WEBVTT - Saving the World with Poop

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<v Speaker 1>Pushkin. If you took a COVID test recently, you probably

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<v Speaker 1>took it at home, and if you tested positive, you

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<v Speaker 1>probably didn't report the results to your local health department,

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<v Speaker 1>which you know fair enough, no judgment. But in the

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<v Speaker 1>big picture this does create a problem. How do we

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<v Speaker 1>keep track of COVID levels as public testing declines. How

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<v Speaker 1>do we look out for new variants? How do we

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<v Speaker 1>monitor new outbreaks? The answer, in a word, is poop.

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<v Speaker 1>I'm Jacob Goldstein and this is what's your problem. My

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<v Speaker 1>guest today is Mariana Matus, co founder and CEO of

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<v Speaker 1>biobot Analytics. The company analyzes sewage samples from hundreds of

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<v Speaker 1>water treatment plants across the US and Canada. They provide

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<v Speaker 1>an aggregate picture of the cod status of tens of

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<v Speaker 1>millions of people. Marianna's problem is this, how do you

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<v Speaker 1>move beyond COVID to turn raw sewage into data that

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<v Speaker 1>can help improve global public health. As it's become clear

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<v Speaker 1>that sampling sewage is one of the best ways to

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<v Speaker 1>track the COVID pandemic, the field of wastewater epidemiology has

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<v Speaker 1>become a big deal. But when Marianna discovered the field,

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<v Speaker 1>it wasn't particularly popular or flashy. It was years before

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<v Speaker 1>the pandemic when she was working on her PhD and

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<v Speaker 1>computational biology at MIT. But Marianna told me studying wastewater

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<v Speaker 1>seemed like a great fit for what she wanted to do.

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<v Speaker 1>When I came across this space of voicewatermology through through

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<v Speaker 1>my laboratory, it was perfect, I think for me as

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<v Speaker 1>a combination of cutting edge science but also like a

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<v Speaker 1>very big societal impact. When you imagined having a big

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<v Speaker 1>societal impact through your study of wastewater epidemiology, like what

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<v Speaker 1>did you imagine? What was like your big, big dream.

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<v Speaker 1>I grew up in Mexico City and in a you know,

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<v Speaker 1>in a very sort of like slummy part of Mexico City,

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<v Speaker 1>not on the nice part of Mexico City. So I

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<v Speaker 1>grew up basically with zero government resources, you know, in

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<v Speaker 1>areas that are ignored and that don't receive any sort

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<v Speaker 1>of like government resources. And I grew up thinking that

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<v Speaker 1>life was like that. And then my career took me

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<v Speaker 1>to study in great universities and places, you know, in

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<v Speaker 1>the Netherlands, in the UK, in Boston, and then I

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<v Speaker 1>just began to realize that it actually it's not like that.

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<v Speaker 1>Not every places like that, And to me, you know,

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<v Speaker 1>I think like the more that dug into it through

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<v Speaker 1>my peach D studies, like I realized that a big

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<v Speaker 1>part of the solution starts with collecting the data to

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<v Speaker 1>demonstrate those health disparities that you know, I know that

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<v Speaker 1>are out there and everybody sort of like knows that

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<v Speaker 1>are out there. But it's just hard to to really

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<v Speaker 1>take action when you just don't have it in your

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<v Speaker 1>face and not don't have a way to measure progress.

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<v Speaker 1>So what I would imagine is, how can we use

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<v Speaker 1>the data from the waste water to give everybody a

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<v Speaker 1>voice and to create maps of what's happening near real

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<v Speaker 1>time everywhere and to and to design action around that data.

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<v Speaker 1>How did you go from that initial kind of big

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<v Speaker 1>dream to starting a company? So when I came to MT,

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<v Speaker 1>it sort of like I had all these dreams in

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<v Speaker 1>my head about like, oh, imagine using science to you know,

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<v Speaker 1>just impact society. Wouldn't that be amazing? But I didn't

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<v Speaker 1>know what that meant in practice. And when I came

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<v Speaker 1>to MT, I discovered entrepreneurship. Entrepreneurship, h yes, I mean

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<v Speaker 1>just like the concept that anybody can be a founder

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<v Speaker 1>and start their own company, right, Like, just that concept,

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<v Speaker 1>I would believe it or not. I mean, I just

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<v Speaker 1>didn't know about that before I came to a m

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<v Speaker 1>I T. I always thought only people that already have

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<v Speaker 1>family money, or like you know that studied business for underground,

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<v Speaker 1>they are the ones who can start businesses. And just

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<v Speaker 1>that concept completely blew my mind. And I think it

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<v Speaker 1>was for me like the missing piece. I realized, Oh, wow, well,

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<v Speaker 1>then that's how I do it. That's how I create

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<v Speaker 1>a company based on science to go and solve a

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<v Speaker 1>big problem. One thing I heard you talking about and

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<v Speaker 1>another interview was when you were starting the company, people

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<v Speaker 1>seem to misunderstand and think that you should be starting

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<v Speaker 1>a nonprofit instead of a company. Is that right and

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<v Speaker 1>if so, can you tell me about that? Absolutely so.

