Yes it’s hot, but

A few years ago, before things calmed down, hurricanes were predicted to become more frequent and more severe. So although global warming fans predicted higher temperatures, they also predicted this. Here’s an example

http://www.climatecentral.org/news/study-projects-more-frequent-and-stronger-hurricanes-worldwide-1620
and another — http://time.com/3706888/hurricane-warning-study/

So far this year (and the major part of the hurricane season is about to begin) the Atlantic hurricane season is the quietest it’s ever been. There have only been 4 tropical storms this year and no hurricanes at all not even one. One of the 4 storms occurred in January, which is rather bizarre.

Scientific theories when faced with a falsified prediction are usually modified or abandoned.  Not so with global warming.  It’s just been rebranded as climate change.

There is a rather imperfect measure of the amount of power produced by the storms of the season, called Accumulated Cyclonic Energy (ACE). The average per year is an ACE of 110. This year (so far) it’s only 6.

ACE is calculated as the square of the wind speed every 6 hours, and is then scaled by a factor of 10,000 for usability. The ACE of a season is the sum of the ACE for each storm and takes into account the number, strength, and duration of all the tropical storms in the season. The caveat to using ACE as a measure of the activity of a season is that it does not take the physical size of the hurricane or tropical storm into account which is why it’s imperfect.

If you know any physics, ACE is velocity squared * time — which is not the dimension of energy (it’s acceleration). I wonder if satellite radar is good enough to give us ground windspeed over small areas, which could be summed over the hurricane area if the division was fine enough. This would allow us to tell big storms from smaller ones. Unfortunately, there would be no way to compare this new measure to ones in the past such as ACE.

You are alive because the lipid bilayer of your plasma membrane is asymmetric

You are an organism with trillions of cells. A mosquito bit you depositing millions of viruses in your tissues. The virus can reproduce only within one of your cells and it has exploited all sorts of protein protein chemistry to get in. Antibodies (if you are fortunate enough to have them) can get rid of the extracellular critters. However, 500,000 have made into the same number of your cells, and are merrily trying to reproduce.

How does the asymmetry of the lipid bilayer of your plasma membrane help you survive. If each virus infected cell killed itself before the virus reproduced, you’d survive. Although 500,000 is a large number is is less than 1 millionth of your cell total.

Well you do have intracellular defenses against viruses, called the innate immune system. One of them is a protein with the ugly name of gasdermin D. The activated innate immune system (in the form of inflammatory caspases) cleaves gasdermin. This breaks up the inhibition of the amino terminal part of gasdermin by the carboxy terminal part giving a fragment which binds to one particular membrane component (phosphatidyl serine) which makes up 20% of the inner leaflet of the cell membrane. It then forms a large diameter (to a cell 140 Angstroms is quite large) pore in the cell membrane. No cell can survive this, so it dies, releasing cellular contents (probably some viral components but not fully formed one). For details see [ Nature vol. 535 pp 111 – 116, 153 – 158 ’16 ]

Wait a minute. The toxic gasdermin fragment is also released. So how come it doesn’t kill everything in sight? Because our cellular membranes keep phosphatidyl serine confined to the inner membrane, normal cells don’t show it on their exterior, so they can be bathed in gasdermin with no ill effect. What is responsible for this asymmetry — believe it or not an ATP consuming enzyme called flippase (about this more later) which takes any phosphatidyl serine it finds on the outer leaflet and schleps it back inside the cell.

There is all sorts of elegant chemistry which explains just how gasdermin binds to phosphatidyl serine and none of the many other phospholipids found on the inner leaflet. There is more elegant chemistry explaining how flippase works (see later).

What chemistry cannot explain, is why organisms would ‘want’ an asymmetric membrane. As soon as you get into the function of a particular compound in an organism, chemistry is powerless to tell you why. Nothing else can explain how a given molecule does what it does on the molecular level but that is not enough for a satisfying explanation.

One further explanation before some hard core cellular biochemistry follows (after ***). Our cells are dying all the time. The lining of your gut is replaced every 5 days. Even the longest lasting element of your blood is gone after half a year, and most other elements are turned over at least once a month. When these cells die, they must be cleaned up, without undue fuss (such as inflammation). The cleaners are cells called macrophages. A dying cell releases chemical signals, actually called ‘eat me’, one of which is phosphatidyl serine found on the membrane fragments of a dead cell. The fact that flippases keep it on the inner leaflet means that macrophages won’t attack a normal cell.

Slick isn’t it?

