"There was no virus"...
...and other fairy stories. Keep your focus. It was never about a virus anyway
Preface: This is a relatively long article which will take you a good 20 minutes. Feel free to skim it and come back to it. It is however essential reading covering multiple aspects of the COVID pandemic falsehoods from both sides. And, before commenting, please at least read the subheading.
TLDR: Not only was there a virus, but it wasn't flu and it did result in deaths. Yet there is so much more to the story...Following the recent opening of the Congressional committee hearing into the origins of the “novel coronavirus” pandemic the following video was posted on twitter. It explains the cover up of the origins in 3 minutes. If you haven’t seen it you can watch it below or if you have an account you should be able to see it there.
It was pretty quickly suppressed after gaining traction as I suspect the select committee investigation was too.
In fact a weird event happened around the same time whereby the select subcommittee released a pdf document containing damning email releases from the likes of Eddie Holmes, but many of the emails were embedded secretly in hidden pictures within the document. They showed that there was intent to hide the man-made origins of the “virus”.
Worse than that, once they realised the hidden emails were in there the document was quickly sanitised and reuploaded. Thanks to one of our intrepid mice we were able to get the pre-sanitised copy. Despite the scandals involved the select committee will probably go through the motions, no doubt finding nobody guilty of anything (whilst millions of people died from being denied antibiotics for their post-viral pneumonia).
Yet the questions remain..
What does it matter, and what actually happened?
If they created a virus that killed people, shouldn’t they be in jail?
Was there even a virus?
Was there a pandemic of deaths?
If there wasn’t a virus, what caused the deaths?
I have already answered some of these questions in this article which asked you to stop focussing on internal fighting over speculative events and concentrate on what you actually could see for yourself was going on:
Were there more deaths in 2020?
Yes - in the UK at least. It’s reasonably well established (if you accept the government data) that there were excess all-cause deaths in 2020. In other words, something happened in the UK (and many European countries) that definitely caused more deaths that year.
This pattern was not present in most of Australia and whether there was an all-cause mortality increase in the US is still a bone of contention.
In case you’re wondering “Why are these two countries your focus?” there is a very simple answer. The UK (via the ONS and UKHSA) and Australia (almost exclusively via NSW data) are really the only places in the world that provided “official” data that allowed us to monitor the impact of COVID vaccines by vaccine status. That is, until it became to obvious that the vaccines were failing.
“Why did they even release this data then?” is the next question. And that can be answered easily too. It’s because they wanted you to believe that these two places were the “honest and transparent” gold standard for COVID data. So they could tell you anything they wanted, and you’d likely believe them without question:
Midazolam and the “3 tablets” scandals
What is now becoming obvious is that much of the all-cause mortality spike in the UK (and by extension other countries such as France) was predominantly driven by the use of midazolam (which is really bad at treating bacterial pneumonia) and withholding of antibiotics to the elderly.
This is the #3tablets scandal that we have touched on but of which much of the work has been on twitter.
Much of the evidence for it came from an analysis of the cyclical antibiotic prescribing data from the UK, which suddenly disappeared in… April 2020 when there was a huge spike in deaths “from COVID”
What the hell is the #3tablets scandal?
Although it’s not really the focus of this article, because it’s so important (and it’s a repeated question in the social media sphere) I will try to summarise here.
This was the post that kicked it off, in December 2022.
The “3 tablets” referred to the full course of Azithromycin, commonly used (for 20 years or so) to treat community acquired pneumonia. One course. 3 tablets.
And then the tagline '“If they hadn’t had the test they would have had the tablets”.
In other words, people died because they had a PCR test.
Wait, what?
Well we have to step back in time a bit, back to the olden days up to 2019. In those days, if an elderly patient had a respiratory virus (flu, RSV, Rhinovirus, whatever) one of two things would happen.
(1) they get better in a week OR
(2) they get a secondary bacterial pneumonia, and if they don’t get antibiotics early they will die.
Which is why any decent GP knew that if they had a patient with a virus, who was frail or elderly or immunosuppressed (does this sound familiar for COVID risk groups?) they would tell them that if they had a chesty cough but weren’t better within 24 hours to start antibiotics. If it was a nursing home patient, the GP would start antibiotics immediately. This is because you have to treat the elderly early in pneumonia, or they will die.
