A medical miracle that needs serious consideration

While the mechanism of injury described in the article could be responsible for the recovery after no oxygen for 25 minutes for some bizarre reason, it is worth having a look whether recovery after longer periods is possible.
There are of course many cold water cases where a person has been under water for a considerable period and been revived, with the arising medical principle that a person in such circumstances isn’t generally considered dead until they are warm and dead.   That is different than the cited case, in which it was a hot August day in the southern Mississipi.
Coming up with an explanation is the interesting part.
The closest normal analogy to death of cells generally due to oxygen is say, coagulative necrosis, where a localized group of cells dies from lack of oxygen.  http://en.wikipedia.org/wiki/Coagulative_necrosis
In that case, the overall architecture is maintained because destructive enzymes are not released as would happen with for example gangrene.
The result can be observed over time when the contents of the cell digest the nucleus.  But how long does that take? Obviously once the cell is permanently damaged there is no going back.  But how long does that take?
The definition of brain death is generally one that shows that activity has ceased in the brain: http://en.wikipedia.org/wiki/Brain_death
But what if, just like the heart can be restarted by CPR, a cell can be jump-started by high oxygen?
There probably would be a race between forces of decomposition and resusitating the cells.   The high flow oxygen may well have killed off some agents in the body that otherwise would be a problem such as any oxygen hostile bacteria that might have been present.
The animal testing has apparently been very promising.
For ethical reasons, human testing would have to be limited initially to cases that otherwise would likely be hopeless or near hopeless.  But for such cases, why not try putting somebody into a decompression chamber with high flow oxygen and see if it works?
The process may in some ways be analogous to the cold water cases, but with less time due to the higher speed of chemical reactions at higher temperatures.  The issue is when damage becomes truly irreversible.  A cell has to restart before it gets taken by say, opportunistic bacteria, but if high oxygen can act as a cell “defibrillator”, that could result in saving many more lives, especially what might be considered highest value targets- kids and otherwise healthy adults suffering from e.g. drowning, who if revived could continue on with productive lives.   Combine it with cold therapy- dropping the body temperature intentionally to reduce damage in certain types of patients, which is now increasingly used to prevent damage in certain types of patients- death could potentially be pushed way back.
There is a philosophical and scientific issue about what is cell death.  Among other things there isn’t really that I know of a separate concept of cell paralysis.  If a cell without oxygen acts like a car without gas that opens up possibilities.

