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Acute kidney failure behind the excess deaths · Cultural Wars | America Out Loud News

• 0:26
  Well, hello everyone, and welcome to the 10 penny files.
• 0:30
  I'm your host, Dr.
• 0:31
  Sherry Tenpenny.
• 0:32
  Here at the 10 Penny Files, we dive into vital and often controversial discussions on topics that will challenge what you've been told and reveal what's been hidden.
• 0:41
  These compelling conversations will make you think, spark debate, and hopefully inspire you to action.
• 0:47
  Join me here each day at 3 p.m.
• 0:48
  Eastern to hear from authors and documentary filmmakers who have something important to say, but have often been censored and silenced.
• 0:57
  Today's conversation is with my guest, John Bodwin to talk about his book, The Real CDC, COVID-19 facts for regular people, the book that delivers truth about how COVID-19 deaths were overcounted and vaccine deaths were hidden.
• 1:14
  John, welcome to the 10penny files.
• 1:16
  Thank you so much for being with me here today.
• 1:18
  Thank you, Dr.
• 1:19
  Tenpenny, and it's great to see you again.
• 1:22
  We always have good conversations.
• 1:23
  Oh, we do.
• 1:24
  We could talk for weeks, I believe, because we always have great things to talk about.
• 1:28
  So before we dive into your book, the real CDC, you told me that you don't like being called the data guy, but you're a whole systems analyst.
• 1:38
  So why why why why don't you like being called the data guy?
• 1:42
  Well, first of all, I don't like statistics.
• 1:45
  Um I grew up, I was born in St.
• 1:47
  Francis Hospital in Hartford, Connecticut, and Hartford, Connecticut has the house of Mark Twain.
• 1:52
  And Mark Twain said there are three kinds of lies lies, damned lies, and statistics.
• 1:57
  And so data kind of people associate that with statistics.
• 2:11
  So I don't like that.
• 2:13
  And it's more important that people know I do investigations beyond just data.
• 2:18
  Okay, these are real people, and they should be considered real people.
• 2:21
  Uh children who died from the vaccine who were said to have died from COVID.
• 2:25
  They're lies, that's fraud in government records.
• 2:28
  I prove it in my book.
• 2:30
  Um, you know, I'm an engineer, I'm not a data guy.
• 2:35
  I have an electrical engineering degree, which takes a lot of a lot of uh studying and heavy and intensive math, uh, not just in statistics, which is very small part, uh, but also understanding you know calculus and uh probably um excuse me, um the time domain frequency domain functions, and then add on that an MBA.
• 2:56
  So economics, finance, accounting, um, and also behavior organizational behavior theory.
• 3:04
  So the psychology of how people act within an organization and how you can more efficient make your organization more efficient by tweaking the organization.
• 3:12
  And so those all those skills together as a whole system uh allowed me to understand uh COVID and what they did to us, and what they did to us was a lot of behavior modification.
• 3:24
  And in the CARES Act, the CARES Act has very little to do with funding the government and and the government funding people to pay for all the negative uh externalities as they call them in economics, but all the negative things that happen to businesses, and here's your $600 check.
• 3:43
  We're gonna close down your business that's been in the family for I don't know, 80 years that you barely scrape by on, and we're gonna give you $600.
• 3:52
  You know, that's doesn't really cut it, but the point was the CARES Act incentivized hospital administrators to do certain things and and coerced doctors to do certain things, and those behavior modifications caused uh a large number of deaths through hospital protocol uh drug protocols and also uh COVID vaccines and the way I'll I'll stop, but that's when I say whole systems analyst, I'm looking at psychology, sociology, organizational behavior, economics, finance, law, law very much, uh, morality, philosophy, and in the little corner is that science, biology, right?
• 4:33
  Um, but people tend to think that the disease is all about biology, it's uh it's a lot more about behavior modification.
• 4:40
  You know, you take issue in your book, you take issues with statistical methods and epidemiology.
• 4:47
  Why do you say that you're that most of us are in the dark ages when it comes to really understanding epidemiology and the statistics surrounding that?
• 4:59
  Well, this does a lot to that.
• 5:00
  Uh let me try to break it down for uh the a regular audience.
• 5:04
  Um so statistics, they're they're looking at the propensity of something happening in the past and and modeling it and trying to forecast what might happen in the future.
• 5:14
  And if you have if you have an event that has never happened before, how how do you take a model from the past?
• 5:20
  And what they do, excuse me, almost overwhelmingly in epidemiology, and it bleeds into um research papers and uh medicine and health and so forth, all the all the medical journals that you know about.
• 5:35
  They apply a normal distribution.
• 5:38
  Now everybody out there knows what a bell curve is, right?
• 5:40
  A bell bell curve, uh they mean it they know what it is, right?
• 5:44
  It looks like a bell.
• 5:45
  But the bell is centered on the mean, the average, and that under that thing, that curve, that's the highest probability that something will happen.
• 5:54
  And as you go out to the edges, you get lower and lower probabilities, and you get to these things called tails on either side of that bell.
• 6:03
  Well, here's an example I use that that people out there should know.
• 6:07
  Um, male cancers.
• 6:09
  If you were to take all the people that got male cancers and you look at their ages, it'll span from you know 18 to 100.
• 6:21
  Now let's look at testicular cancer.
• 6:24
  It's from about 18 to 40.
• 6:26
  It hardly ever happens after 40.
• 6:28
  The average of testicular cancer is around 30.
• 6:32
  It's actually a little lower, lower than 30.
• 6:35
  But if that's set, that bell curve is centered, okay, on 30, and it goes down almost to zero by the time you get to 40.
• 6:43
  And then you say prostate cancer, another male cancer.
• 6:46
  It doesn't happen until 55, 60 years old, it starts going up, you know.
• 6:52
  And then it goes up until the the age, you know, until you die.
• 6:56
  The older you are, the more likely likely you are to get it.
• 7:00
  So you have two separate male cancers.
• 7:03
  One has an average of like 70 something, and the other one has an average of 30.
