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Hello and welcome to another UK 
column interview. 

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My name's Debbie Evans and today
I'm joined by our very good 

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friend Cheryl Granger. 
But I'm also joined by a very, 

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very astute engineer called John
Bodwin. 

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Now, if you have not heard of 
the name John Bodwin, you will 

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now. 
And where have you been? 

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Because John is a very humble 
gentleman, but he is an 

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extraordinary engineer who's got
extraordinary, irrefutable data 

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on excess deaths. 
Why, you ask yourself, how has 

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this man got all of this data? 
Well, he asked for it, and that 

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was very simply what happened. 
What do I mean by irrefutably 

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irrefutable data and 
extraordinary data? 

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Well, I mean, actually over a 
million death certificates. 

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And yes, John asked them and 
John got them. 

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And I'm delighted to be able to 
welcome you from across the 

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ponds in the USA. 
John, welcome to UK column and 

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thank you so much for agreeing 
to talk to us today. 

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Good morning and thank you for 
having me on UK column. 

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Really nice to meet you Debbie 
and Cheryl. 

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Hope we have a good 
conversation. 

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I'm sure we will. 
Oh, I can assure you we will. 

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And on that note, let's welcome 
Cheryl as well, who many of our 

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viewers and listeners will know.
Cheryl is a self-employed 

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pharmaceutical training 
consultant and she's done much 

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work with me on the MHRA, 
vaccines, excess deaths and 

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mRNA. 
So Cheryl, thank you so much for

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a facilitating this interview 
and joining us today. 

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Welcome. 
Thanks, Debbie, and hello, John.

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Nice to be with you both this 
this afternoon actually here. 

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Yeah. 
Thank you. 

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The plan is, is that we're going
to show you a very short piece 

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of video now just to give you a 
flavour of who John is and what 

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he's done, because I think 
you're going to find it 

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extraordinary. 
And then I'm going to hand over 

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to Cheryl because Cheryl's going
to do a bit of data crunching 

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because that's what John is all 
about. 

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So without further ado, here's a
very short video clip just to 

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give you a flavour of who we're 
talking to today. 

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COVID, as I've proven in my book
and in the CDC memorandum, a 

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second publication, 80 to 90% of
the COVID deaths are fraud. 

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They were drug overdoses and car
accidents and they were a bunch 

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of old people dying. 
Out of neglect. 

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And being mistreated by 
hospitals weren't given 

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antibiotics when they had a 
bacterial infection in their 

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lungs. 
These things all combined 

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through a coercion of doctors 
from the American Board of 

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Internal Medicine, Family 
Medicine, Pediatrics, the state 

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licensing boards, the Federation
of State Medical Boards, the 

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NIHFDACDC, all said the same 
thing. 

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You will use this protocol or 
we're going to suspend your 

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license. 
And you can find that on the 

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web. 
The letter is still there. 

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It's a joint statement by the 
CEOs of the American Board of 

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Internal Medicine and the other 
two I mentioned. 

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So with with regard to all of 
the hospital protocols, I call 

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it murder because at some level 
somebody knew what they were 

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doing. 
More than half a million people 

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killed by hospital protocols, 
another half a million killed by

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what they call a vaccine. 
I have all the data. 

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I can show it. 
I now have 1.4 million 

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unredacted, non redacted death 
certificates. 

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That represents 5% of the US 
population. 

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It's the biggest study, biggest 
database ever in anything COVID.

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Thanks, John. 
I think that summarized a lot of

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what we're going to talk about 
today. 

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We just want a bit more 
information. 

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You say and you've got 1.4 
million certificates. 

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How on earth did you get them? 
How on earth did you get to 

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write an FOI and get them to be 
released? 

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What was it about your 
particular request? 

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Do you think that actually 
allowed you to have all this 

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Massachusetts data? 
Oh boy, it's really simple, but 

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it's But it's not. 
So I spread eight public records

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requests. 
Now, that's the Massachusetts 

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State version of the FOIA 
Freedom of Information. 

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And I spread them across 
different people so that the 

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state wouldn't think it was all 
coming from one person, then 

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just denied them all. 
So seven were rejected. 

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And the one that came through 
was the entire death record. 

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And I say record instead of 
certificate because of the 

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certificates are actually the 
pieces of paper, right? 

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The records have a lot more. 
Information I have 315 different

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columns per decedent per person 
who died, 315 different 

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variables across. 
The row of a spreadsheet that's 

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a lot more than it is on a death
certificate. 

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So I have, I have the medical 
examiner's name, license number,

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office address. 
I have the burial plot of the 

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person, whether or not they were
cremated, the causes of death. 

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So I asked for it and they they 
gave it to us and then they, 

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they further said. 
You don't even need. 

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To do a a public records 
request, you just need to ask 

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for it. 
So in Massachusetts we don't 

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have privacy and death laws like
most of the United States states

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do. 
Most of them are 50 to 70 years.

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So that that's how I got 
Massachusetts. 

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Somebody did the same thing in 
Minnesota, unbeknownst to me for

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over a year. 
And she was working on that. 

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And when we found each other 
after over AI think a year and 

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she offered me the the Minnesota
database and I said, yeah, sure.

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So I got that. 
And then I recently got a third.

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Oh, and I work with a gentleman 
in New York City sometimes, 

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Aaron Hertzberg. 
He got Vermont, which is great. 

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It's a very small state, so it's
hard to find signals, but you 

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can find individual cases and 
there are signals that we'll get

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into that later. 
But, and then I got another 

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state, I guess I'd say a fourth 
state very recently. 

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And I'll have a third book 
coming out end of summer. 

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So basically I asked for them. 
That's how I got them. 

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Oh, the 4th one I, I almost 
don't want to say. 

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I applied to do a research 
paper, which I am doing. 

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I have engaged a gentleman at 
MIT who is just graduating now. 

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He's also an electrical engineer
and he's going to use some 

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discrete foliage transform 
electrical engineering 

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techniques to derive the 
seasonality of deaths by cause 

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where pneumonia would have high 
seasonality. 

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More people die in the winter of
pneumonia than in the summer in 

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a northern climate. 
And then it, it doesn't matter 

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with regard to going to pick 
something like cardiac arrest, 

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you know, there's no, well, 
that's probably a bad one 

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because that goes with 
pneumonia. 

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Let me just say a heart attack, 
there aren't any more heart 

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attacks in the winter than the 
summer. 

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So there's no seasonality. 
So the signal to noise ratio 

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would be different. 
Now I'm off on a tangent, so let

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me get back to, yeah, I got the 
4th state and I got it because 

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I'm going to do a research paper
and I will because I said I 

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would. 
And there there will be 100 

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times more incidental findings 
than climate change versus heart

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disease, which is what I applied
to do are. 

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You from Massachusetts? 
Is that why you got this data? 

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I live in Massachusetts. 
Am I from Massachusetts? 

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No, I'm from Connecticut. 
And there's a very big 

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difference, though it's only two
hours away and it's between New 

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York and Boston. 
So you hear people with a Boston

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accent and people with a New 
York accent. 

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Connecticut does not really have
an accent unless you get close 

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to New York. 
Have you had any pushback from 

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the people who gave you these 
death certificates and your 

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analysis? 
Has there been any reaction? 

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No, not at all. 
In fact, I I don't think they 

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can now because I sued the 
governor of the public health 

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commissioner, the chief medical 
examiner, and four individual 

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medical examiners in United 
States District Court District, 

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Massachusetts. 
So they were probably under 

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orders from the attorney 
General's office. 

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Quiet, not talk. 
Everything I've stated is 

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factual and it's sworn to 
underoath in federal court, 

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which means they can put me in 
jail if I'm lying and I'm not 

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lying because it's their data 
and that's all true. 

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Yeah, and I've been working with
the Pfizer Moderna document 

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analysis with daily clout and or
the information that they have, 

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which is primary source 
information. 

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So it's really been Pfizer 
analysis start off with Pfizer 

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haven't sued them because 
obviously they can't. 

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It's their data. 
It's their data that they put 

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forward for licensing. 
So it's the same sort of thing. 

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But I'm just jealous of the fact
that in your system, you can 

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actually get answers to your 
Freedom of Information without 

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people hiding. 
So you have done a lot of 

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comparisons to theirs to find 
these people that you have death

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certificates for. 
And I'm interested in that 

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because you talk about people as
people. 

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You talk about these poor souls 
who have unfortunately died and 

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make the links about them. 
That must have been very 

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difficult for you to do. 
But I think you started off with

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a Cassidy Baraka. 
Yes, it's it is difficult, I 

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hate to say, but human nature 
makes it easier over time. 

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And now it's hard again because 
I grew up in this state, the 4th

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state that I now have. 
And as I dive into it, I see 

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people from my hometown. 
And so became difficult again. 

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But with with with Cassidy, I 
wouldn't write the name for a 

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while. 
And and nobody was paying 

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attention. 
And if you don't say a name, 

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don't believe you. 
So I looked up the obituary 

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public information. 
So everything I say is public 

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information. 
I'm not, you know, by law 

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restricted. 
So I did use her name because 

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it's so important to save the 
living children. 

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And I think she and her mother 
would want that. 

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And I've not contacted her 
mother for other reasons. 

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I was going to, but I was told 
not to. 

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There's, there's something that 
they, I think they, they 

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threatened her to be quiet and I
won't talk about that now. 

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It has to do with a certain 
agency in Massachusetts. 

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But yeah, it's very difficult. 
And the important thing is, is 

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to use the names because 
everybody else out there who's 

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listening, this is not just 
data. 

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And I, and I know that we're 
going to talk about data today 

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and people call me the data guy,
but most conclusive evidence is 

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an individual person who has, 
who had a reaction. 

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And what I did was I correlated 
to bears by looking at the age, 

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the gender, the state. 
OK, how many people died in the 

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state around that time? 
There were 4-7 year old girls 

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who died around that time. 
And in my book in chapter one I 

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go through all four and I show 
how the other three cannot 

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possibly be. 
The person in the bears record, 

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which the bears records are 
anonymous, there's no names, but

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it has to be Cassidy. 
There's no question it's 

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Cassidy. 
And in my lawsuit, I'm asking 

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the state to tell me, did you 
write that she died from COVID? 

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From COVID, whereas I know she 
died from the vaccine. 

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Tell the truth. 
I'm asking the judge to compel 

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the state to tell the truth 
because all the. 

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Parents that read that. 
In the newspaper and listened to

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it on television. 
It was on all the TV stations. 7

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year old dies from COVID. 
They all got their kids and they

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lined them right up for that 
shot and put them in harm's way.

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And I have several many that 
I've correlated with the new 

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state. 
I just found 41 vaccine deaths 

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that it correlated to theirs. 
In Massachusetts I have about 50

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and in Minnesota I have about 50
and then there's nine in in 

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Minnesota that actually state 
the vaccine killed. 

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00:12:02,280 --> 00:12:05,120
And in Massachusetts, there are 
10 that state the vaccine 

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killed, and in the new state, 4 
state that the vaccine killed 

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them. 
But the CDC only coded the first

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three in Minnesota, the first 
one in Massachusetts, and the 

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first one in the fourth state. 
And they stopped coding them 

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after that. 
And being that it's an 

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automatic. 
Software program that codes the 

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English words sent to them on 
the death records. 

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00:12:25,560 --> 00:12:28,000
That means somebody either 
deleted the codes after they 

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were generated and the code 
specifically is Y 59.0. 

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00:12:32,320 --> 00:12:35,320
And I can talk about how that's 
applicable to the UK in a moment

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if you if you want to go there. 
But Y 59.0 means viral vaccine 

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and that was generated for 
Solomon Kizito who died on 

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January 16th in Massachusetts. 
In the year 2021, it's only two 

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weeks into the vaccine program, 
he died, and it says it right on

228
00:12:55,040 --> 00:12:57,920
his death certificate. 
He died from acute bronchial 

229
00:12:57,920 --> 00:13:00,480
pneumonia in the setting of 
idiopathic thrombocytopenia. 

230
00:13:01,160 --> 00:13:03,160
Idiopathic meaning we don't know
where it came from. 

231
00:13:03,800 --> 00:13:05,640
Well, there's all kinds of 
papers showing how 

232
00:13:05,640 --> 00:13:10,480
thrombocytopenia is frequent 
from these vaccines and we, we 

233
00:13:10,480 --> 00:13:12,120
again, it's another thing we can
get into. 

234
00:13:12,120 --> 00:13:14,280
That's chapter 2. 
I show where that there's a 

235
00:13:14,280 --> 00:13:17,480
paper fatal post COVID mRNA 
vaccine associated cerebral 

236
00:13:17,480 --> 00:13:22,200
ischemia is the title and it 
says in every paragraph the 

237
00:13:22,200 --> 00:13:25,760
vaccine killed the 30 year old 
woman by stroke and so forth. 

