Pharmacology Sciences

[Sciences/Pharmacokinetics] Do nano-particles of the Pfizer COVID-19 vaccine cross the blood-brain barrier and infect your brain with mRNA (or will fritz your gonads)?

1. Introduction
[EDIT: Updated the article on 07/05/2021 to reflect some updates on my analysis]

I have recently seen some claims I considered moot resurfacing on social media: first that COVID-19 vaccines render women infertile; second that mRNA vaccines cross the blood-brain barrier and therefore lead to neurological diseases.
These claims have been rebutted by various science communicators including Edward from Deplatform Disease and myself on Skeptical Raptor few months ago, as the Pfizer and Moderna vaccines were rolling out in the US.

Thing is, with anti-vaxxers, claims are never completely dead and keep rising up like some zombies straight out of a Walking Dead episode.

This time, it seems to be materialized through this screenshot, that appear to spread virally on social media over the weekend, especially in various iterations of that screenshot, with a yellow highlight in a table with the following tissue: “ovaries” and total lipid concentrations as only information.

Screenshot depicting estimated aminolipid contents in rats following injection of the Pfizer COVID-19 nanoparticle formulation (source: Facebook).

2. What is the screenshot coming from?

As always, getting back to the source of a document is essential to put this information back into the context. This screenshot appeared to be coming from a leaked document (if I have to judge on the “Pfizer – Confidential” footers) that I was able to find the source. Unfortunately the document is in Japanese but I can speculate this document likely came from an application packet submitted to the Japanese equivalent of the FDA to seek authorization of sale of the vaccine on the Japanese market. 3. What is the document about?
It seems the document provides us with some pharmacokinetics data on the mRNA vaccine done in rats (Wistar Han strain, both males and females) to assess the pharmacokinetics of the nanoparticles inside these rodents to assess the pharmacokinetics of both the lipid nanoparticles and the mRNA (using the luciferase as reporter of mRNA transcription, I will explain it later).
For the majority of the experiments, we have the following situation been used (according to Table 1):

Nanoparticles were used using two aminolipids (ALC-0315 and ALC-1059) at concentrations of 15.3 and 1.96mg/kg respectively. mRNA was encapsulated in these nanoparticles at 1mg/kg (to give you an idea, the actual dose of mRNA in a Pfizer shot is 30ug or 0.03mg from patients ranging of 12 years and older)

Table 1 provides us with some pertinent PK parameters including the half-life (time to eliminate 50% of a drug), the AUC (to compare the relative bioavailability, distribution and calculate the clearance of a drug) and finally the Kanji translated by Google Translate (sorry but that poor Gaijin is illiterate to Japanese despite decades of anime) as “Distribution ratio to the liver“, with 60% of ALC-0315 found in the liver, 20% of ALC-0159 respectively. The number of animals also appear to be N=3/group (male, female as groups).

We have therefore extensive data on the aminolipids metabolism and the metabolites obtained both in vivo (plasma samples mostly), in vitro (using liver microsomes homogenates, a classic in PK/PD studies); distribution of LNPs in tissues and organs using a non-metabolized radio-tracer ([3H]-08-A01-C0 which I quote the document “[3H]-08-A01-C0 = An aqueous dispersion of LNPs, including ALC-0315, ALC-0159,distearoylphosphatidylcholine, cholesterol, mRNA encoding luciferase and trace amounts of radiolabeled [Cholesteryl-1,2-3H(N)]-Cholesteryl Hexadecyl Ether, a non-exchangeable, non-metabolizable lipid marker used to monitor the disposition of the LNPs“, which was given at a dose of 50ug in animals) and finally bio-luminescence assays in which it consisted of injecting 2ug of RNA encapsulated in the LNP formulation in the hind-limbs of rats (we can assume these were adult rats, therefore a weight of 200-250g is not unheard of), followed by live imaging of the animals to track the luciferase activity (following injection of coelenterazine, the conversion of this substrate by luciferase results in bio-luminescence at close proximity which can be detected through a special camera, as Figure 2).

4. What the data is telling us?

The first thing I would tell is that the person behind the yellow highlight not only have absolutely no idea of what to look for in Table 3 but also went into a cherry-picking expedition to use numbers in scaring people with numbers. That person is providing us with amount of the radiolabeled tracer detected in the tissue (e.g. ug/g tissue), with the approximation of total lipids amount in tissue. This assumes that the nanoparticles made it through the tissue complete, but we cannot exclude that we are maybe measuring only the 08-A01-C0 compound accumulation.
In practice, we usually focus our attention on the percentage of injected dose (% ID) when it comes to appreciate the distribution and the delivery of a drug into an organ/tissue. In some fields, like the BBB, such value is usually not sufficient, and we further correct these values to sort the amount that diffused across the blood-brain barrier (BBB) against the amount that is retained in the cerebral vasculature by the time of euthanasia.
Therefore, we have to put our attention on the right-half of the table. I have plotted these values into a plotting software (Graphpad Prism 9) to have a graphical representation.

What we can see is that the LNPs reach a Cmax value of 52.9% of the ID by 1 hour following IM injection and see a biphasic phase of distribution and elimination (which I suspect the drug would follow a 2-model compartment). Liver is the organ with the highest uptake (we know that 60% of the LNPs are uptaken by the liver) with a Cmax of about 18% of the injected dose by 8 hours. This is expected as liver has a formidable blood flow compared to other organ (Q=1500mL/min). Spleen (very important lymphoid organs) comes in as a good second with a Cmax~1%ID by 8 hours. Kidneys in the other hand sees a much lower uptake despite being an organ with a decent blood flow (GFR=~120mL/min) with a Cmax`0.2%ID, suggesting these LNPs maybe eliminated mostly via hepatic clearance route (including metabolism).

[EDIT: I have performed an area-under-the curve analysis, just for the fun of it. We are lacking data, so we will use for informational purpose. The use of the AUC trapezoidal method can allow to guesstimate how much of that radiotracer accumulated in the tissue/organs over the 48 hours period.
If we look at the AUC values of these from 0 to 48 hours, about 57% of the injected dose is found in the liver, 3% in the spleen, 0.25% in the kidneys, 0.17-0.18% in the gonads and finally 0.04% of the injected dose is found in the brain). ]

What about ovaries? Well we are in the same ballpark than kidneys and indeed nothing really much about (0.1%ID after 48 hours). Interestingly, the author hyper focused on female gonads and occulted to show that male gonads (testes) were getting the same %ID (0.07%). I don’t think it was an accident from the author, just a sign of a deliberate attempt to manipulate the narrative by spinning the numbers.
And last, brain, my favorite organ. The amount entering the brain is maybe the lowest of our organ of interest as we measured a meager 0.02% ID there. Keep in mind, we have to be careful on this number as we may have an overestimation here. In the field, when you do brain perfusion and you are about to collect your last plasma timepoint before sacrificing the animal, you have to be sure to perform a “flushing” of your cerebral blood vessels with PBS, to remove any residual blood volume that can contain your drug. Unless you can correct for the vascular volume (which is not as simple), you have to perform this procedure as we did in a paper I collaborated on. Failure to do so can can lead to overestimation of your brain uptake. Until I have evidence of such flushing occurred, we can hypothesize that the investigators sacrificed their animals at the time points, extracted and weighted all organs and proceeded with the radioactive counts. Therefore, that 0.02% ID should be considered as a grand maximum, likely overestimating the real concentration.

Taken together, we can see that aside of the liver and spleen, the uptake of the radiolabeled tracer (and by extension nanoparticles) remains very low in gonads and in the brain, with amounts of 0.1% and 0.01% respectively at 48 hours.

