent study and center for communicable disease dynamics.

01:17:07.000 --> 01:17:13.000
It's just modeling. So he learned all of his modeling from this modeler, Martin Noak.

01:17:13.000 --> 01:17:19.000
And so let's see him give a little brief introduction into how he thinks about evolutionary dynamics.

01:17:20.000 --> 01:17:27.000
And I want you to think about the broad application of these ideas to almost anything in biology.

01:17:27.000 --> 01:17:42.000
And then ask yourself how much of a useful contribution is an evolutionary biologist like this really making to virology or an infectious disease study or anything really that he applies his wizardry to.

01:17:43.000 --> 01:17:50.000
Thank you. The title of my talk is evolutionary dynamics. What fascinates me about evolution is the ability to describe it mathematically.

01:17:50.000 --> 01:17:56.000
So, in my opinion, evolution is actually or has very much become a mathematical theory and can really be best formulated with mathematical equations.

01:17:56.000 --> 01:18:00.000
There are many verbal presentations of evolution kind of out there and some of them are good.

01:18:00.000 --> 01:18:06.000
You know, but they're typically not as accurate as the mathematical one quantity of understanding of evolution requires the mathematical formulation.

01:18:07.000 --> 01:18:14.000
And this is what I wish sort of the field evolutionary dynamics to be the mathematical exploration of the evolutionary process.

01:18:14.000 --> 01:18:27.000
So he actually believes he actually believes that we have such a grasp on evolutionary dynamics that we can usefully explain it with math equations.

01:18:28.000 --> 01:18:39.000
And that you should give him tax payer funded research money in order for him to explore these research equations, these evolutionary dynamics equations.

01:18:41.000 --> 01:18:47.000
Because he is uniquely in a position to use math to describe evolution.

01:18:48.000 --> 01:18:56.000
Let's just give him a few minutes to see how much of this is really based on something that is going to be, you know, convincing to us.

01:19:10.000 --> 01:19:16.000
Did he just say that he looks at the spread of CRISPR based gene drives?

01:19:17.000 --> 01:19:21.000
Well, now you know the answer, don't you? Holy crap!

01:19:22.000 --> 01:19:31.000
That's what you need to understand evolutionary dynamics for the dynamics of molecular and genetic evolution when you overpower it with molecular tools.

01:19:33.000 --> 01:19:39.000
These are the real kinds of gain of function research experiments that they don't want you to think about, gene drives.

01:19:39.000 --> 01:19:43.000
How many times have I mentioned gene drives as the real bad news?

01:19:44.000 --> 01:20:04.000
And how many times have I pointed out to you that Richard E. Bright and Kevin Estvelt, they were asked to testify in front of the United States Senate about the dangers of gain of function virology are two guys that actually understand that gain of function virology is nonsense compared to gene drives.

01:20:05.000 --> 01:20:18.000
And what gene drives might be able to do to your microbiome or to the microbiome of soil or to insect populations or to bacterial cultures around the world or bacteria in nature.

01:20:19.000 --> 01:20:32.000
Gene drives our infinitesimally more dangerous as real threats to our ecology and our planet than anything to do with gain of function viruses.

01:20:33.000 --> 01:20:34.000
He just said it.

01:20:35.000 --> 01:20:44.000
He just said what I've been saying for a few years now that they are, this is the dynamics that they're interested in from a synthetic biology perspective.

01:20:45.000 --> 01:20:48.000
The mathematical exploration of the evolutionary process.

01:20:49.000 --> 01:20:53.000
And the talk today will be more on the abstract side.

01:20:53.000 --> 01:21:02.000
We also work on specific problems, for example, like the spread of CRISPR based gene drives or evolution of cancer viruses.

01:21:03.000 --> 01:21:05.000
Can't say itself is an evolutionary process.

01:21:05.000 --> 01:21:10.000
There are cells get mutations in selection that leads to lesions.

01:21:10.000 --> 01:21:18.000
And so we studied the evolutionary dynamics of response to treatment such as immunotherapy or targeted therapy.

01:21:19.000 --> 01:21:21.000
Or these are our applied research projects.

01:21:21.000 --> 01:21:24.000
See, so their applied research projects are just modeling.