For the more pictorial thinkers the impact of a COVID test on this well established treatment pathway can be represented like this:
None of this is rocket science. There are 7000 papers on community acquired pneumonia on Pubmed up to 2019. Nearly 1000 of these include reference to influenza because the commonest cause of community acquired pneumonia is respiratory viral infection. In other words, the mantra “you don’t use antibiotics for viral pneumonia” is absolute trash when it comes to post-viral pneumonia. To push the point home there are 25,000 papers on “influenza pneumonia” on Pubmed.
The point is this.
Respiratory viruses cause an acute inflammatory pneumonitis for a few days.
This is not primarily fatal as viruses generally cannot kill you without another event. Your lungs will recover within a week, however…
The pneumonitis disturbs the lung’s natural immunity.
Bacteria can then take hold and cause a bacterial pneumonia.
Bacterial pneumonia, if untreated, will kill you.
This is not some “conspiracy theory”. Fauci knew this, which is why he wrote a paper on it in 2008.
And although this was known for influenza, it was also known for coronavirus pneumonia - antibiotics were in the protocol for SARS-1and MERS.
and
If you read that second paper you will notice that it was published in 2019, just before “COVID” and the conclusion was that “macrolide therapy is not associated with a reduction in mortality”. Weird, considering so many of the patients were being given it eh? Well you might also notice that the paper was supervised by the University of Oxford and the authors had vested interests with Gilead (makers of remdesivir) and Regeneron (the expensive treatment given to Donald Trump for “COVID”)
And despite denials of the utility of antibiotics (for bacterial pneumonia) in the early treatment of COVID by the main players (NIH, NIAID, NICE, WHO) we now know that not only were there thousands of patients successfully treated by the Zelenko protocol (or similar, containing azithromycin or doxycycline) but it turns out that deaths from COVID were, in fact, predominantly from bacterial pneumonia.
Bear in mind, too, that “COVID pneumonia” is simply another version of community-acquired (CAP) or atypical pneumonia, which often arises after a previous viral infection.
So how do we treat community acquired pneumonia? With antibiotics of course!
It should also be noted that antibiotics are prescribed in CAP without culturing the sputum for bacteria. This is because the yield of sputum culture is low, and if you are trying to culture “atypical” organisms (like legionella or chlamydia) your patient will be long dead before you get the result. So the protocols usually include a penicillin-type antibiotic for “normal” pneumonia and a macrolide or tetracycline for the “atypical” organisms.
It’s also of interest that the “atypical” organisms like chlamydia and mycoplasma behave a bit like viruses in that they can only survive within a host cell (hence they are called obligate intracellular pathogens) and so it’s not surprising that antibiotics like azithromycin that target these bacteria also have anti-viral activity. So when all those twitter trolls accounts say “antibiotics don’t work against viruses” keep this reference handy:
And remember also, that just because somebody tests positive for “COVID” that doesn’t mean that the “COVID virus” is driving their pneumonia. Here’s a published example (but I’m sure there are many others that weren’t able to get published) showing a patient who actually had Legionnaire’s diseasedriving his pneumonia but he “tested positive for COVID”.
So, what do we know so far?
Just to summarise as we go along - and it is important to recap…
People were presenting with symptoms of a respiratory illness
Some people who were ill were testing positive on a “COVID test”
Some people who were not ill were testing positive (during contact tracing)
The “COVID test” was a PCR test looking for genetic sequences from a “novel coronavirus”
99% of people got better themselves, but without treatment 1% of people got really worse and some died
Nearly all the deaths were from respiratory failure due to pneumonia, with a median time to death of 18 days
The “COVID test” does not look for or exclude a concomitant infection
Now, in that list are a couple of highlights. The first being the PCR test which we will take into a separate section below.
The other is something you might not have realised, which relates to the median time to death in “COVID” - it’s 18 days. That is long after the viral phase has gone (up to 7 days). Everybody knows this. Everybody knows that you will notice a respiratory virus for about a week during which your T-cells deal with it, without any IgG antibodies (that take two weeks to make) needed.
Most people don’t realise that even though they know it. It is fundamental to the con. The con that says that you can’t fight a respiratory virus without massive quantities of antibodies derived from a corporate injection. Yep. You might want to read this paragraph again.
