1 00:00:05,360 --> 00:00:08,600 Speaker 1: On this episode of news World. I first met Alvin 2 00:00:08,600 --> 00:00:12,600 Speaker 1: and Heidi Toffler in the nineteen seventies and worked with 3 00:00:12,640 --> 00:00:18,680 Speaker 1: them on a project about anticipatory democracy. Tafler's book Future 4 00:00:18,680 --> 00:00:23,400 Speaker 1: Shock had really changed how people thought about things. Providentially, 5 00:00:23,800 --> 00:00:26,880 Speaker 1: the publisher decided to bring it out in three different colors, 6 00:00:27,520 --> 00:00:29,920 Speaker 1: so that when you walked into a store, you had 7 00:00:29,920 --> 00:00:33,199 Speaker 1: your own version of Future Shock right there. The Taflers 8 00:00:33,240 --> 00:00:35,440 Speaker 1: went on to write What I Think is in some 9 00:00:35,479 --> 00:00:39,720 Speaker 1: ways their most important book, The Third Wave, published in 10 00:00:39,800 --> 00:00:43,960 Speaker 1: nineteen eighty four. It had an enormous impact and was 11 00:00:44,000 --> 00:00:47,199 Speaker 1: built on some earlier work done by various academics on 12 00:00:47,280 --> 00:00:50,159 Speaker 1: the whole concept of the scale of the information revolution, 13 00:00:50,680 --> 00:00:56,160 Speaker 1: but the Tafflers had a remarkable capacity for popularizing things 14 00:00:56,160 --> 00:00:59,000 Speaker 1: and bringing them home. Alvin and Heidi had a daughter 15 00:00:59,160 --> 00:01:02,680 Speaker 1: named Karen, who tragically died in two thousand of theester 16 00:01:02,800 --> 00:01:06,280 Speaker 1: forty six after more than a decade of suffering from 17 00:01:06,319 --> 00:01:10,480 Speaker 1: gian Barr syndrome. After her death, Alvin and Heidi established 18 00:01:10,520 --> 00:01:14,560 Speaker 1: the Karen Tofler Charitable Trust to help fund neurological medical 19 00:01:14,600 --> 00:01:17,520 Speaker 1: research breakthroughs. Here to talk about the work which the 20 00:01:17,959 --> 00:01:21,600 Speaker 1: Karen Tafler Charitable Trust has developed which they are funding today. 21 00:01:22,080 --> 00:01:26,640 Speaker 1: I'm really pleased to welcome my three guests, Deborah westfal 22 00:01:26,760 --> 00:01:30,520 Speaker 1: the Executive Advisor to the Karen Toaffler Charitable Trust, and 23 00:01:30,600 --> 00:01:35,480 Speaker 1: two of the Trusts Tofler Scholars and grand recipients, Adifia Gopinav, 24 00:01:35,920 --> 00:01:43,360 Speaker 1: postdoctoral Neuroscience, University of Florida, and Vijaya Colachlama, Associate professor 25 00:01:43,360 --> 00:01:47,480 Speaker 1: at Boston University, on how the Karen Toffler Charitable Trust 26 00:01:47,520 --> 00:01:52,720 Speaker 1: helps fund advanced neurological disease research and helps young professional 27 00:01:52,720 --> 00:01:57,160 Speaker 1: researchers early in their career doing this research, including Alzheimer's 28 00:01:57,160 --> 00:02:11,080 Speaker 1: in Parkinson's welcome, It's a pleasure to be here. 29 00:02:11,440 --> 00:02:12,560 Speaker 2: Thank you, sir, pleasure. 30 00:02:12,880 --> 00:02:14,960 Speaker 1: I'm really delighted to have all three of you. And 31 00:02:15,000 --> 00:02:19,600 Speaker 1: I took great pride in taking Tafler down to see 32 00:02:19,639 --> 00:02:21,639 Speaker 1: General Don Starry, who was the head of the Training 33 00:02:21,639 --> 00:02:25,280 Speaker 1: and Doctrine Command, who had read Tafler's work and said, 34 00:02:25,320 --> 00:02:28,399 Speaker 1: if this is accurate, it forces us to change all 35 00:02:28,440 --> 00:02:32,079 Speaker 1: of our thinking and literally had reshaped the Army's battle 36 00:02:32,080 --> 00:02:35,160 Speaker 1: doctrine and led them to develop what they called air 37 00:02:35,280 --> 00:02:39,320 Speaker 1: land battle because they wanted the air Force totally integrated 38 00:02:39,840 --> 00:02:42,560 Speaker 1: with the army based on the whole concept of a 39 00:02:42,600 --> 00:02:46,280 Speaker 1: third wave of information, and as a result, much of 40 00:02:46,360 --> 00:02:50,320 Speaker 1: what we see today as an integrated team across all 41 00:02:50,360 --> 00:02:52,480 Speaker 1: the services grew out of the work of Alvin and 42 00:02:52,480 --> 00:02:56,080 Speaker 1: Heidi Tofler, which is kind of an amazing achievement. I 43 00:02:56,120 --> 00:02:58,320 Speaker 1: also had the great privilege of taking them to see 44 00:02:58,600 --> 00:03:02,359 Speaker 1: Vice President George shtab Bush and talk about the implications 45 00:03:02,840 --> 00:03:06,240 Speaker 1: that their work had for thinking about government and society. 