238
00:13:26,000 --> 00:13:31,280
So the the CDC for the next 9 
did not code the word vaccine or

239
00:13:31,280 --> 00:13:35,440
vaccination where people died 
within 5 minutes of the vaccine 

240
00:13:35,440 --> 00:13:39,160
or one or two hours later or the
next day from a pulmonary 

241
00:13:39,160 --> 00:13:43,480
embolism or a cardiac arrhythmia
or a thrombocytopenia, all very 

242
00:13:43,480 --> 00:13:47,000
common from this vaccine. 
And I show death, death, death, 

243
00:13:47,000 --> 00:13:48,080
death. 
They knew. 

244
00:13:48,080 --> 00:13:50,560
Within weeks of this vaccine, 
they knew. 

245
00:13:50,560 --> 00:13:52,360
That it was killing people and 
they've been hiding it. 

246
00:13:52,360 --> 00:13:55,000
And that's why I say that, you 
know, data is good. 

247
00:13:55,400 --> 00:13:58,440
But individual cases are 
conclusive evidence where data 

248
00:13:58,440 --> 00:14:01,480
is persuasive and supplemental 
to the conclusive evidence. 

249
00:14:02,400 --> 00:14:07,240
It's always very interesting to 
put a name to these cases, John.

250
00:14:07,240 --> 00:14:10,720
And you're saying that this is 
something that has been miscoded

251
00:14:10,720 --> 00:14:13,440
in the UK as well, and that 
presumably this has been 

252
00:14:13,440 --> 00:14:17,480
happening all around the world? 
I can't say miscoded but it's 

253
00:14:17,480 --> 00:14:21,280
been avoided being coded. 
I, I would have to see the 

254
00:14:21,280 --> 00:14:24,720
individual records from the UK 
and the governments are hiding 

255
00:14:24,720 --> 00:14:28,920
all the records from the people.
I was, you know, now there's a 

256
00:14:28,920 --> 00:14:31,720
few of us that have gotten these
records, the woman in Minnesota 

257
00:14:31,720 --> 00:14:35,200
and Aaron Hertzberg in New York.
But we're talking worldwide. 

258
00:14:35,200 --> 00:14:37,200
We have what, three people 
who've gotten these records. 

259
00:14:37,960 --> 00:14:40,920
Oh, Bobby Bounds got some Nevada
and New Mexico too. 

260
00:14:40,920 --> 00:14:44,400
So that's four people. 
Yeah. 

261
00:14:44,400 --> 00:14:47,040
So in the UK. 
The. 

262
00:14:47,080 --> 00:14:51,960
The Y590 that I was mentioning 
has to do with EU 12.9. 

263
00:14:52,000 --> 00:14:55,000
U 12.9 is specific to the COVID 
vaccine. 

264
00:14:55,960 --> 00:15:01,640
And I testified in New Hampshire
with a bunch of factual 

265
00:15:01,640 --> 00:15:04,560
information for maybe 15 minutes
or so. 

266
00:15:04,920 --> 00:15:08,680
And the woman who came up after 
me from the Department of Health

267
00:15:08,680 --> 00:15:12,360
and Human Services of New 
Hampshire, chief of the Bureau 

268
00:15:12,360 --> 00:15:17,080
of Statistics, I believe within 
New Hampshire, stated that in 

269
00:15:17,080 --> 00:15:20,760
preparing for the meeting, she 
had her staff look at U-12 dot 

270
00:15:20,760 --> 00:15:24,400
nine and there were no deaths 
from COVID listed. 

271
00:15:24,880 --> 00:15:28,040
What she didn't know in stating 
that is that the United States 

272
00:15:28,040 --> 00:15:30,920
and the CDC did not adopt U 
12.9. 

273
00:15:31,080 --> 00:15:33,200
It is not mentioned in the CDC 
at all. 

274
00:15:33,200 --> 00:15:37,400
I combed through everything. 
However, I did find that it is 

275
00:15:37,400 --> 00:15:43,240
used by the UK and it is used by
Germany in the context of death 

276
00:15:43,240 --> 00:15:46,240
by COVID vaccine. 
But there's a a specific note 

277
00:15:46,360 --> 00:15:50,400
under that in in the UK, and 
that note states it's a 

278
00:15:50,400 --> 00:15:57,800
subcategory of Y 59.0. 
So you should see Y 59.0 just 

279
00:15:57,800 --> 00:16:00,560
like the United States uses Y59 
dot zero. 

280
00:16:00,560 --> 00:16:04,400
You probably won't see U 12.9. 
It needs to be looked at. 

281
00:16:04,400 --> 00:16:08,000
Find out how many hundreds are 
are coded that and if you find 

282
00:16:08,000 --> 00:16:11,080
them, that's only the tip of the
iceberg because the others are 

283
00:16:11,080 --> 00:16:13,960
probably being hidden like they 
did in the United States. 

284
00:16:14,640 --> 00:16:19,160
I mean, we've had some changes 
made to UK death certification 

285
00:16:19,160 --> 00:16:22,640
when this all started. 
So because we had a doctor 

286
00:16:22,640 --> 00:16:27,280
Shipman who basically bumped a 
lot of people off to basically 

287
00:16:27,720 --> 00:16:34,360
have a monetary gain, older 
people were put to to to sleep 

288
00:16:34,360 --> 00:16:37,000
by him. 
And because of that they changed

289
00:16:37,000 --> 00:16:39,160
the rules that you had to have 
two doctors sign the death 

290
00:16:39,160 --> 00:16:41,640
certificate until COVID came 
along. 

291
00:16:41,800 --> 00:16:46,400
And at the beginning of 2020 
they reduced it down to one 

292
00:16:46,400 --> 00:16:49,800
doctor and the doctor didn't 
even have to see the body, they 

293
00:16:49,800 --> 00:16:51,920
could actually certify the body 
remotely. 

294
00:16:53,160 --> 00:16:58,480
So the whole thing was open to a
lot of interpretation. 

295
00:16:59,320 --> 00:17:02,360
I know this. 
My mum in law who was 101 died. 

296
00:17:02,360 --> 00:17:04,680
Fortunately, on her death 
certificate it said she died of 

297
00:17:04,680 --> 00:17:06,520
old age, which obviously I 
couldn't argue with. 

298
00:17:07,000 --> 00:17:10,200
But at the end of the day, a lot
of people, because they hadn't 

299
00:17:10,200 --> 00:17:13,520
been seen and a lot of people at
the beginning, as you'll go on 

300
00:17:13,520 --> 00:17:17,079
to say we're in care homes, they
were elderly people and they 

301
00:17:17,079 --> 00:17:22,520
actually were basically said to 
have died for COVID. 

302
00:17:23,280 --> 00:17:25,800
There were no oxy autopsies 
being done, which obviously when

303
00:17:25,800 --> 00:17:29,200
a new medication comes out and 
there's deaths, autopsies have 

304
00:17:29,200 --> 00:17:32,080
always been done until this 
year, 2020. 

305
00:17:32,600 --> 00:17:37,360
And also the coroner coroner's 
office in the UK don't seem to 

306
00:17:37,360 --> 00:17:42,040
be centralised. 
We seem to have every coroner's 

307
00:17:42,040 --> 00:17:45,600
office acting independently in 
terms of getting information. 

308
00:17:46,440 --> 00:17:49,240
So that makes it again another 
difficulty. 

309
00:17:49,760 --> 00:17:53,080
But it's so it's, it's, it's 
similar but different in the UK.

310
00:17:53,880 --> 00:17:58,160
You've talked about errors in 
the certification omissions, 

311
00:17:58,160 --> 00:18:03,960
extremely rare labels, 
malfeasance being acted on and 

312
00:18:04,000 --> 00:18:07,720
deliberate fraud happening. 
So you've talked about federal 

313
00:18:07,720 --> 00:18:13,440
felony because the omission of 
root cause of death has been 

314
00:18:13,440 --> 00:18:15,800
omitted. 
And you talk about different 

315
00:18:15,800 --> 00:18:19,600
types of fraud, You talk about 
fraud of Commission and fraud of

316
00:18:19,600 --> 00:18:21,280
omission. 
Can you tell us a little bit 

317
00:18:21,280 --> 00:18:25,440
about that and whether there are
any court cases that have come 

318
00:18:25,440 --> 00:18:28,800
out of that knowledge? 
With regard to the fraud, the 

319
00:18:28,800 --> 00:18:32,520
fraud of omission would be 
omitting a vaccine as a cause of

320
00:18:32,520 --> 00:18:36,880
death where the certifier knows 
that the vaccine caused the 

321
00:18:36,880 --> 00:18:39,240
death. 
In such a case, Cassidy Baraka, 

322
00:18:39,640 --> 00:18:43,480
somebody wrote a bears report. 
So they knew they wrote that 

323
00:18:43,520 --> 00:18:47,000
report before she even died, two
days after she was injected. 

324
00:18:47,160 --> 00:18:51,360
They wrote the report and then 
she expecting her to die and she

325
00:18:51,360 --> 00:18:54,600
did die. 
With regard to Brianna McCarthy,

326
00:18:54,600 --> 00:18:58,440
30 years old, they wrote that 
the doctors at the Beth Israel 

327
00:18:58,440 --> 00:19:01,440
Deaconess Medical Center, 
Harvard Medical College, wrote a

328
00:19:01,440 --> 00:19:05,320
six page report and in that 
report they stated in the title 

329
00:19:05,560 --> 00:19:07,920
that she was killed by the 
vaccine by stroke. 

330
00:19:08,560 --> 00:19:10,120
And in every paragraph they say 
it. 

331
00:19:10,160 --> 00:19:13,840
In fact, they say a number of 
reports on these COVID vaccines 

332
00:19:14,160 --> 00:19:19,440
and several reports and they 
also write where CVST is not in 

333
00:19:19,440 --> 00:19:22,800
this particular case cause 
Brianna died from an ischemic 

334
00:19:22,800 --> 00:19:26,240
stroke. 
It says although this is not a 

335
00:19:26,240 --> 00:19:30,080
CVST, which means cerebral 
venous sinus thrombosis, it's a 

336
00:19:30,080 --> 00:19:34,080
type of hemorrhagic. 
Stroke, while CVST is not. 

337
00:19:35,080 --> 00:19:38,920
She did not die of this. 
There are several reports of CVS

338
00:19:38,920 --> 00:19:42,320
TS occurring from these vaccines
where thrombocytopenia is 

339
00:19:42,520 --> 00:19:45,440
frequent. 
OK, they're using words, several

340
00:19:45,440 --> 00:19:47,640
reports, a number of reports, 
and frequent. 

341
00:19:48,600 --> 00:19:52,800
And yet two weeks before Brianna
was injected on, she was 

342
00:19:52,800 --> 00:19:58,480
injected on March 30th, 2021, 
like 8-9 weeks into the vaccine 

343
00:19:58,480 --> 00:20:00,760
campaign. 
Two weeks before that, in 

344
00:20:00,760 --> 00:20:04,640
Massachusetts, not far away, 
Diane Dubois was 62 years old. 

345
00:20:04,640 --> 00:20:08,440
And on her death certificate it 
actually says, and it's not 

346
00:20:08,440 --> 00:20:13,080
coded OK. 
It actually says acute 

347
00:20:13,080 --> 00:20:17,160
intracranial hemorrhage in the 
setting of thrombocytopenia. 

348
00:20:17,800 --> 00:20:21,920
In a person vaccinated 11 days 
prior, it says vaccine on her 

349
00:20:21,920 --> 00:20:24,440
death record. 
It does not get coded with Y 

350
00:20:24,440 --> 00:20:26,360
59.0. 
It is fraudulent. 

351
00:20:26,360 --> 00:20:27,920
They know it, they're doing it 
on purpose. 

352
00:20:27,920 --> 00:20:31,400
So it's fraud of Commission in 
the fact that it, well, it's 

353
00:20:31,400 --> 00:20:35,240
fraud of omission. 
By omitting the the Y 59.0, the 

354
00:20:35,240 --> 00:20:41,000
fraud of Commission are all the 
acute fentanyl overdoses blunt 

355
00:20:41,000 --> 00:20:43,680
force trauma to the head, blunt 
force trauma to the torso? 

356
00:20:44,080 --> 00:20:45,840
They're testing dead bodies for 
COVID. 

357
00:20:46,040 --> 00:20:47,760
It was not causal in their 
death. 

358
00:20:48,280 --> 00:20:50,600
It is very clear what goes on at
death record. 

359
00:20:50,640 --> 00:20:54,000
It has to be causal in their 
death to be under the causes of 

360
00:20:54,000 --> 00:20:56,840
death. 
And that's part one ABCD right 

361
00:20:57,560 --> 00:21:01,080
in reverse time order. 
The the the most recent thing 

362
00:21:01,080 --> 00:21:04,400
that happened like they stopped 
breathing would be or that their

363
00:21:04,400 --> 00:21:06,560
heart stopped. 
That's cardio pulmonary arrest. 

364
00:21:06,560 --> 00:21:09,040
They they both stopped. 
That's the last thing that 

365
00:21:09,040 --> 00:21:11,240
happens before death. 
It's called the immediate 'cause

366
00:21:11,240 --> 00:21:15,920
that's in cause A. 
So the writing down that they 

367
00:21:15,920 --> 00:21:17,880
died from COVID is the fraud of 
Commish. 

368
00:21:18,200 --> 00:21:19,920
And they've done it over and 
over and over. 

