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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 Peoplethe GuardianVarietyCNNNewsweek, 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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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
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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.
 
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Will these same companies end up controlling robotics too?
 
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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.
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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.
 
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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.
 
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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.
 
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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.
 
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Train on Azure.
 
Run foundation models from OpenAI.
 
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It can't.
 
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Navigating the world of blockchain 🧭
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.

🏛️ 1. Core Architecture: The Base Layer

  • Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
  • Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
  • Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
  • Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.

    • Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
    • Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).

🔑 2. Ownership & Security: Wallets and Keys

  • Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
  • Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
  • Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
  • Hot Wallet vs. Cold Wallet:

    • Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
    • Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

  • Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are met—eliminating the need for intermediaries.
  • dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
  • Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
  • Layer 1 vs. Layer 2:

    • Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
    • Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).

💰 4. Financial & Market Concepts

  • Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
  • DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built on smart contracts without traditional banks or financial intermediaries.
  • Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
  • DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.

💡 Quick Cheat Sheet

"Not your keys, not your coins."

If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

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AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

Source

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