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Blockbuster Yale Study: Millions Of Long COVID Patients Might Actually Be Vaccine Injured
February 24, 2025
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Authored by Paul D. Thacker via The Disinformation Chronicle,

Yale researchers released a study today that posits millions of Americans thought to have Long COVID may have been misdiagnosed and actually have post-vaccination syndrome caused by exposure to the spike protein in COVID vaccines. Spike protein produced by the Pfizer and Moderna vaccines triggers the body’s immune response, and the FDA claimed in a 2023 Politifact fact check that vaccine spike protein is not toxic and does not linger in the body. However, Yale researchers report that some patients, who were never infected with COVID virus, were sick with post-vaccination syndrome (PVS) and had elevated levels of virus spike protein in their blood up to 709 days after vaccination.

“There is considerable overlap in self-reported symptoms between long COVID and PVS, as well as shared exposure to SARS-CoV-2 spike (S) protein in the context of inflammatory responses during infection or vaccination,” noted the study authors.

NIH has poured $1.6 billion into Long COVID research, while ignoring patients harmed by COVID vaccines, causing some well-known patient advocates to hide vaccine injury. After a 13-month battle with Long COVID, Hollywood screenwriter Heidi Ferrer took her own life after deciding death was preferable to another minute in her own “personal hell." Death of the Dawson’s Creek writer made headlines across the media including places such as People, the Guardian, Variety, CNN, Newsweek, and The Daily Mail—each recounting Ferrer’s struggle with Long COVID.

But in a private video circulating among patient groups and obtained by The DisInformation Chronicle, Ferrer’s husband Nick Guthe stated that Moderna’s COVID vaccine was the final straw, causing Heidi to develop tremors and then internal vibrations when she lay down for bed, so that even prescription sleeping pills would not allow her to sleep.

“And that’s when things turned,” Guthe said in the video.

Prominent patient advocate Beth Mazur also committed suicide after a COVID vaccine apparently worsened her struggles with myalgic encephalomyelitis (ME), a chronic illness with many similarities to Long COVID. Mazur co-founded #MEAction for patients with chronic illness. #MEAction reported in early 2021 that a significant number of ME/CFS patients experienced “both new symptoms and long-lasting exacerbations of their pre-existing ME/CFS symptoms” after a COVID vaccine.

“Beth was a compassionate advocate for ME/CFS and a fierce advocate for vaccine injury after she experienced this herself sometime before she took her own life,” said one of Mazur’s personal friends who did not wish to be identified. “Having people come after you for vaccine injury is worse than being sick itself. And people can’t handle that. It’s a shroud of shame.”

One of the study’s lead authors, Yale Medical School’s Akiko Iwasaki, previously shot down public concerns about COVID vaccine side effects. When Houston Methodist Hospital staffers sued to avoid the hospital’s coronavirus vaccine mandate in 2021, Iwasaki told the Washington Post that the employees’ fears were “absurd” because “no safety concerns” had been found in the mRNA vaccine clinical trials.

Along with other prominent health experts, Iwasaki also signed a petition supporting the OSHA COVID-19 vaccine mandate which the Supreme Court later blocked.

Several of the study’s findings as well as research the authors cite in their paper have been labeled as false by federal agencies, medical experts, and fact checkers. Because medical journals have been rejecting studies on vaccine side effects, the authors uploaded their paper to the preprint site medRxiv.

Passages from the paper are examined below, as well as “fake fact checks” with false and misleading statements by federal agencies and medical experts that, in the past, called these new scientific findings fallacious. Hyperlinks to research papers cited by the study authors have been added to replace their footnotes.

Yale researchers point out that vaccine injury has not been totally defined and has been labeled both PVS and PACVS. Unlike Long COVID, health authorities do not officially recognize PVS, so patients get little support or care. See passage from paper:

In addition, some individuals have reported post-vaccination symptoms resembling long COVID beginning shortly after vaccination. This condition, sometimes referred to as post-vaccination syndrome (PVS) or post-acute COVID-19 vaccination syndrome (PACVS), is characterized by symptoms such as exercise intolerance, excessive fatigue, numbness, brain fog, neuropathy, insomnia, palpitations, myalgia, tinnitus or humming in ears, headache, burning sensations, and dizziness. Unlike long COVID, PVS is not officially recognized by health authorities, which has significantly limited patient care and support.

Both Long COVID and PVS rely on patient’s self-reported symptoms, which have quite a bit in common. Exposure to the spike protein during infection or vaccination causes inflammation. Various parts of the mRNA vaccine might also be problematic such as the mRNA itself which then creates the spike protein, or the tiny fat globule that encases vaccine mRNA called a lipid nanoparticle.