***

Flippase is a MgATPdependent aminophospholipid translocase. It localizes phosphatidylserine and phosphatidylethanolamine to the inner membrane leaflet by rapidly translocating them from the outer to the inner leaflet against an electrochemical gradient. The stoichiometry between amino phospholipid translocation and ATP hydrolysis is close to one (how will the cell have enough ATP to do anything else?). The flippase is inhibited by high calcium, and by pseudosubstrates such as vanadate, acetylphosphate and para-nitrophenyl phosphate, and by SH reactive reagents such as N-ethylmaleimide and pyridyldithioethylamine (PDA) a specific inhibitor of phospholipid translocation

[ Proc. Natl. Acad. Sci. vol. 109 pp. 1449 – 1454 ’12 ] P4-ATPases are a subfamily of P-type ATPases. They transport aminophospholipids from the exoplasmic to the cytoplasmic leaflet (and are known as flippases). Man has 14 P4-ATPases, expressed in various cell types. They are thought to be similar to the catalytic subunits of the Ca++ ATPase, and the Na, K ATPase, consisting of cytoplasmic, N, P and A domains and a membrane domain made of 10 transmembrane helices (M1 – M10).

[ Proc. Natl. Acad. Sci. vol. 111 pp. E1334 – E1343 ’14 ] The P4-ATPases are thought to resemble the classic P-type ATPase cation pumps — a transmembrane domain of 10 helices and 3 cytoplasmic domains (P for phosphorylation, N for nucleotide binding and A for actuator). ATP8A2 forms an intermediate phosphorylated on aspartic acid (E2P)and undergoes a catalytic cycle similar to the sodium pump (Na+, K+ ATPase). Dephosphorylation of E2P is activated by the transported substrates phosphatidyl serine (PS) and phosphatidyl ethanolamine (PE), similar to the K+ activation of dephosphorylation in the sodium pump.

PE and PS are 10x as large as the cations transported by the sodium pump. This is known as the giant substrate problem. This work shows that isoleucine #364 (mutated in — patients with the ataxia, retardation and dysequilibrium syndrome Eur. J. Hum. Genet. vol. 21 pp. 281 – 285 ’13 aka CAMRQ syndrome ) forms a hydrophobic gate separating the entry and exit sites of PS. I364 likely directs the sequential formation and annihilation of water filled cavities (as shown by molecular dynamics simulations) allowing transport of the hydrophilic phospholipid head group, in a groove outlined by TMs 1, 2, 4 and 6, with the hydrocarbon chains following passively, still in the membrane lipid phase (and presumably outside the channel) — this must disrupt the hell out of the protein as it passes. They call this the credit card model — only the interaction with part of the molecule is important — just as the magnetic stripe is the only important thing about the credit card.

Functional MRI research is a scientific sewer

First a primer about the science underlying functional Magnetic Resonance Imaging (fMRI). Chemists use MRI all the time, but they call it Nuclear Magnetic Resonance. Docs and researchers quickly changed the name to MRI because no one would put their head in something with Nuclear in the name.

There are now noninvasive methods to study brain activity in man. The most prominent one is called BOLD (Blood Oxygen Level Dependent), and is based on the fact that blood flow increases way past what is needed with increased brain activity. This was actually noted by Wilder Penfield operating on the brain for epilepsy in the 1930s. When a patient had a seizure on the operating table (they could keep things under control by partially paralyzing the patient with curare) the veins in the area producing the seizure turned red. Recall that oxygenated blood is red while the deoxygenated blood in veins is darker and somewhat blue. This implied that more blood was getting to the convulsing area than it could use.

BOLD depends on slight differences in the way oxygenated hemoglobin and deoxygenated hemoglobin interact with the magnetic field used in magnetic resonance imaging (MRI). The technique has had a rather checkered history, because very small differences must be measured, and there is lots of manipulation of the raw data (never seen in papers) to be done. 10 years ago functional magnetic imaging (fMRI) was called pseudocolor phrenology.

Some sort of task or sensory stimulus is given and the parts of the brain showing increased hemoglobin + oxygen are mapped out. As a neurologist as far back as the 90s, I was naturally interested in this work. Very quickly, I smelled a rat. The authors of all the papers always seemed to confirm their initial hunch about which areas of the brain were involved in whatever they were studying. Science just isn’t like that. Look at any issue of Nature or Science and see how many results were unexpected. Results were largely unreproducible. It got so bad that an article in Science 2 August ’02 p. 749 stated that neuroimaging (e.g. functional MRI) has a reputation for producing “pretty pictures” but not replicable data. It has been characterized as pseudocolor phrenology (or words to that effect). Keep reading you’re about to find out just why this was.

