0:00:02.070,0:00:03.195
You don't know them.

0:00:04.150,0:00:05.436
You don't see them.

0:00:06.453,0:00:09.476
But they're always around,

0:00:09.476,0:00:11.265
whispering,

0:00:11.265,0:00:13.775
making secret plans,

0:00:13.775,0:00:17.288
building armies with millions of soldiers.

0:00:18.906,0:00:21.555
And when they decide to attack,

0:00:21.555,0:00:23.700
they all attack at the same time.

0:00:27.154,0:00:28.831
I'm talking about bacteria.

0:00:29.340,0:00:30.342
(Laughter)

0:00:30.342,0:00:32.197
Who did you think I was talking about?

0:00:34.520,0:00:37.313
Bacteria live in communities[br]just like humans.

0:00:37.651,0:00:38.968
They have families,

0:00:38.968,0:00:39.969
they talk

0:00:39.969,0:00:41.520
and they plan their activities.

0:00:41.957,0:00:43.483
And just like humans,

0:00:43.483,0:00:46.434
they trick, deceive and some might[br]even cheat on each other.

0:00:48.199,0:00:52.097
What if I tell you that we can listen[br]to bacterial conversations

0:00:52.097,0:00:55.412
and translate their confidential[br]information into human language?

0:00:56.255,0:01:01.053
And what if I tell you that translating[br]bacterial conversations can save lives?

0:01:02.469,0:01:04.384
I hold a PhD in nanophysics,

0:01:04.610,0:01:08.610
and I've used nanotechnology[br]to develop a real-time translation tool

0:01:08.610,0:01:11.211
that can spy on bacterial communities

0:01:11.211,0:01:13.833
and give us recordings of what[br]bacteria are up to.

0:01:16.323,0:01:18.103
Bacteria live everywhere:

0:01:18.103,0:01:18.353
they're in the soil,

0:01:18.353,0:01:20.023
on our furniture

0:01:20.023,0:01:21.203
and inside our bodies.

0:01:22.330,0:01:26.428
In fact 90 percent of all of life's cells[br]in this theatre are bacterial.

0:01:28.201,0:01:29.752
Some bacteria are good for us:

0:01:29.752,0:01:32.554
they help us digest food[br]or produce antibiotics.

0:01:33.113,0:01:34.778
And some bacteria are bad for us:

0:01:34.778,0:01:36.762
they cause diseases and death.

0:01:37.982,0:01:40.421
To coordinate all the functions[br]bacteria have,

0:01:40.421,0:01:42.551
they have to be able to organize,

0:01:42.551,0:01:44.684
and they do that just like us humans --

0:01:44.684,0:01:45.653
by communicaiting.

0:01:47.013,0:01:48.441
But instead of using words,

0:01:48.441,0:01:51.483
they use signaling molecules[br]to communicate with each other.

0:01:52.293,0:01:53.665
When bacteria are few,

0:01:53.665,0:01:55.641
the signaling molecules just flow away.

0:01:56.361,0:01:58.552
Like the screams of a man[br]alone in the desert.

0:01:59.811,0:02:01.303
But when their are many bacteria,

0:02:01.303,0:02:03.744
the signaling molecules accumulate,

0:02:03.744,0:02:06.313
and the bacteria start sensing[br]that they're not alone.

0:02:07.501,0:02:08.655
They listen to each other.

0:02:09.639,0:02:12.479
In this way, they keep track[br]of how many they are

0:02:12.479,0:02:15.590
and when they're many enough[br]to iniate a new action.

0:02:16.804,0:02:20.662
And when the signaling molecules[br]have reached a certain threshold,

0:02:20.662,0:02:22.882
all the bacteria sense at once

0:02:22.882,0:02:24.733
that they need to act[br]with the same action.

0:02:26.239,0:02:29.703
So bacterial conversation consists[br]of an initiative and a reaction.

0:02:30.609,0:02:33.207
A production of a molecule[br]and the response to it.

0:02:35.312,0:02:38.470
In my research I focused on spying[br]on bacterial communities

0:02:38.470,0:02:39.843
inside the human body.

0:02:40.563,0:02:41.568
How does it work?

0:02:42.631,0:02:44.559
We have a sample from a patient.

0:02:44.559,0:02:46.797
It could be a blood or spit sample.

0:02:47.514,0:02:49.995
We shoot electrons into the sample,

0:02:49.995,0:02:53.931
the electrons will interact with any[br]communication molecules present,

0:02:53.931,0:02:56.325
and this interaction[br]will give us information

0:02:56.325,0:02:58.293
on the identity of the bacteria,

0:02:58.293,0:02:59.857
the type of communication

0:02:59.857,0:03:02.000
and how much the bacteria are talking.