The second set of data we have to look at is the bio-luminescence data (see Page 5). The lab injected 1ug of mRNA in each hind leg, totaling 2ug mRNA in each rat. Considering an average weight of 200g per rat, we can approximate a dose of 10ug/kg for the luciferase assay. As a control (to remove the background noise), control animals were injected with saline buffer. The average bio-luminescence signals were given, and I personally added 10% of this average as an estimated standard deviation to have an error margin, which a value commonly accepted in biological sciences (10% variation around average is considered pretty good data variability).
[Added: The bio-luminescence is also set to a mininum of 10E6 AU, which is important for the rest of the analysis.]

We can see that the luciferase activity at the injection site (which we can refer as our reference tissue) is significantly high within hours of injection (2 hours being the first reported timepoint) and decreases over time. [Added: What is important to note is how does the %ID actually compares to the bioluminescence. The common sense would be the more of the lipids are biodistributing in the tissue, the more mRNA (and therefore luciferease activity) we should detect, no? Well it is more complicated than this. Let’s plot the %ID in the tissue versus the bio-luminescence.

As you can see, an increase of lipid tracer in the tissue does not correlate with an increase in mRNA activity (as seen by Luc activity). It can be meaning two different things:
* The accumulation of the radiotracer present in the LNPs does accumulate in the tissue because of its non-metabolization and therefore may overestimate the half-life of the LNPs.
* Lets assume the LNPs found a way in the tissues, does not mean they made it safely with their cargo. They may accumulate as residues, or may come as empty shells with little or no mRNA left.]

We can assume that the luciferase expression at the injection site last for up to 10 days before being no different of background noise (we also have to be careful to not extrapolate as-is for the spike S protein, as the mRNA and protein kinetics of luciferase enzyme may greatly differ from the recombinant spike protein). However, the risk of off-target effect and having the mRNA expressed outside the injection site seems to be quite dim. Luciferase activity in the liver (which apparently uptake 60% of the injected dose) is down to background level by 48 hours post-injection. [Added: If we look at the profile, we can guess there is some metabolism in the liver that makes the clearance of LNPs and/or mRNA faster than the muscle tissue. From the data of the muscle bio-luminescence, we can see the decay of the bio-luminescence follows a first-order kinetics and puts with a half-life of ~0.75 days].
Ovaries luciferase activity was basically in the range of the saline group (and would be barely detected over noise, if we refer to the expected min. The penetration of the dye emission wavelength should be enough to be caught by the camera, even through solid tissue. If we don’t see any luminescence, it is likely because it is below or same intensity than background in saline) and brain luciferase activity in the brain was basically noise from the beginning to start (remember we have no access on the standard deviation but the numbers being that close from saline suggest we are scrapping background noise).
In conclusion, the risk of having the mRNA expression outside the injection is very unlikely and meaningless when it comes to biological activity.

5. The perils of dismissing the dose and the allometric scale in assessing the risk
So, we have evidence that the LNPs are pretty safe by barely accumulating in gonads and in the brain, that the mRNA activity is mostly not being found to have off-target, but what about the dose and how does it correlate to clinical situation?
This is where important concept of doses and allometric scale have to be introduced.
First, the dose used for the PK study. It was 1mg/kg of mRNA given in rats. As a comparison, the regular dose of the Pfizer vaccine is 30 ug (0.03mg) given to any patient of 12 years and older.  An average 12-years old girl would be 40 kgs per the CDC chart (rounded up to the lower value and for the ease of calculation). This would indicate a dose of 0.00075mg/kg. That’s already a difference of 1333-fold between what we gave to these rats and what we gave to humans, but there is more!
We also have to account to the allometric factor, because rats are not small human. [EDIT: For adjusting to the allometric scale, we will use this calculator ]. The allometric scale tells us that 1mg/kg dose in rats results in a human-equivalent dose (HED) of 68mg/kg if your patient is a 70-kgs adult; 45mg/kg if you are a 40-kgs teenager (~12 year old girl falling in the 50th percentile of the CDC growth chart).

Therefore, we have to multiply it by 45 (40-kgs patient) or 68 (70-kgs), which means if we want to transpose the PK findings as done in the rats, we would need to inject about 60’000 doses of the Pfizer vaccine in ONE girl (91’000 doses if you are a 70-kgs adult). That’s about half one-fourth of all doses distributed to Amarillo until now given to only ONE person [EDIT: One 12-year old teenage girl that is in the 50th percentile], ALL AT ONCE! You see where we going? The very extreme implausibility of the claims that COVID19 vaccines affect ovaries and the brain.
To finish it up, we can also look at the actual mRNA and luciferase.
We know that 8microg/kg was sufficient to see some liver activity, but no activity in gonads and brain. How does it translate to humans? First, lets apply the allometric scale (68x). We would need 544microg/kg for the HED, and translated to a 12-years old girl that would be 21760microg of mRNA delivered, which is about 725 doses of Pfizer given in ONE person at once! You can see that since we cannot detect notable activity if I give 725 doses at once, chance are I will not detect any activity when given a single dose or even two doses of Pfizer.

6. Concluding remarks

In conclusion, we can take the following messages:
– This is a document leaked on the PK of nanoparticles as found in the Pfizer vaccine, showing animal studies have been done before or during the clinical trials and we have the documentation.
– It helps clarify an ambiguous statement made by Pfizer in their summary submitted to the European Medicine Agency a couple of weeks ago about the distribution of the mRNA vaccine.
– The studies were done in a very conservative fashion at doses exceptionally high and impossible to reach in humans
– At such doses, it was shown that aside from the liver and spleen, the distribution of LNPs was minimal in gonads and the brain.
– The amount of mRNA required to be present in the tissue to appreciate an off-target effect is ridiculously low and impossible to achieve in real life and was transient in the liver.
– When accounting for the clinical dose and the allometric scale, this study shows that the Pfizer vaccine is very safe with a very low incidence of the off-target effect. To achieve the same result in humans, it would take a ridiculously high amount and a sheer incompetent healthcare practice to have the probability of having any issues of off-target effect occur in humans.


[Neurosciences/BBB] The SARS-CoV-2 spike protein alters barrier function in 2D static and 3D microfluidic in-vitro models of the human blood–brain barrier (Buzhdygan et al., Neurobiol. Dis. 2020)

Recently, a study from Ramirez and colleagues was brought to me on my attention and asked my view on it, as it was discussing SARS-CoV-2 and the blood-brain barrier (BBB). Myself dealing with manuscripts submissions (2 undergoing revisions, 2 manuscripts in preparation), I had to put it on the back burner until I could address it.
Thanks to Thanksgiving break, I can finally put my hand on and review it as it raises some good questions that several colleagues (including myself) are currently asking: What is the effect of COVID19 on the BBB? Does the neurological symptoms observed in COVID19 are linked at some form with the BBB? Can SARS-CoV-2 cross the BBB? What is the function of ACE2 at the BBB? Servio and his group decided to address that question by running this study in their lab, and after having a pre-print on BioRxiv finally found a place to have it peer-reviewed and published.

Intro and methods: In this study, Ramirez and colleagues decided to investigate how elements of the SARS-CoV-2 spike S surface proteins would impact the cell viability of primary human brain endothelial cells (hBMVECs) and compared it to hCMEC/D3 (a common cell line used as a model of the human BBB in vitro). They used various techniques including cell viability, molecular biology to assess changes in expression at mRNA and protein levels, barrier function (using the ECIS TEER and permeability to FITC-labelled 4kDa dextran), all of it in two models: one 2-dimensional (Petri dish) and one 3-dimensional (microfluidic chip). One of the caveat, and I completely understand as I have the same issue, is the use of recombinant proteins instead of a full SARS-CoV-2 virus. This is because at this point, the use of SARS-CoV-2 in the growth of virus requires a BSL-3 research laboratory. These are very uncommon and restricted to big institutions as it requires a level of biosafety drastically higher than a BSL-2 laboratory that is common place in many research universities.

