Spiked: A shot in the dark

Added: 2026-05-13

[00:00:01]You are most welcome to this talk and the more observant amongst you will realize that we have Dr. Cla Craig with us. Cla, thank you so much for coming.

[00:00:09]>> Thank you for having me, John.

[00:00:11]>> And this is the second video we've done today actually in person, which is pretty wonderful to have Cla here. Thank you so much for coming up all the way from >> Thanks for having me >> from from way way down south. Most people know Cla as a doctor. She is a pathologist. She is a medical researcher. How you get time for doing all these things? I wish I had 10% of your energy. It's amazing the amount of work you produce, including perhaps one of the best books written on the vaccine so far. We've actually re reviewed your um this one on the pandemic in the past. So, we've looked at that expired also. Excellent. We will put the links to these available for very low cost. That spiked CLA. Um we're going to look at a few slides. Um but ju just just as as an introduction to this video so people could decide if they want to watch. What is this book about and therefore what is this presentation about?

[00:01:06]>> So it's a book about the co vaccines.

[00:01:10]>> Um it's a book about how they did not work and how they could not work >> and how people were silenced if they tried to tell you that. Um, and I think it's an important story because other books about the vaccine so far have been about safety issues, >> but you can't have a conversation about safety because you get shut down and told it saved millions of lives.

[00:01:33]>> So, that point has to be addressed first. So, I'm addressing that point because it's simply not true and it could never have been.

[00:01:39]>> I wish I had a pound for every time someone had said that to me. Oh, no.

[00:01:42]based on a few ludicrous preposterously optimistic or pessimistic depending on how you look at it models.

[00:01:50]>> Absolutely.

[00:01:51]>> I hate I hate medical models.

[00:01:53]>> Yeah. And but these particular models are so completely unjustified. You know, they're just they're extreme. They're fantastical. Yeah. And and they're very easily taken down. But of course, people have heard it repeated so many times that they've believed them.

[00:02:07]>> So often enough people will start to believe it.

[00:02:09]>> I know. And it has been said for years now. Let's look at some of these slides, CLA, just as a way of illustration. Um, yeah, there we go. Start us off, please.

[00:02:18]>> Okay. So, I just want to start off actually by saying um who it's for. Y >> because um on you know what I do when I write books is I write what I want to read and I love learning when I read books. So, I've tried to fill it with things that people might not have heard of. Yeah. Um and and that includes people who are very on top of this subject. I want everyone to learn something when they're reading it. Yeah, but it is completely intelligible to the intelligent lay reader. You don't need I found that very readable.

[00:02:48]>> Yeah.

[00:02:48]>> Intelligible.

[00:02:49]>> Um and then I also want it to be read by people who >> haven't heard any of this stuff yet as well.

[00:02:55]>> Um and that's a really difficult thing to do, particularly on a vaccine topic.

[00:02:59]>> Um but I've tried my best to do it gently. I maybe shouldn't have put a black syringe on the cover, but I think you can just pretend it's a murder mystery or something.

[00:03:08]>> No, I don't know. I wouldn't question that.

[00:03:21]So that's on that one. Now we go on to second screen. There we go. Yeah.

[00:03:25]>> Okay.

[00:03:26]>> Yeah.

[00:03:27]>> So the starting point has to be a question that nobody ever asks. The question that was never asked is if we'd had no vaccine, >> what would have actually happened? And of course, we were being told based on modeling all sorts of things that were clearly not true. And in retrospect, you know, we've had a lot of time pass since and we've had so many of these seasonal waves that same magnitude, similar peaks >> um that we can say very clearly this is what would have happened. And so that has to be the starting point because without that you can't say >> you know you can't make any measurement.

[00:04:01]You have to have a baseline and that is the baseline.

[00:04:04]>> Um >> and this is not based on modeling. This is based on empirical observation of previous years and subsequent years.

[00:04:12]>> Yeah, exactly. Just just looking at looking at what happens in the real world.

[00:04:16]>> Yeah.

[00:04:16]>> Um and of course we had we were told extreme pro promises. So there's a whole chapter of extreme promises that were told >> um ranging from you know ending the pandemic, stopping hospitalizations and deaths 95% effective. If any of those things had been true, CO would be have been over in 2021. And they, in fact, people were saying that as well, weren't they? We'll see the end of these waves, the end of this transmission because the pharmaceutical claims were that that is what would have happened. And it did not happen and it could not happen.

[00:04:45]>> Um, and of course, everybody's caught COVID since. And yet, you know, this kind of promise of we're ending this. If you get injected, you won't get this thing.

[00:04:55]>> The pandemic stops with you when you get the injection. the Joe the Joe Biden thing.

[00:05:00]>> Yeah. Um and then you know next to that people then took the vaccines and got it and of course the public stopped getting injected because they could see it wasn't working but the official line continues to be that it worked even though everybody caught it. Now vaccines are meant to prevent illness. These did not work. So that's the starting point.

[00:05:22]Um >> oh what have we got here? um >> we could effectively end this pandemic in 2021.

[00:05:30]>> Yeah. And then later on in 2022, we had the FISA CEO saying two doses off of very limited protection if any because at that point he's trying to sell boosters.

[00:05:41]Um and in February the case rates start to rise. So we had this situation where um the public health authorities were trying to measure the rate of infection in the unvaccinated and various different dosage categories of vaccinated. And for a long time it looked like the unvaccinated were at a higher rate probably because the number of people who were unvaccinated wasn't measured properly in the population. And then over time the rates in the vaccinated were going up and up and up and up and up until they overtook this ex sort of exaggerated rate in the unvaccinated and then they just pulled the data because it got too ugly for them to keep sharing it.

[00:06:21]>> Well, we don't want inconvening inconvenient data.

[00:06:25]>> No. In fact, the first thing they did >> they're such children they won't understand it.

[00:06:28]>> The first thing they did was they changed the case rates in the vaccinated to be printed in pale gray ink instead of black ink. Do you remember that? just sort of please go away, we don't like this result.

[00:06:39]>> Let's pretend it doesn't exist.

[00:06:41]>> Yeah, indeed.

[00:06:42]>> Hang on, you need to go to that.

[00:06:43]>> Yeah. Yeah.

[00:06:44]>> Okay. Yeah.

[00:06:45]>> Um and then um in a poll, a Gallup poll was done where they asked in the US how many people had had CO and when. And of course, some people had died of COVID, so they're not in the survey, but you can do the math to figure that out. And there were um 350,000 co Oh, so this is this isn't a Gallup survey. I'm sorry. I should have revised this. This is just COVID labeled deaths in the US. There were 350,000 in 2020.

[00:07:10]>> This is the overall sort of mega data sort of thing.

[00:07:12]>> Yeah. And 470,000 in 2021. So where's the benefit there? And you know that's that's a that is a problem data point.