46 00:03:06,560 --> 00:03:10,440 Speaker 1: The Taflers were remarkable people, and I don't think there's 47 00:03:10,480 --> 00:03:13,200 Speaker 1: any way to explain to beond that they were one 48 00:03:13,280 --> 00:03:17,720 Speaker 1: hundred percent human. They totally loved life. They were engaged 49 00:03:17,760 --> 00:03:20,200 Speaker 1: in the life of the mind, but they were also 50 00:03:20,320 --> 00:03:23,160 Speaker 1: just engaged in being good friends. I stayed with them 51 00:03:23,480 --> 00:03:25,119 Speaker 1: in New York, and I stayed with them in Los 52 00:03:25,120 --> 00:03:28,360 Speaker 1: Angeles after they moved out there. Every time I'd see them, 53 00:03:28,760 --> 00:03:31,720 Speaker 1: they were remarkably helpful, and as we were developing a 54 00:03:31,800 --> 00:03:34,400 Speaker 1: new set of ideas which led to the contract with America, 55 00:03:34,720 --> 00:03:36,920 Speaker 1: they would come and visit us in the Capitol and 56 00:03:36,960 --> 00:03:39,800 Speaker 1: we'd sit around a brainstorm. Some of our members thought 57 00:03:39,800 --> 00:03:41,240 Speaker 1: we were nuts because we kept talking about all this 58 00:03:41,360 --> 00:03:44,360 Speaker 1: future stuff, but others being to figure out, hey, this 59 00:03:44,560 --> 00:03:48,080 Speaker 1: is real candidly today the house could use a little 60 00:03:48,120 --> 00:03:51,800 Speaker 1: more Tofleriism on a little less stupidity and how it's 61 00:03:51,880 --> 00:03:54,600 Speaker 1: doing things. Let's start Debora if we could with you, 62 00:03:55,360 --> 00:03:59,120 Speaker 1: can you talk about the Karen Tofler Charitable Trust, what 63 00:03:59,280 --> 00:04:02,040 Speaker 1: it is today, how it got started, and how you 64 00:04:02,080 --> 00:04:03,880 Speaker 1: see its evolution and its impact. 65 00:04:04,600 --> 00:04:08,160 Speaker 3: Sure so you did a wonderful job describing the Tofflers. 66 00:04:08,400 --> 00:04:14,120 Speaker 3: They were amazing people, visionary thinkers, renowned authors and futurists, 67 00:04:14,160 --> 00:04:17,840 Speaker 3: and they really dedicated their life to understanding and shaping 68 00:04:17,880 --> 00:04:22,600 Speaker 3: the future across this very rapidly changing world. Through their 69 00:04:22,640 --> 00:04:26,840 Speaker 3: books and their speeches, they connected with people human to 70 00:04:27,000 --> 00:04:31,240 Speaker 3: human to share ideas, to learn, and to champion future 71 00:04:31,320 --> 00:04:36,760 Speaker 3: focus consciousness. To honor their legacy, they continue their mission 72 00:04:36,800 --> 00:04:41,760 Speaker 3: through the Karen Toffler Charitable Trust. All passed away in 73 00:04:41,800 --> 00:04:46,240 Speaker 3: twenty and sixteen. Heidi passed away in twenty nineteen, and 74 00:04:46,279 --> 00:04:50,280 Speaker 3: the trust was established in twenty nineteen, named after their 75 00:04:50,360 --> 00:04:54,000 Speaker 3: daughter Karen, which you knew personally and you talked about 76 00:04:54,120 --> 00:04:57,880 Speaker 3: per disease. The trust is a nonprofit organization and it's 77 00:04:57,920 --> 00:05:03,840 Speaker 3: working to revolutionize metal research, education and technology, and we're 78 00:05:03,880 --> 00:05:09,040 Speaker 3: really focus on these young professionals conducting early stage research 79 00:05:09,160 --> 00:05:14,400 Speaker 3: that explores new ventures and creating new medical knowledge. And 80 00:05:14,440 --> 00:05:17,800 Speaker 3: that's really important because it's these young researchers who are 81 00:05:18,120 --> 00:05:21,680 Speaker 3: just starting their careers. They're starting to build their labs, 82 00:05:22,200 --> 00:05:27,839 Speaker 3: they're maturing the area of focus, and sometimes their research 83 00:05:28,279 --> 00:05:33,200 Speaker 3: is pushing the limits of and outside boundaries, and so 84 00:05:33,360 --> 00:05:35,599 Speaker 3: that support to them is very, very important. 