Chapter 7
The Madhouse of Science (2)
Why are scientists such prissy queens when they come across a bit of mad anarchic humour? It appears that when a mere second of the disorder of real life appears, they all scatter like pantomime dames spilling their baskets of apples all over the place. Quelle calibre! Quelle sophistication! On a certain List recently, a fruit and nut appeared and started saying silly things. Lo, a Nobel Prize winner (and a few other lesser mortals) stamped their tiny feet and minced off the stage, petty coats all a-flutter. The lace curtains twitched, and the corporate conformist dovecotes were all a-tremble.
When they have had a bit more experience of life itself outside the lab and the lecture room, they will realise that they don't have any chance of getting near the heart of the human picture.
Meantime, with their pieces of 17th century clockwork and their bits of maths they strut around the universe as if they were all the Lords of Creation. The social-scientific applications of their "disciplines" in medicine, psychiatry and weaponry alone will be the death of all of us, environmentally and individually. Here are just a couple of their “scientific” catastrophes.
Best of luck!
Colin
(1) Vaccines, Autism and Gulf War Syndrome
From Dr. Betty Martini, D.Hum.
Bettym19@mindspring.com
(2) Why Most Published Research Findings Are False
John P. A. Ioannidis
(1)
This is an excellent report by Dr. Blaylock.
Dr. Blaylock is the author of:
'Excitotoxins: The Taste That Kills', 'Health & Nutrition Secrets To
Save Your Life' and 'Cancer Strategies' all of which are shown on
his site at
http://www.russellblaylockmd.com
An excitotoxin is a product that literally stimulates the neurons of the brain *to death causing permanent brain damage.* The two we think the most of are the aspartic acid in aspartame and MSG.
In Health & Nutrition Secrets, page 125, Dr. Blaylock says: "So, in the case of diet drinks in aluminum cans, the very toxic brain aluminum fluoride compound co-exists with multiple toxins found in aspartame, thus creating the most powerful government-approved toxic soup imaginable. With the strong association between aluminum, excitotoxins, aluminum fluoride complexes and Alzheimer's disease, it would be completely irresponsible to encourage people to consume this toxic mixture. Yet, this is done literally billions of times every year in advertising."
In the Gulf, these diet drinks sat on pallets as long as 8 weeks at a time, piled high, in 120 degree temperatures.
In the protest of the National Soft Drink Association at http://www.dorway.com, you will read that aspartame breaks down at 86 degrees. And it breaks down into a witches' brew of toxins including formaldehyde and diketopiperazine, a brain tumor agent that triggered brain tumors in original studies.
Aspartame destroys the immune system and central nervous system. It not only interacts with drugs because of damage to the mitochondria or life of the cell according to Dr. James Bowen, but as a chemical hypersensitization agent it interacts with vaccines and other toxins. In a lecture on www.dorway.com, Dr. Blaylock also says the reactions to aspartame are not allergic but *toxic*...
just like consuming arsenic and cyanide. Aspartame is an adjuvant and forms antigenic tissue, triggering immunologic attack. This is how it triggers lupus. The immune system turns against the victim's tissues.
In this article Dr. Blaylock brings out that it is accepted by most authorities that vaccines should not be given to individuals with impaired immunity for fear of triggering immune attacks on the central nervous system, such as encephalitis, nerve injuries (peripheral neuropathy, multiple sclerosis and allergic encephalomyelitis.
So, it's really a given with such a toxic soup the troops were exposed to, we truly are looking at diet drink catastrophe. This article makes it easy to understand Gulf War illness. A new study connects brain cancer in Gulf War vets to sarin, but researchers may not have known the aspartame laced pop they were drinking breaks down to a brain tumor agent. A USA Today article of July 26 states sarin has never been shown to cause cancer.
With all this information in mind, here is Dr. Blaylock's excellent article:
By Russell L. Blaylock
MD Neurosurgeon
Most have at least heard about the controversy surrounding possible harmful effects of some of the vaccines. What is less well known is that even greater dangers exist than are being conveyed to the general public. Much of this information is buried in highly technical scientific journals beyond the reach and understanding of the average person.
Of special concern is the relationship between vaccine policy, autism and the Gulf War Syndrome.
I shall use the Gulf War Syndrome as an example of a vaccine policy gone berserk, while including discussions of other dangers as well.
Most scientific observers have attributed the dramatic fall in infectious disease to the appearance of widespread vaccination, despite recent evidence that some of that credit is unjustified. For example, we know that improved public health measures and nutrition played a major role in the sudden decline in most of the infectious diseases plaguing mankind. Likewise, there is growing evidence that vaccinations are not providing the protection that they are touted to provide. For example, all cases of polio occurring after the introduction of the polio vaccine shave been traced to the vaccine itself. Similar findings have been shown for diphtheria.
Convinced that the victory over the major childhood infectious diseases was secondary to vaccine programs, public health officials began to add more diseases to the list, including haemophilus influenza type b, hepatitis B, measles, mumps and now even chickenpox. Present vaccination programs are exposing children to as many as 22 inoculations before attending school. More are being proposed. Driving much of this are the profits being made by vaccine manufacturers and a revolving door between medical university professors with financial interest in these companies and public health officials with the same interest.
Unfortunately, the science supporting the safety of unlimited vaccination of small children and adults does not exist. Most follow-up studies of vaccinated children last no more than two weeks after the vaccine is given, and many of the effects of vaccination on brain development are delayed much longer. In addition, most of these studies look only for blatantly obvious injuries and not subtle changes that can lead to serious future impairment.
