1 00:00:00,960 --> 00:00:09,060 Okay, So could you just start by saying your name and what your current position is and what your previous association with the university was? 2 00:00:09,600 --> 00:00:12,390 So my name is Annie Oakley, Dr. Annie OPA, 3 00:00:12,540 --> 00:00:22,380 and my current position is a group leader at a biotech company at Abyssinian just down the road in Milton Park. 4 00:00:22,920 --> 00:00:32,249 And I used to work at the university as a senior postdoc, as senior immunologist with Frater Lab, Professor John Fritz's lab. 5 00:00:32,250 --> 00:00:37,560 And yeah, I was with the university for about seven years before I left. 6 00:00:37,950 --> 00:00:41,760 Mm hmm. Okay. And I'm going back to the very beginning. 7 00:00:42,510 --> 00:00:48,240 Tell me about your your career to date. How did you first get interested in science and decide you are going to be a researcher? 8 00:00:48,590 --> 00:00:55,950 So I thought about this question, and it's not it wasn't something that because I know that for some people here, 9 00:00:55,950 --> 00:00:59,520 they've always wanted to be a scientist, they've always wanted to be a researcher. 10 00:01:00,150 --> 00:01:04,530 But as you can probably tell from my accent, I did not grow up here. 11 00:01:04,570 --> 00:01:13,680 I'm not from here. And so my I'm Nigerian by origin and I had my first degree there. 12 00:01:13,680 --> 00:01:28,589 I did all of my primary education there. And it's not something we research and science isn't really a third world thing, if I can say it that way. 13 00:01:28,590 --> 00:01:34,920 It's not something that developing countries really think about this issue, but it's not just our priority at the minute. 14 00:01:34,950 --> 00:01:41,640 So even though I had a lot of real world examples of diseases and I was interested in them, 15 00:01:42,570 --> 00:01:46,250 I didn't really think I was going to end up as a scientist or as a researcher. 16 00:01:46,260 --> 00:01:49,409 I think you might be a doctor. Exactly. Exactly. 17 00:01:49,410 --> 00:01:54,750 That's what I was just about. So you it sort of got you took what you were given. 18 00:01:55,230 --> 00:02:02,190 And we had this entrance examination where you sort of state what you want to study at university. 19 00:02:02,190 --> 00:02:08,790 And then that goes out and you write an exam and depending on your if you made the points for it. 20 00:02:08,790 --> 00:02:12,269 But the points were also complicated by what parts of the country you came from. 21 00:02:12,270 --> 00:02:16,620 So if you were considered to be from a part of the country that was highly educated, 22 00:02:16,620 --> 00:02:21,929 your points were much higher and then it depended on what university you picked as well. 23 00:02:21,930 --> 00:02:26,459 If it was within your catchment area, it meant that your points might not be so high. 24 00:02:26,460 --> 00:02:30,390 But if it was outside your catchment area, you know, it just made it quite difficult to get into. 25 00:02:30,390 --> 00:02:37,080 So I wanted to study medicine and be a doctor because those are the things you do. 26 00:02:37,080 --> 00:02:48,120 Engineering, doctor, accountant, pharmacy. But I didn't make the points and so I got, I got given biomedical sciences or biological sciences, 27 00:02:48,120 --> 00:02:51,210 as we called it, and I remember thinking, Oh, what am I going to do with it? 28 00:02:51,220 --> 00:02:53,070 I'm just going to be a teacher afterwards. 29 00:02:54,810 --> 00:03:03,420 And first year was a bit rough and then second year, third year, I think I strapped up my boots and said, Yeah, okay, let's do it. 30 00:03:03,420 --> 00:03:10,380 And then I finished. And I think at that point we were starting to hear a bit more about cancers. 31 00:03:10,890 --> 00:03:14,340 So growing up it was a whole lot of HIV and a whole lot of malaria. 32 00:03:14,340 --> 00:03:17,459 Up towards the time I had I finished, 33 00:03:17,460 --> 00:03:25,920 I started to hear a bit about cancers and I began with my fascination with infectious diseases or just diseases in general. 34 00:03:25,920 --> 00:03:32,220 I wanted to know, you know, what was happening and there wasn't any chance to do that. 35 00:03:32,760 --> 00:03:34,530 So I started out in the bank. 36 00:03:34,530 --> 00:03:44,850 I worked in the bank for about a year, a year and a half, and then I was fortunate, privileged to be able to afford an education here. 37 00:03:45,150 --> 00:03:53,670 And so I moved here for my master's degree, and I started out by doing a master's in molecular medicine with cancer research because I thought, 38 00:03:54,150 --> 00:04:02,460 well, after my master's degree, if I went back home, I can sort of set up a diagnostic lab in Nigeria because we didn't have a lot of that. 39 00:04:02,910 --> 00:04:11,489 I could, you know, do something along those lines to educate the public about how to identify this cancers, 40 00:04:11,490 --> 00:04:17,160 what sort of test, you know, how do you screen for breast cancer and things like that. 41 00:04:17,160 --> 00:04:21,720 And then I'm glad. Did you do it at Brunel University in London? 42 00:04:22,230 --> 00:04:28,230 And then after my master's and the lab, I did my master's in, they had this really interesting project. 43 00:04:28,740 --> 00:04:32,190 It was looking at this pair of jeans. 44 00:04:32,190 --> 00:06:28,139 So they are called the Adequate response jeans. And then I went into a bit more of trying to understand what was going on. 45 00:06:28,140 --> 00:06:35,910 So some more mechanistic studies. And we found out that the genes that we were interested in actually controls 46 00:06:35,910 --> 00:06:42,600 one of the signature transcription factors for a very important T cell subset. 47 00:06:42,600 --> 00:06:52,319 They are called the follicular helper T cells. So they help the B cells make this, you know, the gold standard of antibodies, you know, 48 00:06:52,320 --> 00:06:59,160 the high affinity antibodies that have been semantically hyper mutated and you know, with different sort of response. 49 00:06:59,160 --> 00:07:04,450 So let's, let's just go into that a little bit just so that we've got a good background on. 50 00:07:04,500 --> 00:07:12,960 So what are the the main arms of the immune system and what's the difference between what the T cells do and what the B cells do? 51 00:07:13,110 --> 00:07:21,540 So the main arms of the immune system would be the innate immune arm, which is like your immediate, your first responders is the way I'll put it. 52 00:07:21,540 --> 00:07:27,419 They are very non specialised, but they are on the scene quite quickly and these are the T cells of the T cells. 53 00:07:27,420 --> 00:07:33,540 Yeah, not the T cells. These would be your macrophages, your neutrophils would be the very first ones to arrive on the scene. 54 00:07:34,050 --> 00:07:41,220 And I always use the analogy of a crime scene or an accident scene where you very 55 00:07:41,220 --> 00:07:45,060 quickly you get the emergency responders and they come and they sort of take the, 56 00:07:45,210 --> 00:07:48,450 you know, vital information, write down things on the notepad. 57 00:07:48,450 --> 00:07:54,389 This is exactly what the response does to neutrophils with common or what's going on here. 58 00:07:54,390 --> 00:07:58,250 They survive the place. They start to send out signals. 59 00:07:58,250 --> 00:08:01,830 So like the sirens you get and the like, they start to send all of that out. 60 00:08:02,190 --> 00:08:05,429 Then the macrophages would come in and they start to pick up the pieces, 61 00:08:05,430 --> 00:08:13,950 sort of trying to form a pattern of this invader, of this crime scene, and then they then go away with the macrophages. 62 00:08:13,950 --> 00:08:17,759 And there's another cell as well called the dendritic cell, that they do this quite well. 63 00:08:17,760 --> 00:08:25,230 They engulf the particles that chomp it down into tiny pieces and then present it like a profile and say, look, this is who you're going for. 64 00:08:25,620 --> 00:08:31,019 And then the specialised then come in. This is where we have the T cells and the B cells. 65 00:08:31,020 --> 00:08:37,110 So that would now that that's the transition from the innate immune system to the adaptive immune 66 00:08:37,110 --> 00:08:44,040 system where you have the T cells and the B cells and then they come in and they are so specialised, 67 00:08:44,040 --> 00:08:50,520 they are like your top level snipers or whatever it is, they have the profile, they are going for the profile. 68 00:08:50,760 --> 00:08:55,799 You don't have a lot of collateral damage with them because they know exactly what they are going for whilst with the other ones, 69 00:08:55,800 --> 00:08:59,130 the non specialised guys, you get a whole lot of collateral damage. 70 00:09:00,930 --> 00:09:05,669 So you've got the T cells that sort of targets cellular immune response. 71 00:09:05,670 --> 00:09:12,870 So they target the cells that are infected and then you've got the B cells that are more humoral, 72 00:09:12,870 --> 00:09:17,520 so they secrete this antibodies to shoot them up like 2000 molecules per second. 73 00:09:17,760 --> 00:09:26,819 I think for some of them this antibodies then go through the bloodstream and then able to target pathogens the way the antibodies do it. 74 00:09:26,820 --> 00:09:31,200 Again, kind of like a crime scene where you have, you know, 75 00:09:31,200 --> 00:09:37,200 some of those veins that tell you if you try to steal from it, you get sprayed by something. 76 00:09:37,200 --> 00:09:44,550 Yes. So this B cells, just the antibodies just sort of dot the patogeno, the antigen. 77 00:09:44,850 --> 00:09:50,670 And then that means that all the cells can then come and say, oh, that's you and then engulf it. 78 00:09:51,120 --> 00:09:55,109 So those are the two main things. That's a really good explanation. 79 00:09:55,110 --> 00:09:58,170 Sorry I interrupted you in your story. No, no, that's fine. 80 00:09:58,440 --> 00:10:07,140 I, I like to do this public engagement and I was doing one for a group of schoolchildren and I thought, what's the easiest way to. 81 00:10:09,990 --> 00:10:13,680 Message the immune system to. I thought, oh, first responders. 82 00:10:13,680 --> 00:10:16,980 That was. Yeah. 83 00:10:17,430 --> 00:10:24,450 Pretty good. Yeah. So sorry. I think I interrupted you in mid-flight when you were talking about your PhD, right? 84 00:10:24,540 --> 00:10:28,889 Okay. Yes. So we're talking about the T of hate cells Within the T cells. 85 00:10:28,890 --> 00:10:35,640 You have different types. You have, I think the most common that people would know are the CD4+ T cells, 86 00:10:36,030 --> 00:10:40,470 which we call the help of T cells and the CD8 T cells which are called the cytotoxic T cells. 