[00:07:21]>> Yeah. Yeah. Yeah.

[00:07:22]>> And then of course a COVID label death is not necessarily a death because of a virus. There were all sorts of reasons why people were dying in excess and being labeled as being COVID deaths.

[00:07:33]Yeah. So first of all there was the overdiagnosis problem where um people were being described as dying from COVID when they were dying from other causes.

[00:07:41]Correct.

[00:07:42]>> And as the COVID wave rose and fell, people dying of cancer, people dying of heart disease, those deaths were disappearing at the same rate and then reappearing when CO disappeared again.

[00:07:52]So it was really quite clear we were massively overdiagnosing as as if CO is like protecting you against cancer deaths.

[00:08:01]>> Yeah. It's just ludicrous when you think about it. It >> It's just It's literally people who were dying of cancer who at any other occasion would have been called a cancer death and it was called a COVID death because of a positive test result and that and that was it.

[00:08:15]>> And you know that's not the truth, is it? That's not the whole truth. But the deaths are reduced down to this one word. Yeah.

[00:08:21]>> Um and then there's this problem that we have where people who are dying tend to have um you know one by one their organs fail and one of the things that fails is their immune system. Yes.

[00:08:32]>> So if you go and swab a load of people in a morg who died of any cause >> any cause a large proportion of them will have respiratory virus positive test. And this is not something that was a problem in life and it was not something diagnosed in life but the numbers are huge. So 47% of people in a morg will have a positive respiratory viral test result and 7% will have a positive corona virus respiratory test result. So if you're swabbing the dying, you're going to get positive results even when those people are not dying of that virus.

[00:09:04]>> So it's not a good test to do on the dying. But that is exactly where testing was concentrated. In fact, we even have people testing in the morg after people had died. In fact, if you're testing people that are dying for anything, um if you feed them ice lollies or something, you know, they're dying anyway, >> they're going to die that and you could you could blame it on the ice lollies, but it's not.

[00:09:27]>> No, it's it's uh >> again, it's so obvious.

[00:09:32]>> So obviously wrong.

[00:09:34]>> Um yeah, I think one of the things that's happened is that deaths have been reduced to a single diagnosis when it's deaths aren't like that. They're complex. And um the CDC are good at publishing death data where they say um multiple different causes, but the UK data always gets reduced down to one cause. And when you've got, you know, this kind of political environment we've been in, um COVID becomes one of the one causes and any other cause that was mentioned just disappears out the data and you're just left with COVID.

[00:10:02]>> Yeah. Um, so then in the US there was this problem where there were these incentives that were increasing the rate at which people were diagnosed with COVID. So families were getting $9,000 of funeral expenses >> if there was a co on the death certificate.

[00:10:16]>> It's a lot of money >> that was happening up until September 2025. It was still as late as that.

[00:10:24]>> It was as late as that. Yeah.

[00:10:26]>> Wow. $9,000 if you put CO on the death certificate.

[00:10:29]>> Yeah. And so, you know, if you're a doctor who wants to help out a family, you'll put it on.

[00:10:33]>> Well, I mean I mean that that that's the difference between having a funeral and not having a funeral. I mean, $9,000 is a huge amount of money in for poor people. It's >> it's quite a lot of money for anyone.

[00:10:43]>> Yeah. And if you're a relative who gets, you know, the receiving end of a death certificate and $9,000, you're not going to complain too much, are you?

[00:10:50]>> Come on. Don't put CO down for that.

[00:10:52]Come on. You can do that.

[00:10:54]>> Yeah. So, that I mean, that's not a good way of collecting data, is it?

[00:10:57]>> No, it really is. really is not.

[00:11:00]>> Yeah. Um >> Yeah.

[00:11:01]>> And then there was a problem that people were not being treated the way they normally would be. You know, people who had a community pneumonia >> would normally get seen by a GP. Yeah.

[00:11:10]>> Bit of face to face attention.

[00:11:11]>> Oh, pneumonia is a medical emergency.

[00:11:14]>> Yeah.

[00:11:14]>> Yeah.

[00:11:14]>> And you'd get given antibiotics.

[00:11:16]>> They'd be straight into A&E. They'd be on get the blood cultures done. They'd be on oxygen. They'd be >> Well, even in the community, if they weren't that sick, you know, not everybody has to go to hospital, they still get antibiotics.

[00:11:27]>> Oh, yeah. for sure.

[00:11:28]>> And they weren't getting antibiotics even in the hospital, even when they were dying.

[00:11:32]>> If the sats went down, they would go into hospital for oxygen.

[00:11:34]>> Yeah. And all the pandemic plans were to stockpile antibiotics because this was, you know, secondary bacterial infections are a serious thing that definitely cause people to die because antibiotics work.

[00:11:44]>> Yeah.

[00:11:44]>> And yet instead of having an increase in prescribing, >> the prescribing fell through the floor.

[00:11:50]And if you think antibiotics save lives, and I do, then if you stop prescribing them, >> I've seen untold numbers of lives saved by anti antibiotics. Yeah.

[00:11:59]>> And then you stop prescribing them, >> you'll get excess deaths.

[00:12:02]>> And so, you know, not all of those deaths were co deaths because people were denied antibiotics.

[00:12:07]>> People were dying of bacterial infections when antibiotics were just sitting there doing nothing.

[00:12:11]>> Yeah. Because of this idea that viruses aren't helped by antibiotics. But the reality >> which is is basically true but >> well it's sort of true sort of true. So um aithramyin for example is anti-inflammatory which is good for pneumonia and it has antiviral properties which is good for pneumonia and it stops secondary bacterial infection. So you know it's not as black and white as >> not it's not absolutely yeah >> and then the last one we've got there is dehydration. So, you know, people in care homes >> um are there because they need care. And a big part of that care is help staying hydrated.

[00:12:48]>> Um and when staff are first of all not there because they've been told to isolate or not there because they're scared or not there because they think that if they go into this room where someone's been described as COVID positive, they might be risking their family's lives and they don't have the PPE they think will save them and they don't have enough PPE. then you are not having enough contact with the elderly to keep them hydrated. Yeah.

[00:13:11]>> And these are people who are frail and if they're not kept hydrated it can kill them.

[00:13:16]>> Oh, I mean these patients become dehydrated really quickly.

[00:13:21]>> You know, we know this in hospitals because some some of them are catheterized and you can see the urine collecting in the bag and it goes dark colored really quickly and the volumes drop off.

[00:13:31]>> Yeah. So you get small volumes of really dark concentrated urine really quickly and of course that means that the fluid in the body drops. It means that the fluid in the secretions drop and the secretions become thicker and you're really into a vicious circle then.

[00:13:46]>> Yes.

[00:13:47]>> Even in hospital this is a common problem.