Results: The first results presented is the expression of ACE2 at protein levels in small vessels of human brains tissue sections obtained by post-mortem brains from patients with a history of dementia or hypertension (Fig.1). What is interesting is what appears an up-regulation of ACE2 expression (or an increase in ACE2-positive microvessels) in dementia and hypertensive brains. This is interesting, because it would be interesting to see how those two conditions are accounting as risk factors for COVID19, or if such conditions were associated with higher incidence of neurological symptoms. What is also interesting, is that ACE2 expression is only mildly induced by TNF-alpha or exposure to recombinant spike S proteins produced in E. coli and HEK293T cells.

Fig. 1

The second figure (Fig. 2) investigated the effect of spike S protein subunits (S1, S2 and the RBD) on cell viability following 48 (A) or 72 hours (B). Saponin (a detergent) was used as a positive control to show that the method can assess live-dead cells. We can see a slight but significant increase in number of dead cells (~5%) in RBD-treated cells. The caveat is the concentration used of these protein (1 and 10nM respectively), which brings us the question of how representative such concentration matches to viral load? Is it a realistic concentration in patients on ventilator or it is just overblown? That’s something that would need to be addressed.

Fig. 2

The weird kind of data, in my opinion, is the measurement of the barrier function using the ECIS TEER and permeability. We see a dose-dependent decrease in the barrier function over 24 hours following treatment with S1, S2 or RBD proteins but one thing that bother me is the amplitude of decrease. We are measuring here less than 0.2% change in TEER compared to untreated cells. That’s low, very low to be honest (usually you are aiming at least 20-30% difference) and this is where I stood unimpressed with the data. Especially we have a conundrum here: how do you explain a minute difference in TEER (<0.2% change), while seeing seeing at least 15-20% increase in permeability to a big molecule (4kDa)? I would suspect that the ECIS TEER is not efficient, or these cells are simply too leaky to see drastic changes on the ECIS (this is supported by the use of 4kDa-dextran, which is about 10 times bigger than more common fluorescent paracellular tracers such as fluorescein and Lucifer Yellow).

Fig. 3

Figure 4 kind of reproduce the Figure 3 results, but this time in the 3-dimensional model, seems like S1 treatment resulted in a significantly higher permeability (5x or 500% increase) than the 2-dimensional model. One thing that bother me is why the author did not applied the same treatment groups (TNF-alpha, S1, RBD and S2). It is interesting also what is the rationale that explain such a drastic difference between the 2-D and 3-D models? It is I think interesting to see if it is a fluke or real, because it may give an upper hand to the later, making the microfluidic a more relevant model than the static model.
Figure 5, using flow cytometry, show that treatment with S1 or S2 (but not RBD) are somehow capable to activate hMBVECs and prime them to recruit immune cells from the periphery, as we could see an increased expression of ICAM-1 and VCAM-1 on the cell surface, such up-regulation seems to ocur as early as 4 hours and peak by 24 hours.
Finally, gene expression show that overall S1 seems to be a major driving force in gene expression, as we can see a significant increase in CCL5 and CXCL10, with CCL5 gene expression at 24 hours being peaking for all three treatment (S1, S2 and RBD). In the other hand, all three proteins were sufficient to increase gene expression of MMP3 and MMP12, two matrix metalloproteinases that could possibly induce the disruption of cell-cell junctions.

Conclusion: This is a nice little paper that brings a bit of information on SARS-CoV-2 at the BBB. Surely, spike S protein can interact and transduce some signals at the BBB which ultimately result in change in the barrier function. However, we only have fragments of it as we have to see how changes in tight junction complexes correlate with changes in the barrier function. We also have to address how relevant the concentrations used. I think 10nM is pretty high and would require some huge amount of viral load to happen, hence it would be great to compare and contrast it to viral load (I would see the use of ChadOx adenoviruses expressing spike S proteins as an alternative to the full-swing SARS-CoV-2).

Gothic Metal Metal Music

[Metal/Prog] Ghost – Opus Eponymous (10th anniversary review)

This week marked the 10th anniversary release of “Opus Eponymous” from the Swedish band Ghost, which is fronted by Tobias Forge (aka Papa Emeritus). My listening to this album was in a retrospective as I was introduced to the band through their third album “Meliora” with “Cirice” four years ago. As of today, I still consider their first album as their best album. Maybe for the musical style of it, maybe from the overall atmosphere from it.
It is personally an awesome album to listen to on a regular basis. It is a 9 tracks album that is short (34 minutes shy) but nicely crafted with almost no filler. It is a festival for the hear, with this rough guitar reminding me of the early age of progressive rock, coupled with a primitive sound of a MOOG synthesizer (with primitive not being used as a pejorative connotation here, more like in the idea of the original), giving this late-70s/early-80s feels to it.
We start with “Deus Culpa” which is an church organ, which introduce us quickly to “Con Clavi Con Dio” which has this particular trance effect on me, almost psychedelic. A very good track. “Ritual”, the third track, help us to chill out while navigating through the lyrics.
Then comes the piece of resistance. My favorite track. “Elizabeth”, the fourth track. Completely devoted to tell the tale of Erzebeth Bathory, a Hungarian princess which holds on the most cruel woman from the middle-age, rivaling with Vlad “Dracul” in terms of her lust for blood, including the infamous legend (or myth) of her youth beauty perpetuated by bathing in the blood of murdered virgins. It is fast-paced, with a very complementation of the guitars and keyboards. Listening to “Elizabeth” from the original demo tape and the finished product is showing the refined product that was already there, only perfected by professional recording in a studio. We resume the trip into the darkness with “Stand by Him” and “Satan Prayer”, also very good. The seventh track, “Death Knell”, is my second track. Again, a track already present in the demo tape that was rearranged, with these Black Sabbath bells. The eight track “Prime Mover”, is another of these tracks present in their demo tape. It has this nervous and menacing guitar tones, giving this malveolent atmosphere.
“Genesis” is the closing instrumental track of the album. A bliss of primordial MOOG synthesizer coupled with excellent proggy/psychedelic rock riffs, transitioning into that synthesizer waves resulting in this absolute epic epilogue to the album, reminding me of the early days of the 80s.
If you have to get introduced to Ghost, I would certainly recommend to do it by starting with their first album, remaining at the pinnacle of my library from this band.

Neurosciences Pharmacology

[Sciences/Pharmacology] Death by Benadryl Tik-Tok challenge: making the case on an interesting neurotransmitter

Another day, another “dose makes the poison” day. This time, it is about diphenyhydramine (Benadryl(R)). You surely heard about the recent “Tik-Tok challenge” launched by some folks that is basically overdosing on Benadryl(R) with some report of death as reported here. So far, we have 3 teenagers in Texas that had to be hospitalized following overdose on Benadryl (up to 14 doses at once) and one death on Oklahoma (dose unreported). Diphenyhydramine is usually taken as an anti-allergic due to its anti-histamine activity (which is a major molecule released by basophile white blood cells, responsible for the allergic response). Interestingly, the reason these kids took Benadryl(R) was not for a major allergic reaction to pollen or animals hair. But apparently “to get high”. This raised me some questions as histamine is not a major neurotransmitter as glutamate, or dopamine are.

This raised my curiosity about the role of histamine in the central nervous system (CNS) and how would diphenylhydramine come to play? As usual I love to start with the chemical structures. Histamine is on the left, diphenyhydramine is on the right:

As you can see, histamine is not too far from histidine, an aminoacid. The only thing missing is the carboxyl group (-COOH) on the carbon alpha. Diphenyhydramine that I will call DPH to ease the typing) has not much anything in common with histamine.
Histamine is not a common neurotransmitter, and indeed has a very specific nucleus, according to Haas and Panula (, located in the tuberomammary nucleus, which appears located between the pons and the thalamus, likely part of the hypothalamus. As other nuclei, the histaminergic system is made by projections towards various region of the brain as represented below:

We can see projection into various region including the striatum/substantia nigra (which is involved in movements execution and affected in Parkinson’s disease), cerebellum (involved in the gait posture and coordination in movements like walking), hippocampus (memory formation) or amygdala (which deals with various things including pleasure). What is more interesting is the presence of projection into the medulla, which means it can likely modulate some vegetative functions including breathing or hearbeat regulation.
What is interesting is that such histaminergic system appears well conserved in evolution. We found in mollusk and we found it in mammals, which is interesting. It also has 3 major receptors in the brains (named H1R, H2R and H3R respectively). The biological functions of histamine appears various and include function in the wake/sleep cycle, inhibitor of neural function (which is important as we discuss DPH pharmacology), feeding behavior, fluid intake regulation, thermoregulation and others. But what is interesting is the ability of histamine to act as a hedonist molecules, including impaired reward behavior and altered cognitive functions when volunteers were given H1-antihistamines.

