VivaFrei (2024-02-01) - Determinants of COVID-19 vaccine-induced myocarditis： Live with Jessica Rose!

v48359x (2024-02-01) - Determinants of COVID-19 vaccine-induced myocarditis： Live with Jessica Rose! Viva Frei [vivafrei]

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The next month, when the President is in East Palestine, will he drink the water there?

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I mean, look, what I can tell you is the President's focus has been to do everything that he

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can to support this community from day one.

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We get what's going on on the ground.

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We understand what's going on.

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That's why we've had the EPA.

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That's why we had DOT.

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That's why we had HHS.

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That's why we've had FEMA on the ground.

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This is not about some sort of like political stunt here.

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This is not about, this is not what this is about.

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This is about this President being a President for everyone and showing up, showing up for

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this community.

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That's what this is about.

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I'm not going to get into some sort of political stunts about drinking water.

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What we're going to focus about is making sure they have what they need and the President

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was invited by the mayor, by community leaders.

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He's going to show up.

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He always said he would be there when it was the most helpful.

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It's a year later.

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It's a year later.

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Is in East Palestine.

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Where did I just leave?

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Do you drink the water there?

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Let's bring this down.

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I mean, look, what I can tell you is, the President's focus has been to do everything

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that he can to support this community from day one.

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We get what's going on on the ground.

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We get it.

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We understand what's going on.

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That's why we had DOT.

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That's why we had HHS.

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That's why we had FEMA on the ground.

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This is not about some sort of political stunt here.

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This is not what this is about.

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This is about this President being a President.

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First of all, my hair looks a lot like hers right now.

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I just noticed this.

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Hold on.

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I kind of look like carrying Jean-Pierre right now.

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For everyone and showing up, showing up for this community.

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Putting up for this community on the one-year anniversary after having absolutely neglected

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the community of East Palestine.

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That's what this is about.

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That's what it's about.

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That's what it's about.

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I'm not going to get into some sort of political stunts about drinking water.

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What we're going to focus about is making sure they have what they need.

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That was everything.

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The President was invited by the mayor by community leaders.

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A year later.

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He always said.

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He always said he was going to show up when it was convenient.

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Who would be there when it was the most helpful?

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When it was the most helpful.

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First of all, that old decrepit fool showing up anywhere is useless to everybody.

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Oh my goodness, she's the worst press secretary in the history of press secretaries.

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Okay.

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Everybody.

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Let me just make sure that we are live across the interwebs.

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I have no idea what I just did to my screen here.

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We are not streaming this one on YouTube for obvious reasons.

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My thumbnail man just said he sent me the thumbnail.

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Excellent comma.

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Thank you.

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Exclamation point.

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I'm going to swap out the default thumbnail for the good thumbnail as we do the stream.

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Let me make sure that we are live on the rumbles.

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Well, we'll start with viva barnslaw.locals.com.

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Are we live right here refresh and press play for obvious reasons for obvious reasons.

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We're not doing this one on YouTube because I don't know what the bloody rules are anymore.

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I mean, it's not that the rules are unclear.

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They are deliberately opaque, but even deceptively opaque.

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We're on YouTube.

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As you know now, I'm appealing a recent removal of a video of mine because YouTube removed

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one of my videos for allegedly violating community guidelines.

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Oh, that sounds terrible and scary.

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After having manually approved the video for monetization two days earlier.

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So it's not that the rules, you know, the rules do not make sense.

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There are no rules.

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It's done specifically and deliberately to penalize creators, to shut them up and to

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let me just get past these ads to weaponize the rules so they can go after politically

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disfavored, controversial, creators were live across the interwebs good.

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So by the way, this is going to be a dedicated show to Jessica Rose and a recent, I don't

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know if we call it a study, an analysis, it's going to be amazing.

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Later today, I'm going to be on Owen Shroy, with Owen Shroyer on info was at five o'clock

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this afternoon.

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And I'm going to get in the car and I'm going to do a car vlog at some point throughout

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the day to talk about big Fanny Willis, I was going to say get into spanking, but she didn't

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get a spanking.

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She got her Fanny spared.

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So we're going to open it.

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It's going to be some fun stuff.

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We're going to get back to you know, the politics and law and whatnot, but today we're

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going to talk about my old cases of myocarditis, we're going to talk about the adverse events

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that apparently don't exist and you got a bunch of, I'm not giving them an ounce more

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attention than they deserve, they deserve none and therefore they get none.

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But you got people out there denying reality.

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And what is it?

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Liars figure and figures lie, I forget what the exact expression is.

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And now you may remember Jessica Rose from such podcasts as ours multiple times now.

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She's amazing.

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A scientist, I'll let her explain herself her credentials.

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And as she does that, I'm going to go swap with the thumbnail so that we can not have

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my default thumbnail, whatever, vivabornslaw.locals.com and we're also on rumble, we're not on Twitter

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and we're not on YouTube.

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So I'm going to bring Jessica in, Jess, ready, three, two, one, madam, how goes the battle?

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It's all right.

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How's the, how's the battle with you?

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I am frustrated.

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I'm cranky.

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I don't want to say that I'm getting more cynical than me that I need to be.

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I think I'm just getting realistic about life and stuff like that, but Jess, while I go

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check the audio levels in the various communities, for those who might not know who you are,

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you credentials, what you do, what you're doing and where we are going today with this

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discussion.

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Well, I'm a girl who's got a safety pin holding her clothes together today.

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So besides that, I have some background in immunology, applied mathematics, computational

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biology and molecular biology and also biochemistry.

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So it gives me kind of a unique ability to handle and process a lot of the things that

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have been going on in the last four years, actually study the immunology of viruses.

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So it's even more perfect.

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Never looked into coronaviruses before now, but so yeah, the last four years I've used

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about something from each of those degrees to put together the story of what I think

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is happening here, but I've been doing it from the point of view of analyzing pharmacovigilance

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data because I like the idea of coming.

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First of all, I like the idea of using data that represents people because ultimately

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that's I just want to help people.

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That's all I've ever wanted to do with my abilities, but yeah, it's a hard core way

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to ask people questions because it's government data and it's really easy to see what's going

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on there and all you really have to do is give it back to them and say, explain.

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I just want to back it up a little bit so that people who want to attack credentials will

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either have the fodder or lose the ammunition.

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You have a PhD in what now I forget?

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Computational biology.

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I didn't forget because I would never remember those words.

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How many years have you been doing research and studying for?

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It's going to be like decades now.

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Yeah.

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Well, right out of high school, I graduated high school and went to university and I basically

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never left.

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So I've been a full-time student in my life and I'm all pretty.

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Computational biology.

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Yeah.

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Okay, so now, I know we've discussed this, we're not going to go into the depth that

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we did the first time we did a live stream, but what does that mean?

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So basically it's a way to study biological systems using math.

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In the context that I worked, well, actually, this is like my first experience with a big

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data set.

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It was still about viruses, but it wasn't really about the thing that I was doing in

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my immunology degree program, which was also interdisciplinary.

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I actually had an advisor in the applied mathematics field and the immunology field because neither

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one knew anything about the other field.

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So I'm a mathematical biologist.

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That's a much better way to say it.

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We're really rare breed.

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The mathematicians think the biologists are BS.

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The biologists think the mathematicians are wrong, so it's like to be doing both this

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kind of a weird thing.

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Alex Washington knows this, but so yeah, I study biology using math.

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All right, and you have some, you've dabbled in immunology as well.

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Well, immunology is like the core, I guess, of what I've done.

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It was the second degree that I did, the Bachelor of Science with the combo math thing

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because it was just such a serious program.

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It was three years.

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It was really difficult.

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I was in the level three lab.

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I was studying HIV immunopathogenesis, which is basically the immune response to HIV in

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chronically infected people.

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So I was doing work in the level three lab, analyzing HIV-infected blood, and I was

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also doing the modeling on the math side.

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So I built a mathematical model to try and demonstrate that people could go on interruptions.

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Structured treatment interruptions is what it's called because for those of you who don't

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know, if you get diagnosed with HIV, oftentimes your practitioner will put you on antiretroviral

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drugs to keep your viral load down, and those things are really toxic.

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They've probably gotten better over the years, but back when I was doing this, they were

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very bad.

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I mean, basically you probably didn't feel bad before you take the drugs and then you

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take them and you feel like crap.

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So my idea was to try and help people get off those things for like three week periods.

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So I was trying to show that using the lab data and also the math model.

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And I didn't succeed, which is not a total loss because I learned an enormous amount.

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And about half of the community kind of said, you never could succeed because you can't

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do it, but then there's the other half of us who really believe in the idea.

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And I'm still one of those people.

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I just, I think maybe, you know, maybe if I dedicated my entire life to that, you know,

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keeping that project going, I probably could have done something better, but you know,

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that was my project.

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So yeah, I don't even remember what I was saying, but well, no, that was just so that

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people understand the depth and specialty or specialization of your knowledge, mathematical

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biologists.

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Okay, I like that term.

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And now, just so we, when we originally did the first stream, we talked about a paper

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that you had written with Peter McCullough that was ready for publication.

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I think it was back in how far back 2021?

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Yeah.

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And it was, it was withdrawn now get you to contextualize that for us.

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What happened?

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What you did at the time and why the paper was, was pulled.

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Okay.

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I'll tell you as much as I know.

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And I mean, I mean that because I don't know much because there's not much to know.

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I, it was the third paper that I wrote on the subject of, uh, theirs data.

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So the first paper I wrote was general, it was about like, um, a subtypes of adverse

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event, uh, adverse events and bears like that fit into the realm of cardiovascular neurological

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immunological, this kind of thing.

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The second one I was, uh, examining the pharmacovigilance is of this database, like how well is

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it working?

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Um, and this third one was a more focused approach on what was going on with myocarditis

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reports, because there was, you know, there was talk on the town, the research community

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town that this thing was bad for kids, uh, the, the shots, I mean, we're, we're causing

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problems in, in children, young children, like we're talking 12 to 15.

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Um, and primarily boys, if I may, if I may stop you there, when you say talk within the

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community, there was public talk or people, you know, coming out with certain, uh, suggestions

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or observations, is this like chatter within that's not public among scientists?

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Say like, we're, we're talking among ourselves, but we don't dare go public with this.

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Pretty much like medical doctors who were, who are looking who are doing certain tests

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who are checking troponin levels who are, you know what I mean?

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It's like, nobody, not many of, I don't, I don't exactly know the ratio, but I would

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say not very many general practitioners, uh, we're, we're doing these tests, especially

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in the context of someone coming to their office, suspecting that it was the shots,

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because the first thing I hear that the doctors would say is, no, it can't be the shots.

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We have to look for something else here, some other clubs.

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So it's, it's just something, I mean, I, I, I, I'd have to think harder about how it

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was identified, but, uh, it came around the most important thing, the nurses and the doctors.

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You know, like when, if there's an issue with women giving birth to babies, the best

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people to talk to or the new ones, you know, it was that kind of phenomenon.

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There's a group of people that you can always go to who have the dark, you know what I mean?

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They're hanging around the water cooler, they know everything.

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So this, this was that phenomenon in the medical community.

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So, so I dove into veers and I, I pulled out the myocarditis reports.

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This wasn't pericarditis or myocarditis, and that also has a story because the reports

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that were being done out of the CDC on this issue, once it became not, uh, hideable,

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was, I mean, it's just crazy how many things they did to, in my opinion, to hide the problem.

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They used the Medricode, which is the diagnostic term that you use to enter into veers, myocarditis,

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which is not the same as myocarditis.

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If a doctor gives you a diagnosis of myocarditis and that's entered into veers as myocarditis

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and you seek the word out in a query, myocarditis, you're going to miss all of those other.

