Latest / From First Principles / AI Cancer Vaccines, Strange Fish, Ketamine, and Ancient Life (EP. 34)

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AI Cancer Vaccines, Strange Fish, Ketamine, and Ancient Life (EP. 34) · From First Principles

0:00
So we're going to start today with our first story about a man who fixed his dog's cancer with chat GPT.
0:09
And no, this is not clickbait.
0:11
It's fully female.
0:14
And it's incredible that it survived for so long because it uses cloning ketamine seems to work on them.
0:20
And it's weird for two reasons.
0:22
One, why does ketamine work when all these other antidepressants don't?
0:25
And two, ketamine works at a really fast timescale.
0:29
Life rebounding shockingly fast after the asteroid that killed the dinosaurs.
0:35
Hello, Internet.
0:36
This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Ketam.
0:43
Krishna Chowdhury.
0:44
We are here for another casual rundown episode where we're going to touch on four stories that we did not do a deep dive on this week, but are also interesting and exciting breaking news stories.
0:58
Categories we will cover today include AI, genetics, neuroscience, and we'll end with a paleontology and ancient life story.
1:07
And squeezed in the middle there, we will do another live story.
1:11
lovely exciting round of are you smarter than a scientist where you can test whether your knowledge of the universe is greater than our resident phd krishna we are going to learn about the science from the ground up today as always because this is from first principles
1:46
So we're going to start today with our first story about a man who fixed his dog's cancer with chat GPT.
1:55
And no, this is not clickbait.
1:58
There was a man's dog who was riddled with tumors and dying.
2:01
And he used ChatGPT to design a custom cancer vaccine, which is stunning.
2:07
Researchers, he's a Sydney tech entrepreneur, no background in sort of this research area of oncology.
2:15
Used ChatGPT and some other AI tools to design a world's first personalized cancer vaccine for his dying rescue dog, Worsley.
2:25
Rosie, who had advanced mass cell cancer, and then working with the University of New South Wales, they sequenced the dog's DNA, identified the cancer mutations using AI, and created the custom mRNA vaccine that shrank the tennis ball-sized tumor by 75% within months.
2:46
This is fascinating.
2:47
It's an incredible story.
2:49
And, I mean, we've covered AI a lot on our podcast, and usually it's all been doom and gloom.
2:55
But one of the things that gets me excited about AI is this idea of the democratization of science and the democratization of technology and capability.
3:08
And this is a spectacular example of what it's capable of in the right hands.
3:15
I think that's so cool.
3:16
So there was a Sydney Tech entrepreneur, Paul Cunningham.
3:20
And his rescue dog, Rosie.
3:22
The rescue dog, Rosie, had months to live.
3:24
And he just did not accept that prognosis from the veterinarians.
3:28
So what he did was use Chachi PT to build a personalized cancer vaccine.
3:36
It's pretty incredible.
3:37
He had no, as you were saying, no background in biology, just 17 years as a machine learning engineer.
3:42
So reasonably tech savvy.
3:45
Sure.
3:45
Reasonably has probably a first principles background of how stuff works at the DNA level.
3:51
And here's what he did.
3:52
Okay.
3:53
So first, always, you go to chemotherapy.
3:56
Chemotherapy slowed down the spread.
3:57
Yeah.
3:58
You know, it couldn't actually shrink the tumors of his dog.
4:02
So he spent $3,000 to have Rosie's healthy DNA sequenced and then the tumor DNA sequenced as well.
4:11
Okay.
4:11
From the University of New South Wales.
4:13
They've got, you know, biomedical labs so they can do this.
4:16
And then now that you have the DNA sequencing of what normal cells do,
4:21
in his dog look like and what the cancer cells in his dog look like, you can find mismatches of where the cancer mutations are.
4:28
Yep.
4:28
Okay?
4:29
And then he used the mRNA vaccine, which is the same vaccine that Moderna sort of pioneered for COVID-19.
4:38
The technology is the following.
4:40
You insert a piece of mRNA...
4:43
That then gives your body a blueprint of what to look for in order to have that immune response, right?
4:51
So in the COVID-19 vaccine, the mRNA is for the spike protein on the COVID virus.
4:57
The mRNA goes into our body.
4:59
Our body then translates that mRNA into protein, makes the spike protein, makes the spike protein.
5:05
Our body then creates antibodies that recognize that spike protein so that when the real thing comes in, we can recognize the spike protein.
5:12
We can recognize the virus.
5:14
We don't get full-fledged COVID.
5:16
We actually have an immune response the first time.
5:18
It's effectively training the body's defense forces for what the enemy is going to look like before the enemy is on your shore.
5:24
Exactly.
5:25
And over here, the enemy is already on our shore.
5:27
Yes.
5:27
But the problem is that cancer is very deceptive.
5:31
Cancer has a lot of mechanisms to suppress the immune response.
5:34
And then also the cancer is our own DNA.
5:37
So our immune system isn't like as good at targeting cancer versus something that is fully foreign.
5:44
Right.
5:44
Because when it's fully foreign, it's like obvious.
5:46
But with cancer, it's like your own cells are actually acting up.