This brings us to the pharmacology of anti-histamines. Interestingly, the first generation of anti-histamines was marked by their persistent side effects on the central nervous system (CNS) and included DPH. These first-generation of drugs side effects were somnolence (a common side effect reported with Benadryl), drowsiness, lack of concentration and attention. The reason why such side effects occur is because these compounds have a very good blood-brain barrier (BBB) permeability, which can exert their central effects easily. To remediate with such issue, a second-generation developed in the aim of reduced BBB permeability was developed such as fexofenadine (Allegra(R)) which is commonly sold as “non-drowsy” anti-histamine.

Now if you look at the Lexicomp (which is a drug database pharmacists commonly access to obtain a detailed drug information), there is an important warning on Benadryl(R): “CNS depression: May cause CNS depression, which may impair physical or mental abilities; patients must be cautioned about performing tasks which require mental alertness (eg, operating machinery or driving).”. If we dig in further we can see two major adverse effects reported:
Cardiovascular: Chest tightness, extrasystoles, hypotension, palpitations, tachycardia
Central nervous system: Ataxia, chills, confusion, dizziness, drowsiness, euphoria, excitement, fatigue, headache, irritability, nervousness, neuritis, paradoxical excitation, paresthesia, restlessness, sedation, seizure, vertigo

There is a serious risk on the cardiac side, whereas we can see that on the CNS side we have some effects sought as it use for recreation (euphoria, excitement, paradoxical excitation) but also that can be potentially dangerous (ataxia, sedation, seizure). These reactions are anticipated with a normal dosing, now you can imagine if you significantly increase the uptake with a very high dose.

If you are a parent, please discuss with your children about this challenge in a calm and posed manner and explain them why it is more dangerous that it is.

Pharmacology Sciences

[Sciences/Pharmacology] A tale of death by licorice poisoning

I guess you have heard in the last couple of days about this poor man death from a severe cardiac condition triggered by a severe case of hypokalemia (low potassium levels) as reported by the APNews here. The culprit in this death? Overconsumption of black licorice (one pack a day for few weeks).

So, how can it be possible? Well, to understand how licorice can be dangerous, and how this case is another validation of Paracelsus axiom “the dose makes the substance poison”, we have to go back into some biology and chemistry, all wrapped up in what we call “Pharmacology” and especially a sub-genre of it we call “Toxicology”.

Licorice (Glycyrrhiza galabra, remember that name for later) is an herb commonly found in Europe and West Asia, with edible roots. It can be consumed as-is by chewing and sucking the dry roots (I used to buy them from my pharmacist as a treat) or primarily used for extracting licorice, a black and bittersweet substance.

Like any plants, Glycyrrhiza produce various phytochemicals including chemicals falling into what we call “secondary metabolism”. One of them, glycyrrhizin, is the major chemical sought from these plant, it gives this bittersweet taste that some enjoy and some get repelled (Gosh, I hate processed licorice candies and I would throw my Haribo mixed bags once I ate everything but the licorice ones). Now comes the fun: Here is the structure of licorice:

If you are a chemistry nerd, you will note the two glucuronic acid on the left, but you will find more interest into the polycyclic saturated chain that looks a lot like cholesterol (see below)….

…..or similar to digoxin (a cardiac glycoside that is a potent poison extracted from foxgloves, but also a potent cardiotonic we give to patient suffering from heart failure).

But what is even more interesting is that glycyrrhizin share a lot of structural similarities with steroid hormones including aldosterone (mineralocorticoids, left) and cortisol (glucocorticoids, right):

Glycyrrhizin (or glycyrrhizic acid) is poorly present in the blood and urine, usually found at less than 2% of the injected dose. In the other hand, glycyrrhetic acid (GA), the degradation byproduct is considered the major form that is absorbed and distribute into the body. GA is mostly eliminated via liver metabolism (GA-3-glucuronide) but interestingly can get salvaged by the gut microbiota back into GA and re-enter the body as GA, hence resulting in a pretty long elimination half-life (between 6-10 hours, which would mean it would take 1 to 2 days to clear out a single dose of licorice).

You can appreciate that we are getting closer when it comes to chemical structure to aldosterone and cortisol. Here comes the interesting part. Cortisol can bind to its cognate receptor (glucocorticoid receptor), but also bind to other steroid receptors like the mineralocorticoid receptors (MR). MR target genes are various, but several of them are encoding for sodium (Na+) channels which will work on kidney epithelial cells to induce reabsorption of sodium in the nephrons. This is turn will change the dynamics of electrolytes, as the reabsorption of sodium (Na+) will result in an increased elimination of potassium (K+) by renal excretion. In turn, we will end up in a hypernatremia/hypokalemia situation which will manifest in any excitable cells, in particular in the heart tissue. Both Na+ and K+ play an important role in the heart electrical activity. Mess around with the extracellular concentration of one of these two and you are setting yourself into serious cardiac issues (arrhythmia, fibrillation, conduction block, impaired or asynchronic muscle contractions….weird EKG patterns ahead).

It would be tempting to assume GA would compete with cortisol, or mimic it for binding to the MR. Turns out, GA does not really fit to MR, but fits quite well into the catalytic site of an enzyme called 11-beta-hydroxysteroid dehydrogenase (11-bHSD). This enzyme will convert the hydroxyl group present in the carbon 11 position (see that OH group pointing on the left in the cortisol and aldosterone molecule?) into a keto group (=O). This is enough to kill the ability of cortisol (which now became cortisone) MR activity. What basically happens is that GA will compete with cortisol for 11-bHSD binding, has better affinity for the enzyme and block the transformation of cortisol into cortisone. Result? You create a buildup of cortisol, which means you have an increased activation of MR, increased expression of its target genes, increased Na+ channels and transporters in the kidneys that will increase its resorption during the renal filtration process…..and the resulting hypernatremia and hypokalemia.

It is very unlikely you will an issue in an acute exposure (the FDA recommends to people over 40 to not eat licorice for more than two weeks, keep it below 2 ounces) but likely to occur if ingested chronically. Plus having an history of cardiac events makes you worse.

So please remember the axiom of Paracelsus: “The dose makes the poison”. Limit your licorice as a once-in-a-week treat, limit your intake and avoid it if you have heart issues.


[Rock/Hard Rock] AC/DC – Back in Black (40th Anniversary)

A couple of days ago marked the official release of AC/DC “Back In Black” album, their seventh studio album by the band. Technically, this is the first album of the band with Brian Johnson. For me, this is how I discovered and remember AC/DC. Purists will yell at me about ignoring about the Bon Scott-era, but I stand deep as team Johnson by his rough voice, and by his signature “beret” hat.

You see, AC/DC was my first exposure to hard rock and indirectly to metal (by opening the door to Metallica). I was 11, in middle school and looking to find my own musical niche, in departure of my brother which was fond of new-wave. There was something about the electric guitar, its roughness of it while able to have nice melodic riffs. This is when you get into music because of classmates sharing their in the school yard with their “walk-mans” and bootlegged tapes, sharing the foam headphones with a “listen to that, good stuff”. One of them was “Back in Black”. It marked me for the last 30 years.