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<v Speaker 1>The idea of selling to government, the idea of of

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<v Speaker 1>operating in the space of public health, and the idea

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<v Speaker 1>of having two women co founders out of MIT basically

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<v Speaker 1>two very technical women co founders. Kind of it was

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<v Speaker 1>a kind of a perfect trifecta for investors to think

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<v Speaker 1>about US as a nonprofit. You mentioned gender, You mentioned

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<v Speaker 1>the fact that that you and your co founder are

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<v Speaker 1>both women, and that that played into it. How did that?

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<v Speaker 1>How did that work? What was going on there? Yeah,

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<v Speaker 1>it seems that we you know, I had to be

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<v Speaker 1>very proactive about kind of explaining I'm doing this not

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<v Speaker 1>just to have public impact, not just to have a

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<v Speaker 1>social impact. I'm doing this because I'm also looking to

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<v Speaker 1>have a financial outcome. And I just found it funny

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<v Speaker 1>that I had to be saying like, hey, I want

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<v Speaker 1>to make money, like you know, I personally also want

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<v Speaker 1>to make any Why wouldn't I? Right, So, okay, so

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<v Speaker 1>so you found the company. So so let's jump to

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<v Speaker 1>say twenty nineteen, like beginning of twenty twenty. Now you

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<v Speaker 1>have this little company that's been up and running for

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<v Speaker 1>a couple of years. It's analyzing wastewater, but you have

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<v Speaker 1>no revenue. You have a staff of just like five people.

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<v Speaker 1>You built this little machine that goes into the sewer

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<v Speaker 1>to collect sewage, and you're working on one pilot project.

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<v Speaker 1>And that project isn't even a fur infectious disease. Right,

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<v Speaker 1>You're looking at opioids, You're you're sort of studying the

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<v Speaker 1>opioid epidemic. And yet this is this moment when history

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<v Speaker 1>is like barreling towards you. Right, this pandemic is about

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<v Speaker 1>to you know, change everything, right, And so I'm curious

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<v Speaker 1>when did you start to hear about, you know, about

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<v Speaker 1>this new coronavirus outbreak. Yeah, I think for us, UM

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<v Speaker 1>it really began to catch our attention in like early

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<v Speaker 1>twenty twenty, like January twenty twenty. Do you remember any

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<v Speaker 1>particular moments, any particular conversations or reading anything or talking

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<v Speaker 1>to anybody. I mean definitely, so some of those like

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<v Speaker 1>first conversations that happened where we had been forming a

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<v Speaker 1>relationship with folks within HHS, like the federal HHS Department

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<v Speaker 1>of Health and Human Services, the big federal Apartment of

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<v Speaker 1>Health and Human Services in DC. So I remember we

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<v Speaker 1>were in DC in February of twenty twenty, basically like

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<v Speaker 1>already pitching that we should begin to test the waste

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<v Speaker 1>water of all of the major airports across the country

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<v Speaker 1>to start getting a sense of the level of import

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<v Speaker 1>And we were told, well, this outbreak is going to

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<v Speaker 1>be contained within two weeks, so it would be a

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<v Speaker 1>waste of your resources to try to develop a solution

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<v Speaker 1>around this now because you're not going to be ready

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<v Speaker 1>in less than two weeks. They were like, by the

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<v Speaker 1>time you figure out how to do this test in

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<v Speaker 1>a few weeks, this all things going to have blown over.

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<v Speaker 1>It's not going exactly so. But despite that sort of

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<v Speaker 1>like very strong feedback, we came back from DC and

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<v Speaker 1>we this was February of twenty twenty, and we make

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<v Speaker 1>the decision that we were going to build a COVID

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<v Speaker 1>solution based on the waste water and tell me about

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<v Speaker 1>building that. We had to basically change everything. We had

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<v Speaker 1>to throw away sort of like all of the progress

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<v Speaker 1>that we had made up until that point to start

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<v Speaker 1>over given the constraints at hand. Number one, this was

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<v Speaker 1>there was a sense of emergency and that time was

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<v Speaker 1>of the essence. So the idea of having to manufacture

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<v Speaker 1>hardware in order to collect data at scale just seemed

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<v Speaker 1>pretty hard or impossible in that moment. Also, do you

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<v Speaker 1>know the supply chain issues all of You're not going

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<v Speaker 1>to be able to make this special device. You're not

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<v Speaker 1>gonna be only like a thousand of them in a

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<v Speaker 1>couple of months or whatever, exactly exactly, So we decided, okay,

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<v Speaker 1>where how else can we get wastewater easily? And we realize, oh,

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<v Speaker 1>actually actually, there are sixteen thousand wastewater treatment plants in

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<v Speaker 1>the US, covering eighty percent of the US population, and

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<v Speaker 1>turns out most of them already have a similar type

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<v Speaker 1>of equipment. So it's not portable, it's not as sophisticated,

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<v Speaker 1>but it does the job at collecting that continues twenty

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<v Speaker 1>four hour sample. The minimum viable product already exists at

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<v Speaker 1>sixteen thousand wastewater plants all across the US, so we

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<v Speaker 1>were like, okay, check. Second was the lab component. We