What was going on? The data was never actually shown, just the authors’ manipulation of it. Acquiring the data is quite tricky — the slightest head movement alters the MRI pattern. Also the difference in NMR signal between hemoglobin without oxygen and hemoglobin with oxygen is small (only 1 – 2%). Since the technique involves subtracting two data sets for the same brain region, this doubles the error.

Under two years ago, it was shown that 70% of people having functional MRIs (fMRIs) were asleep during the test, and that until then fMRI researchers hadn’t checked for it. For details please see
https://luysii.wordpress.com/2014/05/18/how-badly-are-thy-researchers-o-default-mode-network/. You don’t have to go to med school, to know that the brain functions quite differently in wake and sleep.

Recent work shows that functional MRI work is even worse. A devastating report in [ Proc. Natl. Acad. Sci. vol. 113 pp. 7699 – 7600, 7900 – 7905 ’16 ] showed that certain common settings in 3 software pacakages (SPM, FSL, AFNI) used to analyze fMRI data gave false positive results ‘up to’ 70% of the time. Some 3,500 of the 40,000 fMRI studies in the literature over the past 20 years used these settings. The paper also notes that a bug (now corrected after being used for 15 years) in one of them also led to false positive results.

Here’s a bit more detail on what they did. It turns out that analyzing one voxel (essentially a single MRI pixel) at a time produces valid results. The problem comes when multiple voxels (clusters) are analyzed together. Clusterwise inference considers both the strength of activity at spots throughout the brain as well as the size of the spots. When a parameter called the cluster defining threshold (CDT) is set too low, the analysis is more likely to be false positive. This was true for all 3 packages tested. Parametric statistical methods produce the problem (not for voxels but for clusters). It relies on Gaussian Random Field Theory (RFT) for clusters , which depends on two other assumptions (1) the spatial autocorrelation function has a squared exponential shape — e.g. Gaussian (2) the spatial smoothness of the fMRI signal is constant over the brain. Neither of these assumptions is correct. Those of you who’ve read Nassim Nicholas Taleb about the stock market know about ‘fat tails’. It turns out that the spatial correlation function has them. Here’s what a fat tail is all about. Human height goes fall quite nicely into a Gaussian distribution. There are 7 and 8 footers about but they are rare. If the human height distribution wasn’t Gaussian but had a fat tail, we’d see 12 and 15 footers.

If that wasn’t bad enough,the following is even worse (in my opinion). 40% of 241 recent fMRI studies didn’t report using well known methods for correcting for multiple testing. They may have done so, but every biomedical paper, and drug study says so explicitly. Not only that but drug studies are required to explicitly state the hypothesis (or hypotheses) they are testing.

This is probably why in the early days, fMRI researchers always confirmed their original hypothesis. They could test the massive fMRI for statistical rarity, and since the data was so large, find it and post hoc propter hoc publish it. Possibly they did so out of ignorance, but even so this is inexcusable

Science proves cognitive training will raise your IQ 5 – 10 points

Who among you doesn’t want to be smarter? A placebo controlled study with 25 people in each group showed that cognitive training raised IQ 5 – 10 points [ Proc. Natl. Acad. Sci. vol. 113 pp. 7470 – 7474 ’16 ].

You know that there has to be a catch and there is. The catch points to a problem with every placebo controlled trial ever done, particularly those with drugs, so drug chemists pay attention.

What was the placebo? It was the way subjects are recruited for these studies. Of 19 previous studies in the literature, 17 recruited patients using terms like ‘cognition’ or ‘brain training’, so the authors put out two ads for subjects.

Here are the two ads they used

Ad #1

Brain Training and Cognitive Enhancement
Numerous studies of ahown that working memory training can increase fluid intelligences (several references cited)
Participate in a study today !
EMail for more information GMUBrainTraining@Gmail.com

Ad #2

EMail Today and Participate in a study
Need SONA credits? (I have no idea what they are)
Sign up for a study today and earn up to 5 credits
Participate in a study today !
cforough@masonlive.gmu.edu

I might mention that the two ads were identical in total size, font sizes, coloration used etc. etc.

” Two individual difference metrics regarding beliefs about cognition and intelligence were also collected as potential moderators. The researchers who interacted with participants were blind to the goal of the experiment and to the experimental condition”  Not bad. Not bad at all.

The results: those recruited with ad #1 showed the increase in IQ, those recruited with ad #2 showed no improvement.

It was an expectancy effect. Those who thought intelligence could be raised by training, showed the greatest IQ improvement.   Every sick patient wants to get better, and any drug trial simply must mention what it is for, the risks and rewards, so this effect is impossible to avoid. It probably explains the high placebo response rate for migraine and depression (over 30% usually).

