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Okay. Could you just start by saying your name and your position here?

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My name is Daniel Hubner, and I'm a principal investigator at the University of Oxford.

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And we're here today with a colleague who is. I'm Stephanie Hatch.

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I'm a postdoctoral research scientist. Okay.

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Stephanie, let's start with you.

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So just going back without telling me your entire life story, going back to how you first got interested in biomedical science.

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Can you just run me through your career up to the position that you have today?

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I've always been interested in science, and I wanted to do something that would help people.

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But the job where I wouldn't have to actually work with people.

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And I did my Ph.D. at UNC-Chapel Hill in the U.S. And then I went to London for a postdoctoral position.

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And then that was in what was what was the subject of my Ph.D. was DNA repair.

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So it was DNA repair in cancer. And then when I went to a move to London, I worked on cell motility.

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So most of my career has been cancer research, but it's cell biology broadly.

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Would you say yes? Yes. And then I joined this lab about six years ago and.

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It's. It's a perfect job for me.

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I really enjoy working with robots and scaling up other people's experiments.

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It's great. Done. Okay. Yeah, I.

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I knew I wanted to be a scientist when I was very young, so I started my professional career in medicine.

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And then my first pharmaceutical job was with Boehringer Ingelheim, and I spent a little over a year with Boehringer.

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And then I went into biotechnology and in biotechnology I was doing assays for HLA typing.

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So for certain types of. Transplants of solid organs, and I started using robots to automate the tests and loved it,

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and then came to the University of Oxford to start a very small screening facility in what was

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then radiation oncology and biology department within the Department of Oncology here at Oxford.

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And then a couple of years after that started the try to write Discovery Institute,

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where the robots got bigger and we got more equipment and we started running target based screens for the entire university,

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and then it just grew to the facility as it is now.

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Well, we'll talk a bit more about that in a moment. But I just wanted to ask, was it a big deal to move out of industry and back into academia?

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Actually, that's quite interesting because when I first started and in research, it was a big deal.

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There were academic research scientists and there were industrial research scientists and there was very little crossover between the two.

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Now there is you'd be hard pressed to find anybody who has really gone back and forth at least once.

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You mean there are people that haven't and spend their time in academia?

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But the relationship between academics and industrial research scientists is very strong now,

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both and the people who cross between the two, but also in the research and the funding.

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We, my group has a tremendous amount of funding comes from industry.

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So yeah, so there's, there's quite a bit of crossover. So I'm just going to stick with that for the moment.

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But you've mentioned a couple of times this place where you work is called the Target Discovery Institute.

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When was that set up and what is it there to do?

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So so that the Target Discovery Institute was set up about 12 years ago and it was

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the culmination of a couple of ideas that coalesced around drug target discovery.

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We were running a small screening facility in the Department of Oncology, and Peter Ratcliffe, who is my boss,

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had an idea which has been tried to bring together equipment and know how to run

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screens for the very first step of drug target discovery or drug development,

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which is drug target discovery. And we.

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We took some equipment from oncology, we took some money from the Nuffield Department of Medicine under Peter's guidance,

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and we've built a small facility in the Wellcome Trust Centre for Human Genetics from the

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building just across from the TV and started running screens for cardiovascular medicine,

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cancer, circadian rhythm.

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And it then became a actual institute, which is quite important in the academic sphere because oftentimes there's these aethereal institutes.

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This was an actual physical presence and we started to publish and started publishing very nice papers.

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So I can take you through what Target Discovery means in very, very simple.

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So Target Discovery is in the TDR.

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Target Discovery means the identification of a novel starting point for drug development.

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It is taking an assay which is predictive of the most predictive assay of a particular pathology and probing

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it to find what protein targets are responsible for either the start of that pathology or then pathologies,

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maintenance or its continuation. And academic research scientists will spend their entire careers in one particular area of research.

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For instance, again, my boss, Peter Ratcliffe, has spent his entire career and won the Nobel Prize for his insights into oxygen consumption.

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So oxygen consumption is very important in cancers.

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This is just at the level of individual cells. Yep. Yeah, exactly.

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It's very important in cancers. So there's nobody in the world who understands and knows the intricacies of oxygen consumption in human cells.

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Well, he is perfectly placed to help develop assays which would probe oxygen consumption and its implications in cancer.

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And that mean if you take that scientist at one scientist and you multiply them across a university is powerful.

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Research is University of Oxford. It was a area that was missing from R from the University of Oxford's research portfolio.

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So it was basically filling a need that was definitely there.

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And it was really well-timed because I could academic perfectly positioned to do that.

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Pharmaceutical companies absolutely rubbish at target ID,

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but extremely good at taking a validated target and generating a commercially viable product out of that.

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So just trying to find something, it's a kind of lock and key thing. It's hard to find something that will attach to complement each other.

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Yeah. Yeah, exactly. So so the target discovery.

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Then there was a big grant that was written to have Key and a major philanthropic donor in cash and then the building was built so effectively.

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Yeah, Target Discovery Institute. I was employee number one I suppose, or Peter probably employee number right now, employee number two.

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But it just started from from cell based high throughput screening platform and now encompasses chemical biology,

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mass spec proteomics, medicinal chemistry.

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So a lot of scientists who are complementary each other but all focus on drug target discovery, the very first stages of drug development pipeline.

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Thank you. So, Stephanie, you got here about halfway through the the life of the institute so far as did you say, six years ago, you started here?

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Yeah, well, with Daniel, I worked and not actually worked in radiation oncology and biology.

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Okay. Before that. So that's where we Daniel and I met the first time when I first moved to Oxford,

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and then I worked for the best GC, the Structural Genomics Consortium on this floor here.

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So I knew, I knew the lab and I used some of the equipment.

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And then six years ago I moved over to this lab. And what was the specific area of research that you were looking?

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Well, when I was in radiation oncology and biology, it was, you know, cancer.

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And we were working on melanoma, just skin cancer.

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And then for the SGC, I, I did more of a, of a test, so I it's kind of medium throughput.

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So now we do high throughput screens, but this was a little bit more by hand.

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And I looked at chemical probes.

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So compounds that targeted epigenetic.

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Proteins. So it was more tests. So I went from, from studying a specific kind of biology to running a test that was more about a protein.

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And specifically here, this is all kinds of assays for all kinds of biology with a lot of automation.

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Yes. And is that something you you like?

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I think I heard you say robots were cool. So what kind of difference is it made to have that level of automation?

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I mean, we can do thousands of of compounds in a day to handle multiple cell lines.

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To think the most plates that I've handled in a day, but it's me can do a lot of samples.

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Yeah. Yes. And the automation is very cool. And how much do you need to understand yourself about the the actual machines?

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I mean, clearly, your expertise is in the biology, but it's a tool.

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You just learn.

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You know, you learn some software and you, you know, so you're translating something that you would tell someone played the cells the second day,

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add some drugs, you know, and a few days later, change the media.

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It's very simple to say that. But when the automation is all right, we're going to play the cells using the flux drop.

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We need to dilute the cells in a glass bottle and put them under pressure and the plates are

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in a stacker and the plates get down stacked and the cells are added with machine as a tips.

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And you know, and these are all things that when, you know,

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you've always seen pictures of people in labs with pipettes doing what hand by hand when they run into an into little vials.

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So essentially what the robot is doing, it's just doing all that for the same thing.

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So one of the we have a machine that you can load a different head which picks up plastic tips,

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so we can load 96 tips or 384 tips and then it just moves liquid.

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So just as you would pipette, pick up, sprayed some liquid and then dispense it somewhere else, the robot does that.

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You just need to do things that you would do automatically.

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You can't tilt the plate on the robot. It's it's flat.

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So you have to choose the height and the speed that you set it to spray the liquid so it doesn't knock the cells off.

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And you said, you know, you tell it where to go to dispense the liquid.

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So it's all things that are. Simple.

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You just need to think through what you're doing and program it to do that.

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And are you essentially providing service to the other researchers in the in this area?

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So people will come to you and say, I need an essay for this particular existential target.

