:00:00. > :00:18.This week, a medical special with a professor plugging himself into
:00:19. > :00:21.everything, and optimists -- octopus fights dementia and a cat that does
:00:22. > :00:37.everything. Every single person
:00:38. > :00:45.on the planet is different. Looks different,
:00:46. > :00:47.likes different things, Those genes can also decide
:00:48. > :01:00.which diseases each But of course, for most of history,
:01:01. > :01:10.genetics was unknown territory. This is the Royal Society
:01:11. > :01:13.of Medicine in central London. And this institution has been at
:01:14. > :01:16.the forefront of promoting and the sharing of information
:01:17. > :01:21.throughout the medical community. And in 2003, that community received
:01:22. > :01:28.an explosion of information. The human genome project
:01:29. > :01:33.was declared complete. And this knowledge paved the way
:01:34. > :01:38.for a far deeper understanding of And these days, the talk is all
:01:39. > :01:48.about personalised medicine. But how useful is this genetic
:01:49. > :01:53.information at the moment? Well, after meeting a leading
:01:54. > :01:56.geneticist in San Francisco, Jane decided to embark on
:01:57. > :02:00.her own genetic journey of If you could unlock all
:02:01. > :02:09.of the secrets of your health, how long you will live, what diseases
:02:10. > :02:12.you were at risk of developing, Stanford's genetics
:02:13. > :02:16.department is hoping that this It is studying 100 healthy people
:02:17. > :02:22.and sequencing their DNA to see if they can predict when they will
:02:23. > :02:36.get sick before they do. Leading
:02:37. > :02:38.the research is Professor Michael I'm always keeping
:02:39. > :02:44.my devices very well charged. He is a one-man tracking machine.
:02:45. > :02:47.Along with sequencing his genome,
:02:48. > :02:51.he wears nine different devices every day to monitor his health
:02:52. > :02:55.outputs, including three smart I have a continual glucose monitor
:02:56. > :03:00.that sits just on top of my skin, that continuously
:03:01. > :03:05.measures my glucose levels. For Professor Snyder, the
:03:06. > :03:07.experiment has already been a He found he had
:03:08. > :03:13.a genetic predisposition to type 2 diabetes, despite showing no typical
:03:14. > :03:18.signs of the condition. My genes predicted a number
:03:19. > :03:21.of risks, And as we are actually doing all of
:03:22. > :03:26.these medical analyses, we discovered that
:03:27. > :03:28.my sugar which have been running on perfectly fine actually shot through
:03:29. > :03:32.the roof, and basically to the point The professor keeps track
:03:33. > :03:41.of his genome It looks complex,
:03:42. > :03:46.but it is showing changes happening The outside affects my gene
:03:47. > :03:54.representation of my genome, and all the different changes I have
:03:55. > :04:11.relative to the inner line here. I think we will see a world where
:04:12. > :04:16.this will be relayed to your smartphone and the information will
:04:17. > :04:17.show up and get integrated. Quite frankly, it will go to your
:04:18. > :04:20.physician as well. Inspired by Professor Snyder,
:04:21. > :04:22.I signed up for 23 and Me. It is one of the better-known
:04:23. > :04:25.and cheap services that offers you Rather than looking
:04:26. > :04:33.at your whole genome, it looks of the ones that are significant,
:04:34. > :04:36.using the latest research. Simply send a off sample,
:04:37. > :04:42.and you're left with this. It is getting back 3 billion bits
:04:43. > :04:46.of info, I received 100. These included risk factors
:04:47. > :04:50.for indicators of Alzheimer's disease and ovarian and breast
:04:51. > :04:57.cancer syndrome. The roles of 41 genetic variants
:04:58. > :05:00.that produced different things from I was lucky to find out I didn't
:05:01. > :05:08.have anything significant to report. In fact, the most interesting
:05:09. > :05:11.thing I found out was I was I've changed my diet accordingly,
:05:12. > :05:14.and it has made This small discovery increase
:05:15. > :05:17.my appetite for more genetic After reading forums,