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<v Speaker 1>had been building a chemistry platform, and obviously and COVID

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<v Speaker 1>is caused by an RNA virus, so the detection or

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<v Speaker 1>detest in the lab would need to be more of

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<v Speaker 1>a molecular biology type of approach. We didn't have any

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<v Speaker 1>of those capabilities, but we had raised seat round, like

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<v Speaker 1>a you know, a four point two million dollars seat round,

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<v Speaker 1>and so we had some money to just start getting

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<v Speaker 1>some basic new equipment in the lab. And also we

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<v Speaker 1>have a good relationship with my PhD advisor at MT

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<v Speaker 1>so together with him, we actually used a lot of

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<v Speaker 1>his lab early on in the pandemic and collaborated with

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<v Speaker 1>his students. So you know, that's how we got started,

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<v Speaker 1>and it took us like four weeks to just go

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<v Speaker 1>from the decision point until having actually not just a

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<v Speaker 1>proof that the test works with waste water, but also

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<v Speaker 1>we wrote a scientific preprint, like a you know, a paper.

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<v Speaker 1>So we were the first ones in the country to

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<v Speaker 1>demonstrate that you can detect and quantify the virus causing

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<v Speaker 1>COVID nineteen in wastewater samples and that it works well.

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<v Speaker 1>And I think, you know, it was just from there

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<v Speaker 1>that everything exploded. I mean, is there fear I assume

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<v Speaker 1>that that there's no risk of transmitting the disease. I mean,

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<v Speaker 1>it's obviously not transmitted through wastewater, but we're people afraid

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<v Speaker 1>that it might be. We didn't know anything about COVID

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<v Speaker 1>at the time. I mean, this is when I was

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<v Speaker 1>still wiping off all my groceries with bleach, right, because

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<v Speaker 1>literally we didn't know. Oh yes, there was a lot

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<v Speaker 1>of concern. And actually that was you know, thank you

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<v Speaker 1>for I feel like my brain probably like tried to

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<v Speaker 1>forget that, but that was a very big deal. Like

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<v Speaker 1>when we just started from for many reasons, nobody knew

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<v Speaker 1>if the virus present in wastewater would be infectious. My

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<v Speaker 1>very small team that we had. They were really afraid

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<v Speaker 1>to have to handle the wastewater sample without knowing if

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<v Speaker 1>they can get infected. So we came up with a

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<v Speaker 1>solution for our lab where every every bottle of wastewater,

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<v Speaker 1>example that arrives to buy a boat is first pasteurized

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<v Speaker 1>in order to basically make it non infectious or like sterile. Yes.

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<v Speaker 1>And the third thing was that I had just I

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<v Speaker 1>was just pregnant at the time. I became pregnant in

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<v Speaker 1>March of twenty twenty, my first pregnancy, my first you

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<v Speaker 1>know baby, and I my husband and I also would

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<v Speaker 1>go into the lab to model to the rest of

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<v Speaker 1>our team that you know this is safe, Like you know,

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<v Speaker 1>I'm pregnant, but I trust that the pasteurization is good

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<v Speaker 1>enough to make this work safe and let me, you know,

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<v Speaker 1>let me demonstrate that with my actions. At the time,

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<v Speaker 1>it was a very big deal. So you have this idea,

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<v Speaker 1>you have the proof that it works, You've convinced the

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<v Speaker 1>staff at your company, at Bio Boat, that it's okay

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<v Speaker 1>to do this. How do you get customers what happens next?

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<v Speaker 1>So we designed a probono campaign where by About would

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<v Speaker 1>absorb the cost of all of the testing for two months,

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<v Speaker 1>and what happened it was beyond our wildest dreams, Like

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<v Speaker 1>it exploded. It got picked up by the media. People

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<v Speaker 1>loft this idea that you can fight COVID with poop. Poop. Yeah,

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<v Speaker 1>you can fight COVID with poop. You know. That was

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<v Speaker 1>that was basically the message being put out there. It

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<v Speaker 1>went viral and we got in just the first ten

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<v Speaker 1>days or so of announcing the campaign, we got four

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<v Speaker 1>hundred plants nationwide wanting to enroll in the program. We

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<v Speaker 1>had aspired to enroll up to one hundred, So it

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<v Speaker 1>was it was crazy. The response was just crazy. But

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<v Speaker 1>you know, that transition was tough because I guess also

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<v Speaker 1>something that I haven't talked about is that in that

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<v Speaker 1>early twenty twenty stage, when we were the first ones,

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<v Speaker 1>the more established academics were skeptical and basically like recommending

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<v Speaker 1>caution with these and slowing things down for us. And

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<v Speaker 1>so it was a very Were they skeptical of wastewater

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<v Speaker 1>sampling in general or were they skeptical of your company?

