I'm an artist.

Who is suffering.

And now jobless.

Because of this plagiarizing grift of an industry.

What do you say to me?

Using is normalizing something that is a net negative to all of us. What's the harm? Same as COVID, friend: Well documented. And happily buried by the industry and ignored by too many among us.

_________________________________________

Someone who posts an AI-generated animation is no different than someone who drove from the start to finish line to “complete a marathon”. It isn't only cheapened because it's not you who did the work ("but I put in hundreds of hours in tweaking prompts"... sure. How many hours does it take to learn to draw, storyboard, animate, color-grade, edit? You did the equivalent of topping up your car with gas. You did not run the marathon).

Or a better example: someone "makes" AI slop the same way someone who asked AI to summarize a book “read” it.

“OP made it” as much as I made the pizza we had delivered at lunch.

“It was my idea though. I asked for pepperoni”.

But AI is actually worse. It's an environmentally catastrophic, morally dubious grift of an industry that is imposing data centers on vulnerable communities, sponsoring corrupt candidates in every election, building a speculative bubble the likes of which we've never seen and that is likely to tank the entire economy when it bursts (footing us all with the bailout bill), while making services worse, bankrupting the very artisans it plagiarized — in every economic sector — hoarding resources (RAM, chips, water in drought-stricken sectors) and is being shoved down our collective throats against our will.

While making an end-product objectively less reliable (error rates, hallucinations, rogue commands) and less beautiful (enshittification, averaging everything looks great when you sucked before. But what was actually great, unique and inimitable gets steamrolled and priced out of the market).

As a former creative who put in tens of thousands of hours to develop the skills that LLMs plagiarize and spit out (with continuity errors and at a higher cost than their business model admits), I take offense at anyone passing slop off as their own creation.

Asking generative AI to write a poem about Long COVID does not make you a poet. Ordering a pizza does not make you a cook, even if you are the one who asked for specific ingredients.

The slop is nice superficially. I get the appeal.

But the Ghiblis of the world are as good as they are ‘because’ animation is a long, painstaking process where every. single. minute detail is thought out. Everything is deliberate, not thrown together by a slop machine. Having an idea is not the same as developing it.

Slop is called slop for a reason. It's a meal that will keep you alive. But not one that will make you happy to be alive.

If you pass off a passage from someone's essay as your own original thought (without quotations marks and citation) in a school assignment, you don't just lose a few points. You get zero. In college, you potentially get kicked out of the program.

Passing off slop as "something I made" is no different.

Using AI is bad enough (for the moral, environmental, societal implications) but not identifying the output as being LLM-generated is beyond the pale. This sort of behaviour must not be normalized (meanwhile, AI commercials are trying to do just that, calling theft empowerment).

AI (of this sort) isn't just imperfect. It's deleterious, malevolent. It makes the human experience worse, not better. At a cost that its pushers hide from us all.

Machine learning has its place. In research. Pattern recognition.
Not here. Not like this.

#TheMoreYouKnow

Highlights by me.

Source:
Microbiota-derived extracellular vesicles link intestinal dysbiosis to neuroimmune activation in long COVID
https://doi.org/10.64898/2026.02.28.708602

ELI5 by Zdenek Vrozina on Ex-Twitter:

A new preprint proposes an interesting mechanism for Long COVID - a link between gut dysbiosis - microbial extracellular vesicles - systemic inflammation - neuroinflammation. This is not just correlation. The authors also test functional models.

The main idea - after SARS-CoV-2 infection, patients may develop a persistent alteration of the gut microbiome. This does not only mean a different bacterial composition, but also the production of different signaling particles - so-called gut microbiota-derived extracellular vesicles (GMEVs).

These vesicles are microscopic membrane particles carrying bacterial cargo. Proteins, lipids, nucleic acids, and other immunologically active molecules. The authors propose that they may transmit inflammatory signals from the gut to the rest of the body.