87 00:10:41,370 --> 00:10:47,220 Now the CAC 40 cells, just as the name implies, they help the cytotoxic T cells. 88 00:10:48,030 --> 00:10:55,229 There are different flavours of those. So you have, depending on the antigen, they c depending on the site of infection. 89 00:10:55,230 --> 00:11:06,330 You have different ones. You could have the th1 CD4 cells which would be more geared to help the CD8 cells with viral infections. 90 00:11:06,720 --> 00:11:10,260 Then you have the to hit 17 that sort of line your mucosal. 91 00:11:10,680 --> 00:11:17,219 So your cells just epithelial surfaces that interact with the bloodstream. 92 00:11:17,220 --> 00:11:25,650 Then you've got that. You've got the th2 as well that performs some important functions. 93 00:11:25,650 --> 00:11:32,760 But at the moment they seem to be they get a bad rep for their role in allergies. 94 00:11:33,120 --> 00:11:37,920 And then you've got the follicular helper T cells, which is the one that I was working on. 95 00:11:38,400 --> 00:11:50,219 Then the states which are the are the types of T cells you have, as the name suggests, the sort of mediate this healing function of the T cells. 96 00:11:50,220 --> 00:11:55,350 So they have this debt packages where they would shut it out, 97 00:11:55,590 --> 00:11:59,400 they come into close proximity with their target and then they shoot up these death packages. 98 00:11:59,670 --> 00:12:06,899 That's one way they would do it. Sometimes they would also engage with the cell they want to kill and then send those signals. 99 00:12:06,900 --> 00:12:13,139 That then means that the cell gets killed. But all of this killing is done in a controlled manner so that you don't have 100 00:12:13,140 --> 00:12:17,550 inflammation and then it's mopped up by other cells in the immune system. 101 00:12:46,100 --> 00:12:55,600 So yeah, that was my pitch. So that sounds as if it was quite exciting because it was discovered something new and demonstrated it both ways, right? 102 00:12:55,640 --> 00:13:03,530 Yeah, yeah, yeah, exactly. Exactly. And that's what you want to do in a season to get something novel and advance the field ever so slightly. 103 00:13:04,760 --> 00:13:07,890 Then I moved to Oxford after my Ph.D. 104 00:13:08,270 --> 00:13:13,520 I started in a lab just down the road in the Indian. 105 00:13:13,520 --> 00:13:24,650 The Indian army. And we were interested in HIV. 106 00:13:24,660 --> 00:13:37,350 And I was so pleased to be there because like I said, growing up, I saw a lot of first hand real world examples of the disease taking hold in Nigeria. 107 00:13:37,350 --> 00:13:42,630 And I, I never really had the facts, but I was just so fascinated about it. 108 00:13:43,230 --> 00:13:48,030 And so to be able to do it at this level, I was really, really pleased. 109 00:13:48,420 --> 00:13:59,430 And what we wanted to do was I had completely left my Ph.D. I had taken those cells that I knew helped to make this really, 110 00:13:59,460 --> 00:14:06,800 you know, gold standard antibody. And we were asking the question if those cells played a role in HIV. 111 00:14:06,810 --> 00:14:13,770 And the reason was because we had found a group of HIV infected people, a very tiny proportion of HIV infected people, 112 00:14:15,030 --> 00:14:26,430 depending on how broadly you look at your antibodies, they're an elite group of infected people that would make this broadly neutralising antibody. 113 00:14:26,440 --> 00:14:30,360 So part of the problem with HIV is that it has a very high mutation rate. 114 00:14:30,840 --> 00:14:34,169 So the diversity in one person alone, 115 00:14:34,170 --> 00:14:41,700 the viral diversity of one person alone is so much that the immune system is always trying to play catch up with the virus. 116 00:14:41,700 --> 00:14:47,610 The virus is evolving, the immune system is trying to catch up, and it just not because the immune system, 117 00:14:48,090 --> 00:15:00,270 but in a group of group of people, we found that the myth is broadly neutralising antibodies that could neutralise a wide strain up to. 118 00:15:00,300 --> 00:15:04,830 I think the figures at the time I left was up to 90% of circulating strains. 119 00:15:05,220 --> 00:15:12,600 But the problem was it had no clinical benefit to them because they are a virus had mutated. 120 00:15:14,380 --> 00:15:22,680 Yes, yes. So it had no clinical benefit. So what we wanted to do was how can we make this broadly neutralising antibodies. 121 00:15:22,680 --> 00:15:28,709 All right, arise earlier in people or how can we even make it into a prophylactic vaccine 122 00:15:28,710 --> 00:15:33,510 so that at risk populations or maybe even the general population can have. 123 00:15:33,510 --> 00:15:36,900 That's just the same way we have our vaccination strategy. 124 00:15:37,350 --> 00:15:45,870 So we thought, well, maybe this cells that play a role in driving this antibody response might be important in this people. 125 00:15:46,380 --> 00:15:50,360 And what we found was that, yeah, they were the same. 126 00:15:50,550 --> 00:15:58,500 It looked like they played a role because in this group of people they seemed to have a higher number of these cells in their circulation. 127 00:15:59,010 --> 00:16:03,690 So we're trying to really get into how they were playing that role. 128 00:16:04,340 --> 00:16:15,330 It was a very big it was it was with a very big consortium funded by the NIH and some collaborators at Duke University in the US. 129 00:16:16,320 --> 00:16:25,410 But we it's quite it was quite a difficult question to ask because in order for this people, 130 00:16:25,410 --> 00:16:29,070 this infected people to make this broadly neutralising antibodies, 131 00:16:29,880 --> 00:16:34,880 it meant that it didn't go on the standard of care, which was antiretroviral therapy. 132 00:16:35,160 --> 00:16:40,649 Yes. And at the moment, as soon as a person gets infected, they get put on ARDS. 133 00:16:40,650 --> 00:16:44,610 So you don't really get to develop this broadly neutralising antibodies anymore. 134 00:16:45,000 --> 00:16:51,329 And the reason why art limits the development of this is that you need the antigenic diversity. 135 00:16:51,330 --> 00:17:01,950 You need the arms race to push the immune system to broaden its response, but as you would imagine, keeps the viral load down. 136 00:17:01,950 --> 00:17:05,910 So there isn't really any evolution of the virus, so very minimal evolution of the virus. 137 00:17:06,480 --> 00:17:14,520 And so the samples we had where from way back before I came in, so they were very precious. 138 00:17:15,510 --> 00:17:25,079 And then because we're looking at subjects that did not go on antiretroviral therapy, 139 00:17:25,080 --> 00:17:29,070 it meant that the immune system had been battered by the virus for years. 140 00:17:29,610 --> 00:17:36,750 And as we know, with HIV, one of its canonical ways of battering the immune system is depleting the city for cells. 141 00:17:37,230 --> 00:17:42,000 So the city fossils, which I was interested in, we're almost completely gone. 142 00:17:42,240 --> 00:17:45,809 And I was looking for 5% of the almost completely gone cells. 143 00:17:45,810 --> 00:17:53,910 Those were the cells I was interested in. So what while I didn't I didn't quite come out of that project with a publication, 144 00:17:54,450 --> 00:17:59,759 I came out of it with a lot of learning because I had to optimise a lot of things. 145 00:17:59,760 --> 00:18:06,329 I had to develop a lot of assays, I had to make my own sort of trying to find a needle in the haystack. 146 00:18:06,330 --> 00:18:09,870 So I had to make my own ways of finding things. 147 00:18:09,870 --> 00:18:19,500 And I think a lot of what I'm doing now is credit to that because it really put the resilience of the. 148 00:18:20,740 --> 00:18:25,200 In the 95% of science is failure. Absolutely. 149 00:18:25,770 --> 00:18:33,560 Yeah, yeah, yeah. And some of the methods that we developed while trying to find that, you know, 150 00:18:33,670 --> 00:18:42,159 elusive or answer the elusive question we had were then used with other projects I was involved in. 151 00:18:42,160 --> 00:18:49,480 For instance, I got involved in the malaria vaccine projects. Some of the the t cell assays I had set up was then used for that. 152 00:18:49,810 --> 00:18:54,220 And I think we might touch on that later with when we talk about the COVID vaccines. 153 00:18:55,660 --> 00:19:06,550 And then I left that lab and then moved to Johns lab again, still keeping within the the theme of HIV and antibodies. 154 00:19:07,810 --> 00:19:19,030 He he had a very interesting project where he was looking at he had moved from we had moved from prophylactic vaccines now to therapeutic modalities. 155 00:19:19,030 --> 00:19:22,060 And Johns Lab is very focussed on HIV cure. 156 00:19:22,570 --> 00:19:28,780 And, you know, while I think for most people in the field, we have accepted that we're never going to have a sterilising cure, 157 00:19:28,780 --> 00:19:34,570 at least not anytime soon, we wouldn't have a sterilising cure for HIV because the virus forms this reservoir. 158 00:19:34,900 --> 00:19:40,090 And as soon as you take people off and through retroviral therapy just replicates very quickly, it rebounds. 159 00:19:41,230 --> 00:19:47,770 What we want to get is some kind of functional cure where people can be off antiretroviral 160 00:19:47,770 --> 00:19:53,110 therapy for a long time because there are issues with antiretroviral therapy like this, 161 00:19:53,110 --> 00:19:57,700 the toxicity issue, there's the resistance and there's also compliance. 162 00:19:58,150 --> 00:20:03,850 And as you can imagine, logistics as well. If you're thinking about remote villages in Africa or South East Asia, 163 00:20:04,240 --> 00:20:11,110 there's also the fact that HIV is still a disease that people discriminate against. 164 00:20:11,110 --> 00:20:18,150 So for some people going to get the antiviral viral therapy is it's it's quite risky for them, you know, 165 00:20:18,160 --> 00:20:25,870 So if we can take that off them and make it in such a way that you don't need to take a pill every day anymore, 166 00:20:25,870 --> 00:20:29,160 you can now take a pill once a year or once in six months. 167 00:20:29,170 --> 00:20:32,200 Even once in two months would be much better than every single day. 168 00:20:32,800 --> 00:20:39,250 And so that's what a lot of the HIV cure studies are focussed on, and that's what Jong's lab is focussed on. 169 00:20:39,250 --> 00:20:51,010 So he got funding from the Gates and the Gates Foundation, and we set up this clinical trial that was just brilliant. 