5:50
So here's what he did.
5:51
He pinpointed the mutations that were driving the cancer in that tumor.
5:55
And then he made an mRNA that would train Rosie's immune system to target that specific tumor.
6:04
Okay.
6:05
He also actually had to...
6:07
additionally put in things that would suppress that immune suppression so it's like a double negative yeah right because like the cancer is telling the immune system i'm don't don't come at me so there's a suppression there he had to put in a bunch of drugs that actually like told the immune system to ramp back up right
6:25
Okay, so this double-edged sword kind of went in, and it's incredible.
6:32
Less than two months after the sequence was finalized, Rosie received her first injection in December, and by March, 75% of that tennis ball-sized tumor was gone.
6:43
There's a great quote from this article from Paul Cunningham, who said, at the start of December, her mobility was way down at the beginning.
6:52
Mm-hmm.
6:53
She started to shut down and it was a bit sad.
6:54
And towards the end of January, she was jumping over a fence to chase a rabbit.
6:59
So not only the biological decrease by March, but even the behavioral change even sooner than that is just unbelievable.
7:08
It's unbelievable.
7:09
And it's like...
7:10
So incredible.
7:11
Such a cool story as a former dog owner.
7:14
You know, like the the ends of the earth that this guy went to to save his dog.
7:21
I really identify with that.
7:23
I thought this was so cool.
7:24
And it's also the first time that there's a personalized cancer vaccine.
7:27
Right.
7:27
That's been designed for a dog.
7:29
Yes.
7:29
Ultimately, this is going to help.
7:31
in humans as well.
7:33
I'm sure the New South Wales researchers at the university are writing a paper right now because this is a great paper.
7:41
If you're not, you really should be.
7:44
This is something that I can see coming out in a very top journal.
7:48
And the bigger picture is actually very cool.
7:49
Moderna and Merck
7:52
the two companies are actually leading the pack when it comes to personalized mRNA vaccines for this purpose.
7:58
So in humans, where they're going to, you know, again, sequence the DNA from the tumor and then go back, create the mRNA vaccine.
8:07
If you can make this cheaper and if you can iterate on this faster, this is huge for cancer in human beings as well.
8:14
I think the next step, what he's going to do is...
8:17
Every single tumor on the body is actually different, right?
8:20
Because every single tumor is a few mutations here and there.
8:23
So that's why only 75% of the tumor went away.
8:27
Yeah.
8:27
Because the core of the tumor likely has other mutations that he didn't sequence.
8:32
Right.
8:32
So you can iterate on this.
8:35
Now you can get to the smaller inner orbit, sequence that, and now get that.
8:39
Yeah, so it's like the second order, third order, so on and so forth.
8:42
I just thought this was a very cool, hopeful story.
8:46
the potential that AI has for just like average people.
8:52
Yes.
8:52
You know, like $3,000 sounds like a lot, but I don't know.
8:56
I would have paid that for another year or two.
8:58
Based on the cost of cancer treatments now, I think that's probably bargain basement or close.
9:05
It's not necessarily, it's not cheap, but healthcare, as many of us know in the United States, especially in cases like this, is extraordinarily expensive.
9:15
Yeah.
9:16
So anything you can do to chip away at the cost while still being safe and having efficacy in all the checkboxes is huge.
9:24
And if you were to compare it to, let's say, like you get a consultant who's a cancer researcher and you get a whole team to actually do this, it's like pennies compared to like actually hiring a full-fledged team to dedicate to your dog.
9:39
right?
9:40
The time and the resources.
9:41
So I think it's really, really cool.
9:44
And a lot of these, the Moderna and the Merck vaccines, they've jointly developed these for melanoma.
9:51
It's already shown a 49% reduction in the risk of cancer recurrence and death over a five-year range.
9:57
So should be coming out soon.
9:59
Yes, this is good.
10:00
Pretty exciting.
10:01
The only way to have made this a more heartfelt, lovely story if the Sydney dog owner's name was John Wick.
10:10
oh yeah yeah yeah yeah that would have just rounded out the story perfectly um but great but cunningham is a good enough name it's paul cunningham paul props to you man yeah awesome stuff yeah really really awesome stuff uh take life into your own hands yeah great start with uh that sort of interesting angle to an ai story our second story is uh
10:34
A similar gene kind of story, but this one I'm very curious.
10:39
This one's weird.
10:40
To talk to you about.
10:41
So the title on this one is Gene Conversion Empowers Natural Selection in Fish Species.
10:48
So there's a tiny Amazon fish that has been a fascinating look for researchers for some time.
10:55
And it should not have survived as a species.
10:59
Yes, absolutely.
11:00
And there's an interesting reason why it should not have survived as a species.
11:04
And this is out of University of Missouri, as well as the Ludwig Maximilian Universitat in Munich, Germany.
11:11
That's a universität.
11:14
Universität.
11:15
Yeah.
11:16
So you'll do the German.
11:17
I'll do the German.
11:18
And I'll do the French.
11:18
Yeah, you do the French, yeah.
11:21
This is a really cool story because this particular fish, it's found in southern Texas, northern Mexico.