So, pretty much all the COVID deaths were in the post-viral phase. Which means something else other than an active virus killed all those people. And that something else was pretty much either bacterial pneumonia (treatable with antibiotics), atypical pneumonia (treatable with antibiotics) or blood clots (rare, but treatable with anticoagulants). That’s assuming they weren’t euthanised before they got to any of those things (see below).
Getting the picture yet? If all those deaths were from pneumonia, most of them could have been prevented by giving a an anti-inflammatory (e.g. hydroxychloroquine or steroid) to reduce early viral pneumonitis and an antibiotic (azithryomycin or doxycycline) to prevent the secondary bacterial pneumonia that killed most of the “COVID” victims.
All those “dying patients on ventilators” you saw were not dying of blood clots. They were dying of preventable pneumonia. And if you haven’t read this fantastic substack on #ECMOgate now is a good time… because it raises the frightening possibility that patients who were unvaccinated were preferentially put onto a pathway with a 40% mortality. You might want to read that last line again.
Of course, you might not agree with me but if that’s the case please answer this question:
What is the mechanism of death brought on by a respiratory virus that is no longer detectable at the time of death?
There basically isn’t one. Sure, having any infection can increase your risk of blood clotting (DVT/VTE) but that is a rare cause of death in “COVID”. And there was no significant or massive increase in deaths from myocarditis in the COVID (pre-vaccine) era. Any subsequent claim of “COVID causes myocarditis” occurred in the post-vaccine era, because the vaccine presumably caused the myocarditis associated with the post-vaccine COVID that it didn’t prevent!
Think about it. It’s a virus. Sure it can get around the body and be annoying and make your blood a bit stickier but it’s like saying a cold can kill you. How? From the fluid loss from a runny nose? Does papilloma virus cause septicaemia? How about herpes?
All of these viruses need a secondary event. In the case of respiratory viruses it’s almost always pneumonia. But we also found out that some victims of “COVID” didn’t even get to the pneumonia stage…
The Midazolam Gerontocide
There was a massive spike in deaths for another reason - euthanasia of the elderly, particularly in the UK. We’re just going to touch on this as it has been covered quite extensively elsewhere including in this Amnesty International Report outlining how the elderly were basically left to die in care homes during COVID because of fearmongering and the closure of hospitals.
This meant that an elderly person with a stroke or a broken leg was told they could not go to hospital, and therefore died. No, I’m not kidding.
As compensation to the elderly person with a treatable condition (broken leg, stroke, and yes even pneumonia) instead of going to hospital they were given a lethal dose of midazolam, haloperidol and levomeprozine. This was to give them a “good death” according to MP Luke Evans seen in this clip asking minister Matt Hancock whether there was enough of the euthanasia drug in stock before the death surge of April 2020.
The “good death” might conceivably be considered humane if it wasn’t for the fact that few of them, if any, had a terminal illness. We are literally talking about people with dementia, falls and urine infections. So the midazolam surge came and with it thousands of deaths.
This has been very much covered in the Jikkyleaks threads on twitter, which of course have been archived. But in amongst them is this confronting chart which needs some explanation.
You see, care home occupancy is usually pretty steady - because lots of elderly die but there is a constant demand for care home spaces. So there is an equilibrium. The demand is relatively stable so if suddenly 55,000 EXCESSpeople died, you won’t just automatically replace them and the occupancy would drop. Under normal circumstances this never happens.
Except this is exactly what happened - occupancy in UK care homes dropped by about 10% accounting for 55,000 elderly who just disappeared overnight.
And you should note that although there was a mass euthanasia in UK care homes in 2020, this happened to other places too, like France.
Here is what happened in Victoria, Australia in August (not April) of 2020, where 90% of the total deaths in Australia in 2020 occurred in one state only. You can see the blip in March-May when the “COVID pandemic” kicked off, amounting to about 100 deaths. Look how Victoria compares to all other states in Australia around August 2020:
And it wasn’t all care homes either, it was specific care homes.
It’s a scandal that you will have never heard about - that the majority of those deaths were concentrated in only 10 care homes. About 400 deaths, averaging up to 40 “COVID” deaths in each care home - in a two month period.
By now I hope we have established the context that most of the “COVID” deaths were caused by the either the mismanagement of bacterial pneumonia (that could have been prevented) or outright involuntary euthanasia.
So that covers the “were there excess deaths” question..
But what about those “cases”?
Now we get to the fun part.
