0:00

YOU AND BEDI AND THE NANCIES

0:02

AND FILLS AND JAMES WILL

0:04

FIND IN THE STUDY OF SCIENCE A

0:07

RICHER more rewarding life.

0:11

Hey, welcome to inquiring minds. I'm Andreyvascontus.

0:15

This is a podcast that explores the

0:17

space where science and society collide. We

0:19

wanna find out what's true, what's left to discover,

0:21

and why it matters.

0:30

And

0:30

this week, we are doing another

0:33

up to date episode. Welcome back, Adam.

0:35

It's

0:35

great to be here, Andre. That's

0:37

Adam Bristol, our UpToDate correspondent.

0:40

Indeed.

0:40

looking forward to it.

0:42

Lots going on in the world of science and technology.

0:44

And

0:44

it's been a while since we've done one of these.

0:47

So I'm excited.

0:48

Oh, me too. Alright. So,

0:50

why don't you just jump right in? Alright.

0:52

Well, I wanna talk to you about one

0:54

paper that I thought was really fascinating. an

0:57

update on something we've talked about in

0:59

a prior episode. And then last

1:01

one that I'd say is kind

1:04

of science adjacent that kind of impacts

1:06

our daily life. Okay. Alright.

1:08

So the first one is a paper

1:10

that came out recently in one of the

1:12

journals of American Chemical Society,

1:14

a journal called Nano, And the

1:16

title is surface topography

1:19

adaptive robotic superstructures for

1:22

biofilm removal and pathogen

1:24

pathogen detection on human

1:26

teeth. And

1:27

the authors here are Minjun O

1:29

and colleagues at the University of Pennsylvania, with

1:32

the senior authors being Edward

1:34

Steiger and Kyung Ku, both

1:36

of whom had the biofilm research

1:39

lab there.

1:40

though So I thought it was interesting because

1:42

when you think about tooth decay in

1:44

oral health more broadly, we think

1:46

of plaque. Right? The stuff we

1:48

scrape off our teeth. The sugar bugs

1:50

is me Yeah. As we call our kid, the sugar

1:52

bugs, exactly. And those

1:54

are biofilms. Right? They're populations

1:57

of microbial cells like bacteria

1:59

and fungi

1:59

that become enmeshed in an extracellular

2:02

matrix that then is firmly attached

2:05

to the surface of our teeth. Now

2:07

biofilms on teeth and in other

2:09

places are really hard to clean.

2:11

Right? Think of the scraping that your dental

2:13

hygienist does, that your semiannual cleaning.

2:16

and they can lead to infections and chronic

2:18

health problems. So it'd be much it'd

2:21

be it'd be great to find some novel way

2:23

of addressing biofilms.

2:26

One thing that makes biofilms on teeth

2:28

so challenging is that the teeth

2:30

themselves are kind of unusual

2:33

shape. They're oddly shaped. They have ridges

2:35

crevices on in between teeth

2:37

that makes it even more challenging to treat.

2:39

Mhmm. So in this paper, the

2:42

authors took a nano technology approach

2:44

and they engineered a system for controlling

2:47

essentially the aggregation and movement

2:49

of iron nanoparticles using

2:52

programmable magnets to

2:54

create adaptable bristle

2:57

and floss like structures to

2:59

clean teeth and remove the biofilms. So

3:02

this was just a proof of concept paper only,

3:04

but I found it fascinating. So So, like, we we

3:06

we so they basically created toothbrushes?

3:09

No. No. No. Well, I mean, they

3:11

have the they have the actions of

3:14

toothbrushes, but they actually enhanced

3:16

toothbrushes. Okay. And I'll tell you why.

3:18

So

3:18

the authors start with

3:21

an aqueous bath

3:23

and that also contains

3:26

iron nanoparticles at known concentration.

3:28

Okay. And on either end of this bath,

3:30

on either side, they can place small

3:32

electromagnet. So when they energize

3:35

one of the magnets, then the iron

3:37

nanoparticles naturally congregate in

3:39

a clump on that side of the bath. Right? So

3:41

nothing new here. But as they

3:43

energize the opposite side magnet,

3:46

simultaneously, now these iron

3:48

nanoparticles self assemble

3:50

into bristle like structures extending

3:53

across the bath. Yeah.

