Coronavirus Crisis: Main Thread

[Belligerent Savant]

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Caveat Lector


https://twitter.com/HegKong/status/1491 ... _P9F6r77fw


https://www.bitchute.com/video/sGEtgddwrVSq/

AND:


https://twitter.com/Artytom/status/1490 ... _P9F6r77fw


https://greatgameindia.com/fauci-threat ... ists-aids/

Dr Anthony Fauci Threatened Indian Scientists To Withdraw Study Linking COVID-19 To AIDS Virus

June 6, 2021

https://vaccinedeaths.com/2022-02-04-do ... -aids.html

Doctors are testifying that COVID-19 vaccines are giving people cancer and AIDS

02/04/2022

More individuals fully vaccinated against the Wuhan coronavirus (COVID-19) are developing cancer and even AIDS. This is according to prominent physicians Dr. Ryan Cole and Dr. Vladimir “Zev” Zelenko.

Cole, a board-certified pathologist and owner of a diagnostics lab in Idaho, was called to participate in Wisconsin Sen. Ron Johnson’s Senate panel called “COVID-19: A Second Opinion.”

This discussion panel featured world-renowned doctors and medical experts who were given a chance to provide alternate opinions regarding the experimental and dangerous COVID-19 vaccines. (Related: Fully vaccinated people make up 71% of new cases and 60% of COVID hospitalizations in US.)

Cole, who was originally invited to talk about early at-home COVID-19 treatments, also provided his testimony.

According to Cole, he conducts around 40,000 biopsies a year as a pathologist and he has been seeing more cancers pop up in people who ordinarily would not be susceptible to developing the kinds of cancers he has been seeing. The only similarity between all of these people is that they have all been vaccinated.

Cole collected all of his data and attempted to contact other laboratories to aggregate a bigger dataset. For pointing out what he has observed, Cole said he was ridiculed and maligned.

“I have oncologists, I have radiation oncologists [tell me] ‘I am seeing an uptick in cancers,’ ‘I’m seeing these odd stable cancers take off like wildfires after the vaccines,'” said Cole. “It is happening. We need federal funding. The NIH [National Institutes of Health] isn’t looking at this. Getting a grant to look at anything related to the vaccines is next to impossible.”

This is not the first time Cole has spoken about the rise in cancers among those vaccinated against COVID-19. In Sept. 2021, Cole went viral after a video of him talking about seeing a rise in cancers and autoimmune diseases among the fully vaccinated was made public.

“Since Jan. 1 [2021], in the laboratory, I’m seeing a 20 times increase of endometrial cancers over what I see on an annual basis,” said Cole in the video. “I’m not exaggerating at all because I look at my numbers year over year. I’m like, ‘Gosh, I’ve never seen this many endometrial cancers before.'”

Vaccine’s immunity-killing properties cause immune deficiency problems among the fully vaccinated
On Jan. 11, Zelenko, a Ukrainian-American family physician, was interviewed by Clay Clark, host of the Thrive Time Show on Brighteon.TV to talk about how the fully vaccinated patients he has been seeing are experiencing immune deficiency problems.

“They have AIDS. But it’s not HIV-induced AIDS,” said Zelenko. “HIV damages your killer T cells and weakens your immune system. So, that syndrome is called AIDS. But there are other things that damage your immune system.”

“By taking a shot that damages your natural killer T cells, damages your tumor suppressor genes, what’s happening is your innate immune system is getting shot,” he continued.

Zelenko then explained that this “vaccine-acquired immunodeficiency syndrome” is the reason that he and many other doctors have seen a massive surge in debilitating illnesses like cancer, autoimmune diseases and other “opportunistic infections” and conditions like heart attacks, strokes and miscarriages.

Ben Armstrong, a journalist working for the New American pointed out on his show, “The Ben Armstrong Show,” that all of this information blows up the entire narrative people have been fed regarding the COVID-19 vaccines. His hope is for this kind of information to be aired worldwide so that more people can see it.

“But of course, that’s never going to happen. That’s a pipe dream,” he said. “So, we’ll have to push it out grassroots style, as we’ve been doing with the truth the whole time. It’s always through the grassroots, [through] people like you spreading this type of information.”