And indeed it was. Yes, AC/DC is technically hard rock, with a strong influence of blues infused in it, but damn to have to give credit to the explosive mix of Malcolm and Angus Young at the guitars and the rough voice of Brian Johnson that makes this group the legend of rock.

The album is a 42 minutes album cut in 10 tracks. We got into “Hells Bells” which starts with a bell toll which later will be reused in some form by one of Metallica’s staple track “For whom the bells toll”. Oh yeah, staple signature track from AC/DC. Next come another classic track from the band “Shoot To Trill”. Some good AC/DC shit. Then interestingly follows a series of songs I have been listening less than usual: “What Do you Do for Money Honey”, “Givin the Dog A Bone” and “Let Me Put My Love Into You”. They are good, but did not make an imprint in my musical ear.

Track 6 “Back in Black” is the one everyone know even without knowing AC/DC. It is so popular that it became the trademark song of “Walmart” commercial. It has all the signature of AC/DC. The 7th song, is another song “You Shook Me All Night Long”, another classic playing in loop in radio. The last three tracks are again pretty low on my playlists in terms of count (“Have A Drink On Me”, “Shake A Leg” and “Rock & Roll Ain’t Noise Pollution” which has the most bluesy tunes of the album). They are good but not good enough unfortunately to leave a mark on my cortex.


[Neurosciences/Junk Science] “Aluminium in human brain tissue from donors without neurodegenerative disease: A comparison with Alzheimer’s disease, multiple sclerosis and autism”: Another Exley study, another evidence of questionable quality study.

Recently, Christopher Exley (Keele University) published a study in the journal “Scientific Reports” about the content of aluminum in the brains of “control” individuals and used it to compare it to brain samples of patients that suffered Alzheimer’s disease (both familial and sporadic form), autism spectrum disorders and multiple sclerosis. In this paper, I would assume Exley is trying to tame down a major criticism that plagued his papers, the lack of valid controls (including his 2017 study on ASD brains [1], which was recently criticized here. Can this latest study silence the critics of Exley and vindicate his claims? Let’s figure this out.

1. About the study:

Unlike most of his other studies, this one is published in Nature’s “Scientific Reports”, one of Nature Publishing Group open-access journals. Unlike traditional academic publishing scheme, the “open-access” publishing scheme shift the burden of cost (aka “the paywall”) from the reader to the author. In exchange, readers can freely access the content of a study.
Although this “open-access” can help in improving the outreach towards science, it is also a double-edged sword as the financial incentive of such publication may clash with the peer-review process if you are an editor of such journal. It may be very tempting to accept a study that is not fully compliant with a rigorous experimental design and displays flawed results, as the costs of the article processing fees (that can reach up to $3000 per study) can represent a non-negligible source of revenue.

Scientific Reports’ main scope is to publish any study that is scientifically and technically sound, regardless of the novelty or innovative aspect of the study (this is a common decision factor in high impact factor journals such as Science and Nature). However, another outcome I consider as reflective of journal integrity is the number of retractions occurring. Surely, it is not an absolute and objective outcome, but it can be indicative of the relative health of a journal. And this is not looking that good for this journal.

In the last two years, the journal retracted over 30 papers, which is quite alarming for a journal that is nine years old. The vast majority of retractions occurred in the last two years, suggesting some changes either in the peer-review quality (which I doubt would happen unless reviewers suddenly decided to downgrade their review criteria), or a change in the editorial decision tree that may override some reviewers decisions, by accepting a study manuscript despite reviewers decision to “reject” or “revise” such manuscript.

Going back to this study, the author’s list is indeed pretty succinct: we have Christopher Exley (Keele University) as first author and Elizabeth Clarkson (Wichita State University). She is not a biologist, but likely contributed here as a statistician (see her profile here: At first sight, I would consider the presence of a statistician in a paper a welcome move to ensure a proper experimental design that is statistically relevant and to provide a decent power of analysis to be able to make sense of the data, in a statistical manner. However, the large amount of discrepancies noted in the manuscript was enough to trigger concerns in myself, armed only with undergraduate bio-statistics classes taken during my junior and senior years. We will discuss more into details in the later portion of this article.

2. The introduction:

The introduction is, to say the least, confusing, unclear with a touch of arrogance that is typical from Exley. Here, there is no “funnel” approach (introducing the topic in general, with a converging flow to bring it to the main goal of this study). What is even more concerning is the author’s over-reliance on self-citations. Out of 13 citations in the introduction, 9 were from studies authored by Exley. An introduction should be written to provide a rapid and summarized overview of the existing literature and cite studies that are either supporting or contradicting of the main hypothesis that will be challenged by the study. By ignoring (or minimizing) the existing literature, the author is indicating to us that he is likely coming on in his hypothesis with a confirmatory bias, which can be indicative of a risk of cherry-picking the data or worse, fabricating data to fit a pre-formatted conclusion.

It is also important to note the following information: “Brain banks have themselves struggled with the concept of what constitutes a true control14. We asked one such brain bank to identify a set of donor brain tissues that could act as a control for brains affected and diagnosed with a neurodegenerative disease. The majority of control brains available through brain banks are from older donors and so most still show some signs of age-related degeneration. Herein we have measured the aluminium content of twenty control brains where in each case there was no overt neurodegeneration, no diagnosis of a neurodegenerative disease but some age-related changes in the older donors. We have then compared these data with data, measured under identical conditions, for donors having died with diagnoses of Alzheimer’s disease, multiple sclerosis and autism.

It is important to note that this is not the first study of Exley looking at aluminum levels in brain samples from “healthy donors”, he has indeed published a study in 2012 in the Metallomics journal [2]. What would be considered a good control? That’s a good question. I would assume any donated sample from a patient that died from a disease other than neurological diseases, with a patient history excluding any co-founding factor such as history of chronic kidney diseases (since aluminum is eliminated over 95% via renal route) and/or prolonged feeding using IV bags (total parenteral nutrition). Since we assume the brain-to-blood clearance of aluminum to be a very slow process, we would assume age is a determinant factor (assuming the older a donor is, the higher the aluminum accumulation occurred). Here, we will compare this study to his 2012 Metallomics study [3] for an important reason: the reproducibility of his findings. This is an important criterion in the scientific method: anyone should be able to reproduce your findings using the same protocol, same technique and same type of sampling population. If – as here – the same lab was unable to reproduce the same findings using the same method, there is a problem.

3. The methodology:

From the methods section: “Brain tissues were obtained from the London Neurodegenerative Diseases Brain Bank following ethical approval (NRES Approval No. 08/MRE09/38+5). Donor brains were chosen on our behalf by the consultant neuropathologist at the brain bank. All had a clinical diagnosis of ‘control’ while some had a pathological diagnosis that included age-related changes in tissue. There were five male and fifteen female donors. They were aged between 47 and 105 years old. Tissues were obtained from frontal, occipital, parietal and temporal lobes and cerebellum from all donors.” The original Metallomics [2] study’s samples were from the MRC CFAS study, and unfortunately, I could not pinpoint the exact location of that brain bank. However, we can assume that these two brain banks are likely distinct. That said, I would not expect major differences between brains collected using the same inclusion criteria and from the same country (both are from donors that resided in the UK). Important notice, in addition to the different cortical regions (temporal, frontal, parietal, occipital), Exley added samples from the cerebellum into that study. To allow a direct comparison, we will ignore this extra sampling site for our review.

The technique used for the study is the same: about 1 gram of tissue were minced off, chemically digested and analyzed using the graphite furnace atomic absorption spectrometry (TH GFAAS). GFAAS is analytical technique used in assessing aluminum in biological samples, with variable sensitivity limits (see the table in the ATSDR website:

4. The results:

As expected, we have the same issues that swamped his previous studies: the extreme variability of aluminum contents in two technical replicates from the same sample and how does Exley consider the use of technical replicates.