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<v Speaker 1>Or was it both both both? So, up until twenty twenty,

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<v Speaker 1>the wastewater epidemiology was a very niche and obscure area

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<v Speaker 1>of science, So the entire field was a little bit like,

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<v Speaker 1>you know, not very well known obviously. And if that

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<v Speaker 1>was within science, just imagine within government, you know, like

0:15:04.956 --> 0:15:07.956
<v Speaker 1>it was just nobody was saying, oh, I need wastewater data,

0:15:08.036 --> 0:15:10.796
<v Speaker 1>like who can provide it for me? Why do you

0:15:10.836 --> 0:15:12.916
<v Speaker 1>think that is? I mean, do you think it's partly

0:15:12.956 --> 0:15:16.396
<v Speaker 1>like because it's poop, because it's sewage? Like why do

0:15:16.436 --> 0:15:19.836
<v Speaker 1>you think people were ignoring this source of data? Really?

0:15:20.036 --> 0:15:22.956
<v Speaker 1>I mean, is it because it's it's poop and people

0:15:22.956 --> 0:15:24.756
<v Speaker 1>are kind of embarrassed by it don't like to talk

0:15:24.796 --> 0:15:28.156
<v Speaker 1>about it? Like has that been a barrier too? I

0:15:28.196 --> 0:15:31.036
<v Speaker 1>mean I think that No. I think that actually people

0:15:31.196 --> 0:15:35.436
<v Speaker 1>love all of the poop jokes and poop humor. I

0:15:35.476 --> 0:15:38.276
<v Speaker 1>feel like that actually tends to be like a plus.

0:15:38.276 --> 0:15:40.836
<v Speaker 1>How do you feel about them at this point? Like

0:15:40.916 --> 0:15:43.156
<v Speaker 1>are you tired of them? Do they work in your favor?

0:15:43.596 --> 0:15:45.556
<v Speaker 1>Like our poop jokes like enough, I never want to

0:15:45.556 --> 0:15:47.796
<v Speaker 1>hear a poop joke again? No, no, no, I love it.

0:15:47.836 --> 0:15:50.636
<v Speaker 1>I love it. And our company culture, you know, celebrates

0:15:50.676 --> 0:15:55.276
<v Speaker 1>the poop humor quite a lot with okay, our you know,

0:15:55.396 --> 0:15:59.236
<v Speaker 1>branding and suave and you know, we we love it.

0:15:59.396 --> 0:16:01.716
<v Speaker 1>We love the users of the poop emoji or they're

0:16:01.756 --> 0:16:05.276
<v Speaker 1>like particularly very big. Oh yeah, the poop emoji is

0:16:05.276 --> 0:16:09.556
<v Speaker 1>the most use emoji in the bay about slack, it

0:16:09.676 --> 0:16:12.116
<v Speaker 1>makes sense. I mean, it's why they're used even at

0:16:12.156 --> 0:16:15.836
<v Speaker 1>places that have nothing to do with it. Right, So yes, no,

0:16:15.956 --> 0:16:17.876
<v Speaker 1>I think that, you know, I think that the barrier

0:16:18.796 --> 0:16:22.516
<v Speaker 1>to research and to you know, first to research and

0:16:22.556 --> 0:16:26.556
<v Speaker 1>then to the adoption of the data is just that

0:16:26.636 --> 0:16:30.156
<v Speaker 1>the bar to get the data in is very high

0:16:30.236 --> 0:16:33.516
<v Speaker 1>in the sense that there's a lot of operational and

0:16:33.676 --> 0:16:38.836
<v Speaker 1>logistical work in order to get data. So when you

0:16:38.876 --> 0:16:43.116
<v Speaker 1>are working within a university or within a government agency,

0:16:43.796 --> 0:16:47.396
<v Speaker 1>who really has the time to be you know, building

0:16:47.676 --> 0:16:51.556
<v Speaker 1>kits to transport wastewater, learning how to the regulations to

0:16:51.636 --> 0:16:56.516
<v Speaker 1>transport it going out there, or finding the relationships to

0:16:56.556 --> 0:16:59.316
<v Speaker 1>send you the waste water in doing all of that

0:16:59.476 --> 0:17:03.316
<v Speaker 1>groundwork in the lab, right groundwork. Right, It's very logistical.

0:17:03.556 --> 0:17:06.716
<v Speaker 1>It's not like high minded, it's not fancy math. It's

0:17:06.796 --> 0:17:11.236
<v Speaker 1>just getting appealed to the lab. Yeah, exactly, exactly. So

0:17:11.316 --> 0:17:13.356
<v Speaker 1>when I was a PITCHD student that in my t

0:17:14.796 --> 0:17:19.756
<v Speaker 1>I was basically like the only one like willing to

0:17:19.796 --> 0:17:23.876
<v Speaker 1>do the groundwork because I started working on always what

0:17:24.076 --> 0:17:27.476
<v Speaker 1>epidemiology during my PhD. That's what my pitchd PSIS was about.