11:27
It's called the Amazon Mali.
11:29
And it's named after the Amazonian tribe of legend, which were an all-female tribe, no men, and they had just figured out a way to procreate.
11:39
This fish, all-female...
11:43
doesn't actually have sexual reproduction with other males.
11:48
Interesting.
11:49
Yeah.
11:49
Okay.
11:49
It's fully female.
11:52
And it's incredible that it survived for so long because it uses cloning effectively to reproduce.
11:58
Yeah, yeah, yeah.
11:59
And all evolutionary theory says that that is a really bad idea.
12:02
Why?
12:02
Because when you clone, you don't get genetic variability.
12:05
Right.
12:05
And so if there's any kind of change in environment, evolutionary pressure, you're gone.
12:11
Right.
12:11
Because everyone is identical.
12:12
Right.
12:12
Right.
12:13
So the big question has been, this thing is at least 100,000 years old.
12:18
How did it happen?
12:19
So it formed when there were two fish species.
12:23
I believe it was the Atlantic molly and the Sail molly.
12:28
They made it.
12:29
They produced this hybrid.
12:30
This hybrid now was a female.
12:33
And every single Amazon molly today can be traced back to that single male.
12:38
Parentage, that single person.
12:40
Okay?
12:40
That single fish, I should say.
12:43
What's weird about it is that, you know, as I said, they don't sexually reproduce.
12:47
Right.
12:48
So they still actually have sex with males.
12:50
Right.
12:51
But there's no sperm that is going to fertilize the egg.
12:55
Okay.
12:55
That sexual process, the act of having sex, triggers this special kind of meiosis in the fish that causes this cloning to happen.
13:05
So you get egg cells that are identical to the mother.
13:07
Yes.
13:08
And then those egg cells become...
13:09
Yes.
13:10
Amazon fish.
13:11
Baby fish.
13:11
Okay.
13:12
So the question becomes, how did it survive, right?
13:15
How do you actually, you know, if there's a deleterious mutation, if there's like something that goes wrong during the replication of DNA, you might be completely sidelined.
13:27
Right.
13:27
Right.
13:28
They use a process.
13:31
where they're actually using gene conversion.
13:34
This is the idea where you've got multiple sets of chromosomes, right?
13:37
If there's ever a mistake, their genetic repair mechanism can use the other copy to make that fix.
13:43
That repair mechanism also creates...
13:48
Genetic variation at exactly the right frequency to have a pretty healthy population that is not completely genetically identical.
13:57
That's interesting.
13:57
But it's got this mechanism such that it can stop mutations from going bad and also create tiny little mutations to create variation.
14:07
So the genetic variability is deriving from the sort of the internal cloning reproductive process as opposed to sharing the chromosomes from the mother and the father or, you know, one into male and female.
14:24
Exactly.
14:24
And that's kind of an important distinction in the difference with how it drives its own ability to survive over long periods of time while still not having...
14:36
a classic sexual reproduction between a male sperm and a female egg yes exactly and usually that's how we get genetic variability right is like from the combination of two individuals genes that's most life actually even trees plants like everything does this kind of mix and match but these guys are doing a copy and paste but that copy and paste has enough little tricks to
15:02
That like shows this is actually possible.
15:04
It challenges this traditional assumption that, you know, asexual reproduction is going to cause sterile species.
15:11
It's not actually true.
15:13
So maybe evolution has many more ways to actually maintain a healthy genome in that tree of life.
15:18
And we've been sequestered into thinking that sexual reproduction is sort of an apex mechanism.
15:24
Might not be.
15:24
Yeah.
15:26
Don't tell that to the people who want to, whatever you call it, when you make people all the same.
15:31
Oh, yeah, yeah.
15:33
Let's not let them know about that.
15:34
Yeah.
15:35
This was in Nature, again, at the University of Missouri.
15:40
And the Ludwig Maximilian Universitat.
15:44
in munich germany tate tate it's okay it's the it's the double dot yeah the umlaut yeah we'll go we will be in germany for oktoberfest maybe this year so for all the fans out in germany let us know we are going to need to be shown around now last time around on the rundown we introduced our new segment
16:10
And we're going to bring it back for another trial test of Are You Smarter Than a Scientist?
16:18
Okay.
16:19
The game show where you at home can find out if you are, in fact, smarter than our resident PhD.
16:26
And the question this week is name the 10 largest organs in the human body by mass.
16:33
Okay.
16:35
And I will make one small caveat about the answers to this, which is that the organs are discreet.
16:44
They're discreet.
16:45
Okay.
16:46
All right.
16:47
Let's see.
16:47
Let's start with the liver.
16:48
I'm pretty sure that's huge.
16:50
So we're going to see where is liver on the board?
16:53
Is it on the board?
16:54
Yes, it is at number two.
16:57
And that's at 1.5, approximately 1.5 kg.
16:59
Okay.
17:01
Okay, 1.5 kilograms.
17:03
What's bigger than the liver?
17:05
All right, let's start with some of the easy ones, right?
17:07
Let's do lungs.
17:09
The lungs is on the board at spot number four at 1 kg.
17:16
Let's do the large intestine.
