# Dark Galaxies, Fuzzy Dark Matter, and an Alzheimer’s Breakthrough (EP. 28)

An episode of [From First Principles](https://invisiblenetwork.ai/podcast/1830375095), 4 Mar 2026, 1 h 52 min.

Audio: https://episode.flightcast.com/v2u8fw9i0c2uk8q5h6l6iq0m.mp3

Hosted by Lester Nare and Krishna Choudhary, this episode has two main stories: an astrophysics update on a candidate “dark galaxy” in the Perseus Cluster (a halo that’s ~99.9% dark matter), and a major Alzheimer’s mechanism paper tracing how exercise protects the brain by repairing the blood–brain barrier—with an actionable drug-like path already emerging. Summary Candidate dark galaxy — Hubble + Euclid stacking and globular clusters reveal an ultra-faint halo that could test missing satellites and the cusp–core problem (and even “fuzzy dark matter”). Exercise → Alzheimer’s mechanism — UCSF links a liver enzyme (GPLD1) to BBB repair via TNAP regulation, plus an oral TNAP inhibitor (SBI-425) that mimics the effect in mice. Rundown — Rubin Observatory’s real-time alert engine, AI-accelerated magnet discovery, a climate-corrected Easter Island history, and the Boba-Kiki effect in baby chicks. Support the showDonate: FFPod.com/donateFollow: @FFPod (X / Instagram / TikTok / Facebook)

## Transcript

[0:00] Dark matter quantum mechanics.
[0:02] That is what we are worried about in this particular scenario.
[0:07] It's a very weird way to think about it, but it might be the case.
[0:11] So this new paper from UC San Francisco talks about links between Alzheimer's disease and exercise, but the conclusion was not...
[0:18] What I thought it would be.
[0:20] Hello, Internet.
[0:20] This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury, who is today donned in his Ferrari race kit.
[0:32] We will get to that later in the rundown.
[0:34] But on this week's episode, we have two great main stories.
[0:38] We'll start off with an astrophysics story about dark matter, and we're going to follow up with a look into the latest in Alzheimer's research.
[0:46] For those of you who may be new to the show, be sure to like, share, and subscribe.
[0:50] If you want to support the show, head to our website.
[0:52] We have a great donation portal.
[0:54] And as you already know, we're going to learn about the science from the ground up today because this is From First Principles.
[1:17] So we're going to begin today with an astrophysics story about dark matter, as we mentioned.
[1:23] Specifically, scientists think they've found a really good candidate for a dark galaxy.
[1:29] This is a galaxy almost completely made up of dark matter, no normal matter, that could give us a little bit of a hint into what dark matter actually is, which continues to be an enduring mystery.
[1:42] So let's dive in.
[1:45] on what this story is about.
[1:47] Yeah, I mean, the central question that almost every cosmologist is trying to answer is what is the universe made out of?
[1:55] Okay?
[1:55] The current paradigm is something called Lambda Cold Dark Matter.
[1:59] This is the model for what our universe is.
[2:02] The normal atoms that we see are only about 5% of everything in the universe.
[2:08] It turns out that 24% of our universe is something called dark matter, which we have no idea what it is.
[2:14] All we know is that it is like matter in that it interacts with gravity, but it's not like matter in that...
[2:21] Light does not care about it.
[2:23] The electromagnetic field does not care about it.
[2:25] So it's not so much as dark as it is transparent in every sense of the word.
[2:31] Like a light beam will just go through dark matter and not interact with it at all.
[2:35] This is very weird because atoms love interacting with photons, right?
[2:38] Because they're electrically charged.
[2:40] Obviously, electromagnetism is a thing.
[2:42] And then finally, there's about 72% that is like dark energy, which we're not even going to get into this time.
[2:49] But just to set the stage, right?
[2:51] Like we only know what 5% of the matter is in our universe.
[2:56] That's pretty insane.
[2:57] Can you just repeat that really quick?
[3:00] It's like 5% normal matter.
[3:01] That's the stuff that we're made out of.
[3:03] Atoms, baryons is what it's called.
[3:05] And the stuff we can see.
[3:07] Mm-hmm.
[3:07] Yeah.
[3:08] And then 95% is stuff that we have no idea about and we're trying to figure out.
[3:16] Yeah.
[3:17] Weird.
[3:18] It's weird.
[3:18] It is quite weird.
[3:19] And what this particular story is about is something called a Candidate Dark Galaxy.
[3:24] This is the second Candidate Dark Galaxy, but it's the one that we have a lot of information on because the first one was kind of...
[3:32] You know, it's a massive dark matter halo that failed to form a population of stars.
[3:39] Hubble first identified it, and now they've worked together with the ESA Euclid Observatory that's also in space to figure out some more stuff about it.
[3:47] It's got an extreme dark matter fraction, meaning 99.9% of this galaxy is dark matter.
[3:56] Mm-hmm.
[3:56] That's way off compared to the Milky Way, which is in the 90s, 80s, 70s type of thing.
[4:02] This is really weird.
[4:04] We'd actually previously done a story on Dark Matter.
[4:08] Yes.
[4:09] Yes.
[4:09] It's something called Cloud Nine.
[4:11] Yes.
[4:11] And that had no stars.
[4:14] Yes.
[4:14] This one has a few.
[4:15] And so that was what they were.
[4:16] Is this the first relic?
[4:18] Yes.
[4:18] Which was the abbreviation.
[4:21] But this is different than Cloud Nine.
[4:23] Yes.
[4:23] Yeah, this could be a proper galaxy.
[4:25] Okay.
[4:26] And there are differences that we're going to highlight.
[4:28] Okay, perfect.
[4:29] Okay, so let's give some historical context.
[4:32] There was this guy, Fritz Vicky.
[4:33] He was at Caltech.
[4:35] He coined the term dark matter in 1933 because he observed this particular cluster called the coma cluster.
[4:43] He observed a bunch of galaxies in the coma cluster, and he figured out what the velocities of these galaxies were.
[4:51] And he figured out that...
[4:53] How fast these galaxies were moving around some central mass distribution, it didn't make any sense.
[5:00] They were moving way too fast.
[5:01] He used something called the Virial Theorem, which is a theorem in basic physics that connects kinetic energy to potential energy.
[5:09] You can apply the Virial Theorem to the solar system, for example, and everything makes sense.
[5:15] Because the farther you are out, the slower you move.
[5:19] The farther you are out, the more potential energy there is, so the less you have for kinetic energy, right?
[5:24] When he applied it to the coma cluster, those observations didn't make any sense.
[5:28] There was way more potential energy from gravity than what he saw.
[5:32] And so he said there's got to be some dark matter there that we can't see that is providing the gravitational potential energy.
[5:40] There has to be a source for the pole that creates these orbitals.
[5:44] Yes.
[5:45] And normally we can see where the source of the gravity is.
[5:49] Yeah, normally it's stars, right?
[5:50] And it'll be bright.
[5:51] And you can't miss it.
[5:52] Yeah.
[5:53] So the thing that's weird about this is we're looking, things are moving very fast.
[5:58] Very fast.
[5:59] But the thing we can see that would drive that movement is...
[6:03] is not large enough to explain why they're moving so quickly.
[6:08] Yeah, and so there's got to be some stuff there that acts like matter in that it's gravitationally bound and it creates a gravitational potential, gravitational force, but it doesn't act like matter in the sense that there's, like, no light coming out of it.
[6:21] Right.
[6:22] Okay, and that's what's weird.
[6:23] There were other...
[6:25] observations.
[6:25] Vera Rubin very famously solidified the concept in the 1970s with galaxy rotation curves.
[6:32] What you can do is figure out how fast stars are moving around a galaxy.
[6:37] And what you expect is something like the solar system.
[6:40] Again, the farther you are out, the smaller your velocity, the slower you should be going.
[6:46] That's why a year on Jupiter is like 15 years, because it takes 15 years to go around the sun.
[6:51] That's not just because the orbit is bigger.
[6:54] like the circle is bigger.
[6:55] It's also that it's moving slower.
[6:57] It shouldn't be that long.
[6:58] But it's moving slower.
[6:59] That's why it's that long.
[7:00] If it was moving as fast as the Earth, it would be like five years or three years.
[7:04] And this is a similar graphic we talked about in our Veriruban deep dive.
[7:08] We're looking at a graph now where we see the expected path for the visible disk sort of dissipating on the X axis.
[7:18] Yeah, and that would be like if it was normal.
[7:21] The farther I get away from the galaxy, the slower I should be moving.
[7:26] What we actually see using Starlight and the hydrogen's 21-centimeter line, 1420 megahertz, is that actually you're just getting faster.
[7:35] So there's a bunch of invisible matter that is pulling on you that we have no idea how to see.
[7:40] Okay?
[7:41] So the question is, what is going on?
[7:44] What is this dark matter?
[7:46] Well, the more and more observations we did, the better we got.
[7:49] In the 1980s, people discovered something called low surface brightness objects.
[7:54] These became known later on in 2015 as ultra-diffuse galaxies.
[8:00] In 2015, we made a discovery using something called the Dragonfly Telephoto Array.
[8:06] Okay, this is a new array that was built by Yale and the University of Toronto.
[8:11] It's very cool, actually.
[8:13] You as a camera guy, you're going to love this.
[8:14] So each of these lenses, so it's in a honeycomb structure, right?
[8:18] And it's called Dragonfly because the Dragonfly eye very much looks like this.
[8:22] It's a bunch of like honeycomb lenses.
[8:24] Each of these lenses is just the off-the-shelf Canon 400mm f2.8 lens.
[8:29] Really?
[8:29] Yeah.
[8:31] It's just no way.
[8:33] It's there's they just they just bought a bunch of them, put them in a hexagonal array, and then they're compiling the light together to create these images.
[8:42] That's actually incredible.
[8:44] Right.
[8:44] This is something you can get like on Amazon.
[8:46] Yeah.
[8:46] But they put a bunch of them together.
[8:48] Now, the question is, why would you do this?
[8:49] Right.
[8:50] Yeah.
[8:50] Yeah.
[8:51] Traditionally, you never want to use lenses because the bigger the lens is, one thing you get is aberration, which is the idea that the bigger the lens is, the weight of the lens is going to deform the lens.
[9:04] Yes.
[9:04] Right?
[9:04] Because glass is not like completely metal.
[9:07] Yes.
[9:08] So there's some fluid property to it.
[9:10] So the larger you make the glass lens, it's going to sort of like deform because of the weight of its own lens.
[9:16] gravity or the other way around and so you're going to get image distortion that way right but what these guys did was a bunch of small lenses put together now what is the advantage now the advantage is for mirrors right if you have a mirror lens which is what modern telescopes all use they all use mirrors the light has to come down and then has to get set back up because it's a mirror so it's going to bounce up and there's a secondary mirror that's going to
[9:41] That then bounces the light back to a detector.
[9:43] Usually it's like Warrington in some way or something.
[9:46] But at the end of the day, you need a secondary mirror.
[9:50] And that mirror needs to be held up by some kind of scaffolding.
[9:53] In the James Webb Space Telescope, it's hexagonal scaffolding.
[9:57] In the Hubble Space Telescope, it's diamond-shaped scaffolding, like in a square 90 degrees.
[10:02] Which is why when we see the images that these telescopes produce...
[10:07] The mirror structure or the architecture actually dictates – for example, I remember when James Webb first came out.
[10:15] Certain objects had a certain star pattern or like a certain visual representation.
[10:20] There's little like spikes.
[10:22] Yes.
[10:23] That was different than Hubble, which is because of this diagonal versus hexagonal structure.
[10:28] Yeah, and those are called diffraction spikes.
[10:30] It happens because of scattered light from that scaffolding.
[10:34] Yes.
[10:34] Well, if you want to image really, really diffuse things, that becomes a really big problem.
[10:40] When you say diffuse, what do you mean?
[10:42] Diffuse by really dim.
[10:44] Okay, really dim.
[10:45] Like not a lot of light.
[10:46] Got it.
[10:46] As in like so little light that we're pushing the sensitivity of the...
[10:53] CCD, the actual charge couple device, the thing that is collecting the light and turning it into an electrical signal.
[11:00] Yes.
[11:00] Right.
[11:00] There's a bunch of silicon at the end of your detector.
[11:03] That's what makes up a charge couple device.
[11:05] And when the photon hits that silicon, it releases an electron.
[11:08] And then you're like, oh, I got a little photon from there.
[11:11] Right.
[11:11] If you're if you're imaging really diffuse things, even that.
[11:16] The scaffolding, the scattering from that scaffolding is going to be a problem.
[11:20] So that's why you want to use lenses.
[11:22] But you can't use big lenses because of the aberration, like the deformation problem.
[11:27] So what if these guys just figured from the Dragonfly telephoto array, what if we just use a bunch?
[11:32] Yes.
[11:32] Right?
[11:32] Like we use like 20 or 30 small lenses.
[11:35] And the compiler.
[11:36] And we compile them together.
[11:37] okay that makes sense and it's it was like relatively cheap i can imagine right because you're not like making custom made lenses this is just canon 400 millimeter f 2.8 that you put together calling up carl zeiss we need a custom giant 13 meter yeah and he's like uh how about 10 million dollars like no so so this was this was a really cool thing and in 2015
[12:01] They came out with these extreme ultra-diffuse galaxies.
[12:05] Dragonfly 44 is an example.
[12:07] This is a galaxy.
[12:08] You can see the faint fuzz in the middle.
[12:11] Yes.
[12:11] Not a lot of stars.
[12:12] Yes.
[12:13] And from what we can tell from how these things are moving around, when you do spectroscopy and you figure out the Doppler shift, so you figure out how much is the red shift in this direction, the blue shift in this direction, from there we can figure out how fast these things are moving around.
[12:27] Yep.
[12:27] And then you back calculate how big does the galaxy have to be.
[12:30] It's like dark matter dominated.
[12:32] 90% of it is dark matter.
[12:34] 95% of it is dark matter.
[12:36] So it's this ultra diffuse galaxy that seems to have a lot of dark matter.
[12:40] Okay.
[12:41] It's been hard to catch before because usually with normal telescopes, the scattering takes that away.
[12:47] But now with this new...
[12:48] new dragonfly telephoto we can start imaging really diffuse structures that that makes it so the the point is with the normal structure uh we need bright objects for it to work well um and these sort of dark matter galaxies where it's predominantly this this thing we don't understand as that does not interact with light it becomes very difficult to see it or to to
[13:11] image it, I should say.
[13:12] Yeah, to image it.
[13:13] To image it.
[13:14] But with this lens based architecture, it lowers it increases the signal to noise.
[13:20] Yes.
[13:21] Such that we can at least get something.
[13:24] Something, yeah, exactly.
[13:25] And then this particular paper
[13:28] is Candidate Dark Galaxy 2.
[13:30] It is taking these ultra-diffuse galaxies to a whole new level.
[13:33] Okay.
[13:34] It's pushing that diffuse into now dark, because now we're getting 99.9% dark matter.