Statistics traps and survival rates in hospitals

Science has various means of getting at explanations including statistics, first hand or “anecdotal” evidence and the theoretical basis for an event.
Ideally you will have all three.   Statistics have the advantage of large samples but the disadvantage that you don’t know what you are measuring, which may be a meaningless correlation or a transitive correlation based on a common root cause.  Anecdotal evidence can be higher quality due to first hand observation.   In scientific circles “anecdotal” is often used in a derogatory manner which may be appropriate for old wives tales and the like where the conclusions are a stretch from the premises.  If on the other hand you burn your hand on the stove or zap yourself with an electrical outlet you can get a highly reliable anecdotal basis for predicting the outcome of similar events in the future based on a single first hand observation.   Dismissing all first hand observation with the wave of a hand as “anecdotal” is not scientific.  Having a theoretical explanation for events is important to help test whether a believed correlation has anything to it or not.
For things like hospital administrative problems anecdotal observations are probably the best starting place, whereas statistics may leave you befuddled.  Take the following for example:
There is a concept in medicine and first aid called the “golden hour”.  For anybody in serious trouble, if you can get them on an operating table within one hour you have the best odds, and after than the odds drop off steeply.
In straightforward cases, like a pedestrian hit by a car, that will operate in a fairly mechanical manner.  Somebody is hit, it gets phoned in, the ambulance comes, and away you go.  Problems with response times due to traffic concerns, inadequate ambulance availability, the size of the area feeding the hospital and the like are likely significant in some areas, especially where there is very bad congestion.
However not all cases are straightforward.   Some are judgment calls and the key to judgment calls is due diligence.   I once had a bad fall and broke both arms.  I didn’t know that, because it wasn’t cinematic- no jagged bones sticking out of skin or anything like that, no severe pain, just mostly undisplaced fractures.   But I knew it was bad with crepitus in my right elbow (a grinding or crackling when it moved) and both arms freezing up, so I went to a clinic.  There was no point sensitivity so the doctor was sneering and suggesting that I was fine- but he did his due diligence, even though he was an ass.  He sent me immediately for x-rays as a precaution and I discovered that I had two broken arms, my right elbow was dislocated and there was bone schrapnel in my left elbow.   The lesson is that the off the cuff, seat of the pants decision with incomplete information is often wrong and you cover yourself.
There was an infamous case in British Columbia where a man was having a heart attack, but was observed and released by several hospitals that decided off the cuff that he was fine and sent him on his way.  He died.  His daughter had been driving around frantically, trying to find a hospital that would take him seriously.  Probably part of the reason is, and by this I do not mean to imply racism, that he was not white.
White people turn pale and can go cyanotic when they are in serious trouble.   The most prominent visual cue that somebody is in trouble for a white person may be unavailable if the skin is dark enough.  So you have to check alternatives such as pulling down the lower eyelid to see if the pink area there is turning blue and use extra diligence on the physical testing to compensate.
I also remember a long time ago when on guard duty in a hospital when somebody came in with classic symptoms of heart attack.  He looked ghastly.  The triage nurse decided to make him wait 15 minutes before sending him into the emergency ward because the orderly was on break.   I was in disbelief.  And if she hadn’t bothered to prioritize him it could easily be an hour from him walking in the door before he ever sees a doctor.  Remember that “golden hour”?  I don’t know what ultimately happened to that man but I’ve often wondered.
I suspect that doctors and nurses get jaded from all of the hypochondriacs they see and all the people that think they are having a heart attack that have heart burn.  But if they get too cynical and start blowing people off in ambiguous cases there will be a body count attached to that.
I think that one of the main factors that would lead to significantly different outcomes in hospitals is something that statistics don’t look at, the triage nurse.  I think that the triage nurse should be the nurse with the best instincts in the hospital.   A blown call by the triage nurse and the “golden hour” is gone.   The patient may code out before ever seeing a doctor and in patients that are in trouble who survive, added delay in treatment may lead to more damage, longer hospital stays etc.
The research statistics may be difficult to use unless you find a way to break them down into lots of different components.  The triage nurse is one obvious one, there are likely others, for instance the ventilation systems of many hospitals are very efficient in distributing infectuous diseases notwithstanding people wearing gowns and masks and having warning signs on doors.   That is again something that lends itself more to hands on than statistical analysis.
There is also an implicit assumption in making a hospital the appropriate unit of measurement.    You may find that the correlations are much higher if specific important people on duty, like triage nurses, surgeons, head surgical nurses, etc., are measured instead of the whole hospital to correlate success and failure when they are on duty.   In most professions from what I’ve seen about 10% are good at what they do, 65% are hit and miss and 25% are dangerously incompetent.   Common sense is more important than I.Q. or grade point and their art work will probably be a better predictor of ability than certificates on their walls.
The doctors that tend to make mistakes tend to make bushels of them, they just don’t get removed.  You need to deal with such issues to get a better handle on whether a given statistic is a hospital issue.
A major problem with getting problems in hospitals fixed is the “white wall”.   Doctors show more internal unity than the Taliban.   They tend to cover for each other and getting doctors that are not actively pursuing a career as plaintiff’s experts to break ranks and say that another doctor screwed up, is notoriously difficult.   There is a surprising degree of solidarity there given that they could probably cut their insurance premiums by more than half if they would drum out the ones they know are morons.  But everybody makes mistakes, and sometimes blown judgment calls aren’t even negligent.    My guess is most doctors have lost a patient or had a patient do poorly due to a blown call which makes them leery of judging anybody else.
One of the problems in the health care system I think is that doctors and nurses are often treated as if they are interchangeable widgets, like a bunch of 3/4″ wrenches at home depot.   You can’t have the right person in the right job if you have a fiction that they are interchangeable.    The “white wall” and nursing equivalent are huge obstacles to fixing problems with the system, because nobody wants to admit mistakes.   One thing necessary to make hospitals better will be to get sufficient data to figure out who is more effective and why, and that will require research that will make doctor and nurse associations extremely resistant and defensive.

