When it comes to “red pills,” I’m about to tell you one of the biggest ones you can take. Ones this big are hard to take all at once. I ask you to suspend disbelief. Before you finish reading, I will give you a simple method to prove me right or prove me wrong.
This is written in stream of consciousness style. It could be a lot better if I had time.
I’m trying to write this in a simple manner with simple terms because it will get into statistics. I want to use common sense language. The implications about what I will tell you are profound. At times I’ll digress into stats, but I’ll also summarize the important implications along the way.
I see a lot of people on social media today waking up to the realization that the entire health care industry is malfunctioning. It’s worse than you think. You haven’t figured out the primary cause and method yet. It was not designed to create health.
I want you to know that this is a positive article, because I’m giving you the solution. A pure, mathematical solution to the exact problem. I think of it like a silver bullet that will kill the pharmaceutical industry. I think of it like that shot Luke Skywalker took at the death star. As bad as things are, you can fix them way faster than you think, because I’m destroying something.
It was Lou Holtz, of all people, former head football coach of Notre Dame and South Carolina, that I learned this lesson of wisdom from. He was talking about how hard it was to build things. He said he spoke with a demolition engineer. The man told him he could quickly destroy anything man had taken a long time to build. Holtz’s point was that anyone could tear something down, but only great men build things. Well in this case, it’s the opposite.
My realization that the entire health care industry was corrupt came in a flash, in one thought, and I want to share that moment with you. It’s anecdotal but I’m warming up to the solution.
I had a friend who told me that he had cancer. I had lost my mother to cancer in 2009, despite having the best doctors around, who told us that chemotherapy was the best option the health care industry had for her form of cancer, even though odds were very low that it would work. It didn’t and I came away thinking that there had to be a better option than chemotherapy. When my friend told me he had cancer, I began to research herbal cures.
The first one I researched was D-Limonene, a compound found in many citrus fruits (Limonene / lemon).
I found a study where researchers were testing D-Limonene against breast cancer. I’m about to make an important point. The researchers excluded a patient from the study because she was too young. Breast cancer has a great deal to do with the hormone estrogen. Her estrogen levels were substantially different from the rest of the group. In the FDA’s clinical trials model, you have to isolate one dependent variable (cancer) against one independent variable (estrogen). The clinical models are a simple technique where you vary one thing, and see what happens to the other, holding everything else constant. And this is where it hit me. This whole model is bad.
When I saw that they had excluded her for age, I thought to myself, why not just add another column? Let me try to explain this.
I used to run something called regressions in college. Regressions use one dependent variable and multiple independent variables.
In this simplified model, the regression should be Delta (change in) Cancer = Quantity D-Limonene + Quantity Estrogen. Then you just regress it. Look up multiple linear regression to understand how it works.
When I was educated in college on how to use regressions, we made models. Our models had to use “theory.” Gross Domestic Product is a result of population, employment rate, capital stock, interest rates, etc. In the model, you put everything that is relevant, and nothing that isn’t. This college, Auburn, wanted to be better than “data mining.” Anybody could get statistics program and run it. We had to know how it worked and how to use it properly.
Here’s the point. You never leave out a good explanatory variable. Never. Why would you, even in common sense? You don’t exclude a good explanatory variable like Estrogen, you include it. The only reason that variable would be excluded in clinical trials is that they don’t know how to sort the data. In multiple linear regression, the more good independent variables you have in your model, the more variance you explain in your dependent variable. When your model is complete according to theory, the values you get for each independent variable will be more accurate. When you account for the effects of estrogen, the value of your data on D-Limonene is more accurate.
Let me give an example. When I was in college, I had a woman professor from Chicago University and I took her course labor economics. The course came around to the topic of wage gender discrimination. She said there are many factors that influence wages. Men would argue that they made higher wages because they had more education, had more time on the job, had a higher aptitude, etc. She said that when you accounted for all those factors, gender discrimination still existed. A simple model would be Yearly Income = Years Education + Years Experience + IQ + Gender. And you can think of some more variables. But if you run a complete model, and the coefficient for Gender comes back -$10,000/Year for females, that’s wage discrimination. You can’t say it was the other factors because they are accounted for. And you have to account for everything to make that claim.
Here’s another point. You don’t need to set up a clinical trial with hypothetical managers hiring and granting raises to hypothetical employees to determine wage factors. The data exists in life. You just have to know how to gather the data and sort it.