What is really impressive (to me at least) is that the improvement was not in a subjective rating scale (such as is used for depression), but in something as objective as it gets. IQ questions have a right and wrong answers. You can argue about whether they ‘really’ measure intelligence, but they measure what they measure and fluid intelligence is one of them.

Medicine is full of fads and fashions, sugar is poison, fat is bad (no it’s good) etc. etc. and this is true in spades for treatments, particularly those touted in the press. Next time you’re in a supermarket, look at the various nostrums mentioned in the magazines at the checkout stand.

When I first started out in practice, one particular headache remedy was getting great results. The rationale behind it seemed bizarre, so I asked a very smart  old GP about it — his advice — “use it while it works”. Rest in peace, Herb

Another fail safe mechanism used by the cell — readthrough

Nothing is perfect in this world, not even the translation of mRNA into protein. The error rate is one amino acid misincorporated into a protein for every 10,000 or so done correctly — but these results are for one celled organisms (E. Coli, yeast). I can’t find a number for mammals, primates etc. etc.

This means that occasionally one of the 3 codons which tell the ribosome to quit (stop codons), will be misread as an amino acid. This is called readthrough, and means that the ribosome will merrily march on producing a much larger protein than coded for by the mRNA until one of two things happens. l. the ribosome reaches the end of the mRNA and stops. 2. the mRNA contains another stop codon (there are 3). The probability of this is 3/64 per codon. If stop codons are randomly distributed (which they are most certainly not in the protein coding segment of an mRNA) the chances of 100 codons in a row not containing a stop codon is under 1% (.822 % to be exact). So any protein containing more than 100 amino acids is a statistical freak in this sense. Since the 3′ untranslated region (3’UTR) of mRNA doesn’t code for protein, they should have stop codons randomly distributed (there being no selective pressure to keep them away).

Enter Nature vol. 534 pp. 719 – 723 ’16 — if you attach a 3′ UTR section of an mRNA to a normal protein sequence (mimicking readthrough) you get much less protein. The authors think the 3’UTRs code for peptide sequences destabilizing the attached protein. They don’t know what this might be, so it’s terra incognita for researchers, and a worthwhile PhD project to figure it out. Another example of ‘coding’ by a presumably nonCoding sequence in the genome. It may also tell us something about protein structure.

You gotta love this structure

Science vol. 352 pp. 1555 – 1559 has a structure you have to love. It is a molecular knot containing a mere 30 pyridines and 10 benzenes all tied together in a knot which looks like a five pointed star. The tying was done by metathesis of benzenes with CH2 CH2 CH = CH2 dangling from them. To think of what needed to be tied to what was extremely clever.

Surprisingly with all this going on the knot coordinates just a single halogen atom. This shows why you must build a model of a complicated organic compound to see what it really looks like, something I learned with adamantane years ago — you can draw all the chairs you want, making it look rather spiky, but the damn thing is actually spherical. Well, no model was built, but the structure was determined using Xray crystallography (figure 3) Anyone playing with tinkertoys back in the day (or Legos now) and loving it will have a natural affinity for organic chemistry

Why you do and don’t need chemistry to understand why we have big brains

You need some serious molecular biological chops to understand why primates such as ourselves have large brains. For this you need organic chemistry. Or do you? Yes and no. Yes to understand how the players are built and how they interact. No because it can be explained without any chemistry at all. In fact, the mechanism is even clearer that way.

It’s an exercise in pure logic. David Hilbert, one of the major mathematicians at the dawn of the 20th century famously said about geometry — “One must be able to say at all times–instead of points, straight lines, and planes–tables, chairs, and beer mugs”. The relationships between the objects of geometry were far more crucial to him than the objects themselves. We’ll take the same tack here.

So instead of the nucleotides Uridine (U), Adenine (A), Guanine (G), Cytosine (C), we’re going to talk about lock and key and hook and eye.

We’re going to talk about long chains of these four items. The order is crucial Two long chains of them can pair up only only if there are segments on each where the locks on one pair with the keys on the other and the hooks with the eyes. How many possible combinations of the four are there on a chain of 20 — just 4^20 or 2^40 = 1,099,511,621,776. So to get two randomly chosen chains to pair up exactly is pretty unlikely, unless in some way you or the blind Watchmaker chose them to do so.

Now you need a Turing machine to take a long string of these 4 items and turn it into a protein. In the case of the crucial Notch protein the string of locks, keys, hooks and eyes contains at least 5,000 of them, and their order is important, just as the order of letters in a word is crucial for its meaning (consider united and untied).