17:22
I won't give you a strike for this because technically it's a part of the digestive tract and would not be considered a discrete organ.
17:31
Oh, interesting.
17:32
So for that first one, I'll give you a pass.
17:34
I'll give you that.
17:35
All right.
17:37
Well, then, wait.
17:38
Would digestive tract be on here?
17:40
No.
17:41
Okay.
17:42
Interesting.
17:44
Again, that's kind of all categorized together.
17:46
Okay.
17:47
Let's kind of put that to the side.
17:48
All right.
17:48
Let's put it to the side.
17:49
So stomach is also not on this?
17:52
I'm going to give you a strike for that one.
17:53
Okay.
17:54
Fine, fine.
17:56
I'm asking too many questions.
17:57
All right.
17:57
Let's do brain.
17:59
Brain is on the board at number three.
18:03
So we have number two, liver, number three, brain, number four, lungs.
18:09
Let's do heart.
18:10
The heart is on the board at number five, approximately 315 grams.
18:20
Um, let's go for is skin on here is skin on the list.
18:30
Yes.
18:31
Big spot at number one.
18:33
Okay.
18:33
Skin is number one.
18:34
For skin.
18:34
I have the top five.
18:36
Very good.
18:36
All right.
18:37
Very good.
18:37
This one's hard.
18:38
Last, last, last.
18:40
The last five.
18:41
One strike.
18:42
Skin, liver, brain, lungs, heart.
18:45
Top five.
18:47
Skin.
18:49
The heart is on there.
18:50
Kidney.
18:51
Kidney.
18:53
Is kidney on the list?
18:56
At number six.
18:57
So we got to get smaller than kidneys now.
19:00
Let's do pancreas.
19:03
Is pancreas on the list?
19:05
At number eight.
19:07
Okay.
19:07
So we are missing seven, nine, and ten.
19:10
You are on a roll.
19:13
Give me...
19:15
These ones are a little tough.
19:17
Give me the bladder.
19:22
Bladder is not on the list.
19:25
Oh, I only have one strike left.
19:27
You have one strike left.
19:29
We have number seven, number nine, and number ten.
19:35
Choose wisely, those listening.
19:37
We only have one strike left.
19:43
Man, what am I thinking of now?
19:47
Now, again, it's discreet.
19:52
I'll give you one hint here, which is one of them is only present in males.
20:01
Oh, it's only present in males.
20:05
Is it going to be prostate or testes?
20:07
I'll do prostate.
20:07
Prostate.
20:08
Prostate is on the board at number 10.
20:12
So we are missing number seven and number nine.
20:15
So there's something that's in between size of kidneys and pancreas, and there's something in between size of pancreas and prostate.
20:23
Okay.
20:24
Gallbladder.
20:26
Is it gallbladder?
20:28
Unfortunately, that will end your trial on round two of Are You Smarter Than a Scientist?
20:36
We had two missing items.
20:38
Number seven was the spleen.
20:41
Okay.
20:42
Should have gotten that one.
20:43
Yeah.
20:43
And our last one, get your guesses in now for those at home, was the thyroid.
20:50
Yeah, I didn't even think of that.
20:52
So our list, skin, liver, brain, lungs, heart, kidneys, spleen, pancreas, thyroid, prostate.
20:59
And I will just note that this was for the male body because you could arguably put the uterus on the list a little bit higher than some of these items.
21:10
So not a bad round.
21:12
We were missing some of those answers.
21:14
We're still going to give you a...
21:15
A nice little fire there for our second round of Are You Smarter Than a Scientist.
21:22
If you are interested in the game show or you would like to see us bring on guest contestants for a head-to-head competition, please let us know in the comments.
21:34
We are going to continue on to our third story, which is about brain scans revealing how ketamine is,
21:43
can quickly lift severe depression.
21:46
If you're in the UK, you might be familiar with this as being called ketamine.
21:53
Here in the US, it's called ketamine.
21:56
This is out of the YCU Advanced Medical Research Center, and it was in molecular psychiatry.
22:02
Yeah, this one's a very interesting...
22:05
paper because I've always wondered why ketamine or ketamine is so effective against depression.
22:12
So major depressive disorder, that's MDD, it's a major global health problem.
22:18
30% who are diagnosed are given treatment-resistant depression.
22:24
That's the tag that they're given because even normal antidepressants don't work on them.
22:29
Ketamine seems to work on them.
22:32
And it's weird for two reasons.
22:34
One, why does ketamine work when all these other antidepressants don't?
22:37
And two, ketamine works at a really fast timescale.
22:43
Most of the time when you take antidepressants, like in pill form...
22:46
The effects are going to come in like a week to a month.
22:49
That's the timescale that you're looking at.
22:51
With ketamine, it's like minutes.
22:53
Really?
22:53
Okay, yeah, yeah.
22:55
It's like you're there at getting the therapy, you know, getting it injected.
22:58
And then like it's like within the hour to like that day is like when you start feeling not depressed.
23:04
Okay, so there's clearly a very different mechanism about what's going on.
23:09
Right.
23:09
And for the longest time, it's been really quite nebulous.