There are a growing number of scientific studies that are demonstrating serious dangers in our present vaccine policy, including altered brain development, seizures and a loss of brain cell connections called synapses. These studies all point to over-vaccination as a real and present danger to our children, and in certain instances, to adults. Unfortunately, most pediatricians and family practitioners are completely unaware of these dangers. Most depend on their specialty societies, such as the American Academy of Pediatrics and the American Academy of Family Practice for answers concerning safety issues. Rarely do these physicians research these important topics themselves. For too often, those serving on vaccine boards within these specialty societies have a vested interest in the financial success of the companies involved, either as investments in stock or direct payments by the companies.
Much controversy and confusing data surrounds the cause of the Gulf War Syndrome (GWS), and despite numerous studies, little solid information as to the cause of the syndrome has appeared. Throughout the entire period since the first Gulf war the Pentagon has been reluctant to admit to a connection between this devastating syndrome (that has left tends of thousands of soldiers and their families chronically ill and many of these children deformed) and military policies on vaccination. Our soldiers were given approximately 17 vaccinations over a short period, despite manufacturer's warnings that many of the vaccines, were to be spaced over a year period. Several hypotheses have been proposed as to the cause of this syndrome, including neurotoxic and immunotoxic effects of pesticides, aspartame degradation products, chemical warfare agents released, toxins from spent uranium shells, combat stress and vaccines.
HOW SCIENTISTS THINK
It is characteristic of modern science to always look for one central cause of a problem rather than explore additive effects or even synergic toxicity of many agents.
Yet the science of toxin synergy is growing and finding some surprising effects caused by combing two or more weak toxins. For example, it is known that when two weakly toxic pesticides are used alone, neither cause Parkinson's syndrome in experimental animals, but when combined, they can cause full-blown Parkinsonism very rapidly. The same is true with fluoride. We know that both fluoride and aluminum individually are brain toxins, but when combined, as we see in fluoridated water, the mix constitutes an extremely powerful brain toxin, destroying numerous neurons in the part of the brain associated with memory and emotional functions.
It is rare that the government agencies test potentially toxic chemicals or even food additives together; instead, they are tested alone. As in the examples above, we are seeing more instances of combinations of chemicals causing devastating injury yet when used alone are either mildly toxic or even non-toxic. Few laymen realize that vaccines contain many chemical additives in addition to the infectious organism being targeted. These include aluminum, mercury, hydrolyzed proteins, monosodium glutamate, oils and many complex molecules known as immune adjuvants. Several of these (aluminum, mercury, hydrolyzed protein and MSG) are known to be directly toxic to the brain.
TOO MANY VACCINES OVER A SHORT PERIOD OF TIME
A considerable amount of research indicates that the Gulf War Syndrome, as well as autism, is triggered by combing too many vaccines over too short a period. This is compounded by numerous other toxic events, especially in the Gulf War veteran. This includes exposure to pesticides, aspartame breakdown products, combat stress, high intake of food-based excitotoxins, possible exposure to released nerve agents, as well as exposure to contaminated vaccines. For example, recent studies by Dr. Garth Nicolson, head of The Institute of Molecular Medicine, have disclosed a high incidence of contamination of the anthrax vaccine with mycoplasma organisms. In addition, he has shown a strong correlation between ALS (Lou Gehrig's disease) and this mycoplasmal infection. Recently, Pentagon officials reluctantly admitted to a 200% increased incidence of ALS in Gulf War veterans.
Many of the toxic exposures named in connection with the GWS have a common effect on the immune system. In most instances, we see impaired cellular immunity (NK cells, T-lymphocytes, etc.) It is accepted by most authorities that vaccines should not be given to individuals with impaired immunity for fear of triggering immune attacks on the central nervous system, such a encephalitis, nerve injuries (peripheral neuropathy), multiple sclerosis, and allergic encephalomyelitis. All of these are considered autoimmune disorders, during which the immune system attacks specific components of the brain and spinal cord by mistake. Recent studies have disclosed a completely new mechanism of injury, referred to as bystander injury.
BYSTANDER INJURY: A GRENADE IN A SHOPPING MALL
In the case of bystander injury, rather than the immune system directly attacking specific parts of the nervous system (molecular mimicry), that is mistaking a part of the nerve cell or neuron for a viral or mycoplasmal invader, the immune system is merely doing its job but in the process killing a lot of innocent bystanders, that is surround normal brain cells. It's sort of like throwing a grenade in a shopping mall that not only kills the enemy, but also kills anyone close by. This occurs because immune cells kill invaders by flooding them with a storm of free radicals. Free radicals are highly reactive particles that destroy everything they encounter, friend or foe. It is the immune cells that generate these free radicals in large numbers. Normally, an immune attack on viruses and other organisms occurs rapidly and is quickly terminated. This is why strong immunity is essential-it minimizes bystander injury. A weakened immune system initiates a smoldering attack that is prolonged; leaving surrounding normal cells and tissue soaked in destructive free radicals, but does not kill the invader.
These destructive free radicals initially accumulate locally, that is, at the site of the invasion of the organisms whereas, with prolonged immune activation, these free radicals can diffuse far out into the surrounding tissues and with time, can flood the entire body. For instance, in the case of a chronic illness, such as lupus, we see high levels of free radicals throughout the body. This is the cause of the widespread symptoms of the disease. The same is true for diabetes, chronic heart failure and rheumatoid arthritis.