At this point I’m thinking, why are these researchers using such an inferior model? I know the researchers are playing by the rules of the industry and the FDA, but why is the whole industry using an inferior model?
Just from a practical perspective, the explanatory power of regression models are light years greater than the clinical trials model. If you gave me a set of data from the population with all the ingredients of the items they consumed, proximity to pollution nuisances, medical data, demographic data, and so on, I could just run regressions on diseases and figure out everything in that data that had a positive and negative correlation with disease. I could figure out more in a brief amount of time with an autoregression program than doctors could figure out in thousands of years. Guess what. Google has all that. And if you think a person with a background in economics and statistics isn’t the right one to help you understand what’s wrong with the world today, I’m going to get to something important on that.
And at this point I’m thinking, there’s no way this model wouldn’t have been used by pharma and government agencies. It’s just too strong. Too obviously superior. There was no way I was the first one in the world to have this idea, and I couldn’t think of any way I was wrong either.
So I began to search for use and discussion of multiple linear regression in the history of the healthcare industry. There was very little. But I finally traced it back to the 1931 at a place I had never heard of – the London School for Hygiene and Tropical Medicine. (LSHTM). In 1931, at LSHTM, Hilda Woods and William Russel published Introduction to Medical Statistics.
This is a rare out of print book, as important as it is. I found enough information on it to understand that they knew the implications of running regressions to understand cures for disease, and it was probably a course book at LSHTM. I can’t find the 1931 text that I saw once. This one is from 1936:
At this point I was pretty sure that science had been running regressions on all sorts of things that broke down the factors that cause disease, death, health, etc. Mainly death. They were keeping the information to themselves and not using it for good purposes either.
In 1937, Austin Bradford Hill followed up with Principles of Medical Statistics. Hill was employed at LSHTM since 1927. Hill was running mortality regressions. Death = A,B,C, etc. You want evidence of what people call “population control.” They identified the factors mathematically.
Hill’s background was economics. Same as mine. So if you’re wondering why you should trust someone with a background in economics and stats to tell you how to solve the problem of the global disease scam to create fascism, realize that it was people with the exact same background who created the problem in the first place.
Later on I saw where Tedros Adhanom Ghebreyesus of the World Health Organization was an “Honorary Fellow” of LSHTM. Bill Gates is associated with LSHTM. Now they have an intelligence center to collect data on the spread of disease. Now they want a Pandemic Treaty.
Their model, dating way back, was to create disease, profit from partial cures that cause more disease, and use the profits to buy governments and rule the world. A sort of fascist death profit model, that included the other evils of mankind that they controlled, such as pollution, war, etc. I’m just focusing on the industry of medicine and how to expose their scam in this article.
This is a tangent: I don’t know when they first broke fascism to a mathematical formula, but they had certainly done so in the 1930s. Later “artificial intelligence” came to implement the fascist across the globe, capable of scattering and coordinating actions in ways that are extremely difficult to see and prove are coordinated. The left hand doesn’t know what the right hand is doing, but the program runs it all. It’s what I’d do, anyway, if I was designing such a program.
The demand for world government is based on misery. War caused misery. Create the UN to stop war. Pandemics cause misery. Create the WHO to stop pandemics. Climate change is another good one. Anything that you can’t control about another country, China for instance, you have to have a world government to have a voice in their regulation. The worse it gets, the more people will submit to their world government goals. It wouldn’t take much for me to write a simple formula. It wouldn’t take much for them either. Then they just need to scatter their program and compartmentalize the information. I don’t even want to get started on the data collection efforts of all information in the world, how advanced artificial intelligence is, and the processing power of quantum computers. Let’s just say I have a good idea about how they do it. I’ve laid out the basics here anyway. The only difference today is that they can put their program on autopilot, and you can’t prove anyone is directly involved in it. We can and should be proving every related crime that we can to prove the larger narrative, but that’s another series of long tangents.
Back to the healthcare industry. I could write lengthy articles with sources on every simple declarative statement I’m making. I can back it all up or I wouldn’t say it. Those articles need to be written one day. Books really. Point is, if it’s a “theory,” I’ll tell you it’s a theory. Otherwise if I state something as a fact, it’s proven to myself beyond a reasonable doubt and I consider it a fact that I can defend, it just takes too much time and effort. I really need a great deal of funding to subcontract writers and researchers for these topics. I have too many topics that need to be exposed in a lifetime by myself. The model I am giving you here is my best shot at defeating the enemy, which will expose their plans to create fascism by the healthcare industry. Of course they work towards that goal in every industry. Back to the point.