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We'll do different levels of essay development. We might take on an experiment where they have a very vague idea of what they want to do,

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and sometimes they have an essay that they already do in a 96 wall plate.

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But we automate that in a 384 wall plate and we yeah, we do that with them.

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So it's. Yeah, different kinds of biology.

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So it's quite cool we get to do all kinds of things. Mm hmm.

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So let's let's get funny to comment, which we haven't mentioned so far.

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So I'll stick with you since I'm looking at you as the moment. Can you remember where you were when you first heard that there was something going on

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in China and it looked like it might turn into something that would affect the world?

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I think it was here. I tend to kind of avoid the news in here.

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And we there's a girl and a woman in the building and her family were from Wuhan, so she'd been hearing about it.

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She came up one day and she was telling me about her parents being in lockdown and what was it like and then how they were getting food.

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And and, you know, it sounds like science fiction in the beginning, you know, that this this is happening somewhere in the world.

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And it's, you know, it just sounds crazy, you know, and then here.

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We were working as usual and they were talking about.

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You know, a lockdown. And and we weren't really sure we were trying to get our experiments done.

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We had an experiment planned with someone in oncology, and they were they knew they were being shut down.

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And we were like, okay, bring us the cells and then we'll try to do the experiment.

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And when it got to the last day, I had been to visit my friends, I think the weekend before, and I was starting to feel a bit under the weather.

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And I and my friend that I had seen on the weekend, she sent me a text message and said that,

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you know, I have to tell you that I have I have symptoms of COVID.

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And I was like, I don't feel well. I don't know what it is. This is before you could get a test.

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And so this is the middle of March 2020.

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And then I ended up send an email saying, I don't know what I've got, but I don't think I should go to work.

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So you guys can. We'll have to do the experiment without me. You know, I'll try to tell you as much of the plan.

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But then the TDA shut down before we. We got to that point anyway.

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So then I spent a week at home sick. Flu like symptoms.

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You know, not not bad. And then I was told that we had some work to do.

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Okay. That sounds like a good time to switch back to Daniel.

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So how was the decision taken that the resources of the TBI could be put to the service of of understanding this?

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Yes. So it was it was interesting because when when the World Health Organisation declared COVID 19 as a higher level of threat,

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it was just at the start of the year and then it just seemed like it was, you know, barrelling towards everybody.

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It was every, you know, every couple of days, it just kind of intensified.

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And I think it was probably around mid-February that Peter Ratcliffe kind of called the PIs into his office and said,

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you know, you really need to start thinking about what you can do in COVID research.

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And this was well before anybody had really started thinking too much about lockdowns,

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but it was really prescient because it ended up turning into exactly that.

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So I, I went home and I was thinking about, you know, what could our impact be?

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And I'm not a virologist, I'm not an epidemiologist. So I struggled to understand or think about what we could do.

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But we got a phone call from a phone call in an email from Dave Stewart at Truby, who said, you know, that's his structural biology teacher,

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structural biology unit, who said, I have the full length spike, which is the main protein that your body develops antibodies against in Sars-cov.

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And, you know, could we think of an assay that we could do to test patients with with this spike to see if they have COVID antibodies?

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So the first thing that popped into my mind was analyser based assay.

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How does that how does that work? It's an enzyme linked immunosorbent assay.

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So it's basically you put the you capture the.

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The antigen of interest and then through a series of incubation with SERA or

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a plasma from a patient and then a detection antibody and then a reporter,

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it generates a positive or negative signal for the presence of antibodies against COVID.

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So basically that's telling you, has this person had COVID, correct?

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Yes. Yep. And and it started sort of as experimentally.

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We actually, you know, we thought of analyses straight away and then they've said, oh, well, I know Gavin's working on the lies as well.

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So we got a protocol from Gavin's Credence Laboratory.

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We modified it, obviously, to run on high throughput robotics.

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And the first experiments we ran were.

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With Spike RBD, which is the receptor binding domain and the nuclear protein.

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So all three components of the body can generate an antibody to and spike was clearly the most sensitive to being specific.

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So the decision was was really quickly made and it started as just an experiment to see if we could just do it.

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We didn't have an actual application. We didn't really know if anybody was going to be interested in something like this.

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But it just it just started, as, you know, speculation on something that we could do.

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And, you know, we our group certainly jumped at it because the implications would be if we weren't working on something like this,

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we would sit idle for, you know,

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what was clearly going to be at least a month, that if you remember that it was a thing,

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the lockdown was like one month or two months or something like that. And Stephanie's right.

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We, we, we were trying to run experiments right up until the moment that, that, that all of the buildings closed,

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not really understanding or appreciating the implications of really just closing down for a proper,

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you know, first time in anybody's memory in lockdown.

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So we were well and back again by the time these experiments began.

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Yes. Yeah, it was yeah, it was only it was right after I got better.

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Yeah, I was just told to come in and make some solutions so I was a bit cross because it's like, yeah,

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so Daniel hadn't been in the lab too much recently and I was a bit worried that he

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was doing the first experiment and all I was about to do was to make some solutions.

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Yeah, and we did. We really developed the assay.

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It wasn't, we didn't just get a protocol and run it. We, we tried different kinds of plates.

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We tried different coding concentrations, coding the plates with the spike protein.

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We tried different concentrations of antibodies and just different concentration serum.

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And we also did that on the robot. And then we scaled the robot, the protocol in the robot up to do 12 plates a day.

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And I think I would agree ultimately what came out is the final what's called Oxford Immunoassay looks nothing like the original experiments,

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and that was what we spent a long time working on and caused,

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you know, some of the stress that we were discussing earlier around doing it it at a massively accelerated pace,

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under the very watchful eyes of all of our bosses and their bosses.

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So tell me. Yes, we weren't recording when we were talking about it before.

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But so tell me again, what what were your days like? Let's start.

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Well, I didn't go to the meeting, not for a few months.

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So it started. It started as the results of one experiment,

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which certainly suggested that we could develop an immunoassay to test convalescent

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patients for the for the presence of sars-cov-2-specific specific antibodies.

199
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Stephanie And I remember when in all of Oxford there were six or nine positive sera sample samples available for actually testing.

200
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And just like any other large, pretty complex project,

201
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it started with people reaching out to acquaintances or colleagues who might fit some of the criteria for for generating an overall assay.

202
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With bioinformatics, with development, with procurement and multiple steps over multiple sites.

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And it was very interesting because what started off, as you know, meetings of two and three people quickly grew to meetings of 25,

204
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30 people initially sort of project managed by people who were repurposed to project manage your these.

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What was basically a project that they had no understanding or very little understanding of, but sort of just came into it with their own expertise.

206
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So for instance, we had the expertise on a lot of the robotics and the hot and liquid handling.

207
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But somebody like Brian Martin, who was brought in very quickly,

208
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is a an I.T. bioinformatics specialist who started developed links between the

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NHS systems so that we could track the samples through from start to finish.

210
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And then David Ayer, who is it was really powerful statistical analysis in epidemiology to help us define

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what the actual assay and the criteria for positive and negative samples would be.

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And then Dave Stewart in Ruby in the SGC to generate different variants of the spike protein to see if one generated more specific or a better result.

213
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And it just sort of snowballed from there.

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And it became quite clear that very quickly it was it would become very important to test all of the scientists in ah, excuse me,

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all the research and staff in the John Radcliffe Hospital to see who was being who was being infected and who was generating antibodies.

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The research stack, not the clinical staff, the clinical and research staff within within the J.R.R.

217
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And it were actually you know, there were some very interesting findings from that which went into,

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I believe, a Lancet paper about who was getting who was getting ill and who was generating antibodies.

219
00:25:15,020 --> 00:25:20,180
And then, yeah, then it went. But tell me, tell me about this atrium meeting that you've mentioned.

220
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Oh, the AA meetings. So the meetings were every single morning and they were they were sort of a

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summation of the experiments from the day before which everybody had worked.

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We would worked up on them up until probably eight or 9:00 at night or through the summer.