:05:18. > :05:22.I discovered a site which said it can unlock more data
:05:23. > :05:30.from 23 and Me for only $5. So I went for it,
:05:31. > :05:35.and rather wished I hadn't. In ten minutes I was flooded with
:05:36. > :05:39.information on 20,000 genomes. These are marked
:05:40. > :05:43.as not said, good or bad. Instead of nothing to report, I seem
:05:44. > :05:54.to have hundreds of bad genes, and I have a high risk of developing type
:05:55. > :05:57.2 diabetes and various cancer. So I put my results to
:05:58. > :05:59.a clinical geneticists. I'm absolutely baffled by the
:06:00. > :06:05.information that is in this report. Do you find any
:06:06. > :06:08.of this information useful to me? As a clinical geneticist who would
:06:09. > :06:18.be looking at your risk of disease, I would say there is nothing
:06:19. > :06:22.in here that we would find clinically actionable, in terms
:06:23. > :06:24.of setting up screening or It may tell you some things
:06:25. > :06:27.about where you are Adam explained the percentages
:06:28. > :06:32.that scared me actually showed that these genes were fairly common
:06:33. > :06:36.in the general population. It's also just one genetic aspect
:06:37. > :06:41.out of 3 billion molecules that make up your genome, and
:06:42. > :06:46.and that single molecule is not going to change that much about you
:06:47. > :06:52.in this context. What do you think my GP would say
:06:53. > :06:55.to me if I brought them this? I think your GP was struggle to find
:06:56. > :06:59.anything in here that they would A more comprehensive insight
:07:00. > :07:06.into our genes may come from the NHS's 100,000
:07:07. > :07:09.Genome project. Participants include people with
:07:10. > :07:12.rare diseases and their families. The NHS wants us to form the base
:07:13. > :07:16.for a genomic medical service, potentially offering new and more
:07:17. > :07:39.effective treatments and diagnoses. We will be in Rome -- world where
:07:40. > :07:46.people get their gene is sequenced before they are born. Many times you
:07:47. > :07:47.see mutations and you're not sure whether they are damaging not and
:07:48. > :07:50.that's what makes it tricky. While it may be many years before
:07:51. > :07:52.we can access useful information
:07:53. > :07:54.about our genomes cheaply on a smartphone, a future when
:07:55. > :08:00.genes play a greater role in the healthcare seems increasingly
:08:01. > :08:02.possible. Well that was Jen,
:08:03. > :08:04.and this is Tony Young who was the leader of innovation at the NHS.
:08:05. > :08:07.You're also a surgeon. What do you make of giving what
:08:08. > :08:13.seems a load of raw genetic data to Because Jen seemed quite freaked
:08:14. > :08:19.out. I can understand that,
:08:20. > :08:22.and there are more and more of these offerings coming
:08:23. > :08:26.from the private sector around doing some element of your genomic
:08:27. > :08:32.screening. And when you have that And the experience Jen had was one
:08:33. > :08:39.of confusion, and there is But it's not just the public who is
:08:40. > :08:50.confused. Many clinicians as well don't know
:08:51. > :08:53.what to do with large swathes of And that is one of the reasons in
:08:54. > :08:57.2012, our Prime Minister launched 100,000 Genome project, which was
:08:58. > :09:00.a world-first, really. It was a larger-scale effort than the country
:09:01. > :09:07.had undertaken to that point, to screen 100,000 whole genomes, and
:09:08. > :09:10.through the population to look at both cancer risk and rare genetic
:09:11. > :09:13.disorders, so we at the NHS could say, the results of confusing data
:09:14. > :09:16.but actually, we're going to take Not relying on a commercial company
:09:17. > :09:21.to give you some advice on the risk of diabetes or our son
:09:22. > :09:39.is that may or may not be relevant. We can crack and solve some of the
:09:40. > :09:42.big challenges we face and genomics is one of them.
:09:43. > :09:45.We learned very recently that Google is using
:09:46. > :09:47.its Deep Mind project, machine learning and artificial intelligence
:09:48. > :09:53.systems, to analyse health data from the NHS patients.