Vaccinations, if too numerous and spaced too close together act like a chronic illness, flooding the entire body with free radicals. Even so, the highest concentration of these radicals is in the vicinity of the immune cells.
Bystander injury can also occur with autoimmune disorders, since the immune attack is so widespread, persistent and intense.
THE PROBLEM WITH VACCINES
When producing vaccines, scientists combine the intended organism, either killed or live, with chemicals that stimulate an immune reaction. These chemicals are called adjuvants. Squalene, one of the common culprits found in GWS veterans, is an immune adjuvant. Usually they add many such adjuvants together. When these adjuvants are injected into the tissues they remain for a long time, continuously stimulating the immune system. If your immune system is normal and healthy, complications are less frequent.
Yet, should you have a defective immune system, or even part of the immune system is defective, your risk of complications goes up considerably. This is because the immune system is made of many components that must act in a specific concerted manner to kill the invader while minimizing the damage to surrounding normal tissues.
One of the more common reasons for immune dysfunction is nutritional deficiency, even for single nutrients. For example, we know that vitamin E (natural E), selenium, zinc, vitamins C and flavonoids (from fruits and vegetables) are critical for normal immune function. These are common deficiencies, especially after middle age. Likewise, some people may have only a deficiency in selenium, which would also impair a cellular immunity. It is now known that even individual nutritional deficiencies can have devastating effects on the immune system. Likewise, certain nutrients in excess can significantly interfere with immune function as well, such as omega-6 fats, MSG, aspartame and sugar. Of particular concern are the omega-6 oils, which are metabolized in the body to produce a powerful immune suppressing substance (PGE2). Corn, safflower, sunflower, peanut and soybean oils are all omega-6 oils. The MREs (meals ready to eat) contain numerous immune suppressing nutrients.
When nutrition-based immune malfunction is combined with the immune toxicity of pesticides, herbicides, chemical warfare agents and stress even greater immune dysfunction occurs.
Numerous experimental studies have shown that when you over stimulate the immune system with immune adjuvants, as would occur when giving numerous vaccines, close together, enormous numbers of free radicals are generated, and because the immune activation takes place over such a long period of time, these free radicals begin to damage normal cells surrounding the sites of attack as well as throughout the body. In other words, it's like producing a chronic illness in a person.
The type of adjuvant also matters significantly. Oil based adjuvants, such as squalene and squalane are known to produce intense, unrelenting immune reactions that can last a lifetime. Recent studies have shown that all of the Gulf War veterans tested have had antibodies to squaelene, even those not serving in Iraq. The source of this squalene adjuvant has been determined to be from a secret, investigational vaccine for anthrax. One of the code names for this adjuvant is MF59.
Aluminum (as alum), also used as an adjuvant not only produces prolonged immune activation, but also travels along nerve tracks into the spinal cord and brain steam. Aluminum is known to produce significant destruction of brain cell connections and development of the same pathological features as Alzheimer's disease. When combined to fluoride, as occurs in vaccines as well as fluoridated drinking water, aluminum-fluoride complex causes a significant loss of brain cells in the hippocampus of the brain, the site of recent memory generation.
THE BRAIN'S SPECIAL IMMUNE SYSTEM
In the case of multiple vaccinations over a short period, something even worse happens - the adjuvants activate the nervous system's special immune cells called the microglia. Microglia cells are dispersed throughout the nervous system. Normally, they lay dormant, that is, asleep. When activated they can migrate throughout the brain, secreting very powerful toxins, free radical and immune related chemicals (cytokines).
These cells are very easy to activate. We know from many experiments that stimulating the body's immune system, as with vaccination, also activates the brain's immune system.
Under normal circumstances these microglia are activated for only short periods and then quickly shut their self off. With over-vaccination, these cells can remain active for very long periods creating considerable bystander damage. This is because they secrete toxic products that diffuse throughout the nervous system killing neurons, destroying synaptic connections and damaging the coverings of nerve fibers. There is growing evidence that prolonged microglial activation is the mechanisms of damage in numerous neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease, autism and amyotrophic lateral sclerosis (ALS).
It is interesting to note that Dr. Garth Nicolson, as well as others, have had significant success in treating the GWS with the prolonged use of antibiotics. A growing number of recent studies have shown that the very antibiotics being used shut down the microglial cells. It may be that the greater part of the beneficial effects of these antibiotics may be stopping the bystander damage by shutting off this type of cell, rather than by actually killing microorganisms. With residual or persistent infections, both effects are needed.
According to recent studies, it may not even be necessary that live organisms be present to cause this bystander damage. First, we know that prolonged immune stimulation by use of immune adjuvants alone can produce severe bystander damage in the nervous system. Likewise, even the persistence of viral components, that is viral debris, can trigger prolonged immune reactions leading to bystander damage. We see this in association with the dementia of HIV infection. One of the great puzzles of AIDS was how it could result in dementia when the brain's neurons were not infected. We now know that a protein particle is shed within the microglia and that this triggers chronic activation of the microglia cell.
One class of toxins released by these microglial cells includes excitotoxins. These powerful chemicals can excite brain cells to death and are thought to play a role in all forms of neurodegenerative diseases, brain trauma, strokes and meningitis. Common forms of excitotoxins include glutamate (as in monosodium glutamate-MSG), aspartate (as used in aspartame) and quinolinic acid (a metabolic product of serotonin breakdown).
