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SARS-CoV2 infects cells ONLY with ACE2 receptors, yes? SO how on earth that suppose to be worse than nanos which cross ANY cellular membrane??? That's the result of having TONS of antibodies delivered by all the cells with outsticking SPikes on them! W@ish there would be some real experts here a not random writers with engineering background...

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First they came for the “well, actually” guy; and I did not say anything, because a he was random writer.

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sorry for this outbreak, but the lack of real professionals who know all the details and are NOT coming forward to end this crime is just terrible and frustrating. And the more 'maybe/may/etc' the more time passes by and more deaths with the obvious criminal setup.

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Mar 1, 2022Liked by Brian Mowrey

How about this ?

Dr. Raszek: Pfizer Docs Show Spike Protein Enters Cell Nucleus

25 Februari 2022

https://uncoverdc.com/2022/02/25/dr-raszek-pfizer-docs-show-spike-protein-enters-cell-nucleus/

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Right, it's more staining. In this case since the ER is also plotted, you can tell there's overlap due to the 2D nature of the image, so it's still a "might" as far as the evidence. But the theory, again, supports being concerned anyway. If the mRNA from the injections is designed to avoid degradation, who knows how much spike each cell makes, whether the normal signal peptide (that puts the spike on the secretion pathway) comes out correctly or what. It's easy to imagine surplus / deformed spike accidentally dumping into the nucleus.

The big question remains whether the immune system takes out the cells in time to prevent secondary effects, and that's where the evidence is disturbing as far as exosomes and germ centers still showing spike for 6 months / 60 days.

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Mar 3, 2022Liked by Brian Mowrey

I'm no expert, but I like to read and try to understand it. Here I include several studies related to LINE-1 and Reverse Transcript, I am sure you are more expert and more understand them better and can provide enlightenment after reading some of these studies. Thank you.

RNA can integrate into cultured human cell genomes and can be expressed in patient-derived tissues

May 6, 2021

https://www.pnas.org/doi/10.1073/pnas.2105968118

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Reverse Transcribed and Integrated into the Human Genome

December 13, 2020

https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7743078/

SARS-CoV-2 RNA

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New Understanding of the Relevant Role of LINE-1 Retrotransposition in Human Diseases and Immune Modulation

August 7, 2020

https://www.frontiersin.org/articles/10.3389/fcell.200.00657/full#:~:text=LINE%2D1%20and%20Autoimmune%20Disease,et%20al.%2C%202016

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LINE-1 in Cancer: Diverse Functions and Potential Clinical Implications

September 3, 2015

https://www.nature.com/articles/gim2015119

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Somatic Expression of LINE-1 Elements in Human Tissue

March 9, 2010

https://academic.oup.com/nar/article/38/12/3909/2409521?login=false

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Long-Lasted Nuclear Element (LINE): Evolution

July 15, 2008

https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470015902.a0005304.pub2

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Feb 28, 2022·edited Feb 28, 2022Liked by Brian Mowrey

As someone who has used all the methods reported in this paper in various contexts, here are my views:

1) Choice of cell line. Cell and molecular biology studies use immortalised cell lines, i.e. those derived from cancer, all the time mainly because they are easy to grow in the lab. A study with primary cells (directly derived from an animal) or an animal model would be much harder to do and would have taken a lot longer. The reason for choosing Huh7 over other cell lines was noted to because of their hepatic lineage, but also was likely because the researchers simply had easy access to those cells; the latter is not a great reason, but reflects the simple reality that most studies like this make such choices due to simple practicalities. Results from cell lines obviously are not necessarily indicative of what would happen in other cells, but are usually the first step in such a study. They should, however, have performed the experiments in multiple cell lines, to improve confidence in these results.

2) Despite the use of a poor model, it does still show that BNT162b2 RNA can be reverse transcribed in vitro in human cells. This is still an improvement over the "theoretical argument that is already available" because theoretical arguments are still theoretical, and experimental evidence is needed to prove this. Despite its flaws, the study provides the first evidence that reverse transcription of BNT162b2 RNA in human cells. This is presumably due to endogenous LINE-1 (as they didn't add any retrovirus), but the evidence that LINE-1 was responsible is circumstantial at best; the data showing induction of LINE-1 is also poor. In addition, the study doesn't itself rule out the presence of retrovirus infection in these cells, which is another potential flaw in the study.

3) There was no realistic way to obtain "blank" mRNA or LNP controls. Pfizer would not have provided these, and any substitutions for "similar" reagents would erode their usefulness as controls. In addition, such controls would be interesting, but not strictly necessary. Even if the effects are occurring due to "other ingredients of the product", the whole product is what is being injected, and the effects don't need to be due to the mRNA itself. (Obviously, the LNP plus mRNA combination is needed to achieve anything, as the mRNA wouldn't get into the cells without the LNP).

