 So I'm pleased to welcome to the stage the second Grand Challenge team, your health in your hands, future personalised medical technologies for a sustainable and effective health care. Presenting this evening from on behalf of the executive leadership team are Anne Bristol from John Curtin School of Medical Research, Matthew Cook from the ONU Medical School, Jane Despera from the Research School of Population Health and Anthony Trickley from the Research School of Engineering. Good evening. My name is Anthony Trickley. I'm an engineer and leader of the now technology research laboratory at ANU. I'm very happy to be with you tonight to speak about a topic that brings us all together independently of where we are from, from which language we speak or culture heritage. This is our health. In the last century, the incredible amount of innovation progress made in medicine and biomedical technology has led to the rise of modern medicine. This has dramatically increased our quality of life and if we look at one of the most common indicators for this, the life expectancy at birth, this has increased from about 40 at the beginning of last century to more than 80 in many countries. Now, however, not always gold. Actually, today, more than ever, we face extreme challenge if we want to ensure that in the future health care remains sustainable and equal for everyone. In particular, if we look at the distribution of life expectancy in the world, in this map, we will see that health outcomes are not uniformly distributed. The Asian Pacific, for example, with this beautiful diversity of culture, geographical space and politics, experience among the biggest variation in life expectancy going from 40 to more than 80 years. This is not the problem which is only for developing countries. Also in developed countries, if we look at the cost of health care as a percentage of the gross domestic product, this has been increased dramatically. In Australia, health care expenses have increased, have doubled in the last 50 years, going from a nominal value of 10 billion to more than 150 billion in the last 35 years. This is not sustainable. And many countries are considering cat to the health care system which are actually a risk to decrease health care outcome for the most vulnerable part of our population. In Australia, despite having one of the best health care systems in the world, in fact we are very green there, not always green. Actually Australia is a microcosmos that represents some of the problem that we face in the Asian Pacific, a remote area, vulnerable community. If we look into a remote area, in Australia, the pattern of disease is almost twice as high as in urban communities. If we look at diabetes, indigenous Australian experienced twice as high incidence rates and three times as high worse health outcomes. There are many issues that relate to this topic. Two of the main that we will address in our grand challenge are one showing this picture, which refers to the accessibility of health care. On the left side you can see the first magnetic resonance image, a morai. It was kind of a scary instrument where you had to wear a magnetic jacket to get through. And now, about 40 years later, it's actually the anniversary now, we have some fantastic instrumentation which has gone far behind our imagination of what we could do and helped us in the agnostic scene of an important medical issue. However, something has not changed. The instrument is still quite big. It's still in a building. It's very hard to access, especially here in a remote location. So transforming this paradigm that we have to be in a big building to get medical health care is a big important issue if we want to change accessibility to health care. The other issue that we are looking at is that we are facing is not easy despite the tremendous progress in the development of precision drugs. Precision drugs are drugs which are fantastic at addressing one mechanism of disease, so they are very effective. We don't know who benefit the most. If you look at the top 10 drugs in the United States, Comaxone is one of them and is utilized for multiple sclerosis. You will have to give it to 60 people, so all the red and the blue, for one person, the blue one, to be positively effective. This brings a tremendous cost and also a side consequence for the people that actually are not beneficially affected. The reason of this is that disease is common. Disease is a combination of our DNA, genotype, how this genotype developed in the course of our lives, and environmental exposure, for example, how much sunlight to be exposed to, what have you been eating, and some other things. So in the future, if you want to be able to provide precision medicine effectively for everyone, we have to go from that picture to that picture where we can use genetic information and environmental exposure information, so how you are doing your metabolism, to target the right person with the right medicine. So this is about our grand challenge. Our grand challenge is to ensure that in the future we will have a sustainable, eco and high quality health care for everyone. We plan to do so by deep personalization of health care, and with this we mean developing new, portable, deployable technology that non-investigate by measuring, by a market that can be measured without blood analysis can tell us how we are doing independently where we are located. We plan to integrate this with an energy-genomic approach that will be a key to understand the diversity of response that our body has. And we plan to use Earth's data to artificial intelligence process to understand diseases better, to understand why they can and how to treat them and provide better health outcomes for everyone. Now, this is not a technological process alone. If we want really to have a transformation of health care, we have to come out of our labs, of our computers, and start working with people. So we will also develop a social, ethical framework to see how this digital transformation of health care, which maybe in some sense is imminent, will be deployed in the world. Good evening, Matthew Cooks. My name is Professor of Medicine and I'm interested in immunology and genomics. Also in clinical practice, I've just come from my clinic, in fact, where there were patients who were attending for their three or six or 12-monthly check-ups. And some of them, when I inquired how they've been going, they said, well, I'm fine. But if I had been here a month ago, it would have been a