1
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Okay. Can you start by saying your name and what your current position is?

2
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Sure. My name's Alex, Alex mensah, and I'm a currently a group leader at the Welcome Centre for Human Genetics.

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And I'm also an academic clinical lecturer. I work as a registrar in Infectious Diseases and General Medicine at Oxford Director Active Hospital.

4
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Okay, So first of all, tell me a little bit about yourself.

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Without giving me your entire life history, but going back to the very beginning, how you first got interested in medicine,

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what made you decide to be a doctor and the main staging posts in your career so far?

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Great. Yeah, sure. So I first became interested in medicine when I was 14.

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I my father had worked a border lots and we went out to really spend quality time in Africa.

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And I was based in Nigeria for a while and I went out there for quite prolonged periods during holidays and

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obviously was quite struck by the levels of public health and poverty out there and levels of infection.

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And that was around. Yes. So in the 1990.

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So really in the aftermath of HIV and obviously with large pandemics of tobacco, you know,

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so I became alert and aware of pressures of infectious disease and impacts on and developing countries.

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So I we went out there repeatedly.

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And so for one of the longer breaks I was out there, I actually started working as an intern, just helping out in some clinics out there,

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even as a young 14 year old and spent several weeks rotating around different

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hospitals and just helping out and observing and witnessing what was being managed.

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And even that was quite protected out there.

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But it certainly gave me a perception of the impact of infection on public health and especially in resource limited settings.

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So I realised that's where my passion lay and what I thought I was good at was think about people compassionately.

21
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And so I decided then that that medicine was what was going to do at school.

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I was that didn't have that many potential medics coming out.

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You know, it was a high performing school, but I didn't really have that many medics coming out of it at the end.

24
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So we didn't have an awful lot of guidance as to how to apply for medical school.

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But I went through rounds and applied for medical school and I was unsuccessful the first time because I didn't achieve my grades,

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which I was obviously devastated at at that time.

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And I had to go through this cycle that I think we all go through at times of questioning what we're doing and whether it's right.

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And then I thought it probably was still right.

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So I took another turn for the following year, took a gap year, reset my A-levels and missed out again.

30
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So medicine became inaccessible to me because I had missed my grades and that first time.

31
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So I again did some soul searching and then went to do biochemistry as Imperial Target and packed into science

32
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and spent two years having a good time in life before realising that I need to think about what I wanted to do.

33
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I was scarred by medicines. I wasn't ready to apply again.

34
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So I did a master's in molecular medicine at Imperial and then got the thirst and hunger for research.

35
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And because it was a six month tutor led and six months research led and again told me

36
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that medicine was really what it was in my soul and what I felt I was destined to do.

37
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So I applied for medicine again and this time I was successful.

38
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So then I went to UCL last minute decision.

39
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I was going to spend another five years at Imperial but decided that nine years at one place was pretty much so let's

40
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use for a different flavour and thoroughly enjoyed my time there and realised that it was exactly what I needed to do.

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I love the course from the minute I started doing it and it gave the opportunities for a combination of science and patient contacts.

42
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That was exactly what I hoped it would be. And then in my final year of Medicine undergraduate,

43
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I worked at Little Free in the Infectious Diseases Department and realised that that was exactly what I wanted to do.

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It was the combination of working with patients with complex disorders and with infections that we

45
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can usually treat and get better and requires general knowledge as well as patient interaction.

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And, and I just realised that's what I wanted to do. So then I all the way throughout my undergraduate degree,

47
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I did research intermittently in molecular biology and then my first placements after medical school was training and joints,

48
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academia and, and clinical work guys in St Thomas's.

49
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And so I got involved in genetics research, human genetics research there.

50
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And we and during my sort of early clinical training and that was fantastic.

51
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And I realised that language ethics is what I likewise enjoyed.

52
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I touched on that in my early science career,

53
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but it was mainly focussed on inflammatory bowel disease because that's the project that was assigned to me and I wanted

54
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when I finished that part of my training that I realised that I wanted to try and combine that with infectious disease.

55
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So then I put together on fellowship ideas and I began to think about reaching out to people who worked on that.

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I had another. Two years of further medical training. So that's four years following my clinical degree and then asked to work with Adrian Hill,

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who became my supervisor for my PhD, and came here to work in Oxford with him on a project that I developed myself.

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With his supervision, of course. And that was working on vaccine responses in African children and understanding how human genetics impacts on that.

59
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And that was amazing. The school that Adrian gave me, I could go out to Uganda, to my young family,

60
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and we spent four or five months out in Uganda working with amazing cohorts and amazing people.

61
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I sort of take me back to where it all started, in a sense, working with the clinics out there,

62
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that working with the participants and studies and talking to them about genetics

63
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that they had never heard of before in their own languages with interpreters and,

64
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and, and then doing that really hardcore science of genetics and immunology readouts.

65
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And so the issue is that you can give every child the same vaccine, but their responses will vary according to their genetic inheritance.

66
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Absolutely. That's right.

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And so it's always been seen that the vaccines themselves, we know they can be very varied and we think of them as 91% effective,

68
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but they're not know even the best vaccines are usually about 95% effective.

69
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And I think retrospectively with COVID, I think we understand that more now.

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But we didn't so much with some of the other vaccines that were giving childhood,

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and even more so in populations that we don't really understand or study very much like in and nor remains Africa.

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But we were using the vaccines almost as a proxy marker of infection because infection itself can be so heterogeneous,

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so varied, and how it presents to people and how people handle it.

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So we tried to use vaccines as a very pure extract of particular infections that

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we give safely and routinely to people all around the world and just use that

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as a first marker of how people respond and try and understand why people respond

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differently to that and what impact that has on their future risk of disease.

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And that's sort of what stimulated a lot of my early work and what diseases we may be looking at in Uganda.

79
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So those were the common childhood vaccines. So pertussis, the vaccine that protects against whooping cough, diphtheria,

80
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hepatitis B and we also looked at measles and Haemophilus vaccine and and tetanus.

81
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So it's a real common vaccines that we deployed routinely around the world and try and protect against those diseases.

82
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And was there a kind of one line conclusion to your Ph.D.?

83
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Yes, I think there are probably two lines in that.

84
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Yes, human. Many things influence a vaccine, varied vaccine response across individuals.

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Human genetic variation has a significant impact.

86
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And one of the strongest impacts, really, that we looked at and as a specific part of the human genome, that's very important.

87
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That was not a surprise looking back. And that's this part of the genome called the human leukocyte antigen complex or HLA,

88
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and which has been well recognised over decades to be a strong influence in how we respond to infections.

89
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And it came out as a striking feature and the only real feature that we found, given the number of people that we looked at.

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So two and a half thousand incidents across three different countries.

91
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And so one of the biggest studies we've ever done on that, especially working with African infants, that hasn't really been done before.

92
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And yet the signals were clear. So that's.

93
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Yes. And then that led on to a variety of other threats and trying to understand

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genetic diversity a bit more in Africa and trying to understand HLA diversity.

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This particular part of the genome in more detail, diversity in Africa,

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but also understanding how we can look at it in more detail functionally to work

97
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out why this is and maybe how we can use this information to improve vaccines.

98
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And so what was your next career step once you finished your dphil?

99
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Yes. So then then I took up with then I had to come back to clinical training because during that time,

100
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so I took five years out of formal clinical training.

101
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And to do that I felt I had two years to find the funding to support myself and then got the funding

102
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and I through a generous welcome scheme and then had had to come back into clinical training.

103
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So that was I decided to come here.

104
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I thought maybe we'll go back to London, but we decided to stay here in Oxford and as is the easiest thing to do.

105
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And I got onto early clinical training here, but then transition that into something called an academic clinical lectureship,

106
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which provides this facility to have 50% research time, 50% clinical time, which we can decide.

107
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We can split that up in the week, we can get it up over months.

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And I decided to do it six months on, six months off, and that's for a four year tenure.

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And I stretched it out to five years through a very complicated system.

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And then I'm just coming down to that phase now.

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But during that time,

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I've set up my own group and so I've been very lucky to have amazing support through the university and to set up a degree of independence.

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And so I've got my own group of post-doc and I've got some dphil students of my own and already

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had some successfully passing that developed and and can take on research questions of my own.

115
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And that's in the Welcome to Centre for Human History. Yes. Yeah. Yeah.

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Okay. So that's, that's more or less got us up to date.

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So yeah, I think I'll come in now with what can you remember.

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I'm asking everybody this. Can you remember where you were, what you were doing when you first heard that there was something going on in Wuhan and,

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and how soon it was before you realised it would be serious and be realised that there was scope to get involved?

120
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Yeah. Great questions. And it does become distant already, but I do remember it really quite well.