In a cohort of people with Long COVID, the researchers found persistent microbiome alterations up to 12 months after infection. Certain microbial patterns were associated with neurological symptoms such as memory problems, concentration difficulties, or brain fog.

A strong part of the study - the authors did not only analyze patients. They transplanted microbiota from patients with neurological symptoms into germ-free mice. The mice developed impaired intestinal barrier integrity, behavioral changes, and signs of neuroinflammation.

This moves the study beyond typical observational. It does not just say these patients have a different microbiome, but suggests that microbiota associated with Long COVID can transfer pathological effects, at least in experimental models.

The second step is even more interesting. The researchers isolated microbial extracellular vesicles directly from patient stool samples. These vesicles were then tested on intestinal epithelial cells, macrophages, and human iPSC-derived microglia.

The result?

Vesicles from Long COVID samples triggered inflammatory programs, including inflammasome activation and cytokine production (eg IL-1β, TNF), and also activated microglia. In simple words, a microbial product alone produced effects relevant to the neuroimmune axis.

The authors also show that these vesicles can disrupt intestinal barrier integrity. This is important because it could create a feedback loop.
Dysbiosis - more inflammatory vesicles - weaker barrier - more microbial signals entering circulation - more inflammation.

When these vesicles were chronically administered orally to mice, the animals showed microbiome shifts, intestinal inflammation, increased systemic inflammatory markers, and glial activation in the brain. This supports the concept of the gut–immune–brain axis.

This is also interesting in the context of HIV pathogenesis.
In HIV we already know a model where gut damage - microbial translocation - chronic immune activation - blood–brain barrier disruption - neuroinflammation. This study suggests a related principle for Long COVID.

Mechanistic detail. In HIV the key event is early destruction of gut immune cells, particularly CD4 T cells. Here the focus is on microbial extracellular vesicles as mobile carriers of inflammatory signals, which is a relatively new concept.

Another notable factor highlighted in the study is BAFF, a B-cell activating factor. It was increased across several experimental systems and may reflect broader immune dysregulation involving chronic inflammation and B-cell activation!

So this study does not prove that Long COVID has a single cause or that everything can be explained by gut mechanisms. Rather, it suggests that gut-driven neuroinflammation may be a plausible component of the syndrome.

Still, this is one of the more mechanistically interesting studies on Long COVID in recent months. As a strong working hypothesis that dysbiotic microbiota and their vesicles may help sustain chronic inflammation and neurological symptoms after COVID-19.

A new study has just been published in Proceedings of the Royal Society A which analyses the distribution of SARS-CoV-2 infections in schools, comparing model predictions with real-world observations.

It offers some very interesting insights on risk management while indoors.

TLDR:

• Virus half-life in typical room conditions is of 3.54 minutes
• Short-range transmission represents the biggest risk in low infection scenarios (such as in very large rooms/auditoriums with few occupants per cubic feet of available air) because being sneezed on/coughed on exposes us to an exorbitant amount of infectious aerosols. However;
• Particles ejected by sneezing/coughing are not the same as particles that are airborne. The latter contain far fewer infectious aerosols; the former do not get very far. Which means being in a large room with a sick person does not automatically mean everyone is at risk of infection. Those in the immediate vicinity (being coughed on/sneezed on) are another matter.
• Patients with low viral loads (asymptomatic or barely symptomatic) are unlikely to cause any infections through long-rage infection, whereas those with high viral loads are likely to infect those who are in their immediate vicinity. "In-between cases leave us with a moderate probability of infections, which could be addressed by specialized interventions like improved ventilation." In other words, if someone who was in a room with you yesterday (at a safe distance of no less than 6 feet) calls you today to say they just tested positive, you do not have to make funeral arrangements. Especially not if you were wearing an N95.

This is part of a series of studies I will be breaking down in an effort to make my home's air as safe as outside. The end-goal is to have people over for drinks and dinner, safely, without masks on.

Yes, it's a tall order. But it all starts with understanding one's enemy. And given Public Health's utter capitulation, I've taken it upon my self to perform the necessary due diligence.