One of the first questions I had was why are we using the clinical testing model in the US? How did we get to that point?
The history of the mission creep and growth of the FDA is how their regulatory power grew as a result of “disasters” such as thalidomide and sulfanalimide. Both of these disasters were intentional. I’ve written a lengthy article already on sulfanalimide. I could write one on thalidomide. These substances were intentionally given to the public to highlight an area of free market that was not regulated yet. They increased their regulatory power by releasing toxins. It’s the same thing that is going on with the WHO’s “Pandemic Treaty” today thanks to a manmade disaster, but on a smaller scale.
These disasters led to the FDA’s clinical trials model. They blocked all medicines known to mankind from “approved” use (hospitals) unless they went through this trial. They effectively blocked all herbal medicine. This created a monopoly over medicine for the pharmaceutical industry. Pharma used chemicals, chemotherapy, for their medicine instead of herbs. Chemicals generally don’t work as well, don’t cure, and have more bad side effects than herbs.
Cannabis, for instance, was banned for everything including medicine. Congress was able to say it had no accepted medical use thanks to the clinical trials model. This despite it being one of the oldest and safest forms of medicine known to mankind. Cannabis was one of the earliest crops cultivated by mankind. Early man appears to have spread the plant long before homo sapiens even emerged as a species. We literally evolved with this plant, and all other plants. We wouldn’t be here today without plants.
This isn’t just some “stoner” rant. After Cannabis was banned for all use, including medical, and medical research was banned, the NIH set up a grow in Mississippi. Not long after, Mississippi published a study that said that epifriedelanol, a compound in the roots, had anti-tumor properties. I read a summary of the study. I can’t even get the text of the original study. I asked someone in grad school to use their access to give me the study. She said she couldn’t even access it through her university.
Get this. The U of Mississippi was tasked to break down every single tiny compound in cannabis and send it all to pharmaceutical companies. This while it was banned for use and research in the public. Schedule 1. Total ban. It still is on the federal level. But pharma got it all from the government.
Later Raphael Mecholaum said the endocannibinoid system was the key to understanding all disease in humans.
Cannabis was banned because the interests who control pharma and government wanted to increase disease in the population. They didn’t want the competition either. It had become a competitor for their opioids. This cartel created a monopoly on opioids. They had international opioid conventions. When you read the actual language of the bills in Congress that banned opioids, the first ones banned “non-medical” opioids. In other words if it had their label on it, they let it pass through customs. If not, they confiscated it. The CIA later did this with NAFTA to create border checkpoints to let drugs through for cartels who paid their toll. Harry Anslinger was a drug trafficker. On and on. Once you break the code on this, it’s broken, I’m telling you. It’s just a matter of how much history you want to analyze after that. That scene in Matrix where the guy says he doesn’t see code anymore, only redhead, brunette… it’s like that. Monopoly through regulation.
They divided production between nations in their opioid conventions. That type thing, command and control of an economy, an international economy in this case, was along the exact same lines as Communism’s command and control. When they got around to banning opium, they had heroin ready to go, which was a “medicine” they did not ban. So on and so forth. All their “medical” derivates of opium still kill a staggering number of people in the world. All approved by their clinical trial model. Clinical trials are just a barrier to entry that the cartels control. Cannabis has never killed anyone, is one of the oldest forms of medicine in the world, is the key to understanding disease, and it’s on Schedule 1.
Again, the model is create disease, addiction, to profit from the sale of harmful medicine, to buy government, so forth, monopolize industry, create barriers to competition. The language in the Flexner Report is pure Marxism. Not coincidentally 7 years before the Bolshevik Revolution in Russia, both financed by the same Globalists.
That which is good is blocked. That which is bad is controlled by monopoly. Clinical trials are a barrier to entry. All herbs are their enemy, it’s just that cannabis was enemy #1.
Cannabis is one well-known example. I’m about to tell you something most people don’t know.
If you want to understand what natural compounds cure and cause disease, let me tell you how a statistician would approach it. Collect every natural substance in the world and test it against all diseases. Sounds like a huge task. I’m sure it was.
That’s what the National Cancer Institute did for cancer, at least. They collected every plant in the world and tested them against cancer. It took them about 30 years. They found over 3.000 plants with anti-cancer properties. Want a list? There isn’t one.