• 7:08
  But in between, you have zero, like not zero, obviously, but very, very low.
• 7:14
  So it's called a bimodal distribution function.
• 7:16
  It goes up and down and up and down.
• 7:19
  So you have two modes.
• 7:21
  What they do is say, if you were to say male cancers, they take all those data points and they apply a normal distribution, which is one.
• 7:30
  And where is that centered?
• 7:31
  It's centered in the lowest probability of either one, but they're telling you in the paper, the research paper they just wrote, it's the highest probability.
• 7:39
  So it doesn't make any sense.
• 7:42
  If you don't understand the real world probability distribution of a disease or accidents happening or whatever you're modeling, if you don't understand the real world probability and you apply a normal distribution, you could be making vital errors in misunderstanding the confounding variables.
• 8:03
  One there might be two causes.
• 8:05
  Okay, one cause makes something happen on the left side of the graph, and the other cause happens on the right side, nothing happens in the middle.
• 8:13
  But when you throw it all together and you model it only with a normal distribution, doesn't make any sense at all.
• 8:19
  So that means when you hear about research papers talking about confidence intervals of 95% confidence interval, I mean a couple of standard deviations.
• 8:27
  What it people don't even understand what that means.
• 8:30
  They're not 95% confident.
• 8:32
  That's not what that means.
• 8:34
  They're just taking an interval under a graph with a model which is idealistic, it's an idealized model.
• 8:40
  The normal distribution is idealized.
• 8:42
  It has no bearing to the real world many times.
• 8:46
  It's useful in banter, but when the scientists take those papers and write articles about them to put them out to the real world, the real world doesn't understand the basis of those statistics.
• 9:01
  It's all I don't want to use a bad word.
• 9:03
  It's garbage.
• 9:06
  Is that a an explanation that your audience might understand?
• 9:09
  How do they do?
• 9:10
  Yeah, I think so.
• 9:11
  What you're saying is that you know, kind of uh lies lies and statistics and you know, numbers lie, figures lie and liars figure, you know, another you know, Twain quote, because if you're mixing data all into one, if you're you know, if you had two vials and in each vial has data in it and you pour them together and mix it together and come up with a new curve, it doesn't tell you anything about either one.
• 9:40
  I have a good example like that in my book.
• 9:42
  I wish I could remember it.
• 9:43
  It's uh something like, you know, uh if you if you if you uh put meat in a grinder, you can't separate the the pork from the beef because you've ground it all together.
• 9:55
  Um but if before you put it, you could say, oh, this is beef and this is pork.
• 10:00
  Um, but why I say they're behind they're in the dark ages is because they're still doing that.
• 10:06
  Now, if you look at the derivative of time, things have been happening over time, and if you can pinpoint in time where a curve started to go up, that begins your investigation.
• 10:18
  That that's not conclusive evidence that oh, the vaccine caused that, or oh, remdesivir caused that.
• 10:25
  That's evidence that something caused that curve to inflect and go up at that point in time, and then you can say in your investigation, what what happened then?
• 10:35
  It's like, oh, is that when Remdesivir was incentivized with 20% adder to the entire hospital bill?
• 10:40
  So if you get a million dollar ICU stay, the hospital gets an extra 200,000 dollars.
• 10:45
  And somebody says, Well, COVID causes that too.
• 10:48
  And I'm talking about acute kidney injury right now, sudden kidney failure.
• 10:52
  Uh, you didn't have a problem before, all of a sudden you have a problem.
• 10:56
  And that when COVID came in, certain areas of the country, it didn't go up.
• 11:02
  When it went up, is two weeks after they incentivize the use of a drug, which is known to cause kidney problems.
• 11:07
  So I'm not saying, oh, that's it.
• 11:09
  That's still not conclusive evidence.
• 11:11
  Dr.
• 11:12
  Tenpenny, the only way to conclusively determine causality is to do a forensic investigation of individual medical files.
• 11:20
  And that's up to doctors.
• 11:22
  Uh, they can't do it because they don't have access to the data.
• 11:24
  The government has all the data.
• 11:27
  They have enough, they have everything they need to end all debate about vaccines, not even just the COVID vaccine.
• 11:33
  All debate can end in two weeks with a team of five to ten doctors and access to databases.
• 11:39
  They won't give the access, they won't do the work themselves, they're hiding the data.
• 11:43
  It is that easy.
• 11:44
  I'm sorry to say, after five years of doing this or four and a half, well, it's five years for the vaccine now.
• 11:50
  Um, that's how easy it is.
• 11:53
  And and yet people are debating research papers.
• 11:56
  We don't need that.
• 11:57
  We need access to when was this person vaccinated and give me the medical file when his kidneys failed?
• 12:04
  Because I can look at the blood labs every day, the vital uh signs, every hour, everything he got, either IV or oral, all medic medicines, all food, all liquid.
• 12:16
  And from that, you can build a timeline to determine when did his creatinine levels change?
• 12:21
  When did the protein start showing up in his urine?
• 12:24
  You know, all those tests, the evidence is there.
• 12:28
  It just takes a doctor, you know, a little bit of time to forensically investigate an individual as to when his kidneys failed.
• 12:36
  And if you find that it was shortly after remdesivir or baracytonip or vancomycin or whatever he had, um, you'll be able to figure it out.
• 12:45
  And also whether or not he was vaccinated.
• 12:48
  Um, so yeah, that's the these are methods that should be used.
• 12:53
  And epidemiology is in the dark ages because they're still using just the normal distribution for almost everything.
• 12:59
  Um, the SIR curve is is good, uh, but they're not using the derivative of time to find the inflection points.
• 13:07
  And frequency domain analysis is beyond them.
• 13:09
  I'm an electrical engineer, so we use it.
• 13:12
  But if they use that, then that would be another way they could figure it out.
• 13:16
  Dude, do you think this is intentional?
• 13:19
  I mean, you know, when you talk to the general public about these things, they don't have any idea what you're talking about.
• 13:25
  And they try to read, you know, articles and they kind of get it, but they don't understand it to the level of an engineer.