223
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We were leaving after dark. So yeah, it was nine or ten a night we would leave, you know.

224
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So we were generating data and then we would pass it off to Brian Marston and David Eyers teams and they would be looking at it you from maybe nine,

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ten until 12 I would think.

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And then they were reporting results to the whole group.

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The whole group would either look at it at sometime around midnight or first thing in the morning before the AA meeting.

228
00:26:12,530 --> 00:26:18,070
And we would come to some conclusions on what had worked, what weren't,

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Zoom meetings and everything, Zoom meetings first thing in the morning at 8:00.

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So we would sort of resort we would, you know, review the results of that set of experiments from the day before.

231
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We would say what worked, what didn't, what was new, if there were new samples to be tested,

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new criteria set by the government for the stringency of the test, because by this point we had we had representatives from the Vaccine Trials Group.

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We had that representative from Blood Bank, a representative from the from the government,

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and then scientists from the John Radcliffe Hospital who David Cook who helped Derek.

235
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Derek Cook. Derek Fisher. Derek Cook, who helped with procurement of samples and the, you know, the relationship with the owners.

236
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And it just sort of all came together at 8:00.

237
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Super stressful because you're under the watchful gaze of your boss and your boss.

238
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His boss and Stephanie, myself, Alison, were really the only people, you know, sort of actually in the laboratory.

239
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Many of those scientists had their own people doing, you know, research as well.

240
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They were also in the laboratory studying other aspects of stories.

241
00:27:45,350 --> 00:27:54,350
But for this, you know, there was probably 30 people in in the lab in the meeting and then not very many people actually working.

242
00:27:54,800 --> 00:27:57,950
So, Stephanie, what was that? Yeah, well, I was going to say, you mentioned Alison.

243
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We haven't seen what's her name is Alison Howarth. She had my job before me, so she knew the robots very well.

244
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And after a few weeks, Daniel brought her in to run the processing the samples in the day or hospital,

245
00:28:12,680 --> 00:28:18,810
so they would array the samples into three or four plates and then they would get carried over to us to do the.

246
00:28:19,640 --> 00:28:23,990
So. So during the beginning that there's the AA meeting, which I didn't attend,

247
00:28:23,990 --> 00:28:32,809
I was asleep and then I would come in at ten or 11 and Daniel would tell me what they what they decided in the meeting.

248
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And then I'd have to figure out what we were in and how we were going to do whatever they'd ask for.

249
00:28:37,790 --> 00:28:41,120
And then we'd work late till nine or ten at night.

250
00:28:42,740 --> 00:28:49,280
There were a couple of times, you know, in the summer where or didn't want to wait for me to unlock my bike because it was dark.

251
00:28:49,730 --> 00:28:54,440
Um, so it was yeah, it was stressful, but.

252
00:28:55,400 --> 00:28:58,790
Yeah. But how well. Well, did the technology hold up?

253
00:28:58,820 --> 00:29:02,540
I mean, clearly, there are things that can go wrong in the course of experiments.

254
00:29:04,470 --> 00:29:14,430
Yeah. So that we a lot of the team they wanted everything to be perfect and it's biology.

255
00:29:14,430 --> 00:29:18,450
We kept saying, well, it's, it's not going to be perfect, but it has to be perfect.

256
00:29:18,450 --> 00:29:22,260
So there was this stress to make every experiment.

257
00:29:23,710 --> 00:29:32,560
You know, everything to go to go well. And you have to watch the robot like a hawk to make sure it's working alright.

258
00:29:32,560 --> 00:29:35,110
Because if the tips don't all load correctly they won't,

259
00:29:35,500 --> 00:29:40,899
they won't aspirate evenly and you just need to watch and make sure and then you have to stop it and start again.

260
00:29:40,900 --> 00:29:46,530
So it's not as. It's not as bad as it sounds.

261
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But then there were a lot of steps which which is what we learned over time, that the steps with things could go wrong.

262
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So we learned to watch it, which is, you know, in the end, the AC took 9 hours to do 12 plays and you had to watch it on a good day.

263
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I would get 15 minutes for lunch and that was the only break that I had in 9 hours.

264
00:30:09,240 --> 00:30:14,370
And then, you know, concentrating or all the rest of the time. Yes, I did learn a lot.

265
00:30:14,490 --> 00:30:20,370
So, yeah, I learned a lot. And we integrated some different equipment that we had I hadn't used before.

266
00:30:20,370 --> 00:30:25,230
So I really got to know the genesis very well.

267
00:30:25,810 --> 00:30:30,030
That what's that? The genesis of the robot that we use, that it has different heads.

268
00:30:30,030 --> 00:30:33,450
So yeah. Janice Oh, I see. It picks up the tips,

269
00:30:33,900 --> 00:30:43,470
so and then I got to learn things that I hadn't done always we barcoded the plates so we didn't have to to follow the samples through.

270
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So in the day all they had, each tube was barcoded of patient zero, and then it would go into a plate which was barcoded and then would come to us.

271
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And then the intermediate dilution plate was barcoded and the assay plate was barcoded.

272
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And that assay plate would go into the into the reader at the end of the day to read the results.

273
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It had a barcode reader. So all of the samples, every individual sample was followed through so they could, you know,

274
00:31:12,000 --> 00:31:18,060
decide who what they call positive and negative, you know, who was positive and who was negative and.

275
00:31:20,430 --> 00:31:25,620
For us that looked like it was pink. So the plate would come out and it would end up pink.

276
00:31:25,620 --> 00:31:30,569
And it was you could tell which ones were not pink and which ones were pink.

277
00:31:30,570 --> 00:31:34,890
And sometimes there would be more pink samples and other times.

278
00:31:36,450 --> 00:31:39,849
Yeah. Yeah, I think. I think.

279
00:31:39,850 --> 00:31:44,780
Well, what was what was stressful. Most stressful is stepping in.

280
00:31:44,790 --> 00:31:48,260
I would know the development of an AC just as a is a product.

281
00:31:48,450 --> 00:31:51,540
It's, it's a refinement. It's a experiment.

282
00:31:51,540 --> 00:31:54,390
Refinement, experiment, refinement, experiment, refinement.

283
00:31:54,900 --> 00:32:05,340
Um, but when you're under, you know, in these real time pressures, you feel like every refinement has to be a perfect refinement.

284
00:32:05,820 --> 00:32:12,180
And, you know, they're never really like that. They, you know, you refine and you, you test and you refine and you test.

285
00:32:13,410 --> 00:32:17,549
And that process is not normally done under intense scrutiny.

286
00:32:17,550 --> 00:32:23,040
It's done between Stephanie and I at a pace it isn't quite so.

287
00:32:24,210 --> 00:32:28,740
Demanding. And I think you might say, oh, today wasn't such a great day, but never mind.

288
00:32:28,740 --> 00:32:32,130
We'll have another game tomorrow. Sleep on it.

289
00:32:32,140 --> 00:32:38,790
Yeah, because, you know, everybody was really hoping that this was going to be something that would inform.

290
00:32:39,090 --> 00:32:47,820
Ultimately, what it became clear is this could be used at a population level and could inform governmental response.

291
00:32:48,150 --> 00:32:55,950
And, you know, there was everybody was, you know, breathing down everybody's neck.

292
00:32:55,950 --> 00:32:57,209
And it was just, you know,

293
00:32:57,210 --> 00:33:05,880
because it was very clear that the way that we were going to get out of COVID was developing the vaccine and tracking the population.

294
00:33:07,390 --> 00:33:16,410
So these large scale, population driven experiments were going to tell us a lot about how and where the encoded

295
00:33:16,410 --> 00:33:20,850
was moving and what it was doing and how you were developing antibodies towards them.

296
00:33:21,180 --> 00:33:29,340
Longitudinal studies suggest that the just the pace was just something that was just was unrelenting.

297
00:33:29,700 --> 00:33:38,220
So you did the study of the healthcare workers and researchers from the J are was your assay also used for,

298
00:33:38,580 --> 00:33:41,640
for example, in a survey that was collecting serology data.