Viability and corroboration TBD, though the recent DoD whistleblower allegations align with the above:

Pentagon Responds To DoD Whistleblowers' Claim Of Spiking Disease Rates In The Military After COVID Vaccine Mandate


Three United States military doctors have blown the whistle on documents allegedly from The United States Department of Defense (DoD) that they had access to, which show “skyrocketing rates of disease” since the introduction and mandating of the virus vaccines in armed forces, human rights attorney Leigh Dundas told The Epoch Times.

Dundas was recently approached by Dr. Samuel Sigoloff, Special Forces flight surgeon Lt. Col. Peter Chambers, and Aerospace occupational medicine specialist Lt. Col. Theresa Long.

They handed documents to Dundas, who appeared recently with attorney Tom Renz in a five-hour hearing organized by Sen. Ron Johnson (R-Wis.) titled “COVID-19: Second Opinion.”

Renz shared some of the numbers related to medical disorders in the U.S. military data with The Epoch Times.

The whistleblowers, who are represented by Renz, gave him the data “under penalty of perjury,” he said during the hearing.

Renz intends to submit the information to the courts, he told Johnson.

...

Renz responded:

“The DoD has claimed that the DMED data from the years 2016-2020 was incorrect. This is absurd. We spend millions of dollars per year on DMED and people monitoring DMED which is one of the premier epidemiological databases in the world. Accuracy in this database is critical as it is used to monitor for health issues in our troops.

https://www.zerohedge.com/covid-19/pent ... fter-covid

Yes, it's Zero Hedge and Epoch Times. So it grants the easy option of dismissal.

Time, however, has a way of distilling truth. It may be years from now. But it'll arrive eventually. Hopefully soon.

[stickdog99]

California Vaccination Data



24.1% of the previously least healthy quartile zip codes are unvaccinated compared to just 7.5% unvaccinated in the previously most healthy quartile zip codes.

How much of the entire supposed "protective effect" of these vaccines against becoming ill or dying while testing positive for COVID-19 does this huge difference in itself account for?

I mean, considering the demographic differences in populations, as well as the fact that you are only considered "fully vaccinated" 6 weeks into your vaccination regime and that anyone for whom a vaccination record not found is classified as unvaccinated, I am certain that the data these vaccines confer "significant protection" against diabetes, domestic violence, and gunshot wounds.

But, unfortunately, I'm not so sure about heart and vascular disease.

[stickdog99]

What relevence is that. YOu seem to be claimed that vaccine efficacy should be measured from the moment of the vaccine?

Why?

Why?

If you want to make an informed decision between whether to get vaccinated or remain unvaccinated, you need to examine the relative overall and not just COVID-19 associated health outcomes of vaccinated populations vs. those of comparable unvaccinated populations. And you need to see data that defines vaccinated as anybody who started these vaccination regimes from the day that they started.

Why the 6 week delay between the first dose and "fully vaccinated" designation unless to make the vaccines look as healthy and effective as possible? Imagine if I were to analyze chemotherapy treatments this way by excluding any hospitalizations or deaths that occurred within 6 weeks of the start of the chemotherapy regime. This would make a steady diet of arsenic look like an effective cancer preventative since anyone surviving this regime after 6 weeks must have been in more robust health to start.

All I have ever wanted are fair data that show the true benefit (or lack thereof) of these vaccines.

That means stop with "only fully vaccinated after two weeks after the second dose" Bayesian data crime that shrinks the vaccinated and boosted numerator while expanding the relative denominator. Since when have any other medications been able to arbitrarily define their start date for outcome analysis as "bad outcomes count against us only if they occur more than two weeks after second dose and 6 weeks after the first dose"?

Just show us raw COVID-19-associated and overall healthcare outcomes for the totally unvaccinated against the vaccinated starting with the day of the first vaccination so we can compare the outcomes of demographically comparable vaccinated and unvaccinated populations. Why are the data that anyone looking for a fair assessment of these vaccines need always too much to ask for?