:09:54. > :09:57.The programme that you mentioned, with Deep Mind, is all
:09:58. > :10:03.So patients are going to hospital and have an altered blood test or
:10:04. > :10:09.a other test, but the early stages of that.
:10:10. > :10:12.And you are still very well and your kidneys are very well,
:10:13. > :10:18.But you're waiting for a human to look at that blood test result, and
:10:19. > :10:21.the whole point of using a machine learning and artificial intelligence
:10:22. > :10:26.is can we use this to actually pick that up much earlier to prevent
:10:27. > :10:30.that person getting kidney damage and renal failure?
:10:31. > :10:32.The data is there, but we're just not using it.
:10:33. > :10:34.So we can deliver safer, better care.
:10:35. > :10:50.And we have one of the biggest and best data sets but it's how we keep
:10:51. > :10:54.it safe and secure. We have a national data Guardian to make sure
:10:55. > :11:00.those things go forward and all data is encrypted so nope patient
:11:01. > :11:06.identifiable information is shared. It's only the clinician who can know
:11:07. > :11:16.it's their patient in front of them with that data and that's the key
:11:17. > :11:21.thing. Hello. I would love to say it was the week that the inventor of
:11:22. > :11:30.bitcoin was unmasked but after years, an Australian claim to offer
:11:31. > :11:34.proof to three media outlets that he was Satoshi Nakamoto, the mysterious
:11:35. > :11:39.inventor of the currency. That is until lots of people said he hadn't.
:11:40. > :11:43.He has backed out from providing further evidence he said would
:11:44. > :11:50.confirm his claim, saying sorry and goodbye. At IBM, they made a
:11:51. > :11:56.functioning quantum processor available to the public online. Many
:11:57. > :12:00.believe it will pave the way for next gen machines, capable of faster
:12:01. > :12:08.calculations than those of today. Scared of going under the knife?
:12:09. > :12:13.Have no fear, Robo surgeon is here. Researchers in America have built a
:12:14. > :12:18.system that can autonomously so soft tissue. Having successfully operated
:12:19. > :12:22.on a live papers-macro in testing, they say it can be safer and more
:12:23. > :12:30.precise than a human surgeon. The winner for the tech PR Stunt of the
:12:31. > :12:36.Week Award goes to "catterbox". Sick of cats sounding like cats? The
:12:37. > :12:40.custom caller mixes me hows with various phrases giving would-be
:12:41. > :12:47.purring felines a would-be voice. Town-macro. Wow! How purr-fectly
:12:48. > :12:54.pointless. Over 47 million people in the world
:12:55. > :12:58.are suffering from dementia, and an ageing population means that that
:12:59. > :13:03.figure is only likely to increase. So I've been looking at
:13:04. > :13:07.the technology, hoping to better the This week, Sea Hero launches.
:13:08. > :13:15.Designed as a game to appeal to gamers,
:13:16. > :13:20.beneath the surface is real science. While you may in think of
:13:21. > :13:23.the main feature being memory loss, one of the earliest things to be
:13:24. > :13:27.affected is actually spatial So after collecting data
:13:28. > :13:32.about how healthy-minded players navigate the game, and comparing
:13:33. > :13:36.that to how someone with dementia plays,
:13:37. > :13:39.a benchmark can be created to both Just two minutes spent
:13:40. > :13:46.on the app will generate the same amount of data as five
:13:47. > :13:54.hours in a research lab. The design
:13:55. > :13:57.of the game was built from the perspective of the scientists,
:13:58. > :14:03.and what data they needed to understand how people would navigate
:14:04. > :14:05.in 3-D space. This should provide not only a
:14:06. > :14:07.standardised measure of quantifying someone's condition, but also
:14:08. > :14:18.the ability to do so remotely. The key with this research is
:14:19. > :14:21.understanding what goes wrong with So having understood that with this
