It's already published.
Except good luck finding it on pubmed.gov by the author name of Castruita... You have to put in Samaniego or remember the pubmed ID. I'm sure it's just a "formatting error" π
Bear in mind that that this was performed on RNA sequencing, not directed PCR. PCR is much more sensitive, you just need the right primers.
So as long as the genome sequence of each vaccine is known (which it is), you can detect it in serum - for weeks.
Which leads to a creepy consequence:
You're tagged.
As long as there is vaccine mRNA in your system (at least 2 months but could be longer) your tag can be identified by specific PCR which does not cross-react between Moderna, Pfizer and #SARSCoV2 virus
Which provides two scenarios (there might be more):
Firstly, these companies can identify whether you are "using" their product. Think it was free? Well, only as long as they decide it's free and as long as they can't detect it....
But you can trust these companies, right?
Remember Monsanto went to extreme lengths to bankrupt and destroy farmers by genomic testing of crops that were on land farmed by farmers that refused to use their products.
But once they identified the genomic tag, they claimed ownership
But if you unblinded the sample IDs (to see who got vaccine vs placebo) that unblinding would be recorded in the clinical trials database and everyone would know..
So maybe you just needed a different method to find out which sample ID belonged to each group...
And then you could perhaps put the samples on different machines or run them at different PCR Ct thresholds.
And if you ran samples at very low Ct thresholds you would only pick up a handful of cases..
Whilst the other group would be collecting cases at the expected rate. In fact, if you *could* do this your chart might look something like this epitome of scientific perfection:
Well, obviously they couldn't do this because there was no way that the same lab that analysed the PCR tests was the same lab that had the serology samples from the participants - because the @NEJM and IRB would have identified that as a red flag wouldn't they?
Ahem...
So we have the company that made the product, testing the product and testing the effect of the product with no oversight and a defined pathway to change the likelihood that a result would be positive in their treated group.
Lucky they are super honest though, right? #taggate
And not only do Pfizer still hold the record for a criminal fine for healthcare fraud, but - in what could be CNN's only ever true news report - they got a friend to take the rap.
With the timing showing that he went from IQVIA (yes that IQVIA) into medical school at around 2014, likely produced his first paper as a med student in 2016 and was fast tracked through - I suspect funded by IQVIA.
Someone on twitter puts out a tweet that says "I have solid information that Corporation X has contaminated the water in Brisbane"
The person makes the disclosure in good faith.
In Australia, where the disclosure is made, it would be protected under the Public Interest Disclosure Act - provided it is made in good faith (outside of this, other offences might apply)
@leelasik To put it another way, the establishment has sold the idea that viraemia (virus in blood) causes sepsis and death in the same way that bacteriaemia does.
Not true. Show me the data.
The median time to death in COVID is 18 days - long after the viral phase.
@leelasik The majority of deaths are caused by the *consequences* of viral infection.
Bacterial pneumonia and thrombosis are the two commonest, both treatable conditions.
There is something fishy going on with this NCBI record. The Protein record was updated in a hurry after the discovery of this protein, with a reference to a seemingly unrelated Nature paper from 2003.
The lead author on the 2003 reference is the supervisor on the 2018 ref
The original 2003 paper from Thien-Fah Mah was written as an affiliate of Dartmouth Med School in New Hampshire.
Immediate red flags are differences in the groups, such as the higher prevalence of smoking in the "COVID" group which hasn't been seen in real world studies. And the smoker group had the exact same educational history - you don't usually see that.
Always worth looking at the supplementary to look for inconsistencies in published data.
These figures on a test negative design show that the "effectiveness" was only 9%. Bearing in mind miscategorisation bias, this means there was negative efficacy against infection.