23:14
how it's actually working.
23:15
And a lot of times with the brain, a lot of things are nebulous because we don't know enough.
23:19
There's so many things that are working around.
23:22
And at some point, you know, you just sort of say, hey, it's working.
23:26
But as scientists, we always want to figure out what's actually going on.
23:29
happening because then we can model things very well we can start thinking okay how is ketamine actually going to affect what is the dosage that we want to give what is the frequency with which we want to give if we have a fundamental understanding then we can actually start doing clinical trials for like different types of therapies different schedules and things like that so it's very important to actually figure out what is going on now there were early
23:53
studies that showed it had something to do with the AMPA-R receptor.
23:57
So a brief overview of neuroscience.
24:00
Our nervous system is built out of neurons.
24:03
Neurons are the single unit of the nervous system.
24:05
They're cells, and they talk to each other using synapses.
24:09
So when one cell fires or turns on, what it's going to do is talk to its postsynaptic cells, the cells that it is synapsing onto, the cells that it can talk to, using these structures called synapses, where one cell comes in kind of like a hand, and the other cell that is downstream comes...
24:29
comes over it like an open palm around it, okay?
24:33
But there's a gap in between.
24:34
They're not actually tied together.
24:36
They're separate cells, and they have a tiny gap called a synaptic junction.
24:40
Now, through that synaptic junction, the way you talk is you release the presynaptic cell, releases chemicals into that synaptic cleft, that gap, and then the postsynaptic cell releases,
24:56
senses those chemicals and then turns on.
24:59
Okay.
25:00
That's effectively most like 99% of neuroscience is just a cell talking to another cell using these chemicals.
25:07
Got it.
25:07
Most of the time that chemical is glutamate.
25:10
It's not, you know, serotonin, dopamine, all this other stuff.
25:13
We talked about this on the Dave Chang podcast.
25:16
This is where MSG comes from.
25:18
MSG is effectively just glutamate with a little bit of sodium.
25:21
Okay.
25:22
So glutamate is the bread and butter.
25:24
It is like the cash money of how neurons talk to one another and transact information.
25:31
And the receptors that sense the glutamate to trigger a response in the postsynaptic cell, in the downstream cell, those receptors, one of them is the AMPA-R receptor.
25:43
Got it.
25:44
Okay?
25:44
Yes.
25:44
And what we've seen in previous studies
25:47
studies is that ketamine has something to do with these AMPA-R receptors.
25:52
Got it.
25:52
Okay?
25:53
We just don't know what.
25:54
Right.
25:54
Okay?
25:54
The receiving end of the chemical signal to turn on.
25:58
Yes.
25:59
It's receiving the glutamate, and somehow that receiver has something to do with ketamine.
26:04
Yes.
26:04
Okay?
26:05
So what these guys did...
26:06
was they used positron emission tomography.
26:09
This is an imaging method where you basically, like, you have the patient inject some radioactive chemical.
26:16
It's not that harmful, but what it does is that chemical becomes a tracer.
26:21
It releases positrons as it makes its way through.
26:24
Positrons are the antimatter version of the electron.
26:28
And if you have a positron detector, then you can detect these positrons and you can see where the chemical is going.
26:34
It's a way to basically look at what's happening inside the body or track what's happening inside the body from outside the body.
26:39
Yes, exactly.
26:40
And so what these guys at the City University of Yokohama, they developed a PET tracer, a positron tracer.
26:52
that had been developed earlier, this tracer allows them to visualize that AMPA-R directly because it somehow either connects to the AMPA-R or interacts with it in some way.
27:04
So we can actually map where the AMPA-R receptors are in the brain.
27:08
Got it.
27:08
Okay?
27:08
That's step one.
27:10
We figured out a way to trace and analyze
27:12
Map out where the AMPLR receptors are.
27:14
Now you give your patients ketamine.
27:16
Because we're first identifying the location.
27:18
Yes.
27:19
That's what we want to do.
27:19
The thing we care about is happening.
27:21
But we didn't actually know where.
27:22
Like we needed to know where the locations were first.
27:24
Yeah.
27:25
We knew that it was this thing.
27:26
Yes.
27:26
But we don't know.
27:27
We have no way of figuring out where it is.
27:29
Yes.
27:29
And like what are the dynamics when somebody gets ketamine?
27:31
How does that change?
27:32
Now we do.
27:33
Now we've got this tracer that goes in.
27:35
It's going to shoot out positrons, and we've got a big positron detector.
27:38
So wherever the positrons are coming from, we know that's where the AMPA-R receptors are.
27:42
Okay?
27:43
Then we give them ketamine.
27:44
Yes.
27:45
And we look at how these antidepressant effects evolve that AMPA-R activity, that receptor activity.
27:51
And that's what they've done.
27:52
Very interesting.
27:53
They used 34 patients that were diagnosed with this treatment-resistant depression and 49 healthy patients that were in control.
28:01
They gave half ketamine, half like just, you know, placebo because you got to have that control.
28:06
And then what it shows is the people who have this treatment-resistant depression, first of all, they had widespread abnormalities in their AMPA-R density.
28:16
Just wonderful.