01:21:24.000 --> 01:21:36.000
Modeling these disease processes using evolutionary dynamics that it is actually just snake oil.

01:21:39.000 --> 01:21:40.000
It is extraordinary.

01:21:40.000 --> 01:21:48.000
Now think that this guy is the guy that taught, that brought up through the PhD process, Mark Lipsich.

01:21:49.000 --> 01:22:09.000
And so Mark Lipsich applies what he learned from this guy to infectious disease and viruses through his affiliations with Harvard School of Public Health, the Center for Communicable Disease Dynamics and infectious models of infectious disease agent studies.

01:22:10.000 --> 01:22:11.000
Something something.

01:22:11.000 --> 01:22:12.000
What does that say there?

01:22:12.000 --> 01:22:13.000
Sorry, I got to move my head.

01:22:14.000 --> 01:22:16.000
It's just called mitus.

01:22:19.000 --> 01:22:24.000
I just want to listen to a few more minutes of this guy before I sign off because I want you to hear.

01:22:24.000 --> 01:22:32.000
But today I will talk mostly about the underlying mathematical formulation of some of the questions that we are very excited about.

01:22:32.000 --> 01:22:37.000
So you will excuse me that it's somewhat more on the abstract side.

01:22:37.000 --> 01:22:40.000
So we begin with the following question.

01:22:40.000 --> 01:22:42.000
What is evolution?

01:22:42.000 --> 01:22:43.000
What is evolution?

01:22:44.000 --> 01:22:48.000
When you hear somebody like this talk, are you tempted to mark the accent or try to copy it?

01:22:48.000 --> 01:22:49.000
I am too.

01:22:49.000 --> 01:22:50.000
But I'm not going to do it.

01:22:50.000 --> 01:22:51.000
I promise.

01:22:51.000 --> 01:23:00.000
And Ernst Naya, an evolutionary biologist who lived a bit more than 100 years of age, he wrote a book, What Evolution Is.

01:23:00.000 --> 01:23:06.000
And in that book, he asks also that question, what is evolution and what is it that evolves?

01:23:06.000 --> 01:23:12.000
And he says figuratively, we talk about say evolution of species that was his field.

01:23:12.000 --> 01:23:14.000
We talk about evolution of genes.

01:23:14.000 --> 01:23:18.000
We talk about evolution of genomes of the evolution of the brain or something like this.

01:23:18.000 --> 01:23:23.000
But the only thing that actually evolves is the population.

01:23:23.000 --> 01:23:29.000
The carrier of the evolutionary process is the population of reproducing individuals.

01:23:29.000 --> 01:23:35.000
So evolution occurs in populations and populations consist of individuals that kind of reproduce.

01:23:35.000 --> 01:23:40.000
And this reproduction we can think of is genetic, but it's not only genetic.

01:23:40.000 --> 01:23:45.000
In the realm of humans that we often wish to explore, that reproduction is also cultural.

01:23:45.000 --> 01:23:48.000
So a person has an idea and that idea spreads.

01:23:48.000 --> 01:23:50.000
And people learn from each other.

01:23:50.000 --> 01:23:54.000
And that learning process, I also analyze this evolutionary dynamic.

01:23:54.000 --> 01:23:58.000
So I constantly try to expand what I would even call evolution.

01:23:58.000 --> 01:24:05.000
And I'm not absolutely sure whether limits are of what we would call evolution once we go into learning and into these sort of things.

01:24:05.000 --> 01:24:12.000
Wow, so he just applies evolutionary dynamics to everything because he's not really sure where they start or begin.

01:24:12.000 --> 01:24:17.000
He's not even really sure if they're relevant, but he just does all this math on everything that he can.

01:24:17.000 --> 01:24:22.000
Even on learning.

01:24:23.000 --> 01:24:35.000
I just wanted you to see a little brief insight into how quickly the abstract, the abstractification of biology can get out of hand.

01:24:35.000 --> 01:24:46.000
And you go from this cartoon model of a biological process that you're trying to understand to just trying to understand the cartoon model.

01:24:46.000 --> 01:24:50.000
And you're not even really probing a biological system anymore.