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So obviously within within the infection department. So I was working clinically that winter.

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The way it rotated for me is that I would always be on research during the summer months and then beyond clinical during the winter months.

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And I was quite closely affiliated with the infectious disease group at that time and putting it in context at the exact studies that I was involved

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in trying to get running and as part of my clinical lectureship post was recruitment of individuals within Oxford hospitals with severe infection.

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And I developed this quite relatively unique system of taking blood samples from patients,

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presenting to hospital with suspected infection and getting blood samples with them

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from them without taking consent at that moment with a deferred consent structure.

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So that meant that we could go and approach individuals later and say, we've taken this blood for me.

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Do you agree for us to use that blood for research or destroy it if you had to reduce it?

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So I, I had sort of tasked myself to set this up in Oxford.

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I hadn't been running before and to show that we could get it up and running.

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So in the summer, just. Can I just ask why you needed to do that with a too ill to give consent when they came?

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It's not clear. So for lots of reasons. So. Historically, we would always get consent from individuals, but they're often too unwell.

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And then by the time we get in contact with the next of kin, which is obviously the right thing to do,

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we need to talk to these people to make sure that they're agreeable.

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By the time we get in touch with them, the storm has passed, we've given them antibiotics, we've given them fluids,

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which is the mainstay of treatment for people with severe infection, and they've begun to get better.

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So then we get the blood tests. They're telling us something about recovery after infection or the patient dies, obviously, unfortunately,

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and we won't get a good sample because we can't say the blood sample from people who died.

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So really good blood sample we need to take is that as soon as they present to hospital,

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when we're all making all these decisions and it's very difficult to get consent from people in that acute phase because there's so much

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going on and you've got high turnover of staff because you've got shift voters that's making sure that people are trained to do that,

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which is what we tried to do in the past is just doesn't work.

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So instead we take this approach of let's get the blood sample. It's a small amount of blood so we can take between two males, 2 to 10 males.

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So just a few teaspoons of blood really in amongst multiple teaspoons of blood,

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which has been taken routinely as part of a clinical practice and then come to them later.

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And in retrospect now we had a 90, 95% acceptance rate and said, well, you take plus you don't need anything else for me.

148
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So of course I agree, it's not a problem. So do these patients mostly have what kind of infections with this?

149
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Of course. So we were talking to people who are presenting with all cause infection and the majority of those patients would

150
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be presenting with severe respiratory infections or pneumonia or severe urinary infections such as pyelonephritis.

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We call it those, the vast majority. But people come in with skin and soft tissue infections of people coming with brain infections.

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Some people come with abdominal infection. So we all have the spectrum.

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The majority is respiratory, urinary. And so I'd run the first highlights of that just before we got started and before COVID was coming.

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And we sort of had proof of concept that we could make it run, but we weren't actually running that at that time.

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By the time I was in clinical sensitivity during clinical. So yes, the winter of 2019 to 2020.

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So obviously it is beginning to be on the news.

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And we've got a WhatsApp group, an active WhatsApp group between all the other registrars who work with an infectious disease.

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And we talk about hot topics. And within a day of the fast notice happening, even around New Year's Eve and New Year's Day around 2020,

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and there was already chatter about this unusual infection in China, which we didn't know anything more about.

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And then over the subsequent weeks, we continue to talk about it and watch the media.

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And we had some meetings about it as a department.

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And the exact times I did look at this and that it was certainly late January, early February, and absolutely,

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certainly and we were talking about this extensively within the department because, of course,

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we were being expected to be prepared to admit patients onto the water,

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to take patients that have been admitted onto the ward with with travel history and consistent with risk of severe respiratory infection.

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And I realised very early on and I can't remember exactly when it was,

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but I remember it's well before we had seen any cases here in the UK and it's when the cases were beginning to are being reported outside of China.

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And I remember coming home from a very long shift working the weekend and it was 11:00, 12:00 at night,

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my house was empty and I was looking at the news and seeing the spread of infection and I had that sinking feeling of

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just knowing that this wave was coming and it was coming to us and there was nothing that we could do to stop it.

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And that was the one and only time that I really feared for my life because we were there.

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We had prepared all the protocols that we were going to be the first responders to any of these patients.

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And we did. We do this anyways. Infection here.

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We the first responders to potential cases of Ebola or Lassa fever.

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And so we would be looking after these patients.

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And with this unknown, it's incredibly high mortality rates.

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And I remember that feeling.

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I'll never forget that feeling of that heart sinking feeling and knowing my family were upstairs and sleeping and wondering.

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This. I've got to do X is what I've trained to do, and it's what I wanted to do and I need to be prepared to do it.

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But at the same time, within half an hour, I had come to a conclusion That's and that's what I was going to do, is what we had to do.

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Someone had to do it. I have the training and the readiness to do it. And it wasn't just that I was going to do it.

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I was going to make sure that our studies were ready to respond because we had the perfect studies to do to respond to that.

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So that night I sent an email to Jillian Michaels, my main collaborator and mentor, and said to Julien, I've got my study, you've got your study.

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Julian, read this. This wave is coming. There's nothing that we can do about it.

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And if it doesn't come in, that's great. But we need to be ready.

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So we need to start getting our ethics in line. And so within that week we recontact.

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We started reaching out to ethics and ethics committees and saying we want to restructure

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everything and get things ready and we need to be prepared for handling samples.

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And I knew through all the stuff that was happening clinically that if we were going to start receiving patients with suspected coronavirus,

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we would have to know how to handle the samples.

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And speaking from experience, if they came after potential patients with Ebola, that is no mean feat and there's a lot of health and safety concerns.

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So I had to start talking within the labs within Oxford to say if we're going

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to be handling these samples and we want to do a really detailed immunology,

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we need to know what we're going to do with these samples, where they're going to go, how are we're going to handle them.

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And so that took me on a massive journey around the university.

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I'm speaking to Andy Pollard, and because they had the only lab that would possibly be able to handle these types of samples in a

197
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safe environment and working with the amazing people there to start getting our risk assessments done.

198
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And alongside of all of this very practical arrangement,

199
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that was quite hypothetical at this time because because we had no patients who were coming, but we could slowly see this encroaching wave.

200
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And obviously this is pretty around the time that we're having the massive outbreak in Italy.

201
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We were still I was still working a lot clinically, so and I had the normal day to day clinical job of looking after patients with nothing like COVID.

202
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But at the same time, we had a night job because this time we were either assessing patients who were coming to hospital

203
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and we had to dress up in all of our hazmat gear and bring these patients in and assess them

204
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clinically and work out that they didn't have COVID and often waiting four or five days for tests

205
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to come back to say that they didn't have COVID and then and work out how to discharge them.

206
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Having gone through so many hoops and then in the end, we moved out of hospital all this testing and we said, Well,

207
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don't come to hospital if you think you've got COVID,

208
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because we're beginning to realise that a lot of people may not have major symptoms, they might not get sick.

209
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So we'll come to you, but they might be very sick because we've never looked after these people before.

210
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So we would get as a trained registrar as we would go out and it was a highly trained paramedic every night and dress up outside of these

211
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people's homes and then creep into their homes and take these gentle swabs from the back of their nose in the back of their throats.

212
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And, you know, us all shaking, dressing, dressed up and sweating buckets and all of our gear,

213
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trying to not draw attention to the fact that we've just got an immediate, urgent response.

214
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And that would be mass panic of us going and stopping these random people in their houses.

215
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And we would do it swabbing ten, 15 people a night every night in various parts of Oxfordshire.

216
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And these were people who might turn out just to have a nasty cold.

217
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They all turned out to be cold. Yeah.

218
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And so the fateful time, I think it was early March.

219
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Yes, I did take out with these timings, but it was early March that we,

220
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that we had our first confirmed case and we were one of the earliest ones because it's one of

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the earliest clusters which goes on to talk about what know my bit more about my research base.

222
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And one of the earliest clusters one of the cases was linked with in Oxfordshire and we tested him and he was

223
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positive and then he got rushed down to the Royal Free and kept at the Royal Free and in the very early days.

224
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And yes, but it became quite obvious that as things began to increase and as we began to get more

225
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cases trickling in and as I got research studies and sampling off the ground and locally,

226
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I couldn't continue to work clinically so and on the day to day job.

227
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So I'd help out occasionally and I had to drop back and got more involved and full time research.

228
00:22:26,240 --> 00:22:30,560
And what was the main research question that you were going to address by taking these samples?

229
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Yeah. So we had we had one main question when we started.

230
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And that was mainly working with Juliette, and we spent a lot of time brainstorming.

231
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And that's the main question we had initially was the same question was on everyone's lips.

232
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And history was why in the hospital are people some people getting so sick and other people are not getting so sick?