0:17:28.476 --> 0:17:32.876
<v Speaker 1>But you know, I really struggled with just like recruiting

0:17:32.916 --> 0:17:37.276
<v Speaker 1>a team around me, because folks were wanting to help

0:17:37.316 --> 0:17:41.516
<v Speaker 1>with the data analysis, the data visualization, but not the

0:17:41.596 --> 0:17:44.996
<v Speaker 1>eighty percent groundwork that goes behind it. So nobody wants

0:17:45.556 --> 0:17:49.556
<v Speaker 1>get the proof exactly. So that's why I thought, always thought,

0:17:49.596 --> 0:17:57.316
<v Speaker 1>you know, if this science is going to really go mainstream,

0:17:57.436 --> 0:18:02.036
<v Speaker 1>it needs an organization dedicated like one to it so

0:18:02.036 --> 0:18:05.356
<v Speaker 1>that we can do that around work and then the scientists. Yes,

0:18:05.516 --> 0:18:08.316
<v Speaker 1>of course, the power is in the data, in the

0:18:08.356 --> 0:18:10.396
<v Speaker 1>insights that you can get out of it, but you

0:18:10.436 --> 0:18:12.876
<v Speaker 1>know there's an eighty percent around work behind it. Well,

0:18:12.916 --> 0:18:14.796
<v Speaker 1>I mean maybe that's part of the case for a

0:18:14.796 --> 0:18:18.756
<v Speaker 1>for profit company, right Like profit is a very good

0:18:18.796 --> 0:18:23.996
<v Speaker 1>incentive to do groundwork, absolutely absolutely Well. How big is

0:18:24.036 --> 0:18:28.876
<v Speaker 1>the company now? Oh yeah, buy about now has one

0:18:28.956 --> 0:18:31.756
<v Speaker 1>hundred employees. How much revenue do you have? More or less?

0:18:32.396 --> 0:18:37.956
<v Speaker 1>This year? We are on track to make over twenty

0:18:37.956 --> 0:18:41.956
<v Speaker 1>million dollars in revenue, okay, which is great also to

0:18:42.036 --> 0:18:44.516
<v Speaker 1>be here given that two years ago we were basically

0:18:44.596 --> 0:18:47.796
<v Speaker 1>pre revenue. So yes, two years ago was zero. From

0:18:47.876 --> 0:18:53.756
<v Speaker 1>zero to twenty million, there's a lot. Are you profitable, No,

0:18:53.996 --> 0:18:57.996
<v Speaker 1>not yet. I mean we are investing heavily in our

0:18:58.156 --> 0:19:00.476
<v Speaker 1>R and D so that we can look at other

0:19:00.516 --> 0:19:06.676
<v Speaker 1>types of data. So story so far. Three years ago,

0:19:07.156 --> 0:19:10.156
<v Speaker 1>Mariana barely had a company, and waste water surveillance was

0:19:10.196 --> 0:19:14.076
<v Speaker 1>this little niche thing in the US. And then by

0:19:14.116 --> 0:19:16.956
<v Speaker 1>the beginning of this year, when the omicron wave hit

0:19:16.996 --> 0:19:21.276
<v Speaker 1>the country, BAA was this big, real company. Wastewater surveillance

0:19:21.356 --> 0:19:24.156
<v Speaker 1>was one of the most important tools public health officials

0:19:24.236 --> 0:19:27.116
<v Speaker 1>used to figure out where cases were spiking, so that

0:19:27.156 --> 0:19:29.956
<v Speaker 1>they could direct resources into the right neighborhoods and you know,

0:19:30.276 --> 0:19:33.956
<v Speaker 1>plan for surges and hospital admissions. And the story is

0:19:33.996 --> 0:19:37.396
<v Speaker 1>not over in a minute. What we still don't know

0:19:37.596 --> 0:19:48.356
<v Speaker 1>about poop and disease. It's a lot. Now back to

0:19:48.396 --> 0:19:52.396
<v Speaker 1>the show. And one thing I'm curious about, Well, what

0:19:52.596 --> 0:19:55.036
<v Speaker 1>is your company's moat? Right? Like, how is what you

0:19:55.156 --> 0:19:57.956
<v Speaker 1>do not just some commodity thing that anybody with the

0:19:58.036 --> 0:20:04.236
<v Speaker 1>lab can do. Yeah, I think that that's a great question. Everybody, anybody,

0:20:04.276 --> 0:20:09.036
<v Speaker 1>anybody out there could go and let's say open amunthole

0:20:10.516 --> 0:20:14.796
<v Speaker 1>and get a bucket in pull up wastewater, take it

0:20:14.796 --> 0:20:18.196
<v Speaker 1>to a lab and they would get a number, a

0:20:18.356 --> 0:20:20.996
<v Speaker 1>number meaning like an amount of COVID that's in there

0:20:21.076 --> 0:20:23.716
<v Speaker 1>or something. You may get an amount of COVID, you make,

0:20:23.916 --> 0:20:27.236
<v Speaker 1>an amount of opioids, an amount of influenza, right like

0:20:27.596 --> 0:20:30.836
<v Speaker 1>any any number. You may get a number, But what

0:20:30.876 --> 0:20:36.516
<v Speaker 1>does that number mean? Like that number in isolation really

0:20:36.556 --> 0:20:42.196
<v Speaker 1>doesn't mean anything. So our secret sauce is how do

0:20:42.276 --> 0:20:48.716
<v Speaker 1>we create how do we make wastewater a data platform?