[stickdog99]

[Belligerent Savant]

[stickdog99]

[stickdog99]

https://odysee.com/@IvorCummins:f/Covid ... e-Tricks:c

[Mask]

Here's a neurobiologist from Pittsburg reviewing the history of gain of function research and summarizing evidence for Covid19 as a lab created virus, all while riding his bike through town in the rain:


Here's the article he references. Can't vouch for the author(s), know nothing about them, and one of them worked at Los Alamos?
https://harvardtothebighouse.com/2020/0 ... 2019-ncov/

Petition to ban gain of function research:
https://www.change.org/p/national-insti ... _dashboard

IIRC this was the first time I heard about HIV sequences found in Covid-19's spike protein. March 2020!

The guy now streams on Twitch, of all places.
https://www.twitch.tv/gigaohmbiological

Link to the latest VOD (1h20):
https://www.twitch.tv/videos/1290723223

At around the 42 min mark, he interviews Sacha (Alexandra) Latypova, who has worked with Dr Mike Yeadon, Craig Paardekooper and others to bring us the website https://howbad.info

[stickdog99]

Sure enough, the state of CA is fudging the case, hospitalization, and death numbers by defining the unvaccinated in this manner:

Total number of laboratory-confirmed COVID-19 cases among persons age 12+ with episode date on the provided date with **no record of any doses of COVID-19 vaccine**. Persons considered partially vaccinated are not included in the unvaccinated cases.

“Episode date” is defined as the earliest of the following dates (if the dates exist): date received, date of diagnosis, date of symptom onset, specimen collection date, or date of death.

**So if they can't find you in their vaccination database for whatever reason on the earliest date of your "episode", you are summarily classified as unvaccinated for the purposes of these massaged statistics.**

https://abc7.com/myvaccinerecord-covid- ... /10806281/

https://covid19.ca.gov/vaccines/#Digital-vaccine-record

If you have trouble getting your record

If you couldn’t get your vaccine record, you may need to correct or add some information. Follow the troubleshooting tips at cdph.ca.gov/covidvaccinerecord.

What might prevent you from getting your COVID-19 vaccination record:

* Your vaccination site does not report to the state’s immunization systems
* Your vaccination site didn’t report your vaccination
* The information you entered doesn’t match your record in the registry

[Belligerent Savant]

IIRC this was the first time I heard about HIV sequences found in Covid-19's spike protein. March 2020!

Not to take away from PWillow's excellent contribution, but the (reported) HIV connection was referenced in the very first post of this thread:

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Breadcrumbs.

Since the S protein of 2019-nCoV shares closest ancestry with SARS GZ02, the sequence coding for spike proteins of these two viruses were compared using MultiAlin software. We found four new insertions in the protein of 2019-nCoV- “GTNGTKR” (IS1), “HKNNKS” (IS2), “GDSSSG” (IS3) and “QTNSPRRA” (IS4) (Figure 2). To our surprise, these sequence insertions were not only absent in S protein of SARS but were also not observed in any other member of the Coronaviridae family (Supplementary figure). This is startling as it is quite unlikely for a virus to have acquired such unique insertions naturally in a short duration of time.

The insertions were observed to be present in all the genomic sequences of 2019-nCoV virus available from the recent clinical isolates. To know the source of these insertions in 2019-nCoV a local alignment was done with BLASTp using these insertions as query with all virus genome. Unexpectedly, all the insertions got aligned with Human immunodeficiency Virus-1 (HIV-1). Further analysis revealed that aligned sequences of HIV-1 with 2019-nCoV were derived from surface glycoprotein gp120 (amino acid sequence positions: 404-409, 462-467, 136-150) and from Gag protein (366-384 amino acid) (Table 1). Gag protein of HIV is involved in host membrane binding, packaging of the virus and for the formation of virus-like particles. Gp120 plays crucial role in recognizing the host cell by binding to the primary receptor CD4.This binding induces structural rearrangements in GP120, creating a high affinity binding site for a chemokine co-receptor like CXCR4 and/or CCR5.

https://www.biorxiv.org/content/10.1101 ... 0.927871v1

A coronavirus that originated in Wuhan, China, has killed 18 people and infected more than 630.