[13:42] Okay?
[13:43] And that's really crazy.
[13:44] Yeah, yeah, yeah.
[13:45] Okay?
[13:45] Yeah, yeah.
[13:46] So why do we even care, right?
[13:47] This intensive hunt for something like a Candidate Dark Galaxy, it's motivated by two crises that...
[13:55] that are in the center of this cosmological model that we called Lambda CDM.
[13:59] You know that thing I told you before about 5% normal matter, 25% dark matter, the rest being dark energy?
[14:05] I think it was this one, yes.
[14:06] Yeah, exactly.
[14:07] So this is the model that we have today.
[14:09] Yes.
[14:09] Now, fine.
[14:10] If that is the model, right?
[14:12] Right.
[14:12] There should be consequences of that model.
[14:14] Yes.
[14:15] There are certain consequences of the model that we do not see.
[14:19] The point being we have an understanding of the universe.
[14:21] Yeah.
[14:22] And we're like, this is our best guess.
[14:24] And then there's always people who are like, but that doesn't really make sense for these two reasons.
[14:28] Got it.
[14:30] It doesn't compute.
[14:31] Yeah, it doesn't compute.
[14:32] And these are the two things specifically that do not compute.
[14:34] The first one is the missing satellite problem.
[14:37] Okay.
[14:37] Okay.
[14:38] These dark matter simulations, when you use that original 5% normal matter, 25% dark matter, blah, blah, blah, you press play on a universe, you set it up in the computer and you press play.
[14:50] Simulations predict that there should be about 500 orbiting dark matter subhalos around something that is the size of the Milky Way.
[14:59] Okay.
[14:59] Okay.
[15:00] The idea is for every big halo of dark matter condensing, right, dark matter is still gravitationally bound.
[15:07] So the universe is expanding.
[15:08] There's a bunch of dark matter.
[15:10] There's going to be tiny fluctuations where there's a little bit more matter here, a little less matter here.
[15:14] And so it's going to start clumping because gravity is going to take that over-dense region is what we call it.
[15:19] And we're going to start clumping that way.
[15:21] Now, around those big clumps, there's going to be smaller clumps.
[15:24] Around those smaller clumps, there's going to be even smaller clumps.
[15:27] There's like a power law structure.
[15:30] What we expect is there should be a lot more small clumps than what we see.
[15:35] Right.
[15:35] That is the missing satellite problem.
[15:38] We see a couple big clumps.
[15:40] Yeah.
[15:40] But the cascading second and third order clumps we should be seeing are not present.
[15:45] Exactly.
[15:45] And that's what we're seeing over here.
[15:47] Okay.
[15:48] The triangles with the error bars, that's observation.
[15:52] Yes.
[15:52] The lines are theory.
[15:54] And you can see that for larger...
[15:59] Increasing mass.
[16:00] We're fine.
[16:01] So when the mass increases, the observation matches the theory.
[16:05] Yes, but for these smaller clubs, these satellite galaxies, there's a massive discrepancy.
[16:10] Orders of magnitude.
[16:11] Because of the power law, we should be seeing significantly more smaller dark matter clusters or halos.
[16:19] Than the large ones But the rate of increase of the number From observation Is very like the increase It's quite low There's like a point where it's just like We just see way too few So the question is Maybe these smallest halos are present But they're completely dark
[16:36] Which is why you don't see them.
[16:37] There's no stars, so what are you going to see?
[16:40] It's a bunch of Cloud 9s from our starless Dark Matter cloud story.
[16:43] Exactly.
[16:44] And so Cloud 9 was trying to solve the similar problem, and this is kind of solving that similar problem, too, if this is truly there, right?
[16:50] Okay.
[16:50] Now, the second, and this is something that Cloud 9 was not doing, so this is novel.
[16:54] Okay.
[16:55] It's called the cusp core problem.
[16:57] Okay.
[16:58] Here's the idea.
[16:58] Dark matter does not interact electromagnetically, right?
[17:02] Right.
[17:03] There's no, like, you know, electrons, they want to move away from each other.
[17:07] And protons want to move away from each other.
[17:09] Even when we have atoms, these atoms, you can't just compact them.
[17:13] But if you have a particle like dark matter that is only interacting with gravity...
[17:18] That means that the closer you get to the center of the gravitational potential, the density should just get higher and higher and higher because gravity is just clumping everything together.
[17:27] There's nothing that's pushing out.
[17:29] Right.
[17:29] Like when we think about like hydrostatic equilibrium and things like that, but there's nothing pushing out.
[17:34] Right.
[17:34] So the closer you get to the center, the density of dark matter should be higher and higher.
[17:39] That is not what we see.
[17:40] Interesting.
[17:40] We see instead of a cusp.
[17:42] which would mean in the center we get like a little pointy.
[17:45] Yeah.
[17:45] We get a core type where it's like at the center, the dark matter density sort of flattens out.
[17:52] That's weird.
[17:53] That is weird.
[17:53] If dark matter is purely gravitational, why would it just...
[17:58] flatten out.
[17:59] It should be continuing.
[18:00] It should continue to get bigger and bigger as it gets closer and closer to the center.
[18:03] The density should be higher and higher.
[18:05] That's not what we're seeing.
[18:06] Now, okay, the defense that the Lambda CDM people make, like the guys who make the cosmology model and they're drinking the Kool-Aid, right?
[18:15] They're like, no, there's something called baryonic feedback.
[18:18] This is the idea that towards the center of the
[18:22] galaxy, there's a bunch of supernova explosions.
[18:24] Okay.
[18:25] Okay?
[18:25] And that supernova explosion is going to send shockwaves in space-time.
[18:30] And those shockwaves are sort of going to diffuse the center of that galaxy.
[18:34] So even though the normal dark matter would create a cusp, right?
[18:37] Because there's so many explosions happening in the center of the galaxy, that's going to sort of flatten out, right?
[18:43] Kind of makes sense.
[18:44] Okay.
[18:44] Okay.
[18:44] Right?
[18:44] That could work.
[18:45] Okay.
[18:46] But this is why this particular galaxy is significant.
[18:48] This galaxy has no stars.
[18:50] Basically no stars.
[18:52] So there's no supernova explosions.
[18:54] Right, right.
[18:54] Right?
[18:55] Which means that this should definitely have a cusp.
[18:57] I see.
[18:58] That is why it is worth studying.
[19:00] So the CDG2, it's interesting because... Yeah, and by the way, CDG2, CDG means Candidate Dark Galaxy for those... Oh, okay.
[19:12] No, my Siri is trying to figure out what a CDG is.
[19:16] One of the intelligence agencies is apparently trying to hack our phones at the moment.
[19:19] Sorry about that, guys.
[19:21] But this is interesting.
[19:22] So there's currently a cosmological model of normal matter, dark matter, and dark energy.
[19:30] Like, what is the composition of the universe?
[19:32] Yeah.
[19:32] That model has two problems that we just talked about, the missing satellite problem and the cusp core problem.
[19:38] Mm-hmm.
[19:39] The folks who are in defense of this Lambda CDM believe that supernova explosions is the reason that we don't see a cusp.
[19:51] Yeah, it's sort of like blurring stuff out.
[19:54] It moves around.
[19:55] It moves around rather than aggregating it, let's say, for example, right?
[19:59] The problem is, observationally, we've now seen something, which is the CDG-2.
[20:06] That has a very few amount of stars, which means very few amount of no supernova explosions.
[20:10] Yeah, no one's dying because no one's alive.
[20:12] So how do you...
[20:14] So, no.
[20:15] Yeah.
[20:16] So this galaxy can now offer a test to your Lambda CDM.
[20:21] And this is actually an interesting point about understanding the intersection between theoretical sciences and experimental sciences.
[20:27] Yeah.
[20:27] And the experimentals are constantly in the real world looking for...
[20:32] ways to test.
[20:34] You need some sort of stability, blah, blah, blah, like something that can be consistently measured.
[20:39] And we have something now.
[20:41] Yes, we finally found something.
[20:42] And we haven't done spectroscopic analysis because we just found it.
[20:48] That's why it's interesting.
[20:49] Because now we can get time on the James Webb.
[20:52] We can get time on the Subaru or the giant Magellan that's going to come up and be like, I want you to stare at that thing for like three days straight.
[21:00] And I just want to quickly note, you know, because getting time on these instruments is everyone.
[21:06] There's very few of them.
[21:07] Everyone thinks their thing that they're studying is the most important thing in the world.
[21:11] But this seems to be because of the importance of dark matter generally as a concept.
[21:15] Yeah.
[21:16] A good candidate to be able to, quote, skip the line.
[21:19] Yes.
[21:20] Okay.
[21:21] Exactly.
[21:21] And so I want to make a case for why this is important.
[21:25] Okay.
[21:25] Right?
[21:25] And that's what these people did in this particular paper.
[21:29] Right?
[21:29] The challenge is how do you defeat Sky Glow?
[21:32] Sky glow being dark galaxies, these types of dark galaxies that, I mean, there's no stars.
[21:37] So what are you going to look at, right?
[21:38] They're super faint.
[21:39] The signal is often so faint that there's a statistical noise limit when it comes to like your CCD, your charge couple device, the sensor in the back of your camera that is like looking at this thing.
[21:49] There's a noise, right?
[21:50] It's going to catch stray photons.
[21:53] And that noise is going to be higher than the signal that we're getting.
[21:55] So we can't just like look at the thing.
[21:57] Right.
[21:57] Right.
[21:58] So how do we actually find this thing?
[21:59] Well, this is for Earth-based.
[22:02] Even though this is even Hubble, dude.
[22:03] Oh, interesting.
[22:04] Yeah.
[22:05] Interesting.
[22:05] Because the Hubble has a CCD on it.
[22:07] I see what you're saying.
[22:08] And even though it's in outer space, like that thing has a noise.
[22:12] And then there's something actually called Zodiac Light.
[22:14] Okay.
[22:15] There is dust in the solar system, right?
[22:18] And that dust is going to scatter sunlight into your detector.
[22:23] Wherever you look, there's going to be dust from the solar system.
[22:25] That makes sense.
[22:26] And that is going to have a faint glow.
[22:27] That makes sense.
[22:28] And if the thing that you're looking at is a smaller glow than literal dust in our solar system, so what?
[22:34] We're going to put a Hubble, like, outside the solar system?
[22:37] We're not doing that.
[22:38] And then wait like four days to get signal back.
[22:41] Well, first of all, getting out there is like, you know, it's ridiculous.
[22:44] So we need to figure out other proxy ways to look for these ultra-
[22:52] Low-density galaxies, these candidate dark galaxies.
[22:56] Okay.
[22:57] Okay?
[22:58] And the proxy that comes up is called the globular cluster.
[23:02] These are some of my favorite objects.
[23:03] They're dense, ancient, they're bright, and they're point-like sources.
[23:08] They're way, way smaller than galaxies.
[23:10] They're like these old, old relics of galaxy formation.
[23:14] Okay.
[23:14] Not to be confused with the acronym relic.
[23:17] Just to be clear.
[23:17] Yeah, yeah, yeah.
[23:18] Not to be confused with the acronym Relic.
[23:20] There's so many of these that are around our Milky Way.
[23:23] They're really small, okay?
[23:24] They're going to be like hundreds of solar systems to thousands of solar systems.
[23:28] Wow, that's really small.
[23:29] It's really small.
[23:31] And the density of stars in that region is insanely small.
[23:37] You'll have like...
[23:39] Like, you know, the closest star to us is like four light years away.
[23:44] Over there, I think it's like on the order of light days to months.
[23:47] So it's like comparing the population density of Koreatown in Los Angeles to like Poughkeepsie, New York.
[23:53] Yes.
[23:53] Or something else.
[23:54] Yes, yes.
[23:56] We are that New York.
[23:57] Yeah, yeah.
[23:59] We are Poughkeepsie, New York.
[24:00] Yeah.
[24:00] But these galaxies look like Koreatowns.
[24:02] Yeah, yeah, yeah.
[24:03] You shouldn't say New York because whenever I think New York, I think New York City.
[24:07] Sorry.
[24:07] So let's try like – Poughkeepsie is like the middle of New York State.
[24:11] Yeah, yeah, yeah.
[24:12] But let's say like – I don't know where that is.
[24:15] I only know New York City.
[24:17] There's only one New York.
[24:17] Yeah.
[24:18] You could say like – You know, Nebraska.
[24:20] Right, right.
[24:20] You know, Omaha.
[24:21] Yeah, Omaha, Nebraska.
[24:22] Okay, fair, fair, fair.
[24:23] Exactly.
[24:24] Let's do that.
[24:27] But that's a helpful reference point.
[24:30] Yeah, yeah.
[24:31] So these things are really small.
[24:33] And so imagine, and these globular clusters, they're actually really important for just history, astronomical history.
[24:41] Harlow Shapley at Mount Wilson at the Carnegie Observatories, which we're going to have a special coming out soon, he mapped that the sun was not at the center of our galaxy by using these globular clusters.
[24:54] He observed a bunch of globular clusters,
[24:57] their positions, how far they were relative to the galactic disk.
[25:01] And he could figure out we're actually off-center.
[25:03] Right?
[25:04] We're not in the center of this galactic, these globular cluster distributions.
[25:09] So they're really tiny little specks around giant, massive galaxies, right?
[25:16] And so they become really nice.
[25:18] for observing a dark galaxy.
[25:20] Because the galaxy couldn't be there, but if we observe a bunch of globular clusters, there's got to be something that is gravitationally binding the globular clusters in that area.
[25:32] This vaguely reminds me, and I know I'm always making these stretch connections, when we talked about the radius of the proton story last week, the idea that you cannot directly observe it,
[25:42] because it's so exponentially small.
[25:44] And so we have to use these second and third order reference points in order to compute the value.
[25:51] And similarly, because dark matter does not interact with light, we're using the proximity and the movement and acceleration and angular momentum and et cetera, et cetera, of these globular clusters to derive understanding of this dark matter.
[26:06] That's exactly right.
[26:07] Yeah, and so this is what they finally did.
[26:09] They looked at archival data from the Piper survey, which is of the Perseus cluster.
[26:14] There's a giant galaxy cluster in the Perseus constellation.
[26:18] Okay.
[26:18] Okay, and there was this survey by Hubble Space Telescope.
[26:22] When they looked there, they reanalyzed that data with something called Dolfot, which is different from Dolfot.
[26:30] It's like a way to...
[26:31] Analyze data from Hubble Space Telescope, taking into account like the engineering of the Hubble Space Telescope, what it's capable of, things like that.
[26:39] And they revealed in this Perseus cluster, they revealed a fourth globular cluster that they hadn't seen before.
[26:46] If you look over there, there's three blue dots and then there's a red dot.
[26:49] Mm-hmm.
[26:50] That red dot is a new globular cluster that they hadn't found before.
[26:54] And from that, they can be like, now, well, wait.
[26:56] If there's a new globular cluster, there's four.