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If you get your drift, absolutely, well, and it's sort of like, uh, it's, it's spreading

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them out as opposed to contemplating them.

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And that's one of the, the biggest things in veers that I never get to talk about enough,

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which I did cover in my second paper.

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It's a phenomenon.

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I think last count, there were like about 10 ways to describe an abortion, like Medricode

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terms.

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So instead of just, just write the bloody word miscarriage and, or abortion, spontaneous,

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or something like that, there's like 10 different ways to say it's, no, and it's like, it could

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be like, whereas you would lump them all together in an ordinary, honest world, they go myocarditis,

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myocarditis, post pseudo myocarditis, myocarditis, and so that it's like, oh, well, we only

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have five cases of myocarditis, but there are 50 of this there, amazing, corrupt, disgusting,

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unless there's a scientific medical need to make that sub-specification and just remind

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everybody or refresh everybody's memory, myocarditis is information of the heart, pericarditis

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is information of the sack around the heart, and, um, uh, what was the other one you just

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set up through?

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Sorry, so what's that?

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That's, uh, bacterial, which is like the lining of the, the inside, uh, area of the heart.

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So that's, that's different, that's caused by bacteria.

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So yeah, peri is the perimeter, myo is, is refers to the muscili, the myocytes are the

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things that help the heart beat, and the endo is like inside, you know, the prefix is refers

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to the inside.

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So, one more look, like I, I made it easy on myself, well, not really, I, so there's

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a presentation given by, it was either Jonsu or Shimabukuro, uh, from the CDC presenting

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this data, and there's a slide that I will send to you so you can put it up here that

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shows the list, it's a long list of different ways to find myocarditis, and they're all

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medically valid, just like you said, but when you're collecting data and you're counting

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myocarditis cases, it's very important to, you know, uh, define these things properly

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and categorize them properly, like put them all under another subheading called myocarditis

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and make that the preferred term, you know what I mean, so how about, how about heart

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issues at large, and then if you want to go subdivide within, uh, they do do that, but

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the thing is the preferred term is kind of like the, it's not the largest category, but

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it's one of the larger ones, and it's the one that they use in there, so it's like,

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anyway, they know these issues, they create more by doing this, by diversifying the diagnosis,

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and they make, uh, data analytics really hard, but I, I, I push back, and I did a search for

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all of these things, and I, I've written a number of articles on that, but for the purposes

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of this paper that I penned in, I think it was May 2021, I finished writing it, um,

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no, no, May 2021 is roughly, I mean, give or take, they started rolling out the jab,

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yeah, what was it like, late December 2020, January 2021? Yeah, so it was early, like my,

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uh, was it 2020? Yeah, it was, um, so it was early, I did all this work, um, we had enough

18:55.520 --> 19:02.880
of a signal and various for this stuff in January, uh, 2021, so it was, it was easy to kind of see,

19:02.880 --> 19:09.600
and it was easy to write a paper about it, so, um, basically I counted the number of myocarditis

19:09.600 --> 19:18.000
reports, and I plotted them, I plotted the age of the people who had diagnosis against, you know,

19:18.000 --> 19:24.080
the number of cases against dose, because I wanted to see like, was there a difference in age,

19:24.080 --> 19:28.560
like the distribution of the reports by age, and was there a difference in the pattern according

19:28.560 --> 19:36.240
to dose? So, um, it was, it was like a sore thumb, it just stuck out like this, like crazy,

19:36.960 --> 19:43.440
the preponderance of reporting was being done in 12 year olds, and when you also, um, um,

19:44.160 --> 19:49.200
checked by sex, you would see that it was mostly boys, like 80 something percent of the

19:49.280 --> 19:54.880
reports were coming from little boys, so it was really obvious that something unique was going on

19:54.880 --> 20:01.920
here, and these increased, the reporting increased fourfold, it was, it was something more back

20:01.920 --> 20:08.560
than it was like fivefold, it remains at about fourfold higher, following those two, so there

20:08.560 --> 20:15.360
was this like double whammy going on here in young boys, and so once, once this kind of got

20:15.440 --> 20:21.280
got out, like they, they knew, because they were presenting data on it, like I said, John Sue and

20:21.280 --> 20:28.480
Shima Bukuro, you can download their, their presentations on, you just have to type in their

20:28.480 --> 20:35.360
names and, and go CDC and myocarditis, and you'll find their presentations, and, and they reported

20:35.360 --> 20:42.640
on this, and they reported on the higher rate in young boys, it's right there, but what they did

20:42.640 --> 20:48.320
was what they have been consistently doing with this DNA contamination story, they're

20:48.320 --> 20:54.720
minimizing it, and they're saying that it doesn't pose a risk, because myocarditis is, is mild

20:54.720 --> 20:59.440
and transient, and it's neither of these things. Look, two, two questions, actually, someone in

20:59.440 --> 21:04.640
our locals community wants you to define dose. The report is per dose, correct?

21:06.000 --> 21:11.760
Yeah, so. The various reports are per dose. Yeah, so each, uh, there's like 52 variables that you

21:11.840 --> 21:18.320
enter when, uh, you combine the three files and bears, so one of them is the, uh, Vax dose series,

21:18.320 --> 21:22.880
so you can see if it's someone's first dose or their second dose or their third dose, etc. So,

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all the information is there. It's like, bears is very well, um, the fields are very well

21:29.600 --> 21:36.800
occupied, and there's like millions of reports now in the context of, uh, this COVID shit, so it's,

21:36.800 --> 21:45.520
it's like really, there's a lot of data. Um, so yeah, uh, I wrote the paper and I thought, well,

21:45.520 --> 21:50.720
what, what the hell do I know about hearts? So I, I thought I knew about Peter McCullough.

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I can't, uh, I think I asked someone for his email. I wrote him an email. I said, hey, I wrote

21:56.400 --> 22:02.480
this paper. I would be nice to have a cardiologist on as an, as a co-author, uh, because, you know,

22:02.560 --> 22:08.640
I need confirmation that I'm correct and blah, blah. And so he, he was, yeah, he said yes right

22:08.640 --> 22:15.040
away and he, he wrote, um, some extra sections on the heart stuff and gave some clinical stuff,

22:15.040 --> 22:20.160
which is really important because, you know, it validates, uh, what you're seeing in the data,

22:20.160 --> 22:27.280
because there's also has information on test measurements, like troponins and, uh, cardiac

22:27.280 --> 22:34.000
MRI data. It's not like enough to be able to draw a conclusion, but it can corroborate what

22:34.000 --> 22:41.680
someone's seeing clinically. So it was important. Um, so yeah, we, we, we got it finished. Uh,

22:41.680 --> 22:46.880
and we, we submitted it, it got accepted. Sorry. Do you want to? Yeah, I wanted to ask one more

22:46.880 --> 22:53.600
question first. Um, when they say mild and transient in terms of minimizing the myocarditis,

22:53.600 --> 23:00.720
have you done any analysis or study or, or number crunching as to prognosis survival rate after

23:00.720 --> 23:05.200
a diagnosis with myocarditis? Cause there's a, I don't believe them, but you know, you see these

23:05.200 --> 23:10.720
memes or these posts that, you know, 50% of people with my, diagnosed with myocarditis are dead after

23:10.720 --> 23:16.720
five years. I don't believe it's quite that, uh, you know, bad, but have you, have you done any

23:16.720 --> 23:22.000
number crunching on survival rate and prognosis for people diagnosed with myocarditis?

23:22.080 --> 23:28.640
No, I haven't, but, uh, I've asked that same question to cardiologists, including Peter McCullough,

23:28.640 --> 23:35.760
and, uh, it's, I'm not sure I would say five, but it's, I've heard 10 years. So here's the thing

23:35.760 --> 23:43.760
about it. I mean, I think of it like this. If, if somebody's got a fibrotic scarring from a myocardial

23:43.760 --> 23:49.760
insult or whatever reason, it's probably the immune system attacking, you know, the spike

23:49.760 --> 23:55.600
protein, which is embedded in, in the, in the cells of the myocardium. It's probably that.

23:56.560 --> 24:05.360
That's the itis, the inflammation of, of this myocardium. Um, then that's the, the bad part about

24:05.360 --> 24:11.520
it. It's just like neurons. They're not replenishable cells. So once they're damaged, and, and the

24:11.520 --> 24:17.280
whole thing with the, these, these cardiomyocytes is that they're flexible. They, they're the ones,

24:17.280 --> 24:24.640
the muscle, the things that let the heart do. So if that gets replaced with scar tissue,

24:25.520 --> 24:32.400
the way that I, I analogize this as I compare it to like, uh, like a rubber band versus like,

24:32.400 --> 24:37.040
like a string, you know, it's like this has a lot of his. And if you replace that with,

24:37.840 --> 24:43.200
you know, something that doesn't have any give, and this thing is supposed to be like beating.

24:44.160 --> 24:48.480
No, I mean, just just the analogy is put a rubber band out in the sun for a year,

24:48.480 --> 24:52.320
and then see how rubber bandied is. And then is it going to, is it going to do what it's supposed

24:52.320 --> 24:56.880
to do as a band? It's going to, it's going to, a, it's not going to retract. And then it's also

24:56.880 --> 24:59.840
going to snap. I don't know if that's analogous to the heart, but the bottom line is, yeah,

24:59.840 --> 25:05.440
it's got to be flexible to, to do the pumping. And, and if it's not, okay. And, and so the,

25:05.440 --> 25:11.360
it's, I mean, oh my goodness. I'm still now thinking back, you can't really say because,

25:11.440 --> 25:18.000
like this is the tricky thing, but this is also why it's preposterous that once it was even like,

25:18.000 --> 25:22.960
there was even a notion of this happening in kids, there should have been a moratorium called,

25:22.960 --> 25:30.960
or, or at least some kind of temporary cessation, because you can't know, you can't really know,

25:30.960 --> 25:36.480
unless you like cut someone open what the magnitude of the scarring or the damage is.

25:36.960 --> 25:43.680
So if it's just minimal, maybe they're going to have a pretty normal life, but who freaking knows,

25:43.680 --> 25:51.360
we, we don't even know which people have maintained spike production protein, protein production,

25:51.360 --> 25:56.960
like we, we do know that it's a thing that spike protein production can be continuous.

25:57.840 --> 26:03.200
We do know that the, you know, anyway, so we have all these unanswered questions that,

26:03.280 --> 26:10.960
that lead to the, the, the end game for most people, the only thing most people care about

26:10.960 --> 26:18.240
is how they feel and their quality of life. So if that basically shortens your life or, or,

26:19.440 --> 26:25.280
like doesn't improve or reduces the quality of life, that's what they're going to care about.

26:25.280 --> 26:32.400
So it's like, basically what was being given to them could potentially do both of those things.

26:32.400 --> 26:36.960
And it's like, why the hell, you know what I mean, you are.

26:38.800 --> 26:43.200
I'm trying to find the tweet as we're talking. It's another guy, the, the writer for the daily,

26:43.200 --> 26:48.240
the daily show, just died of a heart attack. He also, you know, put out a post,

26:48.240 --> 26:52.480
get fucking vax, you fucking fuckers and one of those super posts. Yeah, yeah.

26:52.480 --> 26:57.760
And the, the idea is like, on the one hand, you know, I'm thinking people who are angry and stressed

26:57.760 --> 27:00.880
in general are probably going to be more likely to suffer heart attacks, especially if they're

27:00.880 --> 27:04.640
overweight. This guy looked like he might have been all three, but you're talking about

27:05.280 --> 27:10.000
messing with the heart. And then I had this discussion recently with people where one of the doctors

27:10.640 --> 27:15.680
after the Twitter space, if he's a real person, not even know if these people are bots,

27:15.680 --> 27:20.560
admits that he got myocarditis from his first Moderna shot, but that in his stress test,

27:20.560 --> 27:25.760
he registered 180 beats per minute with there was another term that he's using. And I'm like,

27:25.760 --> 27:29.920
you're not, you're not supposed to reach 100% capacity even during a stress test.