4) Figure 3 is a mess but if you look at the text, they don't actually refer to the cross time-point comparisons ("Significantly increased LINE-1 expression compared to control was observed at 6 h by 2.0 µg/mL BNT162b2, while lower BNT162b2 concentrations decreased LINE-1 expression at all time points (Figure 3)."). They should have removed the statistical comparisons from Figure 3. The criticism about the induction of LINE-1 in the Ctrl at 48 h is valid but is not too surprising - culturing cells easily changes the expression of various genes. Criticism that this could be due to normalisation by housekeepers is also valid, but this is inherit in the vast majority qPCR gene expression designs, which almost invariably use housekeeper normalisation.

5) The differences in fluorescence intensity in Figure 4 do indicate that any quantification of such images is a problem. I'm not too surprised by this - obtaining consistent labelling intensity across different samples using fluorescence microscopy is extremely difficult in practice. Therefore, trying to quantified fluorescence intensity from such images is always a somewhat dubious method. Doing this "properly" generally requires addition of some kind of labelling control on the slide to normalise to, but this is usually not possible for most targets/experimental designs. Nevertheless, this method is used a lot in such studies, despite known issues, so this is no worse there thousands of other cell biology papers doing so. In addition, the researchers appear to have done a flawed statistical analysis for Figure 5 - they have used n=15 cells from two images, and quantified the fluorescence from this n=30. This is an example of pseudo-replication. The cells are NOT proper replicates as treatments are being applied to the wells of cells, not individual cells, and thus the correct level of replication needs to be at the level of wells. Yet, it seems that there was only n=1 well for each condition (for which two images were taken), which is extremely poor. It should be noted, however, that although most cell biology researchers would see the flaw in doing only n=1 well/experiment, most still incorrectly do statistical analysis on the pseudo-replicates of cells rather than wells, reflecting a poor level of training and understanding in statistics in the field (proper analysis of such a design involves treating it as a nested model, which is beyond the capabilities of most cell biologists).

6) I can't see why Ctrl 5/6 are a "red flag". Ctrl 5 and 6 seem to be exactly as stated: RNA extractions with and without RNAse. Both a negative controls, and they lack a positive control in those images. An appropriate positive control would have been RNA/no RNAse with a reverse transcription step before running the PCR. Nevertheless, Ctrl 5 and 6 seem to serve their purpose and are meant to be interpreted together. Ctrl5 (RNA/no RNAse) would indicate whether the PCR amplification occurred from an RNA extract, i.e. the PCR doesn't amplify RNA. As expected, there is no band here, as PCR will not amplify RNA (prior reverse transcription is needed convert to RNA). If Ctrl 5 had been positive, however, it would not be clear that this is due to PCR amplification of RNA instead of DNA contamination of the RNA. This is what Ctrl 6 (RNA/RNAse) is for. If Ctrl 5 had been positive (it wasn't), a positive Ctrl 6 sample would have shown that the Ctrl 5 band was likely due to DNA contaminating the RNA extract (as any RNA would have been degraded by the RNAse). If Ctrl 6 had been negative, it would have shown that the Ctrl5/6 bands were somehow due to amplification of RNA (but they were negative). Ctrl6 also serves to control for the presence of contaminating DNA in the RNAse, which would have been another possible source of a band in the other lanes.

7) Gels of PCR bands are at best semi-quantitative due to a variety of issues (which I can expand on if anyone wants me to), and therefore, in my view, it doesn't make sense to infer much from the band intensities in Figure 5. In addition, the three different images suggest three different gels. Comparing band intensities across gels is a huge no-no. The normal way to interpret these gels is simply the presence or otherwise of bands, which is exactly what the authors did.

In conclusion, I'd say this isn't a great study, and has some clear flaws in it, but it still is the first experimental evidence that BNT162b2 RNA can be reverse transcribed in vitro in human cells. This evidence is limited in that it is only in one cell line, and evidence linking this to LINE-1 is circumstantial and weak. Clearly more studies need to be done on this but (1) I doubt anyone is in a hurry to fund or perform such studies, given the prevailing narrative surrounding these vaccines, which makes questioning them a "cancellable" offence even in the scientific community and (2) such things take time. The flaws in the present study likely reflect the simply practicalities of doing such research when no one wants it do be done.

Really, detailed studies checking for the possibility of endogenous reverse transcription of the BNT162b2 RNA in human cells, and the possibility of genomic integration, should have been demanded from Pfizer prior to approval, so this study also serves to highlight what might have been found if these had been done.