Lupus, the disease of a thousand doctors

Lupus has often been called "the disease of a thousand faces" because supposedly there can be any number of symptoms in different people. 
As shown in the above article, lupus is loosely defined as from a hodge-podge of different symptoms and two people without any overlaping criteria can mysteriously be described as having the same illness.
The creation of illnesses by committee, such as has been done for so long in psychiatry, should be discouraged.  
Traditionally lupus has been a garbage bin diagnosis that collected anything to do with an auto-immune problem not otherwise diagnosed, which would frankly be a better and less misleading operational definition than one that purports to peg it as one specific illness. 
I strongly suspect that the whole concept that there is something called "lupus", is getting in the way of dealing with various diseases that are similar in some ways but without any definitive common source.   One analogy would be, some red dyes can cause an allergic reaction and red paint with lead in it can cause a variety of health problems.   An inference from a similar color to a common root of problems would be a counterproductive conflation. 
The concepts of specificity (roughly, the odds that one positive criterion for the illness correlates with that illness rather than some other illness or ordinary health) and sensitivity (roughly, the odds that any given instance of an illness will have that specific association) are important, and it is good that the Wikipedia article has them. 
The difficulty though is that in the absence of a definitive characteristic of lupus the correlations are only with a diagnosis of lupus, not with having lupus, as a positive diagnosis does not confirm anything in particular beyond what was already known. 
What ought to be done?
Well first of all, pure symptoms have to be removed from diagnositic criteria.   A rash for instance is for most practical purposes an end result and usually not directly illuminating about the physical process at work.  Although some symptoms can be grounds for strong suspicion for further investigation, such as the characteristic butterfly rash, they should be viewed as symptoms rather than causes. 
Anti-DNA antibodies, on the other hand, are at least an intermediate cause and their presence can show a specific cause of certain symptoms.  What causes the antibodies, on the other hand, is another issue.
If one checks the link on Anti-nuclear antibodies, that reveals another methodological issue, which is that there are a lot of non-lupus things that have this same characteristic, including various syndromes and arthritis.  That is, there is a possibility that the definition of lupus is in some ways both too broad and too narrow.
Until one is able to get behind an intermediate cause, it makes more sense to treat it (cautiously) as a discrete illness.  It may be that there will be more than one root cause behind an intermediate cause but it can help focus to look at, e.g. anti-nuclear antibodies as a specific problem and see what treats that problem rather than artificially separate these.  
You can’t treat metaphysical illnesses.  You treat physical ones.  Defining an illness as anything without any specific and definitive physical meaning isn’t medicine, it’s more like poetry. 
Something that could add more focus would be restricting a lupus definition to cases of anti dsDNA antibodies, which occur with 70% of people diagnosed with lupus and only 0.5% of people who are not.   Here the most interesting information is excluded by the definition of lupus.   Note that because the field of people without lupus is considerably larger than the field of people with lupus, the 0.5% may actually be a greater population than the 70%. 
Why this is very important is that the most important information, why some people with anti dsDNA antibodies go on contract "lupus" while others do not, is unlikely to be looked at as rigourously as it ought to be if you do not define them as having the same disorder.   What is different about those who remain asymptomatic?  This may suggest a cure or it may suggest triggers to avoid.  It may also assist with finding patterns in development of the underlying, sometimes benign problem, which could be geographic, genetic, related to toxic exposures, etc.   That in combination with the tendency for lupus to have flare ups of unclear origin suggests to me that much of the essence of the problem has been missed.
According to Dr. D’Adamo, there is a higher association with lupus and blood type "B".  This is interesting because blood type "B" people have generally lesser immunity to viral and bacterial infections on average and so it is curious if they are more likely to have a stronger immune reaction to themselves.  
I have a suspicion that there may be a correlation between autoimmune disorders and mixed blood types.   Blood types are unfortunately defined according to criteria that are properly limited to the highly unnatural activity of blood transfusions, how they affect somebody else, rather than how they affect the person who will be using most of his blood most of the time.  Blood type A and B are dominant, and consequently there are at least two types of A and two types of B- people with two of the gene vs. people that have one A or B gene and the other being an O gene.    D’Adamo has pointed out that, for example, in a person with A and O genes there will be some cells that are blood type O as well as some cells that are blood type A.   He does not however seem to have appreciated the full potential importance of that.
A blood type O person of course cannot have a transfusion from somebody with A or B blood.   So what happens with somebody of mixed heritage?  Presumably AO and BO types have some mechanism so that the O genes do not produce anything that attacks the A or B genes, the mechanism of which would be something of interest. 
So what happens if the latent type O gene does start making immune decisions and decides that A or B cells are the enemy?  A and B don’t react to O generally, so any O gene rebellion might never be stamped out. 
Stress can bring out increased immune reaction, and increased immune disorders.  What if the wrong immune gene becomes activated?
There was an interesting reported case where a woman who was normally blood type A became AB mysteriously and developed "lupus".  Upon suppression of her immune system she reverted to blood type A again.   I don’t know if she was genetically tested for her genetic type but I would be most interested in that.  I would have a suspicion that she had a B gene but that it was deficient in some way such that instead of becoming AB and her body programming to deal with that, that the A gene may have become dominant and the B gene activating later may have caused huge problems.  That of course is a hypothesis.
The treatment for lupus is typically to suppress the immune system with brutal, debilitating immune suppressant drugs with a variety of nasty side effects.  While that may alleviate symptoms, the question really ought to be, why is the body attacking itself in this specific way?