369
00:21:19,920 --> 00:21:23,000
I have hundreds of accidental 
deaths labeled COVID. 

370
00:21:23,760 --> 00:21:27,800
And then I've spoken to, I 
interviewed a medical examiner 

371
00:21:27,800 --> 00:21:30,640
for 3 1/2 hours, reviewed 
hundreds of death certificates 

372
00:21:30,640 --> 00:21:34,840
with him. 
And he told me nobody came to 

373
00:21:34,840 --> 00:21:37,160
the office during that first 
nine weeks of COVID. 

374
00:21:37,240 --> 00:21:41,920
And, and in Massachusetts, it's 
like almost 9000 people. 8800 

375
00:21:41,920 --> 00:21:46,080
access more than normal deaths 
mid March to mid June of 2020. 

376
00:21:46,080 --> 00:21:48,960
That drove the fear of the 
pandemic worldwide, along with 

377
00:21:48,960 --> 00:21:53,720
New York City and New Jersey. 
I can talk about that in a few 

378
00:21:53,720 --> 00:21:56,720
minutes. 
Massachusetts kind of did this 

379
00:21:56,720 --> 00:21:58,360
to the world. 
There's a reason for that. 

380
00:21:58,960 --> 00:22:00,720
You're talking about codes 
there. 

381
00:22:00,840 --> 00:22:04,480
And in the UK we have something 
called ICD codes. 

382
00:22:04,760 --> 00:22:10,400
We have diagnostic codes and we 
do have a code for unvaccinated,

383
00:22:10,640 --> 00:22:13,600
but we don't have a code for 
vaccine injury. 

384
00:22:14,560 --> 00:22:18,560
And I think one piece of data 
that I found particularly 

385
00:22:18,560 --> 00:22:21,160
striking when you were talking 
about it, and I think you were 

386
00:22:21,160 --> 00:22:23,400
just going to perhaps hint on it
now. 

387
00:22:24,280 --> 00:22:27,400
But before I go to that, I want 
to say to people that are 

388
00:22:27,400 --> 00:22:30,640
watching and listening that 
Cheryl will show you in a minute

389
00:22:30,640 --> 00:22:33,440
John's book. 
But everything that John is 

390
00:22:33,440 --> 00:22:36,320
talking about today, he's 
written the most amazing book. 

391
00:22:36,320 --> 00:22:39,600
I don't suppose he ever expected
to be writing a book like this 

392
00:22:39,600 --> 00:22:44,120
in his lifetime. 
But the real cdc.com and that's 

393
00:22:44,120 --> 00:22:49,520
C with a little D and a big C. 
We'll have that on the screen. 

394
00:22:49,520 --> 00:22:53,760
And maybe John can give us a, a 
bit of an answer of why, why 

395
00:22:53,760 --> 00:22:56,320
it's called that because there's
a, there's a good reason behind 

396
00:22:56,320 --> 00:22:58,520
it. 
However, one of the things that 

397
00:22:58,520 --> 00:23:04,120
first triggered you into a bit 
of a red flag, John was the what

398
00:23:04,120 --> 00:23:09,200
you were talking about there was
respiratory deaths in 2020 were 

399
00:23:09,200 --> 00:23:12,680
coded J you were seeing them as 
AJ code. 

400
00:23:13,200 --> 00:23:17,280
And so those were, those were up
in 2020. 

401
00:23:17,280 --> 00:23:22,040
But then following on from that,
the J code kind of fell off the 

402
00:23:22,040 --> 00:23:26,440
radar and was replaced with 
other codes, with other 

403
00:23:26,440 --> 00:23:31,760
conditions, with people much 
younger because you were getting

404
00:23:31,760 --> 00:23:33,880
death certificates. 
I think it's important that we 

405
00:23:33,880 --> 00:23:35,640
highlight this to viewers and 
listeners. 

406
00:23:35,920 --> 00:23:37,440
You were getting them 
unredacted. 

407
00:23:37,640 --> 00:23:40,840
So you were getting their names,
their ages, their addresses, 

408
00:23:41,040 --> 00:23:45,200
their medical histories, I mean,
the most phenomenal amounts of 

409
00:23:45,280 --> 00:23:49,200
information. 
So when when you started to see 

410
00:23:49,200 --> 00:23:53,800
that the J code for respiratory 
conditions, which makes sense 

411
00:23:53,800 --> 00:23:58,960
with a respiratory illness, was 
suddenly disappearing, what were

412
00:23:58,960 --> 00:24:02,120
you noticing from there on? 
What kind of codes were you 

413
00:24:02,120 --> 00:24:04,880
seeing in replace of of the J 
code? 

414
00:24:05,440 --> 00:24:08,440
Yeah, so I, I mean, I'm an 
engineer, not a biologist or a 

415
00:24:08,440 --> 00:24:10,440
doctor, and I didn't even know 
there were codes. 

416
00:24:11,160 --> 00:24:15,880
What I did was I I noticed 
certain things were happening 

417
00:24:15,880 --> 00:24:18,920
more and then I saw these codes 
wait to the right and then 

418
00:24:18,920 --> 00:24:21,120
they're international. 
So your codes are the same as 

419
00:24:21,120 --> 00:24:23,920
our codes. 
We just, each, each nation 

420
00:24:23,920 --> 00:24:27,040
adopts a subset of the overall 
codes that are put out by the 

421
00:24:27,040 --> 00:24:33,200
WHL, but they're all the same. 
So J189 and 189 is pneumonia 

422
00:24:33,200 --> 00:24:37,280
unspecified. 
You know, J12 is viral pneumonia

423
00:24:37,280 --> 00:24:40,600
and J15IS bacterial pneumonia. 
That's important. 

424
00:24:40,600 --> 00:24:42,000
I'll, I'll discuss that in a 
moment. 

425
00:24:42,000 --> 00:24:45,840
But what I did was I just typed 
all the codes in and, and I 

426
00:24:45,840 --> 00:24:49,440
created a quick search tool. 
I did a lot of spreadsheet 

427
00:24:49,440 --> 00:24:53,920
calculations and formula and I 
put them all in a column and I 

428
00:24:53,920 --> 00:24:58,240
had 2020 versus 2021. 
Which one is in greater excess 

429
00:24:59,680 --> 00:25:03,600
from 2015 through 2019? 
So 2015 through 19 is the 

430
00:25:03,600 --> 00:25:08,480
baseline and looking at 2020 and
2021, which one is greater? 

431
00:25:08,640 --> 00:25:13,240
And the J codes, which are all 
respiratory flu, ARDSCOPD, 

432
00:25:13,240 --> 00:25:17,600
pneumonia, they all lit up in 
2020 that was in greater excess 

433
00:25:17,600 --> 00:25:20,120
than 21. 
And then when I got to the I 

434
00:25:20,120 --> 00:25:25,280
codes which are circulatory and 
the D codes, D for Delta, which 

435
00:25:25,280 --> 00:25:29,280
are blood and blood forming 
organs, the I codes and D codes 

436
00:25:29,280 --> 00:25:32,480
lit up in 21. 
So they were in greater numbers.

437
00:25:32,480 --> 00:25:37,160
And so I started then looking 
for hotspots in standard 

438
00:25:37,160 --> 00:25:40,400
deviations above norm and 
pulmonary embolism. 

439
00:25:40,400 --> 00:25:43,160
Popped out big time. 
So I looked at pulmonary 

440
00:25:43,160 --> 00:25:45,800
embolism and pulmonary embolism 
went up. 

441
00:25:46,400 --> 00:25:48,520
It it went up with in the year 
of COVID. 

442
00:25:48,800 --> 00:25:51,320
Because a lot of old people 
didn't get out of bed. 

443
00:25:51,320 --> 00:25:53,000
They were left in bed, not 
clots, right? 

444
00:25:53,000 --> 00:25:56,000
So they ended up with pulmonary 
emboli just by the nature of 

445
00:25:57,120 --> 00:26:00,960
8800 excess deaths. 
When I divide by the number of 

446
00:26:00,960 --> 00:26:03,440
total deaths in the year, 
pulmonary embolism kind of 

447
00:26:03,440 --> 00:26:05,920
disappears. 
It didn't really happen in 2020 

448
00:26:06,280 --> 00:26:09,120
in a prevalence factor, right, 
more than normal. 

449
00:26:09,120 --> 00:26:13,000
But in 2021, it went up 
substantially, like a lot. 

450
00:26:14,760 --> 00:26:19,280
And in 22 also, but, but back 
when I first did it, I only had 

451
00:26:19,280 --> 00:26:21,320
two months to 22. 
So I was really looking at 

452
00:26:21,320 --> 00:26:25,480
2020-2021. 
So pulmonary embolism and I 26.9

453
00:26:26,240 --> 00:26:28,520
pulmonary embolism without 
mention of acute core 

454
00:26:28,520 --> 00:26:32,000
pulmonality is the name of that.
That went up substantially in 

455
00:26:32,000 --> 00:26:35,080
21, not in 20. 
And but COVID at the same time 

456
00:26:35,080 --> 00:26:38,600
went down, pneumonia went down. 
South year over year from 2020 

457
00:26:38,600 --> 00:26:40,640
to 2021, you have all costs go 
down. 

458
00:26:41,040 --> 00:26:42,800
The marginal difference was cut 
in half. 

459
00:26:43,440 --> 00:26:46,800
The pneumonia marginal 
difference was cut in half, the 

460
00:26:46,800 --> 00:26:49,880
number of COVID deaths were cut 
in half, while at the same time 

461
00:26:50,040 --> 00:26:51,960
pulmonary embolism, a clot in 
your lung. 

462
00:26:52,040 --> 00:26:54,800
That kills you from ventricular 
failure. 

463
00:26:55,720 --> 00:26:58,800
That went up substantially. 
That's the opposite direction. 

464
00:26:59,120 --> 00:27:02,120
And so I looked at all the other
codes like and I'll just rattle 

465
00:27:02,120 --> 00:27:04,320
them off like acute post 
hemorrhagic anemia. 

466
00:27:04,800 --> 00:27:08,160
That's sudden blood loss, 
anemia, non traumatic because I 

467
00:27:08,160 --> 00:27:11,880
have the individual records. 
I could look through the 100 

468
00:27:11,880 --> 00:27:15,920
records or so and find that 89% 
of them had no trauma. 

469
00:27:16,160 --> 00:27:20,160
That means Nope, the steering 
wheel of the car didn't hit him 

470
00:27:20,360 --> 00:27:24,480
and rupture their aorta. 
They didn't have a surgeon make 

471
00:27:24,480 --> 00:27:27,320
a mistake. 
These were all of a sudden you 

472
00:27:27,320 --> 00:27:30,840
have a hole eaten through some 
major artery in your body and 

473
00:27:30,840 --> 00:27:32,400
you bleed out in your body and 
die. 

474
00:27:32,480 --> 00:27:36,080
That's that just doesn't happen 
like this, that all these things

475
00:27:36,080 --> 00:27:38,640
started happening more after the
vaccine. 

476
00:27:38,920 --> 00:27:42,400
So acute post hemorrhagic 
anemia, thrombocytopenia, acute,

477
00:27:42,760 --> 00:27:45,280
excuse me, a cardiac arrhythmia,
cardiac arrest. 

478
00:27:45,480 --> 00:27:49,040
Pulmonary embolism, The 
difficult ones were the 

479
00:27:49,040 --> 00:27:51,080
neurological. 
It took me another three or four

480
00:27:51,080 --> 00:27:53,760
months to find those because 
there are about 50 different 

481
00:27:53,760 --> 00:27:55,560
codes associated with 
neurological. 

482
00:27:55,560 --> 00:27:58,720
And that means all the different
kinds of strokes, brain 

483
00:27:58,720 --> 00:28:01,840
herniations, hemorrhagic 
strokes, ischemic strokes, What 

484
00:28:01,840 --> 00:28:04,720
part of the brain? 
By the time you look at all the 

485
00:28:04,720 --> 00:28:07,880
signals, every one of them is so
small that it's easier to go 

486
00:28:07,880 --> 00:28:10,720
through the individual records. 
And that's why Chapter 2 is 

487
00:28:10,720 --> 00:28:13,360
about 3 strokes and three women,
just to show the world. 

488
00:28:13,960 --> 00:28:18,240
That three women died in three 
months very early in vaccination

489
00:28:18,280 --> 00:28:21,120
and they were all covered up and
hidden by the government of 

490
00:28:21,200 --> 00:28:23,480
Massachusetts. 
Just before I ask you something 

491
00:28:23,480 --> 00:28:28,320
else, John, I want everybody to 
see your book, which is a really

492
00:28:28,320 --> 00:28:31,880
easy read. 
You've read written it for the 

493
00:28:31,880 --> 00:28:33,880
common man to actually 
understand. 

494
00:28:34,680 --> 00:28:37,520
Do you want to say a little bit 
about why it gets that title and

495
00:28:37,520 --> 00:28:39,800
why you've got your T-shirt on 
with the same emblem? 