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Thank you for the thorough corrections!

3 mRNA "blank" was meant to suggest LNP + alt mRNA, say for a flu HA if it was plasmid vs plasmid - but you're right, it's proprietary product vs Not Applicable. It's funny that I hadn't even considered your point about how testing the "whole product" makes such controls impossible - in other words, there's no way for the authors to know what's going on.

(This ties into how weird it is that the authors didn't seem curious about the effects of spike at all.)

4 Right, on Fig 3 the authors essentially submit a no comment. I come down harsher.

5/6 Thank you. You probably know that anyone who hasn't run PCR/sequence can only have a vague sense of the steps. (My impression was the first cDNA generation essentially put sample RNA and DNA in the same umbrella, oops.) I'll revise the text, though I still have qualms about the authors' methods here. All they say is "PCR was then performed" with primers, these temps, and 35 cycles. A link to a posted protocol is always reassuring.

7 At best semi-quantitative, and within same study, though without multiple runs and statistical processing. I did include a caveat on that one. I'll consider revising / removing that point though as it is a bit on-a-limb especially since it immediately follows other critiques about result consistency. But overall I just find things implausible here. LINE-1 incorporation at 6hr, before cellular division, smells fishy. Perhaps that's me trying to have it both ways since I started by saying cancer cells don't represent normal conditions.

1/2 I'm not sure we're in disagreement here on more than tone, context. They should have used multiple cell types. And the LINE-1 mechanism wasn't well-established.

At the same time, we probably have *very* different perspectives on the value of evidence vs theory even within science. As for the value outside of science, this blog is in part dedicated to decoding how what science considers "evidence" should, to the lay reader, be balanced against reasonable doubt.

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Ah, I see why I was confused on the PCR point. The strictness of the PCR / RT-PCR distinction in research lingo (as opposed to regular use, where "PCR" means both) is hard to pick up on.

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I was surprised that most other analyses of this paper didn’t also flunk the LINE-1 expression mechanism. Our lying eyes and all.

Given findings from this paper (https://academic.oup.com/nar/article/38/12/3909/2409521) determining that L1-related transcripts were an order of magnitude higher in testis than in other tissues, perhaps using (non-cancerous) testes cells might have given clearer/more useful results (at least regarding L1 expression)?

Also from the paper: “The observation of differential L1 processing in normal human tissues suggests that the production of the full-length L1 mRNA in various tissues may depend not only on the L1 promoter strength but also on the amount of the RNA processing supported by a specific cell type. A primary example is testis where overall high expression of the L1-related mRNAs indicates efficient L1 transcription ( Figure 1 and Supplementary Figure S1A ), whereas the full-length L1 transcripts remain below the detection sensitivity of the assay.”

Could the LINE-1 suppression we see here be due to excess RNA processing leading to non-full-length L1 transcripts? And if so, implications of that?

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Thanks for that link. I can't make their sentence coherent in my head. Are they saying pre-mRNA is high but not post-processing full-length mRNA, so it's efficient transcription and low export - but if so why say "expression" is high? I am going to need to look at the paper more.

If it is referring to "processing" in the context of "RNA processing," I would say the cells are probably receiving feedback that RNA translation is over-capacity (stress granules etc) and so gene expression (including LINE-1) is down-regulated. I think this is plausible in vivo as well. The cell gets overworked and stressed, probably heads to cell death or metaplasia or DNA demethylation or who knows what. This is really what I think is the biggest hazard from the shots - disrupting gene expression directly, rather than an end-run into the genome and back out.

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Andreas Oehler has been digging into this as well:

https://live2fightanotherday.substack.com/p/takedown-of-intracellular-reverse

“When they say 2-ΔΔCT, they actually mean 2^-(ΔΔCT): the negative powers of two in the difference of concentrations of LINE-1 mRNA and some control (house-keeping) genes also present in the cells, between the control (untreated) cells and the treated cells. I hope I make myself clear here. So, the higher bars correspond to lower concentrations of LINE-1 mRNA, and vice versa.”

https://live2fightanotherday.substack.com/p/implications-of-bnt162b2-in-human

References to the paper I linked to previously.

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Mar 4, 2022·edited Mar 4, 2022Author

I saw that, but I think he is confusing a double negative for a single negative. In qPCR, higher cycle counts mean it takes more amplification to hit the threshold. So, lower means there's more. I haven't fully looked into it since I assume that a misunderstanding on my part here would have prompted a remark from TJ Lees above.