170 00:20:51,010 --> 00:20:57,130 So they had this broadly neutralising antibodies, the ones that can neutralise more than 90% of circulating strains, 171 00:20:57,610 --> 00:21:01,570 but they had tweaked them, they had engineered them so that they were now long lasting. 172 00:21:13,410 --> 00:21:18,150 But also if you infused them on already infected people in the already infected people, 173 00:21:18,540 --> 00:21:22,320 you could get a suppression of viremia even in the absence of art. 174 00:21:23,340 --> 00:21:29,280 So the questions we're asking were twofold. Can we replicate this with the long acting b nabs? 175 00:21:29,790 --> 00:21:33,150 And also, if we could, what? 176 00:21:33,420 --> 00:21:37,770 How were they keeping viral load at B? 177 00:22:06,570 --> 00:22:12,299 And what they had done in that study was try to sort of see what cells were controlling it. 178 00:22:12,300 --> 00:22:17,040 So, you know, you would always want to start with something big and keep narrowing it. 179 00:22:17,400 --> 00:22:24,900 So they started with the cells and then they depleted some of the cells that depleted the natural killer cells and the CD8 cells. 180 00:22:24,900 --> 00:22:28,950 And they found when they depleted the cells, they had a spike in viral loads. 181 00:22:28,950 --> 00:22:33,389 So that sort of gave an indication that something with the cells was helping to control it. 182 00:22:33,390 --> 00:22:41,040 But it didn't quite make sense that, you know, how, you know, antibodies don't really interact with CD8 cells. 183 00:22:41,400 --> 00:22:48,240 And antibodies could interact with the NK cells because part of the effect of functional antibodies would recruit the NK cells, 184 00:22:48,540 --> 00:22:51,710 but not quite the seeds. So how were they doing that? 185 00:22:51,720 --> 00:22:56,160 And this was where I came in as an immunologist on the trial. 186 00:22:57,420 --> 00:23:03,899 I was the lead immuno immunologist on the trial. We wanted to first of all characterise what happened after we fused this. 187 00:23:03,900 --> 00:23:11,790 People with this B nabs what happened to their immune response to the dyes and classify what kind of responses were they making. 188 00:23:33,060 --> 00:23:39,770 And so we wanted to sort of. And this did this vaccinale effect that we thought was happening. 189 00:23:41,270 --> 00:23:46,340 Yeah. And how did that go? Well, so we had COVID. 190 00:23:47,060 --> 00:23:50,690 Julius Caesar came along. Yeah. Just. Okay. 191 00:23:50,700 --> 00:23:59,329 Just when we were about to start the clinical trial, I think we had our first patient and we're ready to dose in March, and then COVID happened. 192 00:23:59,330 --> 00:24:14,210 So we had to pause it and then it didn't get we didn't start until a year and a half later for the trials going really far and really well. 193 00:24:14,690 --> 00:24:20,750 And I'm not allowed to share yet, but I think it's looking quite promising. 194 00:24:21,140 --> 00:24:26,240 And I expect that there would be something a publication coming up quite soon. 195 00:24:28,030 --> 00:24:34,759 Yeah. Mm hmm. Isn't. But you got us to open it so that my question that I'm asking everybody is, 196 00:24:34,760 --> 00:24:40,399 can you remember when you first heard that there was some respiratory illness going on in 197 00:24:40,400 --> 00:24:45,460 China and how you came to realise it might be serious and then how you became engaged? 198 00:24:45,470 --> 00:24:47,300 Yeah, in a response. 199 00:24:47,540 --> 00:24:56,209 So I think I heard about it in December because it was in the news towards, you know, the first week, second week of the set about that time. 200 00:24:56,210 --> 00:25:02,360 And then it became more serious as we got to Christmas. And I think I saw it and I was like, Oh, okay, something else on the news. 201 00:25:02,360 --> 00:25:05,000 You know, I didn't really pay a lot of attention to it. 202 00:25:05,660 --> 00:25:13,219 And then I remember going away, it starts to get more and more serious and it was like the only thing on the news. 203 00:25:13,220 --> 00:25:27,140 And I was like, Oh, this disease from China, you know? And then I went to I went on a holiday to Malta, and I remember just having a slight bit of. 204 00:25:29,970 --> 00:25:36,390 Should I be going on this holiday? This was in January. But then we I think they had one or two cases in. 205 00:25:38,010 --> 00:25:45,030 I don't I do remember if it was the UK or somewhere in Europe, but I went there and I had this cold before I went, 206 00:25:45,630 --> 00:25:50,280 Oh, no, I came back with a cold and I couldn't shake off the cold. 207 00:25:50,280 --> 00:25:55,620 And I think like everybody in the UK, when you speak to them, they're like, Oh, I had something in January, I think I go. 208 00:25:57,000 --> 00:26:01,559 So even I was thinking, Oh yeah, I think I had COVID, but I don't think so. 209 00:26:01,560 --> 00:26:02,430 It wasn't COVID. 210 00:26:03,410 --> 00:26:11,730 And then I came back from Malta at the end of January, and I think that was when I really thought, okay, this is getting quite serious. 211 00:26:12,060 --> 00:26:16,020 And also, you have really. At least for me, 212 00:26:16,320 --> 00:26:20,940 I never really thought about it until we had the first case in the UK and then 213 00:26:20,940 --> 00:26:24,929 the scaremongering started and we didn't know anything about this disease. 214 00:26:24,930 --> 00:26:30,300 All we knew was the SARS cov one, which we knew was very deadly. 215 00:26:31,230 --> 00:26:35,430 So I think we started to have a bit of panic. 216 00:26:35,970 --> 00:26:42,660 And I remember thinking this was around February, March time when they were having the debate about the Cheltenham races, 217 00:26:43,530 --> 00:26:47,030 and I think I had gone into full a full blown panic at that point. 218 00:26:47,050 --> 00:26:51,750 I was like, No, the races shouldn't be going on. This is an airborne virus, it should not be going on. 219 00:26:53,130 --> 00:27:00,930 And then everything just sort of happened very quickly and almost like a dream, because, you know, 220 00:27:00,930 --> 00:27:05,040 one day the universe is like we're going to carry on with our work and blah, blah, blah. 221 00:27:05,040 --> 00:27:15,330 And then the next day this talks about a press conference from number ten. 222 00:27:15,810 --> 00:27:18,150 And I think just before the press conference, 223 00:27:18,150 --> 00:27:26,600 which then got the news that everything is to CS in the university and we're all supposed to go work from hormones. 224 00:27:26,610 --> 00:27:32,130 And it just it just felt it just felt like the end of days in a way. 225 00:27:33,630 --> 00:27:48,960 So we were at home for two weeks while we tried to sort out our ethics and stuff, but we always knew that we would be involved in COVID somehow. 226 00:27:49,290 --> 00:27:55,950 So for most of us, I think as soon as we started to hear that the the numbers were going up, 227 00:27:57,540 --> 00:28:05,460 I think almost quite a number of us from here volunteered to work at the hospital and take our skills to the hospital. 228 00:28:06,420 --> 00:28:09,600 All the doctors, all the medics had gone back to the hospital already. 229 00:28:09,930 --> 00:28:16,259 And the scientists like myself, you know, we could do Q PCRs, we could do the PCR, and we had to wait. 230 00:28:16,260 --> 00:28:19,649 Overwhelmed. We could do whatever it was. 231 00:28:19,650 --> 00:28:22,680 You know, we were all trained. So just tell us what you needed. 232 00:28:23,160 --> 00:28:25,799 And a few of us sort of started. 233 00:28:25,800 --> 00:28:36,330 I think personally, I spent like a day or two, two nights in the hospitals doing the some of the assays for detection, 234 00:28:36,660 --> 00:28:41,220 and then we had to be called back in the lab and it was just go, go, go from then on. 235 00:28:41,430 --> 00:28:45,510 So what was the question that the lab set out to address specifically? 236 00:28:45,600 --> 00:28:53,969 So I think at that time we just wanted to know what the immunology of this disease was. 237 00:28:53,970 --> 00:29:00,090 So it was very much observational and that's what you can do on the stands, you know, observational studies. 238 00:29:00,960 --> 00:29:06,090 What was the disease doing to the body? What was the body doing to COVID? 239 00:29:06,420 --> 00:29:10,139 How long was it taken to clear COVID? Why were some people asymptomatic? 240 00:29:10,140 --> 00:29:13,740 Why were some people asymptomatic, and why did some people even die? 241 00:29:14,130 --> 00:29:17,520 And, you know, why did it impact children? 242 00:29:17,520 --> 00:29:22,170 But they did not Why did it impact adults? So we didn't impact children so much. 243 00:29:22,470 --> 00:29:25,530 Why was it more severe in certain ethnicities versus others? 244 00:29:25,530 --> 00:29:31,379 Why was it why were males more impacted than females in terms of disease severity? 245 00:29:31,380 --> 00:29:32,670 So it was all those questions. 246 00:29:33,060 --> 00:29:45,180 So we set up an observational study where we started by just simply it sounds quite simple, but we didn't even know what tools to use. 247 00:29:45,720 --> 00:29:51,270 So it's like you've got all this arsenal you're, you know, and you're like, Right now what do I do? 248 00:29:51,750 --> 00:30:00,389 So what we did was we then took all our arsenal and just hit it with it and said, Which one answers the question? 249 00:30:00,390 --> 00:30:03,900 But then where were you getting your samples from? From the hospital. 250 00:30:03,900 --> 00:30:11,550 The health care worker. We set up a healthcare worker cohort and I mean, it was just fantastic collaboration because this people, 251 00:30:12,390 --> 00:30:17,190 despite the fact that they were so busy, they were worn out, they were coming for regular bleeds. 252 00:30:18,090 --> 00:30:22,260 It was just a beautiful collaboration to see everyone work together like that. 253 00:30:22,260 --> 00:30:26,640 And it was so selfless. So we had the health care workers study. 254 00:30:27,900 --> 00:30:35,950 We got our. Samples. Then from our end, we had the intellectual input as to how we're going to conduct our study. 255 00:30:36,310 --> 00:30:40,540 So first of all, we wanted to know how do we characterise the immune response in this people? 256 00:30:40,990 --> 00:30:45,460 We had the standard T cell acids that we would use, the earliest bullets we had the. 257 00:30:47,070 --> 00:30:58,389 So elispot basically it looks at the functionality of a cell by you get the cells and then you get an antigen or a part of the virus. 