When chronically activated, microglial cells pour out these excitotoxins in large amounts, destroying neurons, synapses and dendrites, that is, the connections between brain cells.
When these same excitotoxins are consumed in foods and drinks, even more damage is done. There is ample evidence that these food-based excitotoxins easily enter the brain. Most processed foods contain one or more excitotoxins, many in disguised forms.
We know that several things can activate microglia, including pesticides, MSG, viruses, mycoplasma, bacteria, stress, aluminum, mercury and immune adjuvants. These are all things the Gulf War veterans were exposed to both in theater as well as out. One of the enigmas has been the high incidence of similar symptoms experienced by family members of Gulf War veterans. Viral and mycoplasmal contaminants of these vaccines could spread to family members and initiate similar microglial activation. This is especially so with highly mutated viruses, which would be expected in the Gulf War veteran as discussed below.
Another possibility is that the immune dysfunction produced by the adjuvants would allow secondary infections to develop, such as mycoplasma and various viral pathogens.
HUMAN GENERATORS OF DEADLY VIRUSES
Several recent studies, which have not been shared with the public, have disclosed a rather frightening process. It has been found that when viruses are exposed to high free radical concentrations, even within a person, the viruses mutate, becoming much more virulent (deadly). Giving vaccines with live-attenuated viruses opens up a completely new danger that is not being discussed. To make a live virus, say for measles virus vaccines, scientists pass the virus through several cultures to reduce the disease causing ability of the virus, that is, its virulence. Scientists refer to this weakened virus as an attenuated virus. What we are not told is that often this virus remains in the body for a lifetime. In fact, a recent study found that live measles viruses were found in 20% of the brains of autopsied adults and in 45% of other organs. In other words, the virus has been hiding in their bodies for a lifetime.
Interestingly, these viruses were found to be highly mutated. The bottom line is that by giving live viruses, either intentionally or as a contaminant of vaccines, you expose that person to a very high risk of viral persistence, especially if they have an impaired immune system. Furthermore, that virus can mutate, becoming much more likely to produce a serious disease, even one not normally associated with that particular virus, such as colitis, encephalitis or a chronic degenerative brain disorder. The danger is not only to the person initially vaccinated, but to also those who come in contact with him or her. That is, they are acting as generators of deadly mutated viruses. It should be noted that the measles virus itself suppresses the immune system. Other viruses are known to have a similar effect.
Chronic illness is characterized by the presence of large numbers of free radicals throughout the body, as we have seen. These free radicals not only damage cells, but also cause any virus living in that person's body to mutate. Likewise, as we have seen, these new mutated viruses are much more likely to cause serious disease. In essence, people with chronic illnesses, because they generate a lot of free radicals, act as living viral mutation incubators. Poor nutrition greatly magnifies this process as well because such people generate significantly more free radicals. That was the great surprise of this study.
The recent panic over the A/Fujian strain of influenza is a case in point. People, especially mothers of small children, are rushing to have their loved ones vaccinated with the flu vaccines. Yet, doing so exposes them to serious dangers. For example, those receiving the killed older flu vaccine are receiving a significant dose of mercury (50ug).
This dose is even more toxic to small children because of their smaller body size.
Mercury has been shown to greatly magnify bystander injury in the brain. In the case of small children and babies, the immune reaction caused directly by the mercury is added to that of the other childhood vaccines, further aggravating bystander damage in their brain. Because the child's brain continues to develop and grow so rapidly up until the age of two years, the danger of bystander damage is much greater than in adults.
Due to the shortage of conventional vaccines, a nasal form of the vaccine has been offered. Those at greatest risk from the nasal vaccine are people with immune deficiencies - diabetics, those with autoimmune diseases, the elderly and the very young. This means they are more likely to suffer from viral persistence and resulting prolonged bystander injury as well as the generation of mutated viruses. The influenza virus has been suspect in triggering atherosclerosis (hardening of the arteries) and in neurological degeneration such as Parkinson's disease. Likewise, people with chronic illnesses, as we have seen, generate large numbers of free radicals all the time, increasing the likelihood that the virus will mutate to a more virulent strain. These are the high-risk groups the Public Health physicians and medical societies are encouraging to take the vaccines.
Second, many of these vaccines, including the anthrax and flu vaccines, contain thimerosal. Thimerosal is a preservative that is composed of 50% mercury. Mercury is a very unique poison, in that it incapacitates numerous enzymes in the cell including those used to neutralize free radicals. In addition, mercury, among all the metals tested, was the only one shown to block the removal of excess glutamate from the nervous system. This removal system is critical to nervous system health. By paralyzing the glutamate removal system, mercury triggers chronic excitotoxicity - that is chronic destruction of the nervous system.
In addition, mercury tends to accumulate in the microglial cell, causing it to become chronically active. This in turn results in the excretion of the two powerful excitotoxins from the microglial cell I mentioned before, called quinolinic acid and glutamate. It is the secretion of these two excitotoxins that causes the dementia associated with the HIV virus. In fact, the HIV virus coat proteins increase quinolinic acid concentrations in the spinal fluid of demented AIDS patients some 300 fold. Other persistent viruses, viral proteins and immune adjuvants have been shown to do the same thing, even in children.
Another recent study, conducted by the US Department of Agriculture, found that exposing mice to mercury prior to infection with the coxsackievirus B3, a virus that destroys the heart muscle, greatly increased the mortality, number of pathological injuries seen in the heart muscle and the number of viruses in the heart's muscle (viral titer) as compared to animals exposed to the virus alone.