27:30.000 --> 27:34.160
What fucking damage has this guy done to his heart? And does he understand it,

27:34.160 --> 27:38.160
despite what he's continuing to promote, or does he convince himself, well, they said it's gone,

27:38.160 --> 27:42.880
so it's gone, and I'll find out in 10 years whether or not it's gone. I would love to know the stats,

27:42.880 --> 27:47.520
like, like meaningful study of the stats that people diagnosed with myocarditis prognosis.

27:47.520 --> 27:52.480
Okay, that answers the question is to, you know, mild, mild, and, you know, never mind,

27:52.480 --> 27:57.200
it's just a little myocarditis in developing kids and hearts. Okay, so you're, the signals are

27:57.200 --> 28:01.600
there. This is the other shocking thing everybody really has to appreciate. And actually, before I

28:01.600 --> 28:06.400
say that, there's a chat in rumble that says, you are one of the crowd's favorite guests on the

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channel, Jessica. Well, it's reasonable people who are smart and, and, and well researched and well,

28:14.560 --> 28:23.840
okay. So the signals are there as early as rollout in January 2021. And it's a game of the deny,

28:23.920 --> 28:29.760
admit, but minimize, admit, but normalize. Yes. Who are the two doctors? You mentioned their name,

28:29.760 --> 28:34.080
there was a Japanese one in there, I think, who are the two doctors talking about it at the time,

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or doing studies on it at the time? Oh, you mentioned it too earlier, and I didn't catch their names.

28:41.440 --> 28:46.800
Well, Peter, obviously, he's one of the only cardiologists I know. I'm trying to remember,

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I mean, a bunch of groups and like one of them is medical doctors. And it was just being talked

28:54.400 --> 29:00.000
about. I try to think about you mentioned two names who were looking at the, looking at the

29:00.000 --> 29:03.520
signals in January, they had talked about it or pointed out, okay, it doesn't matter. So

29:05.120 --> 29:10.000
it'll come out. So you're looking at this at the time, you collect and aggregate the data,

29:10.000 --> 29:15.520
there's it's not just notoriously difficult to use for a layperson. I think it's deliberately

29:15.520 --> 29:22.640
impossible to use. So you are gathering the data, you see these egregious, what we call signals,

29:22.640 --> 29:26.320
you want to get the confirmation of somebody else within, I'd say an equally open mind,

29:26.320 --> 29:30.240
someone else will call them a conspiracy theorist, you get Peter McCullough. Peter McCullough is a

29:30.240 --> 29:39.920
cardiologist, right? Okay. And so you can epidemiologist and I'm losing the term that he uses. Yeah,

29:40.000 --> 29:47.760
he's a cardiologist though. He's for decades, he's very, very well published. He's been editor

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of journals. I mean, the guy is like, he's like what everyone would want to be in the academic

29:53.440 --> 29:58.000
and the clinical world. He kind of reached the height of both of those things. So he's,

29:58.800 --> 30:04.160
yeah, he's a he's a big deal. Like he's what I love is, you know, when you have these discussions,

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people say, well, you're not an epidemiologist. So you shouldn't you don't get to have an opinion,

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a contrary opinion. If you agree with them, you could be a frickin veterinarian, like Albert

30:11.600 --> 30:16.240
Burla, and they'll agree with you. And then when you are the specialist, you are the epidemiologists,

30:16.240 --> 30:20.480
you are the cardiologist, like McCullough, like Malhotra. Well, then they say, well, you're a quack,

30:20.480 --> 30:25.280
so we disregard your opposition. Okay, so you get McCullough, he comes and he looks at your data,

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and what does he say? And how does it lead to the first paper being written and withdrawn?

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Okay, I already wrote it. So the body was there. So his job was easy. He had to come in with the

30:35.200 --> 30:40.000
like cardiology stuff, the clinical stuff that he'd been seeing in his practice,

30:40.000 --> 30:46.320
which is great. Because you know, he could confirm or deny what I was seeing in various from test

30:46.320 --> 30:52.560
measurement point of view, from his practice. So he already he already knew because he's one of

30:52.560 --> 30:58.400
the doctors who's a cardiologist who was seeing patients the whole time. He was seeing his regular

30:58.480 --> 31:05.840
patients and he was seeing COVID patients. So he, he had, he had his, you know, his fishing pole in

31:05.840 --> 31:14.160
the water. So he added sections, he helped with editing. He really, he helped with the whole thing,

31:14.160 --> 31:19.920
and then we decided he's the one who decided on the journal to submit to, because you know,

31:19.920 --> 31:27.120
he knows this stuff. And I'm still kind of a young scientist. So he basically was, you know,

31:27.120 --> 31:36.080
the, the take charge on the, on the submission, we paid the fee, well, he paid the fees. And we

31:36.720 --> 31:44.480
asked for color proofs because the figures would lose meaning without color. But it basically,

31:44.480 --> 31:52.400
we just got to the end. And then it got, it got, we published, it was up on PubMed as well,

31:52.400 --> 31:59.840
which is like, basically, that's it, you know, we, we were done. And then a few,

32:02.000 --> 32:07.440
a few days before we were supposed to get the, we were, we're waiting for the final proofs to be

32:07.440 --> 32:15.200
approved. And instead of getting the approval for the final proofs, we get an email. No, sorry.

32:16.160 --> 32:24.240
Back up, Jess. This was a long time ago. I got a message on my, you know, one of my, my followers

32:24.240 --> 32:31.360
was saying, Hey, how come the title of your paper on Elsevier has withdrawn next to the title? No,

32:31.360 --> 32:38.160
sorry, originally temporarily withdrawn. So I was like, what? And so I was like, you know,

32:38.160 --> 32:44.560
I, I wrote to Peter right away. And I said, did you do this? Like, did you ask for it to be withdrawn?

32:44.640 --> 32:51.040
Because it's, it's got this thing next to it. And so I, I, again, I'm a young scientist. I'm kind

32:51.040 --> 32:56.240
of like, yeah, I don't know if this is normal. So I wrote to everybody I knew, knew in the sphere,

32:56.240 --> 33:02.720
like the academic sphere. And I'm like, is this happening before? And Peter was, he was very sure

33:02.720 --> 33:08.240
right away. He's like, because he knows, right? He's in this world. He said, no, this is not normal.

33:08.880 --> 33:16.080
So he says right to them and asked them what's going on. So they didn't send us an email telling

33:16.080 --> 33:23.360
us that this was going to happen. We found out both by, by other people telling us. So I, I sent

33:23.360 --> 33:30.160
a polite email, you know, being Canadian. And I said, Hey, what's on the go? What's up with that?

33:30.240 --> 33:37.920
And so they wrote back pretty, no, it wasn't pretty fast. It was a few days later, I think.

33:38.720 --> 33:44.880
It might have been a week. Yeah, it was a few days later or something. It was a few days later.

33:44.880 --> 33:49.120
And they said, well, we're, we're reconsidering publishing your paper.

33:49.120 --> 33:55.200
Is your, there's a buzzword that people use. Was your paper peer reviewed? Yes, of course.

33:56.080 --> 34:04.320
Yeah. So I'm skeptical of peer review stuff, just seeing what has been withdrawn, retracted from

34:04.320 --> 34:10.000
the Lancet and other other publications. At that point, it's past peer review. So yeah, and that's

34:10.000 --> 34:15.920
the thing, right? So this is where you can start to see that something wasn't right here. Someone

34:15.920 --> 34:22.640
said, someone said, what the F pulled this shit, like, cut the, cut the feed. This is like the

34:22.720 --> 34:27.360
Eugene, Carol Anderson Cooper, cut the commercial and shut this, shut them up. Okay, amazing.

34:27.360 --> 34:32.080
Yeah, put the screen up with standing by with the dog with the TV. Yeah, it was,

34:32.880 --> 34:38.320
that really does seem like what happened. I'm a rational, logical, skeptical person. But

34:38.320 --> 34:42.720
if I was going to guess, I'd say that's exactly what happened. Now, I just want to click on one

34:42.720 --> 34:46.240
of these just to see what happens. I got to get to the window somewhere in the back.

34:46.560 --> 34:52.800
Due to the, due PubMed withdrawn, a report on myocarditis, it's on PubMed as a withdrawn

34:52.800 --> 34:58.480
article. So anyway, I wrote to them and they said, yeah, we're reconsidering. And I was like,

34:58.480 --> 35:07.840
um, huh? So I, I, I go to a national, whatever this one is, withdrawn. Yeah, who withdrew it?

35:09.600 --> 35:16.160
Listen, I'm getting to that. So I got the advice from Peter as to what to do, how to proceed,

35:16.160 --> 35:23.440
because he knows I don't. And he said, uh, I, I'm going to write them. And I'm, so we all got on

35:23.440 --> 35:28.880
the email then we were all ready on the email. I was just, you see it here. And he, he basically

35:28.880 --> 35:34.640
just said reinstate the paper or we're going to soon because we, we'd already paid the thing. And,

35:34.640 --> 35:38.960
and they said they were going to give our, the money back. And I think that they did. But the,

35:38.960 --> 35:45.440
the big question was, um, you know, what, why did this happen? And so we heard from them a day

35:45.440 --> 35:50.480
later. I should want to point one thing out here. Sue, not just for your fees. This article has

35:50.480 --> 35:55.680
been withdrawn at the request of the authors and or editors. Well, it's certainly, it certainly

35:55.680 --> 36:01.600
wasn't withdrawn at the request of the authors. And that is, um, you know, either false light or

36:01.600 --> 36:08.080
defamatory. Yeah, it's deceptive. And so like, um, they, they came back to us pretty fast after

36:08.080 --> 36:12.080
that. And they had a definitive answer. They said, we're not going to publish your paper. We've

36:12.160 --> 36:19.360
decided, um, you know, it's, it's within our guidelines at any point during the procedure to,

36:19.360 --> 36:25.040
to not continue. And they were correct. But, um, and, and I guess they gave back Peter's money

36:25.040 --> 36:33.360
and stuff. But it's like, why? And, and I, I, I said what, on, on what, um, premise, like,

36:33.360 --> 36:37.440
why are you doing this? There's, there's nothing wrong with the science. You have, you haven't told

36:37.520 --> 36:42.560
us there's a problem with the data, with the conclusion, with the work, with, with, there's a

36:42.560 --> 36:46.560
problem with the conclusion. It's, it's justified by the data. I mean, that's, that's the, that's

36:46.560 --> 36:52.800
the problem. Yeah. So we never got an answer. And it just got left hanging and even retraction

36:52.800 --> 36:58.560
watch contacted me and they're like, what was up? And I told them, and they contacted the editor,

36:58.560 --> 37:04.160
and they didn't get any answer from them. So a lot of people in, in, in the world were watching

37:04.160 --> 37:11.200
this happen. And nobody got an answer from them. Nobody. Nobody. So it's, it's exactly what you

37:11.200 --> 37:17.520
just said. It ended up looking so like me, because if you're an academic and you have a retraction

37:17.520 --> 37:23.760
on your publication list, it's really bad for you. Really. It's like a community notes on Twitter

37:23.760 --> 37:27.120
until you realize it's all political bullshit. And the only people who care about it are the

37:27.120 --> 37:31.840
ones who want to use it against you. But now we know that. But like, historically, it's like,

37:31.920 --> 37:40.400
it's this like scarlet letter. And it's bad. It's not, it's not seen as a, as a thing. Like,

37:40.400 --> 37:46.480
let's just say things were kind of normal. And I was trying to get another postdoc. And I had

37:46.480 --> 37:52.560
this retraction on my, on my resume. You know, it would probably prevent me from getting positions

37:52.560 --> 37:58.720
or it could possibly that that's the thing. And as you pointed out, the only thing that you get

37:58.800 --> 38:04.480
now instead of any text at all is this stupid message that we had nothing to do with.