85 00:05:36,240 --> 00:05:38,240 Speaker 1: I should mention just for a second, because I think 86 00:05:38,279 --> 00:05:43,440 Speaker 1: people don't often realize how powerful an idea can be. 87 00:05:44,360 --> 00:05:48,000 Speaker 1: That Future Shock, for example, when it came out, actually 88 00:05:48,080 --> 00:05:51,440 Speaker 1: sold as many books in Japan as it's sold in 89 00:05:51,440 --> 00:05:54,120 Speaker 1: the United States. Now, considering that we are more than 90 00:05:54,120 --> 00:05:56,919 Speaker 1: twice the size of Japan, gives you some sense of 91 00:05:56,960 --> 00:06:01,719 Speaker 1: how much they penetrated Japanese culture. And when their book 92 00:06:01,760 --> 00:06:06,479 Speaker 1: came out, which really described the information revolution the third Wave, 93 00:06:07,080 --> 00:06:11,039 Speaker 1: the Chinese Communist Party actually made a decision at the 94 00:06:11,120 --> 00:06:15,560 Speaker 1: highest levels that they would popularize their book throughout all 95 00:06:15,600 --> 00:06:18,760 Speaker 1: of China. I remember one time Alvin telling me what 96 00:06:18,800 --> 00:06:21,520 Speaker 1: it was like to be standing in Shanghai and have 97 00:06:21,680 --> 00:06:25,040 Speaker 1: people walk up holding a Chinese copy of his book 98 00:06:25,320 --> 00:06:28,160 Speaker 1: and asking him to sign it. Gorbachev was aware of 99 00:06:28,640 --> 00:06:32,000 Speaker 1: the concepts that they had. And so these are two 100 00:06:32,040 --> 00:06:35,440 Speaker 1: people who had had a remarkable set of insights that 101 00:06:35,520 --> 00:06:39,120 Speaker 1: applied to the entire human race, and who had come 102 00:06:39,160 --> 00:06:44,120 Speaker 1: to understand from their daughter's long, painful disease that really 103 00:06:44,160 --> 00:06:48,479 Speaker 1: one of the most complicated areas of medical development is 104 00:06:48,520 --> 00:06:53,359 Speaker 1: this whole question of neurological activity, whether it is the 105 00:06:53,440 --> 00:06:56,800 Speaker 1: kind of disease that Karen had, or whether it's Alzheimer's 106 00:06:56,839 --> 00:06:59,280 Speaker 1: or Parkinson's or a whole range of other things. And 107 00:06:59,320 --> 00:07:03,640 Speaker 1: I think that's The Tofler Scholars Program is a perfect 108 00:07:03,680 --> 00:07:07,719 Speaker 1: example of what the Toafflers would have favored in having 109 00:07:07,760 --> 00:07:10,920 Speaker 1: a deep belief in the better future and in the potential. 110 00:07:11,360 --> 00:07:14,080 Speaker 1: So why don't you talk a little bit before we 111 00:07:14,120 --> 00:07:17,880 Speaker 1: get to them about the fact you have over sixty 112 00:07:18,280 --> 00:07:22,360 Speaker 1: Toughler scholars from ten universities, and we had specifically picked 113 00:07:22,400 --> 00:07:25,200 Speaker 1: and asked bj and Adithia to be with us today 114 00:07:25,200 --> 00:07:28,080 Speaker 1: because of the unique and remarkable work they're doing. But 115 00:07:28,440 --> 00:07:31,640 Speaker 1: why don't you briefly describe for us, if you would, Debra, 116 00:07:31,800 --> 00:07:35,480 Speaker 1: the whole concept of the Tofler Scholars Program and the 117 00:07:35,560 --> 00:07:36,720 Speaker 1: universities it works with. 118 00:07:37,520 --> 00:07:40,960 Speaker 3: Yeah, so when the trust was created, we looked around 119 00:07:41,000 --> 00:07:46,080 Speaker 3: to see where other funding was in support was being done, 120 00:07:46,280 --> 00:07:48,920 Speaker 3: and you know, there's a lot of work and great 121 00:07:48,960 --> 00:07:53,240 Speaker 3: work that's being done by existing foundations, by government agencies 122 00:07:53,360 --> 00:07:57,080 Speaker 3: in this area of trying to understand the brain and 123 00:07:57,280 --> 00:08:02,560 Speaker 3: the brain body connection. But what seems to be somewhat 124 00:08:02,880 --> 00:08:07,360 Speaker 3: limited or a gap in that