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If you’ll indulge an analogy, how I interpreted it was: you are told to make a widget, and are given tools and an amount of time in which to do it. If the time and correct tools are sufficient, you are able to make the widget, but if either of those things are not correct, you either can’t make a complete widget, or the widgets you make are not functional, and therefore do not count as production even though your work is still the same, or possibly even more. This could be faintly similar to your suggesting over-capacity down-regulation.

To humanize the overall concept, cell “confusion” seems like an interesting name for it. Increased production of SP leading to some cellular interpretation/hierarchy issues (possible mechanism for DNA housekeeping interference), inflamed and overactive immune response (leading to increased clotting), mRNA passage through the BBB (neurological issues), and long-term camping in particular cells with continued production leading to unknown events. With overworked cells terminating as you posited? And confused cells doing who knows what… ? Many question marks.

Caveat: I am fully aware I am grasping at straws, in the dark, in a swimming pool. Thank you for engaging/humoring.

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Right, the analogy is how I take it, it's just not clear in their text if the analogy stops before export. "mRNA" is the result of processing - trimming a longer strand into only the part that actually gets sent out, and adding a bunch of A's after the last letter. If a cell can't do that as well, then less mRNA can go out - but again I can't square that with "high expression."

But to relate that pre-export "processing" to the Pfizer mRNA let's the analogy work on both ends - the tools are all being used up outside the nucleus, there's feedback mechanisms saying there's a backlog outside, so the tools inside the nucleus sit on their thumbs because further initiation of gene expression pauses. It's plausible. But once again there must be cross/confounders since the bars still go up, just not as much as control.

Confusion is the correct word. Direct signaling - hey here's a bunch of testosterone, activate this gene - cannot account for most of how cells "decide" whether they are supposed to grow or stop growing, differentiate, etc. They read the metabolic context.

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I will have to review this study a bit more. As someone who has done Sanger sequencing I wish they included the BNT sequence as well to compare, but that's a personal gripe. I think the analysis of the gel may be incorrect. It appears that Ctrl 5 and 6 were done to show that their amplicon had to have been arrived from DNA, such that Ctrl 5 and 6 essentially did an RNA extraction. Because both did not have any RNA, no amplicon was found. I think their wording made it confusing. They should not have said they performed PCR on RNA purified from the cells but that they did an RNA extraction to see if they could find any RNA to begin with.

It seems like they may have used these cells because of their ability to replicate, so I suppose it may have been done to mimic effects in rapidly dividing cells.

This is an interesting study, but it definitely means more research should be looked into. If true they should try replicating this study in mice models. I think they should have discussed the possible ramifications. Viruses known to cause cancer usually do so by integrating into host genome. Then, when the viral genome is clipped the process is usually sloppy with either a little too much of the host genome being taken or a bit of the viral genome may be left behind causing a frameshift mutation.

I will hold off on this study and reserve judgement, but I would hope it provides more avenues of research to examine.

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Feb 26, 2022·edited Feb 26, 2022Author

I don't find their rationale for the cell line choice compelling. They say it was "because LNPs go to liver." I would semi-agree with a replication rationale, though one could also call that a disadvantage (as they acknowledge), but don't think these cells were fit and the instability of the control seems to verify that.

I added more after hitting publish, about how the timing doesn't support DNA integration. It should get higher at 48 - if DNA is being integrated it shouldn't go away. But maybe the sample was lower as evinced by the ladder. Their effort to rule out false positives certainly at least warranted clearer description!

*edited for rationale reasons

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With that rationale they should have went with animal model studies. I'm still trying to figure out how papers are quantifying their staining or fluorescent images and get "statistically significant" data. Bret Weinstein mentioned it no the DarkHorse podcast today. I'd like to find out more and have people examine the implications in depth.

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Feb 27, 2022·edited Feb 27, 2022Author

Naturally animal models would be superior. I found a good example for plasmids that used a repeated SINE for the primer to look for randomly placed plasmid code (https://www.nature.com/articles/3302213), but immune destruction of transfected cells might complicate this approach for these injections. *edit: Either way, it occurs to me that the plasmid study is a sufficient proof of concept for the mRNA transfections at least pre-immune-clearance.

I added your objection about control 5 and 6 into the text, but kept my analysis as is. I don't think it makes sense to have done an RNAse yes/no step under your interpretation, since a negative on RNAse yes wouldn't rescue a positive on RNAse no in that setup.

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It is most incredible to have disagreements on substack and your article is very interesting. Thanks for discussing it. I am also baffled by LINE-1 expression not matching what I would have expected.

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Thank you, and I agree.

Right - As I allude to, it could have to do with influences on housekeeping gene expression. These are cells that are going to behave "weirdly" in a culture as a baseline, as the control demonstrates.

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