258 00:30:58,390 --> 00:31:06,640 You can either make that synthetically in the lab as protein shock peptides or you can get the 259 00:31:06,670 --> 00:31:11,030 virus Lysate But at that time we weren't allowed to go anywhere near COVID because we didn't know. 260 00:31:11,050 --> 00:31:17,770 So we had the synthetic peptides and then you put that on the cells and if a cell is specific, 261 00:31:18,100 --> 00:31:24,010 if a T cell is specific for that particular antigen, it will start to make the molecules. 262 00:31:24,340 --> 00:31:25,900 So so that's a signal. 263 00:31:26,260 --> 00:31:33,930 And what we then do is we then trap that signalling in a plate on the membrane and then we can quantify how big the response is. 264 00:31:33,970 --> 00:31:37,270 So you're asking the cell, do you recognise do you recognise this? Exactly. 265 00:31:37,270 --> 00:31:44,680 Exactly. Kind of like an interrogation device. Yeah. So what they call it, you know, a line up. 266 00:31:44,740 --> 00:31:48,610 Yeah, I know exactly right. That's what it's called. 267 00:31:49,180 --> 00:31:54,010 Yeah. Yeah. And we were the ones behind the screen and we're saying, Yeah, which one of them is the criminal? 268 00:31:54,220 --> 00:32:07,120 But yeah, we, we did that. And then it secretes this cytokines we call them and it's, it's all of our t cell, as is our own spectrums of sensitivity. 269 00:32:07,480 --> 00:32:13,900 So there's, you know, it's a balance between sensitivity, specificity and, 270 00:32:14,020 --> 00:32:17,829 you know, a number of different things that you may have to compromise with. 271 00:32:17,830 --> 00:32:24,639 In some cases you're able to sort of take the take us the cell, you know, do you recognise this? 272 00:32:24,640 --> 00:32:31,060 But then say, well, if you recognise this, I want to know exactly how you are, what's your own profile? 273 00:32:31,480 --> 00:32:38,410 And so if we're using the analogy of a line up, it's a two way blinded thing where you can say, Oh, 274 00:32:38,410 --> 00:32:43,450 someone here recognises it, but you don't quite know who the person is versus taking that blinds off. 275 00:32:43,570 --> 00:32:52,120 You're like, Well, this person, this female, this male, you know, recognises this culprit. 276 00:32:52,390 --> 00:32:55,030 So you can, you know, it depends on what you want. 277 00:32:55,270 --> 00:33:06,399 So we took all the approach because we wanted to be able to, we wanted to be agnostic about it and come up with the best possible way of detecting it. 278 00:33:06,400 --> 00:33:11,950 And we found that depending on the assay you had, you got different readouts. 279 00:33:12,280 --> 00:33:21,069 So without any spots we're able to pick cross-reactivity but not a whole lot of cross-reactivity. 280 00:33:21,070 --> 00:33:28,840 So cross-reactivity basically is the ability of one particular cell to be able to recognise multiple 281 00:33:28,840 --> 00:33:35,500 antigens and which is advantageous in if you think about diseases that are closely related. 282 00:33:36,010 --> 00:33:41,740 But it can also be disadvantageous because it can also lead to non-specific responses. 283 00:33:42,130 --> 00:33:46,150 So again, that's this is a nuance of the immune system. 284 00:33:46,540 --> 00:33:53,260 So we found that we were able to see some level of this cross-reactivity with the early spots. 285 00:33:53,530 --> 00:34:00,520 We didn't see very much with the ISIS as the ISIS is another assay that's less sensitive than the early spot. 286 00:34:00,850 --> 00:34:09,250 But with the ISIS, you can go into the fine details of what the cells are essentially intracellular, cytokine staining. 287 00:34:09,730 --> 00:34:15,160 And so this would use it, just an in vitro ASR as well. 288 00:34:15,160 --> 00:34:22,870 You stick the same peptide, but rather than letting the cytokines get secreted out of the cell and you trap it on the membrane, 289 00:34:23,110 --> 00:34:28,570 you actually hold it in the cell and then you can come with the probes, 290 00:34:29,800 --> 00:34:31,570 the antibody probes that we make, 291 00:34:31,900 --> 00:34:38,799 and then you can stick all of these probes on them and then pass it through a machine that beams this lasers on it and then tells you, 292 00:34:38,800 --> 00:34:43,630 Oh, it's got this, it's got that. Depending on what you put, what probes you put in there. 293 00:34:43,930 --> 00:34:47,650 But it has it's a much more reduced sensitivity. 294 00:34:48,070 --> 00:34:51,100 But sometimes you want to know exactly what it is. 295 00:34:51,100 --> 00:34:56,200 So you would be willing to take that compromise of a bit less sensitivity. 296 00:34:56,440 --> 00:35:04,209 But I get to know exactly what the cell is. And then there are other aces as well, where especially when the population is really low, 297 00:35:04,210 --> 00:35:12,130 where the number of cells that recognise a particular pathogen is really low, you want to expand them out for a bit. 298 00:35:13,000 --> 00:35:17,980 And what that happened, what that does is that, you know, you increase the sensitivity, 299 00:35:18,220 --> 00:35:27,070 but it means that you don't know what you start, you don't know who started it, it started out who started to make it so. 300 00:35:28,400 --> 00:35:34,130 Basically just say take this and pass it on to people and then a lot of people end up getting it. 301 00:35:34,400 --> 00:35:45,550 But a lot of yeah, if you imagine sharing candy and you've just said pass this on, but you don't quite remember who started the passing on. 302 00:35:45,560 --> 00:35:52,580 Yeah. So you don't get that information from the, the, the proliferation assays. 303 00:35:52,850 --> 00:35:57,380 So we took all of that approach and then we found out depending on what you took, 304 00:35:58,130 --> 00:36:02,209 you were able to answer different questions and that paper went out quite quickly. 305 00:36:02,210 --> 00:36:07,370 And I think that paper is quite well cited now because it showed that, you know, different T sources, 306 00:36:07,640 --> 00:36:12,469 we already knew it in the field, but it sort of solidified that that, you know, 307 00:36:12,470 --> 00:36:17,660 different assays and especially with COVID, you want to be able to either use all all of the assays, 308 00:36:17,660 --> 00:36:22,720 all everything you've got in your arsenal or at least pick the best one that answers your question. 309 00:36:23,300 --> 00:36:33,140 Um, and then we then moved on to, I think quite quickly the vaccine started to come out, thankfully. 310 00:36:33,410 --> 00:36:39,470 So we then moved into a lot of characterising the vaccine immune responses, 311 00:36:40,610 --> 00:36:46,309 you know, knowing what sort of immune responses this vaccine is were making. 312 00:36:46,310 --> 00:36:50,510 How long did it last? We did that in healthcare workers. 313 00:36:50,510 --> 00:37:00,020 We also did comparative studies where we compared the RNA vaccines to the to the adenoviral vector vaccines that were made in Oxford. 314 00:37:00,560 --> 00:37:03,770 Then as part of our work here at Oxford, 315 00:37:04,220 --> 00:37:18,379 we made we had all the the vaccine and part of vaccine and the the trials that you would 316 00:37:18,380 --> 00:37:25,459 have with a vaccine before it's rolled out is also looking in certain groups of people. 317 00:37:25,460 --> 00:37:30,860 So sometimes it would start out in adults and move to kids and then move to other populations. 318 00:37:31,340 --> 00:37:40,880 But because we had a group of HIV infected people that we had been working with for years, they were included in the study. 319 00:37:41,300 --> 00:37:51,590 And so we did some of the things that they regarded as what the phrase is more vulnerable, and then they were regarded as more vulnerable. 320 00:37:51,590 --> 00:37:55,460 Yes. So yeah, exactly. The where we got it is more vulnerable. 321 00:37:55,700 --> 00:38:05,530 So we did a lot of the HIV, the preliminary work on the HIV vaccines for the child because we had a substudy and you know, 322 00:38:05,600 --> 00:38:09,200 we published that in a paper as well. That's also heavily cited. 323 00:38:09,650 --> 00:38:16,459 And I think I'm quite proud of the fact that some of the work we did with that vulnerable population did inform some 324 00:38:16,460 --> 00:38:26,690 of the and the other policies on the management for COVID in HIV infected people and also the vaccination for them. 325 00:38:27,170 --> 00:38:28,970 So what was the what was the outcome? 326 00:38:29,120 --> 00:38:40,130 We found that it was safe in then, because the worry is that for certain vaccines we found that in HIV, when HIV infected people receive it, 327 00:38:40,580 --> 00:38:49,580 it triggers it could potentially reactivate their immune system and then trigger increased replication in the virus. 328 00:38:50,630 --> 00:38:55,760 So we found that, you know, we where we'd seen that we found that it was safe, they didn't have any adverse effect. 329 00:38:55,760 --> 00:39:05,660 And also, when you consider that these are people whose immune systems are still in a way compromised because even though they go on us quite quickly, 330 00:39:05,900 --> 00:39:10,969 artisan's retroviral therapy, they go on that quite quickly. The immune system never fully recovers. 331 00:39:10,970 --> 00:39:21,710 So you have to make sure that whatever you are given to this population, it's well-characterized and is safe for them. 332 00:39:22,130 --> 00:39:25,790 And that was what we did. And were they protected from COVID? 333 00:39:26,030 --> 00:39:34,280 They were protected from COVID, yeah. When they received the vaccine, yes, they were protected to the same level as healthy or uninfected people. 334 00:39:34,670 --> 00:39:39,130 We shouldn't use the word healthy because they are healthy as well. But uninfected people. 335 00:39:39,350 --> 00:39:44,899 Yeah. Mm hmm. Yeah. And I don't want to rush. 336 00:39:44,900 --> 00:39:53,600 You want but you it. I mean, I was just reading a study you've done on volunteers who are taking part in challenge studies. 337 00:39:54,680 --> 00:39:57,860 Is that right? In the challenge? Um. 338 00:39:58,640 --> 00:40:02,180 Oh, no, I'm not. 339 00:40:02,360 --> 00:40:05,720 I'm not familiar. Oh, right. Sorry. I must have. Well, that's okay. 340 00:40:05,880 --> 00:40:12,440 That's all right. That's all right. And but so. 341 00:40:12,580 --> 00:40:21,340 Yes. So I think the paper I'm thinking of was a was about cross-reactivity and whether previous infection with. 342 00:40:23,890 --> 00:40:29,770 I think it was the review that we wrote because we were quite interested in this cross-reactivity. 