“It was the PCR tests”
You hear this a lot on social media and the idea behind it is that the PCR tests, which have false positive rate (as all medical tests do), drove the pandemic. “Without the false positive PCR tests we wouldn’t have had a pandemic at all”.
Well that is partly true. The excellent Martin Neil puts this argument very much in his substack here. However it is not the full story.
You see the PCR tests somewhat drove the casedemic, but are not in themselves enough to account for the illnesses and the deaths.
And this is where Australia comes in. Here are two really interesting graphs of Australian COVID data drawn from the covid19data.com.au website, which mirrors the “official” data, although nobody really knows where that comes from (that story will need to be an article for another day).
There are 3 things to notice from this chart:
Up to July 2021 (halfway through the vaccine rollout) Australia had about 50,000 tests a day but less than 100 cases a day (average 16/day). That’s a test positive rate of less than 0.2%
Source: covid19data.com.au Despite testing there were very few cases all year from June 2020 to June 2021, except for a huge peak in the state of Victoria only (July - August 2020)
Source: covid19data.com.au The “booster” rollout occurred in November to December 2021, following which there was a huge and sudden spike in cases (and deaths).
Just putting aside for one minute that the case and deaths numbers went crazy after the vaccine booster rollout and focusing on the case rates leading up to December 2021, where there were relatively few cases but your rights were removed and lockdowns and other non-evidenced interventions were imposed on the public…
One common argument I see is “well it was flu, obviously”.
But it’s wrong. It’s wrong because the testing that was performed by PCR for “COVID” cannot pick up Influenza, because the genome sequence of the two are completely different. It’s the same reasoning that meant that you didn’t test positive on a PCR test after you had a COVID vaccine, which made your own cells produce an RNA sequence which was similar but not identical to COVID itself. Remember that 70% of Australians were “vaccinated” by August 2021 yet there were almost no positive PCR tests in the year to that date.
“OK but the positive cases were flu they just weren’t tested”
Not true. Australia is unusual in that it is common to test for influenza on the basis of symptoms. An influenza PCR test is just like your COVID PCR test. And they are all recorded and archived on the Influenza sentinel surveillance site.
If you look at the chart you can see that the weekly influenza PCR test numbers were well over 10,000 and peaking at 30,000 in August. In this same report, influenza disappeared completely despite over 10,000 tests a week and from April onwards the test positive rate dropped to 0.1%.
This is totally in keeping with the “false positive” rate of PCR testing, which we turn to next.
The False Discovery Rate
Now we have to delve into some basic medical statistics.
The good news is that, as a reader of this substack, you are automatically of sufficient intelligence to understand basic terms.
The first terms are sensitivity and specificitywhen applied to medical tests. These are basic concepts but many people don’t understand that a medical test may not be accurate. For instance you could do a test which is positive, but you don’t have the disease. So we have terms for the accuracy of a test for both testing positive and testing negative
Sensitivity is the proportion of people with a disease that test positive.
Specificity is the proportion of people without a disease that test negative.
Unfortunately not all scientists know these basics. They might know how to culture cells in a lab but they really don’t understand that not all medical tests are accurate. This is enshrined in COVID legend in this interview between Mathew Crawford and Daniel Wilson, the latter a high roller in the “Mutton Crew”.
Dan has a PhD in molecular biology but couldn’t answer this really simple question from Mathew. It’s an important interview because it shows you how lab scientists don’t generally understand clinical medicine (i.e. treating real patients), and vice versa (your doctor has no idea about cell culture and mRNA).
Now, sensitivity and specificity are terms that are mostly known by most doctors but most doctors can’t even define them. So it’s not actually unexpected that Dan doesn’t know what they mean. What he should have said was that he didn’t know, but he couldn’t. And therein lies the rub. Pretty much all the “Mutton Crew” - mostly made up of lab techs - exhibit similar Dunning-Kruger tendencies and have no idea that their lab tests are not 100% accurate.
But sensitivity and specificity are only useful parameters in lab tests. Things change a lot when you take your test out into the real world. And making this jump is confusing for lab people, just like they struggle to understand why a drug that works on a cell line doesn’t work in actual humans.
What do I mean?