[26:59] Four being in the same small region is really unlikely unless there's something in the middle there that is causing them to clump.
[27:08] I want to make a brief note here, which is this important kind of...
[27:13] note about how the importance of data analysis.
[27:16] Yeah.
[27:16] Because you have a raw data profile.
[27:19] And this is the same raw data profile as before.
[27:20] It's the same raw data profile of like what had happened.
[27:23] And it's just literally, there's no interpretation on that raw data yet.
[27:27] Yeah.
[27:28] However, depending on the parameters and how you filter that raw data, there's a whole spectrum of different conclusions you can make depending on what you're looking for.
[27:38] And so the idea here is that they change the parameters.
[27:41] Yeah.
[27:43] Which is not changing the fundamental data.
[27:45] No, the raw data is.
[27:46] It's the same.
[27:46] It's like if you have a camera with a 400mm lens and you're trying to look at something that's really close to you, it's just going to look like a blur.
[27:53] Yeah.
[27:53] Because a 400mm lens is trying to look at things far away.
[27:56] But if you change to a 30, whatever, 24mm lens, all of a sudden...
[28:01] You can see the thing because you changed the way in which you're trying to analyze the information.
[28:06] Well, but I want to be cautious there because what you just talked about is actually different raw data.
[28:12] Ah, okay.
[28:12] Right?
[28:13] If you're switching out lenses, the raw data is different.
[28:16] Okay, that's fair.
[28:17] Right?
[28:17] No, that's fair.
[28:17] Here, I do want to make a distinction because this is important.
[28:21] Here, we're doing the same.
[28:22] We got the same photo.
[28:24] Yes.
[28:24] Okay?
[28:24] That's a good point.
[28:25] But we're now putting it through...
[28:28] Like, you know, on your computer, on Photoshop.
[28:31] We got, like, Photoshop the newest version versus, like, the old version.
[28:35] And now we can do, like, oh, contrast this way.
[28:38] And, like, the, you know, the color thing.
[28:41] And you're like, oh, I want to do this color more and this color less kind of thing.
[28:46] That's a really good point.
[28:47] That's the data analysis.
[28:48] No, that's fair.
[28:48] If you switch out a lens...
[28:50] That's raw data.
[28:50] No, no, no.
[28:51] You're switching out to engineering.
[28:52] No, that's a very fair point.
[28:55] The Photoshop analogy is correct.
[28:56] Yeah, they did like Dolphot Photoshop instead of Daofot Photoshop.
[29:00] It's a different Instagram filter.
[29:02] Yes, yes.
[29:03] And they're like, oh, there's four instead of three.
[29:04] Yeah.
[29:06] Exactly.
[29:07] Okay.
[29:07] Yes.
[29:07] Exactly.
[29:08] But they did Instagram on the Hubble.
[29:10] On the Hubble.
[29:10] Yes.
[29:11] Yes.
[29:11] Okay.
[29:12] Yes.
[29:12] Yes.
[29:12] Yes.
[29:12] Yes.
[29:12] Yes.
[29:13] Yeah.
[29:13] And then they found this fourth one, right?
[29:14] And that was critical.
[29:15] And when they stacked, so then they got their ESA colleagues at the European Space Agency.
[29:21] European Space Agency has something called Euclid.
[29:23] Okay.
[29:24] Okay.
[29:24] And they used the Euclid image of the same spot with the Hubble image from the Spain same spot, stacked them together.
[29:32] And now you could reveal the same ultra faint diffuse glow in between those four globular clusters.
[29:40] Those four globular clusters that were there, there was actually a little bit of light coming from the middle.
[29:46] So there are a few stars.
[29:47] Not that many, but there's a chance.
[29:50] And this is why cross-referencing data sources...
[29:54] You can get a little more...
[29:58] Fidelity Exactly And so with that diffuse glow Now you can start fitting models And you can say What is the amount of stellar mass That's in that diffuse glow Right in the middle The amount of light that I'm getting is very little But from that I can make an estimate Given how far I know it is How far away it is The Perseus cluster We can be like That's about 10 to the 6 suns So it's about 2 million suns
[30:27] Okay?
[30:27] It's not a lot.
[30:29] But from what we know about the number of globular clusters and how luminous they are, we can also back-calculate the amount of dark matter that should be there.
[30:39] And the dark matter is something like 10 to the 10.
[30:44] So 10 billion suns.
[30:49] So we've got 10 billion suns worth of dark matter and only 2 million suns worth of normal matter, meaning that the halo mass fraction, the amount of dark matter versus normal matter, is 99.94 to 99.98%.
[31:00] This is where we get the 99.98%.
[31:06] Yeah, the size of the dark matter halo is about that of the Large Magellanic Cloud, which is one of the – it's the biggest satellite galaxy to the Milky Way.
[31:15] You can only see it in the southern hemisphere.
[31:17] But the number of stars there is like a thousand times less than what we see in the Large Magellanic Cloud.
[31:23] Okay?
[31:23] So this is like a really, really tiny, tiny fraction.
[31:28] Yeah.
[31:29] Of what should be there.
[31:31] There's not that many stars.
[31:32] There's like one in a thousand stars.
[31:34] Of what it should be.
[31:35] Right.
[31:36] And that's.
[31:37] That's weird.
[31:38] And that goes in direct.
[31:41] That experimental data.
[31:43] Mm-hmm.
[31:43] Goes into direct conflict.
[31:45] With the Lambda CDM.
[31:47] Right.
[31:48] No, no, no.
[31:49] It doesn't necessarily go into direct conflict because the Lambda CDM does suggest that there should be a lot of dark matter galaxies.
[31:58] Okay.
[31:58] It's just now we found one.
[32:00] I see.
[32:00] Right?
[32:01] And now this is where we get into how could this be possible, right?
[32:05] Okay.
[32:05] Now, the first hypothesis is something called the failed giant hypothesis, which is something like Cloud9, which we covered in a very recent episode.
[32:13] Yes.
[32:13] The idea is you've got some massive halo that's destined to be a galaxy.
[32:16] Right.
[32:17] And then early star formation makes the globular clusters around it.
[32:22] But then star formation somehow abruptly just, like, stops.
[32:25] And so you just get a bunch of dark matter, a few stars, and these globular clusters around.
[32:30] If I recall, there's, like, some threshold where it's right in the sweet spot where it will maintain versus continuing.
[32:37] The weird thing is this is bigger than that threshold, though.
[32:40] I did check.
[32:40] Okay.
[32:41] And this 10 to the 10 solar masses, so 10 billion solar masses, is larger than the threshold that we had over there.
[32:48] So it shouldn't just be like cloud nine.
[32:49] Yeah, it's not like cloud nine.
[32:50] Otherwise, it would be a relic and there would be no stars.
[32:52] There's still a few stars.
[32:54] Fair.
[32:54] Right?
[32:55] The other thing could be like something called a quenching mechanism.
[32:57] There's something called ram pressure.
[32:59] So this is a photo of the Perseus cluster.
[33:02] Yes.
[33:02] You can see a lot of the...
[33:04] starlight is coming from these large galaxies, and the blue sort of haze is from the dark matter that we've mapped out because of all of the
[33:14] velocity measurements that we've taken.
[33:16] And one can imagine that in this really vibrant cluster, you had this early dark matter halo with a bunch of dark matter.
[33:23] And it was like, it had a bunch of cold gas is what we call because cold gas is like the gases are moving around.
[33:29] So it can clump in and form stars.
[33:31] So it was destined to be a galaxy kind of like the large Magellanic cloud, but maybe like some other galaxy just rammed into it.
[33:38] and then stripped it of all of the star-making material.
[33:41] So now it didn't have any star-making material, and it's just stuck being like this dark galaxy.
[33:47] That's actually interesting.
[33:49] So there's these theories, and more testing and more observation is going to tell which one is correct.
[33:56] What I'm really excited about is what I was telling earlier, which is this cusp and core model.
[34:01] Why do...
[34:04] Why does dark matter not form a cusp, right?
[34:08] We need to do more spectroscopy.
[34:10] We don't have that data right now.
[34:11] All we have is photometric data.
[34:13] What we'd have to do is figure out the spectra to figure out the redshift and the blueshift and all of the really nitty-gritty details of what that dark matter clump looks like.
[34:24] Because what we'd expect, given that there's no stars, is there's no smoothing out.
[34:29] And because there's no smoothing out, right, then the...
[34:35] We should have a cusp.
[34:36] We should have a higher density of dark matter in the center, and we should have low density out in the outside.
[34:43] We shouldn't have this flattened profile.
[34:45] Right.
[34:45] Where in the middle, there's just a bunch of dark matter, but there's no real big over density right at the center.
[34:51] Right?
[34:51] There's no, like, clump of dark matter that's super dense in the center.
[34:55] So that...
[34:57] That would be the case if we accept the current models of lambda dark matter, which is lambda CDM, which is the idea that dark matter is some kind of particle.
[35:06] It doesn't interact with itself unless it's through gravity.
[35:11] And now we get a cusp.
[35:13] If we see the cusp, great.
[35:15] Right.
[35:15] Okay?
[35:16] If we don't see the cusp, what does that mean?
[35:26] Further clarify, is there a core or is there a cusp?
[35:29] Yes.
[35:30] And if there is a cusp... Then we're good.
[35:33] Great.
[35:34] This Lambda CDM, well done.
[35:36] Yeah.
[35:37] Experimental data is now further validating the theory.
[35:42] Mm-hmm.
[35:43] If it continues to be a core, which is kind of what it appears to be.
[35:48] Yeah.
[35:48] It appears that, like...
[35:49] It appears that way.
[35:50] Yeah.
[35:50] Yeah.
[35:51] Then we got to go look back at the drawing board a little bit.
[35:54] Yes.
[35:54] Then we got to be like, what exactly is dark matter?
[35:57] Is dark matter really just like another particle, like a proton or an electron?
[36:01] That, fine, the only thing that's different about it is it doesn't have any charge and it doesn't interact with electromagnetic field.
[36:08] It doesn't interact with light.
[36:09] Right?
[36:10] That's the normal thing.
[36:11] And that's like...
[36:12] Like, naively, I would expect that.
[36:13] It's just another particle, but it doesn't interact with light, which is why we see its effects across giant scales of the universe, but not, like, you know, nitty-gritty stuff.
[36:23] This one is pretty cool.
[36:25] There's something called fuzzy dark matter.
[36:26] This is very different from lambda-CDM.
[36:30] Okay?
[36:30] Okay.
[36:30] Fuzzy dark matter means that dark matter is not tiny little like particles, but it's fuzzy in the sense that it's super light, meaning billions of times lighter than the electron.
[36:43] And what does that mean?
[36:44] If it's billions and that's and that's.
[36:46] Some of the lightest stuff, it's going to be lighter than quarks, electrons.
[36:51] It's at the order of even lighter than neutrinos type of thing.
[36:54] It is so light that if we were to calculate the quantum mechanics of a particle that's that light, the wavelength...
[37:02] The de Broglie wavelength, because, you know, remember in quantum mechanics, every particle is a wave and blah, blah, blah, blah, blah.
[37:07] Well, the larger the particle is, the shorter the de Broglie wavelength.
[37:11] Okay.
[37:12] So electrons have a wavelength that's like around like ultraviolet X-ray-ish.
[37:18] This thing at 10 to the minus 22 electron volt mass.
[37:24] would have a wavelength that is hundreds of light years big.
[37:29] Oh, my God.
[37:30] Right?
[37:31] Its quantum wavelength, if you were to create a quantum wave out of it, would be the size of several...
[37:37] Like, it would be like a big chunk of the universe.
[37:40] I mean, sorry, of the galaxy.
[37:42] Right?
[37:42] This is...
[37:43] So the wavelength would be larger than the distance between them.
[37:47] Right.
[37:48] Okay?
[37:48] Yeah, yeah, yeah.
[37:49] Now, what does that remind us of?
[37:50] That reminds us of Bose-Einstein condensates.
[37:53] Ah!
[37:54] Right?
[37:55] Yes, yes.
[37:55] Because in a Bose-Einstein condensate, the waves of each particle...
[38:00] pile on top of one another to create a single quantum wave.
[38:04] And that's why they don't sort of clump together.
[38:06] So what this particular theory is saying is there's some kind of inherent quantum pressure that's at the scale of galaxies that is creating a core with a flat density.
[38:19] You've got a Bose-Einstein condensate of this fuzzy dark matter particle that is at the size of a galaxy, and that's why you don't get a cusp.
[38:26] Yeah.
[38:27] It's so fascinating.
[38:29] So this could be, you know, this Canada Dark Galaxy could be a proving ground for something like fuzzy dark matter.
[38:38] It's very interesting.
[38:39] From my layman's perspective, I like only because we've so recently talked about Bose-Einstein Conversates.
[38:47] So I have a mental model of what that actually means.
[38:50] And so I'm like, yeah, that sounds right.
[38:51] Yeah.
[38:52] That sounds about right.
[38:53] That sounds so cool.
[38:54] Yes.
[38:54] Imagine a particle whose quantum wavelength is the size of a galaxy.
[38:59] That's unbelievable.
[39:00] That's unbelievable.
[39:01] That would be very cool.
[39:02] So, I mean, so this is quite a big deal in, you know, in that I'm curious.
[39:09] I would be curious to see how other folks and institutions begin to sort of digest and interpret the paper.
[39:16] Mm-hmm.
[39:17] But I think like we were talking about earlier, there are certain things that happen when it happens.
[39:20] It's like, okay, all the big guns point over there.
[39:23] Yeah, let's point over there.
[39:24] You think it has the potential to reach that threshold?
[39:28] I think so.
[39:29] I mean, we know where it is.
[39:30] Yeah.
[39:31] We know like sort of the base spectrometry data.
[39:35] So I can imagine there's going to be a lot of follow-up studies on this from all the top telescopes and institutions.
[39:42] Because why not?
[39:43] Yeah, it's a testing ground for particle physics in some sense.
[39:47] Yes.
[39:47] Right?
[39:48] Yes.
[39:49] Which is very cool.
[39:49] This is very, very cool.
[39:51] This is very, very good.
[39:53] This was out of the astrophysical journal letters.
[39:58] Mm-hmm.
[39:59] And we have our institutions here.
[40:02] This would have been, because I want to make sure we cover who did the paper for this.
[40:06] Yeah, yeah, yeah.
[40:06] Good point.
[40:07] I'm just pulling this up now.
[40:10] Yeah, so it was in astrophysical journal letters.
[40:14] And it includes, oh, several institutions.
[40:18] Yeah, it's usually several institutions.
[40:21] There's a lot of folks.
[40:21] But if we want to name a few, University of Toronto is a big one.
[40:24] Yes.
[40:25] Um...
[40:27] Yeah, University of Toronto is one of the big ones.
[40:29] Yes, and this was in collaboration.
[40:31] And Yale.
[40:32] Because, like, you know, it's the...
[40:34] These guys love that ultra-dark, you know, diffuse stuff.
[40:39] University of Santa Cruz, California Santa Cruz.