38:06.640 --> 38:12.000
Well, I definitely had nothing to do with it. Like, we just, it's, it's not.

38:12.960 --> 38:19.040
It's to suggest it was the authors is overtly misleading, deceptive and dishonest. Okay. So

38:20.000 --> 38:24.720
it gets canceled. And then what's it's sitting on a shelf and the study that we're going to look

38:24.720 --> 38:30.160
at in a second basically is up to date analysis of the data from then to now.

38:30.160 --> 38:37.600
Yes. Yeah. So I got really upset and I, and I was told, you know, Peter said, yeah, we're going to,

38:37.600 --> 38:46.960
we're going to, you know, sue them and everybody got busy. And it just kind of petered out. And I,

38:47.840 --> 38:54.240
I'm not, you know, I'm not one of these people that that knows anything about suing anyone.

38:54.240 --> 39:00.560
So I can tell you better off avoiding it at all costs, even if you have go and get caught up

39:00.560 --> 39:05.360
in litigation, have a bias court dismiss it, then not only were you withdrawn, it was ratified by

39:05.360 --> 39:11.040
the court and then look at you. Exactly. So that might be what happened. So I, I just, you know,

39:11.040 --> 39:16.080
whatever, it's, it's fine. And I was upset about it for a long time. And then I don't know what

39:16.080 --> 39:23.040
happened one day. It literally was one day. I was like, screw this. I'm going to, I'm going to

39:23.040 --> 39:30.480
update the myocarditis paper and like frickin resubmit it. So that's why I did. I took the idea,

39:30.480 --> 39:37.520
the body, I updated it and it looked even worse. And I, but when you say it looked even worse,

39:37.520 --> 39:42.560
you mean the data and the conclusions? Yeah. So I had way more data. Think about this. I have like

39:42.560 --> 39:51.360
two years more data. So a lot more data points. It not, not only were the original points

39:52.320 --> 39:57.840
more solid as a rock, but I had more data to draw conclusions about what was going on in terms of

39:57.840 --> 40:04.240
severity. So that's the really important thing about the newly updated version that's now been

40:04.240 --> 40:12.480
really published is that we, we show not only that this happens in kids, it happens after those two,

40:12.480 --> 40:20.240
but it's leading to hospitalization in 76% of the time and also to death. So it's, it's definitely

40:20.320 --> 40:27.280
not mild and or transient. So that's what I would say is like the most important thing about

40:28.400 --> 40:34.480
this new paper. It's unfortunate that it came two years too late and.

40:35.120 --> 40:40.000
Well, say, I was going to say unfortunate, but almost necessary because, you know, other than

40:41.440 --> 40:46.720
the passage of time and more data, what's also clear is that public opinion or the,

40:47.440 --> 40:51.680
the, what's the word when you get chastised into not talking the stigma about talking about it

40:51.680 --> 40:56.640
is certainly gone because it's undeniable now. So it's almost like the, the, the scientific

40:56.640 --> 41:01.280
environment is more amenable to even having the discussion because it's so bloody in your face

41:01.280 --> 41:05.760
and undeniable. Whereas two years ago, it was shut up, continue with the rollout because we got

41:05.760 --> 41:10.800
six billion of these to administer. So the culture has changed and we're going to get into the data.

41:10.800 --> 41:14.960
So this one is peer reviewed or re-peer reviewed or peer re-reviewed?

41:15.520 --> 41:20.000
Oh, man. This, this is peer reviewed multiple, multiple, multiple times because

41:21.760 --> 41:28.320
the, the number of times that it got rejected after going through multiple rounds of peer review

41:28.320 --> 41:36.160
with different journals, we're documenting that. We've documented that. So Nick Holster is,

41:36.160 --> 41:44.080
is an additional author on this paper and he, he really is to, to, he's the one responsible

41:44.080 --> 41:50.080
for this thing getting published because he's, he was very, very good at getting this through

41:51.680 --> 41:55.200
and making sure, you know, the revisions were made properly blah, blah, blah.

41:56.320 --> 42:02.080
So this got, this paper has been going through peer review in different journals and getting

42:02.080 --> 42:10.480
rejected at the last minute for months. So this final paper that accepted it is, is one of many.

42:11.040 --> 42:17.600
So it's been through the washer. So anyone who want to say, you know, it's not peer reviewed,

42:17.600 --> 42:22.240
it is, yeah, it's peer reviewed, a lot of years.

42:23.200 --> 42:30.240
And with, with, with that thorough introduction, and if I decide to snip and clip this portion where

42:30.320 --> 42:36.480
you're going to explain the data to YouTube and they can, you know, accept my fingers if they

42:36.480 --> 42:41.840
decide to pull it down, let me pull up the, the, the study and we're going to go through,

42:42.640 --> 42:47.200
why do I see court filings here? Because that's, okay, fine. Um, Jess, I'm going to pull it up.

42:47.200 --> 42:51.360
I'll go to the top and you'll, you'll walk us through the findings. Maybe I don't need it

42:51.360 --> 42:57.440
up here the entire time to, um, to go through the findings, but I'll just read the, well, what

42:57.440 --> 43:01.280
do we want to do background? Let's go to the figures. Okay. Cause that's,

43:01.280 --> 43:06.080
that's what most people are going to, uh, respond to anyway. Tell me where that is.

43:06.800 --> 43:17.040
Oh, just keep going results. One. So yeah, go to figure one. Figure one is this. Yes. So,

43:18.880 --> 43:25.040
okay. I can, I can. This is the story of the adverse events in theirs in total over the last

43:25.120 --> 43:32.880
30 years. So this is basically a comparison of the total number of adverse event reports

43:32.880 --> 43:39.360
filed to theirs. This is all adverse events, not just mile card days for all vaccines combined

43:40.400 --> 43:48.720
until 2021 when I pulled out only the adverse event reports in the context of the COVID products.

43:49.440 --> 43:54.720
So this, this chart is really important to set the stage because it's, you know,

43:55.280 --> 44:01.200
they're a lot prior to all of the gray is all vaccines ever, which includes the annual flu

44:01.200 --> 44:07.600
shot. Yes. Okay. And then the purple is only the COVID shots and only the adverse events reported

44:07.600 --> 44:12.800
in conjunction with the COVID shots. Yes. Okay. And now the question that people ask,

44:12.880 --> 44:20.800
these are the raw numbers at the total, not a percentage of level. Okay. No, these are the,

44:20.800 --> 44:27.840
the, the absolute counts. So, and these are people. So I don't count, I know it says adverse event

44:27.840 --> 44:35.920
on the left, uh, axis, but this is the adverse event in the context of people. So I count the

44:35.920 --> 44:42.400
number of people who actually reported a multitude of adverse events. If I was going to talk about

44:42.400 --> 44:49.920
the number of adverse events per person, this would be like in the five million range. So it's,

44:49.920 --> 44:56.320
this, this is an important point because bears is a, um, it's a database for real people who are

44:56.320 --> 45:02.960
suffering sometimes very severe side effects to report their injuries. So each one of these

45:02.960 --> 45:09.680
points is a person. Let me ask, let me ask another question. The, and just for comparison purposes

45:09.760 --> 45:16.240
so we can digest it internally. In 2020, all vaccines, that's the number in gray. How many

45:16.240 --> 45:21.520
doses of all vaccines were administered in 2020 prior to the COVID jab? Do you know that

45:21.520 --> 45:27.040
offhand or is that, is that, uh, I do know, but I don't know it offhand. I mean, it's, it's,

45:27.040 --> 45:33.440
it's hundreds of millions because it's like, it's hundreds of millions of flu shots alone.

45:33.440 --> 45:37.600
Well, that's, that's where, that's where I want to like it. Just, is it, is it 10 times less? Is

45:37.680 --> 45:45.280
it a hundred times less than the COVID jab in 2021? The total number of shots, I don't know,

45:45.280 --> 45:53.840
but the, the flu, a, the comparison between 2020 and 2021 is that there were 2.3 times as many

45:53.840 --> 46:01.520
COVID shots given out as the flu shots. Okay. So you would go ahead. No, I'm, then that answers

46:01.520 --> 46:06.000
the question. Let's just say two and a half times more COVID jabs than flu shots. Then you would

46:06.000 --> 46:10.560
expect that this little purple bar at most, at most to be two and a half of the little gray bars.

46:10.560 --> 46:17.040
That's right. And the reason why that is is because here, here's the thing people. If there wasn't

46:17.040 --> 46:24.320
something different, inherently different in terms of adverse events between these products,

46:24.320 --> 46:31.040
and let's just say the flu products, and there was a 2.3, 2, 2.3 times as many shots given out

46:31.040 --> 46:37.200
for the COVID products, then we would expect 2.3 times or 2.5 times whatever as many adverse

46:37.200 --> 46:43.760
events because it would be proportional because the, the so-called damages would be equatable.

46:44.640 --> 46:50.160
So this is a very clear, just this one thing is a very clear indication that there's something

46:50.800 --> 46:57.440
different about these things. So a lot of people are saying, no, it's just because they gave out

46:57.520 --> 47:06.800
more shots. No, no, no, no. I, it's not in this paper, but I've, I've broken that down using napkin

47:06.800 --> 47:13.440
math. It's absolutely false. I'm looking now and just, I don't know, it's from the CDC. So 2019 to

47:13.440 --> 47:22.400
2020, 175 million doses of the flu shot. Yep. And so let's just say COVID shot 2021 number.

47:22.400 --> 47:27.120
Oh, I'll have to go with the US, I guess. I'll find it out. But the bottom line is it's, it's

47:27.120 --> 47:30.800
simply, you know, if anybody wants to try to write it off and say, well, they administered

47:30.800 --> 47:37.600
a hundred times more jabs than flu shots, it's simply false. It is. Or it's, even though they

47:37.600 --> 47:44.000
did administer more COVID shots, it's not proportional when we look at the data, like the, the number of

47:44.000 --> 47:51.360
reports, like, even if this was 20 times, maybe, you know, I'd be like, meh, no, okay, so maybe

47:51.360 --> 47:56.560
there's something, some extra immunological component here, but this is really, really

47:56.560 --> 48:00.240
different. This is, this is more than an anomaly. Like,

48:02.080 --> 48:09.120
Okay, no, no, it's even if they administered three times as many COVID shots, then it would be 300,000,

48:09.120 --> 48:13.520
it would be a proportion to claim it's exceedingly disproportionate to the amount of doses administered.

48:13.520 --> 48:17.200
Okay. And that is, that is conclusive and indisputable.