However, there is considerable overlap in self-reported symptoms between long COVID and PVS, as well as shared exposure to SARS-CoV-2 spike (S) protein in the context of inflammatory responses during infection or vaccination. In susceptible individuals, vaccines may contribute to long-term symptoms by multiple mechanisms. For example, vaccine components, such as mRNA, lipid nanoparticles, and adenoviral vectors, trigger activation of pattern recognition receptors.

The Pfizer and Moderna vaccines create a spike protein that is also called “S protein” or “S1”. See passage from paper:

Secondly, it has been shown that the S protein expressed following BNT162b2 [Pfizer] or mRNA-1273 [Moderna] vaccination circulates in the plasma as early as one day after vaccination.

The virus spike protein has two parts called S1 and S2. These might break down into smaller units called peptides. Some patients with PVS have been found with S protein in their blood cells. In animals, the mRNA vaccine has been found to cross over a membrane and enter the brain. If the mRNA then created spike protein, this could cause neurological problems.

Interaction with full-length S, its subunits (S1, S2), and/or peptide fragments with host molecules may result in prolonged symptoms in certain individuals. Recently, a subset of non-classical monocytes has been shown to harbor S protein in patients with PVS. Further, biodistribution studies on mRNA–LNP platforms in animal models indicate its ability to cross the blood-brain barrier, and the local S expression could result in neurocognitive symptoms.

The researcher report that patients with PVS cited the following symptoms:

The most frequent symptoms reported by participants were excessive fatigue (85%), tingling and numbness (80%), exercise intolerance (80%), brain fog (77.5%), difficulty concentrating or focusing (72.5%), trouble falling or staying asleep (70%), neuropathy (70%), muscle aches (70%), anxiety (65%), tinnitus (60%) and burning sensations (57.5%)

Patients reported symptoms from the vaccines around 4 days, and severe symptoms about 10 days after vaccination.

The median number of days for the development of any symptom was 4 [Interquartile range (IQR): 23 days], while for severe symptoms, it was 10 (IQR: 44 days) post-vaccination.

Patients with PVS had higher levels of spike protein or S1 in their blood.

The results indicated that participants with PVS had significantly higher circulating S1 levels compared with the control group (p = 0.01).

This figure from the paper shows that PVS patients had spike protein or S1 in their blood up to 709 days after they were vaccinated.

PVS and Long COVID share similar symptoms probably from exposure to S protein. Researchers have found the full spike protein and the smaller S1 protein in Long COVID patients. See passage from paper:

Given the similarities between PVS and long COVID symptoms, one hypothesis in the literature is that shared exposure to the S protein may play a role and several groups have independently reported the presence of circulating S1 & full-length S in long COVID using various detection methods.

The highest levels of spike protein were found in PVS patients who had been vaccinated but never infected by COVID virus.

Notably, we observed that the highest levels of detectable S1 in the PVS-I group were the furthest away from the last known exposure and ranging between greater than 600-700 days.

Patients vaccinated with mRNA COVID vaccines and the adenovirus COVID vaccine have shown harm. Patients with PVS had poor health as defined by standardized tests called GHVAS and PROMIS29. Few studies have investigated the cause of PVS, which has no agreed upon definition. See passage from paper:

Post-acute conditions following COVID-19 vaccination have been reported for multiple vaccine platforms including mRNA and adenoviral-vectored vaccines. We observed that the general health status of the PVS participants was far below the general US population average based on the GHVAS scores. The patient-reported outcome scores from the PROMIS29 domains were also indicative of lower quality of life. To date, only a few studies have investigated the immunological mechanisms associated with PVS and no consensus definition of this syndrome exists.

Underlying risk factors for developing PVS are similar for Long COVID. This might be due to problems caused by spike protein, but should be studied further.

The demographics at risk of developing PVS and symptom manifestations are similar to those of long COVID. Whether this reflects overlapping underlying mechanisms such as persistent S protein remains to be determined.

High levels of spike protein in the blood were found in PVS patients who had been infected with the COVID virus and those who had never been infected. This makes sense as spike protein has been found in blood cells. Spike protein has been found in the blood of patients who had myocarditis after COVID vaccination. Because PVS and Long COVID are so similar, the spike protein might be causing the chronic health problems.

By contrast, in our study, significantly elevated levels of circulating S1 and S were observed in a subset of PVS participants both in the infection-naive and infection-positive groups up to 709 days post-exposure. This is in line with the findings of S1 persistence in monocytes in people with PVS. Circulating full-length S has also been detected in cases of post-vaccination myocarditis. Given the striking similarities between long COVID and PVS symptoms, there has been speculation regarding the potential causal role of the persistent presence of spike protein driving the chronic symptoms.

 

More reporting on vaccine side effects in patients to come.

We strongly encourage you to subscribe to The Disinformation Chronicle.

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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

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Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

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The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notes, Massachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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