When you perform an experiment, you are always at risk of having differences in values obtained between experiments. This can be due to various factors: heterogeneity within the sample, errors during the sample processing and analysis, or difference in aluminum contents between individuals. However, you can circumvent and correct such differences in the same individual by increasing accuracy using technical replicates. By using several sampling sites of a same brain tissue sample, you can increase the accuracy of your measure by getting an average value. The higher this number of technical replicates is, the more accurate your estimation is. However, this can be compromised if your technical replicates are inconsistent. That’s the issue haunting his graphite furnace assay, which ends up with huge variation between samples.

Let’s plot the first 10 patients’ brains and see how the technical replicates look like.


The further the dots (technical replicates, each dot color originating from the same donor sample) are, the higher the variability within a biological replicate is, with the horizontal bar representing the average. As you can see, we have several instances of huge variability within an individual. Which amount is correct? The lower measurement, or the higher measurement? We can answer that by increasing the number of technical replicates, but you may be limited by the amount of biological material (in this case brain tissue) you have access to. You have also an important variability between individuals, as you can see some individuals show very high levels of aluminum compared to others, in a defined brain region. You cannot exclude these individuals, unless you have a very good reason to (but normally that would be excluded prior the data analysis, by excluding tissue samples from patients aligning one of your exclusion criteria). However, excluding them – even for a reason that seems valid – creates a risk of cherry-picking your data and biasing your statistical analysis. One thing must be clear: You can only use biological replicates (aka individuals) in your statistical analysis. You cannot use technical replicates from the same individual and consider them as separate biological replicates. This is especially where the data becomes murky and unclear when it comes to Table 4. As you can see, the N number cited by Exley for the sporadic form of Alzheimers disease (sAD) cite N=1394 and N=1322 with P-values obtained using the Wilcoxson analytical methods. Where these numbers come from? I don’t know, and Exley fail to clarify this important information.

Unless Exley clarify and provide the aluminum concentrations of each of these N numbers individually (and make it clear which ones are technical replicates from each patients), I will not trust any statistical analysis.

The second issue I have with the results is how to they compare to his 2012 Metallomics paper. Remember, reproducibility is the key event to have a finding scientifically accurate. The inability to have another scientist reproduce your findings using the same methodology than yours (or worse not able to reproduce your own data) is a serious red flag that usually ends up as a retraction. This is the very same issue that cost Judy Mikovits her Science study, as nobody including herself were able to reproduce the ability to detect XMRV in blood samples from patients suffering from chronic fatigue syndrome.

Therefore, using the average concentrations per individuals from his 2012 study, I plotted them against his 2020 study (excluding the cerebellum samples from the analysis, as such samples were not present in his 2012 study). Here is the graphical statistics.


This is concerning.

Out of four brain regions, three showed a significantly lower amount of aluminum in the 2020 samples compared to the 2012 samples (t-test, unpaired, Welch’s correction assuming inequality of variances between groups). It is telling us that not only Exley cannot reproduce his own findings from a decade ago, but he may also consistently underestimate such value, considering that the power of analysis of his 2012 paper surpasses this one by its sample size (the 2012 study has a N number of individuals equal to 60, the 2020 has a N number of only 20. Remember, in statistics, the higher the N the better it is).

How does Exley explain that difference? I would assume the age has nothing much to do, differences in the inclusion criteria of donors should be similar, the experimenter must be the same (Exley authored both papers), the technique used for analysis is the same (TH GFAAS). This leaves us with an emptiness. Nowhere in the discussion does the author discuss this discrepancy, which is important as it changes the whole data interpretation.

The third issue comes with the “pathological samples”. Exley is pretty frugal in telling us where such samples are coming from, even less willingly sharing the detailed information about these samples (in terms of patient’s individual information such as age and sex, aluminum content in terms of technical replicates in brain regions). Exley mentions that data are freely available upon request by email, but I am surprised he did not provide such tables as supplementary materials visible to the public. Is Exley on a fishing expedition to hunt down his detractors? I don’t know but asking him to disclose such data will requires his detractors to fully disclose their identity.

However, I have prepared a table that summarize his data and the previous data I was able to collect from his previous publications [1, 2, 4]. I used individual average brain concentrations and obtained the statistical analysis using the built-in function of Prism 8.0 (GraphPad Software, La Jolla, CA).

Screen Shot 2020-05-08 at 9.59.13 PM

If I have to compare the average values reported in this paper, compared to his previous publications. I would assume the values reported for FAD and ASD comes from his previous work [1, 2], as the average brain concentrations from patients calculated from his table . My question is why does he provide us only with statistics (mean, median) and not providing us with the raw numbers? I would consider Exley would use a transparency approach by displaying all the raw data for AD, ASD and MS samples, in a table form as he did in Table , to allow everyone to perform a statistical analysis to verify his claims.
Yet, he decided not to (but will provide such data upon request).
You can also see that if you pick one control versus another (lets say the controls from 2020 versus the ones from 2012), you can reach a very different conclusion. You can see that if you cherry-pick the right controls, you can tell a very different story. My common sense would be that we should merge both controls into a single group to enhance its statistical power of analysis and re-run the statistics of the diseased groups versus that control group.

What if we merge both the 2012 and 2020 controls into one group? Well it gives an unfavorable outcome to Exley as we can see here in this graph.


The fourth and final issue I have is in the statistics, even more so when you have a statistician onboard. Exley used the analysis of variance (ANOVA) for the statistical analysis. To be able to perform an ANOVA, you need to fulfill three requirements: independence (in other words individuals are not biologically related to each other’s), homoscedasticity (the standard deviations are equals) and the samples follow a normal (Gaussian) distribution. Ideally, you want to achieve a similar N numbers between the groups.

Here is what Exley is telling us: “The distribution of aluminium content data is heavily skewed in the treatment groups. Data are not balanced with the number of observations and their respective lobe varying considerably between treatment groups. There is large variability in repeated measurements taken from the same donor. Analyses were performed for both the unweighted observations and means across all lobes for each individual. The assumption that the data across all groups are normally distributed, an assumption that underlies any ANOVA model, is questionable at best.”

Wait a minute, is Exley telling us that the data quality is so poor that we should be a fool to expect to get an accurate statistical analysis with the tools he chose to use?  If I quote Mark Twain “There are three kind of lies: lies, damned lies and statistics”. If there is something to be learned about bio-statistics is to be careful about its use and the meaning into a context. The search of a positive statistical outcome is not new and is a common bias that can plague studies, commonly referred by Johnathan Ioannidis as “P-hacking”. Surely, you will find an obscure or rarely used statistical method that will tell you there is a significance when there is not.

To be honest, I am not sure you can make a sense of the statistics because of the high variability. Sure, you can find an obscure statistical test that will tell you have a statistical significance. But that’s has a name. It is called “p-hacking” and it is bad.

I am personally concerned that Elizabeth Clarkson gave her seal of approval on that.
As mentioned in the acknowledgments section: “E.C. carried out all statistical analyses. C.E. and E.C. wrote and approved the manuscript.

I would have been more comfortable in having a bio-statistician consulting for the journal to review the statistical analysis because I am not at all convinced by the data.

Final point, on a more sarcastic tone: the last thing I would ever consider in my manuscript is to self-referencing infomercials that were written in non-scientific journals, especially when it is written on a website with questionable credentials as Exley concludes the discussion with that sentence:

We may then live healthily in the aluminium age (

5. Citations:

  1. Mirza, A., et al., Aluminium in brain tissue in familial Alzheimer’s disease. J Trace Elem Med Biol, 2017. 40: p. 30-36.
  2. House, E., et al., Aluminium, iron and copper in human brain tissues donated to the Medical Research Council’s Cognitive Function and Ageing Study. Metallomics, 2012. 4(1): p. 56-65.
  3. Chen, M.B., et al., Brain Endothelial Cells Are Exquisite Sensors of Age-Related Circulatory Cues. Cell Rep, 2020. 30(13): p. 4418-4432 e4.
  4. Mold, M., et al., Aluminium in brain tissue in autism. J Trace Elem Med Biol, 2018. 46: p. 76-82.