48:17.760 --> 48:25.520
Yes. And, and just to add to that, I look deeper into the range of adverse events that are being

48:25.520 --> 48:30.720
reported in the context of the flu shots within a given timeframe, and the range of adverse event

48:30.720 --> 48:39.280
reports for the COVID shots and the same number of days timeframe. And there's a huge discrepancy

48:39.280 --> 48:46.560
between the, the number of types. I'm talking about like the diagnoses associated with the shots

48:46.640 --> 48:53.680
being given in the context of flu being much narrower than for the COVID shots. So this is very

48:53.680 --> 49:01.840
telling it's, it's literally translated something about these is causing more systemic damage. And

49:01.840 --> 49:07.520
it's interesting because that's what we're hearing clinically as well. It's like we're from Bell's

49:07.520 --> 49:16.560
palsy to, to death. I mean, it's, it's there's this huge range of, of clinical pathology

49:16.560 --> 49:23.280
associated with these shots. And it's irrespective of age, it's irrespective of, of, well, maybe not

49:23.280 --> 49:32.160
irrespective of a precondition, but it's, it's, it's certainly you're not, you're not immune because

49:32.160 --> 49:37.920
you're young, for example, from suffering adverse events. I'm sorry, I just got very

49:37.920 --> 49:42.000
frustrated because I now I'm looking up in the CDC, you know, let me bring this up. Just

49:42.960 --> 49:48.320
have to toggle a couple of screens here. Stop screen. Just, I mean, just because I remember

49:48.320 --> 49:52.240
getting this number to figure out what the proportion of claims to doses administered was,

49:52.800 --> 49:58.960
the total number of doses administered as of, as of like beginning to today, 676 million to,

49:59.040 --> 50:03.840
so you accurate, you just go break that down. That's the amount of doses given and claims made

50:03.840 --> 50:07.840
at the various and for whatever you think that they're worth, break it down and you can get your

50:07.840 --> 50:15.360
claim per dose. And I forget what it was now, but it was, it was significant. That's 676 million

50:15.360 --> 50:21.920
over 2021, 2022, 2023, 2024. So let's just say 200 million. And you're, and you're, you're

50:21.920 --> 50:26.640
almost, let's say double, double the flu shot. And so try to make sense of that graph, which

50:26.640 --> 50:32.480
we're going to go back to right now. Okay. Sorry, please carry on to give myself a heart attack

50:32.480 --> 50:34.960
here. Get this back.

50:36.960 --> 50:44.160
So the next one over B is this exact same concept, except for myocarditis reports.

50:44.800 --> 50:50.320
So it's, it's the exact same picture. And you kind of expect it to be because within that

50:51.280 --> 50:57.280
total number of adverse events, you're going to have, you know, subgroups of cardiovascular

50:57.280 --> 51:02.480
reports. And within that, you're going to have myocarditis reports. And in each case,

51:03.040 --> 51:11.200
any query that you do for any adverse event by metric code, it looks like this. It's, it's not

51:11.200 --> 51:18.880
only for myocarditis. So it's, it's not something you can look away from. It's, it's

51:19.680 --> 51:20.640
a question.

51:20.640 --> 51:27.280
Question. I mean, if this is, this is wildly disproportionate, even if we, you know, even

51:27.280 --> 51:30.800
if we're operating on the two and a half times as many doses of the jab administered as the flu,

51:31.520 --> 51:35.200
I don't know, as a matter of policy, do they administer to the flu? Do they administer the

51:35.200 --> 51:39.440
flu shot to children six months and up? Or did they prior to 2021?

51:39.440 --> 51:44.480
I don't know. I would say no, but I really don't know. I know I was nothing about

51:44.560 --> 51:47.040
to my destination anymore.

51:47.600 --> 51:51.920
No, I mean, it's, but the bottom line is I'm just trying to figure out, you know,

51:51.920 --> 51:55.520
because you're going to break this down by age bracket and it's going to make a lot more sense

51:55.520 --> 51:59.760
the number of claims you're getting. And the question I'm asking is the majority of people

51:59.760 --> 52:03.680
who are getting flu shots, yearly flu shots are older people. Yeah. So yeah.

52:03.680 --> 52:07.200
And it's certainly not, it's certainly not been added to the, I know, as far as I know,

52:07.200 --> 52:10.880
the flu shot has not been added to the children's vaccination schedule.

52:10.960 --> 52:15.360
So when we see this number, and you're going to probably tell us that this number in the red 24

52:15.360 --> 52:24.640
or 14 is disproportionately within a younger demographic, this wildly disproportionate graph

52:24.640 --> 52:32.000
makes all the more sense, assuming that the flu shot is not forcibly administered to young boys,

52:32.000 --> 52:36.560
which I don't think it is, certainly not by vaccine passports and requirements to enter a

52:36.560 --> 52:42.240
library. So that'll explain why this graph is even more disproportionate than the overall

52:42.240 --> 52:48.080
VAERS reports year over year 2020 to 2021. Okay. Please continue, Jess.

52:48.800 --> 52:56.240
Okay. So go to the C. So what I did, just as an exploratory thing.

52:58.000 --> 53:01.680
And because we had so much data when I, when I was looking at this again,

53:02.480 --> 53:08.640
I downloaded the our world and data data for the number of doses administered in the states.

53:09.920 --> 53:16.800
So this is again, you know, it's their data. It's not mine. And then I pulled out the myocarditis

53:16.800 --> 53:23.360
cases, you know, that have occurred regardless of age for, you know, since the beginning of the

53:23.360 --> 53:27.760
rollout, which you can see by indicated by the purple line, you can kind of see when it happened

53:27.840 --> 53:36.000
anyway. It's like when the blue line starts to go up. And you can see it's, it's, I laughed when,

53:36.000 --> 53:41.280
when I saw that I see these are coming from two different places. Okay, this is O with data

53:41.280 --> 53:47.280
and VAERS data. And I superimposed them according to the dates of the data points.

53:47.840 --> 53:55.680
And this is what popped out. So it couldn't be more indicative that the myocarditis in red

53:56.560 --> 54:04.400
is tracing the new injections in blue. By what? By two weeks? It's a book. Yeah, I think so.

54:04.400 --> 54:08.960
It was about it was about 10 days, I think. And I got, I'm going to stop you there also,

54:08.960 --> 54:13.760
because I'm going to, I'll, I'll steal man what I know the liars would say. They're going to say

54:14.560 --> 54:20.880
COVID, myocarditis infection, myocarditis from viral infection is a, you're more likely to get it

54:20.880 --> 54:26.000
from COVID than the jab. If that were true, Jessica and you'll correct me. Well, first of all,

54:26.000 --> 54:33.440
you might not see it on the VAERS system. Although maybe people are reporting COVID-induced myocarditis

54:33.440 --> 54:40.320
as an adverse event from the vaccine because they can't distinguish the two. But we don't see

54:40.320 --> 54:44.800
myocarditis being, well, you would not see myocarditis being reported in VAERS until the shots

54:44.800 --> 54:48.640
are being administered. Yeah. Okay, fine. I mean, that's, that's a logical and that was a stupid

54:49.120 --> 54:56.000
thing, right? And so it's not just that though, it's the fact that it, they, you know, they kind of

54:56.000 --> 55:02.400
peak at the same place, just a little, little bit after, and then they trough, and then they peak

55:02.400 --> 55:10.160
again together, and then they trough. And this is one of the criteria that you should satisfy in

55:10.160 --> 55:16.000
the Bradford Hill criteria to provide evidence of causation. It's called reversibility. If you

55:16.000 --> 55:23.600
take away the drug, which is the blue, if the drug is likely causing the myocarditis,

55:24.320 --> 55:32.320
then the, or let's just say the symptom, then the symptom will go away when the drug is removed.

55:32.320 --> 55:39.280
And that's exactly what we see here. It's striking. And the R value here, it's not shown, but it's,

55:39.280 --> 55:43.760
it's 0.8. I did calculate this, which is pretty high, which means that-

55:43.760 --> 55:48.720
Sorry, what, what does the R value mean? So it's, it's the, a measure of the correlation

55:48.720 --> 55:53.360
between these two curves. So, so how well they track together, basically. So

55:54.080 --> 55:59.760
Let me ask you a question here. The, the, the blue number is tracking, um, raw number of new

55:59.760 --> 56:03.200
injections. Correct. That's why we just see fewer and fewer people getting new injections as we go

56:03.200 --> 56:09.520
along. Yes. Yeah. Yeah. We know now that not many people are, are taking these things at all

56:09.520 --> 56:14.000
anymore. So it's, yeah. What explains, so the last bump, you know what I was going to say,

56:14.000 --> 56:18.000
what explains the last bump without a correlative spike, but then I see a spike. Can you see my

56:18.000 --> 56:23.440
cursor? Yeah, you can. Um, so you got a last blue bump right here. And that is in what month are

56:23.440 --> 56:27.920
we in there? October. That looks like October, November, December. And then we get in January,

56:27.920 --> 56:32.640
2020, a little spike right there. Um, would, would that be what you would say was a, you know, a

56:32.640 --> 56:39.360
correlation? Um, maybe, but I wouldn't be too bothered about, I don't know what the bump

56:39.360 --> 56:45.600
is actually, um, in, well, I mean, I know what it is, but a bunch more people. I can't really see

56:45.600 --> 56:50.880
the dates. It's, uh, yeah, it looks like it looks like 10, 10, 1, 2022. So that's, uh, October,

56:50.880 --> 56:58.720
November 1st, 2022. Yeah. It's flu season. Oh, well, yeah, there you go. Do you remember, though,

56:58.720 --> 57:04.160
like, uh, when, when the different versions of these shots were being doled out, because maybe

57:04.160 --> 57:09.360
this represents boosters? I don't know. Well, I do, I do remember they went with the seasonal

57:09.360 --> 57:13.360
push for Thanksgiving, uh, for Thanksgiving and the holidays. So that's, that's the time. And then

57:13.360 --> 57:18.880
you go back here and you look at this one. It's 11, 11, 121. So right about the Christmas time,

57:18.880 --> 57:25.600
New Year's right here. And that's, yeah, I didn't even, uh, you're right. That's a good observation.

57:25.600 --> 57:29.120
Yeah. And then the first, the first one was right after just fucking jack it out into everybody's

57:29.600 --> 57:34.640
arms. I remember like, you know, uh, hokel coming out and, you know, come with their messages.

57:34.640 --> 57:37.920
You want to meet with your friends at Thanksgiving and Christmas, get your shots now. And I'm like,

57:37.920 --> 57:42.400
you guys are already too late. When they were pushing it, it's like, it takes two weeks to,

57:42.400 --> 57:47.200
if it worked, it would take two weeks to work. You're already too late. Okay. So amazing and

57:47.200 --> 57:52.560
fascinating that this, it's, it's, it's a, it's almost like a direct myocarditis reports. And

57:52.560 --> 57:56.160
what should be shocking about this number, we're going to get into the breakdown of the myocarditis.

57:56.160 --> 58:00.960
But this, this wild, uh, correlation is going to be disproportionately

58:01.520 --> 58:08.880
young, young boys or young men, um, after the second dose. Yes. So keep going. Let's, let's,

58:08.880 --> 58:15.040
let's scroll down and move this out here. I remember what, uh, I did. That's, that's just,

58:15.040 --> 58:19.440
you know how, you know how neurotic I am. I hate seeing that open and viewer thing. I got to toggle.

58:19.440 --> 58:25.360
We do. We do. Sorry. There it is again. Damn it. Okay. So I'm going to go back here.

58:26.320 --> 58:29.280
What chart are we looking at here? Number of adverse event reports in VAERS.

58:30.240 --> 58:36.640
Oh, I'm sorry. So this is just, okay. So what do we, this is the number of vaccines on the market

58:36.640 --> 58:43.120
proportionally, uh, uh, related to the number of adverse event reports. So the reason I put this

58:43.120 --> 58:49.680
in is just to show people that between 1990 when VAERS started to 2020, everything was copacetic.

58:49.760 --> 58:56.400
Yep. It's like linear, linear, uh, trend upward, very, you know, not a, not a big slope. Everything

58:56.400 --> 59:00.720
was proportional. Well, I was just gonna ask you like, where's the spike? And then I realized that

59:00.720 --> 59:07.200
the cutoff date is 2020. So I, I suspect the spike is coming. The hockey stick. There's no point

59:07.200 --> 59:14.080
in showing that. So the, the point was I wanted people to know like that to, to, um, to bounce off

59:14.080 --> 59:19.360
figure one, like this is what it used to look like in terms of the number of products on the

59:19.360 --> 59:23.520
market. And the reason why we have the steady increase is because of the increase in the number

59:23.520 --> 59:29.520
of products getting on the market. So an increase of one more product or four more products, you know,

59:29.520 --> 59:36.480
for COVID should not cause any significant rise. It should fall on the diagonal, you know, so

59:37.200 --> 59:40.320
they don't show that here, but that's, that's what would happen.

