So uh slight change of tech. We um a Q mark diagnostics is focused on early cancer detection. And so we set out quite a while ago uh to develop technology that for the first time would be able to detect stage one cancer reliably from blood but also in a very accessible way. And so just to give you a bit of an orientation of you know this this narrative you know we so far to date we have delivered we think the first highly accurate stage one gas detection test from blood uh it's focused on CRC screening first so we playing in the field of um garden health exact sciences etc etc um the market is large it's uh we count 175 million panels uh need uh across Europe the US Japan Japan and South Korea. Um, our stage one and stage two detection accuracy is now greater than exact sciences guard health free grail at existing CPT codes for instance coligard plus $592 per test. The margins are very healthy. This is a bit of a unicum in in the diagnostic uh diagnostic space and the closest very relevant competitors or comps not competitors but comps are net terra in recurrence or MRD exact sciences tempus caras in gardens. So um we of course have always believed that early detection of cancer is important but some investment bankers think the same way. Clearing for instance recently mentioned the promise of a simple blood draw that can detect cancer at the earliest stages when it's accurate and curable continues to be the most valuable feature of the liquid biopsy industry in our view and should remain a major shift in precision medicine and early detection for years to come. The reason this has been difficult is because usually when one increases sensitivity one always sacrifices specificity. One always includes some false positives in doing so. The reverse is true as well. You want to increase specificity. You give up on some of the detection and and the true positives that you need to detect. So you you you um you you sacrifice uh sensitivity. And so that leads us pretty much to where we are today. So on the um for you it's the left side. On the left side you see the blue curve. You see that currently we're very good at stage three detection. And with recent development, it starts sort of bleeding in no pun, but bleeding into stage two detection as well. There's really no good solution for stage one detection. And why is stage one detection important? It's important because that's when survival is the highest for CRC. 90% survival if detected stage one. Uh 70% survival is detected at stage two. So what we set out to do is really develop a technology that not just would have these kind of levels of accuracy, but also would stay very very accessible. And in order to do so, we had to really go back to the drawing board and reinvent new assays. These assays have now um uh achieved 88 to 100% uh sensitivity for stage one. Across the board, stage one to stage four, we achieve 92% sensitivity at 97% specificity. This for the best machine learning models that we use and um of course is a platform. So this is a metilation platform. This is a qPCR based platform. It's novel assayes but we mount it on qPCR and so this is transferable to other cancers and beyond that some age related diseases. Um it's it's qPCR based technology so that means we did this on purpose so to make it very scalable very accessible qPCR which most of you know from infectious diseases such as COVID um is widely commercially available and so we wanted to mount it on qPCR to make it broadly This is high margins, low cost of goods with qPCR type of technologies. A little more. It's not your everyday qPCR, but it's a little more than than normal traditional ones, but still very And then this of course is a frictionless implementation into the medical workflow and the laboratory workflow. Um, importantly, and this is what you see with some of the other companies, um, we do not think we'll have to exclude individuals at a high risk. Um, you know, which is which are the individuals that we think need it Again, we are on a series B. So again, this is a bit of a summary slide and orientation slide. But what we want to do with the series B is we want to get to commercial system lock. Commercial system lock means we can start a pivotal study uh for the FDA. We can we also like to bring the sample size from 350 to 3,000. Uh that according to some robust conversations I think we've had uh would make us at least as an option M&A ready. And then um it also gives you the option to start commercial launch as an LDT to direct payers for instance. The platform itself consists of novel qPCR based assays or methods not instrumentation assays. We combine that with predictive machine learning and AI. And uh we have uh we have built these assays such that it can detect not just narrow panels such as is traditional the case with qPCR but broad market panels. We have designed them so they can do for the first time ultra- low abundance metilation markers which is traditionally not something that qPCR does well and then we combine it with machine learning. Um high level picture we just want to combine high level accuracy with broad accessibility and for that we had to maintain qPCR and we had to build new assets to make it far more precise with less flaws and artifacts. This is a very very highly simplified uh version of course. So but what you see in the middle is blood is being drawn. What you see in the middle is you know proprietary steps. We have eight patent families. The first one was in license in 2014. The last one is in licensed this year. Uh and then we combine it with machine learning models and then the outcome will be positive. Let's go and have a deeper and more granular sort of assessment. If it were me I would want a nextgen sequencing assessment and an imaging negative. go go back home, have a drink, come back next year and borderline cases come back in three months. So again, highly accurate uh and we think that we we combine of course the essays and the results with with with enhancement of uh by AI and we think that that enhancement will only get more the models will only get more robust the more that we grow the database. We checked this so far on 348 samples across two versions. Version one and version 1.1 we call it. Those are two different tweaks of the assay. Uh and so here are some of the results. Uh so the best models this model 44, 48 and 37 you see circled uh produce sensitivities in the validation set uh of 92% 97% specificity. 