233
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What's driving that and how can we? I tend to find aspects that we could perhaps target through through drugs or therapies.

234
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So that's why we were trying to collect samples from people with a variety of kind of disease retrospectively.

235
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We didn't know what we were expecting. We tried to get the people who were most unwell and then actually it turned out in the early phase that

236
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patients weren't that unwell and some people didn't need oxygen at all and some people did need oxygen,

237
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which will bring them into hospital. And then and then obviously later we began to see those patients who became more critically unwell.

238
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And obviously our impression evolved. That's what we aim to do,

239
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is we aimed to recruit as many people as we could in this early phase and then perform a really comprehensive immune assessment of them.

240
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So we said really detailed characterisation of differences in the way that their

241
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immune system was responding to the virus and these people with varied infection.

242
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And we took, because we had recruited individuals pre-COVID with other infections,

243
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we had a distinct dataset that we could compare to this COVID dataset and say, how different is COVID to other severe infections?

244
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Why is COVID different? And why do some people get really, really unwell as opposed to others?

245
00:24:14,900 --> 00:24:24,980
And that was our main and our main focus. And I think we despite all the analysis we've done, I don't think we've got a very good handle on that.

246
00:24:25,060 --> 00:24:37,670
And I think it's very difficult retrospectively as we realise there's lots of confounders and what makes people at risk of severe COVID.

247
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And that is background medical history of immune suppressed.

248
00:24:42,410 --> 00:24:52,090
We saw quite a strong signal with metabolic diseases such as obesity, diabetes and and that really it's, you know,

249
00:24:52,170 --> 00:24:56,060
it has confounded many of our analysis because no matter how many samples we collected locally,

250
00:24:56,570 --> 00:25:02,450
we did reasonably and we were still quite limited in terms of sample size to really understand things.

251
00:25:02,450 --> 00:25:13,099
But our data certainly aggregated and complemented many other datasets of people who did similar analysis around the world to show that, yes,

252
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there's a timing,

253
00:25:14,600 --> 00:25:22,280
there's a dysregulation of the immune system that just becomes unhinged if the infections left uncontrolled for prolonged periods of time.

254
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And then that's what leads to this sort of effect on multiple organs of the body and would lead to death.

255
00:25:28,310 --> 00:25:30,530
And. Well,

256
00:25:30,530 --> 00:25:38,329
I think our study was unique is that actually showed that actually it's not that different to severe infections of people who die from other causes,

257
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such as we were talking about earlier with severe bacterial and severe pneumonia.

258
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So severe urinary infections and which I think was was quite a big statement at the time and

259
00:25:48,170 --> 00:25:54,130
certainly spurred on a lot of novel research now in saying it isn't really that different.

260
00:25:54,170 --> 00:25:58,910
And so perhaps some of the therapies that we've shown can be so effective in COVID,

261
00:25:59,180 --> 00:26:02,870
we can think of similar ways of approaching it with other severe infections.

262
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And so that was the main question that we were thinking of that.

263
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And that really what we realised was that's the sample collection that we had.

264
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And the way it started so quickly and effectively here was really,

265
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really an amazing opportunity to feed into the wider network within Oxford to ask a variety of questions.

266
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And so we began to ask very early questions about how good of the assays that we were using to say whether or not someone's got COVID,

267
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whether or not someone's had COVID. And so we looked at two antibody tests and and then we looked at some antigen tests as well.

268
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And then we could do comparisons across different antibody testing platforms because we were able to define truth,

269
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because I saw we had a lot of samples and often samples taken from people at multiple time points.

270
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So we had a lot of clarity that statistically, how many people did you have in the end in your in your database?

271
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Yes. Well, I mean, we're still collecting, to be honest.

272
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And so obviously the initial sampling was all focussed on inpatients and then we were sort of the waves within inpatients, but.

273
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The majority of our recruitment is and patients, we've probably got a good 350 and individuals who are very kind enough to either consent

274
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themselves or have family consent for them to have the samples kept and access the data.

275
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But we also made sure that any data that we were capturing contributed not only to our local studies but also to national or international efforts.

276
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So on side of what we were doing with our sample recruitment, we were making sure that we would enrol patients into other studies.

277
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So there's was a big study, Rick and Rich and I, I sort of mentioned,

278
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but I'm quite early on I was involved in in sort of the more national recruitment and the sort of standardisation,

279
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and I was actually tasked as part of that to think about the assay development.

280
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And so that's what really scared me on to make sure that we could get and or the assay testing to make

281
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sure we could get lots of samples locally to then make sure we tested the assays and the I in Israel,

282
00:28:27,380 --> 00:28:30,430
Rick, is international. International, yes.

283
00:28:30,430 --> 00:28:33,639
So that doesn't. Right. I used to know what it stood for and I forgot again.

284
00:28:33,640 --> 00:28:40,660
Yes, yes. I can't remember what history itself stands for, but it became the for seed networks.

285
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We call it District four C, so it's headed by an Callum sample up in Liverpool and Kenny Daly up in Edinburgh.

286
00:28:48,820 --> 00:28:56,200
And we're the main leads on it. And I was one of the package leads, but it became for C, which is coronavirus clinical characterisation protocol.

287
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And so what they wanted to do is capture as much information and sort of clinical data of the ages of individuals,

288
00:29:04,480 --> 00:29:11,200
as many people as possible with coronavirus at all hospitals around the country and try and collect samples from some of those individuals.

289
00:29:11,680 --> 00:29:16,260
And so we made sure that the data wasn't just kept on these patients who we recruited locally,

290
00:29:16,270 --> 00:29:23,110
that we got into this work and databases so that immediately this data could all be analysed nationally.

291
00:29:24,460 --> 00:29:29,840
And then at the same time we had studies like recovery that we were wanting to recruit into.

292
00:29:29,840 --> 00:29:39,010
And and as part of the process we recruited the first patient into recovery and we covered mould that patient not only into recovery,

293
00:29:39,220 --> 00:29:42,310
we got them into research and we got them into our study. We took blood tests from them.

294
00:29:42,700 --> 00:29:48,100
So it was the sort of first time that we had ever had this and development of system

295
00:29:48,760 --> 00:29:53,290
where we aim to and cone como patients simultaneously into multiple studies.

296
00:29:54,100 --> 00:29:57,520
Recovery being the trial of repurposed drugs. Exactly.

297
00:29:57,520 --> 00:30:08,409
That's exactly right. Yes. So again, led out of Oxford and that national and in the end international study and became incredibly

298
00:30:08,410 --> 00:30:15,520
informative but we didn't want to go down the road of becoming possessive over our patients and saying,

299
00:30:16,330 --> 00:30:18,850
Oh, you're in our study and you can't be in anyone else's study.

300
00:30:18,850 --> 00:30:25,240
We wanted to make sure that every patient felt as valued and as valuable to every effort possible.

301
00:30:26,320 --> 00:30:30,610
So we said it was quite complicated system with the amazing nursing research nurse

302
00:30:30,610 --> 00:30:35,200
network that we developed here and simultaneously and the training and just said,

303
00:30:35,200 --> 00:30:37,429
Look, we just want you I know this is a natural.

304
00:30:37,430 --> 00:30:42,759
We usually spend a long time going through each study in turn, but we just want them all into all studies.

305
00:30:42,760 --> 00:30:50,020
So I find the patients going towards them or towards the nominated consultee get their permission and if they agree with and take the blood samples,

306
00:30:50,020 --> 00:30:57,940
we can give them some drugs afterwards and we just make sure that we cross talk as much as possible to make it as informative as possible and have,

307
00:30:57,940 --> 00:31:02,319
you know, still got all that blood in the fridge freezer or it isn't.

308
00:31:02,320 --> 00:31:06,060
So it's not just data. You could go back and look at samples again next week.

309
00:31:06,100 --> 00:31:12,210
We certainly can. We've used up a lot of the and we always have this motto in science.

310
00:31:12,220 --> 00:31:16,660
Cannibalise motto was We don't want any sample left at the end of all of this.

311
00:31:16,660 --> 00:31:20,770
We want to use it for every single thing that we possibly can to understand this virus.

312
00:31:21,130 --> 00:31:24,820
And we've definitely tried to follow that mantra so locally.

313
00:31:24,820 --> 00:31:28,810
So if all the early samples that those samples are pretty much all gone,

314
00:31:29,050 --> 00:31:34,120
but we still continue to recruit and although we haven't done so much analysis in some of the later ways of COVID,

315
00:31:34,360 --> 00:31:37,450
they could be very informative because we've seen people respond to steroids

316
00:31:37,450 --> 00:31:40,959
of some of the therapies we've put in and indeed the effects of vaccination.

317
00:31:40,960 --> 00:31:43,540
Why is vaccination more effective in some people than others?