different story. But they weren't there a month ago. They were here today. And this is one of the challenges that we face in medicine medical practice at the moment. It's episodic. Clinical consultations are episodic, and often the investigations that we perform to go along with those consultations are also episodic. Those investigations often involve something like this where a blood test is taken to see how a patient is getting on. So our first challenge is to disrupt this process by devising new mechanisms for monitoring patients continuously. Not through invasive mechanisms like this, but through non-invasive processes where we can assess how things are going by measuring analytes which are present in sweat or tears. And therefore, it will be feasible for individuals to wear these devices. This will also mean that it's possible to monitor patients' irrespective of their location, no matter whether they're in Canberra or Central Australia or indeed elsewhere. Now the second challenge that we face at the moment is insufficient understanding of the mechanism of disease. And Antonio has also already alluded to this, that a major objective in current medicine is to be able to personalise treatments. Now we know that this is crucial because if we take any complex disease that you can think of, not every patient with that diagnosis responds to any particular treatment. So we already know that diseases are heterogeneous. And dissecting this heterogeneity becomes even more important when we have access to drugs that work in a precise manner. Now precise drugs work usually because they target a single molecule in the body. And so to understand which patient should be matched to which particular precision therapy we need to understand mechanism of disease better. It's no longer sufficient to make a diagnosis based on phenomenological criteria, which was the sort of 20th century approach. Now at the moment the best pathway we have to resolve in this disease heterogeneity is genetics, by elucidating the genetic variation that distinguishes patients within a particular disease category. Now for most of the history of genetics, at least in clinical genetics, this has been approached by looking at single genes. But now we have the capacity to look at all of the genes in a person's make-up, the so-called genome. And we can characterise variation across the entire... So our third challenge is to consider this avalanche of data. We're going to be monitoring individuals continuously, monitoring variables with increasing sophistication and then combining that with genome sequences. The genome is large, it's three billion bases and each of us differ from each other by about a million of those bases. So right there characterising the extent of variation is a substantial task. But then if we need to integrate that with phenotypic variation, then the computational challenge will be substantial. So our third challenge is one of computation and machine learning to take advantage of these large data sets. Now each of those significant challenges in their own right, but what we think makes this a grand challenge, is the combination of these approaches to resolve these questions. And we consider this to be the basis of the virtual circle of discovery, where information which is coming in from the biosensors can be interpreted in the light of conventional aspects of patient evaluation. But then we will have additional information based on complete cataloging of the variance across the entire genome implications within the cohort. And if we're able to characterise some of these variants that are likely to be important in the causal pathways of disease, then we'll be making real headway towards understanding mechanism of disease. And that information will then be able to further inform how we actually monitor disease. This is all too much for a human brain, so we need to take advantage of the capacity for machine learning to integrate this information. Now this becomes a virtual circle because discoveries here may influence future choices for analysers to be studying through the biosensors. And then of course we'll be able to evaluate whether that assessment has been correct through further clinical studies. And there will be a constant process of refinement through iteration of the circle. Can you hear me okay if I don't use that? Hi, my name is Jane Desparin. I'm a registered nurse in Meephite and I'm also an early career health services researcher. The questions I always ask are where are the patients? Where are the people? What does this mean for them and for their health? Did I hear someone ask what is clinical genomics? What a great question. Clinical genomics is the use of genome sequencing to inform patient diagnosis and care. We will combine this information with the development of miniaturised, non-invasive wearable devices. Things like a Fitbit, a bracelet, a patch on your skin, even a specially designed toothbrush. The dynamic health data generated by you, the patient or your family member, will radically change our understanding of diseases. As well as improving our ability to diagnose and monitor your health, it will lead to new models of patient driven decision making. Our vision builds on the ANU's culture of academic excellence, bringing together a group of world leading researchers from across the university. Researchers who are passionate about making technology work for all of us. Improving the health of all people, regardless of gender, age, where you live, your social circumstances. Listening to what you, patients, carers, your families, have to say about your experiences and incorporating these needs and desires into the technology as we develop it. And then implementing this technology into the health services that we use every day in a way that is ethical and effective. This grand challenge will act as an incubator for innovation in biomedical research within Australia, the Asia Pacific and further afield. Our implementation frameworks will place ANU in a unique position to advise and lead policymakers on the imminent digitalisation and personalisation of health care. We will use value-sensitive design to embed particular values into the technologies. The most obvious being patient and clinician perspectives and the technical aspects of cyber security. This recognises the range of values, ethical and individual involved and the decisions have to be made between these. For example, patient privacy versus informational security or patient usability versus