Neurosciences Stroke Uncategorized

[Neurosciences/Stroke] Summary of #ISC20 meeting attendance

A couple of days ago, I attended the second full-day of the American Heart Association International Stroke Conference (#ISC20) that held in Los Angeles, CA from February 19th until February 21st.
Considering this is a meeting mostly driven towards clinical science  (physicians, nurses….), it was a very good year for basic sciences as I had a pretty well loaded schedule of scientific sessions.
My goal is not to provide a detailed description of the findings but really try to keep this summary succint and easy to understand for the layman language. Here are some of the highlights that caught my attention:
* We knew that age is an important predictor of stroke severity and recovery outcome. What we did not knew about is one of these parameters involved is inflammation. A study presented data suggesting that the infusion of young plasma in aged mice undergoing stroke fared better than infusion of old plasma , in terms of infarct size and outcomes. A probable mechanism of action seems to involve a shift of balance in the microglia population (pro-inflammatory vs. anti-inflammatory microglia) and seems to involve exosomes (30-40nm sizes)  diffusion from plasma into the brain.
* Further work on the gut-brain axis and stroke, especially this one on the effect of ischemic stroke on the gut. What is interesting is that an ischemic stroke may have a ripple effect on the gut lining in mice, as it can significantly alter the colon integrity. What was interesting is the sex dimorphism observed, as young female were showing an intact GI lining compared to other group, aligning with the well-established finding that young females fared better than other groups in terms of stroke severity and outcome. Two possible gene candidates identified: MUC4 and CD44, as well as mucin-related genes. Changes in intraepithelial cells (IECs) seems a key factor in such changes.
* Microglia is an important type of immune cells, resident cells inside the brain in resting state. However following injury (DAMPs) or infection (PAMPs), these microglial cells can become activated and trigger the first steps of neuroinflammation. In particular, aging seems to increase the microglial cells population secreting TNF-alpha (a well-known pro-inflammatory factor) suggesting that microglia activation maybe inherently bad for stroke severity and outcome. Therefore, blocking or targeting microglial cell population (by depleting them) would be considered beneficial no? Well turns out maybe not. A study targeted microglia by depleting these cells in both young and old animals using PLX-5622 for 21 days. Interestingly, such treatment resulted in worsened stroke outcome, as the infarct size noted in MCAO group was bigger versus the vehicle group. What was also interesting was such animals showed an increased numbers of monocytes and neutrophils, and an increase in reactive astrocytes in aged animals. There was also changes in the gut microbiota as two genus (Verrucombroia and Akkermansia) were increased in this group, as well as a decrease in Iba1+ gut macrophages.
* Ischemic brain has an effect on the macrophage transcriptome. A study looked at macrophage gene expression profile between circulating monocytes/macrophages in the periphery versus macrophages that migrated into the ischemic injury site. More than 3000 genes were identified as differentially expressed, with an important clustering of genes associated with the peroxisome proliferator-activated receptors (PPARs) including PPAR-delta and PPAR-gamma.
*  MicroRNAs (mIR) play an important role in ischemic stroke injury. In particular a study showed that inhibition of mIR-15a and mIR-16-1 following MCAO (for up to 21 days) resulted in an increased post-stroke angiogenesis (using conditional KO animals). Treatment with AZD0530 resulted in a worsening outcome. A possible mechanism of action may occur via Src-dependent mechanism, that still need some brush-up (is Src activation or inhibition needed?).
* This year, the Thomas Willis award and lecture went to Pr. Maiken Nedergaard (University of Rochester) on the contribution of the glymphatic system. I have to say this is still a very controversial topic that remains highly contested within the BBB field (especially from those interested in fluids movements inside and outside the brain). I have been following as a bystander so I just really keep it on what I have seen presented (and not coming with a knowledge of her papers). According to Pr. Nedergaard, it is a “highly polarized” pumping system, pumping the CSF down to the arterial system, pumped by the glia endfeet processes and clearing in the venous system. According to her, meninges and dura have lymphatic vessels. Amongst things exchanged are ions, lactates and she was also able to observe movements of microspheres up to 1 micron size (aggregates moved to same speed than single ones).
The CSF tracer diffusion was much more present when animals were in sleeping state or under anesthesia, all into the cortex. Similar observations were done in patients as well. The flow is fast and occurs from central to periphery. The CSF accounts about 10% of the brain. According to her findings, tracer was flowing in despite reduced blood flow, all along the cerebral arteries and increase observed mainly in the ipsilateral side.
She also reported a decrease in the volume of CSF after stroke, suggesting that the CSF maybe the source of acute edema. Interestingly, she reported an increase of intracellular calcium influx (known to occur during the ischemic depolarization) preceding the CSF flux, and would still occur later after stroke as the  post-stroke vasoconstrictions would drive such CSF flux.
The rest of the day was marked by some other sessions, meeting old collaborators and friends in the stroke field (an opportunity to meet beyond the emails)  and by the visit of some posters.
Overall, it was a good stroke conference this year, and hopefully next year would allow me to spend more time (it is always tenuous to attend it as it falls right in my busiest semester). Next year? It will take place in the (very) mildly hypoxic mile-high city (Denver, CO), allowing me to drive (or maybe just an hour fly away whichever will be the cheapest option). See you in ISC2021 in Denver!



Doom Metal Metal Music Uncategorized

[Metal] Black Sabbath – Black Sabbath (50th Anniversary Release)

Today marks the anniversary of the freshman album by Black Sabbath, released in February 13th (a Friday none of the less) 1970. This album is an important in the history of modern music, as it is considered as the milestone album, “the mother of it-all” of the whole metal genre, and to the doom metal sub-genre in particular.
50 years and this album still keep its freshness. It is also an album that kept its roots into its era, it has this psychedelic rock vibes, as an extension of Deep Purple and Led Zeppelin sound, yet also infused several key elements that will define the metal genre.
The album is about 39 minutes, and has different iterations depending of the re-issue and edition. The album I own is the North American CD version and I will make my summary out of it.
We are entering the album with the eponymous track “Black Sabbath”. Dang! Listening to it in a retrospective fashion, it has all the element of doom metal: slow pace, monotonous guitar, dark thematic. Ozzy is just awesome in this track, depicting and end of the world to it. It is dark, it is occult, but damn it is.

The second track “The Wizard”, for some reasons always makes me think of “Electric Wizard”, a flagship stoner metal band that is considered as representative of the stoner doom metal, the cousin of doom that runs on weed (it is all about weed with stoner metal). Kind of a more classical rock vibe, but an okay song for me.
The third track on the edition I own is “Wasp/Behind the Wall of Sheep/Bassically/NIB”, a quatuor of proto-metal songs, blending old tunes of their time but also experimenting some features that will define the metal genre, including the drumbeat (deviating from the R&B, and more into a synchronized and regular tempo),  heavier guitar riffs, and inclusion of melodic riffs.
The fourth track, “Wicked World”, is interestingly the one that has the closest sound of modern heavy metal in its backbone and its structure, with a very complex solo guitar played to Tommy Iommi, that will become a staple for almost every metal song to come.
The fifth track “A Bit Of Finger/Sleeping Village/Warning”  is also a good track, starting wiht an awesome guitar solo before the drums and the bass take over.
Today was also a big revelation about the mysterious figure of the original album cover. ( A mysterious figure that haunted and led to the widest speculations and conspiracies. The woman in question is named Louisa Livingston. After all these years, the album is keeping its freshness.
It is so much fresh then it perfectly blends with modern cover, for instance my favorite being the live cover of it by Within Temptation, giving it this modern feel in a circular fashion, as performed in their Black X-Mas concert few years ago:


Blood-Brain Barrier Neurosciences Uncategorized

[Sciences/BBB] T Lymphocytes and Cytotoxic Astrocyte Blebs Correlate across Autism Brains (DiStasio et al., Ann Neurol 2019)

I  have been recently approached on social media to discuss about the recent study published by Anderson and colleagues in Annals of Neurology (, in which the authors reported the presence of residing CD8 cytotoxic T-cells in the perivascular space of brain samples from ASD patients. I wrote about it, and gave my first impressions about reading the study. After such discussion, I realized that I have an interesting review that worth being shared here on my blog.
Here is the summary of my review of this paper as I wrote it on social media. I made some corrections (mostly spelling and grammar), as well as some changes in the writing style (I wrote these comments on the spur of the moment, as I went through the paper). Also due to copyrights, I will not show any figures and tables from the study.