299
00:33:41,820 --> 00:33:50,639
Yeah. The assay was used for the study of the J.R. study is now used it was and is now

300
00:33:50,640 --> 00:33:57,299
still used for the onus for tracking the serological data using your machines here,

301
00:33:57,300 --> 00:34:03,360
or has that happened? So about a year after we established the same,

302
00:34:03,360 --> 00:34:14,309
we were running it on an on a daily basis we partnered with Thermo Scientific who installed two

303
00:34:14,310 --> 00:34:23,550
very large robots and commissioned them and adapted the assay tools that a British company,

304
00:34:23,940 --> 00:34:30,180
Thermo, is probably American. I think the device this multinational, let's say, was small, but they have a lab here.

305
00:34:30,600 --> 00:34:37,240
Well, they have equipment here. Yeah. And that's over in the Wellcome Trust.

306
00:34:38,100 --> 00:34:49,350
And Alison Howarth is the scientists use who started the J.R. she took over the project management of what is now essentially a diagnostic test.

307
00:34:50,910 --> 00:34:53,940
And she set up that she set it up.

308
00:34:53,940 --> 00:35:01,620
She she made sure all the robots were tested and then she got the assay working there and then they so over time.

309
00:35:01,620 --> 00:35:06,089
So at first it was me and Daniel and then me, Daniel and Alison that were doing the work.

310
00:35:06,090 --> 00:35:07,950
And then we, we got a little bit of help.

311
00:35:07,950 --> 00:35:19,319
So I had two people and Alison had two people and now she's she manages 16 people and they run that assay in 12 hour shifts.

312
00:35:19,320 --> 00:35:23,820
They they work one four days a week, 12 hours on each person.

313
00:35:23,820 --> 00:35:29,860
And so it's really. Gotten bigger, bigger than just us.

314
00:35:30,280 --> 00:35:34,300
But you. When you were running it here for a full year. Yes.

315
00:35:34,840 --> 00:35:42,400
March to march. On that kind of daily stressful treadmill that you described earlier on.

316
00:35:42,410 --> 00:35:43,930
It was explosive for a while.

317
00:35:43,940 --> 00:35:51,349
I started attending the AA meeting, so I'd get up and do the meeting and eventually, you know, it was only three days a week.

318
00:35:51,350 --> 00:35:58,220
But then I'd come in and then I would run the experiment for 9 hours, then report the data after that.

319
00:35:58,730 --> 00:36:08,510
And, and it was, yeah, it was quite stressful because if the controls didn't work or something went wrong,

320
00:36:08,510 --> 00:36:11,510
they might decide that, that the pilot had failed.

321
00:36:11,510 --> 00:36:17,879
And they Brian Morrison They made a website that we would upload the data on and you'd run it and automatically

322
00:36:17,880 --> 00:36:22,310
it would tell you if the controls were good enough and it would give you a great big red letters,

323
00:36:22,310 --> 00:36:29,390
it would say bail. And that was at the end of a nine hour, you know, shift of work, of constant concentration.

324
00:36:30,590 --> 00:36:37,819
So, yes, that was that could be quite upsetting to get to the end of the day and find that you'd have to do everything again.

325
00:36:37,820 --> 00:36:51,380
And as as the project built up, we got more and more samples and we would run 12 flights a day and someone decided,

326
00:36:51,770 --> 00:36:59,600
I'm looking at Daniel, but that is not it wasn't his decision. Somebody decided that we we could get data back in 72 hours.

327
00:36:59,960 --> 00:37:06,500
And and I felt like, well, nobody asked me if I could return all the data in 72 hours because knowing something might go wrong,

328
00:37:06,920 --> 00:37:14,180
you really want a little bit of leeway there. So there was this pressure that we we now had a full.

329
00:37:16,200 --> 00:37:20,639
Week of samples. And if something messed up, then it was going to push things.

330
00:37:20,640 --> 00:37:24,710
And then there were deadlines and, you know, to get to get the data reported.

331
00:37:26,430 --> 00:37:29,760
Yeah. I mean, it was, you know, when you go, it's fine.

332
00:37:30,960 --> 00:37:41,510
But we did get help and that was interesting, training people to do something that was so technical and in required focus and.

333
00:37:42,750 --> 00:37:48,030
You know, while being under under stress. But I think they're fine now.

334
00:37:50,700 --> 00:38:00,180
If so, how did you feel when the the in his work got transferred to what they did?

335
00:38:00,360 --> 00:38:08,429
I was asked if I wanted to apply for that job and then when the deadline came, I was asked again, be sure you don't want to apply for this job.

336
00:38:08,430 --> 00:38:15,060
And I was like, Oh, I'm sure. I was very happy to go back to my old job.

337
00:38:16,800 --> 00:38:21,959
I'm like I said, we keep saying we learned a lot, but it was so intense.

338
00:38:21,960 --> 00:38:32,490
And it it's it's it it is all the same thing, but it's it never got boring because you had to watch to make sure things were going okay.

339
00:38:32,640 --> 00:38:39,150
And we learned over time, you know, the somethings with the tips and this the SAM samples,

340
00:38:39,150 --> 00:38:47,430
they would get goopy and you'd have to centrifuge them fairly hard to get the whatever was in there down to the bottom so you

341
00:38:47,430 --> 00:38:55,260
wouldn't pick that up tips and then because that would it would clog different steps and you had to watch to make sure that.

342
00:38:56,750 --> 00:39:02,420
That everything looked good the whole time. And yeah, so when it was gone, I was quite happy.

343
00:39:03,290 --> 00:39:10,129
And a picture of the Tui, the deck of the Janus has the deck where words like a support.

344
00:39:10,130 --> 00:39:13,400
And then I had a photograph where I'd taken all of that deck.

345
00:39:13,400 --> 00:39:17,500
Where off? And it was clean. Space.

346
00:39:18,310 --> 00:39:23,800
And I took a photograph of it. And I like to tell my friends, it's nice to be done.

347
00:39:25,060 --> 00:39:27,430
And so by that time had the lab opened up again.

348
00:39:27,580 --> 00:39:32,260
Other people come in to do research so you could start you could restart the work you were doing on the cancer.

349
00:39:32,890 --> 00:39:37,970
Yes, it was kind of slow getting back up and going, but it was good.

350
00:39:37,990 --> 00:39:43,040
So when when are we roughly now? This was spring 20, 21.

351
00:39:43,540 --> 00:39:51,919
Me, because it was the year that the lab and the Wellcome Trust was supposed to be ready in November,

352
00:39:51,920 --> 00:39:55,810
and it was supposed to be ready in December and it was supposed to be ready in January.

353
00:39:56,140 --> 00:40:01,420
So I did go home that Christmas to the U.S. I just.

354
00:40:02,660 --> 00:40:07,370
I hoped it was, you know, all going to be done by the time I got back, but it wasn't.

355
00:40:07,550 --> 00:40:15,140
So where to? Research assistants were running the assay while I was gone back.

356
00:40:15,140 --> 00:40:17,360
And then we had a few more months.

357
00:40:18,140 --> 00:40:31,970
After that, you know, we went to the building, the university had its covered policy, and then each building had its local building policy.

358
00:40:32,310 --> 00:40:36,530
Our own policy was a bit more stringent, I think, than the university.

359
00:40:36,950 --> 00:40:45,229
So we had to be very careful and the numbers of people that could come into the building and we are a collaborative facility.

360
00:40:45,230 --> 00:40:53,450
So we work our our work is done with scientists that come from around the university.

361
00:40:54,410 --> 00:41:03,590
My own research projects started back up, which were, which was nice because we had creek was good enough to give us an extension.

362
00:41:05,360 --> 00:41:13,250
But, you know, it was clear after a year that lots of scientists wanted to get their research up and going again.

363
00:41:13,610 --> 00:41:18,049
So we had to be very careful about, you know, how projects came back in because there were still,

364
00:41:18,050 --> 00:41:26,270
you know, policies in place to limit exposure and the number of people in a particular space.