0:20:48.756 --> 0:20:53.996
<v Speaker 1>And that really means it's a systems level type of

0:20:54.036 --> 0:20:58.396
<v Speaker 1>design and product. We actually need to use the same

0:20:58.436 --> 0:21:03.996
<v Speaker 1>method in every sample. If we want to calculate, if

0:21:03.996 --> 0:21:07.436
<v Speaker 1>we want to compare across time, if we want to

0:21:07.476 --> 0:21:13.716
<v Speaker 1>compare across locations, we want to build a state statewide trend,

0:21:13.756 --> 0:21:16.156
<v Speaker 1>if we want to compare regions, if we want to

0:21:16.236 --> 0:21:21.036
<v Speaker 1>calculate a national average, it's imperative to actually use the

0:21:21.156 --> 0:21:26.716
<v Speaker 1>same methods at every stage for every sample. Otherwise you

0:21:26.756 --> 0:21:30.436
<v Speaker 1>don't have that comparability wastewater. Again, the power of the

0:21:30.476 --> 0:21:34.356
<v Speaker 1>wastewater isn't about what you about the number that you

0:21:34.396 --> 0:21:38.636
<v Speaker 1>get from a single wastewater sample. The power of wastewater

0:21:38.756 --> 0:21:43.076
<v Speaker 1>is about having that bird's eye view, that systems level

0:21:43.156 --> 0:21:46.196
<v Speaker 1>view of what's happening in an entire country, in an

0:21:46.316 --> 0:21:50.956
<v Speaker 1>entire state. You can drill in more geographically narrow, but

0:21:51.036 --> 0:21:54.316
<v Speaker 1>you can also zoom out. That's the platform, that's the

0:21:54.356 --> 0:21:57.596
<v Speaker 1>power that we're building. So in order to accomplish that,

0:21:58.316 --> 0:22:01.876
<v Speaker 1>you know, that's where we come in, all of the relationships,

0:22:01.956 --> 0:22:07.596
<v Speaker 1>all of the scale that eight you know, groundwork that

0:22:07.636 --> 0:22:12.316
<v Speaker 1>I talked about. To accomplish that level of visibility, that's

0:22:12.316 --> 0:22:14.836
<v Speaker 1>a tough problem. So it's like a network effect. There's

0:22:14.836 --> 0:22:17.556
<v Speaker 1>a network effect thing people would want to use your

0:22:17.596 --> 0:22:20.756
<v Speaker 1>company because you have the most data and therefore you

0:22:20.916 --> 0:22:24.796
<v Speaker 1>have the best understanding of what one particular sample means

0:22:24.836 --> 0:22:26.516
<v Speaker 1>because you can put it in the context of all

0:22:26.516 --> 0:22:30.876
<v Speaker 1>the other data that you have gotten, have processed exactly exactly.

0:22:30.996 --> 0:22:35.516
<v Speaker 1>So we have the largest network of sites in the world.

0:22:36.796 --> 0:22:40.716
<v Speaker 1>It's mostly US, some Canada. How big is it now?

0:22:41.596 --> 0:22:45.516
<v Speaker 1>We have over five hundred sites. What's something you haven't

0:22:45.516 --> 0:22:48.236
<v Speaker 1>figured out yet? What's a problem your story of still

0:22:48.276 --> 0:22:50.156
<v Speaker 1>trying to figure out how to solve Maybe you've tried

0:22:50.196 --> 0:22:52.556
<v Speaker 1>it hasn't quite worked yet. What's something you're still working

0:22:52.596 --> 0:22:56.676
<v Speaker 1>on in the lab. There is a lot of interest

0:22:56.756 --> 0:23:01.196
<v Speaker 1>in looking at different targets, so not just COVID, but

0:23:01.276 --> 0:23:06.876
<v Speaker 1>also influenza, neural virus. You can you know, at least

0:23:06.956 --> 0:23:10.556
<v Speaker 1>keeps going right like monkey box where right now in

0:23:10.636 --> 0:23:13.396
<v Speaker 1>advanced R and D for monkey pops, we're gonna have

0:23:13.396 --> 0:23:16.036
<v Speaker 1>a start testing with water for monkey pops too. But

0:23:16.956 --> 0:23:20.356
<v Speaker 1>it remains a challenge to know kind of how to

0:23:21.236 --> 0:23:23.516
<v Speaker 1>which types of data we can collect and how to

0:23:23.556 --> 0:23:29.996
<v Speaker 1>interpret them because we don't really know which pathogens are

0:23:30.676 --> 0:23:36.276
<v Speaker 1>shed in poop. Huh, that's surprising. I would think people

0:23:36.316 --> 0:23:38.076
<v Speaker 1>would just sort of have figured that out by now.