privacy and security. What's exciting is that this is not just business as usual. It's transformative because the data for decision-making is being made available to patients as well as clinicians and researchers. It's a game changer in that it will foster patients' expertise about their own health, leading to better collaboration, better decision-making, better management of symptoms and early recognition of disease progression. As you can see, we have a number of partners for our project, patient representatives including MS Australia and Diabetes Australia, Government, Research Institutes and Key Industry partners. We have good working relationships with many of them and we're eager to collaborate with new partners as they come on board. Our strong local networks will enable us to demonstrate this project locally in the first instance. And then we will extend this work with our national and international partners. I'll hand over to Haim. I'm Haim Le Brusse, an early career researcher with a passion for uterus chorosis. And in the next few minutes, it is my task to get it a little bit more into the project. So basically, our grand challenge, we built on two research pillars. The first one is about early disease detection and management. So we chose diabetes as our model disease in this case. If you look at the map, you see that diabetes is a pandemic disease and affects people all over the world. But if you look at the rest of the subject, we have an extremely high incidence of a disease. When you look at the numbers, this is not going to stop. The disease is going to be more and more prevalent in the future. So bringing that down to Australia, at the moment we have about 1.7 million Australians affected by diabetes. And it's not hard to imagine that this puts a very high burden on our health system. So where are the challenges here? So first of all, diabetes is often underdiagnosed. So one out of four patients actually gets diagnosed very late and has already consequences of the disease. Think about problems with sight, kidneys, even stroke. Another problem already mentioned is that we have about a double as high burden in our indigenous population. And if you think about remote where this city, you have a three times higher risk of dying of diabetes if you're not in direct access to hospitals. So our challenge here is to make early diagnosis and disease monitoring independent of where you actually live. So we defined projects around this problem. One is about kids with diabetes and kids at risk. So here we can combine our genetic approaches with noninvasive wearables, testing for example retinal function and breath to monitor the disease better. Then we are looking at gestational diabetes. This is in particular interesting because it's a very defined thing only affecting pregnant women. And it also has a huge impact not only on the mothers but also on the future risk for diabetes of the baby. So the big challenge here and the overall aim is to actually develop these devices and approaches and bring it out in the remote communities. The second pilot, our research is a more 21st century kind of problem. And as you have heard before, it's about personalizing precision medicine. Here which shows motivus chorosis is our model. So as you've heard, in motivus chorosis it's a very complex disease and includes not only genetic position but also a lot of environmental factors. So what makes it so devastating is that A, it affects the central nervous system. So we don't know, depending on where the disease hits you, what outcome it has. It could affect your torque skills, it could affect your eyesight, it could lead to cognition problems, pain, fatigue. So it is a very, very variable disease. It affects mainly women between 20 and 40 years in a very early age. So we heard that here we have actually a variety of precision therapies available. But the question is how to target the right therapy to the right person. So our project here is to include, what you've heard before, protein agnostics with a continuous data monitoring of extra disease markers feed that to our artificial intelligence machine learning. And that way we can actually develop devices to monitor even more sensible monitoring systems. If we include now the genomic description and your specific genes, then we have a way to maybe find not only that one person who reacts to the treatment but find treatments which react to more people. And if we do that over and over again and feedback into our circle, we might be able to target most of our patients. So just imagine, you live in a remote area and you have kids and you know from your genes, genetics, that they are at risk of developing Taiwan diabetes. So you're not able to go to the doctor every week, so get them checked, you're worried. And now you could get the device, let's say a toothbrush. It actually measures in the breath or in the mouth of your child markers for the disease. So it will be pledged to a cloud, it will inform the clinicians what's happening. So you can detect the disease early on and don't risk to develop bad effects early on because of this misdiagnosis or late diagnosis. Or another example, let's say you get diagnosed with multiple sclerosis. You get put on a treatment. And now you wait, if it works, you cannot do anything, you have to wait another three months to see a doctor. Or you have devices which actually monitor every single day multiple times. Or you're doing certain tests at home, so you can do something actively to inform about your disease progression. And that way you might be able to pick up slight changes in your disease even before it comes to a relapse. So this, just imagine, would transform our thinking of disease monitoring. Tonight you've seen five of us sitting there and this is not enough, obviously, to address such a challenge. We are already 52 researchers from ANU, 27 female, 25 male. We draw strength from all six colleges of ANU involved in this kind of challenge. And we have world-leading expertise in all the disciplines required to make this challenge not only a dream of a vision that we have now, but a reality. With this, I have to say, 52 people, 100,000, is never enough. And we will meet all the health possible. And because of this, I will actually get you home to support this kind of challenge because we believe it can lead to a chance to change the paradigm of healthcare, leading to a better, more sustainable healthcare for everyone in the future. This was a good help from your end. I