[40:42] So, yeah.
[40:43] Big shout-outs to all the team.
[40:44] This is a really interesting story.
[40:45] I mean, I still continue to be so fascinated by Dark Matter.
[40:48] I know, friend of the pod, Dan Gilman has dedicated his life to...
[40:52] And he did a postdoc at the University of Toronto, so I wonder if he knows some of these guys.
[40:57] I'll tell him to send them.
[40:58] Yeah, share it, Dan.
[40:59] Share it with the team.
[41:01] And if folks want to give us any follow-ups, comments, or corrections, please let us know.
[41:04] Yeah.
[41:05] Great astrophysics first story on Dark Matter.
[41:08] A kind of continuation...
[41:10] On our Cloud9 story, which was fascinating and well worth the watch if you haven't seen it, to get a little bit deeper into that use case where the cluster does not reach the level where it can produce stars.
[41:21] Yeah.
[41:21] And so it remains sort of this, like, empty mass.
[41:24] Yeah, the relic.
[41:25] The relics, yes.
[41:26] Cloud9.
[41:26] Cloud9.
[41:27] So we're going to move into the rundown now.
[41:29] And for those who are new to the show, we usually cover several main stories where we do a big deep dive.
[41:35] But the rundown allows us to talk about other breaking science research that we find interesting.
[41:40] We're going to start with a follow-up to a story we covered in episode four.
[41:45] Yes.
[41:45] One of our early stories about the VersiRubin Observatory.
[41:49] It has now launched its real-time discovery engine.
[41:52] So...
[41:53] This is now about eight months later, and it's a pretty major update.
[41:58] Yeah, yeah.
[41:58] This one's insane because the Varosiruban Observatory is unlike any other observatory out there because it's this giant thing where astronomers cannot get time on it, right?
[42:11] Usually when there's an observatory and you're like, oh, like in the last story, hey, I want to look at this candidate dark galaxy in the Perseus cluster.
[42:20] I need four nights.
[42:21] And...
[42:22] That application is going to go to whoever's in charge of that telescope time, and they're going to decide from all the applications who is going to get time to look at what.
[42:31] The Veris-Ereuben Observatory is an observatory that is just taking a photo of the entire night sky every three nights.
[42:38] That is its job, light clockwork, fully automated, and that's all it's doing.
[42:43] It's not focusing on particular things.
[42:45] That's what it's doing.
[42:46] And what that means is it's going to find a lot of data,
[42:50] Every night there's going to be new data.
[42:51] And what it can do is historically look at what did that position in the night sky look like a month ago, a year ago, 10 years ago?
[43:00] Because all of the archival astronomical data of maybe the Hubble pointed at it at some point or maybe the James Webb pointed at it six months ago.
[43:08] It can compare images between Hubble.
[43:11] what it saw that particular night, and what other telescopes have seen that particular night.
[43:17] And if there's anything different, send out an email alert.
[43:20] And so on its first night that this real-time discovery machine was operating, it sent out 800,000 alerts.
[43:30] It found 800,000 candidates for new stuff that scientists could look at.
[43:36] So this is incredible.
[43:38] And we talked about this when we did the story because the technology behind it is unreal in terms of the volume and the – it's like going from CRT 480p TVs to 4K.
[43:54] Yeah.
[43:56] And so that's in terms of resolution.
[43:58] Yes.
[43:59] The main thing is it's going from photographs to movies.
[44:01] Movies, right, which we talked about.
[44:02] And so we're seeing, like, motion.
[44:04] Motion, right.
[44:05] We're seeing, for example, asteroids, because the asteroids will move.
[44:09] And so that's going to be a motion transient that the Vera Rubin can catch.
[44:12] We're seeing supernovae, which are going to be, oh, there was no light bulb there.
[44:17] And then now there's a tiny, faint light bulb on that galaxy.
[44:19] That means a star exploded.
[44:21] And we've got about...
[44:22] you know two or three weeks to catch that star exploding so all these transient events these fleeting celestial events that's what the vera rubin was out for right that's why it's called the legacy survey of space and time the lsst that's the program it's for 10 years it's going to go along these alerts are going to be insane um
[44:46] It's expected to generate up to 7 million alerts per night.
[44:52] Yeah.
[44:52] And we're already on track because the first night it was already 800,000.
[44:56] I mean, this is incredible because there's not that many astronomers out there.
[45:00] Right.
[45:00] Okay, in the world.
[45:01] And those astronomers don't have a lot of time.
[45:03] Right.
[45:04] So this is really the advent, I think, of decentralized astrophysics research.
[45:09] Like, now we can have citizen scientists, for example.
[45:12] If you're, like, in a high school or, like...
[45:16] You know, some advanced program where you want to do scientific research, but you don't have access to data.
[45:22] This is an incredible resource now to do small little projects.
[45:26] I don't know, you want to get ahead in your college application.
[45:29] This is something you could do.
[45:30] You could go on the Vera Rubin website and find data and look at some of these alerts and be like, hey, which alert do I want to like...
[45:38] dig a little bit deeper into things like that um you can also i mean this is going to be huge for machine learning and ai because handling seven million alerts a night no human can do that right but creating a machine learning pipeline that can you know sparse through all those alerts figure out which is the one that actually might require human attention from somebody with a phd in astrophysics and so on it's going to be huge i i i this is this i think when we did our end of the year episode for season one i brought up
[46:06] this episode 4 is one of my favorites because I still just think technically from technical execution largest camera in the world the data pipeline build being able to actually process and make available
[46:26] Immediately, that volume of data Within two minutes, I think Is a significant technological challenge In and of itself And then the open sourcing of it Right, out of the box Meaning there's no gatekeeping There's no this and that I think it's a beautiful testament To the spirit of what
[46:45] science yeah in the modern era can look like yeah there's understandable reasons why not every system is like that yeah and i mean this is this is something that i think the u.s taxpayer should be very proud of yes this is you almost entirely a u.s effort by the u.s national science foundation and the u.s department of energy do we baby like this is this is really cool that our taxpayer dollars are going into something like this very
[47:11] cool first story um we're gonna move into our story number two our story number two this one is a material science and ai story as scientists have used ai to help them find new types of magnets uh which can be used in an innumerable number of industries so what what exactly is going on with these new magnetic materials yeah so finding rare earth free permanent magnets
[47:36] I.e.
[47:37] permanent magnets that don't require these rare earth minerals that like we have to mine for that are really hard to find that maybe we have to make a deal with China with and so on and so forth.
[47:47] Like that's crucial for reducing the manufacturing cost and the supply chain vulnerabilities of electric vehicles and electric vehicles.
[47:56] If we can make them cheaper.
[47:58] Yeah.
[47:59] easier to make, they can be the future, right?
[48:01] Not just electric vehicles.
[48:02] I mean, batteries go in everything, right?
[48:04] Medical devices, renewable energy systems.
[48:06] And what this team at the University of New Hampshire has done is use an AI system to extract data from thousands of scientific reports and train a model that will predict the magnetic behavior of materials, of novel materials, right?
[48:22] And you have now a searchable database of something like 67,000 magnetic compounds.
[48:29] And out of those, they found 25 new candidates that will remain magnetic at high temperatures.
[48:35] Very interesting.
[48:36] Because the hotter you make a magnet, the more jiggling there is, which means that the local alignment between your magnetic elements, like in iron, for example, the iron has like a valence electron that creates a gross magnetic field around the atom.
[48:52] When iron becomes magnetic, like when you take a paperclip and you put it around a permanent magnet, that paperclip becomes magnetic, right?
[48:59] Because all of the iron atoms sort of align to make a big magnetic field.
[49:04] Well, if you heat up that paperclip, you're done.
[49:06] But if you put that paperclip in the freezer and you come back hours later, it'll still be magnetic.
[49:11] The point is we want to find magnetic materials that remain magnetic at high temperatures.
[49:16] Because that will give us industry applications.
[49:20] And that's what this is doing.
[49:22] And it vastly accelerates material science.
[49:26] You know, because millions of time consuming lab tests can now be churned through with this machine learning algorithm and you can find like a searchable digital resource that you can then be like, okay, I want to find a particular magnetic material that does this and this.
[49:42] You got something.
[49:43] This is great.
[49:43] So this was out of Nature Communications.
[49:45] Would it be...
[49:47] So, I mean, obviously, the thing that comes to my mind is...
[49:51] It makes me think of, like, a magnetic version of AlphaFold.
[49:55] Yeah, kind of.
[49:57] It's like a material science version of it.
[49:59] Like, directionally speaking.
[50:00] Yeah, yeah, yeah.
[50:02] I think it's still a little far from I give you a novel material and you predict.
[50:07] This is still...
[50:08] There's all of these...
[50:11] there's all of these thousands of scientific papers that have already come out with a bunch of stuff.
[50:16] How do I centralize that and create an understanding?
[50:19] So it's more so taking the existing data and making someone be able to iterate on ideas based on existing data.
[50:30] AlphaFold was really sort of saying, here's all possible ideas.
[50:35] Protein structures.
[50:36] Yeah, from all of the sequences that we know, even the ones that we haven't found.
[50:39] Which is a different thing because that's generating net new data versus just interpreting past data.
[50:44] Okay, so there's a difference there.
[50:46] Yeah, but it's getting there, right?
[50:48] We're getting close.
[50:48] That's very cool.
[50:49] Very, very, very, very good.
[50:50] Yeah.
[50:51] Story number three is a climate science story with a bit of anthropology.
[50:55] Yeah.
[50:56] There's new science about what actually went wrong.
[51:00] On Easter Island.
[51:02] Yes.
[51:02] What went wrong?
[51:03] So Easter Island is very famous, right?
[51:05] This is Rapa Nui.
[51:07] It has those giant...
[51:10] statues of these like heads with torsos that are facing out into the ocean.
[51:19] And for the longest time, the popular narrative was that the society collapsed there because of self-inflicted ecocide, meaning they cut down all the trees.
[51:29] And then by the time that the European colonizers came, there was a decimated population that was like almost starving and blah, blah, blah.
[51:37] Okay.
[51:39] That turns out to not be entirely true.
[51:41] Shocking.
[51:42] Shocking that the European colonizers made up a... A story about the history.
[51:48] Yeah, a story about the history that is conducive to them and says, oh, the natives didn't know what they were doing.
[51:54] What they were doing.
[51:55] Okay.
[51:56] It turns out from new sediment core data...
[51:59] What they did was they studied hydrogen isotopes of plant leaf waxes.
[52:03] Okay.
[52:04] And these things preserve sort of a climate history across climates, across centuries, right?
[52:10] So by studying the hydrogen isotope data there, what they actually found out was that the annual rainfall there dropped sharply.
[52:19] Yeah.
[52:20] by almost 24 to 31 inches during the mid 16th century.
[52:25] And this is right before sort of the European explorers went and like saw what was happening.
[52:31] Right.
[52:32] And so what's happening is instead of like entirely collapsing due to deforestation and conflict,
[52:38] which is the original theory, what probably happened is the Rapa Nui people adapted to the severe climate stress, and they shifted their rituals, their power structures, and their ceremonial centers to respond to that climate stress.
[52:53] And it emphasizes more like human resilience
[52:56] And that the history of Easter Island is way more complicated than what this overconsumption narrative that the colonizers had pushed.
[53:06] Surprise, surprise.
[53:07] Classic.
[53:08] Very fascinating.
[53:10] We could probably, I'm sure some people would be like, hydrogen isotopes of plant leaf waxes being able to be analyzed over centuries in and of itself.
[53:19] Yeah.
[53:19] I would love to understand that.
[53:20] Yeah, yeah.
[53:21] That's a good one for a deep dive.
[53:22] Because it's...
[53:24] That's so curious to me, but clearly is hugely impactful in both this historical climate science-related research, but also mapping it in this case, which is what's kind of interesting to an anthropological lens as well.
[53:38] Yeah, I mean, it's a technique that's very similar to when people do ice cores.
[53:42] Have you heard of that?
[53:43] No, no, no.
[53:44] They barrel like an ice cylinder in an Antarctic glacier.
[53:47] Okay.
[53:48] And because Antarctic glaciers are like so old, layers of that are going to give you a recorded history of the climate going back almost millions to tens of millions of years.
[53:59] Right.
[53:59] So similarly, plant waxes like leaf waxes are stuff that are very old because it's not like.
[54:08] It's not like a plant in that it was just born.
[54:10] It's like it created the wax, and then the wax has sort of persisted across centuries.
[54:15] It's kind of like in Jurassic Park when you have the resin.
[54:18] Resin is a plant residue.
[54:20] It's not wax, but it's a type of residue that can last forever.
[54:23] Through the ages.
[54:24] Similarly, leaf wax is something that can last through the ages.
[54:27] So it gives us like a diary of what the climate was like in that locality.
[54:33] If we can really analyze it at that atomic resolution, which is what these guys did at the Columbia Climate School.
[54:39] Fascinating.
[54:41] This was a paper in Communications Earth.
[54:44] Yeah.
[54:45] Again, I think we'll continue to see as scientific tools and instruments and the funding that goes to support those endeavors continues to grow in capability.
[54:56] We will be illuminating more about as much about our past.
[55:00] Yes.
[55:01] As we will about where we are going as a species.
[55:05] Our fourth and final story in the rundown.
[55:08] This one's a weird story.
[55:09] About psychology, language, and baby chickens.
[55:13] Yes.
[55:14] And I don't know much about it on purpose.
[55:18] And before we get into it, I would like to take our audience on a trip to Bahrain and ultimately Italy.
[55:26] Okay.
[55:27] We just ended Formula One testing in Bahrain.
[55:30] Yes.
[55:30] The season opener is happening in Australia at the end of this week.
[55:36] For F1.
[55:37] For Formula 1.
[55:37] Of course I'm talking about Formula 1, the greatest sport alive today.
[55:42] And Ferrari is back, baby.
[55:47] Ferrari is so back.
[55:48] This is going to be our year, dude.
[55:51] We haven't had a driver's championship since 2007.
[55:54] We haven't had a constructor's championship since 2008.
[55:57] I'm not blaming anybody.
[55:58] I'm just saying this is the reality of the situation.
[56:03] But we've got Charles Leclerc, who I'm personally a fan of Charles Leclerc.
[56:08] Lewis Hamilton, one of the greatest of all time.
[56:10] Also, congratulations to Charles.
[56:12] I think he just got engaged.
[56:13] He did.
[56:14] No, he just got married.
[56:15] He got married in Monaco.
[56:18] He's a Monogasque, a native Monaco driver.
[56:21] Most drivers go there for the tax reasons.
[56:24] He was...
[56:24] born there I was born in it yeah yeah he was born in it exactly Ferrari had a great testing in Bahrain Charles Leclerc was one second clear of Mercedes George Russell okay
[56:40] I know everyone's going to be like, oh, during testing, everyone is like, you know, they're putting artificial masses on their car and they're like gaming the system to not.
[56:48] But a second is crazy.