• 13:32
  But the people who are writing the papers and doing the research, they certainly know.
• 13:37
  And so is the oh, the, or you think they don't know?
• 13:40
  Is that I don't I don't I think most of them don't know.
• 13:42
  I think most of them have been indoctrinated into the cult of evidence-based medicine where they're applying the same methods that were used before them, and they think, well, this is how we do it.
• 13:50
  This is how we every company I went into, I was always told this is how we do it.
• 13:55
  I say, well, I have a better way, it's more efficient, we'll make more money.
• 13:58
  No, we're gonna do it the way we do.
• 14:00
  And so you have a culture within medicine and all the research papers that they all just do the same thing, but they all do and have done and they've been taught.
• 14:09
  And um, you know, new methods injected into epidemiology, new new numerical and computational methods um would be good for the for the field.
• 14:18
  You know, speaking of methods, you talk about two different methods in your book, EBM and TBM.
• 14:26
  What are they and what's the difference and why should they be used?
• 14:31
  Okay, EVM is evidence-based medicine.
• 14:33
  Um in the middle of um uh the 1960s, around 1965, the Canadian Health Care Act, Canada Health Care Act, whatever it was called, you know, acts fund organizations.
• 14:48
  It's more about money than anything else.
• 14:50
  So the Canadian Healthcare Act funded a lot of research, specifically at McMaster University, and McMaster University was talking about increasing it uh enhancing the scientific method, which was fine for centuries, but now they're going to enhance it.
• 15:09
  And what happened was these two, I can't remember the guys' names, that's in my book.
• 15:13
  Um they wrote a bunch of papers, the papers became very popular, and by the term by the time uh let's see, night around 1981, the term evidence-based medicine was coined.
• 15:23
  They had other acronyms and terms before that, but that one that one took hold.
• 15:27
  That one caught on.
• 15:29
  And by 1992, after all the papers that were written about EBM over that decade, 92, every single medical school in North America was teaching EBM.
• 15:44
  And here's the bad part about it.
• 15:45
  It centralizes medicine.
• 15:47
  And anytime you centralize everything, anything, you have losses at the margins of normalcy, such that if you're outside the normal framework of diagnosis or you know, treatment, um, you might get misdiagnosed or mistreated.
• 16:01
  So here's here's what they basically feel.
• 16:04
  And if you talk to anybody coming out of med school now, which I'm sure you do, you'll hear them say, we do what the literature says.
• 16:12
  We follow the literature, we follow the science.
• 16:16
  That means they don't use their own brains with the patient standing in front of them.
• 16:20
  They do what the research paper says, which is on the little laptop that they have next to them on the little wheeled table that they bring in when you go to your appointment.
• 16:30
  So the little laptop god on the wheelie table is really your doctor, not the doctor in front of you, right?
• 16:36
  Because if they graduated after 92, they're indoctrinated into EBM, evidence-based medicine.
• 16:42
  And and if you listen to the director of the NIH, J Baticharia, and others like you can't you can't get through a sentence without saying we need to get back to evidence-based, evidence-based.
• 16:54
  That all they talk about is evidence-based, but they're discarding other evidence, which is here's one example.
• 17:02
  A guy goes to the doctor in his local town, the doctor knows the people in town, and the doctor treats him by what the literature says.
• 17:10
  And the literature is from a paper in India, halfway around the world with different genetics, different food supply, different minerals in the water, and yet he's talking to a guy from maybe Portuguese descent who can't eat a certain kind of fish.
• 17:23
  I use this example all the time.
• 17:25
  He's not talking to the individual patient or not treating the individual patient before him based on his experience and knowledge.
• 17:31
  He's treating the patient based on the central authority from the research papers given him by NIH CDC.
• 17:39
  Why?
• 17:39
  Because if he doesn't do that and something happens with the patient, he can be sued, loses license, everything in his life can go to crap because he took a greater chance of bad event because he's covered.
• 17:56
  If he uses the central authority protocol, he's he doesn't have to worry.
• 18:01
  But if he does what he thinks is right, he has to worry about being sued if something were to go wrong.
• 18:06
  So you have a systemic, when I say I'm a whole systems analyst, I'm looking at the whole system, it's economics, finance, behavior, everything is involved.
• 18:16
  And and that's so that's EBM.
• 18:18
  Do you want me to do TBM now?
• 18:20
  Sure.
• 18:20
  Okay.
• 18:21
  So when you make chips, I'm a I'm an electrical engineer.
• 18:26
  My career was in selling contracts, large contracts for uh semiconductor and chip and board development and research.
• 18:34
  So when you make a chip, at least on the digital side, there's design and verification.
• 18:40
  In order to tool up a manufacturing plant to make a chip, it costs a lot of money.
• 18:45
  It's millions and millions of dollars.
• 18:47
  So you better get it right.
• 18:48
  Because if you get it wrong, it's going to cost those millions over and over again.
• 18:52
  So we we try to get the we call them spins, try to get the spins down, like the first rev, second rep, third rev, okay, ship the product, it works.
• 19:01
  So design takes it used to take 50% of the time and verification was 50%.
• 19:08
  Nowadays, it's about 5% design and 95% verification.
• 19:13
  So I took that methodology and I adapted it for investigation of anomalies in health.
• 19:19
  So I say top-down uh investigation.
• 19:22
  I look at the high-level data.
• 19:23
  Is there an anomaly?
• 19:24
  Oh, yeah, we have excess deaths.
• 19:26
  Okay, let me go further down into the details at the at the higher level is abstract, and you go down into more and more detail.
• 19:34
  So all caused deaths all the way down to the most detailed 12,000 page medical file.
• 19:40
  Okay.
• 19:41
  So where are the problems?
• 19:43
  Well, uh, pulmonary embolism and acute kidney injury and stroke are causing those excess deaths.
• 19:51
  Okay, what age groups?
• 19:53
  What genders?
• 19:54
  More and more detail.
• 19:55
  You're drilling down into the data.