343 00:40:31,000 --> 00:40:35,410 And it's it's I can give you a bit of history on how we sort of stumbled on it. 344 00:40:35,420 --> 00:40:43,350 So we had this. We're looking for healthy controls that have never been infected with COVID and we led. 345 00:40:44,050 --> 00:40:47,650 I remember that there were 30 people and I played 30 people. 346 00:40:48,700 --> 00:40:51,750 So you are involved in the sample collection? Yeah, yeah, yeah. 347 00:40:51,760 --> 00:40:56,540 So I'm a trained phlebotomist as well, so I was the only one on site that day. 348 00:40:56,560 --> 00:41:06,310 So there was a long queue of people and we thankfully at that time we had stopped doing the assays fresh. 349 00:41:06,580 --> 00:41:10,040 We had started to freeze down the samples, so we had time to go back to it. 350 00:41:10,510 --> 00:41:14,079 But I remember doing the first set of assays because I did them. 351 00:41:14,080 --> 00:41:22,120 I was leading a team of scientists that were doing this assays and I did the first set and with the 352 00:41:22,120 --> 00:41:27,150 proliferation assay you had to wait seven days to wait seven days and then get a result that you don't. 353 00:41:28,900 --> 00:41:37,390 Well, at that time I thought this was a bad result, but to wait seven days and not get what you expect, that can be quite heartbreaking. 354 00:41:37,840 --> 00:41:41,860 But I was looking at it and I was like, no, this doesn't this doesn't look right. 355 00:41:41,860 --> 00:41:45,549 People. This people have all said they haven't been infected with COVID. 356 00:41:45,550 --> 00:41:48,850 It's still very early days, so I'm quite certain they haven't. 357 00:41:49,180 --> 00:41:52,450 But everyone, you know, when you look at almost everyone, 358 00:41:52,450 --> 00:41:58,510 when you look at the part of the envelope protein that almost everyone had the response there. 359 00:41:58,520 --> 00:42:05,680 So where is that coming from? And I remember thinking, I'm going to have to repeat this. 360 00:42:06,070 --> 00:42:12,300 So I set the first set aside and then I did the second set of patients, another ten. 361 00:42:12,310 --> 00:42:17,530 The first set was ten, I did the second set another ten, and it was the same results. 362 00:42:17,800 --> 00:42:23,320 And then I got someone else to repeat the first set, the first set because I was like, okay, maybe I've gone crazy. 363 00:42:24,220 --> 00:42:36,640 And then they got the same thing as I did. So we started to think and then and I think the first the first t cell paper came out around that time. 364 00:42:36,640 --> 00:42:41,950 It was sometime in April or May, and we're like 22, 20, 20. 365 00:42:41,980 --> 00:42:45,280 Yeah, 12, 20, 20, 2020. Yeah, yeah, I remember. 366 00:42:45,280 --> 00:42:49,780 Which we're all waiting for the first papers to come out because we were not ready with any papers yet, 367 00:42:49,780 --> 00:42:59,679 but because you're sort of digging in the dark and you're doing it under so much pressure and the eye of the world suddenly was on you. 368 00:42:59,680 --> 00:43:02,710 Like, if every time a scientist was important, that was. 369 00:43:04,540 --> 00:43:11,830 Yeah. I remember driving through on the motorway and being the only car there with my permit saying I was not important, 370 00:43:12,310 --> 00:43:19,360 a key worker here, I thought, Oh, okay. So we did it. 371 00:43:19,360 --> 00:43:22,750 And then the first few papers started to come out and then we signed from elsewhere. 372 00:43:22,900 --> 00:43:28,240 From elsewhere, I think it was from La Hoya and the Scripps in La Hoya, the first ones. 373 00:43:28,240 --> 00:43:34,060 And we started to decide to report this cross-reactivity by at really low levels. 374 00:43:34,450 --> 00:43:46,960 And so we then started to align the sequence of the virus because I think the sequence we had the sequence for, there was just so much to do. 375 00:43:47,290 --> 00:43:51,780 But we then aligned the sequences with the virus and we saw that, you know, 376 00:43:51,790 --> 00:43:58,569 there were parts of the sequences that were very potent conserved across the coronaviruses, 377 00:43:58,570 --> 00:44:04,090 particularly the beta coronaviruses, and that would include some of the seasonal colds. 378 00:44:04,540 --> 00:44:09,639 So what we were picking up could have been a response to some of the seasonal colds. 379 00:44:09,640 --> 00:44:14,320 But also those responses, as we've come to know with COVID, don't last very long. 380 00:44:14,830 --> 00:44:21,660 But because we're coming out of the winter and people may have encountered those, it was quite strongly you had one put. 381 00:44:21,700 --> 00:44:28,810 Yeah, exactly. And I was quite strongly Well, it was interesting because it was just the envelope. 382 00:44:29,260 --> 00:44:33,480 And so we found out that if you want to look for COVID specific responses, 383 00:44:33,490 --> 00:44:36,880 don't look for the envelope proteins, because that would be quite cross-reactive. 384 00:44:37,240 --> 00:44:43,330 In fact, you would probably be better off looking for something using an AC that's less sensitive, because if it's less sensitive, 385 00:44:43,570 --> 00:44:52,540 you're more likely to pick up more recent infections versus the highly sensitive ones that would pick up within minutes of signals. 386 00:44:53,770 --> 00:45:00,249 And then we sort of started to ask the question, well, what's the role in this? 387 00:45:00,250 --> 00:45:07,000 Cross-reactive response is what do they do? And I remember sitting on it for a really long time. 388 00:45:07,000 --> 00:45:10,030 Like, I just I really I really got into it. 389 00:45:10,030 --> 00:45:13,340 And I think I had written up a grant for funding. 390 00:45:13,360 --> 00:45:18,429 I didn't get the funding, but I really wanted to go into those cross-reactive responses because what I was thinking was, 391 00:45:18,430 --> 00:45:22,900 well, maybe if we can find something that is common to all of. 392 00:45:22,990 --> 00:45:32,170 Then we can make a pun coronavirus vaccine and maybe we don't need to go all the way because we know Sars-cov-1 and Mers-cov are quite lethal. 393 00:45:32,770 --> 00:45:38,259 If we can find something that we can, you know, we don't have a pandemic for those and the epidemics, 394 00:45:38,260 --> 00:45:45,310 I think we're years previously, but maybe we can, you know, protect against future outbreaks. 395 00:45:47,290 --> 00:45:54,160 And so we we're having the meeting and and this time I'm John and I were, 396 00:45:54,220 --> 00:45:59,680 you know, just a meeting on something else which I think Alibaba's some worry. 397 00:46:00,790 --> 00:46:06,820 John Frater we're talking about I think it was the one of the cohorts, I think it was the cancer cohorts. 398 00:46:07,270 --> 00:46:14,830 And we just talked about, oh, you know, I said, I'm very interested in looking at this cross-reactive responses. 399 00:46:15,160 --> 00:46:18,130 And I think no one really knows what's going on with them. 400 00:46:18,190 --> 00:46:21,969 You know, we were one of the first people to report it, and I think we should go a bit deeper. 401 00:46:21,970 --> 00:46:24,490 If nothing if we can't do the experiments, 402 00:46:24,880 --> 00:46:34,440 let's let's put the literature together and see what it sees and then plan and experiment after we know what the literature says. 403 00:46:35,700 --> 00:46:42,520 Yeah. So that went on for two years and the papers just kept coming out and coming out. 404 00:46:43,030 --> 00:46:51,400 But what was quite interesting was that we got to the end of that exercise and we pitched the idea as to the reviewers at Nature. 405 00:46:51,730 --> 00:46:57,400 They were happy with it and they were very, you know, they were very helpful in bouncing ideas. 406 00:46:57,400 --> 00:47:00,820 And we kept talking for like the full two years. 407 00:47:00,820 --> 00:47:12,790 But when we got to the end of it, we realised we may have put it on paper, but it's still not clear. 408 00:47:12,820 --> 00:47:16,570 It's still just as nuanced as when we started. 409 00:47:16,780 --> 00:47:22,720 But at least now you can read the nuance in an untangled. 410 00:47:24,940 --> 00:47:28,719 Piece of work, you know, someone had done the job of bringing it altogether. 411 00:47:28,720 --> 00:47:36,340 So we found that depending on whether it was T cells or B cells, it could be beneficial or non beneficial depending on where you looked at, 412 00:47:36,670 --> 00:47:42,730 depending on who it was, depending on if it was children, depending on if you had natural infection or vaccination. 413 00:47:43,150 --> 00:47:47,560 And it was it was a fantastic piece of work and that, you know. 414 00:47:48,570 --> 00:47:53,950 Yeah. So it's just yeah, it's, it's, it's still very nuanced, I think. 415 00:47:54,310 --> 00:47:57,520 Yeah. Okay, good. 416 00:47:57,700 --> 00:48:09,420 So, um, yeah, I want to talk a bit more about collaboration because, I mean, sometimes science is seen as quite competitive, you know, within labs. 417 00:48:09,820 --> 00:48:13,090 People want to kind of hold onto what they're doing and yeah, obligation. 418 00:48:14,200 --> 00:48:17,530 But you've already talked about the, the extent of the collaboration. 419 00:48:17,620 --> 00:48:20,919 Yeah. Did did that feel very different? It did. 420 00:48:20,920 --> 00:48:26,260 It did. So I think you're right that science can be very competitive. 421 00:48:27,250 --> 00:48:33,940 But I think one of the things I liked about where I was working at the time the 422 00:48:33,940 --> 00:48:38,710 Med were this building was that they were already quite collaborative as a group. 423 00:48:39,700 --> 00:48:42,700 So it was they already had that rapport. 424 00:48:43,540 --> 00:48:51,400 But with COVID, I think I saw collaboration on a scale that I have never seen before. 425 00:48:52,270 --> 00:49:02,710 And for that brief moment where we were allowed to dream of what science could be, I think it was it was just beautiful. 426 00:49:02,920 --> 00:49:07,930 It was beautiful. People did not care about their publications. 427 00:49:07,930 --> 00:49:15,940 I think science came back to what it should be, where the goal is, how can we help? 428 00:49:16,720 --> 00:49:22,750 What good can we do for humanity? Now? It's not a case of how can I increase my publication record? 429 00:49:23,200 --> 00:49:28,509 And everyone just pitched in. There were people with samples, there were people with no house, 430 00:49:28,510 --> 00:49:39,339 there were people with I trained molecular biologist on cell assays and, you know, all the divisions that we would normally have. 431 00:49:39,340 --> 00:49:47,020 Oh, no, I'm a molecular biologist. I don't do this stuff. You know, cells are just weird or, you know, that sort of things all gone. 432 00:49:47,050 --> 00:49:53,840 And but not just that people were just willing to work. 433 00:49:53,840 --> 00:49:58,659 We it was just all hands on deck and it really felt like a frontline. 