01:24:50.000 --> 01:25:00.000
And so easily people who are engaged in biocomputational studies or modeling studies and oftentimes epidemiological studies,

01:25:00.000 --> 01:25:10.000
virological studies and molecular biological studies are using mathematical models to approximate the complexity they are trying to understand.

01:25:10.000 --> 01:25:26.000
And those mathematical models are necessarily falling short because they are derived from the the paucity of experimental measurements that they have at the time that they formulate their hypothesis.

01:25:26.000 --> 01:25:36.000
And if he stays far enough away from the actual nuts and bolts of the irreducible complexity that he purports to be explaining,

01:25:36.000 --> 01:25:42.000
then he actually never puts his theories into any danger at all.

01:25:42.000 --> 01:25:53.000
It's a bit like making a model that recapitulates everything that you want to recapitulate, but knowing that the model itself can't be right.

01:25:53.000 --> 01:26:01.000
But because it works, you just keep working with it. I'm going to give you an example of a model like that just to give you a clue as to what I'm trying the point I'm trying to make.

01:26:01.000 --> 01:26:04.000
And it's probably an imperfect point, but I do want to try it.

01:26:04.000 --> 01:26:18.000
Let me just say if I make a model of day and night, and the model of day and night that I make is a box with a hole in it.

01:26:18.000 --> 01:26:35.000
And in that hole is a ball, and the ball rotates, and half of the ball is black, and the other half of the ball is white.

01:26:36.000 --> 01:26:45.000
And so as we rotate the ball over 24 hours, very many of the, so this ball is rotating this way in the box.

01:26:45.000 --> 01:26:49.000
And so whatever is showing here is the amount of light that represents daytime and nighttime.

01:26:49.000 --> 01:26:57.000
And for many of the aspects of day and night, this model actually recapitulates it.

01:26:57.000 --> 01:27:15.000
Now, obviously this is a dumb model of day and night, but what I'm suggesting to you is that in biology, when you go and try to probe a irreducible complexity with a model, you are necessarily making a model that is this inadequate.

01:27:16.000 --> 01:27:30.000
And then probing the system with this inadequate model, you will never learn anything real about the irreducible complexity that you are trying to probe with this horrible model.

01:27:31.000 --> 01:27:41.000
And the reason why I think this is an apt analogy is because in this analogy, you can see from your own experience that this is not an adequate model of the day and night cycle.

01:27:41.000 --> 01:27:58.000
And so you would definitely suggest to the modeler, you should redo your model before you make any measurements and try to apply them to this model, trying to understand day and night and seasons with this model would be wholly inadequate.

01:27:59.000 --> 01:28:07.000
And so if we are given a model of our immune system that is wholly inadequate, we can never figure it out.

01:28:07.000 --> 01:28:10.000
Antibodies.

01:28:10.000 --> 01:28:25.000
If we are given a model of how RNA viruses and exosomes and these signals are dealt with by our immune system that has room for pandemic potential in it, and we operate with that model, then we're going to be here as well.

01:28:25.000 --> 01:28:39.000
I don't know if that's a point that makes sense or not, but I really think it's important to understand that that's what these modelers are very capable and easily can do to every field that they contribute to to every study that they do.

01:28:39.000 --> 01:28:48.000
The risk of them going here and creating a model that's not useful for us.

01:28:48.000 --> 01:28:51.000
Establishing a model that isn't going to get us anywhere.

01:28:51.000 --> 01:29:01.000
And this guy just admitted that he doesn't know where these ideas should be applied and not applied, so he just spends all of his time applying them everywhere.

01:29:01.000 --> 01:29:07.000
Well, holy crap, does that sound like a bad idea.

01:29:07.000 --> 01:29:10.000
So we can think of a production genetic or cultural.

01:29:10.000 --> 01:29:24.000
And then the two fundamental ingredients of the evolutionary process based going back to Darwin are really mutation and selection, mutation, meaning there's a new type and selection means that something grows faster than something else and these are the two fundamental principles.

01:29:24.000 --> 01:29:30.000
And over the last 20 years, I have tried to add a third principle to this and that would be cooperation.

01:29:30.000 --> 01:29:36.000
And I want to make the point that somehow cooperation will encounter aspects of cooperationals in this talk.