59:40.400 --> 59:45.440
Now, I mean, it's logical if, if the correlation of adverse events typically is one in 10,000,

59:45.440 --> 59:50.080
if you have five on the market, you'll have, you know, it'll, it'll go up like that. Like, like,

59:50.080 --> 59:54.320
what, I'm curious what the, what the little dip is right there. But okay. Interesting.

59:54.320 --> 59:59.440
Way to glitch. Where do I go now? I'm not pulled from the market that year. I bet you that's what

59:59.440 --> 01:00:08.240
it is. Yeah, we can skip that. That's just the classification of the stuff. So here we go. Um,

01:00:09.200 --> 01:00:15.760
so both of these charts are telling. So the one on the left is the, um,

01:00:15.760 --> 01:00:20.160
the absolute counts and the one on the right is per hundred thousand doses. So the one on the

01:00:20.160 --> 01:00:27.840
right is normalized per dose. They tell the same story though. So on, on the left, what we can do

01:00:27.840 --> 01:00:31.840
the normalized data, whatever. That's over here. All right. And let me just read, let me read what

01:00:31.840 --> 01:00:38.160
this. So figure three shows the distribution of myocarditis cases according to the CDC age grouping

01:00:38.160 --> 01:00:44.640
total 30% of all myocarditis reports were made for children aged zero to 20 and 50% of

01:00:44.640 --> 01:00:49.200
all myocarditis reports were made for young adults aged zero to 30. I'd like to know a

01:00:49.200 --> 01:00:53.360
sub breakdown, which we're going to get in a second. Absolute counts were normalized to vaccine

01:00:53.360 --> 01:00:59.200
administration data by age group, figure three B 12 to 17 year olds have the highest myocarditis

01:00:59.200 --> 01:01:02.560
reporting rates. Okay. Now, now we're going to.

01:01:03.360 --> 01:01:11.280
I just want to add here that the original data was even stronger than this because I think the

01:01:11.280 --> 01:01:16.400
reason why it quote unquote looks better, even though it's still bad now, is because of data

01:01:16.400 --> 01:01:23.520
botching in theirs. Um, but that's a whole other topic. So basically what we're looking at here is

01:01:24.720 --> 01:01:30.960
the greatest proportion of reports per dose being reported for 12 to 17 year olds.

01:01:31.680 --> 01:01:36.880
So within this age grouping, there are the 15 year olds. So they're the ones who are hit the worst

01:01:36.880 --> 01:01:43.600
and you can't see it here, but the boys are doing the worst. I'm not even sure if I have

01:01:43.600 --> 01:01:50.480
the boys chart here, maybe I don't. What if I may ask the 3.1, what number does that represent?

01:01:50.480 --> 01:01:58.880
It's one cases per 100,000 doses. So the guys attacking us will say, well, who cares? It's

01:01:58.960 --> 01:02:05.920
only three people per 100,000, but it's like, um, that's not nothing. And when you're talking

01:02:05.920 --> 01:02:12.720
about something, uh, that's considered a serious adverse event in a, in a young person who, who may

01:02:13.360 --> 01:02:20.960
actually, um, succumb to very severe damage. It matters. Like, well, I'll, I'll, I'll, I'll

01:02:20.960 --> 01:02:26.480
stop you there just because it's, if you say 3.1 per 100,000, that's roughly one per 33,000. So

01:02:26.480 --> 01:02:30.400
people are going to say, well, I've, I've heard the stat was myocarditis was one in 800,

01:02:30.400 --> 01:02:34.960
one in 5,000. Oh, now they're going to say it's one in 33,000. And that's at worst.

01:02:35.520 --> 01:02:41.120
No, but this doesn't take into account the underreporting factors. So any, any data that I ever

01:02:41.120 --> 01:02:48.560
present is a huge underestimate. That's just one of the flaws about bears. So at, at, at best,

01:02:48.560 --> 01:02:54.400
at best for the, for the, for the, I will come to call the data deniers. It's at best. It's one

01:02:54.480 --> 01:03:00.960
in 33,000 for. Okay. At best. And we, and to the extent that many people think the

01:03:00.960 --> 01:03:05.760
various reporting accounts for 1% of all adverse events, you can, and then the, the number for

01:03:05.760 --> 01:03:10.880
one in 800 was actually not pulled from various. It was pulled from trial, clinical trial data.

01:03:10.880 --> 01:03:14.720
And that's probably a little more accurate. Okay. Fine. So just just under, we'll understand the

01:03:14.720 --> 01:03:17.760
arguments. They're going to say, Oh, look at that. Even by your own numbers, it's one in 33,000.

01:03:17.760 --> 01:03:22.160
That's nothing. Okay. That doesn't factor in underreporting. And the number of one in 800,

01:03:22.160 --> 01:03:26.160
one in 5000 was not pulled from the various reports, but rather from the clinical data,

01:03:26.160 --> 01:03:30.160
although one study wasn't peer reviewed, apparently. Okay. Where should I go now?

01:03:31.120 --> 01:03:34.640
Keep going down. Let's see. I don't remember just like years ago.

01:03:35.600 --> 01:03:42.640
Seriously. Let's see what I did. Okay. So this reports of myocarditis by age and dose. Oh,

01:03:42.640 --> 01:03:48.480
this is going to be interesting. Okay. So yeah, you can see already, right? This is what I described.

01:03:48.480 --> 01:03:54.000
So all I did was I pulled out the, the number of there's the, there's reports of myocarditis

01:03:54.000 --> 01:04:00.720
by Medricode myocarditis. And I plotted those points against the people's ages.

01:04:01.520 --> 01:04:07.760
And I superimposed three doses because I wanted to see what was going on. In the, in the initial

01:04:07.760 --> 01:04:14.800
paper, I only had dose one and dose two data. So I had the, you know, I had this picture back in

01:04:14.800 --> 01:04:23.520
like, fricking May, minus the blue. Exactly. But I had this picture, this dose two response.

01:04:23.520 --> 01:04:28.080
And it's like, man, what is going on? It's the fuck. I'm sorry. It's the fricking.

01:04:29.040 --> 01:04:32.320
What's the, the, the building in America now in New York?

01:04:33.520 --> 01:04:40.800
It's the empire state building of adverse events. Yeah. And it really is. It does look like it.

01:04:41.760 --> 01:04:46.400
Holy shit. So then that, that is to say, these are the reports. And in the reports, they say

01:04:46.400 --> 01:04:50.880
adverse event. Okay, which number dose are you on? They didn't, people didn't write it. They didn't

01:04:50.880 --> 01:04:55.600
file the various report on the first dose. They filed it on the second. They, they indicated it was

01:04:55.600 --> 01:05:01.520
the second. And this is the tracking by age. And we're, we're looking at whatever that is halfway

01:05:01.520 --> 01:05:09.280
about 12 to 25. That peak bar is 15 year olds. And if you break that down by gender, by sex.

01:05:09.360 --> 01:05:12.960
80 percent boys. Boys. Sorry. What percentage of boys?

01:05:13.520 --> 01:05:20.560
80 something percent, 80 odd. Okay. Wow. So quite clearly, physiologically,

01:05:20.560 --> 01:05:25.440
for whatever the reason, the second dose triggers more adverse event reports.

01:05:26.080 --> 01:05:30.960
Something more severe. So my line of thinking is like this, and correct me if I'm wrong,

01:05:30.960 --> 01:05:37.680
or if you have one of the other ideas, young boys, like I, I, I kind of like was a young

01:05:37.680 --> 01:05:43.440
tomboy. So, you know, I played soccer, and I, I was a competitive swimmer, and I, all my friends

01:05:43.440 --> 01:05:50.480
were boys. So like if I tried to put myself into my, my 15 year old self, and I got a, you know,

01:05:50.480 --> 01:05:56.400
I'm vaccinated out the yin yang, whatever there was I got when I was a little older, not really

01:05:56.400 --> 01:06:02.960
when I was 15. But let's just say this COVID shit happened when I was 15. I probably would have

01:06:02.960 --> 01:06:07.520
gotten it wouldn't have even thought about it. And then all of a sudden, if I started having

01:06:07.520 --> 01:06:14.800
horrible chest pains within like a few days of the shot, I would never, never connect to them.

01:06:14.800 --> 01:06:20.160
I'm telling you this as a first person, I would never have connected them. And then of course,

01:06:20.160 --> 01:06:24.560
it's time to get the next one, because it's three weeks later. So I get the second one,

01:06:25.440 --> 01:06:31.200
and then I pass out. And then my mom's there, and she's like, what the hell? And it happens in,

01:06:31.200 --> 01:06:36.080
in closer temporal proximity to the shot, because that's another trend that we see following those

01:06:36.080 --> 01:06:42.880
two, like the timeframe from injection to onset is shorter, which is another Bradford Hill thing.

01:06:42.880 --> 01:06:49.920
So my thinking is that there's more reporting following the second dose because it is more

01:06:49.920 --> 01:06:56.320
severe. There's something cumulative going on. But also it's, it becomes, it's so severe that

01:06:56.320 --> 01:07:02.160
it's not deniable. So the moms get involved, and they take their kids to the doctor. So that's kind

01:07:02.160 --> 01:07:07.280
of how I was visualizing it. Like, I don't know, maybe there's another reason, but I would have,

01:07:07.280 --> 01:07:11.920
I would have, I mean, for whatever it's worth, just, you know, critical thought, I would have thought

01:07:11.920 --> 01:07:19.920
it would be a cumulative impact. And, and any, any, any sort of symptomatic chest pain might be

01:07:20.560 --> 01:07:27.120
totally, virtually unnoticeable or. Yeah. And then the cumulative impact of having,

01:07:27.120 --> 01:07:31.520
whatever the spike proteins are, recirculate and retrigger an immunological response, and then you

01:07:31.520 --> 01:07:38.080
get a more severe reaction. Yes. It's the second, the sucker punch that knocks you out. It's a,

01:07:38.080 --> 01:07:45.040
it's another massive dose of lipid nanoparticles carrying foreign genetic material,

01:07:45.040 --> 01:07:51.840
and then more transfection, more downstream, you know, so and you're already probably

01:07:51.840 --> 01:07:58.480
immunologically inflamed from the first experience. Nothing thinking out loud, though. Do you know

01:07:58.480 --> 01:08:02.880
the number of second doses administered compared to first doses? Does that number go down because

01:08:02.880 --> 01:08:07.840
that would make this graph even more shocking? Yes, it does. Dramatically, actually, there's far

01:08:07.840 --> 01:08:15.840
more first doses than second. I can't remember exactly, but it might even be like, I think it's

01:08:15.840 --> 01:08:21.840
probably like, Oh gosh, I don't want to guess because it doesn't matter. I mean, it's also just

01:08:21.840 --> 01:08:24.960
logical necessarily second basis will be fewer than the first. The only question is in what

01:08:25.040 --> 01:08:29.200
proportion, if it's statistically significant, the fact that you would then have this

01:08:30.080 --> 01:08:37.040
clear, clear trend of a statistically lesser amount of second doses, that makes it even more

01:08:37.040 --> 01:08:40.480
shocking. And then you see the blue, I mean, I don't know who's getting third doses anyhow.

01:08:42.080 --> 01:08:43.200
Holy crap.

01:08:46.640 --> 01:08:50.480
Okay, all right, the number is hold on adverse events. I'm just trying to like, I'm trying to

01:08:50.480 --> 01:08:56.560
just try not find a way to play. How can you debunk this?