The virus has been reported in at least eight other countries, including the US, where a man in Washington who recently visited China was confirmed to have the illness.

A scientist at Johns Hopkins last year modelled what would happen if a deadly coronavirus reached a pandemic scale. His simulated scenario predicted that 65 million people could die within 18 months.

https://www.businessinsider.com/scienti ... ths-2020-1

[stickdog99]

This a table from official California data:



How much of the supposed efficacy of these mRNA gene therapies against severe COVID-positive outcomes could be the result of in the clear demographical differences in vaccinated vs. unvaccinated population?

I found this article eye-opening: https://dailysceptic.org/2022/02/03/how ... -covid-19/

What percentage of "unvaccinated COVID-19 deaths" do you think fit the descriptions presented in the above articel? Poor medical care bordering on malpractice, iatrophobia, mental disorders, linguistic barriers, significant health co-factors, bad diagnoses, destructive medical interventions, etc.? My guess is that unvaccinated populations include the most indigent, linguistically challenged, deathbed ridden, and hospital and doctor fearing individuals in any society. So of course we would expect more death per 100,000 associated with COVID-19 in this population anytime COVID-19 is prevalent.

How much of the supposed efficacy of these mRNA gene therapies against severe COVID-positive outcomes could be the result of categorizing all of those "for who a vaccination cannot be found" (as California admits to doing)?

How much of the supposed efficacy of these mRNA gene therapies against severe COVID-positive outcomes could be the result of the data advantage that these vaccines are given by not counting anyone as "fully vaccinated" until 2 weeks after the second dose and counting anyone boosted less than than two weeks ago as unboosted? To me, this is the simplest explanation for the UK-HSA's much more strongly negative case efficacy for 2 dose than for 3 dose individuals.

Finally, how much of the supposed efficacy of these mRNA gene therapies against severe COVID-positive outcomes could be the result of placebo vs. nocebo effects on the individuals who test positive for COVID-19 as well as the relative consideration given to them by the healthcare professionals who treat them?

I am just asking for your basic ideas about these relative relevance or lack thereof of these potential issues.

[Grizzly]

https://www.ketk.com/news/local-news/high-school-student-dies-after-collapsing-during-basketball-game-in-alto/
East Texas high school sophomore collapses and dies during basketball game.

[Joe Hillshoist]

What relevence is that. YOu seem to be claimed that vaccine efficacy should be measured from the moment of the vaccine?

Why?

Why?

If you want to make an informed decision between whether to get vaccinated or remain unvaccinated, you need to examine the relative overall and not just COVID-19 associated health outcomes of vaccinated populations vs. those of comparable unvaccinated populations. And you need to see data that defines vaccinated as anybody who started these vaccination regimes from the day that they started.

Why the 6 week delay between the first dose and "fully vaccinated" designation unless to make the vaccines look as healthy and effective as possible? Imagine if I were to analyze chemotherapy treatments this way by excluding any hospitalizations or deaths that occurred within 6 weeks of the start of the chemotherapy regime. This would make a steady diet of arsenic look like an effective cancer preventative since anyone surviving this regime after 6 weeks must have been in more robust health to start.

All I have ever wanted are fair data that show the true benefit (or lack thereof) of these vaccines.

That means stop with "only fully vaccinated after two weeks after the second dose" Bayesian data crime that shrinks the vaccinated and boosted numerator while expanding the relative denominator. Since when have any other medications been able to arbitrarily define their start date for outcome analysis as "bad outcomes count against us only if they occur more than two weeks after second dose and 6 weeks after the first dose"?

Just show us raw COVID-19-associated and overall healthcare outcomes for the totally unvaccinated against the vaccinated starting with the day of the first vaccination so we can compare the outcomes of demographically comparable vaccinated and unvaccinated populations. Why are the data that anyone looking for a fair assessment of these vaccines need always too much to ask for?

Do you accept it takes 6 weeks for someone to have the required doses and the reaction to them or not?

One of our kids didn't go out until two weeks after their first whooping cough shot because there were ongoing outbreaks of it in the area. Because we didn't know the vaccine was effective yet. And without testing you never know but as far as I'm concerned that was fine, if it wasn't gonna work by two weeks it wasn't gonna work anyway.

