frozenborderline
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A new discovery about ecological toxicity sheds light on the ME/CFS epidemic
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Link is broken.
nerd
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https://openletteropenmedicinefound...ical-toxicity-sheds-light-on-me-cfs-epidemic/
I have cyanobacteria in my raw drinking water, but I filter it. Not sure if this removes the toxins.
I had chronic black mold exposure during childhood. I told my mother multiple times when I developed allergies, but they still live with it and marginalize it.
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frozenborderline
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https://openletteropenmedicinefound...ical-toxicity-sheds-light-on-me-cfs-epidemic/
Does the new link work?
Does the new link work?
Martin aka paused||M.E.
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6 years of black mold exposure. Waiting for my lab results since weeks (UK Biolab) …
what I don’t get is: why do others stay healthy when I get sick? I’ve never lived alone and I have never been abstinent... But I'm the only one who got ill with ME. So I don't think mycotoxins alone (!) are the culprit. Maybe in concert with other factors (that my family members, flatmates, friends, partners didn't have).
And I’m sure that Ron worried a lot about Lake Tahoe 85 and environmental factors. Maybe he didn’t show interest bc he didn’t find any toxins in the severely ill patients.
And IMO it doesn’t really fit. Outbreaks are the exception not the norm.
But who knows. Worth investigating!
nerd
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I also don't know what to make of it. Black mold alone is unlikely to be solely responsible, but maybe in conjunction with certain genotypes or certain viruses. Maybe the mold and cyanobacteria have the potential to change something in the microbiome and this is why not everyone is affected.
I'm getting so tired of looking at all the evidence. Everything showing links and that the disease is associated with so many pathologies and organs, symptoms, triggers. I have the notion that we might still not fully understand it in 30 years, even if researchers had the funds. Just like with MS.
Every research team only looks at a certain aspect but not how this aspect can fit into the other findings. Maybe it would be better to just move on and try different therapies with ME patients on a large scale. Some therapy / drug cocktail might stick out. Then they can go figure why it works.
Let's compare it to SARS-CoV-2 and Ivermectin. An incredible amount of funds went into Covid-19 research. You'd imagine that a drug with good efficacy is granted, but no. The drug that shows the best antiviral efficacy so far is a repurposed drug and the best treatment option is an antidepressive, both lucky fundings, if you will, and not funded by antiviral treatment research. No wonder drug exists so far, no panacea. Still, if you combine Ivermectin with Fluvoxamin and Vitamin D during the early phase, you have very good prognosis.
Maybe if we did the same in ME trials, at some point we might be lucky. But not trying anything until there's all the other evidence available, which never comes due to a lack of funding, doesn't give luck a chance in the first place. It leaves us in an endless waiting position.
I hoped that LHS would change things, but neither long hauler patients nor physicians and researchers seem to be interested to learn from the existing ME research, other than ME researchers themselves. They act like ME is something completely different and can not be considered as a predictor for future LHS research. I don't get it.
Martin aka paused||M.E.
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Great post and I agree 100%. That’s why I test and try different things now Janina and me collected over the last months on PR and elsewhere.
Given the severeness of my illness and the constant worsening from the mildest exertions I think I will be dead before anyone understands the etiology of this disease (sorry OMF ✌️🏼Will still support you).
IMO it's genetic predisposition + multiple stressors + maintaining factors. And as @Learner1 often says: it's unlikely that there will be the magic bullet at any time.
Rufous McKinney
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this is a huge frustrating aspect, understandable, yet not.
It seems like we need an AI, Machine learning type thing in which all the research to date gets plugged in and some computer brain tries to make sense out of it. It seems far too complex for any one brain to solve.
If it helps us feel less worse, we may simply not care- Why does it work.
I wonder who is working on long haul related topics. Articles about cures for long haul are now behind paywalls. Wonder what they say?
dylemmaz
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well that’s just depressing. i have recently been more hopeful every day, with long covid research and funding. BC 007 anyone? guess i shouldn’t be. probably deluding myself out of desperation
dylemmaz
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nancy klimas is doing this sort of thing!
Martin aka paused||M.E.
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Well, I have still hope for OMF and Ron's work!
I'm in contact with Berlin Cures. This company is the opposite of professional. A friend who supported the research of Prusty said fittingly “chip shop”.
But of course it's worth investigating this drug.
What you should know is they are trying to instrumentalize LC and ME/CFS patients to collect money for a study while they want to sell their company to big pharma. everyone can think of that what he*she wants!
@mariovitali has been doing this kind of work for a long time, and has even devised several software programmes to run material through. At this point, from my modest perspective, I have to conjecture that major ME researchers aren't accessing his work. Here and there, perhaps there are isolated individuals doing this, I don't know. I believe @joshua.leisk also has done a bit of this kind of work. AI is clearly going to be, and is already, in some parts of the world and in some medical areas, a major instrument of incalculable use. Lawyers have been using software like this for some time.
That would be great, there is just one thing. AI can work only on the given data, so any data bias could easily confuse it. But still, a lot of people and researchers are trying many, many things, so sooner or later there should be some answer.
bensmith
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I have the same feelings and am in a similar position. I seem to be at a stand still with treatment at the moment. I hope they just throw things at the wall and see what sticks like y’all mentioned. It’s our only realistic hope, this situation is far to complex to resolve. I think the world will become unstable and break down before we could truly understand this disease, at our present funding and desire. which always leads me back to a grim choice that i am constantly thinking of. It’s just a ghastly fate, this disease.
Martin aka paused||M.E.