318
00:31:44,140 --> 00:31:48,580
So we've got this bank of samples that we can use and we've got ethical permission to continue to use.

319
00:31:48,580 --> 00:31:55,690
And so the meantime so yes, indeed, we've still got lots of data and we've got permission to go and access that data.

320
00:31:55,690 --> 00:32:01,450
Again, it's all stored in our clinical drives, but we've also got the samples from to these people as well.

321
00:32:02,460 --> 00:32:07,170
And because we're still continuing to recruit people with and variants of the virus.

322
00:32:07,170 --> 00:32:10,469
And so then after these initial waves,

323
00:32:10,470 --> 00:32:17,640
what became my focus is working with Gavin Scruton and also and an understanding and a and

324
00:32:17,640 --> 00:32:21,660
understanding how people are responding to the different variants of the virus as they're

325
00:32:21,660 --> 00:32:29,459
coming in waves with the ever lingering fear that one of these variants that come about may

326
00:32:29,460 --> 00:32:36,990
be far more virulent and cause more damage to the host than what we're seeing at the moment.

327
00:32:36,990 --> 00:32:40,380
And they might escape the effectiveness of the vaccines that we've got.

328
00:32:40,770 --> 00:32:44,669
And so we need to be prepared to have therapeutics that will target those.

329
00:32:44,670 --> 00:32:54,000
So we're continually trying to find the antibodies that work to tackle variants and work out which antibodies might be good for, which variants,

330
00:32:54,300 --> 00:32:59,190
so that we can have this repertoire of protective antibodies that we know work

331
00:32:59,310 --> 00:33:01,860
across the spectrum of different variants so that something doesn't match.

332
00:33:02,100 --> 00:33:07,770
We're going to have a pretty good idea about what we can and can't use so that we can mass rollout antibody if required,

333
00:33:08,130 --> 00:33:11,970
and to treat those who are most vulnerable in the future.

334
00:33:12,390 --> 00:33:17,310
So we're continuing to try and find those people with the new variants of the virus and

335
00:33:17,310 --> 00:33:21,570
then approach them and ask for their permission to be involved and give lots of blood,

336
00:33:21,570 --> 00:33:25,080
basically, so we can understand their immune response against these variants.

337
00:33:25,890 --> 00:33:27,450
And does that include using?

338
00:33:27,750 --> 00:33:37,950
Does that include using that serum to conduct neutralisation assays against the the different variants to see whether indeed they do work?

339
00:33:38,430 --> 00:33:39,450
That's exactly right.

340
00:33:39,450 --> 00:33:49,950
So we use the serum initially and in Gavin's amazing lab and they will if they've got the original virus they've got all these variants,

341
00:33:49,950 --> 00:33:53,220
the virus that they've identified from people who are positive,

342
00:33:53,490 --> 00:34:03,720
they've cultured the virus and they've got the pure strain of that virus, and then they can see how good the serum is at stopping the virus.

343
00:34:03,960 --> 00:34:07,350
And so particular cells and complex neutralisation assay.

344
00:34:08,010 --> 00:34:13,200
But then and Gavin's lab have stepped forward so that they see that people have got very good neutralisation

345
00:34:13,650 --> 00:34:17,670
and they can actually take the cells from because we take quite large volumes of blood from these people.

346
00:34:17,670 --> 00:34:25,200
So 40 or 50 males. So you know, half a cup full of blood and they can take the cells,

347
00:34:25,200 --> 00:34:32,459
the immune cells from the blood and they can extract out those cells that produce antibody and then

348
00:34:32,460 --> 00:34:39,390
grow up that antibody and get the exact specific antibody that is the best neutraliser for that.

349
00:34:39,390 --> 00:34:43,500
And that's exactly what we need and what's been useful at times during the COVID waves.

350
00:34:43,830 --> 00:34:46,590
So there's monoclonal neutralising antibodies,

351
00:34:47,340 --> 00:34:54,930
so we can essentially manufacture that and make sure that we've got a repository of it if needed to, to deploy it as and when required.

352
00:34:56,760 --> 00:35:02,490
Oh. So that's that's the sort of focus of some of the lingering, lingering work in the background now.

353
00:35:03,730 --> 00:35:10,030
And have you returned to working on other infectious diseases as well, or is COVID still taking up most when you're doing research?

354
00:35:10,060 --> 00:35:14,320
Yes, So we had COVID work is ongoing.

355
00:35:14,320 --> 00:35:19,570
We had our maybe our one of our last big heroes in that COVID world.

356
00:35:19,870 --> 00:35:26,860
And that was to take me in LEAP, really. And so after that, the chaos was beginning to bubble down.

357
00:35:27,610 --> 00:35:34,750
I wanted to return to the question of, well, how much this human genetic variation affects how we respond to vaccines against COVID.

358
00:35:35,230 --> 00:35:41,860
So we worked with Andy Pollard and again with Julian Knight and all the other great vaccine trial team,

359
00:35:42,250 --> 00:35:50,440
and we asked questions of performance, genetic analysis and all the individuals that were involved in the early trials of COVID vaccine.

360
00:35:50,800 --> 00:35:56,290
And we've tested to see what parts of the genetics may or may not influence how we respond to vaccines.

361
00:35:56,710 --> 00:36:02,080
And lo and behold, takes us in full cycle. We find that the HLA is the strongest factor there.

362
00:36:02,620 --> 00:36:06,669
And it not only influences the magnitude of antibody.

363
00:36:06,670 --> 00:36:16,540
So we know that how good the vaccine is, we think is related to how good an antibody response is mounted in the individual against Spike.

364
00:36:18,400 --> 00:36:23,200
Then the main antigen as part of COVID that we target is part of vaccinology.

365
00:36:23,530 --> 00:36:27,370
So HLA not only generates the magnitude of response against Spike,

366
00:36:27,910 --> 00:36:36,790
but also influences how likely you are to be protected over time against sort of what we call a breakthrough infection with COVID.

367
00:36:37,300 --> 00:36:41,980
And so, you know, again, that shows not only that antibodies are very important.

368
00:36:42,370 --> 00:36:46,900
Vaccines are very important for generating that antibody, but also the genetic code of how good we are.

369
00:36:47,710 --> 00:36:52,690
Sorry. I'm sorry.

370
00:36:52,730 --> 00:37:00,760
So, yeah, so we showed that again, it was very important to to that to to that effect.

371
00:37:01,180 --> 00:37:04,090
And in looking at the UK population as a whole.

372
00:37:06,460 --> 00:37:11,920
Can you, does that give you some sort of sense of what proportion of people are more or less susceptible?

373
00:37:13,030 --> 00:37:27,130
And so, yeah, it's a very good question. So the particular parts of HLA that we found to drive increased protection was is about 20%.

374
00:37:27,290 --> 00:37:33,550
And so we found that about 20% of the population are very seems to be better protected than others.

375
00:37:33,970 --> 00:37:39,940
But when we say that people aren't responding so well and still mild infections.

376
00:37:40,090 --> 00:37:43,299
You know, obviously with hindsight, we know that well, we didn't think that at the beginning.

377
00:37:43,300 --> 00:37:50,290
We thought if people didn't respond well to vaccines in the early phase and that would be disastrous and people get very well.

378
00:37:50,290 --> 00:37:55,780
But we know that's not the case. Now, if you get COVID, like the majority of the UK population have assumed remote and self-limiting.

379
00:37:57,430 --> 00:38:04,960
So even those people that aren't so well protected will just get mild disease.

380
00:38:05,920 --> 00:38:11,260
But what it seems to say is that and interestingly, after we've published our work,

381
00:38:11,470 --> 00:38:15,220
I've been contacted by a lot of members of the population who have said,

382
00:38:15,580 --> 00:38:23,980
and I haven't had COVID despite lots of my friends having had COVID, you know, several months after I've had my last vaccine.

383
00:38:24,910 --> 00:38:30,190
And those people are probably carry this protection gene and or combination because every person has two copies of

384
00:38:30,190 --> 00:38:34,840
the gene and they probably carry two copies of that protection gene that mean that they're very well protected.

385
00:38:36,160 --> 00:38:39,639
But ultimately, we'd like to try and take that further and say, well, what is it?

386
00:38:39,640 --> 00:38:44,890
What's different about how can we use that information to make sure that everyone as is protected as each other?

387
00:38:45,310 --> 00:38:51,940
And and does that mean that there are some populations that may be lucky around the world that might be lacking that gene,

388
00:38:52,270 --> 00:38:55,120
that maybe we need to know that information really well,

389
00:38:55,120 --> 00:38:59,500
because maybe they're going to be more at risk of vaccine failure and who knows what the future holds.