01:29:36.000 --> 01:29:50.000
Now just imagine that his whole career is based on this little insight that actually the math of evolutionary theory is missing cooperation and I'm going to add it.

01:29:50.000 --> 01:29:59.000
And that he gets grants after grant with these kinds of abstractions and simple cartoons.

01:29:59.000 --> 01:30:04.000
Because that's the reality.

01:30:04.000 --> 01:30:23.000
The reality is, is there are people like this operating in every field of modern physiology and medicine and biology, all fields have guys like this and girls, women like this, that create abstract abstractions of the irreducible complexity

01:30:23.000 --> 01:30:40.000
that can necessarily trap other researchers in these paradigms, which like this model of day and night of a ball in a box that's half black and half white that can recapitulate a lot of what happens during the course of a day is wholly inadequate for us to

01:30:40.000 --> 01:30:47.000
understand seasons and actually what occurs at sunset and all this other stuff.

01:30:47.000 --> 01:30:53.000
If they give us a biological model like that, we would never know.

01:30:53.000 --> 01:30:56.000
Because we're so far from the sacred.

01:30:56.000 --> 01:31:05.000
Because we've just discarded the idea that there even exists such a thing that that it's extraordinary, isn't it?

01:31:05.000 --> 01:31:24.000
I mean, when you really see it, because he doesn't mean bad, he's not meaning to confuse everybody, but that's the end result of perpetuating these kinds of investigations, these kinds of thoughts and rewarding them with grant money.

01:31:24.000 --> 01:31:40.000
And create years and years and whole sequences of PhD students that go on to perpetuate these bad ideas, the tweak, these bad models.

01:31:40.000 --> 01:31:43.000
And that's how the pandemic occurred.

01:31:43.000 --> 01:31:50.000
That's how we have public health, the way that we have it right now. That's really the end.

01:31:50.000 --> 01:31:58.000
You can watch the rest of this on YouTube if you want to. It's a really, it's a brutal talk.

01:31:58.000 --> 01:32:06.000
It's not that it is an interesting, but it's a real philosophical discussion that isn't purporting to solve any big problems.

01:32:06.000 --> 01:32:16.000
And that's really where I think the issue is. And this is one of those things where, darn it.

01:32:16.000 --> 01:32:31.000
Well, speaking of bad models, the model of vaccine induced immunity being based in antibodies is an extremely good example of a bad model like this one.

01:32:31.000 --> 01:32:45.000
And that bad model of the immune system was part of their sales pitch for the vaccines. It was part of their sales pitch for why natural immunity might not be as effective as the vaccine.

01:32:45.000 --> 01:32:53.000
It was their sales pitch for why everybody was immune or not immune because you didn't have antibodies.

01:32:53.000 --> 01:33:13.000
It was their previous big cash cow in monoclonal antibodies. I mean, this whole proxy for immune response being antibodies has been part of this orchestration.

01:33:13.000 --> 01:33:19.000
It's something that they laid down in the background for a long time. This consensus about, okay, this is meaningful.

01:33:19.000 --> 01:33:33.000
Mark Lipsich will tell you in another talk that part of the discussion about pandemic potential and creating pandemic potential pathogens is being able to make antibodies.

01:33:33.000 --> 01:33:41.000
And the idea of a universal vaccine is making universal antibodies are a compliment of them.

01:33:41.000 --> 01:33:52.000
Not T cell memory to the most conserved proteins or the most conserved enzymes. No, no, no, no. Anybody's.

01:33:52.000 --> 01:34:08.000
And that's the bad model that the New World Order is a bad model. It's a bad model of our world. If we think that pandemic potential exists inside of bat caves and that vaccines are the way out of disease and the disease is a real thing that we all have to be worried

01:34:08.000 --> 01:34:13.000
about because we're all vulnerable to this stuff.

01:34:13.000 --> 01:34:25.000
We can't let our college kids accept this, although they have already. They really have. That's why they're wearing masks right now in public, the only ones because the lie is almost the truth for them.

01:34:25.000 --> 01:34:37.000
Leave our house and go to campus and let the university campus people and themselves bamboozled them into this mythology.