3:54

You can kinda see that if you if you play with,

3:56

like, little iron filings and you have them to magnets,

3:58

they kinda make

3:58

these, like,

3:59

little lines. Yeah. When

4:02

I started reading out, I couldn't help but think about

4:04

that children's toy. It's called like willy

4:06

willy or what's the thing where you put the beard

4:08

on the guy with your little you have a

4:10

magnet on a wand and you sort of move it around

4:12

in the iron filings based on your gate.

4:14

Yes. Sure. I mean, I don't know about Billy, but but something

4:17

like that. It's it's something like that. But so anyways,

4:19

what the authors found. So they took it obviously,

4:21

you know, much further than that. They found that if

4:24

they varied the concentration of the iron

4:26

nanoparticles, the strength of the magnetic

4:28

field, the position of the magnets

4:30

relative to each other, and and a variety

4:32

of other factors that they could control the

4:35

shape that

4:36

forms.

4:37

like the bristle length, and

4:39

the movement of the iron nanoparticle

4:41

bristles. They they could create sweeping

4:44

movements almost like a toothbrush. Mhmm.

4:46

Right? So there was a dynamic control

4:49

of the nanoparticle structures with these magnets.

4:51

And what's cool too is that the bristles

4:53

could conform to the topography of

4:56

the object on which they were in contact.

4:58

So they were kind of like shape shifters. Right? They

5:00

could get into the little nooks in the crannies.

5:03

The authors called these surface

5:05

topography adaptive, robotic

5:07

superstructures, or stars. So these

5:09

are these are robot toothbrushes.

5:12

Pretty much. I mean, what's your definition

5:14

of a robot? Right? I mean, these are iron. These are

5:16

particles -- I mean, it's so simple. -- propelling

5:18

-- Yeah. -- you know. Yeah.

5:20

But what's cool is that they found that these

5:23

little bristles, right? These iron these

5:25

super structures that are formed by the the

5:27

kind of congregation in the of the iron

5:29

nanoparticles could generate shear

5:32

stresses as

5:33

they moved in the range of about sixty pascals,

5:35

which exceeds the value needed for biofilm

5:38

removal. So to test this,

5:40

they use experimental objects like

5:42

they use A3D printed square, they use

5:44

three d printed and actual human teeth

5:47

samples, They coated with them with

5:49

saliva, and then they cultured known

5:52

biofilm generating bacteria on

5:54

them. In this case, streptococcus mutans

5:57

which the author's claims creates one of the

5:59

stickiest most

5:59

recalcitrant bio films.

6:03

So

6:03

in the right medium, It only took about

6:05

forty three hours for the biofilm to form.

6:07

Right? So this is creating the model. Right?

6:09

You gotta get biofilms to form

6:11

on your little experimental apparatus. It

6:13

only took forty three hours to That's that's a long

6:15

time me brushing your teeth. No. No. No. No. No.

6:18

We haven't gotten to the brushing yet. Oh, okay. What I'm

6:20

saying is they took teeth, like, Got it.

6:22

So I see it. And we have to form biofilm

6:24

on them to what Oh, oh, that's that's

6:26

forty three hours to form the biofilm.

6:28

I got it. Now they tested

6:30

various bristle movement patterns. On

6:33

those now formed biofilm, and

6:35

they found that they could effectively remove

6:37

the biofilm with the magnet controlled

6:40

bristle movements when it was done

6:42

in a one percent hydrogen peroxide

6:44

solution. So it wasn't clear to me from the

6:46

method just how long it took to to

6:48

to do the sweeping, but it was at least ten

6:51

minutes. Okay. And the hydrogen peroxide

6:53

alone at one percent didn't

6:55

remove the biofilms. Right? You might say is

6:57

that just uses a cleaning. But at

6:59

a one percent concentration, that's actually

7:01

pretty low. Okay. Containing that a lot

7:03

of the over the counter teeth whitening solutions

7:06

usually contain three percent. Yeah. I can't

7:08

watch that. So you could you I see. Okay. So you could Right.