[56:51] And what gives me hope is that Haas, which is an American team that is an engine customer of Ferrari.
[56:59] So Ferrari's engine is the same engine that's in the Haas industry.
[57:03] in the Haas car Haas is also doing really well during testing so that's like it's like a you know in science we look for coincident data to to suggest that there's an underlying invariant right and as a scientist i'm just saying ferrari is bad okay and dude we're we're we're gonna run away with it it's gonna be amazing
[57:23] Can I ask a quick question before we continue, which is, did the wing thing get figured out?
[57:28] No, no, no.
[57:28] Ferrari has an amazing wing.
[57:30] I don't know if we're going to...
[57:31] The upside down thing?
[57:32] Yeah, yeah.
[57:33] So Ferrari has a wing that flips when it goes from a curve to a straight.
[57:39] We don't have DRS this season because...
[57:41] So for those who are also listening, Formula One is all about the formula, right?
[57:45] Which is the...
[57:47] regulations that the formula one gods produce saying these are the rules for how you can build a car and then every car manufacturer looks at those rules and says where can we read between the lines and create little efficiencies that others might not and ferrari figured out that there's nothing against the
[58:09] building a rear wing that will entirely flip 180 degrees, depending on what part of a racetrack you're on.
[58:15] Because during straights, you want low drag,
[58:21] because you just want to go straight.
[58:22] During curves, you want high downforce, because you want more grip so that you can take corners faster.
[58:29] And those two can have different wing configurations on the rear wing, based on the aerodynamics that you get, and so on and so forth.
[58:36] And most people just have a flap that goes like this, and then goes down.
[58:40] This thing is going...
[58:42] is turning around 180 degrees.
[58:45] And all these other teams were like, yeah, we thought of that.
[58:47] I know you didn't.
[58:48] You didn't think of that, okay?
[58:49] Okay, you saw it, and you're like, oh, we better, like, cover our... No, you didn't think of that.
[58:53] Ferrari thought of that.
[58:55] That's right.
[58:56] Anyways, I'm going off about Ferrari because this next...
[59:00] I'm going to bring it back to science.
[59:01] Okay.
[59:02] Okay.
[59:03] This next rundown study is from the University of Padua in Italy.
[59:07] Okay.
[59:08] It was out in the journal Science.
[59:09] Okay.
[59:10] And that's the connection to Italy.
[59:12] Okay.
[59:14] That's it.
[59:15] I just wanted to go on a rant about how Ferrari's going to win.
[59:18] But, dude, this paper is pretty insane.
[59:22] Okay.
[59:22] Because it didn't make any sense to me why it should be possible.
[59:25] Have you ever heard of something called the Boba Kiki effect?
[59:28] I only know about Kiki.
[59:30] Do you love me?
[59:31] Are you riding?
[59:32] But I don't know about the Boba Kiki.
[59:34] All right.
[59:34] So let's pull up the overlay.
[59:36] Okay.
[59:36] Okay.
[59:36] Okay.
[59:37] All right?
[59:38] So you see the two shapes.
[59:41] There's a green sort of blobby shape.
[59:44] Yeah.
[59:44] And then there's a blue pointed shape.
[59:46] Okay.
[59:47] And just for listeners, so we're looking at an image that currently, can I say this?
[59:51] Yeah, go ahead.
[59:52] Okay.
[59:52] Yeah.
[59:53] So it currently has what appeared to be two baby chickens or birds of some kind or ducks.
[1:00:00] in the middle, and these two shapes at the top right and left-hand corners.
[1:00:06] The one on the left is a green blob.
[1:00:09] The one on the right is like a blue star kind of thing.
[1:00:11] Star kind of thing.
[1:00:12] Pointy.
[1:00:13] Which one is Boba and which one is Kiki for those shapes?
[1:00:16] Oh, Boba's obviously the blob and Kiki is obviously the thing.
[1:00:19] Why did you say that?
[1:00:21] Oh, because in my head, I don't know, Kiki is like
[1:00:26] Yeah.
[1:00:26] It's like...
[1:00:27] It's pointy.
[1:00:27] It's pointy.
[1:00:28] Yeah.
[1:00:29] Boba is blobby.
[1:00:31] That's exactly right.
[1:00:32] Like, phonetically?
[1:00:34] Yeah.
[1:00:35] And this is the Boba Kiki effect.
[1:00:36] Oh, is it really?
[1:00:37] That's entirely what it is.
[1:00:39] If anyone said the blue was Boba, you're crazy.
[1:00:41] No, literally nobody does.
[1:00:43] But here's what's crazy.
[1:00:44] Okay.
[1:00:44] You know, you would think that, okay, maybe we're... You know, we grew up in a Western society.
[1:00:49] We are...
[1:00:51] We are exposed to Western language.
[1:00:54] Yes.
[1:00:55] And so perhaps that has something to do with it.
[1:00:57] That makes sense.
[1:00:57] No, they have done research with multiple language groups, most famously the Tamil language group, which is in India, South India.
[1:01:04] That is an entirely different language group from English.
[1:01:08] English comes from the Indo-European family.
[1:01:11] These guys are Tamils.
[1:01:11] They're not even Hindi because Hindi and Sanskrit still comes from the Indo-European family.
[1:01:16] The Tamil language comes from the Dravidian family, which is native to India.
[1:01:20] It is completely separate from Indo-European family language.
[1:01:24] And they still believe that the left is Boba and the right is Kiki.
[1:01:27] You go to other cultures entirely...
[1:01:31] Boba Kiki works.
[1:01:32] You go to infants, Boba Kiki works.
[1:01:35] So it was thought that this Boba Kiki effect, meaning Boba goes to blobby, rounder things and Kiki goes to pointier things, is something that is innate within human language.
[1:01:45] Okay?
[1:01:45] Interesting.
[1:01:46] Across human languages.
[1:01:48] And so far, so good.
[1:01:49] Until this paper.
[1:01:51] This paper played recordings of humans saying either Boba or Kiki to naive three-day-old chicks and presented them with two panels that had food behind it.
[1:02:01] Okay.
[1:02:02] And the chicks knew which one was Boba and which one was Kiki.
[1:02:06] Shut the front door.
[1:02:07] These are three-day-old chicks that had the Boba Kiki effect.
[1:02:12] I don't believe it.
[1:02:13] No, but it works.
[1:02:15] That's so...
[1:02:16] Meaning there is...
[1:02:18] This is not a language thing.
[1:02:19] It's not a language thing.
[1:02:21] That's what's key, yeah.
[1:02:21] This is like...
[1:02:23] There is something inherent in the sound of Boba versus Kiki that maps onto the architecture of physical objects.
[1:02:32] Okay?
[1:02:34] And you can start thinking, like, why?
[1:02:36] And actually, it doesn't seem that weird anymore.
[1:02:39] When you start thinking about the physics of sound and the physics of Boba and Kiki, the word Boba and the sound Buh-
[1:02:49] That's lower frequency.
[1:02:50] You associate that with bass.
[1:02:52] Yep.
[1:02:52] Right?
[1:02:52] Yep.
[1:02:53] Kiki is like the ding, like the high frequencies.
[1:02:57] Yeah.
[1:02:57] Rounder objects, when sound goes through them, the higher frequencies are going to sort of get absorbed.
[1:03:04] Yeah.
[1:03:05] Around that, the rounder frequencies are going to get deflected and so forth and so on and so forth.
[1:03:10] The sharper an interface is on the physics of an object, right?
[1:03:14] The sharper the sound that is going to come out of that object.
[1:03:18] Just because of the way sound is, all right?
[1:03:20] The sharper the thing is, the shorter the wavelength, the shorter the wavelength, the higher the frequency, the more we get that ding sound.
[1:03:28] Right.
[1:03:28] It's literally just Fourier analysis is what we would think if someone's an electrical or sound engineer.
[1:03:35] It's like the Fourier analysis of sound around the physics of objects.
[1:03:39] The smoother an object is, the basier the sound that comes out.
[1:03:44] And so even chicks, their brains, three day old chicks, their brains.
[1:03:50] There's some innate physics calculation that's happening within them that's saying the higher frequency has to come from a pointier object.
[1:03:58] That is so...
[1:04:00] It's so weird.
[1:04:01] Unbelievable.
[1:04:02] But the way that you explained it does... Yeah, then it kind of makes sense.
[1:04:05] Then it kind of makes sense.
[1:04:06] What I'm surprised about is that a three-day-old chick has neural mechanisms within its brain to make that distinction.
[1:04:14] That this is so invariant across anyone that has brains, I guess, because this is not even a mammal.
[1:04:21] Right.
[1:04:22] Right?
[1:04:22] This thing diverged very, very far away from us.
[1:04:24] That's...
[1:04:24] Hundreds of millions of years ago, before the dinosaurs.
[1:04:27] Yeah.
[1:04:28] There's a deeper architecture here.
[1:04:30] Yeah.
[1:04:31] There's a deeper... Not architecture.
[1:04:32] There's a deeper insight about why that would be true.
[1:04:37] Yeah.
[1:04:37] Not only because of the cross-species level similarities, but also that you don't need brains that have developed for a long amount of time to have some sort of like...
[1:04:52] you know, learned understanding of the physics of the universe.
[1:04:55] Yeah.
[1:04:56] Yeah.
[1:04:56] They've just made this association because like over the years, this is what's true, right?
[1:05:00] Right.
[1:05:01] The buzz sound comes from rounder objects and the cause sound comes from pointier objects.
[1:05:06] Right.
[1:05:06] And it's like even like a short, a small neural network that we train with reinforcement learning, for example, would come up with the Boba Kiki effect.
[1:05:15] Where let's say the input of this neural network was the sound and then the output was a decision between the two.
[1:05:21] Through reinforcement learning, it would learn that the lower frequencies are the rounder frequencies.
[1:05:25] things that but the fact that like you know we did this going all the way back to our common ancestor between birds and mammals which is 100 million years plus and before the dinosaurs and it's still true and it's yeah yeah and it's it's across all these animals right i think that's so cool that to think about and also what a weird experiment to run yeah right like who was like hey let's test boba and kiki
[1:05:52] On baby chicks.
[1:05:52] Who knew Drake was so attuned to Frontier Science Research when he made that song?
[1:05:58] Mm-hmm.
[1:05:59] Whatever that song's called.
[1:06:00] Do you love me?
[1:06:01] Four great rundown stories.
[1:06:04] We're going to jump now back into our main story, our second story of the day, which is around Alzheimer's research.
[1:06:12] This one's very cool.
[1:06:13] So this one is a health and medicine story about Alzheimer's disease and exercise.
[1:06:19] Yeah.
[1:06:19] Yeah.
[1:06:19] So scientists think they figured out exactly how exercise reduces the risk of Alzheimer's.
[1:06:23] It reports a new paper in cell by a group out of the University of California, San Francisco, in this Bacar Aging Research Institute.
[1:06:34] And so we've actually done a lot.
[1:06:37] I mean, we just did last episode.
[1:06:38] We did something on ALS.
[1:06:40] We've talked about Alzheimer's research, I think, multiple times.
[1:06:44] Yeah, multiple times.
[1:06:44] Multiple times on the pod previously.
[1:06:50] millions of people, families, siblings, co-workers, etc.
[1:06:56] And so there's a link between the disease and exercise.
[1:07:00] I'm curious what that link is.
[1:07:01] Yeah, and I mean, it's not like that surprising that exercise...
[1:07:07] Is good for you.
[1:07:08] Right.
[1:07:09] This is something we've known.
[1:07:10] But what I love about this study.
[1:07:12] Is we've.
[1:07:13] Again.
[1:07:14] It's about the mechanism.
[1:07:15] It's the Lego blocks that are coming together.
[1:07:18] There's a pathway from exercise.
[1:07:20] To the biochemistry of Alzheimer's.
[1:07:23] That these guys have traced.
[1:07:24] Okay.
[1:07:24] From the.
[1:07:25] From me running.
[1:07:27] To me not getting Alzheimer's.
[1:07:29] Or getting Alzheimer's later.
[1:07:31] Okay.
[1:07:31] And that biochemical.
[1:07:32] Okay.
[1:07:33] string of Lego blocks is what is super interesting to me.
[1:07:36] Okay.
[1:07:38] We potentially have a discovery of the pathway by which this is happening.
[1:07:42] Yes, because if we have a discovery of the pathway, the biochemistry of what this is happening, then perhaps we can get the benefits of...
[1:07:49] Without the exercise.
[1:07:50] Right, right, right.
[1:07:51] Because we understand the mechanistic dynamics that are driving the process.
[1:07:58] And this could be huge because then for people who maybe can't do exercise but have Alzheimer's, this could be a big thing.
[1:08:05] Or for those who want to do exercise but get added benefit, you know, a boost.
[1:08:08] It's huge, right?
[1:08:09] Okay.
[1:08:10] Okay.
[1:08:10] So we're going to start with the human brain.
[1:08:11] 80 billion neurons.
[1:08:13] It's the most complex object in the universe.
[1:08:16] Okay.
[1:08:17] And stuff can go wrong because it is the most recent of our organs to evolve, right?
[1:08:23] Most of the mutations that create human beings have to do with brain development.
[1:08:28] And what that necessarily means is the brain is going to have a lot of stuff go wrong because it's still in the phase of...
[1:08:35] the genetics is trying stuff out we're in the beta version right yeah we're still in the beta version even though the beta version is doing really well like but but the no one's turned off the the code um like edits yeah it's like no one's frozen the main yeah yeah you know github people people are still merging branches exactly it's like it's like no no we're doing like
[1:08:55] We're okay.
[1:08:56] Can we stop?
[1:08:57] We have some bug fixing to do.
[1:08:58] Yeah, exactly.
[1:08:59] And so the bug fixing is what's crucial, and it happens a lot later on in life.
[1:09:03] And for a long time, the brain is sort of studied in isolation.
[1:09:06] There's like a – you imagine like the brain is an island, and we think about the mind, and everything that's happening in the brain is isolated to the brain.
[1:09:14] But very recently, that has progressed to be the brain, the mind, and the body are connected, and we need to figure out a holistic approach.
[1:09:23] understanding of the entire system right and that's where this research comes in they've identified a biochemical pathway that links the liver to the blood to the brain and it shows the systemic benefits of exercise and how they are transformed or transferred to the brain without actually having to do exercise that's the idea oh my goodness okay and it's it's able to it's able to
[1:09:49] trace the entire story from exercise to brain benefits.
[1:09:53] Okay?
[1:09:54] The mechanism is from a particular enzyme that I'm only going to say once because these biochemists and organic chemistry people, they love just making the world's longest word.
[1:10:05] I think that's what they're going for, is like Guinness Book of World Records.
[1:10:08] Anyways, here we go.
[1:10:09] Okay, guys?
[1:10:09] It's called glycosylphosphatidylonocytol, specific glycosylphosphatidylonocytol,
[1:10:17] phospholipase D1.
[1:10:20] We are going to refer to this enzyme throughout our story as GPLD1.