Well, let’s take an example of a HIV test. In normal use, a HIV test is expected to be 99% sensitive and 99% specific. Some can drop to 94%. Assuming the optimal scenario of 99%, if you tested 100 people with HIV 99 would test positive and 1 would missed. Conversely, of 100 people who didn’t have HIV, 99 would test negative and 1 would test positive (meaning they might be put on HIV therapy even though they didn’t have HIV!).
Now, the thing about sensitivity and specificity is that it is measured against a known standard. In this case, you know the cohort has HIV (or doesn’t have HIV) and you are testing to see how many test positive or negative to validate your test. A perfect test would have 100% sensitivity and 100% specificity, but this rarely happens. A good test (like HIV in this example) would have 99% sensitivity and specificity. A not so good test (such as older antenatal screening tests for Downs’ syndrome) might be 80% sensitive, so would miss 20% of cases.
So that’s all well and good for quantifying how great (or not) your test is, but it’s not much good when you want to know whether you have a disease if you test positive.
In that case (you have tested positive) what you want to know is:
What is the probability that I have the disease?
Now, this is where the equation is flipped. For a 99% specific test which had a 1% false positive rate in a known population… you can’t assume that, in a general population where we don’t know whether people have the disease, your test has a 99% chance of being a true positive (or true negative). The question is now
What is the probability that my positive test means that I have the disease?
And the related question
What is the probability that my positive test is wrong, and I don’t have the disease?
Now, these are totally different measures from sensitivity and specificity.
The first is the positive predictive value (PPV)
and the second is the false discovery rate (FDR).
The Positive predictive value of a (medical) test is the probability that you have the disease if the test is positive. The False Discovery Rate of a (medical) test is the probability that you don't have a disease if the test is positive, i.e. it's the probability that the test gave you a false positive result. The point being, that there is a false discovery rate. In particular when applied to COVID tests it means that there is a chance that your “positive COVID test” is falsely positive.
And the primary driver of this is not the sensitivity or specificity (which are fixed for any test because they are validated against a known cohort).
The primary driver of PPV and FDR are the prevalence of the disease in the community at the time.
To demonstrate this here are the formulas for calculating PPV and FDR
But don’t worry. We’ve made it easy for you (thanksOpenVAET for your help with this).
We have our own False Discovery Rate calculator for you here with an example below… (Please check out these examples for yourself! )
To start, here’s an example where the sensitivity and specificity of a COVID test is 99% and the population prevalence is 1:1000 (a reasonable community incidence even at the peak of COVID)
In this case there are 10 false positives for every true positive. Wow!
But, the thing to know about PCR is that it is really specific. Sure, you can pick up junk with a PCR test by ramping up the cycles but when you include a negative control in your set up you would normally discount this kind of junk because it would be in the negative control wells.
So, here is the Roche Australian government document showing that the false positive rate of their PCR test (which was used around most of Australia apart from Victoria, which used Chinese BGI genomics test kits with a false positive rate up to 6%)… was only 0.2%. That means the specificity of their test was 99.8% (0.998).
Roche Covid Testing Policy Positions Final
901KB ∙ PDF file
How do we know that’s true and isn’t just some “advertising junk” from Roche? Because the Australian (NSW) data that is shown above showed you that the maximum false positive rate was 0.2% (because at the lull of COVID there were never more than 0.2% positives!) . This gives a specificity of the Roche test of 99.8%, just like they said in their “brochure”.
So let’s put that in to our calculator.
Oh look - 67% of all positive tests in this situation are false positives. And this is very close to what Martin Neil described in his substack here:
However, there is a caveat.
The assumption in the “most tests are false positives” premise must be that the prevalence of the disease being tested for is low. Which is very true when testing an asymptomaticpopulation. And that is precisely what the government did on multiple occasions. Here is a prime example from NSW health asking asymptomatic people to get tested just because they visited somewhere. In this situation the prevalence of disease will be tiny (0.001 or 0.1% would be a good estimate consistent with the Neil false discovery rate quoted)
However… for people attending for a COVID test when “COVID” is circulating and they have symptoms compatible with “COVID” the prevalence in that situation would be much higher. At least 20% (0.2). So let’s plug that into our calculator, and even using the really poor BGI testing kits with a 6% false positive rate (specificity 0.94)...
You end up with at least 80% of the tests being true positives.
The bottom line being that the false discovery rate is driven primarily by
poor specificity tests and
a low prevalence in the community being tested.