434 00:49:58,660 --> 00:50:03,220 And everybody just wanted to protect and everyone was doing their part. 435 00:50:05,590 --> 00:50:16,719 I, I love that model of science and I don't know if I think since then I've been in a lot of papers. 436 00:50:16,720 --> 00:50:22,390 We've become such as a huge auteur list. 437 00:50:22,390 --> 00:50:31,180 So I'm hoping that that means that something about COVID has stayed with the scientific community. 438 00:50:32,950 --> 00:50:40,540 But I left shortly after COVID, so I don't know what's what's been happening. 439 00:50:40,760 --> 00:50:45,129 Yeah, so have we got to that moment? 440 00:50:45,130 --> 00:50:48,130 I think we may have got to that moment, yes. So. 441 00:50:48,130 --> 00:50:51,490 Yes. So you left in April 2022? 442 00:50:51,490 --> 00:51:02,650 Yes. Right. Yeah. And why was that? I think like a lot of people, I, I was feeling burnt out by the whole thing. 443 00:51:02,680 --> 00:51:08,380 It just it didn't stop. I was feeling a bit. 444 00:51:08,650 --> 00:51:13,870 So while my community, the scientific community, banded together and got stronger, 445 00:51:16,720 --> 00:51:25,240 I was feeling a bit disillusioned by the outside community and the, you know, it didn't matter what we did. 446 00:51:25,270 --> 00:51:32,020 The anti-vaxxers had something to say. You know, we put a paper out there and they took the paper and twisted it. 447 00:51:32,020 --> 00:51:39,579 I think there was some twisting about the cross-reactive immune, you know, And I just and I don't know, 448 00:51:39,580 --> 00:51:47,530 maybe people higher up had like the kind of support to deal with all of that, but certainly not at our level. 449 00:51:47,530 --> 00:51:51,940 We didn't have that support. There was no support for burnout. 450 00:51:52,300 --> 00:52:04,629 There was no support for. So, for instance, some people went on furlough and the they were at home not by choice, but they were at home. 451 00:52:04,630 --> 00:52:10,480 And it meant that their expenses had in a way reduced for me. 452 00:52:10,480 --> 00:52:14,740 My expenses had multiplied because I used to take the train to work. 453 00:52:14,740 --> 00:52:17,830 I could no longer take the train. I had to drive in to work every day. 454 00:52:19,660 --> 00:52:23,300 I you know, and it was just it. 455 00:52:24,290 --> 00:52:29,090 I was just burnt out. I think physically, mentally I was burnt out. 456 00:52:29,840 --> 00:52:38,620 But also it's sort of highlighted another problem for me, which was, um. 457 00:52:41,680 --> 00:52:52,100 I may have reached a glass ceiling for myself and I could stay and keep hammering up that ceiling. 458 00:52:52,610 --> 00:52:57,110 But I think it took a bit more than my local environment to change. 459 00:52:57,560 --> 00:53:06,350 For me to see a change, or at least a change that I wanted it needed, they needed to be structural changes like proper frameworks put into place. 460 00:53:07,460 --> 00:53:19,850 Because, you know, I did a lot of work with COVID and I did that with a lot of my colleagues that did not necessarily look like me. 461 00:53:20,600 --> 00:53:27,080 And I saw the benefits were different. How they came out of it was very different from how I was coming out of it. 462 00:53:27,620 --> 00:53:37,850 And sometimes that stage was just for them, and my stage constantly had to be shed and on and on. 463 00:53:38,270 --> 00:53:43,159 I see it and it wasn't because there was any it wasn't conscious. 464 00:53:43,160 --> 00:53:51,950 It was unconscious the way it happened, because people could see a medic having a group and leading a group in academia. 465 00:53:52,220 --> 00:53:57,290 People could see a white man or a white woman leading a group. 466 00:53:57,320 --> 00:54:02,150 In academia in Oxford, no one could really see a black girl doing that. 467 00:54:03,080 --> 00:54:10,760 And so it just meant that unconsciously you weren't being prepared for that or you weren't being guided along that part. 468 00:54:11,370 --> 00:54:16,310 And I think I just I was just punked out. 469 00:54:18,320 --> 00:54:26,390 Did you? I mean, the university does have diversity people in in the central administration. 470 00:54:27,180 --> 00:54:32,940 Hello? I said, I assume that job is to address that kind of issue. 471 00:54:32,980 --> 00:54:45,340 Yeah. Did you. Did you get so? I don't know how. So I got into this mentorship program with the BSA and the British Society for Immunology. 472 00:54:45,360 --> 00:54:50,579 Right. Yes. And because I felt like I needed I had gone this far on my own. 473 00:54:50,580 --> 00:54:55,500 I needed a mentor. Right now, it's I had gone as far as I can go on my own. 474 00:54:56,100 --> 00:55:03,720 And around about the same time I was writing the paper and my experience as a black woman in academia 475 00:55:04,410 --> 00:55:11,940 and they were talking about their diversity initiatives and some of the things they were doing. 476 00:55:12,270 --> 00:55:15,810 And then I decided to research into it and I saw that. 477 00:55:17,610 --> 00:55:21,720 Do you know the. Athena Swan Mm hmm. Okay. 478 00:55:21,930 --> 00:55:26,729 So most people have heard of Athena Swan, which is the gender equality initiative, 479 00:55:26,730 --> 00:55:33,060 and not a lot of people have heard about the Race Equality initiative, 480 00:55:33,330 --> 00:55:43,080 and I didn't even know that our university was or had pledged to the charter just the same way as they had done with the. 481 00:55:43,080 --> 00:55:46,650 Athena Swan So Athena Swan is very visible. 482 00:55:47,820 --> 00:55:53,310 The race equality initiative is not, it's not, it's not that visible. 483 00:55:54,040 --> 00:56:10,200 I think it's just it's there, but it's sometimes it's felt like it's just a veneer, it's not really being used. 484 00:56:11,130 --> 00:56:15,090 Um, yeah. So. 485 00:56:15,460 --> 00:56:22,950 Mm hmm. So you, did you start looking around for other opportunities? 486 00:56:22,960 --> 00:56:28,200 I did. I did. So I started looking around. I had started to write my own while I was in academia. 487 00:56:28,200 --> 00:56:32,099 I was I was still hoping that I could move on to the next level. 488 00:56:32,100 --> 00:56:39,990 I had postdocs for this long. I wanted to get a fellowship, get, you know, start to start as a junior. 489 00:56:40,040 --> 00:56:43,859 PI get the kind of support I had written grants. 490 00:56:43,860 --> 00:56:48,270 I had some ideas that I wanted to push along, 491 00:56:48,660 --> 00:56:56,040 but I because I didn't quite have the sort of support that I wanted to that I thought I needed at that time. 492 00:56:57,120 --> 00:57:01,199 I started to look around, so I put a linked in that was open to opportunities. 493 00:57:01,200 --> 00:57:13,260 And then I got a recruiter approached me and said, Oh, we've got this opportunity in biotech and are you ready to move? 494 00:57:14,160 --> 00:57:18,330 And I think at that point I was a bit frustrated. 495 00:57:18,330 --> 00:57:19,680 Sounds like, Yeah, sure, why not? 496 00:57:19,680 --> 00:57:27,629 You know, and I went through with the first stage and almost immediately she came back and she said, Oh, they want to meet for the second set. 497 00:57:27,630 --> 00:57:29,640 I was like, Oh, really? Okay, yeah, sure, let's do it. 498 00:57:30,080 --> 00:57:37,049 I think I remember on one of the interviews I was actually analysing some data and because I didn't think it was going to, 499 00:57:37,050 --> 00:57:43,710 I didn't think it was serious. I didn't think it was going to get far. And then we got to the point where, Oh, now you have to meet with the VP's. 500 00:57:43,790 --> 00:57:47,880 And I was like, Oh, it's got quite serious. 501 00:57:48,540 --> 00:57:54,830 I think that's when I started to struggle a bit because I had reached this glass ceiling. 502 00:57:55,740 --> 00:58:02,729 I didn't want to be a quitter. And I kept thinking, you know, you think about certain things. 503 00:58:02,730 --> 00:58:06,809 You think about and not to be dramatic or anything. 504 00:58:06,810 --> 00:58:17,880 Well, you think about the civil rights movement. And I think, well, if they quit some of the rights we've got, we wouldn't have had it, you know, and. 505 00:58:20,100 --> 00:58:24,060 I thought maybe stay, maybe push a bit more. 506 00:58:24,750 --> 00:58:30,180 And then the other part was like, well, you can't sacrifice yourself, right? 507 00:58:32,220 --> 00:58:40,920 But the other thing I struggled with was my identity, as far as I knew was as a scientist, 508 00:58:40,920 --> 00:58:45,840 as a researcher, and not just a researcher because I could I was still going to maintain that. 509 00:58:45,840 --> 00:58:47,460 But I was an academic. 510 00:58:47,790 --> 00:58:56,130 I thought like an academic, I you know, everything had to be broken down into logical arguments and stuff, to the annoyance of my friends. 511 00:58:56,940 --> 00:58:59,970 So that was my identity. What are you doing on the weekend? 512 00:59:00,000 --> 00:59:04,850 Oh, yeah. I've got this paper to read. I've got that. And in a way, it was a bondage ball. 513 00:59:04,860 --> 00:59:13,470 At the same time. It was our identity and I didn't know whether I could be anything other than that. 514 00:59:14,160 --> 00:59:17,700 And so I was struggling with that. If I lost that, if I. 515 00:59:17,700 --> 00:59:26,440 If I was no longer any who worked at the University of Oxford on HIV and other viral diseases, what would I be? 516 00:59:26,470 --> 00:59:32,100 You know, and there's this thing about biotech as well. It's like, Oh, this is where field academics go to. 517 00:59:32,100 --> 00:59:36,270 And, you know, that's that's that's what people say, you know, the ones that couldn't make it. 518 00:59:36,630 --> 00:59:42,150 So I was like, Oh, does that mean I've accepted to female? Oh, just silly mindset. 519 00:59:44,400 --> 00:59:48,720 But then I had the interview and then I thought about it. 520 00:59:49,110 --> 00:59:52,259 I've got a job. It's a group leader position. 521 00:59:52,260 --> 00:59:59,070 So exactly the next step that I was wanting in academia, I still get to do a lot of research. 522 00:59:59,080 --> 01:00:07,880 It's a very important focus that the company I was working with had AIDS cancer. 523 01:00:08,640 --> 01:00:14,160 I met with the team and they seem to have this clear career path. 524 01:00:14,580 --> 01:00:19,290 They had a structure, so I knew from a group leader what was next. 525 01:00:19,530 --> 01:00:23,070 I knew what I had to do to get to the next level. 