Author Archives: Cameron

London Doctors Pioneer Treatment of Hypertension-Causing Adrenal Adenomas

Aldosterone Structure
Aldosterone Structure credit: Wikipedia

Aldosterone is a steroid hormone produced in the adrenal glands. It regulates potassium and sodium exchange in cells, so it can have a drastic long-term effect on blood pressure. Studies have found that between 5% and 20% of cases of high blood pressure is caused by high aldosterone levels caused by benign growths on the adrenal glands. These growths can be classified as adrenal adenomas or bilateral hyperplasias. Other conditions can be caused by these growths, including Cushing’s syndrome and hyperandrogenism.

credit: Wikipedia

Hyperaldosteronism has been diagnosed historically by Adrenal Venous Sampling. It’s accomplished by running a catheter through the femoral vein up to each of the adrenal veins and sampling blood as it leaves the adrenal tissue. The amount and proportions of cortisol and aldosterone are compared. The method carries a risk of producing venal thrombosis and misdiagnosis is possible. If a diagnosis is made adenomas can be removed by laproscopic surgery. Bilateral hyperplasias can’t be removed surgically and need to be treated by medication and lifestyle. The CT images at the bottom of this post show the difference between the two, and demonstrate how difficult visual diagnosis can be.

In a paper published a few days ago in the Journal of Hypertension 25 patients were recruited for an experimental procedure to locate and remove adrenal adenomas from patients suffering from primary aldosteronism. Doctors used a Carbon-11 Metomidate radioactive tracer to mark adenomas for PET-CT identification. Where a diagnosis couldn’t be made via the PET scan, venous sampling was used as a second-line test. The PET process achieved 80% diagnostic accuracy, venous sampling achieved 75% accuracy, and the two combined were 100% accurate.

Twenty of the patients were able to be surgically treated. All of these patients saw their blood aldosterone levels return to normal. For five of them their blood pressure went down to normal levels. Ten others experienced some improvement, and five had no clinical improvement their blood pressure, giving an overall efficacy of 75%. None of the patients reported long term side effects. The surgical procedure itself was took about 10 minutes to carry out.

The study only involved a few patients so further studies are needed, but appears very promising for patients suffering high blood pressure. Patients with Cushing’s syndrome, hyperandrogenism, or hyperfeminization could also potentially benefit.

Black arrow indicates adenoma
Black arrows indicate bilateral hyperplasia

Study of Grey Wolf Packs Examines Pack Stability

A study published in Frontiers of Ecology examined grey wolf pack stability over 33 years from five National Parks in the United States. The parks were located in Alaska, Wyoming, and Minnesota. Many different population management methods have been used throughout American history, ranging from complete eradication to full protection under the Endangered Species Act. The study examined the survival of packs based on mortality levels of pack members.

Grey Wolf
Image Credit: Yellowstone Wolf Project

The largest average pack sizes were found in Yellowstone and Grand Teton National Parks, where sizes ranged from 6 to 8 in the early spring, to 10 to 12 in the fall. The smallest were found in Voyageurs National park, with a range of 4 to 5 members.

The authors found that human caused deaths tended to have much more drastic effects than natural deaths among pack members. Human caused deaths are more likely to involve pack leaders, breeding females, or other healthy members of the pack. When breeding females were lost, the pack was very unlikely to produce pups the next spring. Most human caused deaths were from legal hunting or illegal poaching. Most accidental deaths were caused by vehicle collisions. Packs that experienced no human caused deaths had an 80% or greater chance of persisting into the next year. Human caused deaths reduced that chance to as low as 60%.

The authors noted that the dissolution or collapse of a pack doesn’t necessarily imply the death of all the pack members. Pack dissolution and formation are a natural part of the lives of grey wolves. Overall populations tend to persist even when packs come apart.

Voyageurs National Park Wolf Project

History of Wolves at Yellowstone Video

An Attempt to Build on Dr Grimes Viability of Conspiratorial Beliefs

Got a secret
Can you keep it?
Swear this one you’ll save
Better lock it in your pocket
Taking this one to the grave
If I show you then I know you
Won’t tell what I said
‘Cause two can keep a secret
If one of them is dead?

The Pierces

Like most folks I enjoy a good conspiracy theory, and sometimes even a bad one. They allow the listener to suspend disbelief and consider for a moment that the social world in which he or she resides is an illusion. This is somewhat similar to the ability to slip into the fictional world of a story or novel, and bears similarity to the Gnostic assertion that the material realm is an entrapping illusion. In fact many very good films are based on the idea that conspiracies are afoot–the Bourne, Mission Impossible, Star Wars, and of course the Matrix series.