General effect of innoculation on the immune system needs to be studied

In the course of the present debate on whether to innoculate against the swine flu or the regular flu, an interesting issue arose that needs to be looked at for all innoculations.
Apparently innoculation against one type of flu increases the chance of getting the other type of flu. 
That has implications for how we should measure the success of innoculations.    If innoculations can reduce resistance to other types of infections we need to study how often such an effect occurs and not limit that to studies of flu. 
The way in which immunizations could reduce immunity against other diseases goes like this: the immune system is highly adaptive and has finite resources.  There is some automated way of determining threat probability and allocating resources based on encountered threats.   The implication is that a massive, immunization style flood of an infectious agent may cause the immune system not just to create more of a response to that infectious agent, but also by diverting resources away from either other similar infectious agents or from other immune programs generally.   In the event that any immunizations are additive, it should be looked at whether excess immune response may be taking resources away from other areas of health which may result in other health problems, i.e. does the immune system compete with any other bodily systems for resources. 
I’m not suggesting that immunizations are a bad thing, although they may be.    Our immune systems have not had sufficient time to evolve to meet modern challenges and they may need a boost.    Modern societies bring plagues because diseases go around and around and get to mutate through exposures to billions of people.  In more primitive societies with  more limited interactions and more limited exposures to populations, reducing the chance for mutation, exposure to something like a new flu may have been more like a once in a lifetime rather than a once a year process and the immune system would probably be adapted accordingly, with greater resistance to things like bacteria and fungal spores that are always around.   Immune systems were formed over millions of years of evolution in response to the regular hazards over that period and modern societies have been around for a blink of an eye in that context.  The immune systems would need some help with new challenges although that will eventually fix itself.
On the other hand we do need to look at whether immunizations do something like pushing down one side of a seesaw.