365
00:41:26,270 --> 00:41:32,839
And, you know, the lab is a big open labs, but you have to maintain two metre distance.

366
00:41:32,840 --> 00:41:38,180
And if you were working very closely, you had to track the number of, you know, the people that you were working next to.

367
00:41:38,180 --> 00:41:46,280
So things that we had taken for granted for many years became, you know, very, you know,

368
00:41:46,460 --> 00:41:51,140
difficult to continue to track because you could become so used to just people coming in and out and,

369
00:41:51,470 --> 00:41:55,910
you know, two or three students looking over your shoulder, you had to track all of that.

370
00:41:55,910 --> 00:42:00,620
So so the transition was was slow.

371
00:42:01,100 --> 00:42:08,420
But I would agree with Stephanie. There was we did our bit and it felt very much like we had done our bit.

372
00:42:10,160 --> 00:42:19,950
But it was really nice to go back to the research that we we are, you know, most qualified, I think, to to do.

373
00:42:21,140 --> 00:42:25,310
And that that was that was very nice. And Stephanie is absolutely right.

374
00:42:25,310 --> 00:42:35,600
I mean, a lot of the research we we we do a project and we move to another project or we focus on different aspects of our projects with COVID,

375
00:42:35,600 --> 00:42:41,600
serological testing, it's it's basically the same, the same assay that's done.

376
00:42:41,600 --> 00:42:45,589
Is it almost as a diagnostics? We're really not that type of scientist.

377
00:42:45,590 --> 00:42:54,260
So it was nice to to to have it moved back out and to return to some normality I think was

378
00:42:54,260 --> 00:43:00,080
something that we recognised more from the previous ten years of research versus that year of.

379
00:43:00,950 --> 00:43:04,130
COVID. Mm hmm. But do you feel proud of it?

380
00:43:05,690 --> 00:43:12,259
Actually, yes, very proud of it. I mean, I think it was a proud for several reasons.

381
00:43:12,260 --> 00:43:22,010
One, that, you know, there were so many scientists who came together to develop the actual platform.

382
00:43:23,480 --> 00:43:29,930
Scientists from the SGC, from Stream B, from the J are from diamond light source.

383
00:43:31,190 --> 00:43:38,210
Scientists in the biochemistry department at the John Radcliffe all came together to build what was

384
00:43:39,050 --> 00:43:49,910
a really well developed assay tracking NHS samples from start to finish moving samples efficiently,

385
00:43:49,910 --> 00:43:53,270
both physically and their data around.

386
00:43:53,690 --> 00:43:59,360
I mean, these things don't normally have been, you know,

387
00:44:00,500 --> 00:44:07,880
as from such a diverse set of people in such a large pipeline, it really turned into a big platform.

388
00:44:08,030 --> 00:44:12,890
That's something other people I spoke to have mentioned that normally academic life is quite competitive,

389
00:44:13,490 --> 00:44:20,330
but a lot of these coded projects that had to be that kind of multidisciplinary collaboration, you know, and how good that felt.

390
00:44:20,480 --> 00:44:28,070
Yeah. And that was really nice. And, you know, we were we were actually generating what I think is very impactful data.

391
00:44:28,580 --> 00:44:36,640
You know, I, I, I've, I've published quite a lot of papers in my scientific career and I would,

392
00:44:36,770 --> 00:44:41,890
I had hoped, you know, at some point to get a proper Lancet paper or a paper.

393
00:44:41,960 --> 00:44:53,900
New England Journal of Medicine paper. And we ended up getting six or seven of these really high profile manuscripts that generate you know,

394
00:44:53,900 --> 00:45:01,360
they came out of the research that we were, you know, right up front doing so as an academic research.

395
00:45:01,380 --> 00:45:06,230
And so you don't often see your your research translated so quickly.

396
00:45:07,730 --> 00:45:09,910
And I think that was what we were most proud of.

397
00:45:09,920 --> 00:45:17,300
At least what I was most proud of is is that something we were doing was making an impact almost immediately.

398
00:45:17,510 --> 00:45:24,020
It was impacting the governmental response. It was telling people, yes, you did have COVID, you weren't crazy.

399
00:45:24,200 --> 00:45:27,200
You know, you you know, and you survived it.

400
00:45:27,200 --> 00:45:30,500
And, you know, you did you know, you're now healthy again.

401
00:45:30,680 --> 00:45:35,630
I mean, all of these things were really we're really amazing in it.

402
00:45:35,960 --> 00:45:44,600
You could see your work almost immediately, which, like I said, is is actually from an academic research standpoint, it's really quite rare.

403
00:45:44,940 --> 00:45:52,429
You know, usually maybe at the end of your career or, you know, ten or 15 years into your career, you would see the translation of your research.

404
00:45:52,430 --> 00:45:58,080
This was almost immediate about what was Stephanie's role in getting the whole thing.

405
00:45:59,030 --> 00:46:02,540
So I can say unequivocally that it would not have happened without Stephanie.

406
00:46:03,680 --> 00:46:17,150
I mean, there's no doubt about it. I mean, Stephanie is, you know, who is it is a trained scientist who is is an expert on the robots and who,

407
00:46:18,680 --> 00:46:24,380
you know, certainly saw that what needed to be done and just did it.

408
00:46:26,270 --> 00:46:35,120
You know, I think, yeah, it's it's just it would not have happened without that heavy, heavy support.

409
00:46:35,840 --> 00:46:39,440
Right. I. I could do a lot of the research, but I couldn't.

410
00:46:39,980 --> 00:46:43,730
Certainly couldn't have done. Done this without Stephanie.

411
00:46:45,730 --> 00:46:50,410
I was just going to ask you the same question of whether I know it was extremely stressful, but are you proud of it looking back on it?

412
00:46:51,330 --> 00:46:55,650
I am. It's hard to do.

413
00:46:57,090 --> 00:47:00,110
It's hard to think of being part of history.

414
00:47:00,120 --> 00:47:04,700
You know, you always put your. My heart feels really small, but.

415
00:47:04,710 --> 00:47:13,200
But then my brain will come back and go. But but you you developed this essay because we developed it from, you know, from you know.

416
00:47:14,440 --> 00:47:18,580
Almost nothing. We worked it up an automated and I worked.

417
00:47:18,790 --> 00:47:26,449
I worked a lot. I worked, you know. Ten or 12 hours a day for a year, you know, a year and a half.

418
00:47:26,450 --> 00:47:30,950
And it was really stressful. Yeah, but yeah.

419
00:47:31,770 --> 00:47:37,219
Yeah. Because at one point my both my oven and my washing machine had broken and I was washing my clothes in the

420
00:47:37,220 --> 00:47:43,850
bathtub and I couldn't take a day off to get this fixed because nobody else was going to run it on their own.

421
00:47:44,150 --> 00:47:44,690
The essay.

422
00:47:45,140 --> 00:47:57,650
So yeah to having to spend and it was a few weeks of washing my clothes in the bathroom before I got one day off to get somebody in to get it fixed.

423
00:47:58,580 --> 00:48:03,930
But. No, it was worth it. I mean, we keep saying, you know, I learned a lot.

424
00:48:03,930 --> 00:48:10,350
I learned a lot about myself, you know, what kind of science I wanted to do and where I wanted to go with my career,

425
00:48:10,530 --> 00:48:13,980
you know, that I didn't want to go and manage 16 people, you know?

426
00:48:14,580 --> 00:48:20,250
It was very happy that Alice wanted that job and it made me do it.

427
00:48:20,730 --> 00:48:28,830
Yeah, but it hasn't put you off working in the level together. No, but it's made me clear about what I do and what I don't enjoy.

428
00:48:29,370 --> 00:48:37,560
I really like doing a bunch of different projects and I really like the robot and I like fixing things in the lab, you know?

429
00:48:40,010 --> 00:48:45,620
Yeah. Our lab has a lot of cool stuff, a lot of equipment that is, you know.

430
00:48:46,650 --> 00:48:50,940
Very interesting. But it only works with the people who understand it.

431
00:48:52,170 --> 00:48:55,920
Yeah, absolutely. Yeah.