In modern societies, however, there are many conspiracy theories peddled as truth that cause real harm to their believers. One recent example from my country is PizzaGate–a ridiculous story based on the notion that a major Presidential candidate was operating a pederasty ring through a Washington DC pizza place–that led to a believer carrying out an armed raid. Another example is the belief that childhood vaccination is a cause of autism, an assertion founded on falsified data. Failure to vaccinate has led to numerous outbreaks of dangerous epidemic diseases in just the last few years. I should note that the longest running conspiracy theories I know of aren’t theories at all because in the minds of their believers they aren’t disprovable. In that sense they are fictitious certainties,

In his 2016 paper “On the Viability of Conspiratorial Beliefs” Dr David Grimes of Oxford University applied probability models to four popular conspiracy theories–NASA moon landing hoax, climate change fakery, vaccination-autism link, and cancer cure conspiracy. His model simulates the probability of a conspiracy being leaked using a Poisson distribution with conspiracy population parameters based on exponential decay, the Gompertz function, and no change. In order to understand Dr Grimes work a little better, I coded his equations in Octave and simulated his parameters, replicating the generalized results displayed in Figure 1 of the paper.

Comparison to Grimes general calculations

Three graphs showing my calculations, with Figure 1 from Dr Grimes paper in the lower right quadrant

In examining the work I found one point where I wanted to test a variation. The population parameters and probability distribution used in the paper are continuous–the growth models allow for fractions of a conspirator to leak information. I added an integer rounding function to the population parameters. I also decided on a binomial discrete probability function, where the probability of a leak is treated similarly to the probability of a defective part rolling off an assembly line. I also added a parameter that allows for a leak, presumably to the press, to require verification from additional defectors.

To test this formula I decided to test the fake job numbers conspiracy. The idea is that the US Bureau of Labor Statistics was cooking up job numbers in 2012 to assist the reelection campaign of President Obama. A few problems with this are:

  • The BLS employs more than 2500 individuals with a broad range of political preferences
  • Job figures are also measured by private firms, such as Gallup and ADP, which show different raw numbers but similar trends
  • There really isn’t any evidence that the average American voter pays close attention to economic releases

The down side to the discrete formula is that it uses factorial numbers to calculate the binomial coefficient. Numbers greater than 170! are treated as infinite by Octave. To mitigate this, I used a conspiracy population of 100, with an individual chance of leaking at 0.1% per year, and a press requirement of 2 defectors to publish the leak. While it looks like it would be possible to carry out such a conspiracy one time, the chance of the conspiracy being broken in 80 years is 91.49%. If one assumes a single leaker is sufficient, the chance of discovery rises to more than 99.99%. The chance of carrying it out with the involvement of 2500 must be slim indeed.

Results from discrete probability analysis of the fake job numbers conspiracy forwarded during the 2012 US Presidential election. On the left, requirement of two leakers, on the right requirement of one leaker.

Results from discrete probability analysis of the fake job numbers conspiracy forwarded during the 2012 US Presidential election. On the left, requirement of two leakers, on the right requirement of one leaker.

The m file code for the discrete distribution is below:

 function conspViaDisc(decayType, p, N0, numIt)

x = [1:numIt];     % fill x-vector for x-axis values
alpha = 10^-4;
beta = 0.085;       % alpha & beta for gompretzian function
te = 40;            % mean age of conspirators
lifeExp = 78;       % mean life expectancy of conspirators
lambda = log(2)/(lifeExp-te);
N(1) = N0;
a = 1;  % Number of leakers needed to break the conspiracy

if decayType == “G”
for n = 2:numIt
N(n) = int16(N0*exp(0-lambda*n));     % Gompretzian decay
elseif decayType == “E”
for n = 2:numIt
N(n) = int16(N0*exp((alpha/beta)*(1-exp(beta*(te+n)))));  % Exponential Decay
for n = 2:numIt
N(n) = N0;   % No Decay

L(1) = 0.001;   % Estimated probability of discovery in timestep 1
cumL(1) = L(1);
for n = 2:numIt
if N(n) >= a
summ = 0;
for m = 1:a
s = m-1;
binCoef = factorial(N(n))/(factorial(s)*factorial(N(n)-s));
summ += binCoef*(p^s)*((1-p)^(N(1)-s));
L(n) = (1-summ)*(1-cumL(n-1));
cumL(n) = cumL(n-1) + L(n);
L(n) = 0;
cumL(n) = cumL(n-1) + L(n);


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