Cosackievirus B3 induced cardiomyopathy and heart failure is the number one disease leading to heart transplants in this country. This study was important in that it demonstrated that exposure to mercury greatly increased the lethality of this virus and promoted the replication of the virus. Other studies have confirmed this finding using different viruses.
WHAT SHOULD YOU DO?
In essence, people should be cautious when considering vaccination for themselves and their families. Many parents are choosing to home school their children so that they can avoid the vaccination program. Families that choose to home school will not only benefit by avoiding the vaccines for their children, they will also be better able to build their children's natural immunity by providing a good nutritional program comprised of whole foods and some supplements. As we have seen, vaccine complications increase dramatically when given close together, especially as combined vaccines such as the DPaT and MMR and the other 17 vaccines given to military personnel. The anthrax vaccine alone is given as a six-dose primary series followed by a yearly booster. It has never been shown to be effective, which is especially so against weaponized anthrax. Because rapid deployment of such a large number of soldiers was required, Pentagon officials compressed the vaccine schedule over a little more than a week. This is extremely hazardous, leading to a tremendous increase in complications. If individuals do choose to engage in this risky activity, they should separate vaccinations by 6 months in children and perhaps longer in adults in order to give the immune system time to settle down. For the reasons I have discussed already above, it's my belief that most, if not all, vaccines need to be abandoned, since they have not been shown to be effective and there are reasonable and infinitely safer alternatives.
In addition it is vital to maintain nutritional health. Numerous studies have shown that nutritional depletion, even of one or two nutrients, dramatically increases vaccine complications. This is especially so for Vitamin A (as mixed carotenoids), vitamin E and vitamin D3. I would recommend a daily multivitamin/ mineral supplement without iron. In addition, I would recommend 1000 mg. of ascorbate (as magnesium ascorbate) three times a day between meals, vitamin E either as d-alpha-tocopheryl succinate or mixed tocopherols (natural vitamin E) 400 IU a day and DHA oil capsules-100 mg. three times a day. Dosages for children would have to be adjusted for weight and age.
Vitamin D3 is particularly important since it is known to regulate immune reactions and calm down those reactions that are overactive. New studies have shown that adults should be taking 1000 to 1500 IU of vitamin D3 instead of the previous 400 IU recommended by the government.
A number of experiments have shown that vitamin D3 can significantly reduce the neurological damage caused by multiple sclerosis-like experimental reactions (experimental allergic encephalomyelitis).
So, what are the alternatives to vaccinations? It is now accepted that immune function declines with age and that this is purely secondary to nutritional deficiency. This decline in immunity explains the 36,000 deaths often attributed to the flu each year among the elderly. Most of these deaths could be prevented simply by adding the nutrients known to repair and maximize immune function, which I have listed above. Additional immune activation can be achieved by the use of non-specific immune stimulation as with beta-1,3/1,6-glucan, a highly purified extract of yeast cell walls. To minimize bystander damage one takes this immune stimulant only during periods of high risk, such as flying on an airplane, and at the first sign of infection. Supplementation is terminated three days after the symptoms subside. This is infinitely safer than vaccination and, in my experience, more effective.
A recent study done at the Chemical and Biological Defense Section in Alberta, Canada demonstrated the remarkable effects of beta-1,3/1,6-glucan against anthrax infection. Using mice infected with anthrax, they found that prior immune stimulation using beta-1,3/1,6-glucan reduced mortality from 50% to 0%. In addition, it lowered anthrax bacterial counts in the lungs 4 to 8 fold and doubled the number of bacteria-free animals. In fact, they found the beta-glucan helped considerably even if given after the infection was established, increasing survival from 30% to 90%.
There are many ways to stimulate immunity safely using nutritional methods. In addition, non-specific nutritional immune restoration using beta-1,3/1,6-glucan can be used in high-risk individuals that are excluded from vaccination, such as those with serious heart diseases and neurological diseases. In addition, beta-1,3/1,6-glucan has been shown to protect the bone marrow from radiation damage and to lower cholesterol.
It is important to avoid omega-6 oils, such as corn, safflower,
sunflower, peanut, soybean and Canola oils. The omega-6 oils are powerful immune suppressants. Avoid all forms of sugar, which also suppresses immunity. Drink distilled water or water filtered by reverse osmosis and avoid sweetened drinks, even fruit drinks. Avoid all forms of fluoride, since it damages antioxidant enzymes, increases free radical production, damages DNA repair enzymes, suppresses immunity, produces skeletal and dental fluorosis, hypothyroidism and produces extensive brain cell injury.
Since most foods are contaminated with numerous excitotoxin additives, you should prepare your foods fresh. Your diet should contain at least three servings of fresh fruits and vegetables daily. Vegetables with the deepest color are preferred, but some white vegetables, such as cauliflower are also important.
Recommended Reading:
1. Blaylock, R. L. Interaction of cytokines, excitotoxins and reactive nitrogen and oxygen species in autism spectrum disorders. JANA 2003: 6: 21-35.