support is for the 125 00:08:07,520 --> 00:08:13,640 Speaker 3: ideas that are somewhat outside the current day thinking. And 126 00:08:13,720 --> 00:08:16,800 Speaker 3: as you just mentioned, you know, it's a very Tofler 127 00:08:17,440 --> 00:08:21,600 Speaker 3: esque thing to kind of challenge today's notions, challenge today's 128 00:08:21,760 --> 00:08:25,680 Speaker 3: hypotheses and understanding and kind of step back and question 129 00:08:25,800 --> 00:08:28,120 Speaker 3: what are we doing or how are we doing it? 130 00:08:28,320 --> 00:08:30,280 Speaker 3: Or what do we know or what don't we know. 131 00:08:30,840 --> 00:08:36,280 Speaker 3: The scholars are those individuals. They're courageous, they have ideas, 132 00:08:36,360 --> 00:08:42,120 Speaker 3: they have hypotheses for what research we might be able 133 00:08:42,200 --> 00:08:46,000 Speaker 3: to take on that today doesn't have a lot of support, 134 00:08:46,240 --> 00:08:50,040 Speaker 3: but tomorrow it could lead to the big breakthroughs. And 135 00:08:50,120 --> 00:08:54,000 Speaker 3: so the Karen Toffler Charitable Trust was created to give 136 00:08:54,080 --> 00:08:57,360 Speaker 3: that support. You know, it's the seed money for their 137 00:08:57,360 --> 00:09:02,480 Speaker 3: early pipeline research that is needed so that researchers can 138 00:09:02,520 --> 00:09:06,520 Speaker 3: get started with this research, get a few breakthroughs, publish, 139 00:09:07,120 --> 00:09:10,880 Speaker 3: get their research out there, and then let other organizations 140 00:09:11,000 --> 00:09:14,560 Speaker 3: such as the NIH or the larger foundations see that 141 00:09:14,640 --> 00:09:17,800 Speaker 3: they can make progress and then fund at larger amounts. 142 00:09:17,880 --> 00:09:21,480 Speaker 3: And so we feel like we're filling a real important 143 00:09:21,559 --> 00:09:26,240 Speaker 3: niche and the upfront part of the research pipeline so 144 00:09:26,280 --> 00:09:29,120 Speaker 3: that we can look for that new science, look for 145 00:09:29,160 --> 00:09:33,719 Speaker 3: that new knowledge that really might lead to the breakthrough 146 00:09:34,440 --> 00:09:37,640 Speaker 3: or the end to some of these horrible diseases. 147 00:09:38,400 --> 00:09:42,480 Speaker 1: I had a particular interest in Alzheimer's because I had 148 00:09:42,520 --> 00:09:47,080 Speaker 1: taught the oldest men's Bible study back when I was 149 00:09:47,120 --> 00:09:50,520 Speaker 1: a very young professor. I watched several of the members 150 00:09:50,520 --> 00:09:54,400 Speaker 1: of that Bible study themselves came down with Alzheimer's. My 151 00:09:54,800 --> 00:09:58,600 Speaker 1: sister in law's father came down with Alzheimer's, wife came 152 00:09:58,640 --> 00:10:03,560 Speaker 1: down with Parkinson's, and so it's really a very complicated situation. 153 00:10:04,280 --> 00:10:07,880 Speaker 1: I co chaired with Democratic Senator Bob Carey a three 154 00:10:07,960 --> 00:10:12,720 Speaker 1: year study on Alzheimer's research, which actually when we testified 155 00:10:12,720 --> 00:10:15,800 Speaker 1: at the Senate had the largest number of senators I'd 156 00:10:15,800 --> 00:10:18,560 Speaker 1: ever seen at a hearing because so many of them 157 00:10:18,920 --> 00:10:23,760 Speaker 1: have direct ties through their families with Alzheimer's and Alzheimer's 158 00:10:23,800 --> 00:10:26,120 Speaker 1: is the sixth leading cause of death in the United States. 159 00:10:26,440 --> 00:10:29,600 Speaker 1: There are over five million Americans living with it as 160 00:10:29,760 --> 00:10:33,559 Speaker 1: we age the current projections will be fourteen million by 161 00:10:33,600 --> 00:10:38,840 Speaker 1: twenty fifty. And of course Parkinson's is the second leading 162 00:10:39,200 --> 00:10:44,080 Speaker 1: a neurodegenerative disorder. So Adithya and Colechlamanla, you decide which 163 00:10:44,080 --> 00:10:47,040 Speaker 1: one wants to jump in first. Here? Can you explain 164 00:10:47,080 --> 00:10:49,920 Speaker 1: to us, even with all the attention has been paid 165 00:10:49,920 --> 00:10:52,520 Speaker 1: over the years, we still have a challenge of an 166 00:10:52,559 --> 00:10:57,280 Speaker 1: accurate diagnosis and an early enough diagnosis. Can you all 167 00:10:57,320 --> 00:11:00,000 Speaker 1: talk a little bit about this whole diagnostic channel. 