390
00:38:59,740 --> 00:39:02,470
How long is the vaccine going to be effective for? Is it going to wear out?

391
00:39:03,070 --> 00:39:07,020
Is it going to wear out more preferentially for those people that don't carry that gene? Only time will tell.

392
00:39:07,030 --> 00:39:10,419
So we're continuing to watch that space.

393
00:39:10,420 --> 00:39:13,780
And that's actually what's driving a lot of my research questions.

394
00:39:13,780 --> 00:39:18,790
Now it's thinking a bit more about HLA and what that means for multiple infections.

395
00:39:19,270 --> 00:39:27,129
And so we're we're trying to look at as many infections as possible and and as large scale and as many datasets as

396
00:39:27,130 --> 00:39:34,660
we can and understand how human genetics influences susceptibility to infection or the outcomes with infection.

397
00:39:34,990 --> 00:39:42,100
Suspecting that HLA is probably going to play quite a major role in that and working out what the relationships are between humans,

398
00:39:42,100 --> 00:39:43,329
the bugs and the genetics,

399
00:39:43,330 --> 00:39:51,610
and how we can use that information to improve and therapies, or certainly preventative therapies such as, such as vaccines for the future.

400
00:39:52,450 --> 00:40:03,580
And has the the the creation of these national and international collaborations that develop through COVID, is that going to be sustained?

401
00:40:03,850 --> 00:40:10,540
Do you think those those partnerships will continue to work while looking at other other infections?

402
00:40:11,020 --> 00:40:14,170
Yes. How important how we put this is a sort of obvious question.

403
00:40:14,380 --> 00:40:19,390
How important is it to have a really big set of data? Yeah, great.

404
00:40:19,390 --> 00:40:26,170
Lots of really great questions there. And so in terms of collaboration, almost certainly is going to continue.

405
00:40:26,410 --> 00:40:34,570
And we've already demonstrated that because obviously, just as we're all tired and fed up of COVID and then we had the infectious hepatitis and series

406
00:40:35,050 --> 00:40:38,920
that we all began to gather up for again and look out for cases and get ready to recruit.

407
00:40:38,920 --> 00:40:44,860
And we didn't have many here locally, but I know many of my colleagues who were managing them at sort of larger liver

408
00:40:44,860 --> 00:40:49,780
centres and were recruiting those cases and we put them in links with research again.

409
00:40:49,780 --> 00:40:54,579
So this reinforced foresee that changed its name slightly and took on the mantle

410
00:40:54,580 --> 00:40:58,780
of looking at those cases in more detail and did a fantastic job of that.

411
00:40:59,590 --> 00:41:02,860
And then we had monkeypox obviously, so that monkeypox raised its head.

412
00:41:03,370 --> 00:41:13,090
And again, these networks all reignited and and we grudgingly thought, Oh goodness, we've got something else to respond to now.

413
00:41:13,480 --> 00:41:16,630
And no matter how hard we were got, we got going and.

414
00:41:17,440 --> 00:41:19,630
We've got some nice results coming out there as well.

415
00:41:21,190 --> 00:41:25,270
So I think it's really important to have the networks because you never know where this is going to crop up.

416
00:41:25,750 --> 00:41:30,730
And we need to talk to each other. Whenever I get asked the question in any interviews.

417
00:41:31,600 --> 00:41:34,510
What's the most important thing that I've learned in science and what we need to do?

418
00:41:34,960 --> 00:41:40,090
I just think it's collaboration and you learn so much by working with different people.

419
00:41:41,240 --> 00:41:47,490
And I think especially so in medical research, because everyone sees things differently and medicine presents in different ways,

420
00:41:47,500 --> 00:41:50,770
in different people, and everyone's got a slightly different perception of things.

421
00:41:50,770 --> 00:41:56,640
So talking is just so important and you learn so much and people will say things that you just missed, which are obviously in front of you,

422
00:41:57,130 --> 00:42:05,830
especially in the heat of the moment with acute infectious diseases and then absolutely answering questions,

423
00:42:05,830 --> 00:42:12,430
big data and working in the area of infectious disease genomics.

424
00:42:14,350 --> 00:42:17,610
We've had lots of naysayers over decades have come.

425
00:42:17,650 --> 00:42:23,560
What are you talking about? It's the infectious pathogen that's the most important thing. Human genetics doesn't play a major role.

426
00:42:24,010 --> 00:42:29,120
And really, despite many of us for many years going by that we've got some evidence back.

427
00:42:30,960 --> 00:42:35,400
We could never get the studies big enough because recruiting enough people of a specific infection

428
00:42:35,400 --> 00:42:41,190
was so challenging and cost vast amounts of money that funders weren't willing to to to fund.

429
00:42:42,030 --> 00:42:50,480
Whereas we've now I think a lot of those near says too bad now, because we've done it for COVID,

430
00:42:50,490 --> 00:43:01,620
we've got 25,000 cases of severe COVID as recruited internationally against half a million or a million controls and individuals.

431
00:43:01,920 --> 00:43:08,670
And we clearly see that there are strong genetic effects of susceptibility to cope as this moving away from vaccine response.

432
00:43:08,670 --> 00:43:15,870
Obviously, this is moving towards susceptibility to COVID. It's not something we could address very well here locally and retrospectively.

433
00:43:15,870 --> 00:43:20,700
But what we needed to do is contribute our data to much larger efforts, which is what we've certainly done and pushed towards.

434
00:43:21,150 --> 00:43:32,219
And and we see clear, amazing, beautiful effects of genetic susceptibility, and that has led to immediate translational opportunities.

435
00:43:32,220 --> 00:43:37,800
So we've developed new drugs which have been redeployed on the basis of a genetic analysis.

436
00:43:38,100 --> 00:43:45,930
For the first time ever, and we're using them now when we've got patients with severe refractory COVID and they work.

437
00:43:46,000 --> 00:43:50,879
I'm speaking from experience. I've used this particular drug called Baricitinib,

438
00:43:50,880 --> 00:43:58,500
which we only really used because of the findings in the genetic analysis of COVID and spread out across multiple populations,

439
00:43:58,500 --> 00:44:02,190
but particularly some of the great work Kenney Bailey and others did here in the UK.

440
00:44:02,790 --> 00:44:07,499
And I've used it for my first three patients looking after here in hospital and it works.

441
00:44:07,500 --> 00:44:10,979
It switches off the disease and gives them a better chance of survival.

442
00:44:10,980 --> 00:44:14,670
So that's the drug that was already in existence.

443
00:44:14,670 --> 00:44:21,620
It's not what exactly. So we didn't we haven't developed that, but I'm sure over the year it takes ten years or so to develop new drugs.

444
00:44:21,620 --> 00:44:24,240
So I think there will be new drug developments.

445
00:44:24,840 --> 00:44:31,139
Having a repurposed drug that is so effective and it's sort of the Holy Grail from what we've always wanted to do,

446
00:44:31,140 --> 00:44:35,370
really bedside to bench, to back to bedside.

447
00:44:35,730 --> 00:44:42,389
And we've done it in two and a half years. And so it's very gratifying to have done that.

448
00:44:42,390 --> 00:44:48,000
And being part of that team and effort has generated that and then to be on the

449
00:44:48,000 --> 00:44:52,780
end results and be able to say to patients that thanks to contribution of you,

450
00:44:52,800 --> 00:44:57,090
people have come to for you, we've written this and we can deliver this to you.

451
00:44:57,090 --> 00:45:00,510
So and yeah, a journey, journey in many ways.

452
00:45:00,660 --> 00:45:06,989
Ooh. So how important is it for you personally that you are at the sharp end treating patients

453
00:45:06,990 --> 00:45:11,730
with serious infections in hospital for half your time and in the lab the other half?

454
00:45:12,030 --> 00:45:16,200
Yeah, it's it's critical and it's something that I've always wanted to do for the minute.

455
00:45:16,200 --> 00:45:24,600
I decided from that time of deciding for the third and final time that I was going to do medicine, I knew at that time I was going to be an academic.

456
00:45:25,380 --> 00:45:29,310
That's not the academic that does medicine part time.

457
00:45:29,310 --> 00:45:35,640
I want to work on the acute end and only through understanding and constant reminding yourself of what the clinical need is.

458
00:45:36,300 --> 00:45:45,330
And at the end, can you understand a, how to design the studies, bearing in mind the patients, as we were talking about earlier, that's the one.

459
00:45:45,330 --> 00:45:52,799
Well, but then also the demands on clinical care and how can you work around ethically to make sure you collect the right samples at the right time

460
00:45:52,800 --> 00:46:02,520
from the right patients and in the right place and make sure they get to where they need to be to develop the right translational outputs.