[stickdog99]

https://dailysceptic.org/2022/02/09/hea ... -appendix/

Nature published a comprehensive study this week on cardiovascular risk including a total of over 11 million patients that has made a few headlines. The aim was to identify the cause of increased cardiac pathology. It should have been a very simple study comparing four groups:

Not infected and never vaccinated
Not infected and vaccinated
Infected but not vaccinated
Infected and vaccinated
It is hard to believe the authors did not look at these groups, but whatever was found when comparing them remains a mystery.

Instead, the following groups were compared:

Not infected and never vaccinated data from 2017
Not infected, including vaccinated and not vaccinated
Infected but not vaccinated
Infected with vaccinated people included but using modelled adjustments
When studies with huge datasets use modelling and fail to share data prior to their adjustments alarm bells should start ringing. Therefore, I took a deeper dive to see what else was questionable.

There were serious biases in the paper which need addressing but first let’s look at the critical question of myocarditis (heart inflammation).

Because of the known risk of myocarditis from vaccination it is worth looking particularly closely at the data presented on this. Oddly, for the issue of the day, the data on myocarditis was all hidden in the supplementary appendix to the paper.

The risk of myocarditis appears to be an autoimmune (the immune system attacking the heart after interaction with the spike protein) rather than direct damage by the virus/vaccine spike protein. Therefore, myocarditis could result from the virus or the vaccine. The key question that needs answering is whether vaccination protects or enhances the risk from the virus.

The authors report 370 per million risk of myocarditis after Covid infection in the unvaccinated. The contemporary control rate was 70 per million and the historic one was 40 per million. What was wrong with the contemporary controls?

They made it clear they removed those who had been vaccinated from the calculation in the Covid arm but they did not state they did this for the control arm. Did vaccination lead to a 30 per million increase in myocarditis in the control arm? Given the cohort appears to be old and we know myocarditis incidence is worse in the young a one in 30,000 incidence is significant.

What about those who were vaccinated and had Covid? Once vaccination (and modelling) were included, the rate rose to 500 per million. It is not entirely clear whether supplementary Table 22 excludes those who were not vaccinated, but given that it does not state the unvaccinated were excluded from this data it is fair to assume the 500 per million relates to the whole population.

Given the higher risk of myocarditis after vaccination one might wonder whether this study showed protection from infection due to vaccination, as this would lower risk from the virus. Hidden in the legends of the supplementary tables the authors reveal that 62% of the Covid patients had been vaccinated compared to 56% of the non-infected controls (not a great advert for vaccine effectiveness against infection).

Using the fact that 62% of the Covid cohort were vaccinated and that the unvaccinated had a rate of 370 per million, to get to an overall rate of 500 per million the vaccinated 62% must have had a rate of 580 per million (580×0.62 + 370×0.38 = 500). Therefore, in those with Covid and vaccination the rate (even after modelling) was 210 per million higher (58% higher) than the unvaccinated with Covid. (If supplementary Table 22 did exclude the unvaccinated the incidence of myocarditis after Covid would have been 35% higher in the vaccinated.) An extra 210 per million works out as an additional risk from vaccination of one in 5,000 among a relatively old population. How high was it for the younger men? This critical question was left unanswered.

The data comprised medical records for U.S. veterans who were 90% male, three quarters white and had a mean age of 63 years.

Two control groups were selected:

Patients who had used healthcare in 2017 and were still alive in March 2018.
Patients who had used healthcare in 2019 and were still alive in March 2020.
These groups were compared to patients who tested positive for Covid after March 2020, with each patient being matched to one patient from each control and measuring beginning from the same day as the positive test but two years earlier for the 2018 control.

There was a significant bias between these two control groups and those who tested positive.

The Covid patients (not just those who were sick with it – all those who tested positive) were more obese, saw doctors more often, had more cancer, kidney disease, lung disease, dementia etc.