01:34:37.000 --> 01:34:51.000
We let them play pandemic at campus for three years straight and nobody spoke up. Nobody said anything. Not one faculty member that has their job still was speaking out against transfection or that these young kids shouldn't do it, or that it's

01:34:51.000 --> 01:35:04.000
totally inappropriate for healthy kids or healthy adults. They didn't say and no one stepped up. So from their perspective, it's already the truth. It already happened.

01:35:04.000 --> 01:35:11.000
We should have never let them go. And we could have stopped them.

01:35:11.000 --> 01:35:18.000
And now they believe that the virus and the transfection are hard to separate because they have such similar effects.

01:35:18.000 --> 01:35:24.000
Now the college kids actually believe that maybe it's better if we have a digital ID on the internet.

01:35:24.000 --> 01:35:33.000
Now the college kids actually are not so afraid of the next pandemic because they know that, you know, it's just going to be. That's just the way it is.

01:35:33.000 --> 01:35:47.000
And they're not really afraid of that because the climate change is coming and the earth is going to last for 12 years anyway.

01:35:47.000 --> 01:36:00.000
Can't let our kids get on this train, ladies and gentlemen, I don't know what to tell you, but then we can't let them get on the train where vaccines are just the greatest thing since buttered bread and pandemics happen.

01:36:00.000 --> 01:36:14.000
And that debate that we just listened to today is one of the ways that this mythology was installed in all of the people in academia and all of the people in bureaucracies and in governments and at the who and at the CEPI is to have meetings

01:36:14.000 --> 01:36:22.000
that read papers and meetings that cite papers about the establishment of pandemic potential.

01:36:22.000 --> 01:36:36.000
And what they gloss over is that bringing up RNA virus into the laboratory requisitely requires them to make a DNA clone that they generate infectious RNA from.

01:36:36.000 --> 01:36:44.000
And so RNA virology is a mythology the fidelity of it is exaggerated and they have done this on purpose.

01:36:45.000 --> 01:36:53.000
So that they don't risk any of their wealth. They don't risk any of their offspring.

01:36:53.000 --> 01:36:59.000
And so that we are put in danger by our own ignorance.

01:36:59.000 --> 01:37:11.000
And that's how they convinced us that a RNA release sometime in late 2019 has successfully circulated the globe with high fidelity for five years.

01:37:11.000 --> 01:37:19.000
And that's how they sustained this illusion of consensus with, I don't know what fame and fortune and and and coercion.

01:37:19.000 --> 01:37:23.000
But this faith in a novel virus seems unshakable.

01:37:23.000 --> 01:37:33.000
It's unshakable in people like Robert Malone it's unshakable in people like Pierre Corey and in in Steve Kersch.

01:37:34.000 --> 01:37:44.000
They are not ready to accept that there wasn't a significant spread of a novel virus in 2020 that we can really look back and say yeah the data shows it.

01:37:44.000 --> 01:38:02.000
The only data that shows it is being lifted up by people with connections to the very biosecurity state that we are being attacked by people that work for the human genome project people that worked for anything to do with HIV.

01:38:02.000 --> 01:38:10.000
They're all connected to this. They're all part of maintaining the faith in a novel virus.

01:38:10.000 --> 01:38:16.000
And so we've got to break the faith as a lie and it's not just coronavirus it's also HIV.

01:38:16.000 --> 01:38:20.000
And it's also the vaccine schedule in America.

01:38:20.000 --> 01:38:35.000
It doesn't matter what the background is it doesn't matter if it's back to your phage RNA or if it's coronavirus RNA or if it's exosome RNA from our own immune system signaling between conspecifics it doesn't matter.

01:38:36.000 --> 01:38:47.000
We just know for sure that they have no evidence that this background signal was blank in 2019 and then became hot in 2019 or 2020 they don't have any evidence for it.

01:38:47.000 --> 01:38:58.000
And the entire premise of the pandemic relies on that one assumption that a signal is detectable.

01:38:58.000 --> 01:39:17.000
That is here now that wasn't there before that huge assertion has been assumed from the very beginning ever since Mike Callahan was was published in the in the epoch times and ever since whatever it has been assumed.

01:39:17.000 --> 01:39:24.000
And we don't have any of that information. So I want to advocate for a pause on all childhood vaccination vaccinations in America.