:14:22. > :14:25.experiment, and this big set of data,
:14:26. > :14:27.we'll armed to go on and do new drug trials, for example, and to
:14:28. > :14:32.investigate particular drugs and how they will have a good idea
:14:33. > :14:38.as to how people navigate. The hope is that now in a crowded
:14:39. > :14:44.market of smartphone games, the app will appeal to enough people
:14:45. > :14:49.to make this mission possible. To bring them back to his beloved
:14:50. > :15:05.Sea Hero. But for the millions this will be
:15:06. > :15:09.too late for, there are other ways technology aims to promote
:15:10. > :15:13.independent living for as long as possible. Like this remotely
:15:14. > :15:19.accessible camera setup. I showed you a while ago and app that
:15:20. > :15:23.re-purpose is smartphones and tablets to turn them into security
:15:24. > :15:28.cameras, but there are many things that can be done with a setup like
:15:29. > :15:33.that and one is to be able to monitor seniors. It's not just about
:15:34. > :15:37.watching with a camera, which many may not want to have done to them,
:15:38. > :15:44.but there are a lot of sensors that come with it. There are entry and
:15:45. > :15:49.motion sensors, full sensors and even a smart pillbox. One of the
:15:50. > :15:53.clever things is that it keeps track of someone's day to day habits so
:15:54. > :15:58.that if they change, you will receive an alert that they could be
:15:59. > :16:04.a problem. Of course, it big worry for loved ones is the risk of their
:16:05. > :16:08.relative falling. So this aims to help. This pair of sensor embedded
:16:09. > :16:14.insoles aims to provide smartphone alerts to chosen contacts should
:16:15. > :16:22.wear red take a tumble. They are also providing feedback. Pressing
:16:23. > :16:25.just here soars -- sort of feels like the vibration of an electric
:16:26. > :16:31.toothbrush and that should help those with sensory issues called by
:16:32. > :16:35.conditions like diabetes, MS or Parkinson's disease. For those whose
:16:36. > :16:40.needs are more focused on keeping and engaged mind, this app provides
:16:41. > :16:45.stimulating games for those with the early stages of dementia as well as
:16:46. > :16:49.a place to log their life story, find exercise and nutrition tips and
:16:50. > :16:51.a tool option to set reminders and to do lists.
:16:52. > :17:07.Many diseases leave markers in the blood. Even detecting these can be
:17:08. > :17:12.tricky and slow. Eight bayou marker may only be there in a tiny amount
:17:13. > :17:16.and then your blood has to be collected and sent to a central
:17:17. > :17:19.pathology lab. The whole thing can take hours or days. But here is
:17:20. > :17:30.something that can take minutes. First, take some blood which may or
:17:31. > :17:37.may not smell like blackcurrant cordial and dip in some of this
:17:38. > :17:42.magic test strip. The blood creeps up the tiny capillaries ready for
:17:43. > :17:49.testing. Pop it into the box, at a cheap smartphone fitted with an even
:17:50. > :18:02.cheaper magnifying lens and an LED. Then add some testing liquids which
:18:03. > :18:07.wash away the blood leaving only the bio-marker which glows. The brighter
:18:08. > :18:12.the image the more the clinician needs to make a judgment on your
:18:13. > :18:17.condition. This is another example of how smartphones, even low-cost
:18:18. > :18:23.ones, can be used for diagnostics either in the developing world or
:18:24. > :18:30.even in hospitals here, too. Let's look at heart attack. There is a
:18:31. > :18:36.bio-marker adopted where heart attacks show chest pain. That is
:18:37. > :18:44.responsible for around 1 million patients every year. Only a quarter
:18:45. > :18:50.have a heart problem. Those patients had to stay overnight before the
:18:51. > :18:59.test which takes so long. But with this, you can do things quicker.