461
00:46:03,570 --> 00:46:09,479
And only by working and continuing to work clinically in that moment can you make sure that those studies

462
00:46:09,480 --> 00:46:15,330
work and that you can direct those pressure samples to the right place to to to develop the best outputs?

463
00:46:15,810 --> 00:46:24,990
And so to me, yes, although it's tiring and has taken its toll undoubtedly in lots of ways as well, I'm going to continue to do.

464
00:46:25,260 --> 00:46:30,060
And even if I did, I'm applying for more substantive funding moving forwards.

465
00:46:30,360 --> 00:46:35,069
But I'm always going to want to be on the acute tip of infectious diseases and

466
00:46:35,070 --> 00:46:39,030
continue to work with these patients with devastating infection and making sure

467
00:46:39,030 --> 00:46:42,839
we get the right samples at the right time to hopefully try and provide some

468
00:46:42,840 --> 00:46:46,170
insights moving forwards for patients with other infections as well as with COVID.

469
00:46:46,920 --> 00:46:53,549
Oh, I think I'd like to ask a bit more about your your personal reaction to the pandemic.

470
00:46:53,550 --> 00:47:04,140
You mentioned that for a moment, you felt the fear that so many of us felt at because the mortality looked so, so dreadful.

471
00:47:05,010 --> 00:47:11,130
Did did that continue or did you as as all the precautions came in and so did you?

472
00:47:12,900 --> 00:47:20,610
And the fact as it became apparent that the mortality was tending to affect older or obese or people with other comorbidities,

473
00:47:20,820 --> 00:47:27,149
did you feel less threatened by it? So it definitely waned over time.

474
00:47:27,150 --> 00:47:30,350
But I think I think a lot of us did and. Should.

475
00:47:32,960 --> 00:47:39,050
We almost had to make the decision there. And then we were undoubtedly on the frontline.

476
00:47:39,050 --> 00:47:42,800
We were the frontline, the absolute frontline ourselves in our intensive care colleagues.

477
00:47:43,400 --> 00:47:46,700
Well, that's the way we envisage that perhaps retrospect, that's not the case,

478
00:47:46,700 --> 00:47:53,599
because actually it turned out to be the elderly care colleagues that probably got the most exposed to the viruses as patients in community settings,

479
00:47:53,600 --> 00:47:57,980
in nursing homes and things where, you know, it was rife and we just didn't really know it at that time.

480
00:47:58,400 --> 00:48:03,920
But at the time it was with us and we were the first people to really have to respond to it and come to it.

481
00:48:04,340 --> 00:48:08,059
And after that brief moment, which lasted about half an hour, something just switched.

482
00:48:08,060 --> 00:48:11,120
And I just felt, this is what I'm going to do.

483
00:48:11,930 --> 00:48:19,070
I've got to take that understanding that I could get horribly sick and something awful could happen to me.

484
00:48:20,060 --> 00:48:21,950
So I'm just going to look after myself as best as I can.

485
00:48:21,950 --> 00:48:27,200
I've got quite a health attitude, so make sure I exercise regularly and make sure we're very lucky to have a wonderful wife who,

486
00:48:27,890 --> 00:48:34,990
as a nutritionist, so optimises my nutrition at home and started taking supplements, Fitzsimons said.

487
00:48:35,570 --> 00:48:40,520
Someone's got to do it. I'm going to go and do it and be ready and try to lead by example.

488
00:48:40,520 --> 00:48:45,110
So even with the research group taking blood from the first participants, you know,

489
00:48:45,110 --> 00:48:52,280
I went straight in there and the research nurses were watching from the side of the room from outside.

490
00:48:52,280 --> 00:48:55,290
The double protective doors took consents.

491
00:48:55,640 --> 00:49:05,430
Patient was really quite poorly and got got the blood for the research as well as for the clinical environments and and took that risk.

492
00:49:06,170 --> 00:49:13,610
But again, it's probably helped in the fact that it sort of encountered that because we look two other cases of Akala which was a potential high risk.

493
00:49:13,610 --> 00:49:21,080
So we've got that framework and obviously with hindsight it's lucky that nothing untoward happened.

494
00:49:21,410 --> 00:49:26,510
It's always a balance of risk. But yeah, it's just sort of that switch in mindset.

495
00:49:26,510 --> 00:49:28,100
But having done that,

496
00:49:28,100 --> 00:49:36,590
I then witnessed that effects happen with lots of my colleagues and at various stages throughout the pandemic and the early phases,

497
00:49:37,310 --> 00:49:41,900
some people were just absolutely frantic and panicked and running around going, We can't do this, we can't do this.

498
00:49:42,500 --> 00:49:47,239
And then drastically seeing intensive care nurses break down in tears.

499
00:49:47,240 --> 00:49:53,030
And when it was, we're being overwhelmed and people I'd work with for years and thought of them as

500
00:49:53,030 --> 00:49:57,500
absolutely made of stone just breaking into tears because they just couldn't handle it.

501
00:49:57,690 --> 00:50:04,880
And we all sort of added support to each other and consoled each other as best we could and just got on with the job.

502
00:50:05,480 --> 00:50:10,370
And and obviously with hindsight, fortunately, most of us really were okay.

503
00:50:10,370 --> 00:50:12,260
But there were times where it's horrendous.

504
00:50:12,500 --> 00:50:21,110
We had a nurse we had to work with to understand why she was in intensive care for weeks and died from COVID.

505
00:50:21,490 --> 00:50:26,030
We had supporters who were in intensive care and dies quite quickly,

506
00:50:26,450 --> 00:50:34,310
and that was the worst weekend when we were told we were short of PPE and that was being rationed.

507
00:50:34,790 --> 00:50:39,230
And at the same time the porters had just been admitted to intensive care.

508
00:50:39,590 --> 00:50:45,620
And I mean, your nurse was coming down with an illness which we strongly suspected was COVID.

509
00:50:46,040 --> 00:50:54,830
So that was a very dark weekend. But then we were quite lucky. Oxford procured an international stock of PPE, which boosted morale slightly.

510
00:50:55,760 --> 00:51:00,139
And but that was the darkest weekend was that was sort of an end of March and April.

511
00:51:00,140 --> 00:51:05,660
I think that I remained okay by then.

512
00:51:05,660 --> 00:51:06,170
Bizarrely,

513
00:51:06,170 --> 00:51:15,920
I think I sort of dealt with that and moved on and just out just became focussed on the task at hand and contributing to the bigger cause and

514
00:51:16,040 --> 00:51:21,980
fantastic support and understanding from my family who knew that that was what I wanted to do and what I was going to do no matter what.

515
00:51:24,080 --> 00:51:31,400
But yes, you would have been working. I mean, I'm sure you've always worked long hours, but did do did the hours get ridiculous?

516
00:51:31,490 --> 00:51:33,350
It was horrendous. Yeah. Yeah.

517
00:51:33,530 --> 00:51:38,240
So I was especially at the time when I was still working clinically and even sometimes when I was officially off the clinical vote,

518
00:51:38,260 --> 00:51:42,499
obviously we had an absence because people were off with fever and we didn't have that covered or not.

519
00:51:42,500 --> 00:51:46,340
And, and they're isolating at home. So I'd be drafted in to help.

520
00:51:46,340 --> 00:51:55,550
And yeah, I mean they would routinely start at 430 in the morning to get up and answer emails and get protocols written onto the clinical ward by,

521
00:51:55,760 --> 00:52:00,900
you know, 8:00, you know, working towards the seven, coming home and answering emails till midnight and,

522
00:52:01,310 --> 00:52:06,950
you know, for weeks and weeks, weeks and, and the emails, I mean literally emails.

523
00:52:06,950 --> 00:52:15,410
But that volume of emails was just overwhelming and everyone wants him to do good and be helpful.

524
00:52:15,680 --> 00:52:19,100
So everyone coming out of woodwork and how can we help? You know, you're here, you're doing research.

525
00:52:19,520 --> 00:52:21,770
We would like to look at this. We think that's a really important thing.

526
00:52:21,770 --> 00:52:29,340
Can we have some your samples and you know, trying to politically and keep everyone happy and whilst making.

527
00:52:29,420 --> 00:52:33,920
Ensure that the key questions that I and the university felt that were most

528
00:52:33,920 --> 00:52:37,410
important to address were being addressed and that the patients being looked after.

529
00:52:37,430 --> 00:52:41,000
Yeah, it was it was it was a hard time.

530
00:52:41,240 --> 00:52:44,260
And I don't think a lot of us really recovered from it.

531
00:52:44,310 --> 00:52:47,660
I, I don't feel I've got that stamina anymore. I don't know if I could do that again.

532
00:52:47,990 --> 00:52:50,930
I try sometimes, but I definitely don't have that stamina.