168 00:11:00,720 --> 00:11:03,840 Speaker 4: Yes, thank you for having us. Before I go there, 169 00:11:03,920 --> 00:11:08,200 Speaker 4: I just want to thank the Toughler Trust for believing 170 00:11:08,200 --> 00:11:11,600 Speaker 4: in us, believing in our science, and in fact I 171 00:11:11,679 --> 00:11:15,600 Speaker 4: received the award in twenty twenty one, and already I 172 00:11:15,640 --> 00:11:18,400 Speaker 4: think with that support we've been able to make a 173 00:11:18,400 --> 00:11:22,160 Speaker 4: lot of progress in terms of building novel tools that 174 00:11:22,240 --> 00:11:25,240 Speaker 4: would allow researchers to sort of come up with better 175 00:11:25,280 --> 00:11:29,480 Speaker 4: ways to diagnose Alzheimer's. In terms of just the background, 176 00:11:29,679 --> 00:11:32,760 Speaker 4: I think the way we want to think about Alzheimer's 177 00:11:32,960 --> 00:11:36,400 Speaker 4: is broadly in the category of dementia so dementia is 178 00:11:36,440 --> 00:11:39,120 Speaker 4: the term that you would want to use to describe 179 00:11:39,200 --> 00:11:42,959 Speaker 4: memory loss. And there are many ways dementia can be caused, right, 180 00:11:43,000 --> 00:11:46,520 Speaker 4: And Alzheimer's disease is sort of really the primary cause 181 00:11:46,559 --> 00:11:50,640 Speaker 4: of dementia. About seventy percent of the cases who have dementia, 182 00:11:51,040 --> 00:11:54,400 Speaker 4: that's because they have Alzheimer's. And the complexity is not 183 00:11:54,440 --> 00:11:57,800 Speaker 4: about diagnosing if somebody has dementia, because there are some 184 00:11:58,440 --> 00:12:02,880 Speaker 4: very standardized tests are out there that any practitioner can 185 00:12:02,960 --> 00:12:07,800 Speaker 4: administer to assess if somebody has dementia. The problem is 186 00:12:07,840 --> 00:12:12,559 Speaker 4: actually more related to understanding the root cause of dementia. Right, 187 00:12:12,600 --> 00:12:15,520 Speaker 4: So dementia can happen if somebody actually even has depression, 188 00:12:15,679 --> 00:12:18,120 Speaker 4: or somebody who had a head injury, or somebody who 189 00:12:18,200 --> 00:12:21,760 Speaker 4: had Parkinson's or even Alzheimer's, Right, So the challenge is 190 00:12:21,800 --> 00:12:25,559 Speaker 4: not about dementia, but the challenge is about understanding the 191 00:12:25,640 --> 00:12:31,439 Speaker 4: root cause of dementia. And unfortunately, the gold standard diagnosis 192 00:12:31,440 --> 00:12:34,040 Speaker 4: that is out there today is only when the person 193 00:12:34,120 --> 00:12:37,200 Speaker 4: is dead, so they actually open up the brains and 194 00:12:37,200 --> 00:12:39,480 Speaker 4: then they see what's actually inside and then then they 195 00:12:39,480 --> 00:12:42,880 Speaker 4: can confirm that they have Alzheimer's or some other issue. 196 00:12:43,160 --> 00:12:46,200 Speaker 1: Can I just say, as a non researcher, it's a 197 00:12:46,200 --> 00:12:50,120 Speaker 1: little depressing to learn that we can only diagnose you 198 00:12:50,200 --> 00:12:53,520 Speaker 1: after you die. It strikes me that that limits the 199 00:12:53,520 --> 00:12:56,960 Speaker 1: amount of medical intervention to minimize the damage. 200 00:12:57,720 --> 00:12:57,960 Speaker 2: Right. 201 00:12:58,080 --> 00:13:01,200 Speaker 4: That actually has been the big challenge. But I think 202 00:13:01,400 --> 00:13:04,679 Speaker 4: over the past few years there have been several technologies 203 00:13:04,679 --> 00:13:07,120 Speaker 4: that are out there that are allowing us to come 204 00:13:07,160 --> 00:13:10,840 Speaker 4: up with the best possible ways to assess if somebody 205 00:13:10,880 --> 00:13:15,400 Speaker 4: has Alzheimer's disease. For example, there is research that is 206 00:13:15,440 --> 00:13:17,720 Speaker 4: going on in the world about how do you sort 207 00:13:17,720 --> 00:13:20,400 Speaker 4: of come up with a black test to assess certain 208 00:13:20,480 --> 00:13:23,600 Speaker 4: proteins that point to the risk of Alzheimer's that are 209 00:13:23,640 --> 00:13:27,920 Speaker 4: imaging modality such as pet scans and other ways to 210 00:13:27,960 --> 00:13:31,200 Speaker 4: sort of understand what kind of proteins are deposited in 211 00:13:31,240 --> 00:13:35,679 Speaker 4: the brain to diagnose Alzheimer's. So it's getting better, but 212 00:13:35,800 --> 00:13:37,720 Speaker 4: still there is a belief in the community that the 213 00:13:37,760 --> 00:13:39,400 Speaker 4: gold standard is still post model. 