7:10

So it's just that concentration doesn't account for

7:12

their ability to remove the biofilm. Sure.

7:15

But the hydrogen peroxide is actually a really

7:17

important component here because the iron

7:19

nanoparticles are capable more of just

7:21

the physical abrasive abrasive

7:24

removal of the biofilms. Mhmm. It turns

7:26

out that the iron oxide used in the solution

7:28

which is f three FE-three zero four

7:31

reacts with hydrogen peroxide to

7:33

generate free radicals, like a hydroxyl

7:35

radical which is known to be

7:37

antimicrobial. Mhmm. So the iron

7:39

nanoparticles in this star's

7:42

platform is really a dual

7:44

mechanical approach. right,

7:46

to to biofilm removal. So this would be

7:48

better than just a toothbrush. Right? It'd be it'd be as

7:50

if you had a antimicrobial toothbrush brush

7:53

Got it. Along with the sort of force of

7:55

your of your brushing. So lastly,

7:57

I just wanna throw this in. As an additional application,

8:00

the authors found that after getting

8:02

rid of the biofilm removal after

8:04

getting rid of the biofilm, if they just de energized

8:07

the magnets, that iron nanoparticles

8:09

fell away. Right? because they're no longer magnetized.

8:12

They found that they could collect those nanoparticles,

8:15

do biomarker analysis, and they

8:17

could identify the components

8:19

of the biofilm. So this what are the

8:21

bugs? So this has potential

8:23

possibility for diagnostic uses too.

8:26

Wow. Right.

8:27

Yeah. Sounds like you spit out your filings

8:29

and then you're -- Yeah. -- you're a dentist,

8:31

figure out, you know. You know,

8:32

so my my thinking was, you know, where does this

8:34

go from here? This is clearly proof of concept

8:37

-- Sure. -- type of paper. And

8:39

the idea of nanoparticles for

8:41

oral health isn't that crazy. Of course,

8:44

we have a number of substances

8:46

are already being used commonly like

8:49

hydroxyapatite, which is a

8:51

tooth coating, and titanium dioxide,

8:54

which is used as an intense white pigment,

8:56

intense white pigment for whitening. So

8:59

these are commonly used, but my big

9:01

question when reading the paper was if

9:03

they actually plan to treat human

9:05

teeth in situ. Right? While they're still

9:07

in our mouths -- Mhmm. -- they'll need to design

9:09

a setup that works as a some sort of mouthpiece.

9:12

that contains the iron nanoparticle solution

9:15

with the dilute hydrogen peroxide and

9:17

then position the two magnets on either

9:19

side of the teeth. Because in the paper,

9:21

the bath was just this little small contained,

9:24

you know -- Mhmm. -- bath. It was just a small

9:26

little rectangle. which was useful

9:29

for an experimental system, but it that's

9:31

that's really not what would require for real

9:33

world application. But

9:34

if it takes, you know, like twenty four hours, in this

9:36

case, forty three hours, for the bacteria

9:39

and form, you know, and my dentist

9:41

always tells me that, you know, you can get a cavity in

9:43

twenty four hours. Like, do you think

9:45

that there's ever and, you know, a

9:47

way that this would replace tooth brushing?

9:50

I don't

9:50

think it would be replace tooth brushing. I mean,

9:52

I could see it for your the types

9:54

of work that your dental hygienist already does,

9:56

the scraping at your seventh annual every

9:58

six months. Oh, I see.

9:59

This would be, like, let's get rid of the build

10:02

up of plaque Oh, instead

10:04

of, like, six months with that tool. Exactly.

10:06

So you basically put it on your I could

10:08

see some appliance that you basically put on in the

10:10

dentist office. I'll be back in ten minutes.

10:12

Yeah. I mean, while you watch it's a year ago.