[00:13:49]>> Yes. And I think also it was a problem in hospitals because the catering staff were, you know, nervous and and the normal the people who normally help with this stuff are the visitors >> and the visitors weren't there >> and the relatives will give the nurses a shout saying, "No, my dad's thirsty."

[00:14:07]>> Yeah. Or they'll help them they'll help naturally drink themselves.

[00:14:10]>> And so if you ban visitors, you ban the care that visitors bring.

[00:14:14]>> Yeah.

[00:14:15]>> And that's what happened. M >> um so I think all of those things cannot be discounted as a cause of death.

[00:14:20]>> Y >> um so then we're on to you know what would happen without a vaccine? Well this tsunami is what we were told weren't we? We were told 85% would have it in the first wave that was Neil Ferguson's model.

[00:14:33]>> Now when you look at um you remember the test and trace that cost us 37 billion pounds.

[00:14:40]>> Yeah. 37 billion. What does that even mean? I it's a massive amount of money for >> absolutely vast and it created the most expensive data set in the world >> um where they had measured um when people had a positive test result in a household did other people in the household that then got tested did any of them then test positive >> and from that you can work out a thing called the household transmission rate or the secondary attack rate >> and that um is tells you what percentage of people in that house then caught it.

[00:15:14]But if you look at it another way, it tells you what percentage of people in that house >> were susceptible to that wave.

[00:15:21]>> Yes.

[00:15:21]>> And so um they measured it with PCR and PCR exaggerates the numbers because you're breathing the air in the house and it's far too sensitive and it doesn't tell you anything.

[00:15:31]>> But then they later on measured it also with lateral flow tests.

[00:15:35]>> And so you can >> you can basically translate one test to the other in percentages. The lateral photo tests are what people were positive with when they were symptomatic and actually had COVID. So it's quite a good measure.

[00:15:47]>> Yes.

[00:15:47]>> By that measure for every wave it was 8.5% who were susceptible prior to Omrion.

[00:15:53]>> But Neil Ferguson said 85%.

[00:15:55]>> That's a big difference, isn't it?

[00:15:57]>> It's only a factor of 10 to be fair.

[00:16:00]So the what the implication is is that if he's saying 85% in one wave and it's actually 8.5 that means it will take 10 ridiculous waves.

[00:16:10]>> 10 Yeah.

[00:16:11]>> at that rate >> before everyone's been infected. 10 waves. Now you approach that problem in a very different way, wouldn't you?

[00:16:19]>> Totally. The idea that 8.5% of people get infected in any wave and only a small proportion of them getting sick.

[00:16:27]So this idea of people dying in the streets outside A&E departments, >> in fact, A&E departments in many parts of the country were so quiet that nurses were doing nursing doctors were doing dance routines.

[00:16:39]>> They were.

[00:16:40]>> What was that about?

[00:16:41]>> That was very very strange. It was strange at the time and it's even stranger now.

[00:16:45]>> Very bizarre. Really weird folly of >> Yeah. With everybody sharing these videos.

[00:16:52]>> Yeah. How do you explain how I'm I'm I'm still mystified looking back on that.

[00:16:57]>> Yeah.

[00:16:58]>> You know, I think some of my old tutors in the old days, you know, they've had a complete fit.

[00:17:03]You know, anything that was considered unprofessional behavior, they would tell you off.

[00:17:08]>> Yeah.

[00:17:08]>> You know, you had to have your tie, you had to speak correctly, you had to walk correctly. You know, as you should. It's a professional standard.

[00:17:16]>> Then people dance around in scruffy scrubs. There was something there was something going on there with >> very very weird.

[00:17:22]>> It was a sort of hero worship thing, wasn't there, that people were >> um celebrating the NHS and worshiping the NHS literally on on alterc cloths and projected onto church buildings at one point.

[00:17:33]>> Well, in the reality only people like me work there, you know, it's it's crazy.

[00:17:38]>> Yeah. And but I think the people who were given that hero complex >> that was part of the expression of it maybe.

[00:17:46]>> Yeah. They actually nurses and doctors actually came to believe their own image somehow.

[00:17:50]>> It was very odd.

[00:17:51]>> Very very strange.

[00:17:52]>> Yeah. So the other number on that slide there, that's 6%.

[00:17:56]>> Y >> that's um where blood donors >> um had their blood um screened for antibodies over time and it's about 6% for each wave acquired antibodies. So it's a different way of testing. Yeah.

[00:18:08]It might not capture everyone that was infected. Yeah. It might be more accurate, less accurate. We don't really know. But the point is that also was steady with each wave.

[00:18:16]>> So you've got two measures there of this tiny fraction being susceptible in each wave. It's a bit higher for again >> but until omicron nothing changed.

[00:18:27]>> So nothing changed from all these people vaccinated. It was exactly the same problem we had prior.

[00:18:32]>> Um so yeah the models were completely off and no one ever admitted it. So 92.5% of people were essentially incapable of catching any particular wave.

[00:18:47]>> 8.5 bystanders. They're just bystanders.

[00:18:50]>> They were never going to get it.

[00:18:51]>> Yeah. And and this is because our lungs are lined by thick thick mucus and layer upon layer of it. And the the final layer before you hit a cell >> is so impenetrable. Yeah. that a virus despite its tiny size a single virus particle on its own is not it cannot pass that that through that is that right if it's inact so you have to have something go wrong to that mucous layer in order to get let virus in so sometimes they seem to maybe hijack on bacteria which do have mechanisms for breaking through mucus >> but sometimes something else is happening to that mucous layer that's letting things in >> right >> one example will be covid vaccine themselves disrupt the cytoines that cause thinning of the mucous layer.

[00:19:38]>> Yeah. The the local chemicals that are released by local cells that control the physiology in that area.

[00:19:44]>> And so this our mucus is so much better than any mask could ever be.

[00:19:49]>> Yeah.

[00:19:50]>> But it fails in a proportion tiny proportion each each wave.

[00:19:55]>> So that's a really interesting question.

[00:19:57]Why is it that this mucous layer sometimes fails? Now, if we had approached this whole thing rationally, we could done some research into that and found something about it. But instead, everybody went crazy on masks.

[00:20:08]>> It's amazing. A virus can't get through, but the oxygen and the carbon dioxide gets through no problem at all.

[00:20:12]>> Well, they're a lot tidier.

[00:20:13]>> They are, but it's still pretty impressive that you've got a barrier that's so pvious to gases, but so impervious to viruses.

[00:20:21]>> Yes. It's almost like it evolved that way or something. Really?

[00:20:24]>> It's It's very It's very clever.

[00:20:26]>> Yeah, it's very perfect. It's It works brilliantly. People lost faith in their bodies completely in their immune systems. But the fact is we breathe in a load of mud all the time.