432
00:48:56,190 --> 00:49:00,360
So you said you thought you had COVID early on. Did you run your example of.

433
00:49:00,720 --> 00:49:08,310
You know, I always wanted to. I kept thinking, especially because they the samples were obviously visually positive or negative.

434
00:49:08,340 --> 00:49:09,389
You know, they would be pink.

435
00:49:09,390 --> 00:49:16,380
And it was thought it would be very cool to have my own sample in there and to know which well it was and have it come out and go, Oh, that's me.

436
00:49:16,770 --> 00:49:20,040
But now. So you still don't know if you actually had COVID or not?

437
00:49:20,220 --> 00:49:23,250
It sounds pretty likely that you did it. Yeah.

438
00:49:23,490 --> 00:49:30,060
And then I had something else in January which may have been code, but the lateral flow tests were negative.

439
00:49:31,240 --> 00:49:34,649
But it had. Loss of taste.

440
00:49:34,650 --> 00:49:39,620
So I thought that. So both times it's been like this January that's just gone by.

441
00:49:39,650 --> 00:49:46,559
Yes. I wonder if did you use the short flat, the nose only lateral flow test or the throat?

442
00:49:46,560 --> 00:49:51,720
And I always just want. Well, you know, I figured that'd be through.

443
00:49:51,960 --> 00:49:55,530
Yeah, yeah. But yeah, yeah.

444
00:49:56,340 --> 00:50:05,490
So so this is just again, something I've been asking everybody how scared where you by the infection itself and the possibility of, of catching it.

445
00:50:07,850 --> 00:50:15,200
It was it was stressful because we were running the assay, you know, 9 hours a day and.

446
00:50:16,390 --> 00:50:18,400
I was by myself a lot of that time.

447
00:50:20,170 --> 00:50:27,310
There was like once I went to the supermarket and half the employees had the map, their mask below their nose and mouth.

448
00:50:27,730 --> 00:50:37,660
And I went to the customer service desk and I was you know, this was important to me, not just for the general public, for myself and our lab.

449
00:50:38,860 --> 00:50:45,510
And I was like. But can you ask you just can you ask your employees to wear your mask, wear their masks above their nose?

450
00:50:45,840 --> 00:50:49,739
And he looked at me like like he was about to roll his eyes.

451
00:50:49,740 --> 00:50:55,830
And then I was like, well, I was just trying to ask nicely, but now that you've rolled their eyes at me,

452
00:50:55,830 --> 00:51:00,659
then I made a formal complaint and the head of the supermarket,

453
00:51:00,660 --> 00:51:07,290
they actually rang me at home and told me, you know, we're going to you know, we're going to do things that are safe.

454
00:51:07,290 --> 00:51:11,730
But it's. It was very upsetting to think.

455
00:51:11,880 --> 00:51:18,630
That that I could get sick. And, and then because I'd been in contact with everyone else, that the whole lab would shut down.

456
00:51:18,630 --> 00:51:23,820
So not just like I couldn't come to work, but it would everyone I would have interaction with,

457
00:51:24,360 --> 00:51:28,200
you know, couldn't go to work and then it would shut us down. That was very upsetting.

458
00:51:28,620 --> 00:51:35,070
And then to work all that time and then to see people not wearing masks, you know, or not being careful.

459
00:51:35,460 --> 00:51:37,830
That was yeah, it was stressful.

460
00:51:37,830 --> 00:51:49,739
And we have people that in the lab that have different levels of rule following and we have at least one person that that was very strict.

461
00:51:49,740 --> 00:51:53,790
And that's actually good. You know, it's good to have someone around that, you know.

462
00:51:56,640 --> 00:52:04,030
That lets you use them as an excuse to like you have to wear a mask and it's because this other person, it's going to bother them.

463
00:52:04,050 --> 00:52:07,560
And you use this excuse to get people to be quite careful.

464
00:52:07,920 --> 00:52:20,360
You. So what about you then? So it was interesting because I had volunteered for the vaccine trial and I was actually number 26,

465
00:52:20,360 --> 00:52:26,180
which always got a kick out of the people at the vaccine trials. The whole, you know, for 26, 25 people out of you.

466
00:52:27,950 --> 00:52:37,010
And so I ended up getting, you know, the injections from the from the vaccine trial,

467
00:52:37,260 --> 00:52:42,020
you know, whether it was a really no, no, I wouldn't give in, but we didn't compare.

468
00:52:42,140 --> 00:52:46,220
We sort of came back here and compared notes as, you know, what did your shoulder feel like?

469
00:52:46,700 --> 00:52:50,870
And I was convinced that I had gotten the vaccine.

470
00:52:51,350 --> 00:52:58,759
And I, I followed all the rules pretty rigorously for the very reason Stephanie was saying,

471
00:52:58,760 --> 00:53:05,149
is that if anybody had gotten ill, we would have had to properly shut the lab down.

472
00:53:05,150 --> 00:53:10,850
And that would have been, you know, at least catastrophic for the the output.

473
00:53:12,110 --> 00:53:19,460
So it was really interesting. So I, I felt almost like, okay, I'm definitely I've been vaccinated, so I'm I'm fine.

474
00:53:19,940 --> 00:53:28,490
And then when it came time to actually get the proper vaccination through the NHS, they unblinded the people who were in the,

475
00:53:29,690 --> 00:53:39,020
who were in the trial and I actually had gotten the placebo so I meningitis B vaccine and it was just very funny because I sat thinking,

476
00:53:39,260 --> 00:53:44,990
you know, I just walked around very cavalier thinking that I'm, you know, I'm impervious to this thing,

477
00:53:45,590 --> 00:53:51,469
still following the rules and, you know, cleaning my hands every, you know, quite a lot and wearing the mask and everything.

478
00:53:51,470 --> 00:53:57,049
But yeah, I just didn't really. Just didn't really think it was going to happen to me for.

479
00:53:57,050 --> 00:54:01,580
For that reason, I mean, pretty naively and kind of stupidly.

480
00:54:01,580 --> 00:54:07,430
But I mean, it's I did it was kind of the way I thought, you know, I've been vaccinated, so I'm fine.

481
00:54:08,800 --> 00:54:18,590
So. Yeah.

482
00:54:18,600 --> 00:54:23,669
So what, you. I'm just running down the questions. Think a lot of them you've kind of answered in passing.

483
00:54:23,670 --> 00:54:32,430
So it would be too. I don't need to ask them. Um, but you've moved back to both, move back to doing what you were doing before.

484
00:54:32,820 --> 00:54:43,770
Um, has the experience of, of working on the serology tests changed your approach to the work you do, taught you new things?

485
00:54:43,770 --> 00:54:48,690
Yeah. So it was interesting because as as a principal investigator,

486
00:54:49,080 --> 00:54:59,399
there's a natural progression that you move away from the laboratory because you're responsible for all the papers coming out of the laboratory.

487
00:54:59,400 --> 00:55:07,770
You're responsible for generating the income, the research income, disseminating the results of your labs and going to meetings.

488
00:55:08,640 --> 00:55:13,230
And that leaves very little, if any, time to go into the laboratory anymore,

489
00:55:14,220 --> 00:55:21,090
which is a shame because I quite like the laboratory and I like to say or think that I was actually fairly good in the laboratory.

490
00:55:23,670 --> 00:55:29,820
So when COVID came, I would write, I went back into the laboratory, which was kind of an interesting thing,

491
00:55:30,780 --> 00:55:38,790
having been out of it for six or seven years and I realised that I actually quite liked it and I missed it.

492
00:55:40,350 --> 00:55:45,540
There were some aspects that I didn't miss but you know, you take the, the better for the, for the bad.

493
00:55:47,220 --> 00:55:59,100
So what it's changed for me is now I'm, I try to spend as much time in the lab as I can now on my own projects or on projects to help some people.

494
00:55:59,640 --> 00:56:05,520
And he's not saying anything about his science but the mess.

495
00:56:05,940 --> 00:56:11,399
Yes. So so I had gotten better at the mess.