526 01:00:23,340 --> 01:00:27,840 Whilst in academia, it felt like the goalposts kept changing. 527 01:00:28,170 --> 01:00:33,420 You know, it's you need this paper, that paper. And then when you had that paper, well, it took you so long. 528 01:00:33,420 --> 01:00:43,200 So now you need this paper. And then also the way of setting structural things like one of the major funding bodies, 529 01:00:43,200 --> 01:00:54,299 I don't remember which one now has got this grant that a lot of people who make it often have had that grant. 530 01:00:54,300 --> 01:00:57,390 I think it's either the Henry welcome or the Henry Doe one of them. 531 01:00:59,220 --> 01:01:02,610 But for a long time, I think it's changing now. 532 01:01:03,240 --> 01:01:07,080 You couldn't apply for that grant if you weren't a British or EU citizen. 533 01:01:07,080 --> 01:01:11,910 I see. And I wasn't. So it didn't matter how much good work I was doing. 534 01:01:12,660 --> 01:01:15,690 Grants like that were inaccessible to me. 535 01:01:16,950 --> 01:01:20,759 And it's it's those sort of structural things that needed to change. 536 01:01:20,760 --> 01:01:26,879 And, you know, how long were that? And by the time I got to seven years, they like, oh, certain grants. 537 01:01:26,880 --> 01:01:33,030 You can only apply within your first five years post Ph.D. So it just felt like I it 538 01:01:33,060 --> 01:01:41,910 was just better to leave and then try to establish something else outside of academia. 539 01:01:41,910 --> 01:01:46,830 And I have to say that it's not a choice that I have regretted at all. 540 01:01:48,510 --> 01:01:55,830 It's a much better work life balance. The questions are, you know, very focussed, very translational. 541 01:01:56,100 --> 01:01:58,890 You can see how it's going to impact the patients. 542 01:01:59,160 --> 01:02:05,280 And I still get to do a lot of science, you know, really, really important science and you get to publish. 543 01:02:05,280 --> 01:02:08,339 So and I haven't, I haven't published there. 544 01:02:08,340 --> 01:02:13,530 No, but I think the focus is slightly different. 545 01:02:13,530 --> 01:02:21,840 And also because I was slightly more, I was slightly more downstream of the research. 546 01:02:22,200 --> 01:02:25,470 So you, you have the team that does all of the researching. 547 01:02:26,010 --> 01:02:32,129 So my company did TCR Engineering. So there's a team that does all of that and what's what kind of engineering? 548 01:02:32,130 --> 01:02:42,720 TCR Engineering. So the T cell receptor. All right. The injuries I was more once that's done and it's come, then I characterise it. 549 01:02:42,720 --> 01:02:49,200 Yeah, yeah. Before it goes to the clinic and after after it's gone into the patients and we get the patient sample. 550 01:02:49,590 --> 01:02:55,740 So that was what I was doing. So it's kind of similar to what I have been doing here, characterising the immune response. 551 01:02:56,580 --> 01:03:02,490 So I haven't, no, I haven't published yet, but yeah, but you look happy. 552 01:03:02,850 --> 01:03:09,690 I do. I joke, I joke to my colleagues here that when, when next I come to visit them, 553 01:03:09,690 --> 01:03:18,180 I'm going to come with a helicopter and arrive on the ceiling just so that I can pull the legs of how much better it is in industry. 554 01:03:19,150 --> 01:03:29,469 Yeah. No, I think I'm surprised that I mean, I can I can hear what you say about people saying that it's that you failed academics. 555 01:03:29,470 --> 01:03:34,240 You go doing stupid. There's a lot of to and fro. Some people go one way and then go back the other. 556 01:03:34,270 --> 01:03:42,489 Yeah. I mean it's, it's even it's more to and fro e if there's a word like that these days now because people have accepted them, 557 01:03:42,490 --> 01:03:47,620 industry is making a lot of connection with academia and academia is making a lot of connection with industry 558 01:03:47,620 --> 01:03:53,320 because I think academia has come to a point where it can't sustain the number of people it's churning out. 559 01:03:53,560 --> 01:03:59,889 So it needs to prepare the people. It's heading out for a career in industry and in your company. 560 01:03:59,890 --> 01:04:03,580 It up to me and it was up to Spinner and Oxford University. Spinner I think so. 561 01:04:03,580 --> 01:04:12,559 I think so, yeah, I think so. I think the original founders would have been affiliated with Oxford, but I think almost every biotech in the Oxford. 562 01:04:12,560 --> 01:04:15,650 This is a spinoff from Oxford. 563 01:04:16,220 --> 01:04:25,959 Yeah. Yeah. Um, great. So I'm just going to talk a little bit about your, your personal experience of living through the COVID pandemic. 564 01:04:25,960 --> 01:04:37,330 So did first of all, how threatened did you feel by infection with the virus itself just moving around in the community or, or through your work? 565 01:04:39,100 --> 01:04:47,500 Personally, I didn't. And I think it this is a bit of arrogance where as a person you feel immortal. 566 01:04:48,880 --> 01:04:56,320 And I was like, Well, I've never been diagnosed with any sort of co-morbidities, so. 567 01:04:58,070 --> 01:05:05,420 I'll probably be fine. But I think. And to this day, I still think the same way. 568 01:05:05,420 --> 01:05:13,040 I think about people who are vulnerable and people who had to shield. 569 01:05:14,990 --> 01:05:22,010 And that informed a lot of my actions during the pandemic and even till now, 570 01:05:22,430 --> 01:05:27,919 because it almost feels like the world's moved on and forgotten that there's still a virus. 571 01:05:27,920 --> 01:05:33,710 Maybe it's not a pandemic anymore, but there's still a virus that could potentially be lethal to some people. 572 01:05:34,340 --> 01:05:41,330 And, you know, the world has just said, well, tough, get on with it. 573 01:05:42,530 --> 01:05:46,100 And if I was in that position, how would I feel? 574 01:05:46,910 --> 01:05:50,059 You know, it's their way of life, their work. 575 01:05:50,060 --> 01:05:54,230 You know, they if they couldn't go to work because of it, if they went to work and they had a mask, 576 01:05:54,470 --> 01:06:00,530 you know, people would maybe jokingly, but people would make fun and just yeah, 577 01:06:00,830 --> 01:06:03,510 So personally, I didn't feel threatened, but I, 578 01:06:03,870 --> 01:06:12,650 I took a lot of actions because of people who were vulnerable and because of people who needed to shield. 579 01:06:13,460 --> 01:06:21,500 Yeah. So I didn't I eventually got COVID, but I got COVID quite late. 580 01:06:21,500 --> 01:06:26,959 I got COVID. The summer of last year. 581 01:06:26,960 --> 01:06:32,870 I went for a festival. I know that that's contradicting what I said. 582 01:06:33,980 --> 01:06:38,600 But in my in my defence, I was doing the debate on the immune system at the festival. 583 01:06:39,350 --> 01:06:49,879 And so I went myself and my partner came back with COVID and I remember doing my because I still didn't think it was COVID. 584 01:06:49,880 --> 01:06:52,910 I thought I was just really tired and I felt feverish and stuff. 585 01:06:52,910 --> 01:06:59,810 So maybe it's just the cold. And then then I decided, okay, I might as well just do the test. 586 01:06:59,820 --> 01:07:06,140 So I did it and I had a really, really think positive band and it felt a bit surreal. 587 01:07:06,560 --> 01:07:12,170 But at the same time I felt relieved. Oh, now I can tick the box, you know, it's happened. 588 01:07:12,950 --> 01:07:16,189 And then I went into a slight panic. I was like, Oh, what if I get long COVID? 589 01:07:16,190 --> 01:07:21,170 Because that was the other thing. You could get COVID and you could be fine, but what about long COVID? 590 01:07:21,470 --> 01:07:25,100 And then I was like, Oh, what? What about what if I did it? 591 01:07:25,100 --> 01:07:28,220 No, that had a heart condition and maybe this is it. This is how I died. 592 01:07:30,380 --> 01:07:39,170 But I was fine. We were better in about two weeks, I think, like most people had reported, you get better, 593 01:07:39,170 --> 01:07:43,700 but for like a few weeks after, you're still breathless and you're still tired. 594 01:07:44,810 --> 01:07:48,799 Brain was foggy for a while, but we did get better. We did recover. 595 01:07:48,800 --> 01:07:57,860 And yeah, and thankfully, none of my No one that I knew had had it really bad. 596 01:07:57,860 --> 01:08:02,590 And so that's I think we were fortunate. 597 01:08:03,950 --> 01:08:08,029 But then the the work pressures were a different thing again. 598 01:08:08,030 --> 01:08:12,080 So what kind of hours were you working? 599 01:08:13,190 --> 01:08:20,360 So that's interesting. So we would start it was just it almost didn't stop. 600 01:08:20,750 --> 01:08:27,649 We're having meetings at 8 p.m., 9 p.m. and I say that's, I think we had a good compared to the other guys down the road, 601 01:08:27,650 --> 01:08:38,240 the Jenna because don't quote me on this, but I heard at some point they were running shifts and they were having to pretty much not shut down. 602 01:08:39,770 --> 01:08:47,840 But we were having meetings really neat. We went so we were getting samples from all over the world. 603 01:08:49,310 --> 01:09:01,100 And I remember times when I'll coming to work, I live in Bunbury, I'll come in and then we will have a sample stock at. 604 01:09:01,430 --> 01:09:04,820 I had driven to Birmingham before to pick up a sample. 605 01:09:05,690 --> 01:09:11,239 I was about to drive to Luton to pick up another sample because the airport at Luton to pick up a sample. 606 01:09:11,240 --> 01:09:19,820 So it was quite chaotic and sometimes the work would start early enough so that you could finish by like six, seven and head home. 607 01:09:20,060 --> 01:09:23,150 And all the times the samples were arriving like 4:00. 608 01:09:23,150 --> 01:09:26,660 So you knew that that was going to be a really late night. 609 01:09:27,320 --> 01:09:29,630 You could be here till like 1112. 610 01:09:30,320 --> 01:09:39,980 It wasn't unusual that we either were here till that Leeds or we were at home and we were working till like well past midnight. 611 01:09:41,780 --> 01:09:53,749 I, I remember going home sometimes when they had the claps and I kind of felt judged because I would 612 01:09:53,750 --> 01:09:57,320 be the only car on the road and there were people hitting their palms and clapping for the. 613 01:09:57,390 --> 01:10:04,290 And he chose and they were probably thinking, and this one just gets in their car and just drives to places. 