:19:00. > :19:03.This flu row strip has the same refractive index as water which
:19:04. > :19:07.means it comes completely transparent when filled with a clear
:19:08. > :19:14.liquid giving a brilliantly clearly quid of the offending bio-markers,
:19:15. > :19:17.even at low levels. Crucially, it detects them at such low levels
:19:18. > :19:25.which is what has been difficult up to now. Smartphone is a cheap auto
:19:26. > :19:31.electronic component which can do pretty much the same job as other
:19:32. > :19:37.systems and equipment sitting in pathology labs and that it is where
:19:38. > :19:42.it becomes powerful. It is a level and miniaturised version of
:19:43. > :19:52.sophisticated lab equipment -- a clever. Last week, we met Sir James
:19:53. > :19:55.Dyson as he went to war on noisy, hot hairdryers with his own
:19:56. > :19:56.invention. Now it's time to have a little more leisurely look around
:19:57. > :20:09.his HQ. These are things that mean a lot to
:20:10. > :20:15.me you. They mean something. A Harrier jump jet. I had a special
:20:16. > :20:22.model built for me. It's heavy actually. That's pretty heavy. The
:20:23. > :20:29.one out there is only five tonnes. It's very light. This one is carbon
:20:30. > :20:36.fibre, one piece of carbon fibre than in the early 60s. There it was
:20:37. > :20:42.in the 60s and it's a brilliant edition invention. That was my
:20:43. > :20:50.favourite Walkman, an underwater one. I was a great fan in the early
:20:51. > :20:59.days. That is a lovely Sony phone. A very good system. Nice and easy into
:21:00. > :21:05.your pocket. A nice size. This was developed in the early 50s,
:21:06. > :21:12.immediately after the war and it uses hydraulics for the steering and
:21:13. > :21:17.brakes. Very high end cards are using the same system. It's taken
:21:18. > :21:22.years to catch up. So the thing that attracts you is the genius in
:21:23. > :21:29.engineering? Yes, the genius in that. What would you say your role
:21:30. > :21:36.is in now? I can't see you taking a hands-off role. No, I was with the
:21:37. > :21:41.engineers working on new product so I don't build prototype --
:21:42. > :21:46.prototypes any more but the engineers do. We discuss the design
:21:47. > :21:53.and discuss new technology and how to use it. How many other new ideas
:21:54. > :21:58.of yours? Very few because I have lots of bright young people with the
:21:59. > :22:04.average age of 26 and they are far cleverer than I am. I am like an old
:22:05. > :22:12.tutor going around encouraging them. The vacuum cleaner, I built over
:22:13. > :22:23.5000 prototypes before I got it right. How many? 5100 plus. How long
:22:24. > :22:27.did it take? Three years. I was doing to a day or something like
:22:28. > :22:30.that and testing them then building another one and another one. I was
:22:31. > :22:39.covered in dust but enjoying myself enormously. I asked Twitter what
:22:40. > :22:43.Twitter would like to ask you. My favourite for the hand-held vacuum
:22:44. > :22:47.cleaner, are there plans to bring out the cowboy style holster? If
:22:48. > :22:54.that's what everyone wants, we'll do a holster. More seriously, what
:22:55. > :22:59.inspired you to become an engineer and what ideas inspire you now? I
:23:00. > :23:05.started off being a designer and I realised that just designing the
:23:06. > :23:09.outside of a problem -- product was deeply unsatisfying. You want to
:23:10. > :23:15.understand how its work, how it's made. I wanted to be involved in the
:23:16. > :23:19.whole thing. I went off to an engineering company and it took
:23:20. > :23:24.seven years to learn engineering. Did want to be an engineer at the
:23:25. > :23:30.beginning. I discovered I had to be and then I enjoyed it. What would
:23:31. > :23:34.you say to the next generation of engineers to inspire them if they're
:23:35. > :23:39.trying to decide whether to carry on with it? It's a good point because
:23:40. > :23:44.we're not producing enough engineers. We need more. I would say
:23:45. > :23:49.that engineering is exciting and fulfilling and an -- engineers are
:23:50. > :23:55.the happiest people. Is exciting even though you have failure. You
:23:56. > :23:58.have exciting breakthroughs and you're making real things for people
:23:59. > :24:05.and satisfying their needs. Thanks for having us. Thank you. That was
:24:06. > :24:14.Sir James Dyson. Who knows what secrets are lurking in that
:24:15. > :24:20.building? That if it from us. Thanks for watching and we'll see you soon.