496
00:28:40,440 --> 00:28:44,200
Sure. 
So my friend Mark Girido, whom 

497
00:28:44,200 --> 00:28:47,520
you may want to interview, he, 
he also came out with a book 

498
00:28:47,520 --> 00:28:53,120
recently, but he's in France. 
And I, I was in a group with 

499
00:28:53,120 --> 00:28:56,480
Steve Kirsch, Mark Girardeau, 
Kevin Mccurran, Jessica Rose, 

500
00:28:56,480 --> 00:29:00,160
Stephanie Senoff, Byron Bridal, 
Matthew Crawford and a few other

501
00:29:00,240 --> 00:29:03,800
people. 
We talked 2 hours a week and I 

502
00:29:04,360 --> 00:29:07,280
wanted to write a sub stack and 
I wanted to be anonymous at the 

503
00:29:07,280 --> 00:29:12,480
time. 
And so I have a black dog and I,

504
00:29:12,480 --> 00:29:15,280
I created a logo that Steve 
Kirsch didn't like and he made 

505
00:29:15,280 --> 00:29:18,120
me use just the dog's head. 
I had the profile of a dog 

506
00:29:18,760 --> 00:29:21,800
squatting. 
He didn't like that. 

507
00:29:21,800 --> 00:29:25,560
So I just used the head, but I 
wanted it to be called bad dog 

508
00:29:25,560 --> 00:29:28,720
or Naughty Dog. 
And I said, Mark, you know, how 

509
00:29:28,720 --> 00:29:31,760
do you say that in French? 
And he, he, we went back and 

510
00:29:31,760 --> 00:29:33,960
forth like five times, like, no,
that's not what I mean. 

511
00:29:33,960 --> 00:29:37,880
No, I said, Mark, what would you
yell at your dog if he pooped on

512
00:29:37,880 --> 00:29:41,320
the floor in the house? 
And Mark said cocaine a Chien. 

513
00:29:41,680 --> 00:29:43,640
And I said, OK, that's what I'll
use. 

514
00:29:44,160 --> 00:29:48,760
So I used cocaine Chien and that
was my Substack name for a 

515
00:29:48,760 --> 00:29:50,640
while. 
And that means Bad Dog or 

516
00:29:50,640 --> 00:29:54,480
Naughty Dog in French. 
Well, serendipitous to that is 

517
00:29:54,480 --> 00:29:58,600
the fact that the initials are 
CDC, where the D is lowercase 

518
00:29:58,640 --> 00:30:04,920
for the D Cocaine de Chien, 
That's where it came from, Mark.

519
00:30:04,920 --> 00:30:08,760
Talks about the bolus injection,
how that's that's largely 

520
00:30:08,760 --> 00:30:10,320
responsible for most of the 
problems. 

521
00:30:10,440 --> 00:30:14,280
Just to say you can get it off 
Amazon, I had to go to Amazon 

522
00:30:14,440 --> 00:30:16,800
US, but they would deliver. 
So that's good. 

523
00:30:16,800 --> 00:30:20,640
That's how I got my book. 
So you, you mentioned about 

524
00:30:20,640 --> 00:30:23,560
seasonality. 
So you talk about seasonality 

525
00:30:23,560 --> 00:30:26,520
profiles and aid spectrum 
profiles and then symptom 

526
00:30:26,520 --> 00:30:30,760
profiles. 
So seasonality basically, we're 

527
00:30:30,760 --> 00:30:33,280
used to infections like flu 
starting off in Australia, in 

528
00:30:33,280 --> 00:30:35,440
this country and then eventually
get into the UK. 

529
00:30:37,520 --> 00:30:44,160
In 2020 we had COVID everywhere.
So what do you have to say about

530
00:30:44,160 --> 00:30:46,600
seasonality and the behaviour in
2020? 

531
00:30:47,160 --> 00:30:49,800
So with seasonality in the 
especially in the northern 

532
00:30:49,800 --> 00:30:53,760
climates, you'll have more 
respiratory deaths associated 

533
00:30:53,760 --> 00:30:55,400
deaths in the winter than in the
summer. 

534
00:30:55,560 --> 00:30:59,240
And when you look at an all 
cause deaths analysis or just a 

535
00:30:59,240 --> 00:31:01,120
wave form of. 
All the people. 

536
00:31:01,120 --> 00:31:05,000
Dying in a society in the North,
you will see a sine wave. 

537
00:31:05,400 --> 00:31:08,440
I've, I've been told it's not a 
sine wave like, well, it sure 

538
00:31:08,440 --> 00:31:09,760
looks like one. 
It's pretty close. 

539
00:31:10,360 --> 00:31:12,040
There's more in the winter than 
in the summer. 

540
00:31:12,760 --> 00:31:14,800
And then if you look at 
pneumonia, you'll see more in 

541
00:31:14,800 --> 00:31:18,480
the winter than in the summer. 
And if you look at COVID, you'll

542
00:31:18,480 --> 00:31:22,000
see it enter, say, Massachusetts
around March. 

543
00:31:23,000 --> 00:31:26,120
So it didn't start in November 
like a normal full season would,

544
00:31:26,680 --> 00:31:30,200
but then it goes down in May and
it was pretty much done in June 

545
00:31:30,720 --> 00:31:33,400
and then it came back in 
November and you have a second 

546
00:31:33,400 --> 00:31:35,680
wave. 
And then in the 3rd wave, which 

547
00:31:35,680 --> 00:31:38,920
is non existent, Covid's been 
over for a couple of years, if 

548
00:31:38,920 --> 00:31:40,240
you even believe there was 
COVID. 

549
00:31:42,160 --> 00:31:45,080
So that's, that's just to 
establish what seasonality is 

550
00:31:47,200 --> 00:31:48,760
what. 
Happened when you when you start

551
00:31:48,760 --> 00:31:52,240
stripping away like what's 
normal and you go looking at 

552
00:31:52,240 --> 00:31:56,120
individual causes and you find 
that pneumonia was up. 

553
00:31:56,560 --> 00:31:59,880
It was, it was up in the first 
wave of COVID and then it 

554
00:31:59,880 --> 00:32:02,960
diminished like it should as a 
disease would in the older 

555
00:32:02,960 --> 00:32:06,880
population. 
However, very interestingly, 

556
00:32:06,880 --> 00:32:11,000
after they started vaccinating 
people, death by pneumonia in 

557
00:32:11,000 --> 00:32:16,880
the younger people, and I mean 
like 25 to 75, you'll see as you

558
00:32:16,880 --> 00:32:20,240
get younger and younger, the 3rd
wave is greater than the 1st 

559
00:32:20,240 --> 00:32:22,480
wave. 
You tell me what disease comes 

560
00:32:22,480 --> 00:32:25,160
in that somebody survives 2 
years of and then in the third 

561
00:32:25,160 --> 00:32:29,960
year starts dying of pneumonia? 
I mean, so if you look by cause,

562
00:32:30,480 --> 00:32:35,000
you see that the seasonality, 
it's, it's kind of backwards, 

563
00:32:35,000 --> 00:32:37,640
right? 
But now let's talk about 

564
00:32:37,640 --> 00:32:42,400
something that is not seasonal, 
like cardiac arrhythmia or 

565
00:32:42,400 --> 00:32:46,040
things like that. 
When you strip away all the 

566
00:32:46,040 --> 00:32:50,120
normal, what's leftover is a 
signal that is a linearly 

567
00:32:50,120 --> 00:32:53,280
increasing line that resembles 
the uptake of vaccine. 

568
00:32:54,160 --> 00:32:56,720
So you don't have a seat and you
say, oh, well, that's COVID. 

569
00:32:56,880 --> 00:32:59,720
Oh, COVID causes that? 
You know, I was hiking and with 

570
00:32:59,720 --> 00:33:03,560
doctor I ran into I coached his 
son in football, English 

571
00:33:03,560 --> 00:33:08,200
football, United States soccer. 
And he knows that I lost my son 

572
00:33:08,200 --> 00:33:10,000
and and his wife is a 
pediatrician. 

573
00:33:10,000 --> 00:33:14,840
He's a cardiologist. 
He said what are you doing 

574
00:33:15,160 --> 00:33:16,800
lately? 
Because he knows I'm depressed. 

575
00:33:16,800 --> 00:33:18,320
I was like, I'm just writing a 
book. 

576
00:33:18,320 --> 00:33:20,880
Well, what's it about? 
COVID And then, you know, that's

577
00:33:20,880 --> 00:33:22,960
a serious disease, you know, 
like, yeah, OK. 

578
00:33:23,160 --> 00:33:26,680
And then it came to. 
I'm a cardiologist. 

579
00:33:26,680 --> 00:33:28,880
I know, I know. 
I'm like, OK, what about 

580
00:33:28,920 --> 00:33:32,080
eosinophilic myocarditis or 
lymphohistiocytic myocarditis? 

581
00:33:32,640 --> 00:33:35,200
He starts walking away and he 
turns around and says that 

582
00:33:35,200 --> 00:33:36,800
happens with COVID too, you 
know. 

583
00:33:38,160 --> 00:33:41,360
Yeah, no, it doesn't. 
Not not like this. 

584
00:33:41,680 --> 00:33:45,840
So the the prevalence of the 
seasonality of things like I 

585
00:33:45,840 --> 00:33:48,400
don't like to talk about 
myocarditis because it's only 1%

586
00:33:48,400 --> 00:33:50,680
of the overall deaths. 
I think it's over talked about. 

587
00:33:51,000 --> 00:33:53,240
People are dying of many things 
if you look at pulmonary 

588
00:33:53,240 --> 00:33:55,720
embolism. 
It went along with the uptake of

589
00:33:55,720 --> 00:33:58,320
vaccine. 
It did not come in seasonally 

590
00:33:58,320 --> 00:34:01,320
with COVID. 
It it was not shown to be more 

591
00:34:01,320 --> 00:34:04,520
prevalent in overall death in 
2020. 

592
00:34:04,680 --> 00:34:07,760
It happened in 21 and it 
happened along with the curve of

593
00:34:07,760 --> 00:34:12,000
the uptake of vaccine which is 
linearly increasing curve, not a

594
00:34:12,000 --> 00:34:14,880
seasonal, respiratory or 
seasonal curve. 

595
00:34:15,480 --> 00:34:17,920
The same with thrombocytopenia 
and all the others so. 

596
00:34:18,320 --> 00:34:22,120
The seasonality of when people 
die throughout the year has an 

597
00:34:22,120 --> 00:34:26,560
association mostly with 
respiratory stuff if it gets 

598
00:34:26,560 --> 00:34:29,400
really super cold. 
Then people do die of heart 

599
00:34:29,400 --> 00:34:32,920
attacks and there's probably 
some reason, like, I don't know,

600
00:34:32,920 --> 00:34:37,480
they're snow blowing the snow 
away and we're trying to shovel 

601
00:34:37,480 --> 00:34:38,520
too much and they die of a 
heart. 

602
00:34:38,520 --> 00:34:41,840
Attack but there is a signal, 
but it has to be extreme, has to

603
00:34:41,840 --> 00:34:45,080
be extreme cold or extreme hot. 
They die of heart attacks. 

604
00:34:45,840 --> 00:34:48,880
So yeah, I've, I've proven in 
the data that. 

605
00:34:49,639 --> 00:34:54,040
Respiratory type illnesses die 
more in the winter than the 

606
00:34:54,040 --> 00:34:57,760
summer, but they're dying more 
in the third wave. 

607
00:34:58,000 --> 00:35:00,400
So when you start subtracting 
out, what should happen? 

608
00:35:00,760 --> 00:35:02,240
It's like, well, this shouldn't 
happen. 

609
00:35:02,240 --> 00:35:05,400
Why are people's immune? 
All the immune mechanism, the I8

610
00:35:05,800 --> 00:35:10,800
and D8 codes, mostly the D8 
codes, the immune mechanism, 

611
00:35:11,080 --> 00:35:13,680
It's out of whack. 
It has been from the very, very 

612
00:35:13,680 --> 00:35:16,600
beginning of the vaccine, not 
COVID. 

613
00:35:17,160 --> 00:35:19,760
It's very stark. 
It starts in 21, not 20. 

614
00:35:19,800 --> 00:35:24,200
The biggest wave of COVID was in
purely in 2020 in Massachusetts.

615
00:35:25,120 --> 00:35:28,920
And and so these differences in 
seasonality are very stark and 

616
00:35:28,920 --> 00:35:30,560
they show that it can't be 
COVID. 

617
00:35:30,680 --> 00:35:33,600
It must be something else. 
And so I'm saying that something

618
00:35:33,600 --> 00:35:35,840
else. 
Is likely the vaccine. 

619
00:35:36,520 --> 00:35:39,360
I mean, I find it fascinating 
that you've got these, all of 

620
00:35:39,360 --> 00:35:42,040
these death certificates that 
anybody would even think to send

621
00:35:42,040 --> 00:35:46,240
them to you, quite honestly. 
But the fact is, is that you 

622
00:35:46,240 --> 00:35:50,160
have got them. 
And I'm just thinking to myself,

623
00:35:50,160 --> 00:35:54,920
well, how do we in the UK get 
data like this? 