10:14

It's a year ago. Yeah. They could

10:16

basically put your teeth in almost like

10:18

a mouthpiece thing. Right. put it up

10:20

in there and then have these something

10:23

that would basically have Magnus energizing

10:25

in the right pattern on either side and let

10:27

the iron nanoparticles do the work. Okay.

10:30

This is the the general hygienist. Yeah. I

10:32

I definitely feel like I I screwed you up

10:34

with the forty three hour thing. I was just I was amazed

10:36

to find that only took forty three hours when

10:38

they're getting their experimental system up. Yeah.

10:40

No. No. No. I I see what you're saying. You're saying.

10:42

I mean, it doesn't surprise me because I yeah. I've

10:44

been told that, like, yeah. You know, after

10:47

a day, you can you can actually have a buildup

10:49

of Yeah. For sure. Black and your teeth. Okay.

10:51

This is awesome videos. They show you all they

10:53

show it working. So I'll throw some links on the Patreon

10:56

page. because it's really kinda cool to see

10:58

these little iron

10:59

nanoparticles in action.

11:06

One

11:12

of the articles that caught my eye and I think

11:14

lot of other science journalists and people

11:16

are just interested in science in general, was

11:18

that there seems to finally have been

11:22

a positive trial involving

11:24

an Alzheimer's drug that works

11:26

on amyloid. So

11:29

this has been this is kind of like one of the

11:31

big disappointments in neurology

11:33

was that several decades ago,

11:35

this hypothesis that at

11:37

least one of the pathologies

11:40

related to Alzheimer's disease is caused

11:42

by a buildup of amyloid.

11:45

And and so if we could just get rid of it,

11:47

then we could alleviate or possibly

11:49

even pure the disease, but it

11:51

hasn't mean there have been failure after

11:53

failure after failure when it comes to

11:56

drugs that, you know, attempt to do this.

11:58

And a lot of people have have started

11:59

to just say, like, we just just

12:02

to give up this whole endeavor and

12:04

and leave this hypothesis behind. And

12:06

in fact, this drug,akinumab, came

12:09

out of discovery of a mutation

12:13

described in a Swedish population over

12:15

twenty years ago that potentially

12:18

affects the protofibrils. So these are the

12:20

pre plaques

12:22

you know, when it comes to amyloid is

12:25

the idea that it creates these plaques

12:27

that are part of pathology of Alzheimer's disease.

12:30

So if they can target sort of these

12:32

plact before they become fully

12:34

blacked. I don't wanna say that.

12:37

The the idea is that they could prevent you

12:40

know, this particular pathological part

12:43

of Alzheimer's. So it was developed

12:46

by small Swedish company called BioArctic And

12:49

later on, that company

12:52

made deal with Eisai. And

12:54

so now if you hear about the drug, it's

12:57

a trial by Eisai and

12:59

the parent company Biogen. So

13:02

a couple drug companies, Eisai, in collaboration

13:04

with Biogen, started

13:06

trials in humans. And the

13:09

Phase 2b trial actually

13:11

was it looked like the drug has failed And

13:15

that was that those data read out

13:17

a few years ago, and it just

13:19

looked like not only did

13:21

the drug not really work ultimately,

13:23

but the longer the person was on the drug, so

13:25

going from twelve to eighteen months, the

13:28

sort of more less of an effect

13:30

you saw. So you saw this, like, didn't really

13:32

make a huge difference and then it also, you know,

13:34

didn't get any better if you were on the drug

13:36

for longer. But this phase

13:39

three trial showed something

13:41

a little bit different. Now,

13:44

there's a caveat here, which is that

13:46

the full data is gonna be released

13:48

in November. So we

13:50

don't know exactly what the

13:52

full data set looks like. But

13:55

the thing that's got everybody excited is

13:57

that the primary endpoint for the trial

14:00

was called the clinical dementia

14:02

rating And what they found

14:05

was that the patients that were treated by

14:07

the drug showed a

14:10

reduced decline, which means basically

14:12

they didn't decline as quickly as

14:14

the patients that were on placebo by

14:16

twenty seven percent So

14:19

what does this mean? That means patients are still

14:21

declining, just not as quickly. And

14:24

it does seem to be a significant

14:27

result, which is

14:29

good. But whether this actually translates

14:32

into any kind of real

14:33

world

14:35

observations is still unclear because

14:37

ultimately this effect was on

14:39

the clinical dementia rating scale.