There’s nothing much left of this study. One of the main scientists used to write some articles for a journal on the study. Someone later bound those articles into a book. The publisher was a front company. A leak of what was left, and you can’t even trace the publisher. That book only exists in a few places.
They chose one plant for development- the Yew Tree. They called their product Taxol. That’s right, Taxol from the Yew Tree. They did it for the antisemitic language. It is a horrible and ineffective drug that was given to my mother and a good motivation for my hate of them today.
You and I wouldn’t even know the 3,000 figure if it wasn’t for the Office of Technological Assessment. This was a research arm of Congress, that kept tabs on everything the Executive Branch (CIA) was doing. The OTA made their own reports on “technology” for Congress. They reported the over 3,000 figure. They reported that the government was researching how disease created from pollutants were passed on through genes to future generations. They were probably the ones who leaked that the CIA had spliced two viruses together to create HIV.
Newt Gingrich ran on a platform to downsize government and all that and they defunded this one modestly-funded research arm of Congress that kept tabs on the Executive Branch. Now Congress only knows what the Executive Branch deems to tell them. They have forsaken their role of Legislation. They take turns bitching at the side in power who is responsible for however the CIA is attacking the United States and the free world. “Vote for us,” again. We didn’t do the last one.
“To know all that’s knowable,” and “total information awareness,” these are the goals of CIA. They get it all. They do not use their information for good purposes. They by and large don’t even answer to the President. All their employees are “compartmentalized.” They send all their intelligence to the shadow global government, just like most of the rest of the intelligence communities of the world do. And then the Globalists use this information against us. That’s how it works.
All of these things, and a great deal more, from big pictures to countless smaller ones, lead me to the conclusion that the government has already conducted the type of studies that I would like to do. They did it on everything they thought might cause or cure a disease. They didn’t use a clinical trials model to do it on everything.
They would have used what is called “data mining.” Let me explain the difference between data mining and accepted science. In the first lecture I heard on Econometrics, the Professor told us we had to use theory at this University. Like in the example I gave on wage discrimination, you have to run a model that makes sense, not just put a bunch of variables in it and see what the results were and expect anyone to accept those conclusions.
When you think about it, this is similar to the clinical trials model. Someone has to come up with some theory on how a specific cellular mechanism affects a disease, then get funding for a study, then test it. You can’t just test everything and see what happens. Not practically, even if you could get approval. Not one small compound at a time with the length of time and the amount of money that is required. It would take forever. The clinical testing model drastically lowers the amount of data that the public has on the causes and cures of disease.
Of course the shadow globalist government doesn’t play by those rules, and I don’t intend to either.
Their rules are: You figure out a theory, get funding for it, and see if it works. My plan is the same as their secret plan: Run tests, see what works, and let someone else tell me the medical theory of why it works. Data mine. Break the rules.
Herbs are still legal in the United States. There is something called a “right to try” in the United States. You just have to know how to organize and sort the data from volunteers using herbs. There’s not a damned thing the FDA can do about it, even though they won’t “approve” it for use.
When you take an herb, it has dozens of compound in it. You need to have a chemical analysis of the herb beforehand. That’s the big difference. It creates the method to break the results down. Volunteers should take the herb, record the change in disease. Someone else can compile the data and run regressions on the compounds against the change in disease. Everyone knows that herbs cure disease. The establishment just says that the use of them is unscientific. They say you don’t know exactly what cured the disease in the herb, or how much you should take. Well, this is the scientific way to determine all that. And there’s not much to it other than getting a chemical analysis beforehand and recording results.
Go back a few decades and say that you wanted to figure out how cannabis got you “high.” You broke the compound down into the hundreds of cannabinoids and terpenes. People smoked it and recorded results of how “high” they were on a scale of 1-10. Run regression of the scale 1-10 against the individual compounds. Guess what, it’s THC. You wouldn’t have to be as brilliant as Raphael Mechoulam to figure it out. He could tell you how THC worked later. This is how you can cure cancer and other diseases.
Here’s a technical point. You would need variance in the cannabis samples. This one makes me high, this one not so much. Then the regression can sort the THC. There should be enough variance in “lots” of herbs. By lots I mean individual groups. Composition should vary by regions. You can also test close relatives of the same plant. Both artemisia annua and artemisia afra should be tested, for instance.
Everybody knows that there are plants that help with various forms of cancer. You don’t know exactly what. The government does. Break down the chemical constituents, record results in change of cancer, run the regression.