533
00:52:51,230 --> 00:52:59,240
It's probably that drive and that adrenalin that's running through that determination and and,

534
00:52:59,750 --> 00:53:09,090
and the support that we got the university and obviously thought a lot about how the university responded to it and the support and the,

535
00:53:09,650 --> 00:53:16,430
the approachability of people I'd seen as very and figures but suddenly becoming colleagues

536
00:53:17,030 --> 00:53:21,259
arm in arm who I could call up or email or walk into their office at any time of day,

537
00:53:21,260 --> 00:53:26,989
and they would stop everything and say, It's what you need, you know, let's do this, we need to do this.

538
00:53:26,990 --> 00:53:28,370
You need to deliver these samples.

539
00:53:28,370 --> 00:53:36,110
So what can we provide to to make sure that that happens and and, you know, coordinating things and getting people in line.

540
00:53:37,250 --> 00:53:44,600
Yeah, lots of interesting times. But yeah, it was a whirlwind and a whirlwind, I felt like.

541
00:53:45,080 --> 00:53:53,060
And was there anything that you personally or anybody else was able to do even tiny things to make you feel better about school,

542
00:53:53,330 --> 00:53:54,830
to support your wellbeing?

543
00:53:55,820 --> 00:54:06,740
Well, again, in the early phases of being the clinical responders, we were overwhelmed and the NHS was amazing and well-wishers.

544
00:54:07,640 --> 00:54:15,170
We working in the infectious disease ward and daily would walk in and we would have free deliveries of produce from everywhere around Oxford.

545
00:54:15,410 --> 00:54:20,330
Amazing food. I too much food and frustration support us that we distributed around the hospital.

546
00:54:20,640 --> 00:54:35,270
I would have the same and and you know the crazy thing of claps and you know every every weekday evening come and go I was now five days those days,

547
00:54:35,900 --> 00:54:37,340
you know, that was amazing.

548
00:54:37,480 --> 00:54:45,770
And, you know, to say and even just the gentle things of, you know, the NHS discounts and things like that was an amazing boost.

549
00:54:46,220 --> 00:54:52,700
And I think that was the most positive thing in the university.

550
00:54:53,030 --> 00:54:58,390
It wasn't there wasn't so much wellbeing support. Everyone was sort of scared and I think, well,

551
00:54:58,400 --> 00:55:03,290
probably not with so much with me because I probably I know that there a bit in on

552
00:55:03,290 --> 00:55:07,459
one thought I was mad and being there and doing that and delivering these samples,

553
00:55:07,460 --> 00:55:14,960
but they knew that is what needed to be done. So I didn't get so much support for university and everything just shut down.

554
00:55:14,960 --> 00:55:22,070
But the through the NHS, it was just incredible. Got support and that was amazing in the early days.

555
00:55:22,070 --> 00:55:29,300
But then that's what got tough as COVID wore on because everyone began to forget about COVID and Brockovich and be less interested.

556
00:55:30,220 --> 00:55:36,650
And then we were left with the day to day of having to cope with the fallout of the disastrous effects that COVID has had.

557
00:55:37,250 --> 00:55:41,450
And all that support just went away. We were, you know, our staffing that was went up.

558
00:55:41,450 --> 00:55:45,440
We had people coming left, right and centre to come and help out in the first wave.

559
00:55:45,920 --> 00:55:55,639
And then we had about the third, fourth wave. We had people leaving because of burnout and people were sick with COVID.

560
00:55:55,640 --> 00:56:02,420
And then, you know, even last a few weeks just gone, flus hit, Morale is at an all time low.

561
00:56:02,750 --> 00:56:08,150
And that's probably because we had so much great stuff in the beginning and then that all got taken away and that we've had to pick up the pieces.

562
00:56:09,590 --> 00:56:10,820
So if anything, I feel that.

563
00:56:12,170 --> 00:56:18,410
We need more wellbeing support now more than we did at the beginning, because it was all energy and enthusiasm at the beginning.

564
00:56:18,910 --> 00:56:22,280
But COVID is continuing both the virus itself.

565
00:56:23,540 --> 00:56:29,720
But the After-effects are enormous and the toll has taken on people's mental health.

566
00:56:30,830 --> 00:56:31,610
People's health.

567
00:56:32,150 --> 00:56:41,690
People presenting now with diseases in ways that we haven't seen for decades because they're sitting at home feeling sorry for the NHS,

568
00:56:42,380 --> 00:56:45,680
don't feel they can approach anyone in terms of general practice because they feel

569
00:56:45,680 --> 00:56:51,530
sorry for how busy they are and then present with severe overwhelming disease later,

570
00:56:51,530 --> 00:56:52,520
and especially infections.

571
00:56:53,470 --> 00:57:04,820
And so we are still and of course then the amazing debt that the countries got into because of COVID is having an incredible effect on the NHS.

572
00:57:05,780 --> 00:57:13,639
And so the effects are ongoing and it's really testing that the momentum and the impetus and the strength,

573
00:57:13,640 --> 00:57:22,970
I think now of us more than ever now, I think and the world has continued to do research studies.

574
00:57:23,010 --> 00:57:28,150
I mean, most of my colleagues have completely given up on that and just don't want to think about it too.

575
00:57:29,120 --> 00:57:33,529
But I think we can still use that as a model to understand infection more widely.

576
00:57:33,530 --> 00:57:36,410
And there's amazing resources that we've developed, so we need to make the most of those.

577
00:57:36,980 --> 00:57:41,170
If not for the benefit of the patients and what they've contributed to it.

578
00:57:41,180 --> 00:57:47,209
So yeah, the need continues for wellbeing.

579
00:57:47,210 --> 00:57:49,780
Support I think is important and I mean life.

580
00:57:50,200 --> 00:57:58,160
On some days I go home and I think I feel more gloomy and worse and nowadays than I certainly did during COVID.

581
00:57:58,430 --> 00:58:02,210
It was a base and we had that network and we all had a common goal, whereas that's lost now.

582
00:58:03,330 --> 00:58:15,090
Oh, very interesting. Yeah, I think we've pretty much covered unless there's any particular story that I have managed to elicit from you.

583
00:58:15,750 --> 00:58:21,600
I've got one final question, but I'm just going to. Yeah, check that there isn't anything that you particularly want.

584
00:58:22,530 --> 00:58:28,900
I think. I think it'll be. I think it'll be. Now, they're probably too nice.

585
00:58:28,920 --> 00:58:33,590
They're probably two nine stories, again, with participants involvement that are relevant.

586
00:58:33,600 --> 00:58:36,719
So most of my sampling, only sampling. Mr. Duncan Patience.

587
00:58:36,720 --> 00:58:45,720
But then we spend quality time because we realise that a lot of the patients were well in their at home and we would have to go to people's homes.

588
00:58:45,720 --> 00:58:52,500
And that's why I've done a lot of well. And so in the early phase I mentioned earlier that we had this and one of the

589
00:58:52,500 --> 00:58:56,159
first cases that we had here locally was probably this early cluster and there

590
00:58:56,160 --> 00:59:02,120
was a cluster who had been in a French chalet and was one of the earliest detected

591
00:59:02,160 --> 00:59:05,130
set of cases in Europe because it had been someone who'd been in Singapore,

592
00:59:05,580 --> 00:59:11,970
flown back to Chalet in France, had met up with all their friends, was an annual event there was got scatter,

593
00:59:12,450 --> 00:59:18,300
he was coughing away and at a dinner table, went to bed for the next few days.

594
00:59:19,080 --> 00:59:23,220
And then they all got checked into a French hospital and then got transferred over to the Royal Free.

595
00:59:24,060 --> 00:59:35,390
And they all tested positive for COVID. And so we I through the one person who came here to Oxford and there was so much demand,

596
00:59:35,400 --> 00:59:41,060
everyone's like in England and UK, we need blood from people with COVID and we can't get it.

597
00:59:41,340 --> 00:59:44,610
No one can get it. And there are rumours that there is blood all over the place,

598
00:59:45,690 --> 00:59:51,239
but no one could get any blood and we needed to develop our assays and work out how we could validate all the

599
00:59:51,240 --> 00:59:56,610
assays and make sure that they were working to test whether people like it or not and just get something.

600
00:59:57,600 --> 01:00:01,499
And there's all this hypothetical, Oh yes, we might get blood from this person, but we're too busy.

601
01:00:01,500 --> 01:00:03,060
So I just thought that's it.

602
01:00:03,330 --> 01:00:10,770
So I called the person who was the first tested case and I do link with him and I said, your your first case, can you give some blood?

603
01:00:10,860 --> 01:00:16,980
Because your case number one in the UK that we can definitely get blood into this network of researchers who are desperate for it.

604
01:00:17,400 --> 01:00:19,800
And I can't tell you how important this blood is going to be.