Flu and heart attack connection

Apparently somebody has found a connection between flu and heart attacks. 
I’m surprised that this is a surprise. 
The use of blood thinners and anti-inflammatory medications can cause viral counts in the blood to release when someone is infected.  That is probably because when the blood is flowing more freely it is more difficult to keep the infection under control and isolated. 
Blood thinners and anti-inflammatory agents can also make a person less vulnerable to heart attacks.
It would seem reasonable that if the body is defending and containing infection that it would be desirable on that health front to have the opposite effect from that given by an aspirin or ibuprofen. 
If the body is shifted in a direction opposite to what an aspirin does then the natural hypothesis would be that it would increase heart attacks. 

Prudence and the empirical attitude

There has been a lot of talk in recent days about the recent connection or non-connection of cell phones with cancer.  What has impressed me the most about this is the willingness to jump ahead of the research and advise caution.
Modern science has been colored by industrial interests such that the approach of the makers of asbestos and enhanced tabacco has become the prevailing attitude, as if the makers of a new product should be allowed to continue unless a hazard is proven beyond a reasonable doubt. 
Science has a place but the point of science is not to use it to the exclusion of all else, including common sense. 
Having an empirical attitude is about more than science.   It is also about realizing the limitations of science and knowledge and understanding that what you don’t know can be a problem. 
Any time that we change something in our environment, we are conducting an experiment whether we acknowledge that or not.  When we make changes, especially on a large scale, we really have to wait and see what the consequences are going to be.   Modern western society has thousands of these experiments going on at any given time with new chemicals, radiation frequencies and other changes going on as random, uncontrolled, mostly unsupervised experiments.   Cell phones are only one aspect of this.
When we make a large scale change such as with exposure to new types of radiation with cell phones, there may be cases where we don’t know the full effects of this for decades.
Sometimes we can’t wait for the science to catch up.   There may be cases where taking totally unknown risks may be justified, as with terminal patients that want to try anything to get their health back, but when we look at exposing the entire population to risks that are completely indeterminate, we should be asking ourselves if that is prudent. 
It is human and especially political nature to wait for major disasters to take action that should have been taken pre-emptively and thus far disasters in this area of random experimentation haven’t been too bad, just things like large numbers of babies being born with flippers instead of hands and feet with the Thalydomide crisis 40 years ago.   Cancer rates though have been rising and there is probably a correlation between this and all of these random experimental exposures that we are subjected to.
Cell phones should be used with caution, although I would say that on the basis of prudence, rather than established science.  I am concerned in part that we may lessen science if we exaggerate findings in order to create a reaction that we should have had out of prudence in the first place. 
Do cell phones cause cancer?  I haven’t the faintest idea.  They do bombard us with radiation and it would be surprising if that had no effect at all.  We do have to watch correlation traps as well, as the assumption that all correlations are cause and effect is one of the leading causes of junk science. 
We have to consider whether neurological deficiencies may both cause a person to be more likely to talk on cell phones and predispose a person to cancer. 
We have to consider correlations between habits.  What type of person is most likely to spend the most time on a cell phone?  What else are such people more likely to do?  Are they more likely to be overweight?  More likely to use drugs or alcohol or be "party animals"?  Less likely to exercise?  Might there be a genetic predisposition to engage in that activity that also correlates with a higher genetic probability of cancer?  The odds of all of the other risk factors being exactly the same between cell phone users and non-cellphone users or those who use them less is about as probable as tossing a coin and having it land on edge.   There is still probably a lot of work to do here. 
But do we wait for that work to be done to act prudently?
And there is a bigger question: why do we always wait for somebody to sound the alarm before accepting that we are engaging in what may be a potentially dangerous activity?  Science is always too late for the first victims. 