0:03:04.519,0:03:06.530
But what is it like[br]when bacteria communicate?

0:03:07.977,0:03:11.737
Before I developed the translation tool,

0:03:11.737,0:03:15.466
my first assumption was that bacteria[br]would have a primitive language,

0:03:15.466,0:03:18.584
like infants that haven't developed[br]words and sentences yet.

0:03:19.464,0:03:20.688
When they laugh, they're happy;

0:03:20.688,0:03:21.993
when they cry they're sad.

0:03:22.314,0:03:23.455
Simple as that.

0:03:24.505,0:03:28.012
But bacteria turned out to be nowhere[br]as primitive as I thought they would be.

0:03:28.865,0:03:31.008
A molecule is not just a molecule;

0:03:31.008,0:03:33.642
it can mean different things[br]depending on the context.

0:03:34.610,0:03:37.575
Just liek the crying of babies[br]can mean different things:

0:03:37.575,0:03:39.323
sometimes the baby is hungry,

0:03:39.323,0:03:40.488
sometimes it's wet,

0:03:40.488,0:03:42.093
sometimes it's hurt or afraid.

0:03:42.645,0:03:44.578
Parents know how to decode those cries.

0:03:45.885,0:03:47.680
And to be a real translation tool,

0:03:47.680,0:03:50.648
it had to be able to decode[br]the signaling molecules

0:03:50.648,0:03:54.429
and translate them depending[br]on the context.

0:03:55.687,0:03:56.629
And who knows?

0:03:56.891,0:03:58.905
Maybe Google Translate[br]will adopt this soon.

0:03:59.173,0:04:01.542
(Laughter)

0:04:02.652,0:04:03.851
Let me give you an example.

0:04:04.348,0:04:07.901
I've brought some bacterial data[br]that can be a bit tricky to understand

0:04:07.901,0:04:08.885
if you're not trained,

0:04:08.885,0:04:09.960
but try to take a look.

0:04:11.887,0:04:13.459
(Laughter)

0:04:15.208,0:04:18.406
Here's a happy bacterial family[br]that has infected a patient.

0:04:20.499,0:04:22.352
Let's call them the Montague family.

0:04:24.140,0:04:25.516
They share resources,

0:04:25.516,0:04:26.483
they reproduce

0:04:26.483,0:04:27.552
and they grow.

0:04:28.547,0:04:32.984
One day they get a new neighbor ...

0:04:32.984,0:04:34.564
bacterial family Capulet.

0:04:34.865,0:04:35.809
(Laughter)

0:04:36.379,0:04:38.745
Everything is fine[br]as long as they're working together.

0:04:40.627,0:04:43.227
But then something unplanned happens.

0:04:44.669,0:04:48.410
Romeo from Montague has a relationship[br]with Juliet from Capulet.

0:04:48.771,0:04:50.036
(Laughter)

0:04:51.201,0:04:53.798
And yes, they share genetic material.

0:04:54.187,0:04:56.296
(Laughter)

0:04:58.919,0:05:01.563
Now this gene transfer[br]can be dangerous to the Monagues

0:05:01.563,0:05:03.665
who have the ambition[br]to be the only family

0:05:03.665,0:05:05.125
in the patient they have infected.

0:05:05.731,0:05:07.166
And sharing genes contributes

0:05:07.166,0:05:09.600
to the Capulets developing[br]resistance to antibiotics.

0:05:12.071,0:05:16.626
So the Montagues start talking internally[br]to get rid of this other family

0:05:16.626,0:05:18.238
by releasing this molecule.

0:05:20.898,0:05:22.592
And with subtitles:

0:05:22.592,0:05:24.291
[Let us coordinate an attack.]

0:05:24.549,0:05:25.840
(Laughter)

0:05:26.119,0:05:27.553
Let's coordinate an attack.

0:05:29.365,0:05:32.552
And then everybody at once responds[br]

0:05:32.552,0:05:36.519
by releasing a poison[br]that will kill the other family.

0:05:37.126,0:05:38.299
[Eliminate!]

0:05:40.365,0:05:41.307
(Laughter)

0:05:43.633,0:05:47.825
The Capulets respond[br]by calling for a counterattack.

0:05:48.425,0:05:49.331
[Counterattack!]

0:05:49.544,0:05:51.119
And they have a battle.