• 19:57
  Okay, was it?
• 19:59
  Oh, that's
• 20:00
  Actually, it was just this hospital.
• 20:01
  We have a problem with just this hospital, right?
• 20:04
  These are ways to investigate to find out where the problems are, but you can't prove anything conclusively until you get to the forensic level investigation of a 12,000 page medical file, right?
• 20:16
  Or the autopsies along with that.
• 20:18
  You go into all the details and you really prove it.
• 20:22
  Once you've proved so top-down investigation from the abstract to the detailed, and once you've proven causality, then you can attribute those like, oh, well, this was caused by, say, the vaccine or by Rundeci.
• 20:36
  Now you can quantify.
• 20:38
  So bottom-up quantification, top-down investigation, bottom-up quantification.
• 20:44
  You can quantify.
• 20:45
  Well, we had an excess of 40,000 uh just last month, you know, in acute kidney injury in the United States.
• 20:53
  That number's not real.
• 20:54
  It's an example.
• 20:55
  The real number is 255,000 excess kidney failure deaths in the United States in the last uh four four years, four and a half years, which is huge.
• 21:07
  So I can I've I've investigated if I can determine causality, then I can quantify by going back up and looking at the aggregate where where they normally is.
• 21:15
  So that's what TBM is.
• 21:17
  Um Sarah Meyer testified on June 25th, 2025 before the ASIP ACIP committee.
• 21:26
  She described exactly that.
• 21:28
  She described it's my methodology.
• 21:31
  How can it be my methodology if she said that's what they do?
• 21:35
  But she lied.
• 21:36
  They don't do that.
• 21:37
  Because if they did that, they would find the same things I found.
• 21:40
  And they would be able to determine causality within a week.
• 21:44
  So she lied.
• 21:46
  She talked about what they do when the fact the fact is they purposely don't do that.
• 21:51
  They purposely avoid and omit their required legal duty because who's gonna who's gonna arrest her?
• 21:58
  Oh, the government.
• 22:00
  Really?
• 22:01
  The government that's causing this is going to arrest the person.
• 22:04
  Yeah, I don't think so.
• 22:05
  So yeah, those are the methodologies.
• 22:08
  You know, I want to go back to the evidence-based medicine thing for a second, because I remember, you know, all the time that I did hospital-based medicine, that was the mantra.
• 22:20
  We have to get back to evidence-based medicine.
• 22:22
  It's like, well, aren't you looking at the person sitting in front of you?
• 22:26
  Isn't that evidence?
• 22:27
  I mean, the person is bleeding out.
• 22:29
  They've cut an artery and their arm and they're bleeding out, and they say, well, evidence-based medicine would say the artery doesn't run over that part of their arm.
• 22:39
  It well, doesn't it matter that maybe that person has an abnormal anatomy, has an a variant variant to their anatomy that the the cut, cut an artery.
• 22:48
  No, no, the evidence-based medicine doesn't say that.
• 22:51
  It can't be an artery and they can't be bleeding out.
• 22:54
  And I used to say that analogy a lot because that's where they come from.
• 22:58
  It does, it's evidence-based.
• 23:00
  It's like uh under that bell curve that you were talking about.
• 23:03
  If the vast majority of cases are at the at the at the top of that bell curve, then that's evidence.
• 23:10
  If it's out here at the side somewhere in the tails, as you described, um, even though it's still evidence, that doesn't count.
• 23:17
  That's not evidence-based medicine.
• 23:19
  And so um, and you also rightly said, as medical education has gone on, it's just gotten worse.
• 23:28
  I mean, it's all algorithm-driven medicine.
• 23:30
  It's like if yes, then you do this.
• 23:32
  If no, if yes, no, yes, no, yes, no.
• 23:35
  But it only goes down maybe two or three layers deep.
• 23:38
  And then if they can't figure it out from that, then we'll just skip it and go on to something else.
• 23:44
  It doesn't apply at all.
• 23:46
  We might as well replace them with AI at this point because it's just a digital decision tree, like you said.
• 23:53
  And and it's funny that you used a very similar example to what I use.
• 23:57
  Um triage, right?
• 23:59
  Triage, the guy comes in, you know, he's bleeding like crazy from his leg.
• 24:03
  He has a big gash right where the femoral artery is.
• 24:06
  And um, yeah, what are you gonna do?
• 24:09
  Take his temperature.
• 24:10
  What's the first thing you're gonna do?
• 24:12
  Yeah, hurry up, take his temperature because the the guidebook says to take his temperature first.
• 24:20
  I mean, come on.
• 24:22
  Come on.
• 24:23
  People aren't thinking.
• 24:24
  Um, doctors are robots now.
• 24:26
  That's it.
• 24:27
  Well, there has been a lot of discussions.
• 24:29
  We actually did a webinar on AI and medicine.
• 24:33
  This was maybe about six or seven months ago.
• 24:36
  It was maybe about that long ago because it was right after the inauguration.
• 24:40
  Well, maybe a year ago.
• 24:41
  It was right after the inauguration when when Trump had like um Larry Ellison and all the people in talking about we're gonna, you know, we're gonna just just take a blood test and determine what cancer you may have predicting, and then we're gonna give you an antibody drug to neutralize it ahead of time.
• 24:57
  And I remember even, you know, Dr.
• 24:59
  Malone came out at that time.
• 25:00
  I was on a webinar with him, and he said, that's nonsense.
• 25:04
  He said, We've been trying to do that for 30 years.
• 25:07
  That's nothing new and novel.
• 25:08
  We tried that with mice, we tried it with rabbits, we tried it with rats.
• 25:13
  It doesn't work if it doesn't work on those systems, which are much less complex than what would happen in a human being and levels of complexity.
• 25:21
  How could it ever work on in the way that he stated it?
• 25:25
  He said that was just a bait and switch.
• 25:26
  He said it was just a sales pitch.
• 25:28
  And people, you know, all kind of got into that.
• 25:31
  So well, I don't know if I don't know if you want to cut this out, but there was a deal on the table with Palantir and Trump's first administration.