From junk science to bulk science

So another piecemeal attempt at stopping a disease has failed. 
Eliminating the sticky brain gunk apparently has not stopped the problem.  It was worth a try.  One of the problems that I have though with traditional science even when it is real science is that it is often too piecemeal.   The focus is too narrow.  Time may be lost as treatments are attempted in series. 
One thing that I would like to see happen is what I think of as the massive correlator project.  Put together as many people as possible with their genetic information, family health backgrounds, what they eat, where they came from, do blood tests and see what is their levels of various things, everything from hormones to contaminants, and put all of that information together in a monster database and see what pops up.
Correlations don’t necessarily imply a cause and effect relation.  They can be effect-effect relationships with a common cause, only slightly related or completely random.  There can be intervening causes in a chain reaction.  A correlation just tells us that something is worth a look.  But that can give a lot of starts.
With altzheimers for instance, the gunk that covers the brain is a correlation with the disease (although if it is defined as a characteristic of the disease, we have to beware of a circular definition).   A high correlation between this gunk and a certain level of dysfunction is unlikely to be random, the doctors have figured that out.
But then there is some other gunk that is generally found too, creating what is called brain tangles. 
So here is a completely ad hoc hypothesis which I am not necessarily saying is true.  Suppose that the reason for these two types of junk on the brain is that certain things leak out of brain cells.   Suppose that Alzheimers is not the gunk, but the leak.  Other substances get out through the leak as well, depleting the brain cells of nutrients, or whatever, and the result is that the brain cells don’t work properly because they are missing something.  Suppose that the reason that they can’t find the presence of whatever causes Alzheimers because what it is, is the absence of something. 
This is why complete tests are necessary.  Not just what is there, but what is not there, both on the large scale where we can see gunk and at the cellular level to see if there is too much of something or too little of something and that may give rise to more specific theories.   At the very least I think that we need to find one more correlation to figure this thing out.  It could be related to a contaminant.  It could be genetic.  It could be genetic vulnerability exploited by a contaminant.  It could be related to damage by a virus- or a bacteria, including one that may have run its course and been run out of the body, in which case we’d have to run a full check on antibodies and possibly do genetic tests to see what might have been altered by a virus. 
If you stop an effect-effect correlation, you will probably wind up stumped and wondering where you went wrong.  The first question that came to my mind when reading the article is that the article doesn’t answer the question fully about where the gunk comes from, how it happens to be there.   Figure that out and you probably get closer to what the disease is.  That there are two types of gunk out of control in the brain really makes me suspect a common cause.  Even if they cause the symptoms, the real disease is whatever leads to their buildup, whatever that might be. 
I’d also note in passing that the behaviour reminds me somewhat of prions, another poorly understood disease where the brain fills with crap.   In the case of prions, I’m inclined to agree with the viral theorists as opposed to what might be called the voodoo theorists who now think that proteins can replicate themselves through some magical process.  Sounds neat, but it falls to Oakham’s Razor at this point.   We already know that viruses can reprogram the body to self destruct, there is no reason to guess that there is some mysterious process that contradicts everything that we know about microbiology. 
While I’m on prions, I suspect that such things are common in the animal kingdom and I’m unconvinced at this point that the same agent causes the same disease in cows and humans.   The "proof" of this as far as I can see is quite hypothetical.   It may well be true, say if there is a virus that works in both cows and humans, but we must beware interests that pervert science for economic advantage.  Whether the connection is correct or not, the real function of these amazing discoveries in animals is for economic advantage.  Nobody cares if the science is good if it means that an economic competitor is placed at a disadvantage.  Look at the mass hysteria around beef- one infected cow lead to an extension of the ban of Canadian beef from the US.  This discovery came mysteriously at the same day that some American panel or other was considering reopening the border to Canadian beef.  When there was a lawsuit to resume trade, who was the foremost opponent to trade resuming?  Montana cattlemen.  Not the government, not scientists, but an economic competitor. 
The sad thing about this apart from the descent into junk science in the cattle matter is that if real science were done on all related matters, it could increase understanding.  It could be, while studying prions for example, that somebody has a stroke of brilliance that goes a long ways to solving the problem of Alzheimers or some other disease that involves the mangling of the brain processes.  Alzheimers is associated with an amyloid problem, prions are associated with an amyloid problem.  It could be that they are related in some way or another.   It could be that they are completely distinct as well.  But there are lots of things to look at here and the focus should be broadened before it narrows into specific lines of enquiry.