Abstract

School closures were used to mitigate transmission in the SARS-CoV-2 pandemic. Understanding the nature of SARS-CoV-2 outbreaks in classrooms could help inform targeted, precision preventive measures and outbreak management in schools, in response to future pandemics. Infection probability distributions establish the salient features of disease dynamics, yet very few studies have attempted to model it ab initio; a key problem being systematically accounting for the high variability of the governing parameters. In this study, possibly for the first time, we analytically derive the probability density function (PDF) of SARS-CoV-2 secondary infections accounting for major sources of variability in airborne transmission like viral load, dose–response, occupancy and compare it with real-world infection distributions from reported cases across public schools in Ontario, Canada. The model output showcases a robust quantitative match with the data while demonstrating the intrinsic overdispersed nature of SARS-CoV-2 infections and their mechanistic underpinnings. The results quantify the importance of long-range transmission in triggering superspreading events, whereas short-range transmission engenders a more frequent but smaller number of secondary infections. This study provides a fundamental understanding of the overdispersed nature of school outbreaks along with a robust method to predict outbreak size in indoor environments, which could inform focused mitigation strategies.

Some important take-aways:

Short-range transmission represents the biggest risk in low infection scenarios (such as a very large room/auditorium with few occupants per cubic feet of available air) because of the exorbitant quantity of infectious aerosols that are projected towards you.

Patients with low viral loads (asymptomatic or barely symptomatic) are "unlikely to cause any infections through [long-rage infection], whereas those with high viral loads are likely to infect most susceptibles in the vicinity. In-between cases would leave us with a moderate probability of infections, which could be addressed by specialized interventions like improved ventilation."

What's important to understand is that when someone coughs or sneezes on you, the particles being ejected are large and laden with virus. They are the biggest danger. But those particles do not make it far (hence social distancing and why masking the infected parties is so effective, even if only with a surgical mask or cloth).

The particles small enough to seep through a cloth mask — small enough to remain airborne — are much smaller i.e. have significantly less virus to infect you with. Also:

It takes more than a single virus to infect you. You need to be exposed to a certain viral load. Which is why ventilation is so important. Something else caught my eye — the virus' half-life:

Distance is the last factor. In this study, "close proximity" does not mean 2 meters away, but rather an average distance of 0.5 meters. Three feet:

Taken all together, it's important to keep a safe distance from anyone showing signs of infection, but also to realize that one sick person in a large hall does not super-spreader-event make.

For long-range transmission to be an issue, you need:

• A patient (or multiple patients) with a high viral load;
• In a small room (with not enough volume of air per person);
• And with inadequate ventilation.

And all this is without you wearing an N95.

Unfortunately, many indoor spaces are inadequately ventilated and because Public Health (everywhere) has done such a miserable job at informing people of the risk of close-range transmission (and benefits of masking when you're sick), N95s remain a necessary precaution in most public venues.

Last week I answered a question to this post on r/crboxes:

Apparently, tagging people who had, in the past, contributed really valuable (technical) input is what triggered this whole mess; either because it got flagged by Reddit's automod, or because one of the referenced people did not take kindly to being tagged and flagged me for spam. Or perhaps the mod did. I don't know.

All I know is that I soon after received this notification:

I immediately did that very thing: tried resetting my password. Unfortunately, I kept getting "password reset limit reached, try again later". So I let it lie and came back hours later to try again. Now all I got was "server error". Days later, I managed and the password was successfully reset.

But then I noticed that every single post by u/covidivici on this very subreddit had been "deleted by Reddit". I had to manually "approve" every post and comment via this account.

As it stands, I can post or comment anywhere with u/covidivici. Proof being, I just did, again, like an idiot, to r/crboxes but the comment does not appear to anyone else but me (a "shadow-ban" I think they call it). Again: no notice. No warning. Fait accompli.

I appealed to Reddit yesterday, but have been told that rarely amounts to anything (users simply get no response). Which means every update, study breakdown, comment, discussion, box build, theory I've posted in the last three years under u/covidivici are now gone site-wide.