• 25:39
  Uh since he lost, it was tabled.
• 25:41
  As soon as he got in, that deal was on again.
• 25:43
  It's worth billions and billions of dollars to Peter Thiel, and it's to bring AI to medicine.
• 25:48
  Yeah, exactly.
• 25:50
  That's it.
• 25:50
  I mean, it they wanted to do this, and it's all about money, you know, they just power, but I mean, at this level, money that I'm talking about.
• 25:57
  That the power level is way up there in the elites.
• 26:02
  Exactly.
• 26:03
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• 33:37
  Let me tell you a little bit more about our guest, John Bodwin.
• 33:40
  He's an author of two books, The Real CDC, COVID Facts for Regular People, and the CDC Memorandum, A Notice of Criminal Liability.
• 33:48
  He's posted more than 150 substacks and has introduced three bills in New Hampshire and Minnesota.
• 33:54
  After obtaining 1.6 million official death records from four states, John conclusively determined that federal and state governments extremely overcounted COVID deaths and purposefully deleted COVID-19 vaccine deaths as a COVID-19 as a cause of death from the records to make COVID look like it was 10 times worth than what it really was, and to make the deaths from vaccines appear negligible and as though the COVID jabs were safe.
• 34:25
  Jod is also a podcast also has a podcast called The Last Boomer with John Bowen Sr.
• 34:33
  I guess that's your dad.
• 34:35
  And so it's it's a really great, really great topic, a really great conversation on the last boover called the last boomer.
• 34:42
  John, you have said there is an overemphasis on myocarditis while ignoring deaths from kidney failure.
• 34:50
  What signal of excess deaths were missed or ignored?
• 34:54
  And what about sudden kidney failure as in this that have been coupled into these excess deaths that you've uncovered by examining all those death records?
• 35:04
  Yeah, so I'm this is a tough one to explain in a short time, but I'll try.
• 35:10
  Um I look at in the data, the acute kidney injury, which is also called acute renal failure, which I call for the public sudden kidney failure.
• 35:22
  That's not that that's not chronic kidney disease.
• 35:24
  That's it, that's a stage one, two, three, four longer term um issue.
• 35:29
  And you know, if you survive your acute kidney injury event, you end up with CKD, right?
• 35:36
  Your kidneys are impaired and you degrade over time.
• 35:38
  But if you didn't have a problem before and all of a sudden you have a problem, where did that come from?
• 35:43
  So just to give you an idea, the excess acute kidney injury involved deaths over the last five years, or 150 times more than the myocarditis uh involved deaths in this is the United States.
• 36:02
  150 times more.
• 36:04
  And what they also do with the myocarditis is they tell you that it's it's young males.
• 36:10
  Young male athletes.
• 36:11
  So we have three qualifiers.
• 36:14
  So you have young male athletes.
• 36:17
  Each one of those makes the pool smaller and smaller and smaller.
• 36:22
  I couldn't separate out athletes from the death date, but I can look at the ages.
• 36:26
  I looked at ages 15 through 34.
• 36:29
  I think that was I'm pretty sure that was it.
• 36:31
  15 through 30.
• 36:32
  I have it in a graph behind this window.
• 36:35
  And males.
• 36:36
  So I separated males 15 to 34.
• 36:38
  So acute kidney injury is 1,500 times more, 1500 times more than deaths involving myocarditis in young male athletes.
• 36:49
  It's happening in older people.
• 36:50
  It's happening in middle-aged people.
• 36:52
  It's happening in both genders.
• 36:54
  But because somebody wrote a paper a while back and somebody else wrote a paper and they happen to be young males.
• 37:00
  All the doctors are pointing to young males.
• 37:03
  And yeah, it's evidence-based medicine.
• 37:05
  They're looking at the evidence, which is only the papers, right?
• 37:09
  When an older person dies, or even a middle-aged person dies in their sleep or drops dead at work, they might have a diagnosis of cardiac arrhythmia, cardiac arrest, uh, aphib.
• 37:22
  The medical examiner doesn't really know.
• 37:24
  So they like, well, probably something to do with the heart.
• 37:26
  We don't have time to do an autopsy.
• 37:28
  They're not doing autopsies.
• 37:30
  They stopped.
• 37:32
  Obviously, they're doing some, but the the autopsies required by law.
• 37:37
  The law is being broken all the time.
• 37:39
  Because they know they know it's the vaccine.
• 37:41
  They know that if they put those myocytes from the myocardium, the heart cells under a microscope, they will find infiltrates from the vaccine.
• 37:50
  They know it.
• 37:51
  So they're not doing that.
• 37:52
  Um, but back to the point.
• 37:55
  The um myocarditis would be in data a lot bigger if they did investigations of all these individual deaths at 80, 90.
• 38:06
  There's one lady.
• 38:07
  This lady, Ethel in Vermont.
• 38:10
  Uh, Ethel was 98 years old.
• 38:13
  I matched her bears record with her death record, because I have the Vermont death records too.
• 38:19
  She died of a heart attack.
• 38:21
  She's 98 years old.
• 38:22
  Nobody's gonna say anything.
• 38:24
  And nobody's gonna investigate.
• 38:25
  They're not gonna do an autopsy on her.
• 38:28
  But here's what happened.
• 38:30
  She got injected.
• 38:32
  And within hours, her heart rate went to 145 beats per minute.
• 38:36
  145 beats per minute in a 98-year-old lady.
• 38:40
  She's dead in two days.
• 38:42
  She had a heart attack.
• 38:44
  They're not going to oh, is it the vaccine?
• 38:46
  Oh, we don't want to know.
• 38:47
  We don't want to look.
• 38:48
  We're not going to investigate.
• 38:50
  Was it myocarditis in a 98-year-old female?
• 38:54
  Maybe.
• 38:55
  Who knows?
• 38:56
  Why do you think why did um all of medicine and all of primarily uh functional medicine just glob on to this whole concept of myocarditis, which is real, but to the exclusion of all the problems with kidneys.