0:23:38.196 --> 0:23:39.556
<v Speaker 1>Is that Does that go back to the fact that

0:23:39.636 --> 0:23:44.716
<v Speaker 1>it's kind of an understudied area exactly exactly. There's a

0:23:44.756 --> 0:23:48.316
<v Speaker 1>lot of information about how you can diagnose a disease

0:23:48.556 --> 0:23:52.996
<v Speaker 1>in a person via like a serom sample, via maybe

0:23:53.036 --> 0:23:57.116
<v Speaker 1>a blood test, yeah, a blood test, or maybe a

0:23:57.716 --> 0:24:00.556
<v Speaker 1>you know, a swab or like a you know, a

0:24:00.596 --> 0:24:05.036
<v Speaker 1>nasal swab or a saliva suab sometimes in in p

0:24:05.796 --> 0:24:09.596
<v Speaker 1>But but pooh, I mean, why would they have to

0:24:09.676 --> 0:24:12.796
<v Speaker 1>I mean, the clinic the worst way to get us

0:24:12.836 --> 0:24:15.436
<v Speaker 1>to a sample for the doctor, right exactly, So so

0:24:15.476 --> 0:24:18.316
<v Speaker 1>for the doctor like you know, again, poop is an

0:24:18.396 --> 0:24:23.196
<v Speaker 1>understudied like sort of like diagnostic matrix. So you know,

0:24:23.276 --> 0:24:26.476
<v Speaker 1>one wild dream of mine is for us to develop

0:24:27.076 --> 0:24:31.156
<v Speaker 1>the clinical collaborations to to be able to know in

0:24:31.276 --> 0:24:36.236
<v Speaker 1>people like how different diseases are um shedding in poop,

0:24:36.356 --> 0:24:39.676
<v Speaker 1>because that that will help us to understand which targets

0:24:39.676 --> 0:24:44.036
<v Speaker 1>will be easier to track via the waste water and

0:24:44.156 --> 0:24:47.916
<v Speaker 1>how to interpret it. You mentioned sort of starting out

0:24:47.956 --> 0:24:52.196
<v Speaker 1>in the field with this the dream of being able

0:24:52.276 --> 0:24:56.956
<v Speaker 1>to to look at a city like Mexico City and

0:24:57.036 --> 0:24:58.916
<v Speaker 1>get you know, sort of a map of what's going

0:24:58.956 --> 0:25:02.076
<v Speaker 1>on with the health of people in the city. How

0:25:02.076 --> 0:25:03.796
<v Speaker 1>do you get to there from where you are now?

0:25:04.276 --> 0:25:08.356
<v Speaker 1>Because we have done some pilots in Mexico Ecuador Ry

0:25:09.476 --> 0:25:12.756
<v Speaker 1>in partnership already with a World Bank, so you know,

0:25:12.916 --> 0:25:14.796
<v Speaker 1>it's kind of how do we build on top of

0:25:14.836 --> 0:25:18.636
<v Speaker 1>those early pilots that were very successful. So you sample,

0:25:18.916 --> 0:25:22.916
<v Speaker 1>you know, big sewage systems in the US that allows

0:25:22.916 --> 0:25:26.236
<v Speaker 1>you to capture something like eighty percent of the population,

0:25:26.716 --> 0:25:29.516
<v Speaker 1>but it seems like in large parts of the developing

0:25:29.556 --> 0:25:32.636
<v Speaker 1>world you have places you know, that are less developed,

0:25:32.676 --> 0:25:35.116
<v Speaker 1>where where there are not sewage systems. What do you

0:25:35.156 --> 0:25:39.356
<v Speaker 1>do there. There's many areas that don't have any sort

0:25:39.356 --> 0:25:44.356
<v Speaker 1>of sanitation, right, so I think that for those areas

0:25:45.076 --> 0:25:48.956
<v Speaker 1>there's just immediate action, which is we can use basically

0:25:49.036 --> 0:25:54.156
<v Speaker 1>the same type of sampling approaches to open sewers, like

0:25:54.196 --> 0:25:58.036
<v Speaker 1>basically which are rivers where the waste makes it into

0:25:58.076 --> 0:26:01.276
<v Speaker 1>the river, and that that is possible, that that works,

0:26:01.316 --> 0:26:04.156
<v Speaker 1>that's how some people already do waste. What are epithimolity

0:26:04.156 --> 0:26:07.756
<v Speaker 1>You work in areas without the infrastructure. But I think

0:26:07.756 --> 0:26:13.516
<v Speaker 1>you're right at over time this technology could be a

0:26:13.596 --> 0:26:17.156
<v Speaker 1>new reason to invest in waste, what in building the

0:26:17.196 --> 0:26:21.436
<v Speaker 1>waste water infrastructure where it doesn't exist, because now we're

0:26:21.476 --> 0:26:26.076
<v Speaker 1>giving it a second second use, a second mission. This

0:26:26.156 --> 0:26:29.796
<v Speaker 1>is no longer just about the collection and safe disposal

0:26:29.876 --> 0:26:33.836
<v Speaker 1>of waste from people. This is about intelligence that you

0:26:33.876 --> 0:26:38.316
<v Speaker 1>cannot really get any in any other way. So hopefully

0:26:38.316 --> 0:26:42.556
<v Speaker 1>it can be a virtual cycle where it can foster

0:26:43.396 --> 0:26:47.836
<v Speaker 1>the investment in the building of this infrastructure, which would

0:26:47.836 --> 0:26:52.516
<v Speaker 1>only of course also improve public health because then would

0:26:52.516 --> 0:26:59.396
<v Speaker 1>reduce the spread of disease. In a minute, the lightning

0:26:59.436 --> 0:27:04.076
<v Speaker 1>round with questions about immigration, entrepreneurship, and what not to