624
00:35:54,920 --> 00:35:56,600
We're probably not going to get 
it. 

625
00:35:56,600 --> 00:36:00,360
But one thing that you didn't, 
I'm presuming wasn't on the 

626
00:36:00,360 --> 00:36:04,880
death certificates was 
vaccination status, because we 

627
00:36:04,880 --> 00:36:10,280
don't seem to have any data just
with the with the correlation of

628
00:36:10,280 --> 00:36:13,440
vaccinated and unvaccination. 
If we had that, I guess that 

629
00:36:13,440 --> 00:36:16,840
would be the Holy Grail. 
But am I right in thinking that 

630
00:36:16,920 --> 00:36:19,400
you didn't have the vaccination 
status on the death 

631
00:36:19,400 --> 00:36:21,440
certificates? 
That's correct. 

632
00:36:21,680 --> 00:36:24,040
I, I used the bears database to 
correlate. 

633
00:36:24,040 --> 00:36:27,240
I wanted to make sure that I 
have the right person and that's

634
00:36:27,240 --> 00:36:30,120
where the there's a chapter in 
the book that says bears 

635
00:36:30,120 --> 00:36:32,600
correlation, right? 
I can't remember the name of the

636
00:36:32,600 --> 00:36:36,280
title of the chapter, but yeah, 
it's where I correlate the two 

637
00:36:36,280 --> 00:36:38,280
and and the correlation is is 
sound. 

638
00:36:38,280 --> 00:36:42,800
I show you every person who died
that is that age, that gender. 

639
00:36:42,920 --> 00:36:45,200
Died on that. 
Date, right, because sometimes 

640
00:36:45,200 --> 00:36:49,240
the bears records say of date 
they died and then I find other 

641
00:36:49,240 --> 00:36:52,880
conditions and writings within 
the the bears record. 

642
00:36:52,880 --> 00:36:55,240
In fact, there's 112 year old 
girl. 

643
00:36:55,880 --> 00:36:59,880
It's it appears that somebody 
wrote the Bears record using the

644
00:36:59,880 --> 00:37:03,080
language from the death record, 
which is cerebellar, tonsillar 

645
00:37:03,080 --> 00:37:07,560
and bilateral uncle herniation. 
It's the only time I saw that in

646
00:37:07,720 --> 00:37:10,160
a million records. 
And there it is. 

647
00:37:10,200 --> 00:37:13,400
It's in both the Veris record 
and the death record and she was

648
00:37:13,400 --> 00:37:15,960
injected on March 3rd. 
This is where I get the 

649
00:37:15,960 --> 00:37:19,560
injection dates on Veris record,
March 3rd, 2022. 

650
00:37:20,000 --> 00:37:24,480
She died on March 29th. 
She had four injections 4 and 

651
00:37:24,480 --> 00:37:31,120
that was HPV meningococcal Tdap.
And her third COVID vaccine, 3 

652
00:37:31,120 --> 00:37:33,040
in her left arm, one in her 
right arm. 

653
00:37:33,400 --> 00:37:36,240
So yeah, I have detailed 
information from those records. 

654
00:37:36,560 --> 00:37:40,720
And in fact, I told you earlier,
some of the death records 

655
00:37:40,720 --> 00:37:43,400
actually state that the vaccine 
killed the person. 

656
00:37:43,920 --> 00:37:47,520
And because they're not coded by
the CDC, like I said, the first 

657
00:37:47,520 --> 00:37:49,640
one in Massachusetts was coded 
the next 9. 

658
00:37:49,640 --> 00:37:52,720
Were not the first three in 
Minnesota, the next six were not

659
00:37:52,920 --> 00:37:56,440
the first one in the fourth 
state, and the next three were 

660
00:37:56,440 --> 00:38:00,280
not meaning. 
It says it on the death record 

661
00:38:00,280 --> 00:38:01,880
that the vaccine killed these 
people. 

662
00:38:01,960 --> 00:38:05,800
And if you extrapolate that you 
have hundreds across the US, but

663
00:38:05,800 --> 00:38:09,000
you don't see it in the data. 
And that's all anybody can find 

664
00:38:09,000 --> 00:38:10,680
is the data like in the UK, 
right. 

665
00:38:10,680 --> 00:38:15,120
You need you need to see the the
data being the codes, the I CD10

666
00:38:15,120 --> 00:38:16,480
codes. 
That's all you really get to 

667
00:38:16,480 --> 00:38:18,600
see. 
You don't see the individual 

668
00:38:18,600 --> 00:38:20,800
writings. 
What did the medical examiner? 

669
00:38:20,800 --> 00:38:23,400
Or doctor write. 
On the death certificate and 

670
00:38:23,400 --> 00:38:26,360
that's why record level source 
data is so important. 

671
00:38:26,960 --> 00:38:30,080
And I and I could, I could 
really tell you how many of the 

672
00:38:30,080 --> 00:38:33,640
papers written around the world 
by all the PhDs are wrong 

673
00:38:33,800 --> 00:38:36,240
because they're making 
assumptions as to the underlying

674
00:38:36,240 --> 00:38:39,480
meaning of the codes and the 
data, especially with regard to 

675
00:38:39,480 --> 00:38:43,320
cardiac arrest. 
I got something coming out on 

676
00:38:43,320 --> 00:38:48,080
that last 30 years of papers 
using I4 codes are probably 

677
00:38:48,080 --> 00:38:50,680
wrong. 
I think it's important that that

678
00:38:50,680 --> 00:38:55,240
we also remind our viewers that 
the VARES data that you get in 

679
00:38:55,240 --> 00:38:59,320
the USA is very different from 
the MHRA data which we get here,

680
00:38:59,320 --> 00:39:02,600
because we've always said that 
with VARES you get a lot more 

681
00:39:02,600 --> 00:39:06,120
detail. 
With the MHRA data, we get 

682
00:39:06,120 --> 00:39:10,480
absolutely nothing. 
But it's not just the injections

683
00:39:10,480 --> 00:39:12,880
that you've noticed. 
And I know that I'm going to 

684
00:39:12,880 --> 00:39:17,560
hand over to Cheryl because she 
wants to ask you about serious 

685
00:39:17,560 --> 00:39:20,400
adverse reactions and also 
remdesivir. 

686
00:39:20,760 --> 00:39:23,800
And there's so many questions we
both want to ask you. 

687
00:39:23,800 --> 00:39:26,000
I think we're going to have to 
ask you to come back if you'd 

688
00:39:26,000 --> 00:39:30,160
agree to, for a second time 
because I want to ask you also 

689
00:39:30,160 --> 00:39:34,480
about antibiotics, which I've 
noticed that you've noticed. 

690
00:39:34,480 --> 00:39:37,720
And I'm feeling that that is of 
great significance. 

691
00:39:37,720 --> 00:39:42,360
So I'll hand back across to 
Cheryl to pick up where she left

692
00:39:42,360 --> 00:39:44,280
off. 
And then maybe if we've got 

693
00:39:44,280 --> 00:39:47,520
time, we can just briefly talk 
about the future. 

694
00:39:47,720 --> 00:39:50,960
So over to Cheryl. 
The cardiologist you spoke of a 

695
00:39:50,960 --> 00:39:53,920
little while ago, these are 
doctors that are seeing things 

696
00:39:53,920 --> 00:39:56,920
that they don't normally see and
yet are not noticing them and, 

697
00:39:56,920 --> 00:40:00,000
and recording them. 
Or if anybody talks about these 

698
00:40:00,000 --> 00:40:03,280
things, it's always. 
Extremely rare, very rare. 

699
00:40:03,640 --> 00:40:06,680
I mean, I don't know how you 
classify rare, but it's not the 

700
00:40:06,680 --> 00:40:10,520
way that I classify rare. 
When we've seen this data, the 

701
00:40:10,960 --> 00:40:17,080
AstraZeneca vaccine was running 
at about one in 253 doses as 

702
00:40:17,080 --> 00:40:21,200
adverse serious adverse reaction
and Pfizer in the UK was running

703
00:40:21,200 --> 00:40:26,400
at one in 535 doses. 
So it's it's not rare and it 

704
00:40:26,400 --> 00:40:28,120
just depends what that 
definition is. 

705
00:40:28,400 --> 00:40:32,440
But when we go into acute renal 
failure, when we've got 

706
00:40:33,040 --> 00:40:36,720
remdesivir that of course Fauci 
you mentioned was involved with 

707
00:40:36,920 --> 00:40:41,760
and his wife, then we've got 
remdesivir being given out with 

708
00:40:42,120 --> 00:40:46,800
COVID shots with an 
immunomodulator and vancomycin, 

709
00:40:46,800 --> 00:40:50,000
which is obviously a serious 
antibiotic. 

710
00:40:50,880 --> 00:40:54,280
All of these things reduce 
immunity and they also have 

711
00:40:54,360 --> 00:40:57,040
effects on the kidneys and 
hepatic effects. 

712
00:40:57,360 --> 00:41:01,080
So when you're seeing acute 
renal failure, do you blame the 

713
00:41:01,080 --> 00:41:02,680
protocols that were being 
followed? 

714
00:41:03,520 --> 00:41:05,880
Absolutely. 
I've, I've knew something new 

715
00:41:05,880 --> 00:41:08,640
coming out, whether they died in
hospital or not. 

716
00:41:08,840 --> 00:41:12,560
And it's, it's very difficult to
see because most people who die 

717
00:41:12,560 --> 00:41:15,320
from acute renal failure, acute 
is sudden, right? 

718
00:41:15,480 --> 00:41:16,760
You didn't have a problem 
before. 

719
00:41:16,760 --> 00:41:18,640
Now all of a sudden you have a 
problem and you die from it. 

720
00:41:19,680 --> 00:41:23,680
But it still went up despite the
fact that you'd expect all acute

721
00:41:23,680 --> 00:41:26,160
renal failure deaths to end up 
in the hospital because 

722
00:41:26,160 --> 00:41:28,400
somebody's kidneys not working, 
they degrade. 

723
00:41:28,400 --> 00:41:29,680
It's like, oh, I'm feeling 
terrible. 

724
00:41:29,680 --> 00:41:32,040
I go to the hospital, oh, your 
kidneys are failing. 

725
00:41:32,040 --> 00:41:36,120
Oh, you die, right. 
So but it's it's still up And 

726
00:41:36,120 --> 00:41:40,720
and the fact that it's happening
in the hospital more than not in

727
00:41:40,720 --> 00:41:43,000
the in the marginal difference 
of data. 

728
00:41:43,240 --> 00:41:45,040
Let me, let me. 
Forget about the data and just 

729
00:41:45,040 --> 00:41:49,680
get to the point. the United 
States has hospital protocols 

730
00:41:49,680 --> 00:41:52,640
different from other places. 
I'm, I'm asking somebody in 

731
00:41:52,640 --> 00:41:54,520
Europe. 
I have a couple of people I've 

732
00:41:54,520 --> 00:41:56,560
asked. 
I won't say their name yet 

733
00:41:56,560 --> 00:41:57,760
because they haven't agreed to 
do it. 

734
00:41:58,520 --> 00:42:01,880
If they can show acute renal 
failure in the UK and in Europe,

735
00:42:02,040 --> 00:42:04,640
different from the United 
States, then we can show that 

736
00:42:04,640 --> 00:42:07,600
the the hospital protocols were 
different the United States and 

737
00:42:07,600 --> 00:42:09,200
it was the hospital. 
Protocols that killed the 

738
00:42:09,200 --> 00:42:11,040
people. 
We're not talking about a few 

739
00:42:11,040 --> 00:42:14,000
people. 
This is 153,000 excess people in

740
00:42:14,000 --> 00:42:16,680
the last 3 1/2 years from acute 
renal failure alone. 

741
00:42:17,320 --> 00:42:20,920
That should be the biggest 
single cause of death since over

742
00:42:20,920 --> 00:42:24,160
100 years from something in the 
United States. 

743
00:42:24,520 --> 00:42:28,640
It and you know, I'll just say 
Ed Dowd and Finance Technologies

744
00:42:28,640 --> 00:42:30,520
will be coming out with the same
study. 

745
00:42:30,520 --> 00:42:33,040
We have a different or different
group than me. 

746
00:42:33,560 --> 00:42:36,800
They're using a different 
database than I have and they 

747
00:42:36,800 --> 00:42:39,240
have different people and 
methodologies that I'm using. 

748
00:42:39,760 --> 00:42:41,160
And they came up with the same 
number. 

749
00:42:42,080 --> 00:42:44,600
It's the big, it's the biggest 
story nobody's talking about. 

750
00:42:44,800 --> 00:42:47,960
And what is it? 
You mentioned vancomycin. 

751
00:42:47,960 --> 00:42:50,200
So antibiotic, that's 
vancomycin. 

752
00:42:50,600 --> 00:42:52,960
They're giving it for sepsis. 
The person's probably going to 

753
00:42:52,960 --> 00:42:57,400
die anyway because their immune 
system's shattered and it's too.

754
00:42:57,400 --> 00:43:01,840
Late at that point, but. 
It seems in the records and I 

755
00:43:01,840 --> 00:43:05,440
have 5 maybe six different 
patients records. 