214 00:13:40,160 --> 00:13:44,160 Speaker 1: But as a potential patient, the earlier we can intervene 215 00:13:45,400 --> 00:13:48,480 Speaker 1: and the earlier we have some sense of slowing down 216 00:13:48,559 --> 00:13:52,319 Speaker 1: the rate of the disease, the better the likelihood of 217 00:13:52,320 --> 00:13:56,200 Speaker 1: a successful intervention. So if I can only intervene after 218 00:13:56,240 --> 00:13:59,720 Speaker 1: you die. I've sort of lost all of my opportunities here. 219 00:14:00,080 --> 00:14:03,320 Speaker 1: What is the work like because you almost need a 220 00:14:03,480 --> 00:14:08,280 Speaker 1: relatively and expensive, widely usable test, even in your thirties 221 00:14:08,840 --> 00:14:11,240 Speaker 1: to begin to find early on set. Correct me if 222 00:14:11,240 --> 00:14:11,959 Speaker 1: I get this wrong. 223 00:14:12,640 --> 00:14:15,839 Speaker 4: Well, the relatively cheap aspect I think is very attractive. 224 00:14:16,000 --> 00:14:17,880 Speaker 4: I think we are hopefully going to get there at 225 00:14:17,880 --> 00:14:21,240 Speaker 4: some point. And also detecting the disease early on, I 226 00:14:21,240 --> 00:14:26,720 Speaker 4: think is very very important, crucial, especially today because I'm 227 00:14:26,760 --> 00:14:29,240 Speaker 4: sure you have seen the news about two drugs that 228 00:14:29,280 --> 00:14:32,400 Speaker 4: were recently approved by the FDA. One of them is 229 00:14:32,840 --> 00:14:36,560 Speaker 4: named as licanimab and the other one named as aducanemab. 230 00:14:36,920 --> 00:14:39,400 Speaker 4: So finally we have some hope that there are drugs 231 00:14:39,880 --> 00:14:43,200 Speaker 4: that can potentially cure Alzheimer's right. So, because of the 232 00:14:43,240 --> 00:14:45,280 Speaker 4: fact that there are these drugs that are approved, there 233 00:14:45,320 --> 00:14:47,880 Speaker 4: is now a huge push in the community to really 234 00:14:47,880 --> 00:14:50,000 Speaker 4: think about how to detect the disease at the right 235 00:14:50,040 --> 00:14:53,160 Speaker 4: time so that these drugs can be given to those patients. 236 00:14:53,520 --> 00:14:56,520 Speaker 4: So a lot of technologies and a lot of research 237 00:14:56,560 --> 00:14:59,240 Speaker 4: has actually been done to sort of identify those patients 238 00:14:59,240 --> 00:15:02,320 Speaker 4: who might actually get benefit from these drugs. So clearly 239 00:15:02,720 --> 00:15:05,600 Speaker 4: things are getting better, and in fact, one of the 240 00:15:05,600 --> 00:15:07,120 Speaker 4: things that we are trying to do in our lab 241 00:15:07,240 --> 00:15:11,120 Speaker 4: is to come up with AI based approaches to take 242 00:15:11,200 --> 00:15:13,880 Speaker 4: routine collected data clinical data, because you know, when a 243 00:15:14,000 --> 00:15:17,320 Speaker 4: patient walks into the hospital, whether it's a neurologist or 244 00:15:17,360 --> 00:15:20,520 Speaker 4: even a general practitioner, they try to basically gather a 245 00:15:20,520 --> 00:15:24,000 Speaker 4: lot of information. So based on their examination, based on 246 00:15:24,040 --> 00:15:27,400 Speaker 4: the demographics of the patient, based on their medical history, 247 00:15:27,800 --> 00:15:30,720 Speaker 4: based on their family history, they sort of get all 248 00:15:30,760 --> 00:15:32,680 Speaker 4: that information then try to come up with the best 249 00:15:32,680 --> 00:15:35,440 Speaker 4: possible way to diagnose the disease and hopefully early in 250 00:15:35,520 --> 