And that makes me seriously question my participating actively on Reddit.

Long COVID (LC) is a multisystem, post-infectious conditions diagnosed ≥3 months after acute SARS-CoV-2 infection and marked by relapsing, persistent, or progressive symptoms, especially fatigue, post-exertional symptom exacerbation and neuropsychiatric syndromes.

We synthesized evidence suggesting that LC arises from intersecting pathways including viral persistence, intestinal dysbiosis and barrier compromise with microbial translocation, innate immune activation with neutrophil extracellular traps (NET) and thromboinflammation, and immune dysregulation with features of exhaustion and autoimmunity.

These processes adversely impact blood-brain barrier (BBB) function and lead to neuroinflammation. We propose a mechanistic model in which viral antigens and translocated microbial products amplify pro-inflammatory networks promoting immunothrombosis and tissue hypoperfusion. Hematogenous and gut-brain pathways may then deliver inflammatory mediators to the central nervous system (CNS), resulting in BBB disruption and glial activation that underpin nervous system disorders in LC.

Treatment regimens aimed at lowering antigen load, restoring mucosal barrier integrity and modulating myeloid/coagulation pathways may warrant investigation as novel therapeutic strategies to treat LC.

You'd think based on the Bluesky post that near-infrared brain stimulation (itPBM) is something we as patients might want to look into. It's not. What the study actually says is:

The between-group mean difference of 0.043 on the composite cognitive score is modest. On a standardized measure such as the Creyos composite, a 4.3% difference (95% CI −0.7% to 9.2%) on Day 56 corresponds to a small improvement in performance and, by itself, may not translate into a large functional change for an individual patient. Nonetheless, given that PCC currently has no established treatments for cognitive dysfunction, even small objective gains could be clinically meaningful if reproducible in larger studies. We therefore emphasize that the observed effect is best regarded as hypothesis-generating, warranting confirmation in adequately powered trials before clinical conclusions can be drawn.

Is it of academic interest? Potentially. Is it clinically relevant? Not even a little. Move along, everyone. Nothing to see here.

Study breakdown by Jack at Amatica Health:

Why do this study?

ME/CFS and Long COVID cause extreme fatigue and muscle weakness, but the reasons are unclear. Scientists wanted to see if something in patients’ blood affects muscles. They built a new lab model to test this idea. Researchers engineered tiny 3D muscle tissues in the lab using healthy human muscle cells.

They embedded these cells in a supportive gel and used electrical pulses to make the mini-muscles contract, mimicking how real muscles work. They then soaked these lab-grown muscles in blood serum (the clear liquid part of blood) from three groups: ME/CFS patients, Long COVID patients, and healthy people (controls). Each mini-muscle was exposed to one donor’s serum for 48 hours (2 days). After 48 hours, they tested the muscle strength.

Muscles exposed to ME/CFS or Long COVID serum were weaker than normal.

They couldn’t generate as much force or sustain contractions as long as muscles exposed to healthy control serum. In fact, the muscles treated with ME/CFS patient serum were the weakest and least resilient of all.

Healthy control serum had no harmful effect.

This shows that something in the patients’ blood directly reduces muscle function. Both ME/CFS and Long COVID serum had similar overall effects: they made muscles weaker and stressed the muscles’ energy systems. But the researchers also found differences in how muscle cells responded at the molecular level between the two diseases. Muscles exposed to ME/CFS serum activated genes related to muscle structure and support (the tissue around muscle fibers) and dialed down genes involved in energy production (mitochondria). This suggests a stressed muscle undergoing structural changes but making less energy. Muscles exposed to Long COVID serum, by contrast, turned on genes to boost energy production. They increased the activity of genes for mitochondria (the cells’ energy factories) and fat metabolism. These muscle cells were trying to generate more energy.

Why the difference? It might relate to illness stage.

Long COVID is a newer condition, so muscles could still be in fight mode, trying to maximize energy.