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Josh plans to do it. But it's way more complicated that one might think. One little mistake and you can forget the whole program.
Yes, it is tricky. But the key is that the AI specialist can be even more enhanced when working with ME researchers or doctors. For some reason the ME researchers have been very slow to use AI. @mariovitali has 20 years of AI experience and a good deal of it dedicated to ME. I am puzzled as to why OMF or any of their researchers have not invited him to present his research. I guess people's habits are slow to change, and that applies to research as well. I don't know. But using AI would speed things up enormously, and would filter out a good deal of unnecessary stuff.
Rufous McKinney
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I'm hoping OMF Sweden and more data metabolic data from long haulers: Might break open something!
nerd
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Machine learning was one of my focus points at university and most of my scientific work focused on it. The current generation of machine learning algorithms is too rudimentary for this purpose. You have to know what you're looking for in order to use it. It's questionable if current machine learning algorithms really deserve the term "Artificial Intelligence", even when they utilize neural networks.
In order for an algorithm to figure out the etiology of the disease, we need a complete and accurate gene- and metabolitism-based simulation of the body that is also capable of simulating cells and molecular Interactions. It's basically a complete simulation of what happens in an organ or organism. At the moment, we can simulate these things separately, with use-case specific models. We can even simulate atoms accurately, but not in such a quantity and scale. Neural nets could theoretically combine all of this into a single multi-purposed algorithm. It would be a huge project, but I think it is realizable in this decade.
Pharmaceutical companies might not be interested though, because it allows you to predict outcomes and safety in any use case with any repurposed drug. You could create a disease simulation and run the whole database of drugs and supplements on it, also in combination, and the result might be far more reliable than what you have in in vivo animal models today, thereby possibly taking the RCT polypoly away from big pharma. Imagine you could simulate the whole RCT with zero confounding and perfect representation of all genotypes and phenotypes. Better accuracy of RCT outcome prediction allows small biotech startups to get the RCT funding from investors without big pharma participation. It would be a safe investment and a novel market.
In order for an algorithm to figure out the etiology of the disease, we need a complete and accurate gene- and metabolitism-based simulation of the body that is also capable of simulating cells and molecular Interactions. It's basically a complete simulation of what happens in an organ or organism. At the moment, we can simulate these things separately, with use-case specific models. We can even simulate atoms accurately, but not in such a quantity and scale. Neural nets could theoretically combine all of this into a single multi-purposed algorithm. It would be a huge project, but I think it is realizable in this decade.
Pharmaceutical companies might not be interested though, because it allows you to predict outcomes and safety in any use case with any repurposed drug. You could create a disease simulation and run the whole database of drugs and supplements on it, also in combination, and the result might be far more reliable than what you have in in vivo animal models today, thereby possibly taking the RCT polypoly away from big pharma. Imagine you could simulate the whole RCT with zero confounding and perfect representation of all genotypes and phenotypes. Better accuracy of RCT outcome prediction allows small biotech startups to get the RCT funding from investors without big pharma participation. It would be a safe investment and a novel market.
I don't think this will be possible very soon, human organism is too complex to map, this algorithm would need to have information about literally everything, missing one thing would bring a totally different outcome. And now think gut microbiome, we've just recently started looking into that, every bacteria has different properties, it works differently depending on environment, other bacteria, what it is fed, there is also quorum sensing, we would need to know first this whole bacteria and there are hundreds of them and combinations of their interactions, they can also change their actions and evolve. That's just human body, and what is the exact influence of all things we encounter every day, pesticides, herbicides, pollution, everything we eat, inhale, touch, stress factors, exposure to the sun and light, this is never ending.
But I think algorithm could get a pretty good guess based on collected research data, at least it would be as good as the data.
This is another important factor. What if AI would say for example something like, most drugs are harmful and the body needs just more healthy food and vitamins? It would kill the whole industry and doctors would not be needed if AI could properly diagnose and choose the right treatment before illness even started. That's why I think they will not work on it unless they could control it and benefit from it, but this would mean that such algorithm is not focused on health but instead on making profits for medical and pharmaceutical business, so no use to us, we've got that already in many cases.
nerd
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Most of it already exists in one way or another, but not in an integrated way.
Quantum chips can provide the leap to process all of this in a swift. We're currently at the crossing point of where quantum chips can be used in real use cases. Still with a lot of limitations, but let's wait a few more years and this might change the whole processing landscape.
You are correct in that it will be impossible to stimulate this. It would be bias in the training data sets. This means that the simulations can only represent patients with average microbiomes or with colonies for which sufficient real world data can be mapped.
This is already happening. Even current "AI" tools can provide more reliable predictions of diagnoses, e.g. in the field of radiology and skin cancer where existing algorithms could be easily adopted. When machine learning is adopted more and more to use cases that have formerly been performed by physicians, they will lose their importance. But they are lucky in that the system is still structured in a way that always requires the confirmation by a physician and most tools aren't even available to patients even though they are simple to use. But you can't overburden a patient with a potential diagnose, right? So the doctor just reads the screen and tells you the same, just as they read the pathology reports from laboratories and tell you what's in it. Then, everything is in order.
There's one hope though, that is the Indian market. India is overpopulated and can't copy the western medical system without optimizations. They currently use a lot of telemedicine. But I'm sure they will jump on the AI market once it's ready. China will probably have it as well, but the west doesn't bother what China does domestically. It might be different for India. They can lead the way. They can run studies and show how effective and safe the system is.
Maybe I'm too optimistic. Maybe we'll not even be done with COVID-19 until then. I don't know. The influence of big pharma can not be underestimated.