00:15:38,040 Speaker 4: the stage. And what we are doing in our lab 251 00:15:38,160 --> 00:15:42,840 Speaker 4: is to combine all this information using artificial intelligence and 252 00:15:42,880 --> 00:15:44,640 Speaker 4: then seeing if there is a way for us to 253 00:15:44,680 --> 00:15:46,960 Speaker 4: come up with a better way to diagnose the disease, 254 00:15:47,760 --> 00:15:51,520 Speaker 4: especially sort of understand whether the patient has let's say, 255 00:15:51,520 --> 00:15:55,280 Speaker 4: Alzheimer's and maybe some combination of Alzheimer's with other kinds 256 00:15:55,320 --> 00:15:57,520 Speaker 4: of things that are going on in the patient. So 257 00:15:57,600 --> 00:16:01,400 Speaker 4: if we do that well, then hopefully we can identify 258 00:16:01,440 --> 00:16:03,880 Speaker 4: those patients at the right time, and if we do that, 259 00:16:04,280 --> 00:16:07,240 Speaker 4: hopefully these drugs can be given to those patients at 260 00:16:07,280 --> 00:16:11,120 Speaker 4: the right time. I think that's the plan. And because 261 00:16:11,160 --> 00:16:12,920 Speaker 4: of the fact that we have these drugs that are 262 00:16:12,920 --> 00:16:15,480 Speaker 4: recently approved, there is a lot of push in the 263 00:16:15,520 --> 00:16:17,640 Speaker 4: community to sort of really come up with better ways 264 00:16:17,680 --> 00:16:20,880 Speaker 4: to diagnose the disease, because if we didn't have any drugs, 265 00:16:21,280 --> 00:16:23,800 Speaker 4: then it's all about just managing the patient, which is 266 00:16:23,840 --> 00:16:26,640 Speaker 4: completely different as opposed to actually giving the patient a 267 00:16:26,720 --> 00:16:28,880 Speaker 4: drug and then hoping there is going to be some 268 00:16:29,000 --> 00:16:32,200 Speaker 4: benefit that's coming out. Right, So this is a good time, 269 00:16:32,360 --> 00:16:34,120 Speaker 4: and I think things are only getting better. 270 00:16:45,160 --> 00:16:47,840 Speaker 1: Hi, this is newt and my new book, Marks the Majority, 271 00:16:47,920 --> 00:16:51,400 Speaker 1: the real story of the Republican Revolution. I offer strategies 272 00:16:51,400 --> 00:16:55,120 Speaker 1: and insights for everyday citizens and for season politicians. It's 273 00:16:55,160 --> 00:16:58,200 Speaker 1: both a guide for political success and for winning back 274 00:16:58,240 --> 00:17:01,440 Speaker 1: the Majority. In twenty twenty four, March to the Majority 275 00:17:01,480 --> 00:17:05,520 Speaker 1: outlines the sixteen year campaign to write the Contract with America. 276 00:17:05,960 --> 00:17:09,399 Speaker 1: Explains how we elected the first Republican House majority in 277 00:17:09,560 --> 00:17:13,000 Speaker 1: forty years, and how we worked with President Bill Clinton 278 00:17:13,280 --> 00:17:18,520 Speaker 1: to pass major reforms, including four consecutive balance budgets. March 279 00:17:18,560 --> 00:17:21,560 Speaker 1: to the Majority tells the behind the scenes story of 280 00:17:21,640 --> 00:17:24,240 Speaker 1: how we got it done. Here's a special offer for 281 00:17:24,320 --> 00:17:28,160 Speaker 1: my podcast listeners. You can order March the Majority right 282 00:17:28,200 --> 00:17:31,680 Speaker 1: now at gingrishtree sixty dot com slash book and it'll 283 00:17:31,720 --> 00:17:34,280 Speaker 1: be shipped directly to you. Don't miss out on the 284 00:17:34,320 --> 00:17:37,800 Speaker 1: special offer. Go to gingishtree sixty dot com slash book 285 00:17:38,080 --> 00:17:41,520 Speaker 1: and order your copy now. Order it today at gingishtree 286 00:17:41,520 --> 00:17:54,280 Speaker 1: sixty dot com slash book. You know, it really reminds 287 00:17:54,320 --> 00:17:58,119 Speaker 1: me of the period around nineteen seventy when Richard Nixon 288 00:17:58,680 --> 00:18:01,680 Speaker 1: proposed a war on cana. At that stage, we had 289 00:18:01,800 --> 00:18:06,320 Speaker 1: very few tools that were truly successful. But over the 290 00:18:06,400 --> 00:18:09,320 Speaker 1: depth and the range of the research, we just have 291 00:18:09,400 --> 00:18:13,560 Speaker 1: made amazing strides in turning cancer into something