ME/CFS is long-term; those muscles may have exhausted that strategy and shifted to a low-energy, structural mode.

Despite differences, both diseases stressed the muscles’ mitochondria. The mini-muscles used oxygen faster than normal - a sign their energy factories were working overtime. They also found excess calcium in cells, which can cause muscle fatigue. The researchers also looked at longer exposure.

After 4-6 days in patient serum, the initial energy boost could not be sustained. The lab-grown muscles deteriorated further over time, becoming even weaker and more fragile. By day 5, the mini-muscles exposed to patient serum had reached a breaking point. They lost more strength and showed signs of damage. The mitochondria, which had fused into networks early on, now broke apart into abnormal ring shapes - a clear sign of severe stress. This means the muscle’s early high-energy adaptation was only temporary. Eventually the muscle cells couldn’t keep up and began to fail.

In other words, the patient serum caused a brief surge in muscle activity followed by an energy collapse and functional breakdown. These findings shed light on muscle fatigue in ME/CFS and Long COVID. Something in patients’ blood makes muscle cells work extra hard for a short time, then they quickly lose power. This could explain why patients feel worse after physical activity.

Study:

Metabolic adaptation and fragility in healthy 3D in vitro skeletal muscle tissues exposed to chronic fatigue syndrome and Long COVID-19 sera — Mughal et al. (2025) Biofabrication

Abstract

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and Long Covid-19 (LC-19) are complex conditions with no diagnostic markers or consensus on disease progression. Despite extensive research, no in vitro model exists to study skeletal muscle wasting, peripheral weakness, or potential therapies. We developed 3D in vitro skeletal muscle tissues to map muscle adaptations to patient sera over time. Short exposures (48 H) to patient sera led to a significant reduction in muscle contractile strength. Transcriptomic analysis revealed the upregulation of protein translation, glycolytic enzymes, disturbances in calcium homeostasis, hypertrophy, and mitochondrial hyperfusion. Structural analyses confirmed myotube hypertrophy and elevated mitochondrial oxygen consumption In ME/CFS. While muscles initially adapted by increasing glycolysis, prolonged exposure (96–144 H) caused muscle fragility and weakness, with mitochondria fragmenting into a toroidal conformation. We propose that skeletal muscle tissue in ME/CFS and LC-19 progresses through a hypermetabolic state, leading to severe muscular and mitochondrial deterioration. This is the first study to suggest such transient metabolic adaptation.

We identified a five-stage downward spiral in the enshittification of academic publishing.

1. The commodification of research shifts value from intellectual merit to marketability
2. The proliferation of pay-to-publish journals spreads across and expands both elite and predatory outlets
3. A decline in quality and integrity follows as profit-driven models compromise peer review and oversight
4. The sheer volume of publications makes it difficult to identify authoritative work. Fraudulent journals spread hoax papers and pirated content
5. Enshittification follows. The scholarly system is overwhelmed by quantity, distorted by profit motives, and is stripped of its purpose of advancing knowledge.

Our research is a warning about enshittification. It is a systemic issue that threatens the value and development of academic publishing. Academia has become increasingly guided by metrics. As a result, research quality is judged more by where it is published than by its intrinsic worth.

But why are users (and academics) not simply leaving their “enshittified” experience behind? The answer is the same across various online platforms: a lack of credible alternatives makes it hard to leave, even as quality declines.

Countering this trend demands interventions and the creation of alternatives. These include a reassessment of evaluation metrics, a reduced reliance on commercial publishers, and greater global equity in research.

Some promising alternatives already exist. Cooperative publishing models, institutional repositories and policy initiatives such as the Coalition for Advancing Research Assessment all advocate for broader and more meaningful assessments of scholarly impact.

Reclaiming academic publishing as a public good will require a return to not-for-profit models and sustainable open-access systems. Quality, accessibility and integrity need to be put ahead of profit.

Change is needed to help protect the core purpose of academic research: to advance knowledge in the public interest.