[00:20:38]>> And so having this mucous layer that mops it all up. Sweeps it all up with those hair cells. They just sweep it all up and into your silia wafting it all up.

[00:20:46]>> Yep. And then you swallow it and it hits the acid of your stomach and it dies.

[00:20:50]It's >> good system.

[00:20:51]>> It is. It's amazing. Yep.

[00:20:54]>> Right. Where are we on?

[00:20:57]You messing everything up again? Let's try again.

[00:21:02]Sorry. You need to go up again.

[00:21:06]>> No, I'm not moving anything.

[00:21:07]>> What's going on here?

[00:21:10]>> There we are. Okay.

[00:21:11]>> No, we're not on that screen there.

[00:21:13]>> No, but at least it's on there.

[00:21:14]>> This is me being silly. It's nothing to do with There we are. There we are.

[00:21:19]>> Okay, so this is one of my favorite graphs. This is a measure of virus levels in the waste water in the USA.

[00:21:28]>> Oh yeah.

[00:21:28]>> It's not a pure measure. They've had to do a bit of modeling because they didn't have measuring sites everywhere at the beginning.

[00:21:34]>> Um so the early numbers might be a bit you know averaged out.

[00:21:38]>> Yeah.

[00:21:38]>> But broadly that is a level of virus.

[00:21:41]>> It's a reasonable extrapolation.

[00:21:42]>> Yeah. And you can see this like something crazy happened with omicron there. That's the thing that >> oh that's the omicron that spikes the omicrron. Yeah. That's that first omicron wave when um when if you look at the area under the curve. So the amount of space beneath the curve for that omicron wave and the area under the curve for the other waves >> they're actually comparable.

[00:22:02]>> So it's actually quite that big tall spiky one's actually quite thin.

[00:22:07]>> It's very thin.

[00:22:07]>> So it didn't last for long.

[00:22:08]>> It didn't last for long. So the same number of people approximately were infected as for other omic >> but they all were infected in very quick succession.

[00:22:17]>> Right. So the number of people infected is the area under the curve.

[00:22:20]>> Yeah.

[00:22:20]>> Yeah.

[00:22:21]>> So it just happened very very quickly.

[00:22:23]>> Yeah.

[00:22:23]>> Um but the the actual overall impact was similar.

[00:22:26]>> Um but ignoring that very big spike what you see is a series of waves >> about the same duration >> peaking in about the same level >> and you know show me on there where the vaccines came in.

[00:22:41]>> Uh >> I can tell you because you and I know.

[00:22:44]>> We know but there's no way you can tell from the data. No, you can't tell from the data. And what you see with Omicron, apart from that crazy big spike, is that the periods between the waves don't revert back to zero anymore.

[00:22:58]>> You've got them sort of hovering above the baseline still.

[00:23:01]>> Um, so apart from that change, nothing happened before and after the vaccines. This is the same thing just progressing over time.

[00:23:12]>> So the vaccines basically did nothing there on that graph.

[00:23:15]>> They did nothing.

[00:23:16]Well, if they did anything, >> I know they cause a lot of adverse reactions.

[00:23:22]>> So, what what they the vaccines potentially have done here is they've created a situation where the first armor wave was very very sudden.

[00:23:30]>> This was at Christmas time if you remember everybody was being encouraged to get a third dose and they did.

[00:23:35]>> Y >> and in that period people are more susceptible and then you get this very big spike potentially. It might not have been that. I mean it was quite a big spike in South Africa and there weren't very many vaccinated people there.

[00:23:46]>> And then subsequently with omocron you've got some kind of immune problem where it never goes to zero anymore.

[00:23:53]This is you know it's bad.

[00:23:54]>> So things have not got better.

[00:24:04]>> Hang on. We will get there.

[00:24:09]There we go.

[00:24:10]>> Okay. So this is the point that it couldn't work.

[00:24:14]>> This is something that's been known.

[00:24:15]>> This could have been anticipated.

[00:24:17]>> Well, it should have been like the the the reason that you know the reason we've set up heart and have the confidence to do to speak out on all of this.

[00:24:25]>> What does that stand for, CLA?

[00:24:26]>> The health well actually we changed it.

[00:24:28]But so now it's the health advocacy and research team, right?

[00:24:33]>> But it was once the health advisory and recovery team, >> right? Yeah.

[00:24:37]>> But you know it's same same ethos.

[00:24:40]>> Yeah. Um yes so the the point is that this was studied for decades. So the NIH um which was you know the where Fouchy worked the National Institutes of Health in the USA made a publication in the year 2000 >> um saying that respiratory virus vaccines couldn't prevent infection because you don't get mucosal antibodies that can stop the virus getting into the epithelium. you just get antibodies in the blood.

[00:25:10]>> In the year 2000, you know, this is stuff that was known and established and in the textbooks.

[00:25:17]>> Yeah. The mucosal compartment as opposed to the intravascular or body fluid compartment.

[00:25:22]>> Yeah. So you you doesn't work. So you've got your virus coming in to your respiratory mucosa and the only way to stop an infection is if you can stop it getting into the epithelial cells to replicate.

[00:25:34]>> But the vaccines injected into the arm.

[00:25:36]it creates antibodies in the blood.

[00:25:38]>> Um, and so, you know, you've got a sort of it coming through the back door problem. It's not It's >> Well, you're protecting the wrong place >> and you're protecting the wrong place.

[00:25:47]>> Hang on.

[00:25:48]>> Uh, >> and of course, a load of money was thrown at nasal vaccines afterwards because they knew that that was true.

[00:25:54]>> So, here's the thing. This is the quote from that um year 2000 book. Rarely, if ever, induce mucosal immune responses that may prevent infection. No, they can't do that. In January of 2021, so this is, you know, in December 2020, America approves the vaccines. A month later, Fouchi and um colleagues write in a paper that injected vaccines alone typically do not result in potent mucosal immunity that might interrupt infection or transmission.

[00:26:26]>> And he said, you know, we might have to rethink this vaccine program. We're going to learn a lot in the next few months. M >> so you know this was an interesting time January 2021 because actually >> um through December and into the beginning of January there was um common sense being spoken people being rational they're saying we're never going to vaccinate the children we're going to aim at particular groups and then this weird thing happened where suddenly switched and everybody was singing about it and crying about it and it became highly emotional. It was very very strange. Um and then after that period of vaccine worship, >> we got to January 2023 and Fouchy again publishes with some colleagues in a paper. It is not surprising that none of the predominantly mucosal respiratory viruses have ever been effectively controlled by a vaccine.

[00:27:23]So you know they could not work. They knew they could not work. They pretended for a few years that they could and then they went back to saying they could not work and nobody's holding them to account for this.

[00:27:35]>> No.

[00:27:36]>> Um so how did it >> treat yourself all you like and the world ignores it?