01:39:24.000 --> 01:39:29.000
I want to advocate for our strict liability on all pharmaceutical products including the vaccine schedule.

01:39:29.000 --> 01:39:37.000
I want to investigate the use of deadly protocols to cause mass casualty events and also fraud to make the appearance of mass casualty events.

01:39:37.000 --> 01:39:55.000
I want to investigate the use of transfection to create the illusion of spread including the transfection to just the spike protein or the seating of an entire DNA sequence or RNA sequence of a clone of the coronavirus so that an accurate sequence

01:39:55.000 --> 01:40:00.000
could be reproduced with culturing in the places that it was going to be done.

01:40:00.000 --> 01:40:13.000
And that illusion of a pandemic is something we need to break and not pass to our children by making sure that they understand the sovereignty that they have over their bodies and we need to get our country out of the who and the UN entirely.

01:40:13.000 --> 01:40:24.000
You can see the meddlers in this situation because they're not advocating for these things. They're not using the words transfection. They're not talking about how RNA virology is wholly dependent on the methodology of infectious clones.

01:40:24.000 --> 01:40:32.000
They're not summarizing across the whole show and they're not talking about how AI is a joke. They're actually saying the opposite that it's very dangerous.

01:40:32.000 --> 01:40:46.000
So you can see a pattern. They believe and gain a function. They believe in climate change. They believe in the dangers of AI and all of these things are a mythology that are being blown out of control so that our children will reject us.

01:40:47.000 --> 01:40:52.000
So that our young adult population will reject us.

01:40:52.000 --> 01:40:58.000
That's what they're doing and they're going to use AI and social media to make that happen.

01:40:58.000 --> 01:41:11.000
I'm just warning you right now. This is what I feel is coming for the coming election fiasco that isn't almost certainly going to be used to control Demolish America or at least take a big chunk out of it.

01:41:11.000 --> 01:41:20.000
And the only way we're going to stop it is if we wake up. I'm going to learn the biology and that means stopping all transfections in humans because they are trying to eliminate the control group.

01:41:20.000 --> 01:41:30.000
By any means necessary and it does mean sharing this work wherever you can. Intramuscular injection and a combination of substances with the intent of augmenting the immune system is dumb.

01:41:30.000 --> 01:41:36.000
Transfection and healthy humans is criminally negligent and lots of people should have known better.

01:41:36.000 --> 01:41:43.000
And viruses aren't pattern integrity. We're going to carve with that in the next few weeks. I know I haven't started it yet, but I got the slides ready to go.

01:41:43.000 --> 01:41:48.000
It's just a matter of when to pull the trigger. Thank you very much for joining me. This has been getting home biological.

01:41:48.000 --> 01:41:52.000
A high resistance Illinois's information brief brought to you by a biologist.

01:41:52.000 --> 01:41:59.000
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You can also find me on Substack now and if you're already on Substack and want to help me out that way that's also fine.

01:42:13.000 --> 01:42:18.000
But if not and you subscribe, you're here, you're going to get a Substack subscription.

01:42:18.000 --> 01:42:23.000
I'm not going to put anything on the paywall in Substack anyway, so don't worry about that.

01:42:23.000 --> 01:42:27.000
And I guess I'm just going to see you tomorrow, so thanks a lot for joining me.

01:42:27.000 --> 01:42:32.000
These are the supporters of Giggle Home Biological. It's not an updated list yet.

01:42:32.000 --> 01:42:42.000
I apologize for that. Simon Fabrizio, Katherine, Carolyn, Lori, Michelle, Leo, and Jeffrey have contributed quite a bit in the last week or so

01:42:42.000 --> 01:42:56.000
and that's why their names are up there. And actually, I should also be mentioning John, John Ganges on this list.

01:42:56.000 --> 01:43:03.000
He's over here and John also made a separate donation this week and I got to thank you very much for that, John.

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It's really above and beyond. John is right there. He's also a subscriber, but he also made a separate donation this week, which is just beyond generous and my family appreciates it just very, very much.

01:43:14.000 --> 01:43:17.000
So thank you very much for sharing the work and I will see you tomorrow.