28:17
Without even the treatment and everything.
28:18
I see.
28:19
Those who have depression have a very different AMPA-R density in their brain compared to healthy patients.
28:25
Can I do a quick pause here on that point?
28:28
Because just to double click on it, there is a structural, a physical structural difference.
28:36
Mm-hmm.
28:37
At the neuron level for people who have this treatment-resistant depression, then the control, then others.
28:45
So for all the boomers who say depression is a made-up thing in people's head, it has a physical manifestation.
28:52
Yes, it is in their head, and it is like a very much...
28:56
Physical manifestation of like literal receptors and chemicals are structured.
29:00
Yeah, there's a difference.
29:02
Okay.
29:02
Right.
29:02
And so now we get into, okay, what is the ketamine actually doing?
29:07
Right.
29:08
And surprisingly, it was not uniform.
29:11
There was like what they would do is figure out what is the amp hour density before the treatment.
29:15
And then you give them ketamine and then you figure out what is the after.
29:17
Right.
29:18
Not everywhere in the brain changed.
29:20
Very specific regions of the brain actually changed.
29:23
There's this place where there's reward processing.
29:26
It's called a habanula.
29:28
Okay.
29:28
Okay.
29:30
a lot of AMPA-R shifts were happening there.
29:33
And this region-specific shift is strongly connected to the improvement in the depressive symptoms.
29:40
So we can also see that.
29:41
Not only is there change, we can also see that how much change is related to how much the depression gets better.
29:47
So clearly we have some mechanism here.
29:50
And this is very early.
29:52
This is very early.
29:53
But I think it's very, very cool.
29:55
Because now we're seeing these dynamic changes in something as fundamental as the AMPA receptor.
29:59
Right.
29:59
This is not like serotonin.
30:00
Again, this is not serotonin or dopamine or things like that, which are very specific to certain circuits that have to do with reward, that have to do with pleasure and things like that.
30:08
AMPA is like how, like, it's like the, again, cash.
30:13
It's not a weird commodity like gold or something.
30:17
This is how all neurons talk to one another, right?
30:20
And so this is a very fundamental thing.
30:22
Substrate.
30:23
Can I try to make a different analogy maybe here?
30:27
Would it be like saying these AMPA receptors are like the...
30:31
the the under the underwater internet pipes whereas the serotonin are like a web page or a specific domain that is accessible on the internet but it's not the actual system that information is sent through like i'm trying to i don't know that i quite understand the cash analogy it doesn't track in my head as well um so i'm trying to understand the how to differentiate thinking about serotonin and dopamine as different from these receptors yeah
30:58
Yeah.
30:59
Okay.
30:59
So let's go with the internet analogy, right?
31:03
If you say that the glutamate, what I'm saying is like glutamate and another one, which is GABA, that's the inhibition.
31:12
Glutamate and GABA are like your cash.
31:14
What I mean by that is like, it's like, I shouldn't say cash.
31:17
I should really say they're the dollars.
31:19
Okay.
31:19
You know what I mean?
31:20
Yeah, yeah, yeah.
31:21
Like if you want to transact value between one person to another, what do we normally use?
31:27
We use dollars.
31:28
Yes, yeah.
31:28
Or if you're in another country, you use whatever denomination is the fiat currency.
31:32
Yeah, okay.
31:33
Okay, got it.
31:33
But there are other ways to transfer value.
31:36
I can give you a house.
31:37
I can give you land.
31:39
I can give you Bitcoin.
31:40
Yes.
31:40
Those are your serotonin, your dopamine, your acetylcholine things.
31:44
That makes sense.
32:09
is tied to AMPA receptors, which are receptive to glutamate, shows just how nitty-gritty it's getting into.
32:17
And perhaps that is a clue as to why it's so fast.
32:21
Because it's not waiting for the Bitcoin network to settle.
32:24
You don't need to produce dopamine.
32:27
So on and so forth.
32:31
So this is very early, but I think it's very, very cool.
32:34
And it's only going to get cooler and cooler.
32:36
And I think another piece of this that is interesting is being able to have – I mean, obviously, there are a variety of these new studies now that are trying to look at substances that have historically been viewed as just purely detrimental to the human condition.
32:52
Yeah, yeah.
32:53
And having no positive –
32:55
upside, primarily because they were viewed as recreational purely.
33:00
But there is a clinical, a potentially clinical application here that, again, when you actually understand the structures and fundamentals of what is happening...
33:11
we're kind of now starting to see why is it that ketamine can possibly – has this positive effect on treatment-resistant depression.
33:20
Yeah.
33:21
Because the way in which it interacts with our brain is fundamentally different than – I can't remember what the terminology is for the kind of existing set of – Antidepressants.
33:30
There's another like –
33:32
There's another technical name for it.
33:33
It's escaping me right now.
33:34
No, I don't know.
33:35
But, you know, all the Klonopin and all the stuff that people talk about in TV shows and things like that, which there's been a lot of both.
33:42
There's been a lot of kind of pushback on what are the while it might treat the dopamine or the serotonin piece.
33:49
It has a lot of these other potentially negative consequences for people.