[00:27:40]>> Yeah, it seems that way. Yeah. If you if you've got the if you've got a position of authority >> then whatever you say is true even when it makes no sense. It seems >> Yeah. um which is maybe a human societal thing but it's something we really ought to stop.

[00:27:57]>> Yeah. Well, that's what it's just the anti-thesis of science, isn't it? That's the whole point.

[00:28:02]>> Absolutely.

[00:28:03]>> Um so then the question becomes if they didn't work, why does everybody think they did?

[00:28:09]>> And that comes down to uh the clinical trials. But actually there was a lot of research papers claiming they worked.

[00:28:17]>> Yes. And this this is almost entirely down to one statistical trick. There was a few other statistical tricks, but most of it is this statistical trick. So what was happening was people were getting injected with these new products.

[00:28:31]>> The and the the products were designed to make the cells of around your body produce this foreign spike protein. Yes.

[00:28:38]And probably other foreign proteins because the message was slipping >> cells all around the body. Yeah. Yeah.

[00:28:44]>> And then um when that happens, this mucus barrier thins because of the cytoine damage >> and a very important cytoine called interferon was suppressed.

[00:28:56]>> Now that's like the general of the immune system that that tells the immune system what to do and it was taken out for a while by the vaccine.

[00:29:04]>> And the interferon does have antiviral properties, doesn't it? I think.

[00:29:07]>> Yeah. And it's just not functioning for a while. So you've just disarmed the army. Um and then what happens is effectively that person has immune suppression and their risk of infection is far higher than it would have been if they hadn't been vaccinated. Yeah.

[00:29:25]>> And so what we saw was a surge of infections in that first two week period after the injections.

[00:29:31]>> And it wasn't just COVID.

[00:29:33]>> Um shingles rates were 40 times higher than expected.

[00:29:36]>> Wow. Were they all infections higher? Um so shingles that shingles measure was from a a clinic with um patients who had rheumatoid arthritis and were imunosuppressed. Um so that's a quite good population to measure it in because you get high numbers and you can actually see it. Um so in that population it was 40 times what you'd expect but there were papers showing herpes simplex virus went up herpes virus shingles um CMV EBV. So basically virus viruses >> yeah all of the viruses apart from COVID in that list are DNA viruses >> and they're all viruses that we know go latent in the body.

[00:30:14]>> Yeah.

[00:30:14]>> So CO is an exception. Well SARS COV 2 is an exception there because it's RNA >> and it probably respiratory viruses generally don't go latent as far as we know >> but nevertheless these infections were all occurring including CO. Um so that's what happened in the first two weeks.

[00:30:30]But what did they do? They said, "Oh, the vaccines can't do anything in that period, so we should ignore it."

[00:30:36]>> And so they they pretend those vaccinated people didn't get sick.

[00:30:41]>> And then on occasions, they described vaccinated people in that twoe period as unvaccinated.

[00:30:49]So, as well as ignoring the vaccine people who were sick, they were putting those illnesses onto the unvaccinated group and exaggerating the problem for the unvaccinated. So, you've got a sort of double whammy.

[00:31:02]>> Double whammy. Yeah.

[00:31:03]>> Yeah.

[00:31:04]>> Um, >> pretty sly trick really.

[00:31:06]>> It is a sly trick and we were calling it out and it's never been addressed. No.

[00:31:10]And people have continued to publish and ignore that twoe period. And they I only ever managed to find two papers where they published on the incidence of COVID um in a vaccinated population for the whole wave. So like from the moment the injection went in the arm right through to the end of the wave. And it's only with data like that that you can say, well, over the course of the wave, 8.5% of the unvaccinated got sick and over the course of the wave, 8.5% of the vaccinated got sick, too. Only theirs was all sort of in the earlier period, >> but people just were hiding the data.

[00:31:51]>> Makes it so clear, CLA. Yeah, it's just uh it so that one and that one. Try now. There we go.

[00:32:03]>> Yeah. So, this is this is the trick that I've shown.

[00:32:05]>> Oh, yeah.

[00:32:06]>> So, this is if you look at it with smiley faces and unhappy faces.

[00:32:09]>> Um so, if you look at the the top row is unvaccinated, the bottom row is vaccinated, and the totals at the end are the same. You got the same number of unhappy people, but it's just spread out differently. So the unvac the vaccinated get it all in that first two weeks and afterwards they're done. And the unvaccinated I've done it as if we're sort of coming off the top of a curve which we were at the time.

[00:32:31]>> So it's more in the first week than the second and the third and the fourth. But if you only measure and compare week three and week four, you look like you've got a very effective vaccine. And you you didn't you just made people sicker earlier. It's a complete I guess I I guess the correct word is is illusion in psychiatry. An illusion is a a misrepresentation >> of actual events.

[00:32:57]>> Yeah. I I called it the illusion of benefits in the book >> which which leads to a delusion which is a false belief.

[00:33:03]>> Yes. Yes.

[00:33:05]>> Rather expensive false belief it has to be said.

[00:33:07]>> Expensive in many ways. Um, one of the costly parts of this is that if you have a population that are going to get CO in that way, >> but you make them immun suppressed, then first of all, a higher percentage get it.

[00:33:23]>> Yep.

[00:33:24]>> And second of all, they're very sick when they get it >> because they're already immunosuppressed.

[00:33:29]>> Because they're already immune suppressed.

[00:33:31]>> And if you make all these people have their infections earlier, >> Yeah. Then rather than protecting the NHS, you're bringing forward all of the infections into January. Yeah.

[00:33:43]>> In the most frail people and overwhelming the NHS, which is apparently what we were trying not to do.

[00:33:48]>> It's exactly the opposite of a flatten the sombrero that Boris Johnson talked about, wasn't it? Yeah, >> that's exactly right.

[00:33:54]>> Wait, wait till I go back on to this.

[00:34:07]go from that one to that one to that one. There we go.

[00:34:10]>> There we go. So, this is um a graph of late 2020 into spring 2021. Yes.

[00:34:17]>> And the little dotted line there are COVID labeled deaths in Europe.

[00:34:22]>> And you can see they had an autumn wave that was bang on the same as the UK one, which is the solid line.

[00:34:28]>> Yeah. So the first part on the left is all is all the >> Yeah. And then we had this roll out that they're so proud of how quick it was.

[00:34:36]>> Yeah.

[00:34:36]>> And look what happens with our roll out.

[00:34:38]>> We have all of these cases like a witch's hat on top of the winter spike >> and at the end of it >> it's done. Like you know all the people that might die >> have died.

[00:34:49]>> It's way near the baseline. Yeah.

[00:34:51]>> Yeah. So by spring 2021 it's over.

[00:34:54]>> Whereas the rest of Europe who are much slower at doing all of this had a much more gentle spread out wave. Well, is that what we really wanted? Because that's not great. And then when you measure the area under this curve, so again, that's the number of people that did die.