92% sensitivity, 95% specificity and so on. We break that down into stages and you see that across stage one and stage two, these models come out very strong. Some of them behave a little more like um the current technologies like model 36 is a classic mirror of what other companies have achieved. But then you look at 34, 35, 38, 44 and see very strong stage one and stage two detection rates. This is consistent. Then we move to version 1.1. And what you see here is we this is a version that just economized on the primers a little more. So it becomes more cost effective, even more cost effective. And so here actually the results improved a little bit was smaller sample set under 32. But we did detect accurately with very high uh marker scoring. We detected stage one, stage two and stage three, stage four nearly perfectly. You see a couple of borderline cases uh right at the uh right uh bottom panel. Uh those borderline cases were healthies where we saw increased levels um were were case were were uh were cases where we saw increased level for the healthy signal. And so if that would have risen a little bit, we might have called it a [snorts] borderline case and then asked the person to come back in 3 months time. Anyway, so here you see the marker panel is 40 markers and uh with the exception of two markers. You see marker two and three, they don't really work very well. We don't really know why, but we don't really care. uh we have more than enough markers to get to these results, but you see that across the board uh the CRC cases score far higher and consistently so than the healthy cases for each of these markers. Um so again a platform that's very accurate but at the same time is scalable, accessible, high margin uh and does not include individuals at high risk. Um and what it comes down to is really the following. So here you see a sample of some of the incumbents in the space and so what everybody has achieved to date and so it's really stage one and stage two that we care about and so um stage one is of course very important to us because survival rates and that uh crosses into other cancers as well. Uh next to the accuracy we've made the assets in such a way that we can actually make them also very accessible that we can mount them into kits for wide deployment and fast And uh it's a it's a platform that carries over to other to other to other cancers. Um in our mind uh pricing at current CPT codes we aim to keep that um so that we get to high volume. So high volumes times high margins equals high profitability. and uh so again QPCR based so this uh fits seamlessly into the medical workflow. We didn't want to reinvent the wheel where we don't have to. uh highly scalable and this eventually will be able to be run in hospital labs in labs that have the appropriate QPCR based Um from the beginning we really wanted to design something that has incentives for all stakeholders. So the stakeholders are of course the providers and administrators you know they need to focus their resources where the need lies most. So on those individuals that have been detected as accurate um for the patients, you know, the peace of mind of being able to take a simple blood blood sample every one or two years and the peace of mind that it reli reliably detects stage one uh of course reduces anxiety and makes it very convenient for them. And then for payers it really dramatically lowers to risk is a large market of course our pipeline um is uh is first focused on CRC screening and CRC recurrence. So CRC screening the field that exact sciences is playing in CRC recurrence the field that Nera and garden are playing in um but then again we go we move to all the single cancers and all the multi-cancer panels uh beyond that so once we have the CRC test launched we'll start working or advancing the tests that we have in the pipeline further so again under 75 million panels for a bianual screening test uh at assuming 80% compliance across uh US, EU, Japan and South Korea. Um that is at current uh CPT coding. You should put that in perspective with what WHO has been saying. WHO has been saying well two trillion dollar cost of cancer uh per year uh globally. Of course it's a big number but uh so we think uh we think there's a fairly um fairly large need in the healthcare space. uh so then based on com so based on all these advantages that I mentioned to you we we think that this approach really lifts the limitation to bring accurate stage one bloodbased cancer screening into the mainstream finally and then again we play in the same fields exact sciences and we combine recurrence and and and CRC screening as our first product um there's some clear comps the comps uh are a reflection of that large market um exact sciences just got overtaken by neta the year before last I think with relatively low unit volumes. Uh Tempest sort of came out of nowhere again low unit volumes high net losses and then uh Gardens has matured a fair bit as well and stays at a $56 billion level and then in terms of M&A has been a little older but Thrive got acquired from Oxford for 400 million that was an older tech and technique that's that that's younger than where we are today. Exact science acquired uh um sorry B genomics got acquired for $400 million. Thrive got acquired for $2 billion by exact and then many of you will have heard of the transaction between GR and aluminina which was an $8 billion transaction. Now where what what specifically do we want to do with our $25 million series B? So that brings us to M&A readiness. That brings us to PMA start readiness. Uh that will get us validated in a further 3,000 individuals. Um and um and so we we really look forward to reaching that stage. Why? Because at that point in time, I think we have a clear uh we'll have a clear picture of to what extent we deserve a seat at the table with some of the incumbents. So uh in summary, we have clear demonstrated now uh accuracy for stage one and stage two blood based early stage cancer detection. simple two qPCR based so very accessible scalable low cost of goods um and with that we have the opportunity to open up stage one cancer detection space broadly for populationwide screening um this approach marries oops sorry about that this approach marries um uh high accuracy with uh uh broad accessibility importantly patients at risk do not need to be excluded um you know we we think this will be highly high compliance will be easier to reach than with for instance blood uh with with for instance uh stool tests and then um you know it's a very clinicfriendly and hospital friendly type of approach that goes hand inhand with some of the other approaches such as sequencing and imaging. Thank you so much for your attention.