01:08:57.600 --> 01:09:02.240
So the number the number here we're at adverse events is what is the is it thousands on the left

01:09:02.240 --> 01:09:10.320
or is it? No, this is total numbers. So this is bare bones, domestic data, not foreign data,

01:09:10.320 --> 01:09:19.280
only myocarditis. And it's like I narrowed the query to be very, very strict, which makes the

01:09:19.280 --> 01:09:25.600
number look very low. And there's no wonder this is a this is 80 reports. Yes. And so people

01:09:25.600 --> 01:09:30.960
are going to say the total number of reports here. Let's just say following those two reported

01:09:30.960 --> 01:09:36.960
to bears for myocarditis in 15 year old boys. That's not that's not enough to worry about.

01:09:36.960 --> 01:09:43.600
But that's not the point. The pattern is the point. There's two important things about this graph

01:09:43.600 --> 01:09:48.400
and has nothing to do with the absolute counts. It has to do with the dosing phenomenon.

01:09:49.280 --> 01:09:59.440
And the age. And those two that they signify something going on immunologically or physiologically

01:09:59.440 --> 01:10:07.760
in those in that age group. And following those two, which also satisfies Bradford Hill criteria

01:10:07.760 --> 01:10:15.440
for specificity and temporality. Yeah, but the argument is going to be, let's just say of the

01:10:15.440 --> 01:10:20.000
12 years from 12 to 24. Well, let's just say it most is going to be a thousand cases. Well,

01:10:20.000 --> 01:10:27.280
we've we've administered it to 70. Yeah, 50 million kids. So fine. It's showing a statistically

01:10:27.280 --> 01:10:33.280
significant trend on a statistically insignificant blip move on. What are you complaining about?

01:10:33.280 --> 01:10:39.040
That's 80. I would say, yeah, I would say my kids not a blip, asshole, like not to you. But that's

01:10:39.040 --> 01:10:44.240
what I would say to someone who's making that argument. The thing about these data is that they're

01:10:44.240 --> 01:10:52.240
they're not they're not only people, they're little kids. Just revert to the under the statistical

01:10:52.240 --> 01:10:56.880
underreporting, the necessary underreporting. And this number could be as high as 800. It could

01:10:56.880 --> 01:11:03.200
be as high as 8,000. And that's one one demographic for something which would never have put them in

01:11:03.200 --> 01:11:09.920
the hospital in the first place. The thing that you're saying right now is fact, that's why I'm

01:11:09.920 --> 01:11:17.120
saying the absolute count doesn't matter here, really, because this is only it's like the magnitude

01:11:17.120 --> 01:11:25.280
could be anything, but the pattern is going to remain the same. And so like you're absolutely

01:11:25.280 --> 01:11:33.120
right. I mean, the underreporting factor could be 100. It could be this this actually, if you added,

01:11:33.120 --> 01:11:39.520
for example, pericarditis and myoparicarditis or versions of myocarditis, this could spike into

01:11:39.520 --> 01:11:45.600
the 10,000. And that's it could spike into the 10,000s at the unreported level. I mean, so this is

01:11:45.600 --> 01:11:50.880
why hypothetically, it could be 100 times and not even hypothetically, but like logically and

01:11:50.880 --> 01:11:56.160
predictably, 100 times more. So you would have what is 80 times 100? It's 8,000. You would have 8,000

01:11:56.160 --> 01:12:03.360
cases of myocarditis alone for 15 year old boys. And we need I need to get someone to pull out the

01:12:03.360 --> 01:12:09.280
numbers as to the prognosis for myocarditis diagnosis in terms of lifespan. But okay, so but but

01:12:09.280 --> 01:12:13.040
one thing is for certain, as you astutely point out, the relevant thing is here,

01:12:13.040 --> 01:12:20.640
the medical scientific biological correlation trend following between the second dose and myocarditis.

01:12:21.440 --> 01:12:29.200
Yeah. And so like, you know, if they were another, I don't usually, I think it's called the the

01:12:29.200 --> 01:12:36.880
steel man, myself, but like if if I was gonna, let's just say I was working for them, okay?

01:12:36.960 --> 01:12:42.960
And I had to come up with ways to like if I was like one of the people trying to justify this data

01:12:42.960 --> 01:12:49.200
and make people not worry about it, I would say, well, maybe what we can do to satisfy people's

01:12:49.200 --> 01:12:58.720
worries or concerns is just not give the the the children male male children, let's say,

01:12:58.720 --> 01:13:03.760
a second dose. You know what I mean? It's like there's always there's there's something they could

01:13:03.760 --> 01:13:10.080
have said that at least would have acknowledged this data. But that's the point, this data,

01:13:10.720 --> 01:13:15.760
until this this paper got published has not been acknowledged. It's actually been suppressed.

01:13:16.480 --> 01:13:21.920
Jess, have you done this correlation? I mean, I don't know what other vaccines are administered

01:13:22.480 --> 01:13:28.400
in doses. I don't think the flu shot is you don't take two doses of that, even during any flu season.

01:13:28.400 --> 01:13:32.960
Have you tried to correlate other vaccines that are administered to children or the same age

01:13:32.960 --> 01:13:38.800
bracket in doses to see what the correlation would be? Are you are you asking me if I've looked

01:13:38.800 --> 01:13:44.080
at other vaccine products? Yeah, other other vaccine products that are administered in doses

01:13:44.080 --> 01:13:49.040
where you could compare just to show hypothesis. I mean, I don't even know what other vaccines

01:13:49.040 --> 01:13:59.360
are administered in doses. No, but maybe hepatitis. So if you do a comparison between any other

01:13:59.440 --> 01:14:04.320
vaccine, I'm not even still calling this one a vaccine, but any other vaccine to see what

01:14:04.320 --> 01:14:10.160
I don't think you're going to see it. I haven't, but it's a good idea. I don't think you're going

01:14:10.160 --> 01:14:17.040
to see it because the conventional vaccines aren't operating as the same as these reports are coming

01:14:17.040 --> 01:14:21.680
from the modified MRNA products, right? Because most of the people in the States got the Pfizer

01:14:21.680 --> 01:14:27.280
or the Moderna, like there are Novavax and Jensen products out there. Yeah, they put they put the

01:14:27.280 --> 01:14:31.120
Johnson and Johnson in Canada, I think after the 46 year old woman data blood clot. So yeah,

01:14:31.120 --> 01:14:36.960
they pull there's no data on that because they pulled it. Yeah. Well, exactly. And so the mechanism

01:14:36.960 --> 01:14:42.560
of action here is obviously the problem as you saw in the other figures. So I'm not sure that

01:14:42.560 --> 01:14:47.760
you're going to get. No, you will you will not get the same correlation predictably, but it might

01:14:47.760 --> 01:14:54.640
still be useful to show. Yeah, something is absolutely right. I mean, lack of evidence is also evidence.

01:14:54.640 --> 01:15:01.600
So the thing that I did check though was this phenomenon, this pattern for any other adverse

01:15:01.600 --> 01:15:09.760
event and guess how many I found that had the same pattern? None. None. So I mean, I didn't check

01:15:09.760 --> 01:15:17.520
all 14,000, but I did check the Biggies and I didn't see this. So it's like, it's kind of a

01:15:17.520 --> 01:15:24.880
phenomenon that's unique to myocarditis, which is kind of fascinating when you think about it. I

01:15:24.880 --> 01:15:31.680
mean, there's I imagine has something to do with androgens, like the male hormones that are linked

01:15:31.680 --> 01:15:37.440
to like puberty and stuff, but and I'm sure there are people doing work on this. But this is another

01:15:37.440 --> 01:15:42.880
thing. It's like, we'll get the data. We'll get the we'll get those answers in 75 years.

01:15:42.880 --> 01:15:51.680
So do I, should I go back? Is there are more there are more grafts in here? Should we go?

01:15:52.320 --> 01:15:54.800
Let me see what else is there. I should probably know.

01:15:57.520 --> 01:16:02.000
So what about three years? Anyway, yeah, no, but it's been three years, buddy. You got three

01:16:02.000 --> 01:16:07.600
years more data. There's reports of cardiac adverse events as of okay. So this is just cardiac

01:16:08.240 --> 01:16:13.680
as a cluster. So I put this in because I wanted to show people that, you know,

01:16:13.680 --> 01:16:20.240
myocarditis is like one adverse event in the cluster of cardiac related adverse events.

01:16:20.240 --> 01:16:26.720
There's like thousands of them. So this gives you a better idea of the absolute number.

01:16:27.280 --> 01:16:32.160
This on the left is the absolute counts per age group of people. That's a lot. That's a lot of

01:16:32.160 --> 01:16:37.280
this. And this is not underreporting. And this is not definitive. I mean,

01:16:37.840 --> 01:16:43.520
or comprehend like it's comprehensive, but it's not. There's no way I could have included all

01:16:43.520 --> 01:16:47.760
of the metric codes for cardiac related events. So I just picked like the big ones.

01:16:47.760 --> 01:16:53.120
All right. And this is this is as of the first date of administration of the of the of the

01:16:53.120 --> 01:16:59.280
jab from the beginning. And this is normalized on the right. So you can see that most of the people

01:16:59.280 --> 01:17:06.560
who are reporting cardiac stuff are the older people. It starts around 25, which is old, but like,

01:17:06.560 --> 01:17:12.080
you know what I mean? Like, but again, this is as of August 11. So who knows what's happened

01:17:12.080 --> 01:17:17.760
since then maybe more kids because they've been giving it out to more kids have reported. I

01:17:17.760 --> 01:17:22.640
guarantee you that that's true. And now I'm trying to think of the steelman to to counter this would

01:17:22.640 --> 01:17:29.760
be the argument of have you checked overall incidents of cardiac issues to see if it coral

01:17:29.760 --> 01:17:35.520
if it's on the increase or if there's if there's a market increase in cardiac incidents, not

01:17:35.520 --> 01:17:40.160
various related, but aggregate. I don't even know how you'd find that data post COVID jab.

01:17:41.360 --> 01:17:47.840
Yeah, that's I don't know. I I imagine that, you know, another

01:17:48.480 --> 01:17:53.360
Sorry, no, no, I was gonna say we've we've heard reports about it. And then and then people say,

01:17:53.360 --> 01:17:58.960
well, excess death is not up excess heart could. I mean, that would be I mean, I'm sure Ed

01:17:58.960 --> 01:18:05.200
dad would actually have that data or bode went john bode when who you'd have to get the hospital

01:18:05.200 --> 01:18:10.800
records to see. Okay, that would be interesting to correlate. Now I go down a lot of talky talky

01:18:10.880 --> 01:18:14.320
and that's it conclusion. Yeah, don't read.

01:18:16.320 --> 01:18:23.760
Why not? Why just don't read the last sentence. Yeah, it kind of refers to like,

01:18:25.440 --> 01:18:30.560
it was a sentence that was thrown in to get it published. And it kind of refers to like keeping

01:18:31.600 --> 01:18:36.080
stop at you. And we found a very strong safety signal for COVID-19 vaccine-induced

01:18:36.080 --> 01:18:40.160
myocarditis, particularly in children and young adults that result in hospitalization or death.

01:18:40.160 --> 01:18:45.280
Did we miss the death? The death graph. And I don't mean that that sounds very sinister.