496
00:56:11,400 --> 00:56:15,360
I do leave a bit of a wake of mess behind me, but I've gotten better.

497
00:56:15,480 --> 00:56:20,040
So this is kind of like cooking, isn't it, how different people are in the kitchen and whether they live there?

498
00:56:20,430 --> 00:56:25,600
I guess so. I think you got used to. I don't know.

499
00:56:26,950 --> 00:56:28,360
I don't know other people cleaning up.

500
00:56:28,880 --> 00:56:40,000
You know, you've definitely got a lot better because I think the rule, it's hard to remember the rules of, you know, and the facilities have changed.

501
00:56:40,780 --> 00:56:44,859
The system here had changed where we ended up having to do a lot more stuff ourselves.

502
00:56:44,860 --> 00:56:51,760
We have to empty the autoclave bins, you know, for the lab waste because they just had too much work to do.

503
00:56:52,700 --> 00:56:54,669
Yeah. So, yeah, so I've gotten better.

504
00:56:54,670 --> 00:57:04,450
But I think that's the one thing that I would say has certainly changed, that I've actually rekindled my love of being in the laboratory.

505
00:57:04,450 --> 00:57:14,830
So I actually tried to get into lab at least a couple of hours a day to just to do the research that I kind of want to do it.

506
00:57:14,920 --> 00:57:16,870
It kind of it spills over.

507
00:57:16,870 --> 00:57:25,780
So I have to spend a little bit more time doing the admin in the in the, in the other things that I just have to do just to keep.

508
00:57:26,920 --> 00:57:34,750
To be a principal investigator. But I it's a trade-off that I'm willing to make because it just it's just fun for me.

509
00:57:36,670 --> 00:57:48,999
And Stephanie. But is there anything about the way research is managed in the kind of hierarchy of research from the, you know,

510
00:57:49,000 --> 00:57:53,200
heads of department who kind of decree what should be done and the people who are actually

511
00:57:53,200 --> 00:57:58,240
doing the work that you think in the light of your experience could be improved.

512
00:57:59,290 --> 00:58:06,360
I mean, do you think you have enough of a voice in saying things like, once I started started joining the ADA and meetings?

513
00:58:06,370 --> 00:58:15,690
You know, I at one point I looked around the meeting and there were nine principal investigators and then myself and Alison.

514
00:58:15,700 --> 00:58:20,049
So at the time I was like, there's there's all these people, the head of this and the head of that.

515
00:58:20,050 --> 00:58:25,780
There are so many heads of things. And then there was me doing the work and there were only two of us doing physical work,

516
00:58:26,350 --> 00:58:34,329
but I was a part of that and I spoke up because I didn't have time to be shy, you know, I just spoke up.

517
00:58:34,330 --> 00:58:42,260
And one time I pitched a fit and Richard Cornell rang me personally to make sure I was okay.

518
00:58:42,610 --> 00:58:46,960
And by that time, I'd cooled down and I was embarrassed that I had pitched a it.

519
00:58:48,190 --> 00:58:54,490
But it was that was I really felt like I was important to the team, which, you know.

520
00:58:56,890 --> 00:59:00,110
Yeah. It was a big team and we worked hard and.

521
00:59:00,710 --> 00:59:05,300
Yeah. And is that something you think you've got confidence enough to do in future if you think something's not right?

522
00:59:08,070 --> 00:59:16,230
I guess. So you. I am an expert in what I do.

523
00:59:16,320 --> 00:59:23,399
I might not be an expert in. A type of biology, but I'm expert in what I do.

524
00:59:23,400 --> 00:59:30,260
So. You know, sometimes you do have to argue for what you think is best, but, you know, that's.

525
00:59:33,390 --> 00:59:39,790
Yeah. Through the process. I mean, I hadn't I had taught students, but I had managed people.

526
00:59:39,810 --> 00:59:47,040
And then during the process of training two people and managing them, I was like, I want a demotion.

527
00:59:50,880 --> 00:59:56,190
So that that's what I got out of career wise, is that I didn't want to.

528
00:59:56,280 --> 00:59:59,730
I want to be in the lab and training people.

529
01:00:00,240 --> 01:00:04,020
Yeah, it's it is different. That was a special situation.

530
01:00:04,650 --> 01:00:08,950
But I definitely want to be. In the lab, not in an office.

531
01:00:10,500 --> 01:00:15,510
Yes. So, yeah. You like to be in control of what you're doing and.

532
01:00:15,810 --> 01:00:25,940
And doing it yourself. Yeah. Oh, yes.

533
01:00:27,710 --> 01:00:32,420
I always find this question difficult to ask when people have already told me that the work was really stressful.

534
01:00:32,750 --> 01:00:38,780
But did the fact that you were working on something that was so important help to support your own well-being, do you think?

535
01:00:40,020 --> 01:00:49,350
I mean, it's a time when a lot of people, even if they weren't ill, were very down because of the constraints of living under pandemic restrictions.

536
01:00:50,130 --> 01:01:02,660
I can absolutely say yes. From from the PR standpoint, it was as we worked as we were leading up to the COVID shutdowns,

537
01:01:03,140 --> 01:01:10,100
you think about how you were going to manage pain for all of the scientists in your group,

538
01:01:10,280 --> 01:01:13,640
because there's certain level of, you know, just the responsibility that you feel.

539
01:01:15,500 --> 01:01:20,680
You think it's right for the church, the charities to or the, you know,

540
01:01:20,690 --> 01:01:25,909
funding to sort of give you an extension or to furlough or something like that.

541
01:01:25,910 --> 01:01:32,840
But you don't know how it's going to work out. You know, it's just going to be difficult and, you know, it's going to be a lot of paperwork.

542
01:01:33,770 --> 01:01:44,560
So. And then, you know, an idle research scientist, spring is a bad thing because we're trained to to think of the things that can go wrong.

543
01:01:45,040 --> 01:01:53,260
Right. And then we design an experiment to answer questions, but also to mitigate any potential errors.

544
01:01:54,850 --> 01:02:05,680
So my I would have probably focussed on some of those negative aspects of being home, idle or not, you know, into work every day.

545
01:02:07,300 --> 01:02:14,060
So for me, coming in every. The research was absolutely crucial to my well-being.

546
01:02:16,030 --> 01:02:22,840
And if it hadn't been for this, I think it would have been a very, very difficult episode.

547
01:02:24,850 --> 01:02:25,780
What about you, Stephanie?

548
01:02:26,140 --> 01:02:36,820
It's hard to imagine like a heavy state, you know, because I was busy constantly and my level of anxiety was was at the maximum.

549
01:02:37,030 --> 01:02:46,330
You know, it's hard to imagine, but we did have people that were very upset by not being able to help.

550
01:02:46,960 --> 01:02:50,980
And at one point, someone told me I was selfish because I was doing all the work.

551
01:02:51,610 --> 01:02:58,690
And and, you know, when you just you what do you say to that?

552
01:02:58,900 --> 01:03:09,280
You're like, I'm not being selfish. I'm working 12 hours a day, you know, I'm sure I'm sorry I can't share this with you, you know,

553
01:03:09,310 --> 01:03:16,060
and it's it's still hard for me to properly process that different point of view, you know, that.

554
01:03:16,630 --> 01:03:26,560
Yes, it I can understand, but it's hard to feel what they would feel, you know, that they they were upset that they couldn't help more.

555
01:03:26,590 --> 01:03:31,120
Mm hmm. Which must make you even more anxious to have that in front of you.

556
01:03:31,420 --> 01:03:36,280
So, I mean, how difficult have you found it now that we're looking back, you know?

557
01:03:36,310 --> 01:03:40,960
Yeah, almost two years. Just think and talk about that time.

558
01:03:46,340 --> 01:03:51,130
We would talk about it some. But after that, the COVID screening left our lab.

559
01:03:51,140 --> 01:03:55,550
You know, it was kind of I was kind of back in the real world again.