14:42

which doesn't always translate easily

14:44

to sort of the things that you kinda

14:46

need to do in your daily life. Like,

14:48

you know, does it affect your ability

14:50

to remember people's names or where you left your keys

14:52

or what do you need to buy at the grocery store?

14:55

That it's not clear whether this kind

14:57

of benefit of the drug

14:59

actually has an impact in those kinds of

15:01

real world situations. So

15:03

it's a exciting in the sense

15:05

that finally there's a drug that does seem to be going

15:08

in the right direction. It still

15:10

has, you know, a lot of we sold

15:12

a lot of work to do to figure out whether this is

15:14

actually a meaningful drug. And

15:17

finally, of course, it's not

15:19

anywhere near where we really wish

15:21

we could we could be which is closer to stopping

15:24

the progression, you know, entirely or

15:26

even reversing the effects of Alzheimer's. So

15:28

it's exciting. It's the first positive trial

15:30

for a disease of aging,

15:33

like Alzheimer's disease. And

15:36

the jury's still out whether this

15:38

statistically significant change. This

15:41

decrease of twenty seven

15:43

percent in the decline is going be clinically

15:45

significant. That is, is it going to make difference to

15:47

the patients? We'll we'll

15:50

learn more in November, but it's something to

15:52

keep your eye out. So

15:53

what else was on your desk, Adam?

15:55

Okay. Do you remember back in episode

15:58

three sixty four? That was in October

16:00

of twenty twenty one -- Okay. -- that

16:02

we discussed a NASA project known

16:04

as the double asteroid redirect test

16:06

for Oh, yeah. This is like where they're gonna

16:08

shoot away an asteroid. Right.

16:11

Right. This was an air spacecraft launch

16:13

into space. subsequent to our podcast,

16:15

it was in November twenty twenty one, it was

16:17

on a one way mission to test

16:19

the viability of kinetic

16:22

impact onto and asteroid,

16:25

and then redirecting its orbit.

16:27

Uh-huh. The idea being this is part of the

16:29

planetary defense possibility.

16:32

Right? Yeah. That is, can NASA navigate

16:34

its fast spacecraft to

16:36

hit an asteroid and then deflect

16:38

it off its course? Yeah. Well, I'm

16:41

proud. I'm pleased to say. I'm excited

16:43

to say that on Monday of this

16:45

week of the week, we're recording this on September

16:48

twenty six two thousand twenty two, about

16:50

ten months after the launch.

16:52

The craft successfully smashed

16:55

into the asteroid known as

16:57

De Morpheus. and it did so at fourteen

17:00

thousand miles per hour, seven

17:02

million miles from Earth as planned.

17:04

Wait. So we kind of avoided situation and

17:06

don't look up.

17:07

Well, remember this you the the Earth was

17:09

never at risk. You remember this was a

17:11

dual asteroid. I forgot the name of

17:14

it now, but remember this was a unique

17:16

experimental

17:16

situation that it

17:18

afforded an interesting experimental situation

17:20

because dimorphous was an asteroid

17:23

that was actually orbiting around a very

17:25

another asteroid. Okay. And that

17:27

asteroid was called Didimos. Right.

17:30

So the idea is we could we could see

17:32

these two asteroids, which by the way, they were never

17:34

earthbound. Right. But the point is they provided

17:36

this interesting system in which

17:38

we could basically deflect

17:40

dimorphous orbit

17:43

around its own little did demos

17:46

satellite. It was satellite around

17:49

did demos. So As a proof of

17:51

concept, to see if that was the proof of concept. Exactly.

17:53

It seems to be the theme this episode. Now

17:55

the effect is supposed to be small only

17:58

about a one percent difference in

17:59

its

18:00

prior orbit, but that's measurable.