There are two ways to deal with such biases. One is to match the 150,000 Covid patients with similarly sick patients from the over five millions controls. This reduces the size of the control group but when it is already so large this should not be a concern. Instead, the authors modelled the data until the groups seemed similar. Using an algorithm they claimed the same total number of people were present in the Covid cohort, but whereas 49,407 actually had diabetes in the raw data, 11,903 (24%) no longer had diabetes according to the weighted data. Similarly, 14% were ‘cured’ of lung disease, 14% of cancer and a full 35% of the dementia patients no longer had dementia.

There was no discussion in the paper about the reasons for this unhealthy bias among the Covid patients. All positive test results were included and anyone can catch SARS-CoV-2, so the factors that increase the risk of serious disease and hospitalisation should not have biased a dataset based only on infection. Instead the authors discuss the hypothetical issue of people in the non-infected control group having Covid but not getting tested such that the damage caused by Covid could be worse than the paper reports.

It has been well established that hospital transmission dominates as a source of spread and SAGE has reported that up to 40.5% of cases could be traced back to hospital spread and a majority of hospitalised patients in June 2020 were linked to hospital spread. In Scotland, in December 2020, 60% of the acutely ill with Covid acquired the infection in hospital. Patients accessing hospital are highly likely to be less healthy than the general population. Indeed, we know that the Covid patients in the study accessed hospital more frequently than the controls. If the bias was related to hospital acquired infection then the whole study is called into question, as people who attend hospitals are more likely to be sick.

The authors picked some control conditions to attempt to show they had not introduced a bias. Given the study was about cardiovascular diseases, including those that are an immediate threat to life and those that are very common, I would have picked conditions that might kill you within a year, like lung, pancreatic or oesophageal cancer and common conditions e.g. urinary tract infections, diabetes or prostate cancer.

The authors chose three rare malignancies, all with a one-year survival rate of over 80%, and pre-invasive melanoma – why not include invasive melanoma? They then included rare conditions and odd selection of: hypertrichosis (‘werewolf syndrome’ with excessive facial hair), sickle cell trait and perforated ear drums. When the choices are so niche it begs the question of what the results would have been if more obvious choices had been selected.

The group that tested positive for Covid did badly: 13% ended up in (or began in) hospital and 4% in ICU. The mean age was 63 years which may explain part of the high percentage of sick Covid patients, but it does, again, suggest this group may have been more vulnerable than the control.

They then compared the risk of various cardiac outcomes against the controls. However, they used the same control to compare non-hospitalised patients as patients who had received ICU care. Of course, people who have needed ICU care will be more likely to have cardiovascular complications. Indeed, many of the patients may still have been in the ICU when the measuring period began 30 days after the positive test. A fair study would have only compared the ICU outcomes with the sickest people within the control group, not the average of the whole control group.



The risk to the non-hospitalised Covid patients was low for almost all the cardiovascular risk factors.

The risk to the hospitalised was higher (but remember the controls had significant biases).

Those on ICU had a much higher risk. What is not clear is how much of this is because of the virus.



It is not a surprise for people who have had an ICU stay to be unwell for some time afterwards. The risk of ICU admission for Covid was higher than for influenza, but it is important to understand how much of the cardiovascular risk resulted from the virus and how much from the stay in intensive care per se. How do these Covid ICU patients compare to other ICU patients? The paper did not say.

Similarly the paper makes no attempt to unpick how many of the Covid patients tested positive only after being admitted to hospital. If, as in other studies, a significant proportion acquired Covid in hospital, then a higher risk of being diagnosed with other conditions would be highly likely.

Having failed to examine the above two questions – how much cardiovascular disease was a confounder of hospital transmission and how much is secondary to ICU harm – the overall risk of consequent cardiovascular problems included all the above cardiovascular conditions and thereby inflated the average for the Covid population as a whole.

Nature has published this paper which presents data in an obtuse way that should never have passed peer review. The results were presented as showing how dangerous the Covid virus was for cardiovascular complications without suitable controls to enable that conclusion to be drawn. The evidence on vaccination risks was hidden and not presented in a meaningful way for different age groups. Even then, they demonstrated a significant risk of myocarditis after vaccination, particularly after then encountering the virus but this key finding was hidden in the supplementary appendix. Why?

[Joe Hillshoist]

Was gonna post this in the Canada Music thread.