[1:10:26] So whenever you hear me say that word, that is the particular enzyme that we have discovered in the liver and all of its benefits all the way up to the brain.
[1:10:35] I just want to take a brief note to distinguish that although it has all of the same letters with a couple of extras.
[1:10:44] Yes, it's not Ozempic.
[1:10:44] Is that where you're going?
[1:10:45] It is not a G. We're not talking about GLP-1.
[1:10:47] Yes.
[1:10:48] This is GPLD-1.
[1:10:50] Exactly.
[1:10:50] Yes.
[1:10:50] So those are different things.
[1:10:51] Those are very different things.
[1:10:52] GPLD-1.
[1:10:53] This is not Ozempic.
[1:10:55] And what it does is act on blood vessels surrounding the brain to repair age-related damage.
[1:11:01] And that's what we're going to get into.
[1:11:02] So this is like a cleanup crew that goes in to fix stuff.
[1:11:06] Mm-hmm.
[1:11:08] But the way they fix stuff is very interesting.
[1:11:10] Okay.
[1:11:10] Okay?
[1:11:11] And that's where the Lego blocks comes in.
[1:11:13] Okay.
[1:11:13] And that's what I like about it.
[1:11:14] All right.
[1:11:15] So let's talk about Alzheimer's real quick.
[1:11:18] Most common cause of dementia, 60% to 70% of all cases of dementia are Alzheimer's.
[1:11:24] Globally, there's about 50 million people living with dementia.
[1:11:27] Okay.
[1:11:28] Right now in 2021.
[1:11:29] And if we look at a 2050 projection in the future, right, people are living longer.
[1:11:34] And if people are living longer, these age-related diseases are going to go up.
[1:11:38] And so by 2050, there's projections of 100 to 150 million individuals globally.
[1:11:45] That's nearly triple of today living through these symptoms.
[1:11:49] Okay?
[1:11:50] Okay.
[1:11:51] Black Americans are two times more likely.
[1:11:53] Hispanic Americans are 1.5 times more likely.
[1:11:57] Women make up approximately two-thirds of the U.S. Alzheimer's population.
[1:12:02] Yeah, and nearly twice the risk, lifetime risk, versus men at the age of 65.
[1:12:08] So there's a lot of these disparities that go on.
[1:12:10] We still don't know much about why.
[1:12:12] I mean, you can imagine economic and social...
[1:12:14] issues, diet issues with black Americans and Hispanic Americans versus white people who just they're in a different economic bracket on average.
[1:12:24] So these on average statistics comes through and it's a big crisis in medicine to figure out what is going on and how do we help it.
[1:12:33] It was first identified in 1906 by Dr. Alois Alzheimer.
[1:12:37] He was a German doctor.
[1:12:39] He identified these extracellular amyloid plaques
[1:12:43] in patients with dementia.
[1:12:45] And since then, we've been studying these things called amyloid plaques.
[1:12:50] The idea is you've got this accumulation of a protein called beta amyloid, okay?
[1:12:56] There are these peptides, and that's the primary cause.
[1:13:00] You get a bunch of amyloid precursor proteins, so this is whatever is the precursor to amyloid.
[1:13:07] They denature in some way, and they
[1:13:10] get together, right?
[1:13:12] And when they get together, they create these plaques of amyloid.
[1:13:16] And those plaques then get in the way of all of the neural machinery.
[1:13:20] I get you.
[1:13:22] There's a highway, and then these roadblocks just start auto-generating and blocking the flow of stuff.
[1:13:28] Yeah, and there was genetic link, right?
[1:13:30] Because early-onset familial Alzheimer's was caused by mutations in this protein.
[1:13:35] Got it.
[1:13:36] In the gene for this protein.
[1:13:37] Got it.
[1:13:38] So you can imagine, okay, that seems like something that we should target.
[1:13:43] There was a big discrepancy between targeting this pathway and actual clinical advantage.
[1:13:51] Okay.
[1:13:52] The drug failure rate from 2002 to 2012 is something like 99.6%.
[1:13:59] And even today, like this is a headline from last year, Novo Nordisk shares plunge after the Alzheimer's drug trial fails to hit the key target.
[1:14:08] Interesting.
[1:14:09] Okay, so there's so much research that is going into Alzheimer's, but when we try stuff in animal studies, when we try stuff in a Petri dish, and then when we go in and try to fix the myeloid crisis in the brain of patients, it doesn't quite work.
[1:14:22] There's only a weak correlation, it turns out, the more of the research we do.
[1:14:25] There's only a weak correlation between the total plaque load in the brain and cognitive severity.
[1:14:30] Because the more and more that we've been able to do brain scans of healthy patients, we figured out that some of these healthy patients...
[1:14:38] Have a myeloid plaques So that can't be the whole story It's not a single source It's not the single source of the problem It may be part of it Yeah, but it can't be that Oh, a myeloid plaque, One Direction Causes Alzheimer's Because there's plenty of people that have a myeloid plaques That don't have Alzheimer's Okay?
[1:14:57] Mm-hmm
[1:14:58] Furthermore, there's a timing problem because the amyloid plaques, they start accumulating like 20 years before the actual symptoms.
[1:15:05] So by the time you've got the symptom and you're trying to treat this thing, it's like the forest fire has already started.
[1:15:09] Yeah.
[1:15:10] Yeah.
[1:15:10] Right.
[1:15:11] Yep.
[1:15:11] That tracks.
[1:15:11] Yeah.
[1:15:12] And so there's been recent approval of certain drugs, but it only shows a modestly slow decline in early stage patients.
[1:15:22] But it's not a cure.
[1:15:23] It doesn't reverse the effects of Alzheimer's.
[1:15:25] Right.
[1:15:26] And sometimes it carries risks like brain swelling and hemorrhage.
[1:15:30] So this amyloid paradigm is really under pressure.
[1:15:34] Right.
[1:15:34] And we need to find a new way to figure out what's going wrong in Alzheimer's brains.
[1:15:39] And is there a way to reverse the effects?
[1:15:41] Not just stop, not just slow down, but reverse.
[1:15:44] Because, I mean, even just looking at this data, the drug failure rate that you mentioned of 99.6% was across almost over 200 programs in a 10-year time span.
[1:15:54] Yeah.
[1:15:54] So it's not like it's from a lack of trying.
[1:15:57] No, no.
[1:15:57] It's like we're trying, but the fundamental issue we're having is maybe the philosophy with which we're going about.
[1:16:04] Right.
[1:16:04] This.
[1:16:05] If you're the Dallas Cowboys, you're like, why haven't we been to the playoffs in 30 years?
[1:16:09] It's like you're looking at everything but the source of the problem, which might be, you know.
[1:16:13] Yeah.
[1:16:14] Or if you're Ferrari.
[1:16:15] Yeah.
[1:16:16] Never mind.
[1:16:16] That's a source.
[1:16:17] For another day.
[1:16:19] Yeah.
[1:16:19] I don't want to get into that.
[1:16:21] We're doing well.
[1:16:21] It's going to be fine.
[1:16:22] Anyways.
[1:16:23] So we need to change the way that we're approaching this problem.
[1:16:26] Right.
[1:16:26] Okay.
[1:16:27] So now let's think about the brain from a mechanistic sense.
[1:16:30] Okay.
[1:16:30] The brain is only about 2% of the body mass.
[1:16:33] Mm-hmm.
[1:16:33] But it uses up 20% of the body's energy.
[1:16:38] Okay?
[1:16:39] All of that glucose, all of that oxygen, 20% is going to our brain.
[1:16:43] Okay?
[1:16:44] It's got about 400 miles of capillaries in our brain that is supplying energy to the neurons.
[1:16:52] Now, all of that energy has to get through from the blood into the neurons.
[1:16:58] Right?
[1:16:59] And it has to go through something called the blood-brain barrier.
[1:17:02] Mm-hmm.
[1:17:03] This is an incredible plumbing system that we have in our brain.
[1:17:08] Okay?
[1:17:09] It's different from everywhere else in the body.
[1:17:11] Everywhere else in the body, normal blood vessels, they carry blood.
[1:17:15] Okay?
[1:17:16] And they've got these, like, gaps where...
[1:17:22] Stuff can go in, stuff can go out from the blood.
[1:17:25] So, you know, hemoglobin can go through, oxygen can go through, glucose can go through, little tiny metabolites can go through from, let's say, our muscles to the blood back and forth.
[1:17:38] The junctions in the piping are loose, literally.
[1:17:43] There's like gaps.
[1:17:46] In the blood-brain barrier, the junctions are what we call tight junctions.
[1:17:51] These brain endothelial cells, which are the cells that line the blood vessels in our brain—
[1:17:57] have tight junctions, and they're so tight that 99% of large molecules and 95% of small molecules do not make it through.
[1:18:06] Not even glucose can go through unescorted.
[1:18:09] There are literally things called transport proteins that have to take glucose from the blood through a special kind of passage into the brain.
[1:18:19] It's like a clean room.
[1:18:21] The analogy I think of, it's like flying.
[1:18:24] Regular blood vessels are like flying back in the day where there's no TSA.
[1:18:27] Yeah, dude.
[1:18:28] Yeah.
[1:18:29] But the blood-brain barrier is like TSA.
[1:18:30] Yes.
[1:18:31] It is exactly like.
[1:18:32] Take your shoes off.
[1:18:33] Yeah.
[1:18:33] Got to scan your bags.
[1:18:34] Yeah, yeah.
[1:18:35] Random search Come to the back room Yeah exactly The brain is like Because the brain is like Arguably the most important thing for our humans Like there's a lot of security Between what's coming in And what's going out Which makes sense Totally makes sense And
[1:18:56] If these blood-brain barriers fail, if these junctions fail, the tight junctions, if they become leaky like with normal blood vessels, then that's going to lead to nonspecific leakiness.
[1:19:08] We're going to get inflammatory compounds that come in.
[1:19:11] We're going to get cytokines.
[1:19:13] That's going to drive neuroinflammation, and that's going to exacerbate any Alzheimer's-related pathology.
[1:19:18] I see.
[1:19:19] I see.
[1:19:19] So now there's a new hypothesis here.
[1:19:21] Not the amyloid hypothesis, but the vascular hypothesis.
[1:19:25] This is the hypothesis that says systematic vascular disruption caused by aging, caused by high blood pressure...
[1:19:35] That's going to loosen up the capillaries in our brain.
[1:19:38] And that's actually a primary driver for neurodegradation.
[1:19:44] Okay?
[1:19:44] Yeah.
[1:19:44] So the idea is we have two sort of thought processes here.
[1:19:50] One is this myeloid plaque accumulation is the driver of this neurodegenerative stuff.
[1:19:58] Then the other idea is that
[1:20:02] The degradation of the blood blame barrier on the capillaries, the 400 miles of capillaries in your brain, is like the inciting incident that then enables a whole slew of derivative stuff.
[1:20:15] Yes.
[1:20:16] Like that is a – It's a precursor to the amyloid stuff.
[1:20:18] Got it.
[1:20:19] Ah, that's right.
[1:20:20] Yes, right.
[1:20:21] Like it's not just that the amyloid piece is not connected at all.
[1:20:24] No, no.
[1:20:25] But it's not the first.
[1:20:26] It's not the first thing.
[1:20:27] And perhaps there's something upstream that we can tackle.
[1:20:30] Okay.
[1:20:30] That we can put a dam on.
[1:20:32] Okay.
[1:20:32] Okay.
[1:20:33] And usually this stuff is a hurdle, right?
[1:20:34] Because the blood-brain barrier structurally prevents most drugs from reaching the brain.
[1:20:39] Right.
[1:20:39] Right?
[1:20:40] We've heard about that.
[1:20:40] Like, oh, this thing can cross the blood-brain barrier.
[1:20:43] And then, like, the valuation goes up by a lot.
[1:20:46] Right.
[1:20:46] because it can cross the blood-brain barrier, right?
[1:20:48] People are trying to do nanoparticles.
[1:20:50] You might have heard of nanoparticles that go through the blood-brain barrier.
[1:20:53] You can also do sonic disruption, where you send high-frequency sound, and that sound disrupts the blood-brain barrier.
[1:21:00] But it's like, well, if we're trying to fix the blood-brain barrier, we don't want to... You know, it's like a...
[1:21:06] Catch-22 type situation.
[1:21:07] Yes.
[1:21:08] And this particular paper is leveraging that hypothesis to actually treat the brain by targeting that blood-brain barrier itself.
[1:21:16] You're not going to pass through it.
[1:21:17] We're trying to repair the plumbing there.
[1:21:21] Yes.
[1:21:22] Okay?
[1:21:22] Yes.
[1:21:23] Now let's get into this particular paper in this particular lab at UCSF.
[1:21:28] So they first started out by doing this technique called heterochronic parabiosis.
[1:21:34] The idea is you take an old mouse and young mouse and you join up...
[1:21:40] their vasculature, their blood.
[1:21:42] Okay.
[1:21:43] And so it's kind of like that thing on Netflix where, like, you know, the old guy is getting, like, the... Yeah, it's what Peter Thiel's doing, right?
[1:21:48] Yeah, like the young person's blood.
[1:21:51] Well, this is, like, the genesis of that.
[1:21:54] Okay.
[1:21:54] Okay?
[1:21:55] They did this in Mice First, and the old mice that were exposed to the young blood showed neurological rejuvenation.
[1:22:03] So new neurons, better memory.
[1:22:05] It was actually reversing the effects of dementia.
[1:22:08] Very interesting.
[1:22:09] Okay?
[1:22:09] The introduction of new blood was actually basically having neurological positive benefits.
[1:22:14] Exactly.
[1:22:15] And so now you can start thinking, well, okay –
[1:22:17] maybe other interventions could make the blood young.
[1:22:21] One of the easy things is aerobic exercise, right?
[1:22:25] Like cardio.
[1:22:27] It's a potent intervention.
[1:22:28] It delays cognitive decline.
[1:22:30] And maybe that cardio and exercise is doing something else to the blood that is causing that blood to rejuvenate.
[1:22:36] Okay, I just want to pause really quick because I'm tracking.
[1:22:38] So the idea is this heterochronic parabiosis is this idea of a younger person
[1:22:47] rodent uh has better the blood is the blood is better it's better so if you're like reversing all this like stuff yeah and so if you put that younger rodent blood in an older rodent it makes the older rodent better like that's the first yeah that's the first thing we know that's true that yeah yeah you did the surgery you make them share blood boom it's done yeah and so
[1:23:11] When we look at an activity like exercise, so the point is it's indicating that, oh, like the blood has an impact on neurological maintenance and growth and degradation.
[1:23:24] Exactly, yeah.
[1:23:25] And exercise impacts the blood.
[1:23:28] Yeah.
[1:23:28] Yeah.
[1:23:30] The more you exercise, the more it keeps it sort of, let's say, youngish.
[1:23:33] Exactly.
[1:23:33] And actually, the exercise impacts the blood in a very specific way because they're signaling molecules that are released by our organs during exercise.
[1:23:41] They're called exerkinds.