The cell has tons of such Turing machines (called ribosomes) and lots of copies of strings coding for Notch (called Notch mRNAs).

The more Notch protein around in the developing brain, the more the proliferating precursors to neurons proliferate before differentiating into neurons, resulting in a bigger brain.

The Notch string doesn’t all code for protein, at one end is a stretch of locks, keys, hooks and eyes which bind other strings, which when bound cause the Notch string to be degraded, mean less Notch and a smaller brain. The other strings are about 20 long and are called microRNAs.

So to get more Notch and a bigger brain, you need to decrease the number of microRNAs specifically binding to the Notch string. One particular microRNA (called miR-143-3p) has it in for the Notch string. So how did primates get rid of miR-143-3p they have an insert (unique to them) in another string which contains 16 binding sites for miR-143-3p. So this string called lincND essentially acts as a sponge for miR-143-3p meaning it can’t get to the Notch string, meaning that neuronal precursor cells proliferate more, and primate brains get bigger.

So can you forget organic chemistry if you want to understand why we have big brains? In the above sense you can. Your understanding won’t be particularly rich, but it will be at a level where chemical explanation is powerless.

No amount of understanding of polyribonucleotide double helices will tell you why a particular choice out of the 1,099,511,621,776 possible strings of 20 will be important. Literally we have moved from physicality to the realm of pure ideas, crossing the Cartesian dichotomy in the process.

Here’s a copy of the original post with lots of chemistry in it and all the references you need to get the molecular biological chops you’ll need.

Why our brains are large: the elegance of its molecular biology

Primates have much larger brains in proportion to their body size than other mammals. Here’s why. The mechanism is incredibly elegant. Unfortunately, you must put a sizable chunk of recent molecular biology under your belt before you can comprehend it. Anyone can listen to Mozart without knowing how to read or write music. Not so here.

I doubt that anyone can start from ground zero and climb all the way up, but here is all the background you need to comprehend what follows. Start here — https://luysii.wordpress.com/2010/07/07/molecular-biology-survival-guide-for-chemists-i-dna-and-protein-coding-gene-structure/
and follow the links (there are 5 more articles).

Also you should be conversant with competitive endogenous RNA (ceRNA) — here’s a link — https://luysii.wordpress.com/2014/01/20/why-drug-discovery-is-so-hard-reason-24-is-the-3-untranslated-region-of-every-protein-a-cerna/

Also you should understand what microRNAs are — we’re still discovering all the things they do — here’s the background you need — https://luysii.wordpress.com/2015/03/22/why-drug-discovery-is-so-hard-reason-26-were-discovering-new-players-all-the-time/weith.

Still game?

Now we must delve into the embryology of the brain, something few chemists or nonbiological type scientists have dealt with.

You’ve probably heard of the term ‘water on the brain’. This refers to enlargement of the ventricular system, a series of cavities in all our brains. In the fetus, all nearly all our neurons are formed from cells called neuronal precursor cells (NPCs) lining the fetal ventricle. Once formed they migrate to their final positions.

Each NPC has two choices — Choice #1 –divide into two NPCs, or Choice #2 — divide into an NPC and a daughter cell which will divide no further, but which will mature, migrate and become an adult neuron. So to get a big brain make NPCs adopt choice #1.

This is essentially a choice between proliferation and maturation. It doesn’t take many doublings of a NPC to eventually make a lot of neurons. Naturally cancer biologists are very interested in the mechanism of this choice.

Well to make a long story short, there is a protein called NOTCH — vitally important in embryology and in cancer biology which, when present, causes NPCs to make choice #1. So to make a big brain keep Notch around.

Well we know that some microRNAs bind to the mRNA for NOTCH which helps speed its degradation, meaning less NOTCH protein. One such microRNA is called miR-143-3p.

We also know that the brain contains a lncRNA called lncND (ND for Neural Development). The incredible elegance is that there is a primate specific insert in lncND which contains 16 (yes 16) binding sites for miR-143-3p. So lncND acts as a sponge for miR-143-3p meaning it can’t bind to the mRNA for NOTCH, meaning that there is more NOTCH around. Is this elegant or what. Let’s hear it for the Blind Watchmaker, assuming you have the faith to believe in such things.

Fortunately lncND is confined to the brain, otherwise we’d all be dead of cancer.

Should you want to read about this, here’s the reference [ Neuron vol. 90 pp. 1141 – 1143, 1255 – 1262 ’16 ] where there’s a lot more.