560
01:03:56,180 --> 01:04:03,829
But then when the orderly and they approached us about about our experience and about, you know,

561
01:04:03,830 --> 01:04:10,070
having this interview and about physical things that we could give to the library,

562
01:04:10,880 --> 01:04:20,330
we had a meeting and I, I, I just found I was, I was very upset for a few days.

563
01:04:20,330 --> 01:04:26,450
I was really grumpy and, and I was like, it's like I have PTSD.

564
01:04:26,750 --> 01:04:32,299
And I kind of joked, but then I thought about it and I was like, well, actually it's very like, I have PTSD.

565
01:04:32,300 --> 01:04:41,270
But having had that conversation and brought bringing it all up, it was very upsetting and and just brought back all that stress.

566
01:04:41,270 --> 01:04:53,149
And, and it took me a few days to calm down and then I would get emails about, about, you know, from the library, and I would just not read them.

567
01:04:53,150 --> 01:05:00,830
And that's I'm usually the kind of person that reads all their emails as they come, and I would just leave them unread and I couldn't read them.

568
01:05:00,950 --> 01:05:06,349
But but then when it was just wait for Daniel to respond and then I'm like, I'm fine doing it.

569
01:05:06,350 --> 01:05:14,659
But I also didn't want to do this interview by myself because I didn't want to come across that it was an awful time, you know?

570
01:05:14,660 --> 01:05:22,970
But it's better when other people around and and it more becomes a conversation instead of just me venting and me complaining and,

571
01:05:23,390 --> 01:05:26,810
you know, talking about how how stressful it was.

572
01:05:28,890 --> 01:05:33,660
You know. You know, whenever I don't know, whenever I complain and it's like I can complain a lot.

573
01:05:33,660 --> 01:05:41,120
And then the last thing I want to say at the end of all this, you know, is that, you know, it we learned a lot.

574
01:05:41,130 --> 01:05:45,240
I keep saying we learned a lot. And I was glad that I could do something to help, you know?

575
01:05:46,350 --> 01:05:52,680
Well, I really hope that the experience of being interviewed doesn't lead you to be grumpy in doing this for another couple of weeks,

576
01:05:53,130 --> 01:05:57,210
but you seem a little more relaxed. Yeah, I did find my notebook.

577
01:05:57,360 --> 01:06:04,940
So after the library asked, you know, if we had anything, I did find my lab notebook, and I was like, Oh, no, I couldn't.

578
01:06:05,160 --> 01:06:11,729
I couldn't. But it is interesting. It goes from, you know, like the dates go the dates and, you know, the meetings that we had.

579
01:06:11,730 --> 01:06:15,540
And then the next day it has a date and make solutions.

580
01:06:15,540 --> 01:06:20,100
And that's all I have in my lab notebook. And then it becomes more involved as as we get.

581
01:06:20,700 --> 01:06:28,499
I got to do more. But yeah, it's kind of personal for me that they could possibly scan it.

582
01:06:28,500 --> 01:06:36,540
But anyway, yeah, for me, I remember that August there was a little tiny window when we were actually able to travel

583
01:06:37,020 --> 01:06:44,969
in Greece was open and my partner Louis and I went to Santorini and we spent I think,

584
01:06:44,970 --> 01:06:48,090
six days there and we got there.

585
01:06:48,090 --> 01:06:52,650
And, you know, Greece is really pretty, very warm.

586
01:06:53,790 --> 01:07:02,400
And on day three, it was like it was like it was within like 20 minutes.

587
01:07:02,820 --> 01:07:06,270
It felt like I was completely deflated.

588
01:07:06,840 --> 01:07:15,690
Um, all the, you know, all that the stress of the past several months just came flooding out.

589
01:07:16,770 --> 01:07:24,780
And it was very it was just an unusual experience because you are under a fair bit of stress for some time.

590
01:07:24,780 --> 01:07:30,180
And then you go on holiday and you, you know, you relax and that's exactly what holiday is.

591
01:07:30,600 --> 01:07:37,889
But I normally on a holiday, you sort of just read into it and it becomes nice that what it was like almost at some point,

592
01:07:37,890 --> 01:07:43,410
it was like somebody pulled out, you know, a drain plug and it just all drained away.

593
01:07:44,650 --> 01:07:50,910
And it was an interesting experience here. And I remember on the plane coming back,

594
01:07:51,180 --> 01:08:00,570
there was sort of been elation to to come back and see how things had continued to go and felt, you know, if there were any problems.

595
01:08:00,990 --> 01:08:07,830
But, you know, the closer we came to the United Kingdom, the more you sort of think, oh, I wonder how it went.

596
01:08:07,830 --> 01:08:10,980
Were there any problems? What do I have to deal with?

597
01:08:11,400 --> 01:08:14,490
Um, so that was.

598
01:08:16,750 --> 01:08:27,500
You could feel that, you know, the stress was was focussed around what are the problems and what do we need to do to to solve them versus,

599
01:08:27,500 --> 01:08:32,150
you know, the excitement of, you know, what was been discovered and you know what?

600
01:08:32,780 --> 01:08:36,830
You know, what would what can we put into a paper? What can we really, you know, investigate?

601
01:08:37,520 --> 01:08:41,930
Very different. Experience of returning back to that.

602
01:08:42,710 --> 01:08:50,960
After holiday versus normal research, which is usually when the scientists continue to do their research on their projects and they come in excited,

603
01:08:50,960 --> 01:08:56,000
they tell you we did this or we did that, or we completed this screen or This has been published.

604
01:08:57,500 --> 01:09:04,700
It was kind of coming back to potential problems versus something instead of coming back to sort of exciting results.

605
01:09:06,550 --> 01:09:09,670
But it's got to be more about the site, you know?

606
01:09:10,560 --> 01:09:16,200
Now, you know, like you said, it took a while, but then now you can be excited about science again.

607
01:09:16,440 --> 01:09:24,150
Instead of working to do a bit project on this, you know, we have.

608
01:09:25,900 --> 01:09:29,010
All kinds of projects going. It can be.

609
01:09:29,890 --> 01:09:40,000
It's interesting again. Yeah. And I think it's it's in now it is sort of an exciting chapter, one year chapter in our research lives.

610
01:09:41,740 --> 01:09:47,740
And it's seen, you know, and interestingly, a lot of the data that we generated at the time is still being analysed and looked at.

611
01:09:49,530 --> 01:09:59,400
In generating manuscripts. But yeah, I think we now I think it's now, you know, really become very clear that,

612
01:10:00,180 --> 01:10:04,749
you know, COVID, at least that platform really won't return to to our laboratory.

613
01:10:04,750 --> 01:10:12,060
And it's it's nice for probably a few months I was thinking, okay, we're, you know, at some point we're part of the business contingency plan.

614
01:10:12,450 --> 01:10:15,630
So if the robots over there go down, they can come back here.

615
01:10:15,930 --> 01:10:22,720
And that's was like sort of like for if it ever came back here, it would have to be a massive juggling, you know,

616
01:10:22,860 --> 01:10:29,010
my own research be massive juggling exercise and you know, we would just be thrust straight back into that.

617
01:10:29,460 --> 01:10:32,700
Um, but it's clear that those robots are really robust.

618
01:10:32,700 --> 01:10:35,340
They're running reliably.

619
01:10:35,610 --> 01:10:43,440
They have Allison, who is more than capable of making sure that, you know, they maintain the output that they that they normally do.

620
01:10:43,710 --> 01:10:47,730
And it's kind of like now they get year out from the year, it's just like, okay,

621
01:10:49,100 --> 01:10:56,430
it is looks like it's well and truly out of my lab and and sort of off and robustly doing what it was designed to do.

622
01:10:56,760 --> 01:11:01,620
But for the first few months, yeah, I was, we were waiting for it to break and come back.

623
01:11:03,600 --> 01:11:07,159
In in fortunately. I mean, they're really good scientists over there.

624
01:11:07,160 --> 01:11:10,200
And they made sure everything. Continue to work and.

625
01:11:10,800 --> 01:11:13,910
Produce data. Kept going.

626
01:11:15,010 --> 01:11:18,230
Good. Good.