33:52
But maybe there's a new path here.
33:53
Super, super fascinating story.
33:56
Yeah.
33:57
And we'll see where it goes early, like you said.
33:58
Yeah, early, but very cool.
34:00
But we need to be able to research a variety of different areas and explore opportunities in controlled and safe environments.
34:07
So we love to see that.
34:08
Our last story of the day is our paleontology and ancient life story.
34:14
about life rebounding shockingly fast after the asteroid that killed the dinosaurs.
34:20
This is out of University of Texas at Austin.
34:23
It was published in Geology.
34:25
And basically what they're saying is new research reveals that microscopic plankton
34:30
began evolving into new species within just a few thousand years, possibly under 2000, after the asteroid impact that killed the dinosaurs that we all remember from high school was 66 million years ago.
34:44
And we all remember the amber from Dr. Hammond's staff in Jurassic Park where they extracted dino DNA and merged it with amphibian DNA.
34:54
But the idea here is that
34:56
there was not an expectation that life after such a catastrophic impact would re-arise so quickly.
35:03
It was an interesting way in which scientists have actually found this to be true.
35:07
Yes, this is actually really cool.
35:09
So, you know, 66 million years ago, an asteroid hit...
35:14
Mexico, effectively, and created the Chixioclub Crater.
35:18
All the dinosaurs died.
35:19
There was massive extinction all over the planet.
35:22
There was a bunch of climate change.
35:24
It was pretty bad.
35:25
And what this paper is suggesting is that within 2,000 years, there was a new species of plankton that had evolved.
35:32
And 2,000 years is a blink when it comes to 66 million years ago, right?
35:36
This is pretty insane.
35:38
It's ridiculously fast.
35:40
Ridiculously fast.
35:42
It is.
35:42
And that's literally what Chris Lowry, who's the study's lead author from the University of Texas, he said, it's ridiculously fast, is what he said.
35:52
So earlier they had done work where they had gotten samples from the Chiksa Club crater around the Chiksa Club crater.
36:00
And you can figure out where the fossils are in the fossil record.
36:03
And then you've got some kind of model for how fast sediment gets deposited.
36:07
Right.
36:08
And so from that, you can figure out how old stuff is.
36:10
Yes.
36:11
Right?
36:11
Because the deeper you go, the older it is.
36:13
And if you have some rate of how much sediment goes through, you can calculate.
36:17
You can back calculate from where the boundary is.
36:21
There's a layer of sediment from the meteorite all over the earth.
36:25
Mm-hmm.
36:26
when the meteor had smlashed in and like created that sediment in the atmosphere.
36:30
So if that's our, you know, zero 66 million years ago from there, we can then figure out how fast fossils were coming up.
36:37
That's actually a good point in that because that impact surrounded the atmosphere and the planet with the ejecta from that impact.
36:46
It's a, it's a bookmark in the sedimentary layers of, it's a time bookmark.
36:51
Yes.
36:52
Um,
36:53
Which, that's actually interesting.
36:54
It's actually cool, right?
36:55
It actually took Luis Alvarez, who he's mentioned in the Oppenheimer movie.
37:00
We're going to do a special on all the scientists of the Oppenheimer movie, and we're going to rank them.
37:04
You're going to see where he falls.
37:05
But Luis Alvarez, he's one of Lawrence's PhD students.
37:12
And...
37:12
He actually later on in his life, he started doing lots of random stuff.
37:16
Like he did this Chixi Club Crater thing.
37:17
He also mapped the pyramids.
37:19
Like once he won his Nobel Prize in physics, he was just doing side quests.
37:23
And each side quest was like, wow.
37:25
You know?
37:26
That's awesome.
37:27
So anyways, so that's what we normally do, right?
37:33
Now...
37:34
That's built on an assumption.
37:35
This whole sediment thing, right?
37:37
You take the zero and then you figure out where the fossil is and how much sediment is.
37:42
From that, it's built on the assumption that the rate at which sediment is deposited is the same before and after.
37:51
Okay.
37:52
Now, that's not entirely true, right?
37:53
Because if there's a giant mass extinction— Right.
37:55
—and then—
37:57
All your trees are dead.
37:57
All your plants are dead.
37:59
There's going to be more erosion, so there's going to be faster sediment being deposited.
38:02
So you might have an over or an underestimate on when actually these plankton got where they are.
38:08
That makes sense.
38:09
So how do you resolve this discrepancy?
38:10
Yes.
38:11
What they did was they used...
38:12
The radioactive isotope helium-3, this is an incredibly rare isotope.
38:17
So already this piqued my interest because helium-3 is incredibly rare.
38:22
It's one of the most expensive substances on Earth because helium-3 is used for very specific industrial applications.
38:28
For example, if you have something called a dilution refrigerator, which is what you use to cool down things like quantum computers, you require a lot of helium-3 materials.
38:37
in order to do that.
38:38
Helium-3 is two protons, one neutron, instead of normal helium-4, which is two protons, two neutrons.
38:44
That's the more stable isotope.
38:45
Here, you've got helium-3.
38:47
That's an isotope, right?