One more big step. Selectively breed that herb for the compound or compounds you want, the same way people started doing it with cannabis. Look at how that plant changed over time. Get funding to build greenhouses or do outdoor grows to selectively breed the plants for the compounds you want. This will destroy Pharma, as long as we highlight that government and pharma knew about these things the whole time.
It’s important for studies of this nature to be a “proof of concept.” If I say this compound cures cancer, and the information was leaked from a government data mining project, then you test it, and it’s right, that proves what the government did. It doesn’t prove that I’m a doctor. That compound to me is epifriedelanol.
Actually there are two studies I would like to do. One is artemisia against SARS2. The other is epifriedelanol against some forms of cancer in women.
I want to do artemisia because it is so obvious. I want to do epifriedelanol because it is so obscure.
On artemisia, I think it’s fair to say that artemisia should so obviously been chosen for testing by the establishment against COVID that it clearly demonstrates that the establishment intended to suppress an effective cure for a pandemic that has killed millions.
On epifriedelanol, I think I can demonstrate that the only way I would have ever heard of this compound is that it was leaked from government data mining studies.
First, I’ll lay out some facts on artemisia, then I’ll do epifriedelanol.
Artemisia is one of the oldest and most effective forms of antiviral medication known to mankind. The Chinese were writing about it in their ancient medical texts long before Christ. During the Vietnam War, the Chinese set about to find an effective antimalarial drug to aid the VC. They searched through all their ancient texts and tested everything that they though was promising against the malaria virus. Artemisia was the best one. They isolated artemisinin from it. It came to replace chloroquine and hydroxychloroquine, two compounds derived from quinine, in turn derived from cinchona tree bark, that were the most widely used antimalarial drugs. There are multiple antiviral compounds in artemisia, however. Two are listed as essential drugs by the World Health Organization. That’s right, two. This brings up a very important point. Why the hell break the plant up into separate compounds?
There is something called an “entourage effect.” This term was coined for the combination effect of CBD and THC in cannabis. It’s just not the same thing to take them separately. Anyone who smokes cannabis can tell you that. There’s another version of an entourage effect in the news lately, that of zinc ionophores and zinc. Some common zinc ionophores are ivermectin, quercetin, hydroxychloroquine, and EGCG.
As best described by Dr. Zelenko, a zinc ionophore doesn’t work without the zinc. And the zinc doesn’t work without the zinc ionophore. He describes them as a gun and a bullet, one pretty much useless without the other.
In the clinical testing model, what are the odds that you are going to pick out two compounds in the right combination? You square the odds. This is another way that clinical testing model limits information. You can find these entourage effects when you test the whole plant. In tests on mice, the whole leaf of artemisia was found to be 40 times more effective against malaria than artemisinin alone. And why not? The plant has at least two great antiviral compounds in it, and probably several more. The WHO led the effort to break the plant up into chemical compounds. They actually recommended that derivatives of artemisia be used only in combination with other drugs, but never two drugs from the same plant. Chemotherapy is a criminal scam.
People in the hardest-hit areas of the world for malaria could grow their cures, if they knew how to selectively breed artemisia. Bill Gates won’t fund that. It wouldn’t take much.
How many zinc ionophores and THC/CBD combinations are out there waiting to be identified? The odds of picking two together are very low squared. The odds of finding two together in a regression of herbs that are known to work against a disease are extremely high. When a scientist accidentally finds something they weren’t looking for, they call it serendipity. The regression data mining of herbs is serendipity on tap.
Back to SARS2/Artemisia. Yeah, it was tested on SARS2. It was tested on SARS1. The Chinese, once again, in 2004, tested every herb that they thought might be effective against SARS1. Artemisia was the second most effective this time. This was in vitro testing. In other words it killed the stuff in a petri dish.
Artemisia was tested against SARS2 in the United States. It worked. No publicity on that of course. They used the whole leaf, in vivo, humans. They debated what compound worked. I say break down the leaf into compounds and run regressions and see what worked, including any entourage effects, instead of speculating about one compound or another.
Test artemisia with zinc too. Run it with multivitamins. Here’s an important point: you can intentionally include diet and other factors that might influence results. You think time spent listening to music could influence the results? Add a column. The more you include and identify, the more you know. (There are rows and columns in a spreadsheet, with each row being a patient).
And another red pill tangent. Organizations like google already have information on medical data, demographics, and everything you consume. They have the data to run regressions on everything that is causing or impeding disease. The world is their lab.