0:05:52.320,0:05:56.552
This is a video of real bacteria dueling[br]with sword-like organelles

0:05:56.552,0:05:58.419
where they try to kill each other

0:05:58.419,0:06:01.017
by literally stabbing[br]and rupturing each other.

0:06:03.042,0:06:06.534
Whoever's family wins this battle[br]becomes the dominant bacteria.

0:06:08.769,0:06:11.743
So what I can do is to detect[br]bacterial conversations

0:06:11.743,0:06:13.885
that lead to different[br]collective behaviors

0:06:13.885,0:06:15.160
like the fight you just saw.

0:06:15.911,0:06:18.618
And what I did was to spy[br]on bacterial communities

0:06:18.618,0:06:20.731
inside the human body

0:06:20.731,0:06:22.327
in patients at a hospital.

0:06:23.057,0:06:25.175
I followed 62 patients in an experiment

0:06:25.175,0:06:29.086
where I tested the patient samples[br]for one particular infection

0:06:29.086,0:06:31.847
without knowing the results[br]of the traditional diagnostic test.

0:06:32.664,0:06:38.677
Now, in bacterial diagnostics,[br]a sample is smeared out on a plate,

0:06:38.677,0:06:41.811
and if the bacteria grow within five days,

0:06:41.811,0:06:44.035
the patient is diagnosed as infected.

0:06:46.003,0:06:47.172
When I finished the study

0:06:47.172,0:06:50.612
and I compared the tool results[br]to the traditional diagnostic test

0:06:50.612,0:06:52.112
and the validation test,

0:06:52.112,0:06:53.160
I was shocked.

0:06:53.617,0:06:56.829
It was far more astonishing[br]than I had ever anticipated.

0:06:58.260,0:06:59.144
But before I tell you[br]what the tool revealed,

0:06:59.144,0:07:02.755
I would like to tell you about[br]a specific patient I [followed].

0:07:03.462,0:07:04.409
A young girl.

0:07:05.079,0:07:06.412
She had cystic fibrosis,

0:07:06.412,0:07:09.821
a genetic disease that made her lungs[br]susceptible to bacterial infections.

0:07:11.017,0:07:13.057
This girl wasn't a part[br]of the clinical trial.

0:07:13.540,0:07:15.940
I followed her because I knew[br]from her medical record

0:07:15.940,0:07:18.250
that she had never had[br]an infection before.

0:07:19.668,0:07:23.181
Once a month this girl went the hospital[br]to cough up a [.. ... ] sample

0:07:23.181,0:07:24.509
which she spit in a cup.

0:07:25.096,0:07:27.916
This sample was transferred[br]for bacterial analysis

0:07:27.916,0:07:30.245
at the central laboratory

0:07:30.245,0:07:33.379
so the doctor's could act quickly[br]if they discovered an infection.

0:07:34.339,0:07:36.933
And it allowed my to test my device[br]on her samples as well.

0:07:36.933,0:07:37.699
The first two months I measured[br]on her samples there was nothing.

0:07:37.699,0:07:43.186
But the third month,

0:07:43.186,0:07:45.514
I discovered some bacterial chatter[br]in her sample.

0:07:46.778,0:07:49.370
The bacteria were coordinating[br]to damage her lung tissue.

0:07:50.783,0:07:54.383
But the traditional diagnostics[br]showed no bacteria at all.

0:07:55.950,0:07:57.900
I measured again the next month,

0:07:57.900,0:08:01.018
and I could see that the bacterial[br]conversations became even more aggressive.

0:08:02.367,0:08:04.729
Still the traditional[br]diagnostics showed nothing.

0:08:06.682,0:08:07.871
My study ended,

0:08:07.871,0:08:08.937
but a half a year later[br]I followed up on her status

0:08:08.937,0:08:13.470
to see if the bacteria[br]only I knew about had disappeared

0:08:13.470,0:08:15.245
without medical intervention.

0:08:16.438,0:08:17.516
They hadn't.

0:08:18.298,0:08:19.784
But the girl was now diagnosed

0:08:19.784,0:08:22.078
with a severe infection[br]of deadly bacteria.

0:08:23.678,0:08:27.360
It was the very same bacteria[br]my tool discovered earlier.

0:08:28.777,0:08:31.296
And despite aggressive[br]antibiotic treatment,

0:08:31.296,0:08:33.513
it was impossible[br]to eradicate the infection.

0:08:34.996,0:08:37.908
Doctors deemed that she would not[br]survive her 20's.

0:08:40.724,0:08:42.778
When I measured on this girl's samples,

0:08:42.778,0:08:44.416
my tool was still in the initial stage.