To put this in more understandable terms:
The "casedemic" fear was driven by false positives in situations where asymptomatic communities were asked to be tested.
Where symptomatic communities were tested, the false positive rate (false discovery rate) was low. Most people with symptoms testing positive had "COVID". Now exactly how this virus managed to maintain spread and prevalence for 3 years is another matter altogether, and will probably be an article for another day. Suffice to say that we have touched on the infectious clone theory previously in “It doesn’t matter” as has Mathew Crawford in a similar article here
What we do know is that MERS was deliberately seeded in multiple places. That’s because two really important people told us - back in 2014, before they became embedded into the establishment. Raina MacIntyre is a very high profile scientist who used to freely talk about how these viruses were spread by non-natural (aka man-made) methods. Bioterrorism, essentially - except when the government does it you’re not allowed to call it that. And Lauren Gardner became - really, really coincidentally - the person who ran the Johns Hopkins COVID dashboard on which you could monitor real time COVID data - supplied by China (yes you read that correctly).
So what?
If you have stuck with this so far, congratulations.
The real take home from this article is that there was a virus that was associated with a spike in deaths. The arguments that you might see such as “viruses don’t exist”, “there was no virus”, “it was just flu” are false and are intended to distract, conflate and discredit you from discussing the number 1 issue:
Many of those deaths could have been avoided.
In order for the deaths to have occurred in the presence of a real virus with low pathogenicity it was necessary not only to scare the population but also to run a psychological operation that allowed the ridicule and discrediting of dissenters who were led to believe either that “there was a scary novel virus that couldn’t be treated” or that there “was no virus” and “COVID was just false positive PCR tests”.
None of these were true. What is true is the following:
There was a virus (or an infectious clone of a virus, which is the same thing) of usual pathogenicity.
PCR tests have limitations, but if you have symptoms of a viral disease and you test positive for that viral disease on a PCR you probably have the disease.
If you don’t have symptoms of the disease and you test positive, you are more likely not to have the disease but it doesn’t matter because nothing will happen to you. The sole purpose of testing asymptomatic people was to scare the population.
If you took a PCR test for COVID and tested positive you would be denied the very antibiotics that would prevent your death from pneumonia.
If you were elderly in a nursing home and the nursing home staff, living in fear of COVID, decided that the nursing home had a COVID infestation due to someone testing positive (whether it was real or not), there was a good chance that you would be euthanised by midazolam if you showed the slightest sign of an infection or other condition requiring hospital attendance.
And that, dear reader, is COVID.
A viral infection spread by unknown methods that resulted in positive PCR tests and for which you were then denied treatment.
The result was more than 6 millions deaths and the imposition of a biofascist tyranny that enforced an experimental GMO vaccine on the population of every “democratic” country.
The COVID test was simply part of a game being played by those who created a virus that they could scare the world’s population with.
As far as COVID itself was concerned your decision in the game was this:
Take the test, or don’t take the test.
This is not a game you can win by playing.
The only winning move is not to play.
How about a nice game of chess?
This is the original congressional committee report containing hidden emails that were cropped out for public copy but remain visible in Acrobat Pro
2023 07 11 Sscp Interim Staff Report Re Proximal Origin Final
34.1MB ∙ PDF file
A very interesting thread on twitter explains all about the origins of the Johns Hopkins dashboard run by Lauren Gardner - whose expertise was in data synthesis - and why the numbers that underpin it are almost certainly synthetic, originating via China. https://archive.is/W4Iec
It should be noted that Dr Wilson made the subsequent claim that he was talking about “analytical sensitivity” which is the limit of detection of a test. This is not a term that is used in clinical medicine in general, and “sensitivity” in this context (clinical testing) is not “analytical sensitivity”. However he does reference the CDC/Labcorp report on the performance of the COVID PCR tests which I have included here for transparency. In their testing the specificity of the COVID PCR test was 100%, but with a confidence interval of 92-100% (p16). It’s quite an interesting report as it shows that the limits of detection (analytical sensitivity) of the PCR test equate to a Ct value of around 33-35. Which means that tests that detected at higher Ct values (over 35) were almost certainly irrelevant.
Eua Labcorp Covid Euasum 1
1.26MB ∙ PDF file
Additional corroboration of this (specificity rate and its impact on the false discovery rate) is in a single page document from Prof Carl Henegan which is copied here:
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