2. Blaylock, R. L. The Central Role of Excitotoxicity in autism spectrum disorders. JANA 6: 11-19, 2003
3. Blaylock, R. L. The central role of chronic microglial activation and excitotoxicity in the Gulf War Syndrome and autism. Journal of American Physicians and Surgeons 9: 46-51, 2004
Dr. Russell Blaylock is editor of The Blaylock Wellness Report, published by NewsMax.com
Board-certified neurosurgeon Dr. Russell Blaylock was a Clinical Asst Professor of Neurosurgery at the Medical University of Mississippi. He practiced Neurosurgey for 24 years and practiced nutritional medicine. Dr. Blaylock's first book, Excitotoxins: The Taste That Kills, demonstrated the link between food additives and degenerative diseases. He also has contributed to medical textbooks and written and illustrated booklets on multiple sclerosis and bioterrorism . Dr. Blaylock serves on the editorial staff of the Journal of the American Nutraceutical Association and the editorial board of the Medical Sentinel, the official journal of the Association of American Physicians and Surgeons. He is also author of Health & Nutrition Secrets to Save Your Life and Cancer Strategies.
Dr. Blaylock's article on autism can be found on www.dorway.com plus other articles. There are also some on www.wnho.net And we have an Aspartame Information List on this site.
Dr. Blaylock can also be seen in the aspartame documentary, Sweet Misery: A Poisoned World, www.docworkers.com
Dr. Betty Martini, Founder, Mission Possible International, 9270 River Club Parkway, Duluth, Georgia 30097 770 242-2599 http://www.wnho.net and http://www.dorway.com Aspartame Toxicity Center, www.holisticmed.com/aspartame
(2) Why Most Published Research Findings Are False
John P. A. Ioannidis
http://medicine.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pmed.0020124
Summary
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
John P. A. Ioannidis is in the Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece, and Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical Center, Tufts University School of Medicine, Boston, Massachusetts, United States of America. E-mail: jioannid@cc.uoi.gr
Competing Interests: The author has declared that no competing interests exist.
Published: August 30, 2005
DOI: 10.1371/journal.pmed.0020124
Copyright: © 2005 John P. A. Ioannidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abbreviation: PPV, positive predictive value
Citation: Ioannidis JPA (2005) Why Most Published Research Findings Are False. PLoS Med 2(8): e124
Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [1–3] to the most modern molecular research [4,5]. There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [6–8]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.
Modeling the Framework for False Positive Findings
Several methodologists have pointed out [9–11] that the high rate of nonreplication (lack of confirmation) of research discoveries is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p-value less than 0.05. Research is not most appropriately represented and summarized by p-values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p-values. Research findings are defined here as any relationship reaching formal statistical significance, e.g., effective interventions, informative predictors, risk factors, or associations. “Negative” research is also very useful. “Negative” is actually a misnomer, and the misinterpretation is widespread. However, here we will target relationships that investigators claim exist, rather than null findings.
It can be proven that most claimed research findings are false.
As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true (before doing the study), the statistical power of the study, and the level of statistical significance [10,11]. Consider a 2 × 2 table in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both true and false hypotheses can be made about the presence of relationships. Let R be the ratio of the number of “true relationships” to “no relationships” among those tested in the field. R is characteristic of the field and can vary a lot depending on whether the field targets highly likely relationships or searches for only one or a few true relationships among thousands and millions of hypotheses that may be postulated. Let us also consider, for computational simplicity, circumscribed fields where either there is only one true relationship (among many that can be hypothesized) or the power is similar to find any of the several existing true relationships. The pre-study probability of a relationship being true is R/(R + 1). The probability of a study finding a true relationship reflects the power 1 − β (one minus the Type II error rate). The probability of claiming a relationship when none truly exists reflects the Type I error rate, α. Assuming that c relationships are being probed in the field, the expected values of the 2 × 2 table are given in Table 1. After a research finding has been claimed based on achieving formal statistical significance, the post-study probability that it is true is the positive predictive value, PPV. The PPV is also the complementary probability of what Wacholder et al. have called the false positive report probability [10]. According to the 2 × 2 table, one gets PPV = (1 − β)R/(R − βR + α). A research finding is thus more likely true than false if (1 − β)R > α. Since usually the vast majority of investigators depend on α = 0.05, this means that a research finding is more likely true than false if (1 − β)R > 0.05.
Table 1. Research Findings and True Relationships
What is less well appreciated is that bias and the extent of repeated independent testing by different teams of investigators around the globe may further distort this picture and may lead to even smaller probabilities of the research findings being indeed true. We will try to model these two factors in the context of similar 2 × 2 tables.
Bias
First, let us define bias as the combination of various design, data, analysis, and presentation factors that tend to produce research findings when they should not be produced. Let u be the proportion of probed analyses that would not have been “research findings,” but nevertheless end up presented and reported as such, because of bias. Bias should not be confused with chance variability that causes some findings to be false by chance even though the study design, data, analysis, and presentation are perfect. Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias. We may assume that u does not depend on whether a true relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true. In the presence of bias (Table 2), one gets PPV = ([1 − β]R + uβR)/(R + α − βR + u − uα + uβR), and PPV decreases with increasing u, unless 1 − β ≤ α, i.e., 1 − β ≤ 0.05 for most situations. Thus, with increasing bias, the chances that a research finding is true diminish considerably. This is shown for different levels of power and for different pre-study odds in Figure 1.
Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret research experiments in isolation. An increasing number of questions have at least one study claiming a research finding, and this receives unilateral attention. The probability that at least one study, among several done on the same question, claims a statistically significant research finding is easy to estimate. For n independent studies of equal power, the 2 × 2 table is shown in Table 3: PPV = R(1 − βn)/(R + 1 − [1 − α]n − Rβn) (not considering bias). With increasing number of independent studies, PPV tends to decrease, unless 1 − β < α, i.e., typically 1 − β < 0.05. This is shown for different levels of power and for different pre-study odds in Figure 2. For n studies of different power, the term βn is replaced by the product of the terms βi for i = 1 to n, but inferences are similar.
Figure 1. PPV (Probability That a Research Finding Is True) as a Function of the Pre-Study Odds for Various Levels of Bias, u
Panels correspond to power of 0.20, 0.50, and 0.80.
Table 2. Research Findings and True Relationships in the Presence of Bias
Conversely, true research findings may occasionally be annulled because of reverse bias. For example, with large measurement errors relationships are lost in noise [12], or investigators use data inefficiently or fail to notice statistically significant relationships, or there may be conflicts of interest that tend to “bury” significant findings [13]. There is no good large-scale empirical evidence on how frequently such reverse bias may occur across diverse research fields. However, it is probably fair to say that reverse bias is not as common. Moreover measurement errors and inefficient use of data are probably becoming less frequent problems, since measurement error has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated about their data. Regardless, reverse bias may be modeled in the same way as bias above. Also reverse bias should not be confused with chance variability that may lead to missing a true relationship because of chance.
Testing by Several Independent Teams
Several independent teams may be
addressing the same sets of research questions. As research efforts
are globalized, it is practically the rule that several research
teams, often dozens of them, may probe the same or similar
questions. 
Figure
2. PPV (Probability That a Research Finding Is True) as a
Function of the Pre-Study Odds for Various Numbers of Conducted
Studies, n
Panels correspond to power of 0.20, 0.50, and 0.80.
Table 3. Research Findings and True Relationships in the Presence of Multiple Studies
Corollaries
A practical example is shown in Box 1. Based on the above considerations, one may deduce several interesting corollaries about the probability that a research finding is indeed true.
Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Small sample size means smaller power and, for all functions above, the PPV for a true research finding decreases as power decreases towards 1 − β = 0.05. Thus, other factors being equal, research findings are more likely true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology (several thousand subjects randomized) [14] than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller) [15].
Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. Power is also related to the effect size. Thus research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease (relative risks 3–20), than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.1–1.5) [7]. Modern epidemiology is increasingly obliged to target smaller effect sizes [16]. Consequently, the proportion of true research findings is expected to decrease.
In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous false positive claims. For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1.05, genetic or nutritional epidemiology would be largely utopian endeavors.
Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. As shown above, the post-study probability that a finding is true (PPV) depends a lot on the pre-study odds (R). Thus, research findings are more likely true in confirmatory designs, such as large phase III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested information, such as microarrays and other high-throughput discovery-oriented research [4,8,17], should have extremely low PPV.
Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true. Flexibility increases the potential for transforming what would be “negative” results into “positive” results, i.e., bias, u. For several research designs, e.g., randomized controlled trials [18–20] or meta-analyses [21,22], there have been efforts to standardize their conduct and reporting. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes. True findings may be more common when outcomes are unequivocal and universally agreed (e.g., death) rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes) [23]. Similarly, fields that use commonly agreed, stereotyped analytical methods (e.g., Kaplan-Meier plots and the log-rank test) [24] may yield a larger proportion of true findings than fields where analytical methods are still under experimentation (e.g., artificial intelligence methods) and only “best” results are reported. Regardless, even in the most stringent research designs, bias seems to be a major problem. For example, there is strong evidence that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a common problem even for randomized trails [25]. Simply abolishing selective publication would not make this problem go away.
Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. Conflicts of interest and prejudice may increase bias, u. Conflicts of interest are very common in biomedical research [26], and typically they are inadequately and sparsely reported [26,27]. Prejudice may not necessarily have financial roots. Scientists in a given field may be prejudiced purely because of their belief in a scientific theory or commitment to their own findings. Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may also lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review process the appearance and dissemination of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [28].
Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in fields that draw wide attention. With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its most impressive “positive” results. “Negative” results may become attractive for dissemination only if some other team has found a “positive” association on the same question. In that case, it may be attractive to refute a claim made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [29]. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics [29].
These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings. Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.
Most Research Findings Are False for Most Research Designs and for Most Fields
In the described framework, a PPV exceeding 50% is quite difficult to get. Table 4 provides the results of simulations using the formulas developed for the influence of power, ratio of true to non-true relationships, and bias, for various types of situations that may be characteristic of specific study designs and settings. A finding from a well-conducted, adequately powered randomized controlled trial starting with a 50% pre-study chance that the intervention is effective is eventually true about 85% of the time. A fairly similar performance is expected of a confirmatory meta-analysis of good-quality randomized trials: potential bias probably increases, but power and pre-test chances are higher compared to a single randomized trial. Conversely, a meta-analytic finding from inconclusive studies where pooling is used to “correct” the low power of single studies, is probably false if R ≤ 1:3. Research findings from underpowered, early-phase clinical trials would be true about one in four times, or even less frequently if bias is present. Epidemiological studies of an exploratory nature perform even worse, especially when underpowered, but even well-powered epidemiological studies may have only a one in five chance being true, if R = 1:10. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones 1,000-fold (e.g., 30,000 genes tested, of which 30 may be the true culprits) [30,31], PPV for each claimed relationship is extremely low, even with considerable standardization of laboratory and statistical methods, outcomes, and reporting thereof to minimize bias.