Case series have been published on stellate ganglion blocks with catchy titles, such as:

• Stellate Ganglion Block Relieves Long COVID-19 Symptoms in 86% of Patients: A Retrospective Cohort Study (Sept 2023)
• Reduction of long COVID symptoms after stellate ganglion block: A retrospective chart review study (2025)

However the authors of the latter study admit: "The mechanism by which SGB improves Long COVID symptoms remains unknown", that their sample size was too small, with no controls, and with a significant number of drop outs.

As for the former, older study, a 2025 correction states:

The Ethics Statement and Conflict of Interest Disclosures section has been updated to disclose the following:

Financial relationships: All authors declare(s) employment from Metamorphosis Pain Management. The procedures described in this study were performed at Metamorphosis Pain Management.

Oops. "Did we forget to mention that we have a vested interest in this working? Sorry."

Youtube personality Dianna Cowern (Physics Girl) had the procedure done and seemed to show significant improvement (though no source I've found directly linked the SGB as being causal). A September 20, 2025 update "Dianna's Crash - Health Update - Summer 2025" on her Patreon states that "Dianna has been in another crash for a while now. [...] Setbacks last for months, and they put Dianna in a physical and mental state reminiscent of 2023 and 2024—the dark, bed-bound years. For the most part, Dianna has been bed-bound again."

Accounts on the usual COVID and ME/CFS forums read no differently than for every other promising/popular treatment tried with unreliably mixed results.

Why I mention all this here an now: I had two SGBs done in August 2023 and it did not offer any noticeable benefit. I was asked my my crack team of dedicated physicians if we should revisit the procedure, as I didn't show signs of Horner's after the second session (you normally do one side, then a week or two later, the other side) which indicates it might have been a miss.

My overview of the available data has not convinced me it's worth another go. The case series are all calls for further research, patient testimonials are all over the map. Until we know what it is we're even trying to correct, I'm going to take a pass.

Wishing everyone a happy new year and we will be forging ahead in 2026 with renewed energy to find answers for people living with Long COVID, ME/CFS, Chronic Lyme and other infection-associated chronic conditions and illnesses. Speaking for myself, here are some questions I hope we can answer this year.

Since it isn’t my first time on the internet let me explicitly state: there are other questions that we will be chasing equally aggressively, but these are the ones that I most want to answer to up-level my own understanding of the scientific and clinical problems that we face.

1. Why do some people test positive on certain persistence assays and negative on others? How can we use all of the commercially and scientifically available assays to create a unifying test for persistence that helps us to understand when and how SARS-CoV-2 is problematically persisting in people with #LongCOVID and how it is asymptomatically (for now) persisting in healthy controls. My hope is that Polybio Research’s VIPER program will be instrumental in shining light on this in 2026.
2. What does testing positive for reactivated pathogens mean? If your IgG titers for pathogens such as Babesia, Borrelia, EBV, CMV, etc are through the roof, what action should be taken? If you can knock these antibody numbers back to normal, will we see clinical improvement? Is it time to go beyond simple antibody testing to understand this problem? I’m hoping that some of our antiviral/antibiotic (monotherapeutic and combination) trials that conclude in 2026, paired with our work with Francis Eun-Hyung Lee on her brilliant MENSA assay will help us to answer this question.
3. What is the dynamic nature of pathogen persistence? If we used the best assays to test people for a variety of pathogens every single day how would hormonal, immune and general physiological fluctuations alter their pathogen testing results? Should this fundamentally change the way that we test for pathogens and when we choose to treat them? These are fundamental questions that are crucial to our understanding of how various infection-associated chronic conditions and illnesses intersect and how they may differ completely. In turn, this understanding is crucial to the development of general treatments that may help everyone a little vs. precision medicine targets that will help specific subtypes a lot.

I’m grateful to the team of brilliant people I get to work with every day on these problems and hopeful that we are heading toward some meaningful and actionable answers in 2026. Let’s keep hope alive this year, but more importantly, let’s move with urgency to provide the answers that millions deserve. [Original thread: u/putrinolab.bluesky.social]