would like to thank you for your attention and we would like to try to answer a few questions in the next minutes. Thank you very much. Again, I'd like to invite the panel to stand at the front of the room. And again, I would open the floor for questions, please. There's a bit of a mic over here. And there's one over there. Hi, Ian Peters from Diabetes Australia. Good to see the priorities are on the right things. And of course, in this, we have a lot of challenges in diabetes and my great focus in all the things I do every day is about trying to early go and get us figured because we live in far too long. And all of this is really exciting, very exciting, really. But one thing that I sort of struck me as we went along is the indigenous people who are more prone to diabetes and the part that is always a challenge to us is the remoteness. And it may be an unfair question to ask you about the technology and how it can actually go. This was clear, you're not going to be able to read it every day. I'd like to understand how that's conveying back defence, you know, something that you can answer. But also about the practicality of getting into remote indigenous communities and being able to sort of organise that and make sure that things happen and what the effect would be. Thank you. This is an excellent question. I will answer to the first part, which is the most technological aspect. The detection of diabetes type one, for example. This is very important because we have seen that there are tremendous consequences that this has done to many, many side effects. In the last years we have been able to demonstrate that you can measure biomarkers for diabetes, non-imbessivity throughout, not with blood analysis, but by other means. And these biomarkers can be utilised for the non-imbessive diagnostic of diabetes. One of the most important is the amount of acetone, a ketonic, that can out from your breath of your skin. We have developed a device, which is smaller than one millimetre, which is able to capture a difference in acetone concentration in your breath if your diabetes type one is treated or not. Now, in terms of how we can access this technology remotely, if you could have one of these chips in a bracelet on your phone, this can transmit 4,000 tons of data and this can help with the remote diagnostics of diabetes type one, already almost today, if you are able to push the technology a bit forward. Now, with respect to a bridge of community, will that change? Thank you for your question. I think I need to point out, to start with, often when we think of Indigenous communities, we automatically go to thinking of remote, whereas a large number of Indigenous people live in urban environments, so engaging with Indigenous people. Probably in the first instance, it starts on a local basis. And we acknowledge that, in fact, engaging with Indigenous people and engaging with them on this project involves starting with community, starting with country, and involving them from the very start in the co-design of what's going to work for them, what do they want, what sort of devices would they use? Actually, do they want to use these devices? You know, so it's a very slow and iterative process and part of our very strong focus on patient engagement from the beginning and implementation when involved engaging with Aboriginal and Torres Strait Islander people in the way that works for them and starting that as early as possible. Great. Thank you. Just a quick one. We've got another three questions and I'm going to have to wrap up. Sorry, we're over time. So just how I see it is you think of a pregnancy test. So it's not substituting the clinician. It's just an initiation to go to see one. Hi, my name is Crystal Hammond. I'm part of the community who has type 1 diabetes. So this really interests me a lot. I think it's a great idea. I was just wondering how people make these machines and how will they be given to the communities that are not in big cities and how will this be funded? That's an excellent question. So it's a good how we bring our discoveries outside of the labs. So we already contacted and we have received extra interest from four major companies which are active in the area of biomedical device. So we plan to partner with them to develop the device. Now in terms of, let's say, cost, funding and the planning of the device we plan to actually partner with different organizations, mostly political government organizations and we hope to be able to convince them that if you invest early on in us, on your health the return will be such that the health care costs will decrease while the quality of health care will increase. So we hope to be able to achieve this on the long term. Thank you. My name is Vanessa Penning and I'm a person with MS. I think anyone who has MS would be incredibly excited about this project because our experience is that we send along to have MRIs. That's a very expensive technology. We do it probably every year, sometimes smaller ones a year and it's well established now. I think that it's a very blunt instrument and it doesn't tell you much about the progression of the disease. So what you're proposing to do is fantastic and very exciting. I think people in MS are very good at monitoring and gathering knowledge themselves and gathering evidence. There was a passing reference to microbes and there's a lot of discovery now happening about the microbiome. I just wondered whether that's going to be looped into your project. So it's an excellent question. And so I think it was part of the slide that talked about the interaction between the environment and the genome to cause disease. And so you know the microbiome might be considered part of us. It also is thought to bring about disease through its interactions. We're setting ourselves a fairly substantial task already by looking at the influence of variation in our own genomes with various physiological modes. But we're also doing that to try and get a better understanding of what the mechanism of disease is. And so when we're looking at diseases of immunity, such as MS or type 1 diabetes, then often those mechanisms lead us back to how we interact with the microbiome. So indirectly we circle back to that question. Again, I appreciate there's a lot of interest in other questions that I'd ask you to reserve those. And I'm sure again you have the names of the members of the team now I'd be delighted to engage with you after the session. But please join with me in thanking King King.