756
00:43:05,520 --> 00:43:08,280
That's in the CDC memorandum, my
second publication. 

757
00:43:09,880 --> 00:43:12,800
They were given, two were given 
baricitinib, two were given 

758
00:43:12,800 --> 00:43:17,080
remdesivir, all five were given 
vancomycin and the timing at 

759
00:43:17,080 --> 00:43:20,880
which the the liver enzymes and 
creatinine levels, the, the, the

760
00:43:20,880 --> 00:43:24,800
tests for kidney function and 
liver function, it didn't seem 

761
00:43:24,800 --> 00:43:28,480
to start failing until after the
vancomycin. 

762
00:43:28,960 --> 00:43:31,960
But I don't know is it the 
vancomycin in combination with 

763
00:43:31,960 --> 00:43:36,000
Remdesivir or you you mentioned 
an immunomodulator that would be

764
00:43:36,040 --> 00:43:38,400
baricitinib. 
As one of them I believe you're 

765
00:43:38,400 --> 00:43:41,480
talking about. 
These were all incentivized for 

766
00:43:41,480 --> 00:43:46,520
the hospitals to use a 20% adder
on $1,000,000 hospital stay is 

767
00:43:46,520 --> 00:43:50,440
an extra $200,000 to pump 
remdesivir through somebody's 

768
00:43:50,440 --> 00:43:53,920
veins on simply a positive COVID
test, even if they're sitting 

769
00:43:53,920 --> 00:43:57,120
there with 95% oxygen levels and
normal vital signs. 

770
00:43:57,320 --> 00:43:59,720
And they did do this. 
They did it to many patients. 

771
00:44:00,200 --> 00:44:03,800
The important thing about acute 
renal failure is also that it's 

772
00:44:03,800 --> 00:44:08,520
not just the old people dying. 
This goes down into the 25 to 44

773
00:44:08,520 --> 00:44:10,400
range. 
I have new slides I've 

774
00:44:10,400 --> 00:44:14,440
generated. 
I just did 121 last night, 121 

775
00:44:14,440 --> 00:44:20,000
different sets of graphs, each 
being 6 graphs per set or three 

776
00:44:20,000 --> 00:44:22,760
graphs per set depending on 
which slide it is. 

777
00:44:23,400 --> 00:44:26,040
So that's that's like 400 
different graphs I just 

778
00:44:26,040 --> 00:44:29,440
generated for acute renal 
failure all the way down to 25 

779
00:44:29,440 --> 00:44:32,520
years old. 
There's 100% increase, 100%, 

780
00:44:32,800 --> 00:44:36,320
which totals 150,000 people and 
the life years lost for these 

781
00:44:36,320 --> 00:44:39,760
young people with families, they
have parents living, they have 

782
00:44:39,760 --> 00:44:43,120
children, they're being killed 
because they get a positive 

783
00:44:43,120 --> 00:44:45,640
COVID test going in the hospital
and the hospital starts them on 

784
00:44:45,640 --> 00:44:47,920
a drug protocol put out by the 
NIH. 

785
00:44:48,280 --> 00:44:51,000
And the NIH knows darn well 
these people are dying and 

786
00:44:51,000 --> 00:44:53,600
they're still killing people and
that's murder. 

787
00:44:53,600 --> 00:44:56,560
I hope I answered the question. 
Sometimes I. 

788
00:44:57,240 --> 00:45:00,000
Go on a little bit. 
We're grateful for your answer. 

789
00:45:00,480 --> 00:45:03,840
In the UK, it was midazolam and 
morphine that was particularly 

790
00:45:03,840 --> 00:45:07,280
used in the care homes and that 
obviously that's a death row 

791
00:45:07,280 --> 00:45:10,280
drug. 
And I don't know whether much 

792
00:45:10,280 --> 00:45:14,880
midazolam was used in the 
States, but not only was it a 

793
00:45:14,880 --> 00:45:19,560
lot was ordered, they used 2 1/2
times the annual amount in about

794
00:45:19,560 --> 00:45:23,520
three to four months and 
basically at very high doses. 

795
00:45:24,080 --> 00:45:27,800
And, and why would you give 
midazolam and morphine, both 

796
00:45:27,800 --> 00:45:30,720
respiratory depressants for 
somebody who got a respiratory 

797
00:45:30,720 --> 00:45:32,960
infection? 
It doesn't make sense. 

798
00:45:33,360 --> 00:45:37,520
And those are the the products 
that we're very worried about. 

799
00:45:37,720 --> 00:45:41,000
But in terms of what might be 
coming our way, I know Debbie 

800
00:45:41,000 --> 00:45:45,600
has major worries about 
vancomycin and how they're even 

801
00:45:45,920 --> 00:45:50,960
talking about paediatric doses 
of it, which sounds quite 

802
00:45:50,960 --> 00:45:53,360
horrific to me. 
I've listened to many of your 

803
00:45:53,360 --> 00:45:57,560
interviews, John fascinated. 
And I've been looking at 

804
00:45:57,560 --> 00:46:01,440
antimicrobial resistance and 
I've been looking at the use of 

805
00:46:01,440 --> 00:46:04,480
antibiotics and I've been 
looking at what we've been using

806
00:46:04,480 --> 00:46:07,560
in the UK. 
And we, we do use vancomycin, 

807
00:46:07,560 --> 00:46:11,200
but there's another one that I'm
looking into that's part of the 

808
00:46:11,200 --> 00:46:15,560
same family. 
And the dangers of vancomycin 

809
00:46:15,560 --> 00:46:20,560
obviously are renal failure. 
And with the agenda moving 

810
00:46:20,560 --> 00:46:25,160
forward and with all that you've
seen with the whole COVID 

811
00:46:25,160 --> 00:46:29,720
narrative and the COVID data and
the people associated to that 

812
00:46:29,720 --> 00:46:33,880
data, I'm wondering if what 
we're looking forward to is 

813
00:46:33,880 --> 00:46:39,280
another pandemic, but this time 
of antimicrobial resistance 

814
00:46:39,280 --> 00:46:43,160
where we're going to be looking 
at more mRNA shots coming down 

815
00:46:43,160 --> 00:46:50,400
the line for AMR, probably 
mrnas, more combination novel 

816
00:46:50,480 --> 00:46:54,200
antimicrobials, which of course 
takes into consideration not 

817
00:46:54,200 --> 00:46:57,800
just antibiotics, but it takes 
into consideration antifungals 

818
00:46:57,800 --> 00:47:02,040
and anti parasitics as well. 
So the whole family of 

819
00:47:02,040 --> 00:47:06,840
antibiotics and I don't know, 
noticing what you had noticed in

820
00:47:06,840 --> 00:47:09,680
that people were getting really 
sick and was that the 

821
00:47:09,680 --> 00:47:12,160
contraindication? 
I just wondered what your 

822
00:47:12,160 --> 00:47:15,880
thoughts were with regards to 
moving forward on antibiotics 

823
00:47:15,880 --> 00:47:20,520
because what we're seeing here 
seems to be a narrative that's 

824
00:47:20,520 --> 00:47:26,440
pointing towards Shigella, E 
coli, Cryptosporidium, water 

825
00:47:26,440 --> 00:47:30,560
based wastewater and clean 
water. 

826
00:47:30,960 --> 00:47:34,760
And we're looking at E coli 
being one of the most studied 

827
00:47:34,760 --> 00:47:38,320
organisms in history, but 
Shigella being particularly 

828
00:47:39,000 --> 00:47:42,920
dangerous and a new variant that
may require a new antibiotic. 

829
00:47:43,560 --> 00:47:46,240
What are your thoughts moving 
forward on that? 

830
00:47:46,840 --> 00:47:50,480
Oh boy, the antibiotic question,
that's that's a tough one. 

831
00:47:51,400 --> 00:47:53,680
There's just so much to it. 
And I'm not a doctor, I'm not a 

832
00:47:53,680 --> 00:47:57,960
scientist, I'm not a biologist. 
I was given a combination of 

833
00:47:57,960 --> 00:48:01,560
streptomycin and penicillin at 
four years old, and I went 

834
00:48:01,560 --> 00:48:04,440
completely deaf in my right ear.
And that's how they learned. 

835
00:48:04,440 --> 00:48:07,400
You can't mix those two. 
It'll cause small children to go

836
00:48:07,400 --> 00:48:08,760
deaf. 
Why did they? 

837
00:48:08,760 --> 00:48:11,000
Give me the. 
Shot because they did not have a

838
00:48:11,000 --> 00:48:13,560
vaccine for the Hong Kong flu in
1968. 

839
00:48:13,560 --> 00:48:16,480
So they wanted to make money on 
injecting something, so they 

840
00:48:16,480 --> 00:48:20,160
injected that. 
That's that's where my my 

841
00:48:20,280 --> 00:48:26,000
antibiotic life starts. 
I, I think there's, I think 

842
00:48:26,000 --> 00:48:30,080
antibiotics have saved my life a
number of times with strep 

843
00:48:30,080 --> 00:48:33,880
throat, bronchitis, 104° fever. 
There is a, there is a place for

844
00:48:33,880 --> 00:48:35,720
them. 
And I and I believe strongly 

845
00:48:35,880 --> 00:48:39,040
that antibiotics are important 
for the immune system. 

846
00:48:39,800 --> 00:48:42,880
The immune system, if you're 
vaccinated is absolutely 

847
00:48:42,880 --> 00:48:47,440
crushed. 
Your bone marrow and lymph prove

848
00:48:47,440 --> 00:48:51,800
that not only are is at the 
beginning of the cancers that I 

849
00:48:51,800 --> 00:48:56,480
showed while, you know, two 
years ago, in fact, lymph node 

850
00:48:56,480 --> 00:48:59,360
cancer is now up 400% of normal 
in Massachusetts. 

851
00:49:00,320 --> 00:49:03,200
But everything else that we're 
talking about thrombocytopenia, 

852
00:49:03,360 --> 00:49:07,560
you know, one would expect and, 
and I thought you're not making 

853
00:49:07,560 --> 00:49:10,240
enough platelets. 
I said platelet creation or 

854
00:49:10,240 --> 00:49:12,360
thrombocyte creation is 
dysregulated. 

855
00:49:13,400 --> 00:49:16,200
I, I might have been wrong in 
that it's not that you're not 

856
00:49:16,200 --> 00:49:19,640
making enough, it's that your 
macrophages, you certain white 

857
00:49:19,640 --> 00:49:24,560
cells that eat up your, your 
dying platelets are dysregulated

858
00:49:24,560 --> 00:49:28,120
in that instead of eating the 
senescent or the, the dying 

859
00:49:28,720 --> 00:49:30,960
platelets. 
So every 10 to 12 days, you, 

860
00:49:31,080 --> 00:49:33,480
they die, right? 
Your platelets, they're always 

861
00:49:33,480 --> 00:49:36,200
being replenished. 
But if your macrophages are out 

862
00:49:36,200 --> 00:49:39,520
of control and start eating the 
healthy ones, that will cause 

863
00:49:39,520 --> 00:49:44,080
thrombocytopenia. 
So the blood and blood forming 

864
00:49:44,080 --> 00:49:48,600
and creation of these cells that
basically kill pathogens in your

865
00:49:48,600 --> 00:49:51,120
body. 
There's T cells, B cells, 

866
00:49:51,120 --> 00:49:54,200
lymphocytes, leukocytes, 
neutrophils, acinophils, all 

867
00:49:54,200 --> 00:49:58,120
these macrophages, all these 
weird names for white cells. 

868
00:49:58,120 --> 00:50:01,520
Let's just say white cells. 
The creation of white cells that

869
00:50:01,520 --> 00:50:03,720
the stem cell level is being 
dysregulated. 

870
00:50:03,720 --> 00:50:07,320
You're creating cells that you 
don't need, and you're not 

871
00:50:07,320 --> 00:50:10,280
creating cells that you do need.
So now you're open to certain 

872
00:50:10,280 --> 00:50:13,960
pathogens attacking you. 
Shingles was on the rise after 

873
00:50:13,960 --> 00:50:18,080
the vaccine, RSV was on the rise
after the vaccine. 

874
00:50:18,880 --> 00:50:20,680
All kinds of stuff that were 
latent. 

875
00:50:20,760 --> 00:50:23,960
They were latent in individuals 
because those individuals had 

876
00:50:23,960 --> 00:50:27,960
natural defenses for decades in 
their own bodies against those 

877
00:50:27,960 --> 00:50:29,960
things. 
And when their immune system was

878
00:50:29,960 --> 00:50:34,080
crushed by the vaccine, they're,
oh, you have antibodies against 

879
00:50:34,080 --> 00:50:36,520
the spike protein now. 
It's like, oh, great, but I 

880
00:50:36,520 --> 00:50:39,320
don't need that because that's 
not even circulating in society 

881
00:50:39,320 --> 00:50:41,320
anymore. 
And you just crushed my immune 

882
00:50:41,320 --> 00:50:43,400
system, so I can't fight off 
other things. 