18:03

So this is going to be now it's

18:05

gonna take some time on the order of months

18:08

to gather the data, to determine whether

18:10

in fact they were successful in deflecting

18:13

and altering now -- Mhmm. -- its orbit

18:15

going around Didi Mose. Cool. But the

18:18

fact that we were able to successfully launch,

18:21

direct, and hit, successfully

18:24

dimorphs, is fascinating. And

18:26

if you haven't done so already, you have

18:29

to see the photos and the video

18:31

of the final moments. Okay. Because

18:33

just like the moon the Mars Landing we had

18:36

not long ago, where you just are

18:39

mouth on the floor at how

18:41

amazing these these these images

18:43

are, the

18:46

dart craft

18:48

was sending photos back

18:50

-- Mhmm. -- as it was approaching the

18:53

asteroid and it got bigger and bigger until

18:55

you basically the

18:57

saw the surface. of this

18:59

Astra seven million miles away -- Wow.

19:02

-- Kaboom. Wow. It

19:04

sounds very satisfying. Yeah. And there was actually like,

19:06

the other thing I'll say too is there's an attempt I saw

19:09

on social media that there is a

19:11

asteroid detecting or asteroid surveillance,

19:14

you know, telescope and they directed

19:16

it towards the dibimos, dimorphous

19:19

pairing. And you could actually see

19:22

the explosion that a change in the

19:24

light and all the debris and it was like

19:26

Oh, cool. It's really it's just it's just fascinating.

19:29

Amazing. Alright. Well, I have

19:31

one more for you too. And I'm gonna end on this one.

19:33

And I say this one's kinda science light.

19:35

It struck me because it impacted

19:37

my life. You know how much I use

19:39

YouTube -- Yeah. -- to fix

19:41

stuff, to learn stuff. I

19:44

call it YouTube University, It's

19:46

fascinating, but I was dismayed

19:48

to learn that the feedback tools

19:51

that allow you to presumably

19:54

shape or sculpt

19:56

the recommendations and what videos

19:59

you see and what it serves to you next up.

20:01

And those feedback tools, like,

20:03

don't like not interested, don't

20:05

recommend, they

20:08

seem to be much like the closed

20:10

door button in an elevator. No. They

20:12

don't really work. No. They're

20:14

just -- Yes. -- to make you -- They're like -- The

20:16

illusion. -- back of the illusion of

20:18

control. So the Mozilla Foundation which

20:21

is a not for pro nonprofit dedicated

20:23

to, they say, quote, shaping the future of

20:25

the web for public good. They

20:28

found that the feedback tools that YouTube

20:30

uses Well, they don't

20:32

work that well. So here's what they did. So

20:34

I was actually impressed with the approach

20:37

and the quantitative analysis

20:39

they were able to achieve because again, This

20:41

is all proprietary tools and data.

20:44

Exactly. Exactly. Okay. So they

20:46

had twenty two thousand seven hundred

20:48

twenty two volunteers. Okay. install

20:50

a browser extension they call regrets

20:53

reporter. Mhmm. So they could don't first

20:55

they could do, they would donate their data to Mozilla.

20:57

And this dataset over time came to be

20:59

five hundred and sixty five hundred

21:02

sixty million video recommendations. Yeah.

21:04

People watch a lot of videos on YouTube. Mhmm.

21:07

But what was cool is the regrets reporter

21:10

allowed Mozilla

21:12

Foundation to basically run a controlled experiment.

21:14

Mhmm. And this then

21:16

became the largest experimental audit

21:18

of YouTube recommendations by an independent

21:21

research group ever. so depending

21:23

on which experimental group that the volunteer

21:25

participant was part of. Clicking

21:27

the button on the regrets reporter

21:29

would send one of several types

21:32

a feedback to YouTube like do not

21:34

recommend channel, dislike, or what

21:36

have you, or it would send no

21:38

feedback at all. if the participant

21:40

was in the control group. That's important. Right? So

21:42

for those participants who opted into

21:44

the research, their extension kept

21:47

track of which visit videos that

21:49

the stop recommending button pressed

21:51

and what videos YouTube subsequently recommended.