About the authors: The first author is Dr. Marcello DiStasio (MD/PhD) and the senior author is Dr. Matthew P. Anderson, MD/PhD. He is a clinical faculty of Harvard Medical School and member of the Department of Neurology and Pathology a Beth Israel Deaconess Medical Center, with an affiliation with Boston Children’s Hospital Intellectual and Developmental Disabilities Research Center.  So we have some high profile and experts in neurodevelopment disabilities.

About the journal: The journal is published under Wiley, and is the official journal of the American Neurological Association and the Child Neurological Society. It has an impact factor of 9.49 and is in the top quartiles of neurology and neurosciences journal. Therefore, we have a study published in a very good journal that is relevant to the topic.

About the study design: The study is mostly observational and relies mostly on histological methods (tissue sections followed by staining using chemical dyes and/or antibodies targeting specific proteins). Tissue samples are freshly isolated from postmortem patients (which is a big plus compared to formalin-fixed samples, and opens up the ability to perform protein and RNA analysis if the samples are immediately treated for extraction). The sample size is pretty decent (N=25-30) with a large age spectrum and various types of ASD represented. First interesting to note, 50% of the ASD brain (N=25, not bad) have a history of seizures. Less than 30% of control brains (N=30) have history of seizures. Important thing to consider as a comorbidities and evenutally as a cofounding factor.

About the results: This is my summary of the different figures. By copyright concerns I am not showing the actual figures, but you can overlap my comments to the figures as I have separated them into sections.
Figure 1: Me being cranky, I wanted to see control brain pictures, but not avail. Also with immuno pictures, you have to be super-precautious because there is a high risk of cherry-picking a brain slice, and claim it is representative of all brain. Nevertheless lets discuss it here. On the left panel, we have an H&E staining. Pretty much a vanilla staining. Now, if you want to show a certain protein, you can do it with the right antibody and staining (DAB/peroxidase stain). It appears as the dark brown color. We can see a strong GFAP staining around the vasculature (hollow structure). Astrocytes usually line up blood vessels by forming end-feet process. S100B and ALDH1L1 are pretty standard proteins for astrocytes. However seeing GFAP expression in human astrocytes means these cells are stressed out and are reactive. This is what the quantitative bar graph is telling us. We can also see some CD8 in this perivascular space. These are the cytotoxic T-cells. I wonder what they are doing there, as they can cross the BBB only during brain injury, as microglial cells will äctivate” these endothelial cells and allow white blood cells to adhere on their surface and cross the BBB into a complicated tango dance.
Figure 2: Here there is an attempt of some matrix correlation. GFAP versus CD8 cells. And we can see there is some (expected) correlation between these two (see linear regression and R2) with ASD brains have higher rates of both compared to controls. Interestingly we see similar pattern between ASD that are genetic versus the idiopathic.
Figure 3: digs in more about the immune cells and some correlations (although the scatter of the ASD brains is less convincing here). Overall it seems we have a higher number of lymphocytes in the ASD brain compared to control, both in the white matter (WM, this is where our cables go through) and grey matter (GM, this is where our processing units are) and lepomeningeal (LM, this is our brain surface protective skin, basically the meninges, pours the CSF into the veinous blood). What seems interesting is that at young age we have lymphocytes sitting in our perivascular space, doing nothing(?) and decrease as we age (thats interesting for the non-neuroimmunologist that I am). However, these number at best slightly increase in ASD brain as we age (and I guess not convincingly enough, otherwise the authors would have reported the R2 value), or at least remain the same. With the exception of the medulla (brain stem) most blood vessels show a higher number in ASD brains versus control brains. Interestingly, cortical blood vessels being the predominant population harboring such feature. Very few NK cells to be honest on the panel.
Figure 4: Again this one makes me cringe a bit as a reviewer, because the author do not show the controls data. Yeah, I am pissed. Anyway. Again, perivascular space. Again immune cells highly present (CD3+), CD8 lymphocytes being the predominant type of T cells, in contrast not many CD4 lymphocytes (usually the T helpers, but my immunology is outdated for 20 years at least). Granzyme staining denotes the presence of natural killer (NK) cells. CD20 is a marker for B cells. What is interesting is mostly not much B-cells in either ASD and control brains, the graph more like a refried version of what we already know (higher number of immune cells and CD8 cells).
Figure 5:  is a bit of a useless graph, I don’t see anything that brings us more information than before.
Figure 6: It shows us a Masson trichrome staining. It is a chemical (histological) staining aimed to make collagen fibers visible under a certain color compared to other tissues. Collagens exist in different forms (based on their alpha-fibrils) but the one you expect to see around the vasculature is Collagen Type IV (COL4A1). This one forms a basement membrane (BM) that is like a net around blood vessels. Think about standing on a trampoline. Thats it. Collagen IV forms the net that supports the BBB. Normally, you would expect the BM to be thinned out, this is the case after stroke as cells secrete protein-degrading enzymes (like matrix metalloproteinases MMP2 and MMP9) that will break it down into pieces. But here it is thickened and hhad a bigger thickness than normally. Why so? What does not mean? I dont know. The only time I have seen such things was in transgenic mice overexpression erythropoietin (EPO). These mice had a such high hematocrit that would make blood be like Aunt Jemima corn syrup. If I remember, my former PhD adviser had a collaboration with an electron microscopist and observed similar thickening. Why this is happening is a good question that is likely the next study.
Figure 7: See Figure 5 comment.
Overall thoughts, limitations and outlook: Overall, it is very interesting study, that has some methodological limitations to be noted. First, we are missing any information about the BBB integrity in general, as I wish the authors would have shown some immunofluorescence to compare changes in tight junction (TJ) proteins expression (claudin-5, occludin) in blood vessels and assess differences in TJ strands. The second problem is the lack of information about microglia activation (that would be done by Iba1 staining. The authors noted they performed it, observed a higher expression in ASD brain but decided not to show the data) and more importantly the status of endothelial cell activation (using ICAM1, VCAM1 staining). Are these residing CD8 T cells freshly migrated or just being duck sitting for a while? Is the inflammation status in the brain (and the BBB) ON or OFF at the time of autopsy? What about pro-inflammatory cytokines levels between ASD and control patients? As usual, studies like that opens much more questions that it answers. The second problem is that the study by itself fairly descriptive and observational. It would be interesting how this would compare to clinical findings done in patients. Do we see flares on the MRI indicating of the BBB opening? How does that data compares to patients with MS?
I would also be very careful on making any claim about a “leaky” or “down-regulated” BBB at this point. There is no data about the barrier integrity (TJ complexes integrity) or assessment of the barrier function (for example an MRI scan with gadolinium as contrasting agent) to support the claim. I have seen this claim floating around in non peer-reviewed articles (including in The Scientist), as a BBB expert I would not jump into that conclusion quickly until I see real data on that
This is the type of interesting study, because it opens 100 questions that incentivize to further look down the road. I hope that the authors were able to prepare RNA and protein samples for transcriptome analysis (RNAseq) and proteome analysis (2D-electrophoresis coupled with MALDI) that could help us learn more.