• 39:16
  Um, that's that's uh, you know, I don't want to get in trouble with a lot of people I like.
• 39:21
  Uh, but how does it happen?
• 39:24
  Operation Warp Speed was a joint mission between um HHS, Health and Human Services, and the Department of Defense.
• 39:32
  The Department of Defense ran distribution.
• 39:36
  I did not see green army trucks driving up to CVS to deliver vaccines.
• 39:41
  Uh they had nothing to do with really distribution.
• 39:44
  Their job was PsyOps, psychological operations against the people of the United States.
• 39:49
  Their job was to shut down social media posts with any comment adverse to vaccines.
• 39:57
  Um, and if you think about all the people who are
• 40:00
  Suppressed.
• 40:00
  Like I was, I was uh suspended maybe four or five times on Twitter.
• 40:06
  I can't use LinkedIn at all.
• 40:08
  It's the worst platform.
• 40:09
  You can't write anything.
• 40:10
  There's no free speech at all.
• 40:12
  Um, I didn't use Facebook very much back then.
• 40:16
  So if they can suppress somebody's speech, they can also promote somebody's speech.
• 40:24
  And so where they have a full spectrum of narratives across, you know, tens of thousands of doctors posting on X.
• 40:33
  It was Twitter, now it's X.
• 40:36
  They don't have to tell anybody what to post.
• 40:38
  They don't have to post anything themselves.
• 40:40
  With the entire spectrum that they have, they can choose who gets the views.
• 40:44
  They can choose whom to feed to everybody.
• 40:47
  And they chose the narrative of, I believe, this is my opinion.
• 40:51
  They knew that myocarditis was going to be extremely rare and extremely low in the data.
• 40:57
  And they chose it.
• 40:59
  They chose the people who talked about it.
• 41:01
  So they can change the narrative in the United States, whether it's climate change or the border.
• 41:06
  They can change people's mind about a country in an hour.
• 41:11
  They can change the minds of 50 million people in an hour through social media promotion of certain posts.
• 41:18
  I believe Dr.
• 41:19
  Tenpenny, that's how it happened.
• 41:22
  But the numbers are so overwhelming, John.
• 41:25
  And in favor of the well, in favor, that's probably the wrong word, but a slanted towards, you know, what'd you say 155 times greater people dying from kidney disease and this whole died suddenly is not even close to being over.
• 41:42
  I mean, it's going to continue on for years and years.
• 41:45
  You know, I have a friend who's attended since COVID began, he's attended almost 200 funerals from people who have died suddenly or died from turbo cancers or died from other some other complication of the COVID jab because they that they had to get for their employment because they were in the military, because of we work for the government or whatever it was.
• 42:06
  And um, the last conversation I had with him, I said, man, this is when do you think this is ever going to stop?
• 42:13
  And he said, not any time soon.
• 42:15
  Do you believe that that's true?
• 42:17
  That's accurate.
• 42:19
  Uh, yeah, for a couple of reasons.
• 42:21
  Um, well, you know, I should mention uh Steve Um Steve Connolly in Massachusetts asked me, he kept sending me stuff, and he would take screenshots.
• 42:32
  I was like, I can't deal with these screenshots.
• 42:34
  Can you put it in a spreadsheet?
• 42:36
  I said, he said, I don't know.
• 42:38
  I said, I'll do the spreadsheet, you populate it.
• 42:40
  And what we did was there's this legacy.com, which had they aggregate all the obituaries.
• 42:47
  And I said, Well, any instance of the word suddenly or unexpectedly in an obituary, let's track them over 15 years.
• 42:58
  And wow, that the graphs just took off and not in 2020, in 2021.
• 43:05
  21, all these graphs.
• 43:07
  It I have an article on it, it's got all the graphs in it.
• 43:10
  So that's one thing.
• 43:12
  The other one, um, you know, as far as any time soon people who damage their hearts, um, um for for the for your audience, let's not forget I'm not a biologist, I'm not a doctor.
• 43:27
  Okay, so I don't you gotta check everything I say.
• 43:31
  But what you'll find is that myocytes in the myocardium, they do not regenerate like other parts of your body.
• 43:38
  There are certain cells in your body that just don't.
• 43:41
  The the younger you are, the better chance you have of regenerating.
• 43:45
  I mean, if something happens to an infant or a two-year-old, they have a lot of stem cells, they can they can do that repair.
• 43:51
  But when you get older, by the time you're 18, you know, if you do damage to your heart, it's damaged for life.
• 43:57
  When you damage the heart and create scars, scarring, call it scarring if you want, uh, calcification left over from apoptosis.
• 44:04
  We don't want to get into big words though.
• 44:07
  Um, you change the impedance of the heart.
• 44:10
  Impedance is like resistance and capacitance, right?
• 44:13
  It's it's the the electrical structure of your heart.
• 44:16
  When you change the impedance due to scarring and other things that happened from the myocardias that you think you recovered from your ECG or EKG, uh the patterns of electrical behavior of your heart are going to change over time.
• 44:32
  Uh, not sorry, not over time, but they have been changed in that instant that you you had that event that caused the scarring.
• 44:39
  Unless you have a full set of ECG to do a before and after comparison, you may never know you've done damage, but it can manifest in a couple of different ways.
• 44:50
  If you're a performance athlete, and that means testing the performance and the limits of your capability.
• 44:57
  If you're a four and a half minute miler, you run a mile in a four and a half minutes, you take the shot.
• 45:04
  Let's say you can never get to four thirty ever again.
• 45:08
  You're at 450.
• 45:08
  You lost 20 seconds off your mile time, you have no idea why, because you've damaged the overall performance of your heart.
• 45:15
  The other thing is if you hit peak performance, like in football, they don't run for ever.
• 45:22
  They have these short bursts of five and eight seconds and 10 second plays.
• 45:27
  So they have a short burst of energy where they're using as many muscles as they can as fast as they can.
• 45:33
  And that short burst, um, that burst to peak performance, that's when people drop right after those events.