21:54

But if they if if they were in the control group and

21:56

there was no feedback going to YouTube.

21:58

Mhmm. They could have a baseline rate

22:00

of the similarity and the of

22:02

the recommended videos. Oh, wow. because

22:04

ultimately looking here is, like, I get a video

22:07

and you know I watch a lot of bicycle maintenance

22:09

videos. Yeah. If I say all of a sudden, don't wanna see

22:11

those anymore. And then they send me another

22:13

video to recommend and it is in fact a bicycle

22:15

maintenance video, there's a high similarity

22:17

there. Yeah. And so the recommendation was poor.

22:20

And that's the bad recommendation rate.

22:23

So by comparing the results

22:25

across these different experimental arms against

22:27

the baseline rate, they

22:29

were able to measure the effectiveness of YouTube's

22:31

user controls. and to

22:33

sort of cut to the chase, what they found

22:35

that even the most effective user controls,

22:38

those would be saying you don't

22:40

want any recommendations from a particular channel,

22:43

or you can remove something from your

22:45

watch history. Those

22:47

prevented less than half of

22:49

the bad recommendations. The best they could

22:52

get was a forty three percent reduction

22:54

in recommendations. So this

22:58

for most people is probably a

23:00

mere frustration. Right. Right? But

23:02

there are potentially some real world

23:05

consequences to this.

23:06

If there's a problem with disinformation,

23:09

if there's a problem with online

23:11

hate speech, or things that

23:14

are more malevolent. you

23:15

could imagine people being served

23:17

things saying, I I don't wanna see that. don't wanna

23:19

see that and yet continually being the recipient

23:22

of it. Yeah. Especially if you have, like, you know, a

23:24

phobia like nophobia or,

23:26

you know, some kind you've experienced some trauma

23:28

and you just don't

23:28

wanna deal you know, don't wanna see And Mozilla

23:31

Foundation cites those types of examples.

23:33

Sure. Yeah. But it makes

23:34

me wonder, like, is it just that

23:36

the YouTube algorithm doesn't

23:39

want to stop giving you these recommendations

23:41

because they know that these are the, like, highly

23:43

watched videos and you're very likely to see these, so

23:45

it doesn't really care. Like, then it kinda is

23:47

like the closed door button, or is it

23:49

just that it's not that good? Is it just like

23:51

I don't know. Because it's a different

23:53

you know, the similarity rating that Mozilla

23:55

uses is not gonna be necessarily the same

23:57

as what YouTube uses to decide

23:59

whether something is, you know,

24:01

Yeah.

24:02

I mean, reading the Mozilla Foundation's report,

24:04

they would say it's more of the latter, which is they're

24:07

more interested in engagement. Right.

24:09

And they are in you

24:11

know, user control. Right?

24:13

And and sort of tailoring into the user's own

24:15

interests. Yeah. But

24:18

anyways, I just saw that again thinking, you

24:21

know, I I hit those little not

24:23

interested buttons all the time. And

24:25

yet, know, yet. It's really doing

24:27

very little. Yeah. Oh, well.

24:30

So that's it for another episode.

24:33

Thanks for listening. If you wanna hear more, don't forget

24:35

to subscribe. And if you'd like to get an

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ad free version of the show, consider supporting

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us at patreon dot com slash inquiring

24:42

lines. I wanna especially thank David

24:44

Noelle, herring Tang, Sean Johnson,

24:46

Jordan Miller, Kai Ryhala, Mark

24:48

Michael Galgol, Eric Clark, Yuchelin,

24:50

Clark, Lindgren, Joel, Stefan

24:53

Meyer A Wald, Dale Master,

24:54

and Charles Blial. Encurring

24:56

lines is produced by Adam Isaac,

24:59

who also edited this episode I'm your

25:01

host, Andrey Vasconjes.

25:02

And I'm Adam Bristol. See you

25:04

next time.