Table 4. PPV of Research Findings for Various Combinations of Power (1 − β), Ratio of True to Not-True Relationships (R), and Bias (u)
Claimed Research Findings May Often Be Simply Accurate Measures of the Prevailing Bias
As shown, the majority of modern biomedical research is operating in areas with very low pre- and post-study probability for true findings. Let us suppose that in a research field there are no true findings at all to be discovered. History of science teaches us that scientific endeavor has often in the past wasted effort in fields with absolutely no yield of true scientific information, at least based on our current understanding. In such a “null field,” one would ideally expect all observed effect sizes to vary by chance around the null in the absence of bias. The extent that observed findings deviate from what is expected by chance alone would be simply a pure measure of the prevailing bias.
For example, let us suppose that no nutrients or dietary patterns are actually important determinants for the risk of developing a specific tumor. Let us also suppose that the scientific literature has examined 60 nutrients and claims all of them to be related to the risk of developing this tumor with relative risks in the range of 1.2 to 1.4 for the comparison of the upper to lower intake tertiles. Then the claimed effect sizes are simply measuring nothing else but the net bias that has been involved in the generation of this scientific literature. Claimed effect sizes are in fact the most accurate estimates of the net bias. It even follows that between “null fields,” the fields that claim stronger effects (often with accompanying claims of medical or public health importance) are simply those that have sustained the worst biases.
For fields with very low PPV, the few true relationships would not distort this overall picture much. Even if a few relationships are true, the shape of the distribution of the observed effects would still yield a clear measure of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators have viewed large and highly significant effects with excitement, as signs of important discoveries. Too large and too highly significant effects may actually be more likely to be signs of large bias in most fields of modern research. They should lead investigators to careful critical thinking about what might have gone wrong with their data, analyses, and results.
Of course, investigators working in any field are likely to resist accepting that the whole field in which
they have spent their careers is a “null field.” However, other lines of evidence, or advances in technology and experimentation, may lead eventually to the dismantling of a scientific field. Obtaining measures of the net bias in one field may also be useful for obtaining insight into what might be the range of bias operating in other fields where similar analytical methods, technologies, and conflicts may be operating.
How Can We Improve the Situation?
Is it unavoidable that most research findings are false, or can we improve the situation? A major problem is that it is impossible to know with 100% certainty what the truth is in any research question. In this regard, the pure “gold” standard is unattainable. However, there are several approaches to improve the post-study probability.
Better powered evidence, e.g., large studies or low-bias meta-analyses, may help, as it comes closer to the unknown “gold” standard. However, large studies may still have biases and these should be acknowledged and avoided. Moreover, large-scale evidence is impossible to obtain for all of the millions and trillions of research questions posed in current research. Large-scale evidence should be targeted for research questions where the pre-study probability is already considerably high, so that a significant research finding will lead to a post-test probability that would be considered quite definitive. Large-scale evidence is also particularly indicated when it can test major concepts rather than narrow, specific questions. A negative finding can then refute not only a specific proposed claim, but a whole field or considerable portion thereof. Selecting the performance of large-scale studies based on narrow-minded criteria, such as the marketing promotion of a specific drug, is largely wasted research. Moreover, one should be cautious that extremely large studies may be more likely to find a formally statistical significant difference for a trivial effect that is not really meaningfully different from the null [32–34].
Second, most research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to achieve. In some research designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [35]. Registration would pose a challenge for hypothesis-generating research. Some kind of registration or networking of data collections or investigators within fields may be more feasible than registration of each and every hypothesis-generating experiment. Regardless, even if we do not see a great deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.
Finally, instead of chasing statistical significance, we should improve our understanding of the range of R values—the pre-study odds—where research efforts operate [10]. Before running an experiment, investigators should consider what they believe the chances are that they are testing a true rather than a non-true relationship. Speculated high R values may sometimes then be ascertained. As described above, whenever ethically acceptable, large studies with minimal bias should be performed on research findings that are considered relatively established, to see how often they are indeed confirmed. I suspect several established “classics” will fail the test [36].
Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the report of a single study gives only a partial picture, without knowing how much testing has been done outside the report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [37], usually it is impossible to decipher how much data dredging by the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would not inform us about the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true among those probed across the relevant research fields and research designs. The wider field may yield some guidance for estimating this probability for the isolated research project. Experiences from biases detected in other neighboring fields would also be useful to draw upon. Even though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context.
Box 1. An Example: Science at Low Pre-Study Odds
Let us assume that a team of investigators performs a whole genome association study to test whether any of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify any of them. Then R = 10/100,000 = 10−4, and the pre-study probability for any polymorphism to be associated with schizophrenia is also R/(R + 1) = 10−4. Let us also suppose that the study has 60% power to find an association with an odds ratio of 1.3 at α = 0.05. Then it can be estimated that if a statistically significant association is found with the p-value barely crossing the 0.05 threshold, the post-study probability that this is true increases about 12-fold compared with the pre-study probability, but it is still only 12 × 10−4.
Now let us suppose that the investigators manipulate their design, analyses, and reporting so as to make more relationships cross the p = 0.05 threshold even though this would not have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original study plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially available “data mining” packages actually are proud of their ability to yield statistically significant results through data dredging. In the presence of bias with u = 0.10, the post-study probability that a research finding is true is only 4.4 × 10−4. Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1.5 × 10−4, hardly any higher than the probability we had before any of this extensive research was undertaken!
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