• 45:41
  If somebody drives to the hoop in basketball, right after he drove to the hoop, he's walking back, that's when he drops.
• 45:48
  And so that's another thing that happens.
• 45:50
  The other one, Dr.
• 45:51
  Tenpenny is longevity, where if you're 20 years old and you've scarred your heart, you may have taken 30 years off your life.
• 46:00
  You were gonna live to be 90, but now you're only gonna live to be 60, or you were gonna live to be 80, now you're only gonna be live to be 50.
• 46:07
  But the insidious part is you're not gonna know it for 30 to 40 years.
• 46:12
  No one will know that you've done this to your heart through taking that injection until 30 to 40 years from now, and they're not gonna make the causal attribution.
• 46:22
  They're not going to attribute the early death at 60 to the vaccine that happened 40 years earlier.
• 46:30
  So that yes, I believe that's my explanation.
• 46:34
  And nobody, as of today, as of this conversation you and I are having, nobody has been held accountable for this.
• 46:43
  Nobody has nobody from the the smallest um from the nurse, smallest nurse from the nurse in the smallest tiny little community hospital somewhere, all the way up to um the the highest levels of government, the highest levels of industry.
• 46:58
  I mean, nobody has been held accountable for this mass demo side that's happened across uh the US country and around the world.
• 47:07
  I mean, you've brought lawsuits, you brought lawsuits about about thing about about the the data that you found.
• 47:14
  Um, where did you file them and over what legal other revenue what legal avenues have you tried to get this data to somebody's attention?
• 47:24
  Oh uh, so I filed a lawsuit against the governor, the public health commissioner, the chief medical examiner, and four individual medical examiners in Massachusetts for massive amounts of fraud on death records because I got I went to law school at 56.
• 47:38
  I got thrown out for not getting the COVID vaccine.
• 47:41
  So, but but for the uh fraudulent death records that Massachusetts did.
• 47:48
  Um, I would not have gotten thrown out of school because they would not have enacted mandates if they knew that five miles from my school, Brianna McCarthy died from the vaccine, and on the death record, they wrote COVID.
• 47:58
  And three years later, after a bunch of pressure from her family from my lawsuit, they finally added the vaccine to her death record.
• 48:06
  And they didn't remove COVID, even though she was only positive for COVID three months earlier asymptomatically, no symptoms.
• 48:13
  So that's one thing.
• 48:15
  Um that case was dismissed on standing, and then I analyzed the U.S.
• 48:20
  district court system.
• 48:22
  And uh I appealed to the First Circuit and the First Circuit Rubber Stampedate.
• 48:26
  They have two paragraphs, they reiterated some facts, and they have one sentence in the entire I paid $600.
• 48:32
  So they broke the contract with me.
• 48:34
  I contracted with them for a service of reviewing de novo, my dismissal.
• 48:39
  They didn't review it.
• 48:41
  They they just said, oh, we agree with what they said in one sentence, effectively.
• 48:46
  So I appealed that to the Supreme Court of the United States.
• 48:49
  It's docket number 25-600.
• 48:54
  January 9, they'll decide whether or not they want to hear my case.
• 49:04
  Kevin McCarnin finding the SV40 and the origin of replication, finding the DNA plasmid sequences that are left over in the Pfizer vaccines, it is huge.
• 49:17
  That that means they knew not only that, they purposely omitted it, they deleted it from the there's this map that's created by software, and there's a deletion in there that nobody knew about.
• 49:29
  Well, because of his work, um, we know that there is it was purposeful, Dr.
• 49:36
  Tempenny.
• 49:37
  They purposely lied to the FDA and the FDA now knowing that Pfizer lied, what are they doing about it?
• 49:45
  Nothing, nothing.
• 49:47
  And there's hundreds of thousands of people dead, and this is what's causing likely causing the cancers.
• 49:53
  Um, and Jessica Rose also doing that work, and you know, we've we've proven from
• 50:00
  My aggregated data analyses, and I hate to be called the data guy, but you know, the analyses I have are irrefutable.
• 50:09
  I have individual records that say on the record, reacted in five minutes to the vaccine, dropped dead in the clinic.
• 50:17
  And they don't use vaccine as a cause of death.
• 50:20
  It's missing, it's omitted by the CDC purposely deleted them.
• 50:25
  And I could go on with people who died in hours from uh pulmonary emboli, um, from stroke and so forth.
• 50:33
  So you've got Kevin at the molecular genetic level, Jessica at the microbiological level, and me at the data level, but among the three of us, it's it's over.
• 50:43
  And it should have been over in the first two weeks of January 2021.
• 50:47
  The fact that it isn't means they did it purposely.
• 50:51
  And is that what your court case is going to be about when if the Supreme Court Supreme Court agrees to hear it?
• 50:58
  If if they well, if they hear my case, my case is more I did an analysis of the U.S.
• 51:03
  district court system and found that in uh 2020, the first year of COVID, there was a 62% increase in civil cases, civil litigation cases filed in all U.S.
• 51:14
  district courts.
• 51:15
  To give you an example, the prior quarter century, 25 years, the greatest increase in 25 years was 5.7%.
• 51:25
  And in 2020, it was 62.2%.
• 51:28
  Wow.
• 51:29
  More than 10 times.
• 51:31
  There's no way they can handle those cases.
• 51:33
  There's no possible way.
• 51:34
  They did not hire 62% more people.
• 51:37
  They did not work 62% more hours in the every single day for the entire year.
• 51:43
  So the only thing they could do is dismiss meritorious cases that they should have heard, which is a violation of our constitutional rights.
• 51:50
  Now, how can I prove that?
• 51:52
  Well, I looked at the number of uh case citations in the history of the United States.
• 51:57
  What are the 35 most cited cases in the history of the United States?
• 52:00
  And what I found was the two cases that are used to dismiss cases right at the beginning.
• 52:06
  You get a motion of dismiss right after a lawsuit.
• 52:09
  The two cases that are cited are based on standing doctrine and failure to state a claim.
• 52:14
  These are mostly failure to stay declined.