[1:23:43] Kinds meaning like these signaling molecules.
[1:23:46] Exer meaning it's coming from exercise.
[1:23:47] Yes.
[1:23:48] And examples are like irisin, which stimulates brain-derived neurotropic factors.
[1:23:52] Neurons live longer that way.
[1:23:54] There's more synaptic plasticity.
[1:23:56] There's clustering, which reduces neuroinflammation.
[1:23:58] So all of our organs are releasing this.
[1:24:01] The muscles release stuff.
[1:24:02] The heart releases stuff.
[1:24:03] The kidneys, the liver releases stuff.
[1:24:05] Even the bone releases stuff into our blood.
[1:24:08] And these are all signaling factors that facilitate inter-organ communication.
[1:24:13] Like if the brain wants to coordinate with the liver, the liver wants to coordinate with the kidneys and so on and so forth.
[1:24:17] And all of this is happening in our blood.
[1:24:19] And so the key insight here is that, well, how do we make the blood younger?
[1:24:26] It's because of these extra kinds, which are...
[1:24:29] released during exercise.
[1:24:31] And what Saul Valeda's lab in UCSF did in 2020 is they reported in Science that the exercise was actually...
[1:24:44] The effects of exercise were happening because of this liver-derived enzyme, GPLD1, whose name I'm not going to say.
[1:24:53] But that's the subject of this particular paper.
[1:24:58] In 2020, they discovered this enzyme.
[1:25:02] Can we just hold on this for a quick second?
[1:25:04] Because I think this is important.
[1:25:06] So what you're saying is in this 2020 paper, in science...
[1:25:11] We had this understanding that when you exercise, all your body starts sending these extra kinds.
[1:25:17] And the benefit of that is it helps sort of sustain.
[1:25:24] It's almost like this maintenance process for the body where it keeps you at stable state, for lack of a better way to put it.
[1:25:32] We know that it comes from exercise, which drives a process that the body enacts.
[1:25:38] The discovery that there is a singular protein that comes out of the liver in this 2020 paper.
[1:25:45] That is improving cognition.
[1:25:47] Is improving cognition out of all the extra kinds.
[1:25:50] Yeah.
[1:25:50] And other ones could be as well, right?
[1:25:52] But this one particularly found this, that is like the signal is way high.
[1:25:57] Of all the impacts, this one is the one that has the most and is a key for focus and attention.
[1:26:02] I just think that's kind of a crazy idea in and of itself.
[1:26:07] Yeah.
[1:26:07] There's an enzyme coming out of the liver.
[1:26:10] Yeah.
[1:26:10] There's an enzyme coming out of the liver that is improving cognition demonstrably in older mice when they do exercise.
[1:26:19] Right.
[1:26:19] Right.
[1:26:20] That's fascinating.
[1:26:22] And that was in 2020.
[1:26:22] There's a problem, though.
[1:26:25] This enzyme, GPLD1, is massive.
[1:26:28] Okay.
[1:26:29] So it's not making it through the blood-brain barrier.
[1:26:33] So what exactly is it doing?
[1:26:34] And that's why there's a giant... You see the question mark?
[1:26:36] Yes.
[1:26:36] BBB, and then there's a question mark, and it points to the hippocampus.
[1:26:39] Like, yeah, what is going on?
[1:26:41] How is this massive GPLD1 enzyme that cannot cross the blood-brain barrier...
[1:26:46] I mean, even glucose can't pass the blood-brain barrier.
[1:26:49] Let alone an enzyme of this size.
[1:26:50] Okay.
[1:26:51] So how exactly is it making mice smarter and their brains bigger?
[1:26:55] Because it's not going directly to the source.
[1:26:57] Exactly.
[1:26:57] Apparently, because it can't pass.
[1:26:58] Because it can't.
[1:26:59] It can't.
[1:27:00] So what is it exactly doing?
[1:27:01] Okay.
[1:27:01] And that is what this 2026 breakthrough is about.
[1:27:04] Okay.
[1:27:05] Okay?
[1:27:05] It's unpacking the mechanistic...
[1:27:08] Reason how that GPLD1 Is affecting the brain It's not actually going through the blood brain barrier It's maintaining the blood brain barrier Itself Okay It's like the gates themselves The TSA Is what this thing is maintaining Yes Very interesting So here's the mechanism GPLD1 is something called a phospholipase That's what the D is there Somehow Okay
[1:27:38] And what it's doing is its function is to cleave something called the GPI anchor.
[1:27:45] These are anchors that tether proteins to cell membranes.
[1:27:49] The cell has an outside.
[1:27:51] That's the cell membrane.
[1:27:52] Yes.
[1:27:52] And there's a bunch of proteins that are tethered to the cell membrane.
[1:27:55] You can imagine a boat on a dock, right?
[1:27:57] The boat is not actually attached to the dock.
[1:28:00] There's like rope that attaches a boat to the dock and a bunch of like those like sailor knots.
[1:28:05] Yes.
[1:28:05] Right?
[1:28:05] Yes.
[1:28:05] Yes.
[1:28:06] That's a tethering protein.
[1:28:08] Okay.
[1:28:09] And what this GPLD1 is doing is attacking that tethering protein.
[1:28:14] Okay.
[1:28:14] Now, why is that a good thing?
[1:28:16] Yeah.
[1:28:17] You would think that if a protein is tethered to the cell membrane, you want to keep it there.
[1:28:22] Yes.
[1:28:23] Right.
[1:28:24] That's exactly what I think.
[1:28:25] That's what you should think.
[1:28:27] If there's too many proteins, though, on that cell membrane, what's going to happen to your dock?
[1:28:32] The dock's going to break down.
[1:28:33] The dock's going to break down.
[1:28:34] If there's too many boats, that's why you've got to pay for slips.
[1:28:38] You can't just free-for-all dock to the dock, right?
[1:28:43] If there's too many boats, the dock is going to float away, and then it's just chaos.
[1:28:47] Yes.
[1:28:48] So you've got to have a way to have slips and, like, untether boats that are causing the dock architecture to rupture.
[1:28:56] Yes.
[1:28:56] That is what GPLD1 is doing.
[1:28:58] I see.
[1:28:58] I see.
[1:29:00] They got a target.
[1:29:02] This target must be on the blood-brain interface.
[1:29:04] And then they used actually transcriptomics to figure out that there's this specific protein called TNAP, tissue nonspecific alkaline phosphatase.
[1:29:12] This is a boat that docks to the membrane.
[1:29:16] Yeah.
[1:29:16] Okay?
[1:29:18] And this thing on its own has its own, like...
[1:29:21] job.
[1:29:22] This particular T-nap protein, it involves promoting bone mineralization, and in the brain it does a variety of other stuff.
[1:29:30] But in the brain, if it's anchored to the cell membrane, if there's too many of that anchoring,
[1:29:38] then the cell membrane of your blood-brain barrier is going to start breaking down.
[1:29:43] And then all of a sudden, your tight junctions start becoming loose.
[1:29:46] That was the hypothesis.
[1:29:48] Okay.
[1:29:48] Okay?
[1:29:49] Okay.
[1:29:49] And the key action was that if you circulate this GPLD1 drug, which is something that cuts, it's a scissor for the ropes that tether the boat, then what that's going to do is act as a molecular scissor.
[1:30:01] It's going to cut that anchor.
[1:30:02] The TNAP is going to go off.
[1:30:04] Mm-hmm.
[1:30:05] And you're going to maintain the blood-brain barrier structure.
[1:30:10] And that is what is key.
[1:30:11] So to recap, you exercise.
[1:30:14] The liver produces this GPLD1 enzyme.
[1:30:18] That GPLD1 enzyme that goes into the brain and it maintains the blood-brain barrier by cutting...
[1:30:27] The anchor between this TNAP gene and the cells that maintain that blood-brain barrier.
[1:30:34] Because the TNAP at too much volume begins to degrade the actual structure of these tight joints in the blood-brain barrier.
[1:30:46] And they're not currently, for whatever reason, self-regulated.
[1:30:49] There are no slips.
[1:30:50] Yes, exactly.
[1:30:51] Yeah, I mean, yeah, the self-regulation is the GPLD-1.
[1:30:55] Right, right.
[1:30:57] That's the function.
[1:30:58] Right.
[1:30:59] GPLD-1 is kind of like the boat, I guess the dock.
[1:31:02] Coast Guard.
[1:31:03] Yeah, the Coast Guard.
[1:31:04] It's like you don't have a slip.
[1:31:06] Right.
[1:31:07] But currently, at least in the context of this study, the only source –
[1:31:14] for the GPL-D1 is exercise.
[1:31:18] Is the liver during exercise.
[1:31:20] That is, in the context of what we're looking at, that is the only way we can generate enough Coast Guard people to patrol the docks to maintain the structural integrity of the blood-bain barrier, which can then be a driving force for the...
[1:31:35] The plaques we talked about earlier.
[1:31:37] Yeah.
[1:31:37] Because now when, you know, random stuff gets in, that maybe starts denaturing the plaque proteins, which causes amyloid plaques, which causes Alzheimer's.
[1:31:48] Okay.
[1:31:48] Right?
[1:31:49] This is very cool.
[1:31:49] From the top down.
[1:31:50] And I think it's worth really, like, lingering on this for a bit.
[1:31:54] Yeah, yeah, yeah.
[1:31:54] We've created a molecular cascading mechanism.
[1:31:58] Yes.
[1:31:58] From exercise to preventing Alzheimer's.
[1:32:02] Yes.
[1:32:02] Yes.
[1:32:02] Right?
[1:32:03] Right.
[1:32:03] There's now a link between the two.
[1:32:04] Yes.
[1:32:05] And it's literally exercise.
[1:32:08] The liver produces a specific exerkind, which is this GPLD1.
[1:32:13] Yes.
[1:32:13] That then goes to maintain the blood-brain barrier.
[1:32:15] Yes.
[1:32:16] Once the blood-brain barrier is maintained...
[1:32:18] Alzheimer's gets reduced because maybe that is the key issue.
[1:32:22] Right?
[1:32:23] The key issue is potentially there's no TSA preventing whoever wants to come into the airport getting into the airport.
[1:32:30] Exactly.
[1:32:31] And then what we're looking at when we had the amyeloid hypothesis was a specific type of –
[1:32:38] Person that was getting through at the airport.
[1:32:40] Yeah.
[1:32:40] But not... And like causing problems inside the terminal.
[1:32:43] Right.
[1:32:43] Yeah.
[1:32:44] But that was...
[1:32:44] It's potentially just a single use case.
[1:32:46] Yeah.
[1:32:47] Versus understanding the systemic structure.
[1:32:48] Exactly.
[1:32:48] Yeah.
[1:32:49] Okay.
[1:32:49] And let's just go through some of the experiments that they did.
[1:32:51] So they did alkaline phosphate labeling of TNAP.
[1:32:54] Yes.
[1:32:54] TNAP is this thing that...
[1:32:56] The boat that is docking too often...
[1:32:58] Yes.
[1:32:58] ...to the blood-brain barrier.
[1:32:59] And you can see on the left there is old animals.
[1:33:02] There's lots of TNAP along the blood vessels.
[1:33:05] And on the right is an old animal that does exercise.
[1:33:08] the amount of TNAP is drastically reduced.
[1:33:11] That's incredible.
[1:33:11] Right?
[1:33:12] So there you can see just very much in the staining of this particular enzyme that we're getting a lot.
[1:33:17] They also did novel object recognition and YMASE, which are these behavioral tests for old mice.
[1:33:24] Okay.
[1:33:24] Like, if the old mice have dementia, they can't do well on these where's the cheese type of task.
[1:33:30] But you can actually recover dementia.
[1:33:33] and reverse cognitive decline by treating old mice with GPLD1.
[1:33:39] You could treat old mice with GPLD1 without having them do exercise, and then there was a profound reversal in the deficits in this particular behavioral maze, right?
[1:33:48] So they could find the cheese faster and all that other kind of stuff, and remember where the cheese was.
[1:33:53] And just to be clear, what you're saying here is...
[1:33:58] You can artificially introduce GPLD1 into these aged mice who already have an accumulation of TNAP happening.
[1:34:08] Yeah.
[1:34:08] Yeah.
[1:34:10] And it still works when it's not necessarily coming from exercise.
[1:34:14] Because it's the same enzyme.
[1:34:16] Yeah.
[1:34:16] So it doesn't necessarily – it doesn't have to come from exercise.
[1:34:18] It's like this singular benefit from exercise we can replicate by just injecting.
[1:34:23] And then we're good to go.
[1:34:23] And we're seeing that even when the – you don't have to do it preemptively or preventatively.
[1:34:29] No.
[1:34:29] You can do it even after.
[1:34:30] This is already an old mice that has Alzheimer's-like symptoms.
[1:34:32] Yes.
[1:34:33] It's bad at the Y maze.
[1:34:34] Right.
[1:34:35] And you –
[1:34:36] Artificially insert it, and it gets better.
[1:34:39] It gets better.
[1:34:39] It finds the cheese, right?
[1:34:41] So this is huge.
[1:34:42] And for blood-brain barrier repair, right?
[1:34:46] So let's look at the difference between how much leakage is happening when you've got an aged mice that has leakage versus one where we treat it with GPLD1.
[1:34:59] On the left, you can see the pink is all of where...
[1:35:04] The leakage happened.
[1:35:05] Okay.
[1:35:05] Okay?
[1:35:06] It's all over the hippocampus.
[1:35:07] This is a slice of the hippocampus.
[1:35:09] On the right, most of the pink is on that blood-brain barrier in the corner.
[1:35:13] There's no huge spots in the middle of the neurons because there's not that much leakage.
[1:35:18] Leakage.
[1:35:18] Because you've treated them with GPLD1, right?
[1:35:21] So I think this is really, really cool.
[1:35:24] This is – I mean – so it's cool for a couple of reasons.
[1:35:27] It's, again, understanding –
[1:35:31] Think what you talked about, connecting the biochemistry processes of the body to these neurological long-term symptoms and being able to trace that story from...
[1:35:46] Which, again, we only talked about one of the exerkinds, which is this GPL.
[1:35:50] GPL.
[1:35:51] There could be more.
[1:35:52] There could be more.
[1:35:53] So, again, if there's a fundamental that's interesting, independent of the specifics of them looking at Alzheimer's as their initial use case.
[1:36:03] And then how they then tried to test this.
[1:36:12] In mice.
[1:36:12] Yeah.
[1:36:13] And saw the exact...
[1:36:14] They did all the right things.
[1:36:15] Right?
[1:36:16] And when we compared it to the amyloid experimentation and why it didn't work, it clearly works or it doesn't.
[1:36:24] Yeah.
[1:36:25] You know what I mean?
[1:36:26] Exactly.
[1:36:27] Yeah.
[1:36:27] Like, you have amyloid plaques in people, and they're fine.
[1:36:30] But when you introduce this GLPD-1 into...
[1:36:33] It's not like they just continue being...