So with the positive results of testing of artemisia against both SARS1 and SARS2 already, this will almost certainly work, and we’ll know why it worked.
Now on to epifriedelanol. As I said previously, the University of Mississippi published a study that said epifriedelanol had anti-tumor properties, I just can’t get access to the actual study.
Leslie Taylor wrote “Then in an 1976 plant screening program by the National Cancer Institute, an alcohol and water extract of the leaves was documented with cytotoxicity against cancer cells at very low dosages.” This is why I say that epifriedelanol is a “leak” from this cancer data mining program. I can’t quote the government on that fact. The whole study is gone. 30 years worth, over 3,000 plant with anti-cancer properties, gone. But you have these reports of epifriedelanol that are based on that program. I say test it and find out if it works. If it does, it won’t be a coincidence.
The plant Taylor was referring to was maytenus ilicifolia, a common small tree in South America. It has epifriedelanol in it. This is the plant that I think is best for testing, even though I’d like to do cannabis roots as well. Really any plants with epifriedelanol. They have been used against cancer all over the world wherever they are found. Maytenus ilicifolia has the most successful track record. It had been used in South America to treat cancers and also for women’s health issues. It is a “phytoestrogen,” meaning a compound in plants that behaves like estrogen in humans. The highest selling breast cancer drug in the world, Tamoxifen, was originally developed as birth control then retasked for breast cancer.
Maytenus ilicifolia also has “friedelins” in it. The term “epi” in epifriedelanol appears to be a reference to epifriedelanol being the parent compound of all the other friedelins. This is why you want to run tests and regressions of the whole leaf. It may only work with a a combination of compounds. The leaf works, we know that much. There’s good reason to test the root bark too. You get the picture. Just pick good candidates to test and use regression.
This study will definitely need experts in women’s cancer to be involved. It seems to me like the best candidates for testing are “estrogen-negative” cancers. Some cancers could get worse. Taylor wrote that people with estrogen-positive cancers should not use this plant.
And here’s another “for instance” on the concept. Recently someone on twitter posted an old study on how native Americans used purple pitcher root to treat smallpox. It may work on “monkeypox.” So if that starts going around, a regression of that plant might be in order. Some of these plants are going to be endangered though, and it would be ethical to make sure that natural habitat is protected. I’ve seen plants like these growing in Florida. You just make sure that the land has enough standing water that isn’t drained, and they grow like crazy. Anyway, think ahead on that.
As for the actual testing model, I call it “double see,” as opposed to double blind. You don’t need a control group to compare results to. The regression takes into account all those variables that the clinical trials model cannot process mathematically, that they claim “confound” their results. Everyone participating, volunteers and administrators, need to have the same set of information. Everyone has to know what the medical theory is. Test results need to become pubic instantly. If something is going wrong, stop it. If Plant A is working far better than Plant B, obviously you want to switch. Etc. I’m not playing by their games. I’m not trying to publish a study. I’m trying to help people understand how to cure disease, and then watch them do it.
Frederick Gauss said that multiple linear regression was his gift to the world. It’s a shame what’s been done with it.
People can participate in these types of studies at home. They can just fill out something with the data. I look at it like this. Take the herb and do a complete chemical analysis by lot. Every lot has a lot number that has the chemical constituents. (You also need to test for toxins in herbs, by the way. You can’t take for granted that they don’t have pesticides or other toxins from the soil where they came from). And some toxins are natural, of course.
Anyway, complete chemical analysis. The big natural supplement companies could do this voluntarily. I would not try to force regulations of them, even though I am concerned that some anti-herb forces may be willing to sabotage the industry if they don’t start doing testing. Some probably already do. Medicine has been sabotages several times already with things like sulfanilamide and thalidomide. It wouldn’t take much to do it again with herbs to bring them under the FDA. Think ahead on this one too.
So ideally I’m talking about quantifying and organizing the herbal industry in a manner that will expose exactly how the herbs work against specific diseases. Then the industry can begin selectively breeding the plants for medicine. That will destroy chemotherapy and the pharmaceutical industry.
Think of what will happen when all the data sets converge from volunteers recording results. You’ll start to know as much as the globalists about medicine, and what they did.
There’s a lot more I can add to this but I think it’s enough for the reader to understand the significance of this type of analysis of herbal cures for disease.
I fully expect people to be skeptical and ask me questions. I wouldn’t have it any other way. Even if you agree, play devil’s advocate and make me defend it. I would really like for people to understand this.