01:18:45.280 --> 01:18:51.760
Where was the chart graph is a death graph if memory recalls because it's 3%. And it's like,

01:18:51.760 --> 01:18:56.960
it's not really a graph that's going to be like, you know, so I you can have a limited number of

01:18:56.960 --> 01:19:02.400
figures in your in your publication and you kind of want it to be like, you know, the ones that are

01:19:02.400 --> 01:19:08.640
irrefutable. So, you know, it would probably be another way for them to say, yo, so, but it's

01:19:09.280 --> 01:19:16.960
I'm going to ask Bodewin and Dowd and or Dowd for the like the overall cardiac related by age

01:19:16.960 --> 01:19:22.480
overall cardiac related and also holy crap. How am I going to ask if I just forgot what I was

01:19:22.480 --> 01:19:27.600
going to ask them? Oh, the life span, life span, someone in the chat, take a note of this because

01:19:27.600 --> 01:19:32.480
I'm going to forget the lifespan prognosis once you're diagnosed with myocarditis. That's that's

01:19:32.480 --> 01:19:36.960
that is the most important thing where we've been told is mild just will tell you it's probably

01:19:36.960 --> 01:19:42.560
like on average 10 years, but it's really hard to ascertain. But here's the thing. This is just

01:19:42.560 --> 01:19:48.480
common sense. The younger you are, and especially if you're prepubescent when you're still developing,

01:19:49.200 --> 01:19:54.000
if you sustain heart damage, like in with regard to fibrotic scarring,

01:19:56.080 --> 01:20:01.520
it's not hard to guess that it's going to reduce your lifespan. Like, maybe we don't know exactly

01:20:01.520 --> 01:20:07.120
by now much, but. Okay, now I'm going to do this. I'm going to share a screen one more time so I

01:20:07.120 --> 01:20:12.160
can get to the chats because I see some chats in rumble. Jesse, yeah, this about covers it for

01:20:12.160 --> 01:20:19.120
the study, correct? What that covers the study that just got published approved, approved, it's

01:20:19.120 --> 01:20:22.960
out there undeniable. I'm going to share this because there's a few chats we got that Finn

01:20:22.960 --> 01:20:26.720
boy slick in the bottom says just a little something to echo the chat and say that Jessica is one of

01:20:26.720 --> 01:20:31.360
our favorite guests ginger ninja who I just had on yesterday. He's a member of our locals community.

01:20:31.920 --> 01:20:36.880
Says re East Palestine. I don't know about the other alphabet agencies, but FEMA is absolute trash.

01:20:36.880 --> 01:20:41.840
Trump sent FEMA to Tennessee when we were hit with the tornado that tornado. I don't know of one

01:20:41.840 --> 01:20:46.800
person that got any aid relief. FEMA lost lost. This is just goes back to what we were talking about

01:20:46.800 --> 01:20:52.000
in Hawaii, Lahaina. Yeah, lost your home but had bad insurance denied lost your home but didn't

01:20:52.000 --> 01:20:56.320
have insurance should have had insurance denied lost your husband child. Hope he had life insurance

01:20:56.320 --> 01:21:03.840
denied and the ginger ninja survived the the Tennessee tornado of March 3 2020 which I didn't

01:21:03.840 --> 01:21:09.360
know until we had our discussion last night and had no idea of the severity of that that disaster.

01:21:09.360 --> 01:21:15.040
And we got mighty Megatron says this COVID scare scam was a test in compliance. A medical

01:21:15.040 --> 01:21:18.960
experiment some might even say and then we got William V cases. Are there any recommendations

01:21:18.960 --> 01:21:25.520
on solutions for people that did get the vaccine? Any remedies known to help myocarditis is probably

01:21:25.520 --> 01:21:29.840
the the specific question but there are I don't want to judge the detox things out there. I've

01:21:29.840 --> 01:21:34.240
heard people make a number of recommendations. People who care about me that think that you know

01:21:34.240 --> 01:21:40.960
because I got the I got two shots that I'm gonna you know die. I people are representing some stuff.

01:21:40.960 --> 01:21:48.000
Well, I'm I look I was I'm neurotic. I remember I don't take naps period. I remember it was either

01:21:48.000 --> 01:21:51.600
the first or second shot. I had to take a 20 minute nap and I'm like well that's that's a little odd

01:21:51.600 --> 01:21:56.560
never had chest pains. And I looked at the bad batch reporting for my batch and it was

01:21:56.560 --> 01:22:00.560
virtually nil. And you know this was back in the day when we might have been getting inert

01:22:00.560 --> 01:22:06.800
whatever the hell that in their stuff. But are you do you have any personal opinion on any of

01:22:06.800 --> 01:22:13.040
the traditional detox stuff that people recommend for for vaccine injury or vaccine the jab stuff?

01:22:13.920 --> 01:22:18.720
No, not other people's but I and I'm not a medical doctor. So I can't give advice. But

01:22:18.800 --> 01:22:25.920
what I can tell you is that it's it's the advice I gave you for your sinus. Turmeric is like one

01:22:25.920 --> 01:22:32.800
of the most potent anti-inflammatories you like you can ingest and it's tastes like feet sometimes

01:22:32.800 --> 01:22:37.200
but if you combine it with coconut milk and boiling water and honey, it tastes quite nice. It's

01:22:37.200 --> 01:22:44.800
actually called golden milk. And I would just recommend anybody for anything drinking a glass

01:22:44.800 --> 01:22:50.560
of that a day because it's not going to hurt you. But it's a magic thing when it comes to

01:22:52.000 --> 01:22:57.920
bacterial infections, inflammation, even viral, you know, keeping viruses at bay,

01:22:59.040 --> 01:23:05.040
balancing out your immune responses. It probably helps your t-rex somehow because that's all built

01:23:05.040 --> 01:23:12.320
into inflammation. I'm I don't know maybe it even helps autoimmunity. I don't know but

01:23:13.280 --> 01:23:18.400
it you can't you can't OD on turmeric? No, of course not. I always have it a lot of it when

01:23:18.400 --> 01:23:22.400
you told me to. But by that time I was already also on antibiotics. So that probably helped

01:23:22.400 --> 01:23:28.320
the sinus infection. Yeah, you sometimes you got to do the antibiotics. But if you if you can,

01:23:28.320 --> 01:23:33.200
well, on the subject of sinus infections, which I have a lot of experience in the past,

01:23:34.000 --> 01:23:39.440
if you know the signs or like I'm a surfer and I surf in dirty water sometimes. So it's like I'm

01:23:40.000 --> 01:23:45.760
you know, it can become a cesspool in here real quick if you don't if you're not

01:23:46.560 --> 01:23:52.800
preemptive or prophylactic. So that's why I drink the turmeric milk. A surfer suggested it to me

01:23:52.800 --> 01:24:01.280
and ever since he did, I have not had one single problem and it's been a long time knock on wood.

01:24:02.400 --> 01:24:07.840
One of the things that I've I had never done in my entire life was a sinus cleanse. I had never

01:24:07.920 --> 01:24:12.880
done it before. And well, you warm up some water, you mix in the

01:24:13.840 --> 01:24:17.120
no, you just go like this, you go and then and then it literally just pours out the other

01:24:17.120 --> 01:24:20.800
sinus. I'd never done it before it doesn't doesn't feel good. It goes down your throat. And but

01:24:21.360 --> 01:24:25.600
that was probably the most useful thing is like just flushing out all that disgusting crap.

01:24:26.240 --> 01:24:31.200
Jess, do you have time for a brief supporters exclusive on locals only Q&A?

01:24:31.920 --> 01:24:35.840
Yeah. Everybody who's watching now on Rumble, I'm going to give you the link again. But where

01:24:35.840 --> 01:24:40.880
can everybody find you? And thank you. That was a question and an affirmation. Where can

01:24:40.880 --> 01:24:45.200
people find you? And thank you for everything you're doing. You're welcome. I have a website,

01:24:45.200 --> 01:24:50.880
Jessica's universe that I just updated payment for. So it's back online. Someone tried to steal

01:24:50.880 --> 01:24:54.480
my domain name. Oh, you're you're target. You better keep up with that. Otherwise,

01:24:54.480 --> 01:24:59.680
they're going to do it and redirect it to Jessica Rose. Well, that's that's what they did. But I

01:24:59.760 --> 01:25:05.440
think it's remedied now. It was quite scary there. Sketch a ramam. And these people are nuts.

01:25:05.440 --> 01:25:09.760
They're really nuts. They need to get a life. And my substacks, of course,

01:25:11.200 --> 01:25:19.040
Jessica 5B3.substack.com is more like current events and presentations. And Jessica are.substack.com

01:25:19.040 --> 01:25:25.920
is more about like write-ups of papers like this new one that was talking about frame

01:25:25.920 --> 01:25:30.960
shifting and stuff. So and I have a Twitter, which, you know, I, I,

01:25:30.960 --> 01:25:36.720
allegedly, I have 90 something thousand followers. But when I post something, only three people see.

01:25:39.680 --> 01:25:44.480
You're on a list and I'm noticing it. I'm noticing some severe fuckery on YouTube right now. Like

01:25:44.480 --> 01:25:49.840
I put out a video and I'm on the list. You have to be. I got put on a list. What is it called?

01:25:49.840 --> 01:25:53.680
Center for Countering Digital Hate. And I've noted, I put out a video.

01:25:56.080 --> 01:26:01.200
Dude, we'll talk about that when we get over to Locals. But YouTube is not even recommending

01:26:01.200 --> 01:26:05.360
my own videos to my my own followers. So I'm going to, you know, it doesn't matter. And I do.

01:26:06.000 --> 01:26:11.120
That's just it. The tide will write itself. The ships, whatever you don't think. Okay.

01:26:11.840 --> 01:26:15.920
Jess, I'm going to end this on Rumble. Come on over to vivabornslaw.locals.com. The link is

01:26:15.920 --> 01:26:19.040
I'm going to turn it to support is only we're going to do a Q&A. And I'm going to ask you

01:26:20.080 --> 01:26:23.680
about hemorrhoids. I'm going to ask you about my hemorrhoids. When we get over to, I'm joking.

01:26:23.680 --> 01:26:29.760
I'm not going to. I can help you with that too. Oh, well, you know, maybe I will. Okay, we're

01:26:29.760 --> 01:26:34.800
ending it on. We're ending it on Rumble. Jess, thank you very much. Everybody, you've gone through

01:26:34.800 --> 01:26:39.040
it now, people. And you can see how people are going to steal. You know what the arguments against

01:26:39.040 --> 01:26:43.120
it are going to be. You can have the proper responses and you can assess it accordingly.

01:26:43.120 --> 01:26:47.520
And when they say, Oh, that's just that's just only 80 kids, that 80, 15 year olds. Well, yeah,

01:26:47.520 --> 01:26:51.280
that's that's that's assuming that the reporting is all the number and it probably represents

01:26:51.280 --> 01:26:58.640
1% if history is any if past is prologue. And besides the fact that it still boils down to the

01:26:58.640 --> 01:27:03.120
fact that we should be allowed to decide for ourselves what we inject into our bodies. So,

01:27:03.120 --> 01:27:08.720
No, and to have the discussion publicly. And also, there was the other thing I was going to say.

01:27:10.000 --> 01:27:13.920
We'll ever and also not being demonized for not subjecting our kids to the risk of that.

01:27:14.640 --> 01:27:19.760
As a solution to something that was of not meaningful risk to them. And it doesn't prevent

01:27:19.760 --> 01:27:22.880
the transmission of it anyhow. But then you deal with the arguments that well, if you get my if

01:27:22.880 --> 01:27:27.920
you get COVID after, you're less likely to get my all of these creative arguments. Okay, ending

01:27:27.920 --> 01:27:31.200
it on Rumble people. Thank you very much and stay tuned. I'm going to be with Owen Schroyer on

01:27:31.200 --> 01:27:35.280
Infowars at five o'clock today. And I'll put out a car blog. So ending on Rumble coming over to

01:27:35.280 --> 01:27:42.080
the locals. Three, two, one now. Jess, I'm never doing the rubber band

01:27:42.080 --> 01:27:49.840
ligation procedure again ever. Do you know what rubber band ligation is? No. Oh, hold on.