38:49
And if you look at the dynamics of helium-3, helium-3 actually accumulates in the ocean sediments at a steady rate.
38:59
So when the sediment...
39:01
builds up slowly, you're going to have higher concentrations.
39:05
But if it's being deposited really fast, you're going to have lower concentrations in that layer of sediment, whatever core that you drilled.
39:13
So by looking at the relative abundance of helium-3 versus helium-2, or like whatever, not helium-2 obviously, but like other elements that it could decay into, we can figure out...
39:23
What was the rate at which the sediment was being deposited?
39:27
Because the helium-3's abundance level is correlated with the rate of sediment accumulation.
39:34
Exactly, yeah.
39:35
The faster it gets accumulated, the lower the density, right?
39:38
Because it doesn't have enough time to really pile up.
39:41
And so from that, they got...
39:45
The KP boundary location, you know, that layer of meteorite-like layer, they got samples from that layer of sediment all the way from Europe, North Africa, and the Gulf of Mexico, so all over the planet.
40:00
And they looked at this helium-3 data to figure out what was the sedimentation rate.
40:06
And from that, they actually figured out that...
40:11
The normal plankton species that we usually think about, it's called P. iujubina.
40:17
I hope I'm saying that right.
40:18
I don't really know how plankton is pronounced, honestly.
40:21
I think it's pronounced chum bucket.
40:23
I'm not sure, though.
40:25
Yeah, but this normal plankton species that we normally look for showed up about 3.5 to 11,000 years after the Chiksa Club happened.
40:36
impact so that's already pretty fast yes there were certain species there were that there were certain species that appeared fewer than 2 000 years after the asteroid strike and that's pretty ridiculous yeah no that's very ridiculous right bb
40:53
Like 2,000 years later, these guys are already like, wow, no one is alive.
40:57
I have all the sun to myself.
40:59
Right.
40:59
You know?
41:00
So the speedy recovery shows just how resilient life is.
41:04
Yes.
41:05
Right?
41:06
To have – this is not like bacteria.
41:08
This is plankton.
41:09
So this is pretty complex life.
41:10
Mm-hmm.
41:11
Sure, it's still like, you know, single cell to fewer, not that many cells, but it's still, it reestablished itself within a geological heartbeat.
41:20
Yeah, which is, I mean, it's unbelievable.
41:22
And it's sort of, you know, there's all of the...
41:28
When people look at the history of this planet and there's all these epics that are kind of opaque to us.
41:35
And how many times have we seen these either regional or global ecosystem collapses and then rebirths?
41:45
And there could be a number of them.
41:48
And given how quickly now we're seeing the recovery from the asteroid from 66 million years ago introduced, again, simple life but so quickly, it does recalibrate how you think about a variety of sort of the catastrophe scenarios you see in movies.
42:04
Yeah.
42:04
Mother Earth will be okay.
42:06
Yeah.
42:06
Humanity may not.
42:07
Yeah.
42:08
But Mother Earth will be fine.
42:09
Yeah.
42:10
And life will continue to find a way to live and prosper.
42:13
I want to make a funny, not like a quick side note about helium three, which is for anyone who's a fan of For All Mankind, the Apple TV show that does the revisionist history about who got to space first and the moon first.
42:25
Yeah.
42:26
Spoiler alert.
42:28
One of the battles and one of the reasons for the permanent moon base location is the massive deposits of helium three.
42:34
Yeah.
42:34
Oh, okay.
42:35
That makes sense.
42:35
Which then allows them to power all the stuff locally.
42:38
And then if you have... And then there's all the movies where we run out of energy on Earth.
42:41
We need to go to the moon to go get...
42:43
I think also that's part of the plot point in...
42:49
Don't Look Up was that there was helium-3 on the moon and the tech guy wanted to go mine it or some crazy nonsense.
42:53
So it's one of those things people hear about in pop culture quite often, but not necessarily knowing real-world applications.
42:59
So this is a very interesting, maybe non-traditional use of the helium-3 isotope in terms of identifying.
43:08
Fantastic.
43:09
We love a dinosaur story.
43:10
We had AI, genetics, neuroscience, paleontology.
43:15
We had a great round two of Are You Smarter Than a Scientist?
43:20
In the comments below, let us know if you would like to see us continue the game show.
43:24
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43:32
And if you're still watching and you are a first-time listener or a long-time listener, this show is being able to be created and produced multiple days a week just with...
43:42
the two of us.
43:43
And so your support is always super, super helpful for us to be able to continue to get the latest and greatest breaking science in a way that's digestible and entertaining for all those who are curious.
43:54
So a like, share, a comment, bring it into the lunch, put it in your email list serve is super helpful.
44:01
As well as if you want to support us, you can go to our website at ffppod.com
44:07
Backslash donate and become a patron.
44:10
Also, if you haven't checked out the website, all of the research papers we cover on every episode are right on that website.
44:17
We have our episodes with chaptering and a whole bunch of features.
44:21
So if you want to really dig in, if we've piqued your interest with any individual story, there's plenty to dig in and jump in on the website online.
44:29
I am Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury.
44:36
Thank you all for joining us for another week.
44:39
And we will see you next week.