605
01:00:20,460 --> 01:00:25,790
And he said, Oh, yeah, I'd like to be in contact with the cluster, so if you want, I can put you in contact with everyone.

606
01:00:25,800 --> 01:00:31,410
So immediately I got in contact with them all and went on a drive down to Brighton, which is where they were based,

607
01:00:31,860 --> 01:00:37,980
and we had this collection of people coming down and I said, You don't mind how much, how much blood?

608
01:00:38,280 --> 01:00:42,390
Do you mind if I take can I take 100 mils? So, you know, that is a cut, cut full of blood.

609
01:00:42,810 --> 01:00:45,900
And they were actually quite young, 30 something individuals.

610
01:00:45,900 --> 01:00:49,440
They want to see something, right? Yeah. Let's do it. So got that consent.

611
01:00:50,860 --> 01:00:56,549
You talking through the risks? It's just phlebotomy and sharing data and had a mass phlebotomy session.

612
01:00:56,550 --> 01:00:59,810
So I walked away with about 700 miles of blood.

613
01:00:59,890 --> 01:01:06,570
Initially, seven participants drove back and then immediately on the phone, obviously on a note,

614
01:01:06,600 --> 01:01:13,620
hands driving back from Brighton saying, I've got the blood, I've got the lab set up, we're going to harvest the blood.

615
01:01:14,610 --> 01:01:16,450
You can get this blood because that's what you need.

616
01:01:16,470 --> 01:01:23,550
Get it to the national, you know, blood service technology teams that you can start setting up your assays.

617
01:01:23,910 --> 01:01:29,160
We're going to start collecting from one or two channels and we're going to start doing immune profiling of these individuals.

618
01:01:29,490 --> 01:01:34,740
And, you know, systems go and immediately I could just feel this immediate network of everything,

619
01:01:34,740 --> 01:01:38,700
this ripple effects careers were being ordered and overnight careers.

620
01:01:38,700 --> 01:01:43,439
And, you know, that was probably the most exciting aspect.

621
01:01:43,440 --> 01:01:53,909
And just the devotion that got Brighton lots were involved in some of the many times afterwards because so many questions and they were the first lot.

622
01:01:53,910 --> 01:01:59,309
And so at each stage of us understanding how long antibodies lasted for and how protective it makes,

623
01:01:59,310 --> 01:02:03,450
it is always the never ending question Are we going to get it again? How long are we going to be protected for?

624
01:02:03,750 --> 01:02:08,549
And so we were I would I myself would drive down to Brighton, which wasn't too bad a drive,

625
01:02:08,550 --> 01:02:14,280
obviously, and every few weeks to go and take more gallons of blood from these lovely participants.

626
01:02:14,760 --> 01:02:19,980
And in the end I had to second because I was getting too busy here, so and got my other colleagues to go in and do that.

627
01:02:20,930 --> 01:02:25,430
And so they were they were all recovered. By the time they were recovered, people were not certified.

628
01:02:25,670 --> 01:02:33,350
They barely had any oxygen requirements at all. And, you know, it's a really rare prime example of mild infection in young, otherwise healthy people.

629
01:02:34,210 --> 01:02:35,780
And so that was the first one.

630
01:02:35,800 --> 01:02:44,030
And then the final one in sort of a similar ilk was, you know, in the middle of the first wave there, there's always the theory of,

631
01:02:44,030 --> 01:02:55,250
well, had COVID hit our shores earlier and did we actually have cases of COVID before on English soil before this all happened?

632
01:02:55,760 --> 01:03:06,970
And so we were put in contact with a choir in and in Bradford and through an infectious disease physician up there who

633
01:03:06,980 --> 01:03:16,130
had been approached and said that they felt that there was a traveller back from Wuhan who had sung in a choir in,

634
01:03:16,400 --> 01:03:21,979
I think, late December, who had viral illness and was coughing a lot.

635
01:03:21,980 --> 01:03:27,950
And then the entire choir came down with an unknown viral illness and many of them

636
01:03:27,950 --> 01:03:32,720
were in bed having the worst infection that they had ever experienced in their life,

637
01:03:32,990 --> 01:03:36,440
all in late December and 2019.

638
01:03:37,400 --> 01:03:41,750
So there was a strong suspicion epidemiologically that this could have been the first cluster.

639
01:03:42,740 --> 01:03:47,090
And of course, by this time we knew epidemiologically that choirs were at risk for transmission.

640
01:03:47,300 --> 01:03:54,320
And so that was well documented risk. And we had the antibody assays and the T-cell ASIC,

641
01:03:54,320 --> 01:03:58,610
we could look at the different parts of the immune response to work out whether people had likely

642
01:03:58,610 --> 01:04:06,140
had and COVID or not in the past so we could create a signature to understand that COVID.

643
01:04:06,590 --> 01:04:11,250
And so we coordinated.

644
01:04:11,420 --> 01:04:18,469
This was really complex and ethically because there was a different site and one of our studies didn't involve Bradford at that time.

645
01:04:18,470 --> 01:04:23,630
So we spent two weeks and lots of rigmarole trying to work out how we could ethically get blood from that.

646
01:04:23,900 --> 01:04:25,680
But then we finally got some ethical approval through,

647
01:04:25,700 --> 01:04:34,339
and then I got a medical student who had become my blood take a number one who was always free because he was not doing his course.

648
01:04:34,340 --> 01:04:38,780
And he was he had a car. So he became my and my research number one.

649
01:04:39,610 --> 01:04:47,540
I sent him on his first trip up to Bradford to go and meet all these choir singers and take a series of blood to bring them back.

650
01:04:47,540 --> 01:04:52,970
And Bill waited bated breath, and there was no evidence that any of them had had COVID before.

651
01:04:53,220 --> 01:05:00,140
And it just showed how, you know, we could begin to ask these really interesting questions and how sampling could be

652
01:05:00,140 --> 01:05:05,660
used in various ways to inject these interesting aspects of of COVID moving forwards.

653
01:05:06,590 --> 01:05:14,000
Many stories. Many stories. But as far as the ones that we remember and always remember and probably tell the grandkids.

654
01:05:15,480 --> 01:05:24,549
So this is the final question. Has the experience of working through the COVID pandemic changed your attitude or your approach to your work?

655
01:05:24,550 --> 01:05:27,160
And what would you like to see change in the future?

656
01:05:28,450 --> 01:05:38,760
And I think it has taught me reinforced the importance of trying to maintain a work life balance and teach you how to be a family.

657
01:05:40,180 --> 01:05:45,250
I did not respect that need for balance in the first wave.

658
01:05:46,720 --> 01:05:52,670
And, you know, in retrospect. And so really, I think all of us have we've sat back.

659
01:05:52,670 --> 01:05:57,950
We really were all close to being burnt out. We need to make sure we get rest.

660
01:05:57,950 --> 01:06:04,040
And when we take a rest, it's a decent amount of rest and that we spend it with our family and really try and switch off to work.

661
01:06:04,640 --> 01:06:18,049
And uh, and so that's, I think my sort of personal take home message from Kovac and my research take home message is that there are several,

662
01:06:18,050 --> 01:06:21,050
obviously, and slightly gloating.

663
01:06:21,500 --> 01:06:29,930
Genetics has a major effect and we should be able to use that genetics to develop novel therapeutics and preventative strategies.

664
01:06:30,380 --> 01:06:38,900
And and if we look and can characterise infection carefully enough and individuals, we will keep answers.

665
01:06:39,590 --> 01:06:46,549
And if we do that and doing these studies can be of substantial benefit to individuals and infection

666
01:06:46,550 --> 01:06:52,820
continues to have the biggest amount of mortality certainly on inpatients patients come into hospital.

667
01:06:53,000 --> 01:06:55,010
Yes, of course we got cancer and cardiovascular disease,

668
01:06:55,310 --> 01:07:00,560
but if patients who are admitted to hospital and infection is still the most likely cause for that.

669
01:07:00,560 --> 01:07:08,240
So there's a lot of work still to be done and infection and COVID is showing us that with concerted efforts and carefully designed studies,

670
01:07:08,690 --> 01:07:17,600
we can develop those changes very quickly and that we just need to do the studies correctly and uh,

671
01:07:18,680 --> 01:07:25,040
and make sure we've got buy in from, from the participants themselves and as best way we can and,

672
01:07:25,310 --> 01:07:32,299
and hopefully continue that work so that we can make inroads for infection and for the future.

673
01:07:32,300 --> 01:07:37,340
So I think those are the two main lessons learnt for me.

674
01:07:39,060 --> 01:07:43,460
That's great. Thank you very much. All this keeps happening over again.

675
01:07:43,900 --> 01:07:44,090
But.