0:08:44.987,0:08:47.599
I didn't even know[br]if my method worked at all,

0:08:47.599,0:08:49.370
therefore I had an agreement[br]with the doctors

0:08:49.370,0:08:51.269
not to tell them what my tool revealed

0:08:51.269,0:08:53.289
in order to not[br]compromise their treatment.

0:08:54.374,0:08:57.129
So when I saw these results[br]that weren't even validated,

0:08:57.129,0:08:58.434
I didn't dare to tell,

0:08:58.434,0:09:01.555
because treating a patient[br]without an actual infection

0:09:01.555,0:09:03.504
also has negative consequences[br]for the patient.

0:09:05.258,0:09:06.953
But now we know better,

0:09:06.953,0:09:10.299
and there are many young boys[br]and girls that still can be saved.

0:09:11.390,0:09:14.457
Because unfortunately this scenario[br]happens very often.

0:09:14.760,0:09:16.264
Patients get infected,

0:09:16.264,0:09:19.764
the bacteria somehow don't show[br]on the traditional diagnostic test

0:09:19.764,0:09:23.329
and suddenly the infection breaks out[br]in the patient with severe symptoms.

0:09:23.806,0:09:25.804
And at that point it's already too late.

0:09:27.463,0:09:31.040
The surprising result[br]of the 62 patients I followed

0:09:31.040,0:09:33.464
was that my device[br]caught bacterial conversations

0:09:33.464,0:09:35.743
in more than half of the patient samples[br]

0:09:35.743,0:09:38.524
that were diagnosed as negative[br]by traditional methods.

0:09:39.691,0:09:43.205
In other words, more than half[br]of these patients went home thinking

0:09:43.205,0:09:44.913
they were free from infection,

0:09:44.913,0:09:47.531
although they actually carried[br]very dangerous bacteria.

0:09:49.407,0:09:51.786
Inside these wrongly diagnosed patients,

0:09:51.786,0:09:54.526
bacteria were coordinating[br]a synchronized attack.

0:09:55.742,0:09:57.260
They were whispering to each other.

0:09:58.134,0:09:59.757
What I call whispering bacteria

0:09:59.757,0:10:02.454
are bacteria that traditional methods[br]cannot diagnose.

0:10:03.661,0:10:07.074
So far it's only the translation tool[br]that can catch those whispers.

0:10:08.637,0:10:12.017
I believe that the time frame[br]in which bacteria are still whispering

0:10:12.017,0:10:14.751
is the window of opportunity[br]for targeted treatment.

0:10:15.880,0:10:19.467
If the girl had been treated[br]in this window of opportunity,

0:10:19.467,0:10:21.438
it might have been possible[br]to kill the bacteria

0:10:21.438,0:10:22.908
in their initial stage,

0:10:22.908,0:10:25.219
before the infection got out of hand.

0:10:27.344,0:10:31.218
What I experienced with this young girl[br]made me decide to do everything I can

0:10:31.218,0:10:33.303
to push this technology into the hospital.

0:10:34.416,0:10:35.394
Together with doctors,

0:10:35.394,0:10:38.574
I'm already working[br]on implementing this tool in clinics

0:10:38.574,0:10:40.246
to diagnose early infections.

0:10:41.619,0:10:44.406
Although it's still not known[br]how doctors should treat patients

0:10:44.406,0:10:46.418
during the whispering phase,

0:10:46.418,0:10:50.047
this tool can help doctors[br]keep a closer eye on patients in risk.

0:10:50.776,0:10:54.034
It could help them confirm[br]if a treatment had worked or not

0:10:54.034,0:10:56.790
and it could help answer simple questions:

0:10:56.790,0:10:58.587
is the patient infected

0:10:58.587,0:11:00.237
and what are the bacteria up to?

0:11:01.188,0:11:02.959
Bacteria talk,

0:11:02.959,0:11:05.029
they make secret plans

0:11:05.029,0:11:07.752
and they send confidential[br]information to each other.

0:11:08.496,0:11:11.166
But not only can we catch them whispering,

0:11:11.166,0:11:13.683
we can all learn their secret language

0:11:13.683,0:11:16.342
and become ourselves,[br]bacterial whisperers.

0:11:17.177,0:11:19.913
And as bacteria would say,

0:11:19.913,0:11:20.163
"3-oxo-C12-analine."

0:11:20.163,0:11:24.184
(Laughter)

0:11:24.574,0:11:25.733
(Applause)

0:11:26.114,0:11:27.248
Thank you.