• 52:16
  Uh there's this other standing doctrine ones.
• 52:18
  But so Ashcroft versus Ickball and Bell Atlantic versus Swambley in 2007 and 2009, uh, 16 and 18 years ago.
• 52:26
  Those two cases are 380,000 citations each.
• 52:31
  They're 10 times more than any other citation, any other case that has to do with civil procedure in only 16 years, average between the two, 16 years.
• 52:42
  So in 16 years, the two cases that are used in motions to dismiss are the most cited cases in the history of the United States by a factor of about 10, unless you count summary judgment in 1986.
• 52:56
  That's another issue that I found when I was doing this research.
• 53:00
  So I've basically proven that they use those cases to dismiss all the 2020, 2021, 22, 23.
• 53:07
  And it has become a habit that is formed in the U.S.
• 53:10
  district court system, making the the courts inaccessible to the regular people.
• 53:15
  So all the cases you hear about with vaccines, all the cases you hear about hospital treatments that are every civil case, they're using dismissal doctrines, whether it's failure to state a claim, standing, latches mootness, ripeness.
• 53:29
  You got the LAUSD case that they they said was moot three times, three times smoothing three times.
• 53:36
  Oh, we canceled our vaccine mandate.
• 53:38
  Oh, now that the lawsuit's done, we reinstated the vaccine mandate.
• 53:42
  Oh, you sued us again.
• 53:43
  We cancel the vaccine mandate.
• 53:44
  Now it's moot.
• 53:46
  Oh, and then after you lose the case, we reinstate the vaccine mandate.
• 53:50
  They're playing with the court system.
• 53:52
  So Dr.
• 53:54
  Tenpenny, this this is the biggest thing I've done.
• 53:56
  It has nothing to do specifically with COVID.
• 53:59
  But the bit the biggest work, most important work I've done as a management systems analysis of the U.S.
• 54:05
  district court system, which shows the people have not had access to the courts.
• 54:09
  We've lost an entire one of the three branches of government where there's supposed to be a check against the other two branches for having abused us.
• 54:19
  I'm probably going on too long, but it's no, that's this is so important, John.
• 54:24
  I mean, it's so important.
• 54:25
  That's the reason that I wanted to have you on to talk about this and to talk about, you know, the what's happening at the CDC.
• 54:32
  But in just the last two minutes that we have together here this morning, what would be your biggest takeaway from all this voluminous amount of work that you've done?
• 54:43
  Um, yeah, it has to be the courts, you know, because COVID, they killed a million people, a million, a million American Americans.
• 54:51
  Okay, there's a lot more around the world, but a million Americans were murdered, and it and the number continues to grow.
• 54:57
  And yet people, oh, I'm tired of hearing about COVID.
• 55:00
  It's like, okay, do you know why your kid is half dead?
• 55:03
  Do you know why he's paralyzed?
• 55:04
  You know why he has transverse myelitis?
• 55:08
  So there's there's the COVID issue, but then there's the border issue.
• 55:13
  And there's the election issue.
• 55:15
  Are they fixing the elections?
• 55:16
  Doesn't look like it to me.
• 55:17
  He's been in office almost a year now.
• 55:20
  They haven't fixed that.
• 55:21
  And they brought 60 cases back in 2020.
• 55:24
  That's part of that number, part of that big number increase.
• 55:27
  60 cases brought.
• 55:29
  None of them made it to a substantive discussion.
• 55:32
  And and the Ashcroft versus Zickball was cited by the Supreme Court.
• 55:36
  They denied Missouri and Texas standing.
• 55:39
  So that's the biggest thing.
• 55:40
  If if um if people want to uh look into it, um I I need I need support.
• 55:49
  They're not going to hear my case unless a bunch of people call in or send faxes.
• 55:53
  It's too late for amicus briefs um to support my case.
• 55:57
  But if the Supreme Court ever heard my case, it would fix America.
• 56:01
  It would fix everything.
• 56:03
  Wow.
• 56:04
  Well, tell us where we can find more information about you, John.
• 56:07
  Where can we get your books and how can we best follow you so that people can get involved can get involved with this?
• 56:13
  So the books can be found uh right here at the real CDC.com.
• 56:19
  Uh my articles are the uh the real cdc.substack.com.
• 56:25
  So you just put substack in the middle there.
• 56:28
  And uh let's see, I have the podcast on Rumble.
• 56:31
  I just did my ninth episode.
• 56:33
  Um, doctor, I interviewed Dr.
• 56:34
  Uh Jessica Rhodes, and that went pretty well.
• 56:38
  Uh, and that's called The Last Boomer on Rumble, the Last Boomer podcast with John Bodwin Sr.
• 56:44
  And um, oh, I say senior because um I usually have it written, it's not here right now, but I'm I'm the senior, my son was the junior, so he he passed away in a motorcycle accident in 2018, which is why I do all this.
• 56:57
  I do all this because I don't want other people to lose their kids.
• 57:01
  That's it.
• 57:02
  That's how I got into this.
• 57:04
  God is putting all this in front of me.
• 57:06
  I I'm just a guy.
• 57:07
  How did I find out all this stuff?
• 57:09
  I have no idea.
• 57:10
  Well, it's because you're a brilliant guy, and it's just so great to have you here today, John, and to thank you about all this stuff.
• 57:16
  And we'll have all of those links to everything in the in the show notes.
• 57:20
  And um, it's just been a great conversation again, because we've you and I have had these conversations before.
• 57:26
  So with that, I want to close out with one of my favorite verses from the Bible.
• 57:29
  I close all my podcasts with, whether they're live or pre-recorded, which is Romans 12, 12.
• 57:34
  Rejoice and hope, be patient in trouble and be persistent in prayer.
• 57:38
  So we'll see you again here tomorrow, same place, same time at 3 p.m.
• 57:42
  Eastern on iHeartRadio or at AmericaOut Loud.news.
• 57:46
  Well, have a great rest of your day, everybody.
• 57:48
  Goodbye and God bless.