Thank you very much. So I will talk about clocks. We've heard a little bit about them today. And I'll mention a number of them. But keep in mind, I'm an expert on one. So watching the glycan side, and I'll name all the experts on the others. So first, why should we measure biological age? Why would we need this as a marker? Well, we know that chronological age is not a good enough measure of health, all of us and I'll give you two examples. We recently tested a number of centenarians in Costa Rica, and there were 100 100, free under five. And none of them actually had any diagnosed condition at that point, none that we can diagnose with traditional health care. And they're already achieving extreme longevity. While on the other hand, we have friends, we have a colleague, who started working with a company when she was 19. By 16, she got a diagnosis of rheumatoid arthritis. Then at 20, she got a diagnosis of MS. And she has a life expectancy which is greatly reduced to where she's living. So biological age should give us a better indication of health than chronological age, and also this risk of disease and mortality, especially in people who are not aware of it or younger individuals who are have this accelerated risk, but it's going undetected. And aging clocks have been the mission for a very long time. Actually, the first assumed biomarker of systemic aging were telomeres. But then we learned that telomeres are really good marker of aging of a single cell. But they're not as good of a marker of aging on the system level. Because we have cells, trillions of cells with many different ages. So we collect the sample and try to calculate an average age, it doesn't equal a very good systemic age. And for systemic age, now we have two very good candidates. So the first, molecular systemic age clocks were the glycan clock and the safe harbor DNA methylation clock. And by complete coincidence, they were published on the exact same date in 2014. So last year, we celebrated their 10th anniversary, and the first likened clock was developed in creation. And Steve Horvat, who's the inventor of the epigenetic clock, his surname means creation. So we decided to hold a conference in Croatia to celebrate our anniversary. And if you Google, actually, I have a link on the other page, like we shared with you afterwards, you can see all the recordings if you look for the longevity symposium and see if so, the epigenetic clock and mentor, he was actively looking for aging. Looking at the epigenome, we were not looking for it, we stumbled on it by complete random coincidence, by looking at a molecule that's mainly neglected in research. And we didn't have a lot of tools to look at it at scale in the past. In fact, our lab pioneered high throughput glycomics. Back in 2007, we analyze the first 1000 Human glycans together with a lab in Dublin led by Polina. And to date, our small, small lab and are a research institute in Croatia, lifted 85% of the total human glycan, we generate 85% of high throughput, like studies globally. And this is a review paper by chemical reviews. So these are just published, like comes out 191 published like comes our lab generated 160,000 of those. And we this in collaboration with all the top universities from Oxford Park, Harvard, pretty much everyone. And we found that when you look at these large cohorts, you see aging, especially on IgG glycans, we would see that certain structures would accumulate with age, and other structures would decline with age. And if you see on the graphs here, if I can love figure out a ticker, women are red. And they have a very different aging curve to men. Men have a very liberal path. And then women we see this acceleration that's linked to menopause. And I'm going to get into that a little bit later. So then we're curious. Can we see this agent in every population and together with the human glycan project, we looked at 27 different populations around the world 100 individuals in each and we saw that this prediction that the glycan was associated with like expected lifespan, but actually the strongest association was development index of a country. And we had a replication cohort in In the UK, of British minorities, the separate cohort, where we got samples from the first and second generation of British minorities, Indian, British, Caribbean, and Eastern European, what. And we saw that in the first generation there glycans would look like their original country. But in the second generation, their glycans will start to be more similar to white British, despite the ethnicity. So there is a very strong impact of environment. And that goes beyond ethnicity. And a number of these clocks are good predictors of mortality and morbidity. So the key thing they have to do, if you have an accelerated clock, Does it predict disease? And Does it predict mortality? And this was a nice comparison done by Ricardo Mariani. On the generation Scotland biobank, where he looked at if you have an accelerated like an age, or a grim age or other epigenetic ages, it, how does it predict diabetes? And how does it predict all cause mortality. And interestingly, although our clock is not modeled on reality, like the Grim age, were equally predictive of all cause mortality as women age, and fino each, which encompasses Clinical Biomarkers was less predictive than the grim age things like an age and predicting diabetes, so they are more predictive than some of the traditional markers we have. And maybe the most interesting fun finally, we've had in the recent years is that these clocks don't well, the clocks correlate because all of them correlate with logical age. But if you see if you look at acceleration in these clocks, epigenetic clocks, and glycan clocks are on an opposite spectrum, we do not correlate at all, which is very interesting, because both clocks are predictors of mortality and morbidity, which means that aging is not one thing. It's multifaceted. There's a number of different mechanisms driving it. And maybe the most, something that Steve Horvat did recently, he developed a clock to predict maximum lifespan in humans. And then he tried to correlate that clock with his clock to predict mortality and morbidity. And these clocks didn't coordinate, which means whatever governs our maximum lifespan is very different to what kills us before we get out there. And that's one of the key things we're focusing on. But it also means that there's different theories of aging, like one is a program. If we reprogram it, can we extend maximum lifespan, maybe, but also, we need to fix the things that kill us in between. And that's the part that we're focused on. Now, then, there was an idea that if we connect all of these different omics, we will develop the ultimate agent clock, and that was done in Edinburgh with the ricardas biobank, they tried to they looked at all these different multi omics aging clocks, and connected in one case here in the middle, created a multi omic or mega mega clock, they put them all together. And the pluses are prediction of hospitalization. And you can see in this ultimate clock, there was zero prediction of health outcomes, it basically became the perfect measurement of your chronological age. So putting these two, this, these things together doesn't work, we have to look at them independently. And these are potentially very early mechanisms of disease that start with aging itself. But they're not one thing. And different clocks predicted different things. But there are talks which are stronger at predicting mortality and morbidity. And if I tell you that glycans are a great biomarker for this, I would be very biased. But when somebody else says it for us, then we can, we can just agree. This was 10 years ago, this was a theory. But now we have more and more independent data coming out of how important like how important lichens are. And this is a paper in preprint. That's just about to be published in Nature Communications, done in way Cornell and Doha, where they looked at over 6300 molecular trades, and which ones of these were associated most strongly with age. And now the 20 associations nine of these were glycans, and the rest were predominantly sex hormones.