[00:35:09]>> It was 13% higher in the UK than in Europe.

[00:35:13]>> And that's before we even mentioned madazzylam and morphine.

[00:35:17]>> It is before all of that. It's just it's just like just comparison, you know, comparing like for like >> that we were >> Yeah. doing the opposite of what we were meant to be trying to do.

[00:35:28]>> Yeah.

[00:35:42]>> Now >> we will hang on we'll get the second screen. Try now. There we go.

[00:35:47]>> Oh, okay. So, this is a delusion.

[00:35:50]>> Oh, yeah. Delusions. Good.

[00:35:52]>> Yeah. So the delusion was you know come and roll up your sleeves get a vaccine and once you've been once we've injected you you are unvaccinated for two weeks.

[00:36:00]>> So by being vaccinated you're unvaccinated.

[00:36:02]>> Yes it >> makes perfect sense.

[00:36:04]>> And then of course that delusion only works when you've got this delusion that the whole world is going to catch fire.

[00:36:10]Yeah. That was what we told the whole world is about to have this catastrophic fire.

[00:36:14]>> Um and we going to give you all fire protection systems.

[00:36:18]>> Mhm. And when the fire protection systems end up shorting and causing fires, >> uh we're told that it's too um too soon for a fire protection system to have had a benefit.

[00:36:29]>> Um and therefore the fires are not the fault of the fire protection system.

[00:36:32]That's basically what happens.

[00:36:34]>> Yeah.

[00:36:34]>> And then when all the um sprinklers go on and people get other harms, they're told it's a coincidence >> um and nothing to do with the protection system. And if you talked about the fire protection systems and said that you didn't like them, you were anti- fire protection systems.

[00:36:50]>> Yep.

[00:36:51]>> Yeah, that's my little analogy.

[00:36:53]>> Yeah. No, that is a good analogy.

[00:36:55]>> Um, so this is back onto that number that I was trying to quote earlier, the Gallup poll when they asked the Americans who'd had COVID.

[00:37:02]>> And if in 2020, um, based on this poll, 85 million cases there were um, and there were 116 million cases in 2021. Now, if the vaccine had reduced the case fatality rate, >> then the percentage of cases that died should have been lower in 2021.

[00:37:22]>> Of course, >> it was4% before and after the vaccines.

[00:37:26]>> So, you've got the same you've got more deaths in 2021.

[00:37:30]>> Um, you've but you've got, you know, broadly a similar number of cases and deaths and the same death rate.

[00:37:36]>> So, where's the vaccine benefit? So, it's not just that the infections weren't stopped because the death rate's the same.

[00:37:42]>> Death rate is the same. Yeah.

[00:37:44]>> Um, and so, you know, the 20 million lives saved claim is really all they've got left.

[00:37:52]>> All they've got left is the model data.

[00:37:54]And, you know, I've just listed off here some of the things that are in the book, some of the examples of things that should be obvious to say this this worked. So one lovely one is when you compare CO deaths in Israel and Sweden which um coincidentally had a ratio of two all the way through. So Israel's quite a young population and Sweden's older but you could the two lines map perfectly at a ratio of two. Israel went hard and early on the vaccines. Sweden didn't and there was no difference.

[00:38:25]>> So twice as many people in Israel.

[00:38:29]>> No in Sweden. the rates higher in Sweden because they're older.

[00:38:32]>> Oh, right. Okay. Yeah. Yeah. Yeah. Of course. Yeah.

[00:38:34]>> But the point is that they vaccinated at very different times.

[00:38:37]>> Yeah.

[00:38:37]>> And so the line should have deviated for a period and they don't.

[00:38:41]>> And that's true. Like every time you try and find these examples, you don't see the benefit that there should be. And besides which if there was a massive benefit and the you know there there was the claimed risk where is this country that didn't vaccinate very much where lots of people died you know where is their sort of you know pitch postcard country they always point to to say everybody died here because they didn't get the vaccine there isn't one because they can't you know there's a lot of African countries that didn't vaccinate.

[00:39:10]>> Oh yeah yeah I know I know my friends in Uganda hardly any of them.

[00:39:14]>> Yeah. Everyone got sick with a cold.

[00:39:18]>> Yeah.

[00:39:19]>> For about a fortnight in 2021 and that was it.

[00:39:21]>> Yeah. And I mean they were a young population over but Yeah. But there you know even the older people weren't getting >> Yeah. And there there's HIV positive people there who are high risk.

[00:39:31]>> That's a really good point.

[00:39:33]>> Yeah.

[00:39:34]>> Yeah. I mean you're probably talking about I'm not sure about Uganda but you know some African countries you're talking about 10 20% of the population are HIV positive. and there were CO deaths to measure beforehand >> um but nothing nothing got worse compared to elsewhere. So you know that there is no control group they can show to say this is what would have happened if we hadn't done it.

[00:39:56]>> Um and then these are more US wastewater graphs which I quite like. So we've got the >> um solid black line is the level of virus up to the point where Omicron comes and I cut the graph off.

[00:40:07]>> Right. And on the left we're comparing it to hospital admissions which is the dotted line.

[00:40:13]>> And you can see that it maps very very very closely.

[00:40:17]>> Yeah.

[00:40:17]>> Um and the two lines separate in autumn of 2021 and then later December 2021 Omicron comes along and they separate in a big way.

[00:40:29]>> Um but they do separate just a little bit there. And I think what was happening in autumn 2021 is the US had done a big push to get monoconal antibodies. So these are um antibbody treatment where you've got an antibbody to the virus that you can give someone in an IV drip.

[00:40:47]>> Yeah.

[00:40:47]>> Um and it was shown to reduce hospitalizations and they were out there with all the high-risisk people giving them these drips in their homes to stop them coming into hospital and I think it was working a little bit. reduced hospitalizations by about six% and you can kind of see it in the population.

[00:41:02]>> It does make sense. The trouble is that the the virus mutates so quickly that the MAB would be out of date very quickly. But >> well, yeah, it didn't work for >> at all. They stopped using it, which is interesting because um they admitted that the the monoconal antibbody to the spike >> that was working a little bit >> did not work for omicron >> and yet they were claiming that the vaccine antibbody >> did. I mean they were still injecting people in December 2021 with the Wuhan spike.

[00:41:31]>> Yeah. And we know that antibbody did nothing.

[00:41:33]>> So the the antibbody doesn't work, but the antibbody generator mysteriously still does work. And of course it doesn't.

[00:41:39]>> Yeah. And in 2022, they were injecting children with the Wuhan spike. It was outrageous to be doing that.

[00:41:46]>> Yeah. Yeah.