0:27:04.196 --> 0:27:15.196
<v Speaker 1>flush down the toilet now back to the show. Let's

0:27:15.236 --> 0:27:18.036
<v Speaker 1>just do let's do like a lightning round. I'm just

0:27:18.076 --> 0:27:19.996
<v Speaker 1>gonna ask you a bunch of fast questions and you

0:27:20.036 --> 0:27:24.116
<v Speaker 1>can answer them fast. Why do you think immigrants are

0:27:24.156 --> 0:27:30.156
<v Speaker 1>more likely than native born citizens to become entrepreneurs? That's

0:27:30.156 --> 0:27:32.316
<v Speaker 1>a good question, so I I mean, I can only

0:27:32.356 --> 0:27:35.956
<v Speaker 1>speak about my kind of experience, but at least in

0:27:35.956 --> 0:27:39.636
<v Speaker 1>my case, I felt that a little bit like I

0:27:39.676 --> 0:27:44.316
<v Speaker 1>had nothing to lose, So why not try? Like, why

0:27:44.316 --> 0:27:48.436
<v Speaker 1>wouldn't I try? What's one thing people should never ever

0:27:48.596 --> 0:27:59.836
<v Speaker 1>flush down the toilet? Well, that's a great question. Never

0:27:59.996 --> 0:28:06.596
<v Speaker 1>flushed down the so called flushable wipes okay, not actually

0:28:06.836 --> 0:28:13.556
<v Speaker 1>flushable at do not actually flushable. They don't dissolve in water,

0:28:14.356 --> 0:28:18.876
<v Speaker 1>So what happens is that they create this massive the

0:28:19.116 --> 0:28:23.276
<v Speaker 1>glock systems everywhere. The Q tips are also pretty bad

0:28:23.396 --> 0:28:26.876
<v Speaker 1>because they have size tends to be the same as

0:28:26.996 --> 0:28:30.836
<v Speaker 1>like some of the sort of like filters that capture

0:28:31.316 --> 0:28:33.716
<v Speaker 1>just like big, big junk out of the waste water,

0:28:33.756 --> 0:28:37.036
<v Speaker 1>so they also block those systems. The Q tips are

0:28:37.076 --> 0:28:41.436
<v Speaker 1>not great to fulush either. Feel like I touch the

0:28:41.556 --> 0:28:46.916
<v Speaker 1>nerve here? Yes? Oh yeah? If everything goes well, what

0:28:47.076 --> 0:28:49.236
<v Speaker 1>problem will you be trying to solve in five years,

0:28:50.756 --> 0:28:55.076
<v Speaker 1>due to climate change, we can expect to see more

0:28:55.996 --> 0:29:00.996
<v Speaker 1>infectious outbreaks than before in a more frequent manner. The

0:29:01.036 --> 0:29:07.596
<v Speaker 1>patterns of you know, just wildlife of insects, factor insects

0:29:07.596 --> 0:29:12.756
<v Speaker 1>are just changing, so so we can expect to see

0:29:12.796 --> 0:29:17.876
<v Speaker 1>more zonoric events of infectious diseases jumping to humans. So,

0:29:18.756 --> 0:29:20.836
<v Speaker 1>in my opinion, I think that the next five to

0:29:20.956 --> 0:29:25.356
<v Speaker 1>ten years should be more about that international collaboration around

0:29:25.356 --> 0:29:29.396
<v Speaker 1>health understanding. You know, what we do here won't be enough.

0:29:29.556 --> 0:29:32.476
<v Speaker 1>We need to be connected. So I would love if

0:29:32.996 --> 0:29:35.916
<v Speaker 1>we have been so successful at collecting, you know, at

0:29:35.916 --> 0:29:40.116
<v Speaker 1>building this data asset and having the trust of different

0:29:40.156 --> 0:29:43.236
<v Speaker 1>governments that we can even begin to be part of

0:29:43.276 --> 0:29:48.516
<v Speaker 1>that collaboration. What's the most interesting thing you ever saw

0:29:48.756 --> 0:29:52.196
<v Speaker 1>pulled out of a sewer? Oh? Wow, Yeah, that's another

0:29:52.276 --> 0:29:59.076
<v Speaker 1>good one. Well, I mean I didn't pull this myself,

0:29:59.436 --> 0:30:03.276
<v Speaker 1>but we were touring one of the plants that we

0:30:03.396 --> 0:30:07.396
<v Speaker 1>work with in in Portland, Maine just a few weeks ago,

0:30:08.076 --> 0:30:12.356
<v Speaker 1>and they were telling us that they recently pulled the

0:30:12.436 --> 0:30:19.116
<v Speaker 1>door of a car. I don't know what happened, but

0:30:19.196 --> 0:30:22.156
<v Speaker 1>there was a door of a car that they had

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<v Speaker 1>to pull out of the stream. Marianna Matus is co

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<v Speaker 1>founder and CEO of biobot Analytics. Today's show was produced

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<v Speaker 1>by Edith Ruslow, engineered by Amanda ka Wong, and edited

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<v Speaker 1>by Robert Smith. I'm Jacob Goldstein, and we'll be back

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<v Speaker 1>next week with another episode of What's Your Problem