Historically, this was one of the criticisms of the Star Wars Missile Defense — the Russians wouldn’t send over a few missles, they’d send hundreds which would act as sponges to our defense. Whether or not attempting to put Star Wars in place led to Russia’s demise is debatable, but a society where it was a crime to own a copying machine, could never compete technically to produce such a thing.

Where did this quote appear?

The following quote appeared in a major newspaper the day before the Brexit vote. Guess which one.

“David Cameron, the British prime minister has no one to blame but himself… made a promise … if re-elected, he would hold an in or out referendum on continued British membership” (in the EU).

The article goes on in this vein about what a mistake this was. Allowing people to actually vote, or as the article says “what many consider to be a wholly unnecessary roll of the dice”.

Various British mandarins are quoted as to the wisdom of Cameron’s decision, and a variety of arguments against Brexit are trotted out “sharp tones of xenophobia, racism, nativism and Islamophobia” — this by the authors of the article. No arguments for Brexit are given (as if any reasonable person could be in favor).

So where was it published? Pravda? Granma? People’s Daily?

No, the front page of the New York Times.

It’s the typical New York Times ploy of masquerading an opinion piece as a news article.

This is something I despise (see — https://luysii.wordpress.com/2016/02/03/helping-hillary-along/).

Not this time though. It is a perfect example of the elitist (and leftist) impulse of the Times in full cry. We know what’s best. The people are not to be trusted, but ruled by decree by their betters (vide Obama’s 13 million amnesty, and the BLM’s attempt to control fracking despite a law passed by congress).

It’s very good to see elite opinion lose. Americans should be aware that Brexit was opposed by the heads of all political parties, business elites, academic elites, Nature and the scientific elites, the church — essentially every class of elite imaginable. Perhaps this was its high tide.

Why our brains are large: the elegance of its molecular biology

Primates have much larger brains in proportion to their body size than other mammals. Here’s why. The mechanism is incredibly elegant. Unfortunately, you must put a sizable chunk of recent molecular biology under your belt before you can comprehend it. Anyone can listen to Mozart without knowing how to read or write music. Not so here.

I doubt that anyone can start from ground zero and climb all the way up, but here is all the background you need to comprehend what follows. Start here — https://luysii.wordpress.com/2010/07/07/molecular-biology-survival-guide-for-chemists-i-dna-and-protein-coding-gene-structure/
and follow the links (there are 5 more articles).

Also you should be conversant with competitive endogenous RNA (ceRNA) — here’s a link — https://luysii.wordpress.com/2014/01/20/why-drug-discovery-is-so-hard-reason-24-is-the-3-untranslated-region-of-every-protein-a-cerna/

Also you should understand what microRNAs are — we’re still discovering all the things they do — here’s the background you need — https://luysii.wordpress.com/2015/03/22/why-drug-discovery-is-so-hard-reason-26-were-discovering-new-players-all-the-time/weith.

Still game?

Now we must delve into the embryology of the brain, something few chemists or nonbiological type scientists have dealt with.

You’ve probably heard of the term ‘water on the brain’. This refers to enlargement of the ventricular system, a series of cavities in all our brains. In the fetus, all nearly all our neurons are formed from cells called neuronal precursor cells (NPCs) lining the fetal ventricle. Once formed they migrate to their final positions.

Each NPC has two choices — Choice #1 –divide into two NPCs, or Choice #2 — divide into an NPC and a daughter cell which will divide no further, but which will mature, migrate and become an adult neuron. So to get a big brain make NPCs adopt choice #1.

This is essentially a choice between proliferation and maturation. It doesn’t take many doublings of a NPC to eventually make a lot of neurons. Naturally cancer biologists are very interested in the mechanism of this choice.

Well to make a long story short, there is a protein called NOTCH — vitally important in embryology and in cancer biology which, when present, causes NPCs to make choice #1. So to make a big brain keep Notch around.

Well we know that some microRNAs bind to the mRNA for NOTCH which helps speed its degradation, meaning less NOTCH protein. One such microRNA is called miR-143-3p.

We also know that the brain contains a lncRNA called lncND (ND for Neural Development). The incredible elegance is that there is a primate specific insert in lncND which contains 16 (yes 16) binding sites for miR-143-3p. So lncND acts as a sponge for miR-143-3p meaning it can’t bind to the mRNA for NOTCH, meaning that there is more NOTCH around. Is this elegant or what. Let’s hear it for the Blind Watchmaker, assuming you have the faith to believe in such things.

Fortunately lncND is confined to the brain, otherwise we’d all be dead of cancer.

Should you want to read about this, here’s the reference [ Neuron vol. 90 pp. 1141 – 1143, 1255 – 1262 ’16 ] where there’s a lot more.