[00:41:47]>> And then that graph next to it is the deaths. So the black line is the waste water and the little dotted line is the deaths. And now what you see is that in that same period with fewer hospitalizations, >> the people who were going to die still died.

[00:42:03]>> Yeah.

[00:42:03]>> So it did keep people out of hospital, but it didn't actually affect the death rate at all.

[00:42:07]>> Saving lives. Yeah.

[00:42:08]>> No. And so if you've got no separation between um excess deaths and wastewater levels, >> Yeah.

[00:42:15]>> then you haven't succeeded, have you?

[00:42:17]>> Correct.

[00:42:19]>> Oh, and then so this is the fantasy claim graph. So the graph on the left >> Yeah.

[00:42:24]>> is all of the COVID labeled deaths in the world >> accumulating over time. So it's a gra a line that can only go up.

[00:42:31]>> Yep. Of course.

[00:42:32]>> Um and it you know it goes up at a particular rate up until the end of 2020.

[00:42:39]>> So the steepness of the curve on the left is the >> that's the key >> number of deaths. Yeah.

[00:42:43]>> Yeah. And then the vaccines arrive and it gets steeper but it's pretty constant. And then at the end, Omicron arrives.

[00:42:51]>> A third is deadly. And you can see it plateau off. You can see this changing gradient. And there's far fewer deaths accumulating.

[00:42:59]>> Now, what Omicron shows there is what the vaccines were meant to do.

[00:43:02]>> They're meant to flatten this curve off.

[00:43:04]And where is the flattening? There is no flattening.

[00:43:07]>> The vaccines didn't do it, but the omicron did.

[00:43:09]>> That's right. And then this this graph on the right is actually the same graph.

[00:43:14]The solid line is the same line and the dotted line is what we're told would have happened um if we hadn't had vaccines. Well, hang on a minute. Why would it suddenly escalate to that degree?

[00:43:28]>> There's absolutely no reason to think it would have taken off like a rocket, but that's what they claim.

[00:43:34]>> Um so that was the the third delusion was that the vaccines made Omicron less deadly. No, was intrinsically less in >> it was a sparkler, not a flamethrower.

[00:43:46]>> Yes. Good analogy. Yeah.

[00:43:49]Um, and so then we get to the point that people weren't told any of this >> and still haven't heard this >> and so they find it really hard to believe. Like if you I've said all of that and people f cannot believe it because they've had years of being told the opposite >> and told it in sound bouts and told it repeatedly told it by people in authority um when it was not true and they can't get their heads around that.

[00:44:14]>> Mainstream media if it's on TV it must be right. Mhm.

[00:44:18]>> Yeah.

[00:44:18]>> And and the fact is that first of all, the centers were being punished. People were seeing the centers being punished and didn't want to join in because they didn't want to have any of that. Careers were destroyed. They still are being destroyed currently >> in Australia, which was appalling.

[00:44:34]>> Yeah.

[00:44:34]>> It's going on in Ireland. Yeah.

[00:44:36]>> Right. You know, five years later.

[00:44:38]>> Um and then there's the problem that, you know, the authorities sort of started to hide the data when it wasn't convenient >> and still are. There's there's all sorts of data not released. You know, we don't have the death the excess deaths related to vaccine status.

[00:44:56]>> The government have released that data to the pharmaceutical companies, but not to you.

[00:45:00]>> That's right.

[00:45:00]>> Outrageous.

[00:45:01]>> Yeah. We've also got that the vaccine safety reports when they started to look nasty about um arrhythmias and heart disease that those have still not been published and they were being published.

[00:45:12]They were sharing the previous reports.

[00:45:14]Soon as it looks uncomfortable, we're like years into not being shown.

[00:45:18]>> Yeah. Yeah. No. All sorts of data streams just disappeared.

[00:45:22]>> Yes. Yeah.

[00:45:23]>> And then there's a peerreview problem, which is a problem that's been going on for decades. It's a problem that the editors of the journals themselves have acknowledged.

[00:45:32]>> Yes.

[00:45:32]>> That there's, you know, basically the system doesn't work as a healthy gatekeeper. It's an unhealthy gatekeeper. Um it's it's corrupted. Um and so if the press rely on peer review as their measure of quality, they're relying on a corrupted measure.

[00:45:50]>> But it's not only that. I mean, um all the national guidelines, you know, the NICE guidelines.

[00:45:56]>> Yeah.

[00:45:57]>> National Institute for Health and Care Excellence generate their guidelines based on peer-reviewed publications. And they they say, well, there's no peer-reviewed publications. There's no evidence. M and the world we're in and it's absolutely insane that we have these publishers as a barrier.

[00:46:16]>> Well, they're just massively profitm for no benefit. There's no public benefit. I can see there. I think we should >> It's a real It's a real legacy shackle, isn't it?

[00:46:26]>> Yeah.

[00:46:27]>> It's a millstone rounder neck that's been there since >> well, I suppose it goes back to mid mid 1850s and things like that when the only way to publish something was on a hot piece of metal. Yeah, which is fair enough to have a publisher then.

[00:46:42]But at the moment, >> but now publishing is not too big of a problem, strangely enough.

[00:46:45]>> No, it's cheap and and free and I mean like you know you and me both ourselves.

[00:46:50]>> Yeah. And so we should be seeing science published and shared and publicly peer-reviewed.

[00:46:56]>> Re Yeah. It really is annoying that you know we're paying through for this research through our taxes.

[00:47:01]>> Yeah.

[00:47:01]>> Then you have to pay again to read it.

[00:47:03]It's just outrageous.

[00:47:04]>> Yes. So, I don't know why that I don't know when we'll ever get to that stage, but we need to get there. We do.

[00:47:10]>> It's like vested interest is is got a an interest in maintaining this legacy system that discriminates against the majority of us.

[00:47:18]>> Yeah. Yeah.

[00:47:21]>> Um and then we've got this silly crazy situation where the FISA are telling the FDA, the regulators, they didn't understand the mechanism of protection.

[00:47:31]Of course, they didn't understand the mechanism of protection because one. Exactly. But they're happy to say to the regulator, "Oh, we've no idea how this thing is working." And then the public gets told it's safe and effective.

[00:47:43]>> Yeah.

[00:47:43]>> Like, well, that's a big disconnect there, isn't there? And >> Yep.

[00:47:46]>> Yeah. And you can't have informed consent when >> Yeah. That that was the thing that really got me. I I I actually had three of these things before I rambled rumbled. It was a >> it was a bit of a con. And uh yeah, it means I was not I couldn't give informed consent because the the the information wasn't there in the public domain. You simply couldn't get it.

[00:48:08]>> No, >> it was it was controlled. You know, in in in our great democracy, information was controlled.

[00:48:16]>> Yeah.