883
00:50:43,840 --> 00:50:47,440
So with any of these mRNA 
vaccines, the, the body is going

884
00:50:47,440 --> 00:50:50,360
to be disrupted. 
It's not just about the spike 

885
00:50:50,360 --> 00:50:52,360
protein. 
It's mRNA technology. 

886
00:50:52,680 --> 00:50:55,160
It was never safe. 
They brought it forward. 

887
00:50:55,160 --> 00:50:59,760
Just they went past all the 
regulatory normal licensing of 

888
00:50:59,760 --> 00:51:03,600
so many years. 
So yeah, the the antibiotics 

889
00:51:04,640 --> 00:51:07,600
vancomycin is a last ditch 
effort to save somebody with 

890
00:51:07,600 --> 00:51:10,720
sepsis. 
They have they have multiple 

891
00:51:10,720 --> 00:51:14,560
organ failure from it. 
The adeno vector virus 

892
00:51:16,320 --> 00:51:20,360
injections. 
So like AstraZeneca, they are 

893
00:51:20,360 --> 00:51:22,920
also responsible for 
thrombocytopenia as well. 

894
00:51:23,280 --> 00:51:26,800
So it isn't just the mRNA, 
obviously the the DNA ones go to

895
00:51:26,800 --> 00:51:30,600
mRNA, but the, we have a court 
case over here. 

896
00:51:30,600 --> 00:51:32,080
I don't know whether you're 
aware of that. 

897
00:51:32,760 --> 00:51:35,960
And that is under a Consumer 
Protection Act. 

898
00:51:36,240 --> 00:51:39,400
So we're basically saying it 
doesn't actually do what it says

899
00:51:39,400 --> 00:51:41,400
on the tin. 
It's not safe and effective and 

900
00:51:41,400 --> 00:51:44,880
that's been shown. 
And basically they there are 

901
00:51:44,880 --> 00:51:51,200
about 480 cases in yellow card 
of thrombocytopenia and it kills

902
00:51:51,200 --> 00:51:57,160
about one in five unfortunately.
So there's a court case ongoing 

903
00:51:57,160 --> 00:51:59,000
at the time. 
Yeah, absolutely. 

904
00:51:59,000 --> 00:52:01,840
Thrombocytopenia is huge. 
There's the signal on that 

905
00:52:02,000 --> 00:52:06,360
across all all the states that I
have is very high and there's 

906
00:52:06,360 --> 00:52:09,960
different kinds and the the the 
problem is. 

907
00:52:10,680 --> 00:52:14,520
The government that's supposed 
to be about healthcare and doing

908
00:52:14,520 --> 00:52:18,320
all this research is doing 
nothing because the signals are 

909
00:52:18,320 --> 00:52:20,880
strong. 
I proved that for example, I'll,

910
00:52:20,880 --> 00:52:24,760
I'll use acute renal failure 
again for an example, 150,000 

911
00:52:24,760 --> 00:52:28,800
excess people and all it would 
take is one man week for them to

912
00:52:28,800 --> 00:52:32,520
look in some files to figure out
what is the sequence of events 

913
00:52:32,520 --> 00:52:38,040
with vital signs, medication 
being given, blood labs and you 

914
00:52:38,040 --> 00:52:40,320
know, the different things that 
are being caused in people 

915
00:52:40,320 --> 00:52:44,080
sequelae. 
As people say, it is not hard 

916
00:52:44,080 --> 00:52:46,560
for them to find this. 
They're purposely not looking 

917
00:52:47,160 --> 00:52:49,400
and that means they're killing a
lot of people through their 

918
00:52:49,400 --> 00:52:52,840
inaction where they have a legal
duty to act concurrent with 

919
00:52:52,840 --> 00:52:56,280
their inaction. 
Now back to thrombocytopenia, 

920
00:52:56,280 --> 00:52:58,440
another signal they're not 
looking at it. 

921
00:52:59,000 --> 00:53:02,720
They could study this. 
Is it a lack of production of 

922
00:53:02,720 --> 00:53:05,920
traumacytes or is it that the 
macrophages are eating your good

923
00:53:05,920 --> 00:53:09,800
thrombocytes so fast that you 
have low platelet count? 

924
00:53:09,880 --> 00:53:13,200
And how is clotting happening at
the same time of not being able 

925
00:53:13,200 --> 00:53:15,760
to clot? 
You know, these are things that 

926
00:53:16,160 --> 00:53:18,720
they did exist. 
We had these names for very, 

927
00:53:18,720 --> 00:53:21,480
very, very rare things that are 
now happening frequently. 

928
00:53:21,480 --> 00:53:24,720
And the doctors, you know, most 
doctors have never seen these 

929
00:53:24,720 --> 00:53:28,520
things in their careers. 
Some have not seen something in 

930
00:53:28,520 --> 00:53:32,240
10,000 patients like a 
neurosurgeon doing a brain 

931
00:53:32,240 --> 00:53:37,000
operation back in 2020. 
Now this is how I know that or 

932
00:53:37,000 --> 00:53:40,640
how I believe that COVID causes.
The same thing the vaccine 

933
00:53:40,640 --> 00:53:41,960
causes. 
It's just so rare. 

934
00:53:41,960 --> 00:53:44,520
There's no signal. 
So I do believe COVID is a real 

935
00:53:44,520 --> 00:53:46,680
disease and I do believe it's 
prothrombotic. 

936
00:53:46,800 --> 00:53:49,280
It causes clots, but very 
rarely. 

937
00:53:49,640 --> 00:53:53,560
Otherwise it's just a bad cold a
surgeon was operating. 

938
00:53:54,120 --> 00:53:56,320
And he was removing a clot in 
somebody's brain. 

939
00:53:56,640 --> 00:53:59,440
While he was removing it, he saw
two forming right in front of 

940
00:53:59,440 --> 00:54:01,120
him. 
He'd never seen that in 

941
00:54:01,120 --> 00:54:05,720
thousands of operation that 
that's specific to COVID. 

942
00:54:05,760 --> 00:54:08,000
And then there are other things.
But yeah, so they're not 

943
00:54:08,000 --> 00:54:09,920
studying it. 
Thrombocytopenia is frequent. 

944
00:54:10,560 --> 00:54:13,080
There's vaccine induced 
thrombotic thrombocytopenia, 

945
00:54:13,080 --> 00:54:15,400
heparin induced 
thrombocytopenia. 

946
00:54:15,800 --> 00:54:16,960
Yeah. 
They're giving too much heparin 

947
00:54:16,960 --> 00:54:18,600
because people are clotting. 
So they're giving too much 

948
00:54:18,600 --> 00:54:20,680
heparin in the hospital. 
They get thrombocytopenia. 

949
00:54:21,160 --> 00:54:24,400
There's all kinds of reasons. 
Unfortunately you you don't have

950
00:54:24,400 --> 00:54:28,480
the detailed records to figure 
out what is the the root cause 

951
00:54:28,480 --> 00:54:30,480
of this particular 
thrombocytopenia. 

952
00:54:30,480 --> 00:54:32,000
Is it? 
Does it present in? 

953
00:54:32,000 --> 00:54:34,640
Corpora. 
You know, the the red splotches 

954
00:54:34,640 --> 00:54:38,640
on your skin, that's extremely 
dangerous 'cause if it's a 

955
00:54:38,640 --> 00:54:41,440
presenting on the skin, what's 
happening in your body, in your 

956
00:54:41,440 --> 00:54:42,960
organs, in your brain, in your 
lungs. 

957
00:54:44,200 --> 00:54:45,480
Yeah. 
So that's another one. 

958
00:54:45,760 --> 00:54:47,840
I mean, I want to ask you about 
long COVID. 

959
00:54:47,840 --> 00:54:51,040
I want to ask you where flu went
to there's and I know that 

960
00:54:51,040 --> 00:54:53,840
Cheryl's got a load more 
questions for you as well. 

961
00:54:53,840 --> 00:54:57,880
But before we ask you if you 
would be happy to come back and 

962
00:54:57,880 --> 00:55:02,560
talk to us again, I want to ask 
you for people that are watching

963
00:55:02,560 --> 00:55:04,760
now. 
Because what we're seeing is a 

964
00:55:04,760 --> 00:55:08,440
lot of people are saying to us, 
we're looking back and we're 

965
00:55:08,440 --> 00:55:11,400
seeing that our loved one, our 
mother, our father, our brother,

966
00:55:11,400 --> 00:55:14,960
our sister was killed, was 
killed by the state. 

967
00:55:14,960 --> 00:55:17,960
We're seeing that now. 
And it's very difficult for 

968
00:55:17,960 --> 00:55:21,800
people to take a lot of this 
data on board and this 

969
00:55:21,800 --> 00:55:25,800
information on board. 
And we're still finding quite a 

970
00:55:25,800 --> 00:55:30,320
bit of resistance with families 
that find it too painful, too 

971
00:55:30,320 --> 00:55:33,840
painful to hear and are still in
denial. 

972
00:55:34,440 --> 00:55:38,920
When do you think we will come 
to the tipping point where 

973
00:55:39,000 --> 00:55:43,600
people will realize what has 
gone on in the past and what is 

974
00:55:43,600 --> 00:55:47,480
still going on and perhaps 
moving forward is going to come 

975
00:55:47,480 --> 00:55:49,520
again and hit us in a different 
way? 

976
00:55:49,960 --> 00:55:54,680
What can we say to people when 
they're saying, well, I know my 

977
00:55:54,680 --> 00:55:58,360
friend down the road, her 
family, they won't accept it, 

978
00:55:58,360 --> 00:56:02,800
but I know that that person 
should have come out of hospital

979
00:56:02,800 --> 00:56:06,120
and didn't. 
How do we tackle situations like

980
00:56:06,120 --> 00:56:09,760
that? 
Yeah, so maybe you you said I'm 

981
00:56:09,960 --> 00:56:12,320
kind of a data guy and we talked
about data a lot. 

982
00:56:12,520 --> 00:56:15,000
I hate data. 
It's it's very little of what I 

983
00:56:15,000 --> 00:56:18,440
do. 
I'm a a whole systems analyst, 

984
00:56:18,440 --> 00:56:22,160
the whole system being the 
behavioral, psychological, 

985
00:56:22,160 --> 00:56:28,720
sociological, economic, finance,
and then of course the biology 

986
00:56:28,720 --> 00:56:30,240
and stuff. 
I just don't even do it. 

987
00:56:30,240 --> 00:56:32,120
And I'm looking at data. 
How does it all play? 

988
00:56:33,120 --> 00:56:35,600
The CARES Act is behavioral 
modification. 

989
00:56:35,640 --> 00:56:39,120
The state medical boards are 
coercing boards. 

990
00:56:39,520 --> 00:56:42,240
We call it topping off. 
You get about 60% of the doctors

991
00:56:42,240 --> 00:56:46,920
to comply with solicitation. 
You get another 30% with 

992
00:56:46,920 --> 00:56:49,160
coercion. 
You get another 5% by making 

993
00:56:49,160 --> 00:56:53,440
examples out of doctors like 
Merrill Mast, John Littell and 

994
00:56:53,440 --> 00:56:57,240
others and the 5% you suppress 
on social media. 

995
00:56:57,720 --> 00:57:00,400
Now, all this combines to make 
the average person out in 

996
00:57:00,400 --> 00:57:05,200
society a little bit shy also 
about confronting their own 

997
00:57:05,200 --> 00:57:07,840
mortality and what they've done 
to their family members, what 

998
00:57:07,840 --> 00:57:09,960
they've done to their friends by
saying you should get the 

999
00:57:09,960 --> 00:57:12,280
vaccine. 
So there's a little bit of a 

1000
00:57:12,280 --> 00:57:14,640
guilt and culpability in each 
person. 

1001
00:57:14,640 --> 00:57:16,840
But I I don't I. 
Don't like to use broad sweeping

1002
00:57:16,840 --> 00:57:21,040
statements, which I just did. 
But when it comes to 

1003
00:57:21,040 --> 00:57:24,440
individuals, every, everybody's 
got a different reason. 

1004
00:57:24,920 --> 00:57:27,640
And yeah, it's difficult to 
approach a family member. 

1005
00:57:27,640 --> 00:57:30,160
I have cousins. 
One was doing horribly. 

1006
00:57:30,160 --> 00:57:34,920
She lost a lot of she's down to 
I think under 100 lbs. 

1007
00:57:35,440 --> 00:57:38,880
She was dying and they they did 
save her in the hospital after a

1008
00:57:38,880 --> 00:57:41,720
year long battle. 
She got all her vaccines and 

1009
00:57:41,720 --> 00:57:43,400
boosters and completely believes
in it. 

1010
00:57:43,680 --> 00:57:46,200
Do I tell her? 
Do I say, hey, cousin, you know,

1011
00:57:46,200 --> 00:57:47,720
I wrote this book, you should 
read it. 

1012
00:57:47,720 --> 00:57:49,560
And this all has to do with the 
vaccine. 

1013
00:57:50,200 --> 00:57:52,920
It's tough. 
It's tough to approach people 

1014
00:57:52,920 --> 00:57:56,080
that you know and say you did 
this to yourself, but they 

1015
00:57:56,080 --> 00:57:58,120
didn't do it to themselves. 
That's the problem. 