Historically, this was one of the criticisms of the Star Wars Missile Defense — the Russians wouldn’t send over a few missles, they’d send hundreds which would act as sponges to our defense. Whether or not attempting to put Star Wars in place led to Russia’s demise is debatable, but a society where it was a crime to own a copying machine, could never compete technically to produce such a thing.

Overweight is Good, Obesity is not.

“The attached article from the latest edition of Science News reports on a new study showing that the BMI associated with lowest mortality is 27 — FAT!” If a Berkeley PhD can be led astray by such an article, it’s time to set the record straight. The problem comes from conflating a term of art (overweight — BMI { Body Mass Index } between 25 and 30) with another (obese — BMI over 30). A BMI of 27 isn’t FAT but a BMI of 30 is. But normal people (even a Berkeley PhD) use the words fat and overweight interchangeably. To an obesity researcher they are not (in fact they don’t use the term fat at all).

To get started, calculate your own BMI– http://bmicalculator.cc/?gclid=CM66rIG2tc0CFYQ2gQodOdINEg. Don’t worry that BMI is usually given in kiloGrams and Meters, the site lets you put in your weight in pounds and your height in feet and inches. A 6 footer would have to weigh 222 pounds to be obese.

I’ve been posting that something is wrong with our model of obesity and mortality for years. The Nation continues to get fatter and fatter, and yet lifespan continues to increase. After *** you’ll find a post of 2013 about a paper showing that as we get older, the lowest mortality is with a BMI over 25, increasing each decade.

The new paper cited is interesting, as several different cohorts of the (rather homogeneous) Danish population were studied over time. The BMI of least mortality changed depending on when the cohort was recruited (1976 to 1978) vs. 2003 to 2013. The minimum mortality was 23.7 for the first cohort and 27 for the second.

Should you gain or lose weight to get to a BMI of 27? Not at all, although every continuous curve must have one low point, the mortality rate is pretty much the same between BMIs of 25 and 30. It would be like not going to Yellowstone to increase your chance of survival. Granted road travel has a risk and there are probably no bears where you live, but the increment in survival is not worth tying yourself in knots about.

What you should absolutely not conclude, is that if a BMI of 27 is OK, a BMI 30 and over is as well.  It most certainly is not, and mortality rapidly increases with BMIs over 30.  The higher you go the worse it gets.

Here’s the older post with a lot more discussion of these matters.

****

Something is wrong with the model — take 2

Nearly 4 years ago I wrote a post about the disconnect between the increasing longevity of the US population and its increasing obesity. You can read the whole thing after the ****. The post was titled “Something is Wrong with the Model”. Indeed something is. It doesn’t fit with a lot of data. Those proposing the model don’t like this at all. You can read all about the brouhaha in the 23 May issue of Nature (pp. 428 – 430).

In general I tend to skip medical articles involving meta-analysis under the garbage in garbage out theory. The most egregious example was Women’s Health Initiative when 3 separate meta-analyses of a bunch of uncontrolled studies concluded that estrogen replacement therapy decreased the risk of coronary heart disease by 35 – 50%. The gigantic (161,100 women followed for 12 years, with 1,000,000 clinic visits) Women’s Health Initiative trial of hormone therapy to prevent coronary disease was halted earlier than planned when it was found estrogen based therapies increasedthe risk of coronary heart disease, stroke and breast cancer.

The excitement was over a paper [ J. Am. Med. Assoc. vol. 309 pp. 71 – 82 ’13 ] which performed a meta-analysis on 97 studies of body weight and mortality which in aggregate involved nearly 3 million people.

A popular measure of weight is the body mass index (BMI) which is weight in kiloGrams divided by your height in Meters squared. Not something which is obvious. If you want to figure yours know that a kiloGram is 2.2 pounds, and a meter is 39.37 inches.

At any rate a BMI over 25 is considered overweight, and one over 30 is considered obese. At 6 feet 1+ (which I used to be) a weight of 190 puts me at 24.69. To be obese (BMI over 30) I’d need to weight 228 (which I almost did 50 years ago).

When you plot BMI vs. probability of death you get a U shaped cure, with the very thin and the very fat showing increased risk of dying (mortality). The Nature paper is interesting as it shows 6 curves for people at ages 20, 30, 40, 50, 60, 70. As one might expect the curves for each age lie below the next oldest. All of them rise with BMIs under 20 and over 30, so there’s no argument about whether obesity is bad for longevity.