[00:48:17]>> And we weren't allowed it because we're just the plebs. And what some people might say to that is um well we didn't know all of this stuff about the safety at the time and we didn't know how effective it would be. You're like well okay I think there's some truth in that.

[00:48:32]It's not entirely true but there's some truth in that.

[00:48:35]>> But the point is you can still communicate under certainty >> and and for example >> Yes. But even with the phase 2 studies there was obvious bio distribution for example.

[00:48:45]>> Yeah. Um and for the FISA vaccine in particular um in their phase three trial which are 22,000 people in the placebo and 22,000 people in the vaccine group of that the people they selected you know there was a certain number that were dying. Now, if their product had caused the death, this is hypothetical, just the maths.

[00:49:08]>> If it was possible that that product had caused a death of one in 4,000 of the 22,000 people, it would not have been statistically significant.

[00:49:17]>> Which means that at the end of that, when you've got the trial result, you can say to your patient, "This is still a new product. We haven't tested it thoroughly, but I can tell you that it doesn't cause death at a rate of more than one in 4,000, but I can't reassure you that it's that or less than that.

[00:49:36]>> And that that's that's the truth. That was the level of information that people had available. There's no way anyone was going to say that, were they? Because it was just safe and effective and everyone was a bit hysterical.

[00:49:46]>> That's what we were told. Yeah.

[00:49:48]>> And I don't think that level just to be clear.

[00:49:51]>> No, >> it didn't. No, but but that's the knowledge the actual power of the statistical technique >> that that it could that was the level of detail it could penetrate down to.

[00:50:01]>> Yeah. Because >> because with the trial even a trial that size >> um there wasn't a difference in the death rate >> because the thing they were trying to measure wasn't very deadly. You know, if you've got 22,000 people >> and you can't see any difference in these severe outcomes >> because it's not really much of a problem.

[00:50:22]>> Whereas if people were being shot in the head, you would see it with a much smaller number.

[00:50:25]>> You would more people would die. Yeah.

[00:50:28]>> Yeah. Just that one. Uh try now. There we go.

[00:50:33]>> Um so yeah, I just sort I've put in a note about vaccine safety. So I do mention it a few times in the book, but it's not the focus of the book. Um, so the focus of the book, as I said at the beginning, is about efficacy. Um, because I just think that's a really, really important story that people just haven't talked about. It's not really being addressed. You know, people are there's a lot of people concerned about safety >> and people don't talk about the efficacy problem. Um, and >> yeah, it Yeah. It's not safe and it doesn't work. Yeah.

[00:51:03]>> Yeah. Um and the the efficacy problem of course relates to you know what they're trying to do next when we've you know we've got what was it 100 million pounds >> no sorry 100 million doses per year of madna being produced on a government contract in this country >> with potential for it to be higher if they it needs to be high who is being injected with all these things >> like one and a half injections per person per year. M yeah with a product that didn't work when it was twiled in this massive way >> and has huge amounts of adverse reactions.

[00:51:40]>> Absolutely. Absolutely. And you know governments don't like admitting that they've signed contracts and wasted public money, but that's what they've done and they need to admit it >> on a scale which is like off the scale.

[00:51:52]>> Yeah. The amount of money that we've added to the national debt is I suppose some people might be able to calculate it, but it's incalculable and and enough to >> revolutionize many parts of the country if it had been used properly.

[00:52:06]>> I know it's really tragic.

[00:52:09]>> It's awful.

[00:52:11]>> Okay. Um, so that's my conclusion. I think I did 1,372 references. It's a bit of a reference textbook if I know it's a huge number.

[00:52:22]Yeah.

[00:52:23]>> Yeah. And I was also cuz, you know, I just I wanted what I'd learned to be written down somewhere because I just think we have to have these written records.

[00:52:32]>> It's a historical document, isn't it? I mean, it's uh Yeah. And and and and when when you read this, it's kind of gratifying. You think, "Oh, yeah, that that that's right. That that CLA just puts it in a way that um vents your frustrations in in the proper way." But it's so important because we need to not make these mistakes next time round.

[00:52:54]>> That's right.

[00:52:54]>> That's right. And I' just to add, it's not all maths. And not at all. So I've got stories in there. I've got I've got all of these quotes from these officials as we went along the way.

[00:53:05]>> And um and I've also got a whole load of stuff about the censorship and what that was like. And in fact, what happened to me in terms of the GMC and all of that stuff in there, too.

[00:53:15]>> Yeah. I mean, I I was I was censored just from making a few videos, >> particular things, >> you know, some worldleading doctors I talked to, I was censored for talking to them. It was just >> I um I made a ridiculous I had an interview on GB News at one point >> and um they put it up on YouTube and it was a fine interview. I they were talking about vaccine side effects which was controversial at the time and I I just you know I didn't want to get them their channel taken down. So I thought I'll talk about GM Barry syndrome because the government had admitted that one. So I just focused on that one thing, >> the paralysis, >> right? So that there was no issue there, you know, it was just an example of how you approach these things.

[00:53:55]>> And um my mom wanted to see it and she couldn't make it work. And I thought, well, I'll just put it up on my YouTube channel and I can share it with her on that. So I shared it in a private setting on my YouTube so that she could see it really >> and I got a strike. you know, well, how come GB News are allowed it and I'm not?

[00:54:13]>> Yeah. Then Twitter, of course, at the in the early years, pre pre-Elon Musk was terrible, >> as was Facebook. I'd put something on Facebook and some fact checker would come and talk a load of dril about it, you know.

[00:54:26]>> Ah, there were fact checks all over the place, weren't there? But I mean, Twitter, Twitter at the moment, um, is thankfully allowing us to talk about hand virus and what's really going on there, >> but there are a lot of people I know who have got voices that ought to be heard, who are saying important things who are not. You're one of them. You're not on it at the moment.

[00:54:47]>> No, I got hacked.

[00:54:48]>> Yeah, Professor Fenton.

[00:54:49]>> Exactly. He's not on it. And people who are on it officially are completely shadowbanned. Nobody sees what they say.

[00:54:57]So what's the point?

[00:54:58]>> Um, so it's not as free as people think.

[00:55:02]>> Yeah, I know a guy who knows a guy who knows a guy who knows Elon. So maybe send send a message.

[00:55:10]>> Cla, great stuff. Um, CLA's books are available. So expired on the COVID pandemic.

[00:55:17]Um, and spiked a shot in the dark on the vaccines and some of the stuff we've just been looking at. Um, I'll put the links. Get get them, buy them, read them, keep them for the next generation. They really are an important historical document as indeed hopefully are these videos which you've shared all the same information. So, that's brilliant. So, as always, CLA, thank you so much. It's been great. And thank you especially for coming up. It's been >> It's been tremendous. Yeah. And we'll talk soon.

[00:55:49]>> All right. Byebye.

[00:55:50]>> Bye-bye.