1016
00:57:58,520 --> 00:58:01,200
They need to be told you're not.
You're not at. 

1017
00:58:01,200 --> 00:58:02,960
Fault for this. 
The government did this to you 

1018
00:58:02,960 --> 00:58:07,480
and they did it on purpose. 
So I mean, I know what you're 

1019
00:58:07,480 --> 00:58:09,160
asking. 
It's hard to answer because it's

1020
00:58:09,160 --> 00:58:11,720
individualistic. 
Each person is different. 

1021
00:58:13,240 --> 00:58:16,160
I'm I can be brutally honest 
because that's been my 

1022
00:58:16,160 --> 00:58:19,600
personality my whole life. 
I'm an engineer felt like it is.

1023
00:58:20,760 --> 00:58:25,240
I try to use some a little 
trepidation in approaching some 

1024
00:58:25,240 --> 00:58:29,960
people, but I try to just say, 
hey, you know, I don't care if 

1025
00:58:29,960 --> 00:58:31,520
you're going to hate me, doesn't
matter. 

1026
00:58:31,520 --> 00:58:34,040
You need to hear this because 
you know what? 

1027
00:58:35,080 --> 00:58:37,600
You might prevent them from 
getting the booster that kills 

1028
00:58:37,600 --> 00:58:40,520
them. 
If you might lose a friendship, 

1029
00:58:41,320 --> 00:58:44,800
you might have a relative be 
angry at you for a year, not 

1030
00:58:44,800 --> 00:58:49,480
talk to you. 
But you're obligated morally to 

1031
00:58:49,480 --> 00:58:52,840
tell them. 
And if they hate you, So what? 

1032
00:58:53,160 --> 00:58:54,960
But the next? 
Time they go in to get that 

1033
00:58:54,960 --> 00:58:56,720
shot. 
They might be thinking about 

1034
00:58:56,720 --> 00:59:00,280
what you said, so be brave and 
tell people. 

1035
00:59:00,880 --> 00:59:03,280
John, where do we find you? 
There's going to be a lot of 

1036
00:59:03,280 --> 00:59:07,120
people watching that because you
have put out an extraordinary 

1037
00:59:07,120 --> 00:59:10,960
amount of material. 
There's slide presentations, 

1038
00:59:10,960 --> 00:59:15,480
graphs, all of your evidence, 
your book, your website. 

1039
00:59:15,880 --> 00:59:18,680
Just let everybody know. 
Before I throw to Cheryl for her

1040
00:59:18,680 --> 00:59:21,680
last word, let me ask you where 
we find you. 

1041
00:59:22,280 --> 00:59:23,760
So the the. 
Book can be found at 

1042
00:59:23,760 --> 00:59:27,360
therealcdc.com in the United 
States. 

1043
00:59:27,360 --> 00:59:30,120
Unfortunately, I don't have 
anything set up for Europe yet 

1044
00:59:30,120 --> 00:59:32,480
to sell the book, which is it's 
my fault. 

1045
00:59:32,560 --> 00:59:36,360
I have to figure that out. 
It is sold on Amazon.com and I 

1046
00:59:36,360 --> 00:59:38,480
was surprised that you even have
a book and. 

1047
00:59:39,160 --> 00:59:40,800
You know, I don't know how you 
got it. 

1048
00:59:40,800 --> 00:59:43,080
You said Amazon's like, oh, 
they'll ship there. 

1049
00:59:43,720 --> 00:59:47,600
So yeah, the real cdc.com, the 
book is on Amazon, Barnes and 

1050
00:59:47,600 --> 00:59:51,040
Noble. 
Oh, in Canada it's on, Oh, I 

1051
00:59:51,040 --> 00:59:54,160
forgot the name of that place. 
It's on that big book sale place

1052
00:59:54,160 --> 00:59:57,080
in Canada. 
Oh, I can't believe it escapes 

1053
00:59:57,080 --> 00:59:59,720
me. 
I also have a website called Via

1054
00:59:59,720 --> 01:00:02,360
Vera Vita. 
I'm trying to move people away 

1055
01:00:02,360 --> 01:00:04,800
from it, but it still has a lot 
of data and it has a lot of 

1056
01:00:04,800 --> 01:00:08,200
links. 
If you go to therealcdc.com at 

1057
01:00:08,200 --> 01:00:11,800
the bottom you'll see links to 
my podcast interviews that 

1058
01:00:11,800 --> 01:00:15,080
actually directs to Via Vera 
Vita, which means The Way, the 

1059
01:00:15,080 --> 01:00:17,360
Truth and the Life, John, 
chapter 14, verse 6. 

1060
01:00:19,280 --> 01:00:24,160
So that's a good one. 
VIAVERA vita.com I'm prolific on

1061
01:00:24,160 --> 01:00:30,560
Twitter at the at sign 
JOHNBEAUDOINSR John Beaudoin 

1062
01:00:30,560 --> 01:00:34,920
Senior Facebook, I think John 
Paul Beaudoin. 

1063
01:00:35,360 --> 01:00:39,240
I don't do much there, but I'm 
trying to do more and that's oh,

1064
01:00:39,240 --> 01:00:42,080
Rumble channel. 
I just renamed my rumble channel

1065
01:00:42,080 --> 01:00:47,200
the real CDC from via Vera Beta.
So now it's called the real CDC.

1066
01:00:47,200 --> 01:00:49,960
I'm moving everything to the 
real CDC because it's easy. 

1067
01:00:50,080 --> 01:00:51,360
It's easy for people to 
remember. 

1068
01:00:51,640 --> 01:00:54,720
So rumble channel, I'm going to 
try to start building the rumble

1069
01:00:54,720 --> 01:00:57,640
channel. 
Oh, and on sub stack, 

1070
01:00:57,640 --> 01:01:03,040
coquandasheon.substack.com, COQ,
uin.substack.com. 

1071
01:01:03,400 --> 01:01:07,560
I'll stop now. 
Don't stop, please don't stop 

1072
01:01:07,560 --> 01:01:11,640
because without people like you,
we wouldn't have this incredible

1073
01:01:11,640 --> 01:01:15,760
wealth of knowledge. 
And on that note, John, I'm 

1074
01:01:15,760 --> 01:01:18,800
going to thank you so much for 
agreeing to join us. 

1075
01:01:18,800 --> 01:01:21,080
And I do hope you're going to 
join us again because we've got 

1076
01:01:21,080 --> 01:01:23,840
so much to say. 
And I'm going to give Cheryl an 

1077
01:01:23,840 --> 01:01:28,200
opportunity to a have her last 
word, but also to tell you where

1078
01:01:28,200 --> 01:01:30,400
did she get that book? 
Cheryl, over to you. 

1079
01:01:30,840 --> 01:01:35,920
Me and my book, yes I got it 
from Amazon US and had to pay 

1080
01:01:35,920 --> 01:01:37,960
some postage but at least they 
post it here. 

1081
01:01:38,680 --> 01:01:41,320
I can't remember exactly how 
much it cost, but I thought it 

1082
01:01:41,320 --> 01:01:45,200
was well worth the, the value 
because we found out that more 

1083
01:01:45,200 --> 01:01:48,000
than half a million people are 
killed by hospital protocols and

1084
01:01:48,000 --> 01:01:51,040
another half million have been 
killed by the COVID-19 vaccine. 

1085
01:01:51,040 --> 01:01:53,000
From all the evidence that's in 
this book. 

1086
01:01:53,280 --> 01:01:57,200
And the, the, the main strength 
that we have is in evidence is 

1087
01:01:57,200 --> 01:02:01,360
in detail, is an understanding 
what's gone on so that if there 

1088
01:02:01,360 --> 01:02:04,240
are people out there who have 
become I'll because of the 

1089
01:02:04,240 --> 01:02:08,040
vaccines, they can actually do 
something hopefully to help 

1090
01:02:08,040 --> 01:02:10,000
themselves. 
First of all, by not having 

1091
01:02:10,000 --> 01:02:14,640
anything more, no more mRNA, 
whatever it is for. 

1092
01:02:15,680 --> 01:02:21,400
And on top of that, there are 
things that people can do to try

1093
01:02:21,400 --> 01:02:25,400
and alleviate themselves from 
the damage that has been caused.

1094
01:02:25,680 --> 01:02:30,160
And I just leave those thoughts 
with you in terms of 

1095
01:02:30,240 --> 01:02:32,960
understanding what John has been
saying. 

1096
01:02:33,000 --> 01:02:35,680
I thank John very much and I 
hope to see him again and have 

1097
01:02:35,680 --> 01:02:39,360
another chat. 
And I too, want to thank you, 

1098
01:02:39,360 --> 01:02:41,840
John, so much. 
And I'm sure that we'll talk 

1099
01:02:41,840 --> 01:02:44,920
again very soon. 
And if you like what the UK 

1100
01:02:44,920 --> 01:02:50,200
column are doing, please share 
this video because this is vital

1101
01:02:50,200 --> 01:02:53,920
information. 
And whoever it goes to, it might

1102
01:02:53,920 --> 01:02:56,880
just, it might just save a few 
lives. 

1103
01:02:57,040 --> 01:02:58,760
This is really, really 
important. 

1104
01:02:59,160 --> 01:03:03,120
So John, on that note, I'd like 
to leave you as normal as I 

1105
01:03:03,120 --> 01:03:08,640
always do, with grateful thanks 
and also your last word and it's

1106
01:03:08,640 --> 01:03:10,160
over to you. 
Thank you, John. 

1107
01:03:11,040 --> 01:03:13,880
Thank you very much for having 
me and for this opportunity. 

1108
01:03:14,600 --> 01:03:18,000
I don't get to speak to a lot of
people in the UK very often, but

1109
01:03:18,000 --> 01:03:19,600
I know you have American 
audience too. 

1110
01:03:19,640 --> 01:03:23,440
So last words, it's over. 
It's been proven. 

1111
01:03:24,240 --> 01:03:30,120
I know, I'm just one guy. 
I'm not a doctor, but the people

1112
01:03:30,120 --> 01:03:33,560
of the world for 10,000 years, 
however long we've been on the 

1113
01:03:33,560 --> 01:03:37,040
planet, have had the ability to 
discern facts that are in front 

1114
01:03:37,040 --> 01:03:39,480
of us. 
And now for some reason, we 

1115
01:03:39,480 --> 01:03:44,600
abdicate that ability to discern
to somebody in a white coat that

1116
01:03:44,600 --> 01:03:47,520
the white coat is not going to 
save you. 

1117
01:03:47,880 --> 01:03:49,760
The information is right in 
front of you. 

1118
01:03:49,760 --> 01:03:52,040
People are dying from this. 
It's clear. 

1119
01:03:52,080 --> 01:03:55,920
I've proven it over and over 
with hard, conclusive evidence 

1120
01:03:55,920 --> 01:03:57,560
that would stand up in any 
court. 

1121
01:03:58,360 --> 01:04:01,800
The CDC memorandum, my second 
publication, is a notice of 

1122
01:04:01,800 --> 01:04:05,240
criminal liability. 
It was served to the directors 

1123
01:04:05,240 --> 01:04:09,000
of the CDCNIHFDA and their 
deputy directors. 

1124
01:04:09,800 --> 01:04:13,320
They now are in a knowing state 
of mind and if they don't act to

1125
01:04:13,320 --> 01:04:16,200
investigate, then they are 
guilty of the subsequent deaths 

1126
01:04:16,320 --> 01:04:19,120
and that is murder that happened
to other people. 

1127
01:04:19,520 --> 01:04:22,840
The conclusive evidence is what 
is presented. 

1128
01:04:22,840 --> 01:04:26,960
Somebody dies within 5 minutes 
or couple hours over and over 

1129
01:04:26,960 --> 01:04:29,120
from the same thing. 
That is what I've proven. 

1130
01:04:29,880 --> 01:04:32,200
I don't need a research paper, a
peer review. 

1131
01:04:32,200 --> 01:04:35,360
I don't need somebody in a white
coat to tell me so. 

1132
01:04:35,360 --> 01:04:37,040
I want to thank everybody for 
listening. 

1133
01:04:37,360 --> 01:04:41,040
Use your own brain. 
This is hard, factual evidence. 

1134
01:04:41,040 --> 01:04:43,840
It's irrefutable. 
That's the difference between 

1135
01:04:43,840 --> 01:04:46,960
what I do and a peer reviewed 
research paper that gets 

1136
01:04:46,960 --> 01:04:50,040
bantered and argued and debated 
for the next 10 years. 

1137
01:04:50,280 --> 01:04:53,440
That's not what I do. 
I give you hard evidence, you 

1138
01:04:53,440 --> 01:04:56,800
can make decision on your own. 
So make that decision. 

1139
01:04:56,960 --> 01:05:00,040
And I strongly recommend you 
stay away from what the 

1140
01:05:00,040 --> 01:05:03,120
government tells you to do, 
whether it's remdesivir or a 

1141
01:05:03,120 --> 01:05:05,560
vaccine. 
Use your own brain. 

1142
01:05:05,920 --> 01:05:07,920
Thank you very much. 
Appreciate everybody's time.