Well, if the curve is U shaped, it has a minimum. The excitement comes in because the healthiest weight (the minimum) is a BMI of just over 25 for those in their 60s and around 26 for those in their 70s. Also in ALL 6 age groups the curve is pretty flat between 25 and 30, rising on either side of the range.

Naturally people who’ve invested their research careers in telling everyone to diet and that weight is bad, don’t like this, and a symposium involving 200 unhappy people convened 20 February at the Harvard School of Public Health is described, along with a lot of the back and forth between the authoress of the study (Flegel) and Willett of Harvard who didn’t like it one bit. The best comment IMHO is from Robert Eckel “We’re scientists. We pay attention to data, we don’t try to un-explain them.” Read the article, it’s well written and there’s a lot more.

One final point, which might explain why the minima of the curves shift to higher BMIs at older age — which the article didn’t contain. People lose height as they age, yet the BMI is quite sensitive to it (remember the denominator has height squared). The great thing about BMI is that it’s easily measured, and doesn’t rely on what people remember about their weight or their height. Well as a high school basketball player my height was 6′ 1”+, now (at age 75) its 6’0″. So even with constant weight my BMI goes up.

Well it’s time to do the calculation to see what a fairly common shrinkage from 73.5 inches to 72 would to to the BMI (at a constant weight). Surprisingly it is not trivial — (72/73.5) * (72/73.5) = .9596. So the divisor is 4% less meaning the BMI is 4% more, which is almost exactly what the low point on the curve does with each passing decade after 50 ! ! ! This might even be an original observation, and it would explain a lot.

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Something is wrong with the model

Back in grad school when a theory came up with a wrong prediction, we all clapped hands because it showed us exactly where a new theory was needed, and just how it failed. No casting about for something to work on. A program that crashes intermittently is very hard to fix. Once you’ve found input that consistently makes it crash the job becomes much easier.

The Center for Disease Control released new data for 2007 (based on 90% of all USA death certificiates) showing that mortality rates dropped again (by over 2%) to 760/100,000 population. It’s been dropping for the past 8 years, and viewed longer term is half of what it was 60 years ago. Interestingly death rates from heart disease dropped a staggering 5% and even cancer dropped 2%.

But the populace is fat and getting fatter. This has been going on for 30 years. You can Google NHANES for the gory details, but the following should be enough. [ Science vol. 299 pp. 853 – 855, 856 – 858 ’03 ] The data from a recent NHANES (’99 – ’00) shows that the percentage of obese (as opposed just overweight) increased from 23% in the surveys from ’88 to ’94 to 31%. This is based on the body mass index (BMI). Someone 6′ 1″ would have to weigh 225 pounds to be obese.

We are told to be prepared for an epidemic of diabetes, high blood pressure, elevated blood lipids because of this. Every doc has seen blood sugar drop, blood pressure lowered, lipids come down in people with any/all of the above when they are able to lose a significant amount of weight. These diseases are significant only if they kill people, which they certainly seem to do in my experience. The next time you’re visiting a friend in the hospital, look at what’s lying in the beds. Very likely, many more than 31% of them are obese.

So why are death rates dropping and people living longer? Something must be wrong with the model — it’s pretty hard to quarrel with the data as being inadequate. Certainly the increased incidence of obesity should have produced something by this time (it started 30 years ago).

Well, the self serving answer for the drug developers is that their drugs are better. MDs would like to think it’s due to better care. Possibly. Here’s some detail.

#1: More people are exercising than they used to. How many joggers and walkers did you see on the streets 20, 30 years ago?

#2: Fewer people are smoking. Forget lung cancer (if you can). The big risk for smokers is premature vascular disease. Normally we all have carbon monoxide in our blood (it comes from the breakdown of hemoglobin). [ Brit. Med. J. vol. 296 pp. 78 – 79 ’88 ] Natural carbon monoxide production would lead to a carboxyhemoglobin level of .4 – .7%, but normal levels in nonsmokers in urban areas are 1 – 2%. Cigarette smoke contains 4% carbon monoxide, so smokers have levels of 5 – 6%. This can’t be good for their blood vessels.

#3: Doctors know more than they did. My brother is a very competent internist. He took over the practice of a similarly competent internist after his very untimely many death years ago. Naturally he got all the medical records on the patients. He found letters (now over 25 years old) from the late MD to his patients informing them of their lab results, and assuring them that their cholesterol was just fine at 250 mg%.

#4: The drugs are better. In addition they may be working in ways that we have yet to fathom. Consider the statins — their effect on vascular disease is far greater than their effect on blood lipids (cholesterol, triglycerides) — particularly when compared to other agents that lower blood lipids to the same extent.

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