614 01:10:04,290 --> 01:10:07,580 And I was like, If only you knew. 615 01:10:07,590 --> 01:10:11,870 I'm so tired from work. Yeah. 616 01:10:11,880 --> 01:10:14,940 So it was it was chaotic at work. 617 01:10:15,000 --> 01:10:22,230 And was there anything you could do or that you managed to make time to do to support your, your wellbeing? 618 01:10:22,530 --> 01:10:26,370 I think I had my partner was a real source of support for me. 619 01:10:27,000 --> 01:10:30,180 We had formed a bubble, so that was really good. 620 01:10:31,770 --> 01:10:38,520 It was difficult as well because most times you try to have a separation between work and home, 621 01:10:38,940 --> 01:10:43,530 but with work moving home, there was no separation anymore. 622 01:10:45,810 --> 01:10:51,750 I remember taking lots of walks. I used to do lots of walks when I could. 623 01:10:53,580 --> 01:11:06,150 What else did I talk to? I tried painting. I wasn't very good at it, but I felt like we didn't have enough time to consider other things. 624 01:11:06,600 --> 01:11:12,569 It was. There was just always something else and we needed to move really fast. 625 01:11:12,570 --> 01:11:18,900 It's almost like you need to channel the data in real time with the eyes of the world watching. 626 01:11:18,930 --> 01:11:23,910 So you need to make sure that there was no mistakes and it was just hard. 627 01:11:24,240 --> 01:11:26,700 We never really switched off. 628 01:11:27,270 --> 01:11:37,170 And did that outweigh the sense of achievement or the sense of the sense that you were doing something important in terms of giving you up, 629 01:11:38,880 --> 01:11:47,430 you know, supporting your wellbeing? I don't think so. I don't think so because I was I was exhausted, but I was happy. 630 01:11:47,580 --> 01:11:51,629 Oh, you were happy. I was. I was. I was happy. I didn't that I love. 631 01:11:51,630 --> 01:11:55,140 Yeah, I it was it was manic but it was. 632 01:11:57,210 --> 01:12:04,920 This sort of. Let's do it. Let's do it. And then I had a lot of I had a lot of responsibility during COVID as well. 633 01:12:04,920 --> 01:12:13,110 So while it was scary, I knew that I needed that. 634 01:12:13,110 --> 01:12:19,050 I needed that. That's that being thrown in that situation to develop. 635 01:12:20,100 --> 01:12:31,440 So that really brought out skills that I needed for the next step of my career, leadership skills, you know, deciding what's perfect and what's good, 636 01:12:31,440 --> 01:12:42,629 when to draw the line, you know, between perfection and good learning how to triage things and just yeah, I think I was really I was really happy. 637 01:12:42,630 --> 01:12:44,940 It didn't outweigh it at all. I enjoyed it. 638 01:12:44,940 --> 01:12:52,970 And I would always tell my partner that, oh, you know, walking on the front lines, I he's like, oh, you sound you make it sound like you went to war. 639 01:12:53,280 --> 01:13:06,239 I think it was like I think I've heard one person I interviewed said that when monkeypox first arose and that for just a moment, it's only a few days. 640 01:13:06,240 --> 01:13:13,440 I think there was a fear that it might become very big. He said the people in his group really were showing signs of PTSD. 641 01:13:13,470 --> 01:13:16,920 Yeah, that they thought, Oh no, not to get going again. 642 01:13:16,920 --> 01:13:17,730 Yeah, yeah. 643 01:13:18,080 --> 01:13:26,370 I think a lot of us were bummed out in the end, but we didn't realise because we're driving on adrenaline, I think, and we just didn't realise. 644 01:13:28,900 --> 01:13:29,260 You know, 645 01:13:29,260 --> 01:13:37,479 when you're writing publications or you're writing papers for really high publications and in fact you write in the paper at all that goes out. 646 01:13:37,480 --> 01:13:43,450 It's your name out there. You don't want to have to, Oh, I made a huge mistake and I have to redact. 647 01:13:43,450 --> 01:13:45,520 It is it's within. 648 01:13:45,790 --> 01:13:54,760 You had to be so focussed in what you're doing so that and you were the first name on some of those papers that have also lists half a page long. 649 01:13:54,790 --> 01:13:59,550 Yes. Yours is the first. Yeah. That's how I found. Yeah. 650 01:13:59,890 --> 01:14:04,090 So it wasn't pressure. Yeah. It was a whole lot of pressure because you're making shut out. 651 01:14:04,160 --> 01:14:07,990 Yeah. Yeah. You're like, my name is on this. 652 01:14:08,530 --> 01:14:16,059 It needs to be done. Well, I need to be able to defend it years from now and see, you know, things change. 653 01:14:16,060 --> 01:14:19,840 You know, certain things will change. 654 01:14:19,840 --> 01:14:24,069 And this is where I would always say that science cannot be. You can't be dogmatic about it. 655 01:14:24,070 --> 01:14:32,260 You need to be able to change your stance with changing evidence, but you need to be able to see with the information we had then, 656 01:14:32,620 --> 01:14:35,920 with what we had, with the materials, with the reagents, with everything we did. 657 01:14:36,190 --> 01:14:40,360 We took the right decision. We made the right decision. And that's what's in this paper. 658 01:14:40,780 --> 01:14:45,040 And, you know, you just want to be able to sleep at night. And it was that pressure. 659 01:14:45,070 --> 01:14:48,760 Yeah, but I enjoyed every minute of it. Yes. 660 01:14:49,600 --> 01:14:54,009 So final question. Has the I mean, you've probably partially answered this, 661 01:14:54,010 --> 01:15:00,130 but has the experience of working through the pandemic changed your attitude or your approach to your work? 662 01:15:03,010 --> 01:15:18,580 In a way, yes. What it showed me is as a scientist, I've always been quite engaged with the with the public about my work, 663 01:15:20,530 --> 01:15:22,330 like I said, to the annoyance of my friends. 664 01:15:22,780 --> 01:15:33,940 But I think what I realised was that we don't talk enough about things we do in the lab and we, we as scientists, 665 01:15:34,300 --> 01:15:41,950 we're not the best with social skills, but, but we, we tend to just close ourselves in and then keep doing our work. 666 01:15:42,340 --> 01:15:49,479 And we don't sometimes we don't think about the translational side of it, but all the times we don't think about letting the world know. 667 01:15:49,480 --> 01:15:57,790 We publish it in this very, very scientific papers that only another scientist would read. 668 01:16:00,160 --> 01:16:08,139 So for me it's meant that I am always open to opportunities to engage with the public now, 669 01:16:08,140 --> 01:16:16,540 because I think the public needs to know if they knew that the vaccine did not just spring up after a few months, 670 01:16:16,960 --> 01:16:21,490 You know, it was years and years of research that went into this. 671 01:16:21,700 --> 01:16:23,560 Maybe we would have had less stick. 672 01:16:23,860 --> 01:16:34,269 Maybe we would have had less vaccine hesitant people if they knew that we acknowledge and we empathise with some of the we some of their feelings, 673 01:16:34,270 --> 01:16:42,219 you know, some of the mistakes that science had made in the past with certain communities that were vaccine hesitant if they knew that, 674 01:16:42,220 --> 01:16:50,920 okay, we understand where you're coming from. But look, we're honestly, you know, it's not we're not we're not we're not playing the game here. 675 01:16:50,920 --> 01:17:00,130 We're really trying to help. That could have we had to do a lot of emergency public health, public engagement. 676 01:17:00,460 --> 01:17:06,040 But I think we need to build that into our work. 677 01:17:06,040 --> 01:17:14,410 And that's what I try to do now. I try to talk to people about what's going on in science recently. 678 01:17:15,640 --> 01:17:21,640 What I'm trying to do is set up a podcast where I take on different papers that have 679 01:17:21,640 --> 01:17:28,630 been published and then just break it down for the layperson and get my partner. 680 01:17:29,410 --> 01:17:31,840 Well, well, it might be a podcast, it might be a vlog. 681 01:17:31,960 --> 01:17:38,470 Not sure yet, but get my partner who is a software developer, to ask me questions about the paper. 682 01:17:38,470 --> 01:17:48,430 And then if I can't explain it well enough for him to understand, then I assume I have an extremely well enough for a layperson to understand. 683 01:17:48,430 --> 01:17:53,950 So that's that's what I want to do. I want the public to know how science is moving. 684 01:17:53,950 --> 01:17:56,350 It shouldn't science shouldn't be closed anymore. 685 01:17:56,620 --> 01:18:04,179 And I think if we can bring the public in more, we can have a lot more collaboration and a lot more advancement with what we're doing, 686 01:18:04,180 --> 01:18:08,200 you know, because, yeah, they can be missing just for us. 687 01:18:08,200 --> 01:18:12,609 They can, you know, we could have had people who would have said, No, I know about this work. 688 01:18:12,610 --> 01:18:15,280 You know, they've been working on this many vaccines for years. 689 01:18:17,380 --> 01:18:27,700 Part of the one one interesting point that someone made during COVID was, oh, if making a vaccine was so easy, why don't we have a vaccine for. 690 01:18:27,900 --> 01:18:32,700 HIV and why do we have a vaccine for cancer? And those are two things I was involved in. 691 01:18:32,710 --> 01:18:36,510 I was like, That is such a simple answer. 692 01:18:36,750 --> 01:18:41,370 If the public doesn't know that, then we failed in engaging with the public. 693 01:18:41,370 --> 01:18:48,149 You know, with HIV, it's just a really difficult virus to make a vaccine for. 694 01:18:48,150 --> 01:18:58,260 And although traditional methods have failed, in fact, the Moderna vaccine was to be trialled in HIV before it was very quickly pushed to COVID. 695 01:18:58,560 --> 01:19:05,040 So a lot of what we know for COVID and a lot of other infectious diseases came from the work we've done with HIV, 696 01:19:05,040 --> 01:19:08,250 and that's why we could really ramp it up so quickly. 697 01:19:08,760 --> 01:19:11,250 And then we cancers, there isn't just one. 698 01:19:11,880 --> 01:19:18,180 You know, we have a vaccine for HPV, but we can't get a vaccine for any of the others because it's so diverse. 699 01:19:18,630 --> 01:19:26,880 So if simple things like that, the public are not, I'm like, why should they know if we haven't been told them, you know? 700 01:19:26,880 --> 01:19:31,350 So for me, that's that was the biggest change that I got out of COVID. 701 01:19:31,350 --> 01:19:34,499 I need to engage more with the public. What? 702 01:19:34,500 --> 01:19:40,200 I think you'd be very good at it. I evidence this conversation because I just talk too much. 703 01:19:40,200 --> 01:19:43,800 That's just what I want. 704 01:19:44,100 --> 01:19:45,600 Okay. Thanks so much.