[1:36:36] ineffective at the YMAZ.
[1:36:37] Exactly, yeah.
[1:36:38] And, you know, one could say, oh, this is in mice, there's obviously going to be issues, right?
[1:36:43] Always.
[1:36:44] But that's what's cool about this.
[1:36:46] Okay.
[1:36:46] That's what's cool about this.
[1:36:47] So TNAP, like, okay, so...
[1:36:51] How did they implement GPLD1 in mice?
[1:36:53] Yes.
[1:36:54] They've got this procedure where what you effectively do is you increase... You put the gene for GPLD1 inside a viral vector or like in some kind of plasmid chunk of DNA.
[1:37:05] You stick that into mice.
[1:37:07] You increase the blood pressure such that the liver gets infused with this piece of DNA.
[1:37:12] And then the liver makes the GPLD1.
[1:37:15] Can't do that in humans.
[1:37:16] Yeah, yeah, yeah.
[1:37:17] Okay?
[1:37:17] But here's what's cool.
[1:37:19] Okay.
[1:37:19] The TNAP...
[1:37:21] Is already something that we've been worried about.
[1:37:23] Okay.
[1:37:24] Okay.
[1:37:24] That TNAP gene, the boat that gets anchored, that's already something that we've worried about.
[1:37:29] And there is a small molecule drug called SBI-425 that is already approved to inhibit TNAP.
[1:37:38] Okay.
[1:37:39] The boat.
[1:37:40] Okay.
[1:37:40] Yeah, yeah, yeah.
[1:37:41] It's already getting in the way of the boat.
[1:37:42] Okay.
[1:37:43] And it's successfully been used in preclinical studies to prevent blood vessel calcification.
[1:37:48] Okay.
[1:37:48] Because remember, TNAP has to do with calcification and bone meturation.
[1:37:52] Yeah, yeah, yeah.
[1:37:52] So, like, sometimes, like, there's calcification, like, calcium deposits that happen in blood.
[1:37:57] Yeah.
[1:37:57] And so this particular drug was developed in order to circumnavigate that.
[1:38:02] Yes.
[1:38:02] But it's like, well...
[1:38:04] This thing doesn't have to cross the blood-brain barrier because what it could do is go into the blood and inhibit the boats from anchoring.
[1:38:13] Right?
[1:38:13] Now, this is a—we're taking our understanding from the study that it's about boats anchoring to the dock and too many boats anchoring to the dock that is then—
[1:38:23] creating the physics of the dock to not be tight and be loose.
[1:38:27] And now there's leakage.
[1:38:28] Well, what if we just like messed with the boats?
[1:38:31] Right.
[1:38:32] Right.
[1:38:33] And that's what they did.
[1:38:34] Okay.
[1:38:35] And the oral SBI 425, the,
[1:38:40] It successfully mimicked this GPLD1 gene therapy in the mice.
[1:38:45] Wait, wait, wait, wait, wait, wait.
[1:38:46] This is actually probably the craziest piece of this.
[1:38:49] So in the mice, we had to do gene therapy in order to instigate.
[1:38:53] We had to do this weird stuff that we can't do in humans.
[1:38:57] But now it's like, what if we just feed the mice this drug?
[1:39:01] Right, which is already approved for a different use case.
[1:39:05] Yeah, some random, you know, calcification.
[1:39:07] But the point is it has the same – it creates the same exact GPLD1.
[1:39:13] I keep saying GLP1.
[1:39:15] GPLD1 –
[1:39:19] generation, enzyme generation.
[1:39:21] Yeah.
[1:39:22] Just take, and it's already in oral format.
[1:39:24] Yeah, it's already in oral format, and it's mimicking the gene therapy, right?
[1:39:27] That's unbelievable.
[1:39:28] Maybe it's not doing what the gene therapy exactly is doing, but effectively, what do we want?
[1:39:33] Right.
[1:39:33] We want to maintain the blood-brain barrier.
[1:39:35] Yes.
[1:39:35] That is our end case.
[1:39:36] Yes.
[1:39:37] Right?
[1:39:37] Yes.
[1:39:37] And by whatever means necessary.
[1:39:39] Well, we already have...
[1:39:40] SBI-425 that's been approved for other stuff.
[1:39:43] Yes.
[1:39:44] And it's doing it.
[1:39:45] Right.
[1:39:45] Right.
[1:39:46] So now this is now a bioavailable pill.
[1:39:49] Yes.
[1:39:50] That can treat Alzheimer's.
[1:39:51] Yes.
[1:39:52] Purely by targeting the blood-brain barrier.
[1:39:54] Yes.
[1:39:55] By targeting this vascular interface.
[1:39:57] Yes.
[1:39:57] Between the blood and the brain.
[1:39:58] Maintaining that.
[1:40:00] It circumnavigates this historic blood brain barrier drug delivery problem we don't have to worry about.
[1:40:05] Right.
[1:40:06] Is it going to get through?
[1:40:07] Because what we're worried about is actually maintaining it in the first place.
[1:40:10] Yes.
[1:40:11] We want to make sure that in the analogy we brought up about TSA in the airport or the boat in the dock in the Coast Guard.
[1:40:18] We want to make sure that the Coast Guard is always patrolling the dock.
[1:40:20] Exactly.
[1:40:21] Because what's happening is Coast Guard, over time, people retire, and then there's not enough staffing issues.
[1:40:27] Yes.
[1:40:27] And then all of a sudden, these TNAPs are just having a field day, kind of like the Chinese fishermen people off the coast of Chile and all over the place that are just totally unregulated.
[1:40:37] Yeah.
[1:40:38] But if you had some Coast Guard there, and then there's no less TNAP.
[1:40:40] Yeah.
[1:40:41] And the final thing I want to bring up about this paper is they did do their homework with actual human tissue.
[1:40:47] Because so far, it's always been in...
[1:40:51] In mice, in particular mice.
[1:40:54] So in humans, they looked at autopsied cortical tissue of older adults with Alzheimer's dementia.
[1:41:01] The TNAP protein levels are elevated there, which means there's a lot of these TNAP boats that are anchored to the dock of the blood-brain barrier.
[1:41:10] The GPLD1 levels were elevated in active, healthy, elderly humans.
[1:41:16] So those who are active and healthy...
[1:41:18] The Coast Guard was up.
[1:41:20] So already we're seeing these like large scale correlations.
[1:41:24] Right.
[1:41:25] And it's hope that this could actually work perhaps in humans.
[1:41:29] I thought it was a very cool study because they did a comprehensive research in mice.
[1:41:34] Yes.
[1:41:35] And they didn't leave the human part.
[1:41:39] Because the point is the insights that were derived from the studies in mice are in terms of autopsy data from past Alzheimer's patients, both those who are active and inactive.
[1:41:52] basically mimic the levels both in the TNAP inhibitors being more present on the blood, as well as exercise increasing this enzyme level.
[1:42:06] Yeah, this GPLD one.
[1:42:08] That helps to maintain the structure of the blood vessels and the tight junctions of the blood-beam barrier, which, again, seems to be as a...
[1:42:16] competing theory to the amyloid hypothesis, a potential high, it's like lower in the stack of processes because you want to basically attack a problem as close to the source as possible.
[1:42:32] The amyloid plaques is not the source.
[1:42:34] Yeah, it seems it's not.
[1:42:35] It seems it's not, but this might be.
[1:42:38] Getting closer.
[1:42:39] To what it's going on.
[1:42:40] And also just an interesting conceptual way
[1:42:47] neurodegenerative diseases.
[1:42:52] Exactly.
[1:42:52] As a holistic thing.
[1:42:53] As a holistic thing.
[1:42:54] It's like this thing involves the liver, the blood, and the brain.
[1:42:57] Yes.
[1:42:58] The brain is no longer like an island that you have to think about.
[1:43:01] Right.
[1:43:01] I mean, I think the future is looking very bright for this particular study.
[1:43:06] Of course, I don't want to do commentator's curse, but the GPLD-1, which is like your Coast Guard, that thing targets all kinds of boats.
[1:43:13] Mm-hmm.
[1:43:13] Not just TNAP.
[1:43:14] It's targeting all sorts of GPI-anchored proteins, hundreds of others.
[1:43:19] So those could be targets for new therapies, right?
[1:43:23] You could have a...
[1:43:26] I mean, I anticipate there's going to be a surge in peripheral TNAP inhibitors, not just...
[1:43:31] this SBI 425, there could be other stuff.
[1:43:35] Right.
[1:43:35] So, yeah.
[1:43:36] And I think that's an important point that the, the SBI 425 already being an approved drug, like is huge because it sort of shows the, the drug development, the,
[1:43:46] It's scaffolding to get to an end result, even if you don't use exactly – like you can at least reach – like see what the pathway is to success here around how to approach it.
[1:43:57] Because there's the research, but then the whole drug discovery process from stage one or phase one to phase four, quite complex.
[1:44:04] You have early stage drug discovery where they're just trying to figure out like the active ingredients.
[1:44:09] Then you give it to the chemists because they have to figure out how do you actually deliver it to the patient.
[1:44:13] Right.
[1:44:14] Is it intravenous?
[1:44:14] Is it oral?
[1:44:15] Yeah.
[1:44:16] Dosing?
[1:44:17] What is the half-life?
[1:44:18] All that kind of stuff.
[1:44:19] Then you get into sort of early preclinical trials.
[1:44:22] Yeah, where it's like, okay, let's make sure it doesn't kill the mice.
[1:44:24] Right.
[1:44:25] Then you get into clinical trials, FDA approval.
[1:44:27] I mean, it is a very complex and long thing.
[1:44:30] And this is already halfway through.
[1:44:31] So that's a huge deal.
[1:44:33] Yeah.
[1:44:34] Fascinating story.
[1:44:36] Yeah, I thought that was very cool, actually.
[1:44:37] It's very cool.
[1:44:38] And I think what's been so interesting in this journey on the show, for me, right, having the benefit of just having you able to really, again, having a first principles general science understanding is not super common, unfortunately.
[1:44:51] Mm-hmm.
[1:44:56] almost 30 episodes in to be able to make these connections across stories because there's fundamentals about the process of this early stage research or frontier research that track.
[1:45:09] I mean, we talked earlier about the proton piece and how that's like measurement not of direct observation but indirect observation is a concept, and it can manifest when you're zooming in really, really small or when you're zooming out really, really far.
[1:45:23] I mean, this is a really, really cool story.
[1:45:26] And great work.
[1:45:27] This, again, was from UCSF.
[1:45:31] Yeah, UCSF.
[1:45:31] They do amazing work there.
[1:45:33] Lots of Nobel Prizes.
[1:45:35] Oh, my God.
[1:45:35] Fascinating, fascinating story.
[1:45:37] And, again, I just want to make a quick sort of caveat here.
[1:45:41] The point of this show is to talk about breakthrough and frontier research from first principles.
[1:45:47] There is a large delta or a big gap in between a research paper coming out and it having –
[1:45:54] you know, over-the-counter or prescription medication available to people.
[1:45:59] And so just to be very clear about that point, we very much understand that, one, there needs to be replication.
[1:46:05] There's a whole process of implementation.
[1:46:07] There's a lot of stuff that can go wrong in the middle.
[1:46:09] And so the middle is there's a lot there.
[1:46:11] Mm-hmm.
[1:46:12] this show is not about the middle.
[1:46:13] It is, it is about kind of the beginning of the story, but it's important just to be clear that, that, that is, that is the case.
[1:46:19] Yeah.
[1:46:20] Yeah.
[1:46:21] And so, you know, I know some folks will be like, yeah, this is still early.
[1:46:23] Yes.
[1:46:24] Yeah.
[1:46:24] But isn't it cool that we figured out this Coast Guard boat docking thing?
[1:46:29] It's the concepts and the ideas are really an understanding how we got there.
[1:46:34] Yeah.
[1:46:34] Is the point.
[1:46:35] Yeah.
[1:46:35] That's the point.
[1:46:36] I mean, it's kind of cool.
[1:46:38] Because there's so many things you can get from this.
[1:46:40] So in any event, um,
[1:46:42] Fantastic.
[1:46:43] We started with dark galaxies.
[1:46:45] Again, space and then neuroscience, some great rundown stories.
[1:46:50] I want to do a couple of quick housekeeping notes before we wrap up for the day.
[1:46:54] Number one is if you are listening to this episode and you've made it all this way.
[1:46:59] congratulations because some of the stuff is really hard to grok when you're just only listening.
[1:47:05] And if you are curious and want to know more, we have a video podcast.
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[1:48:34] Maybe for those who have reached the end of the episode, maybe I'll do a little tease here.
[1:48:38] That, you know, you only will know.
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[1:48:44] We have recently had the wonderful experience of getting to be interviewed on the Dave Chang show with David Chang, the famous chef and owner of the Momofuku restaurant group.
[1:48:59] Being able to have food made for you by one of the goats is.
[1:49:04] Yeah, it wasn't like, oh, we got takeout from Momofuku.
[1:49:07] Yeah, no, no.
[1:49:08] Dave Chang is making me.
[1:49:10] I can't say, actually.
[1:49:11] I want you guys to see what he made us.
[1:49:12] You'll have to have a last episode.
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[1:49:37] Big shout out to Dave's team.
[1:49:40] David Meyer, Christine, you guys are fantastic.
[1:49:43] We had a great time.
[1:49:44] And we look forward to you inviting Krishna when you guys need the science of food or food science expertise.
[1:49:52] I will do anything for food.
[1:49:53] Friend of the pod.
[1:49:54] Shout out to Dave Chang and co. Really appreciate that.
[1:49:58] My name is Lester Nare.
[1:50:00] Hold on.
[1:50:00] Yes.
[1:50:03] The comments.
[1:50:04] I want you guys to give me an alternate comment.
[1:50:09] GPLD1.
[1:50:11] What does it stand for?
[1:50:12] Yes.
[1:50:12] Because the one that we have, which, you know, just for old time's sake, I'm going to try.
[1:50:17] Glycosylphosphatidylonatosol specific phospholipase D1.
[1:50:24] I would like something better.
[1:50:25] Thank you.
[1:50:26] That's a great call.
[1:50:27] Yes.
[1:50:27] So, again, listening all the way, you'll get a little couple secret nuggets in here.
[1:50:32] Send over the comment.
[1:50:34] Keep an eye out for us on Dave Chang.
[1:50:36] And we may or may not start to do some in-pod giveaways.
[1:50:40] And so, you know, you might want to listen because he might be maybe someone who gets some special merch or who knows?
[1:50:48] Who knows?
[1:50:48] Who knows?
[1:50:51] Bringing it back, I'm Lester Nari, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
[1:50:59] We really appreciate you guys so much, especially those who listen all the way to us ranting for two hours.
[1:51:04] We tried to do two stories this week to make it last.
[1:51:06] Yeah, and it's still two hours.
[1:51:08] And it's still two hours.
[1:51:08] We got to work on that.
[1:51:10] We will be back next week with our next episode.
[1:51:13] This is From First Principles.

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