which people 292 00:18:13,600 --> 00:18:16,360 Speaker 1: can survive and which in many cases they go into 293 00:18:16,400 --> 00:18:20,679 Speaker 1: remission and in other cases it's manageable for decades. So 294 00:18:20,720 --> 00:18:22,800 Speaker 1: there's some help here that we're in the very beginning 295 00:18:22,880 --> 00:18:27,959 Speaker 1: stages of being able to deal with neurological conditions in 296 00:18:27,960 --> 00:18:31,200 Speaker 1: the same kind of science based pattern of gradually learning 297 00:18:31,280 --> 00:18:34,760 Speaker 1: more and more and having greater and greater tools. Now, 298 00:18:35,119 --> 00:18:38,800 Speaker 1: let me ask Aditya. You focus a lot on Parkinson's. 299 00:18:39,200 --> 00:18:41,080 Speaker 1: First of all, can you talk a little bit about 300 00:18:41,119 --> 00:18:46,000 Speaker 1: the difference between Alzheimer's and Parkinson's as they manifest themselves 301 00:18:46,760 --> 00:18:49,680 Speaker 1: both in physical characteristics but also in terms of what's 302 00:18:49,720 --> 00:18:51,760 Speaker 1: happening in your neurological system. 303 00:18:52,240 --> 00:18:55,760 Speaker 2: Yes, absolutely, so, to start to echo what Vijaya said, 304 00:18:55,800 --> 00:18:57,879 Speaker 2: we'd like to thank the Trust for believing in us 305 00:18:57,920 --> 00:18:59,840 Speaker 2: this far, and of course I'd like to thank you 306 00:18:59,880 --> 00:19:02,680 Speaker 2: for inviting us here to talk with you today, as 307 00:19:02,760 --> 00:19:06,320 Speaker 2: Vijaya was also telling us, often Alzheimer's is considered a dementia, 308 00:19:06,440 --> 00:19:09,240 Speaker 2: so it can be diagnosed by a skilled clinician using 309 00:19:09,240 --> 00:19:13,840 Speaker 2: a battery of tests. Parkinson's typically does not present as dementia, 310 00:19:13,960 --> 00:19:16,880 Speaker 2: so a person would usually go to see their primary 311 00:19:16,920 --> 00:19:20,040 Speaker 2: care physician or a neurologist a specialist if they have one, 312 00:19:20,359 --> 00:19:23,640 Speaker 2: because they suddenly noticed that they're not able to perform 313 00:19:23,760 --> 00:19:26,800 Speaker 2: certain tasks. So when somebody's reaching for a mug of 314 00:19:26,840 --> 00:19:29,640 Speaker 2: coffee in the morning, they suddenly find that their hand 315 00:19:29,680 --> 00:19:31,400 Speaker 2: doesn't quite make it all the way to the mug, 316 00:19:31,480 --> 00:19:33,600 Speaker 2: or once they grasp the mug, their hands are shaking, 317 00:19:34,160 --> 00:19:36,439 Speaker 2: and that's usually one of the first signs they have 318 00:19:36,480 --> 00:19:39,680 Speaker 2: a tremor in one hand or the other. Now, when 319 00:19:39,760 --> 00:19:42,480 Speaker 2: a person has this kind of issue, they would see 320 00:19:42,520 --> 00:19:46,080 Speaker 2: their doctor might suggest that they go see a neurologist 321 00:19:46,080 --> 00:19:48,760 Speaker 2: as specialists, and in the hands of a skilled specialist 322 00:19:49,280 --> 00:19:51,800 Speaker 2: usually they can make a seventy to eighty percent accurate 323 00:19:51,840 --> 00:19:57,000 Speaker 2: diagnosis of Parkinson's based on the movement problems that they're having. Now. 324 00:19:57,160 --> 00:19:59,920 Speaker 2: On the other hand, by the time a person had 325 00:20:00,560 --> 00:20:05,080 Speaker 2: presented with these movement problems, sixty seventy percent of the 326 00:20:05,200 --> 00:20:07,520 Speaker 2: cells and the brain that signal movement. These are called 327 00:20:07,560 --> 00:20:11,639 Speaker 2: dopamine neurons they make a certain transmitter called dopamine, sixty 328 00:20:11,640 --> 00:20:14,440 Speaker 2: to seventy percent of these cells have already died. So 329 00:20:14,840 --> 00:20:17,399 Speaker 2: by the time a person has the movement symptoms that 330 00:20:17,400 --> 00:20:20,680 Speaker 2: they'd go to the doctor for, as far as we're aware, 331 00:20:20,760 --> 00:20:23,399 Speaker 2: at this point in time, they may be beyond the 332 00:20:23,440 --> 00:20:26,320 Speaker 2: point where we can bring it back to where they 333 00:20:26,359 --> 00:20:29,919 Speaker 2: were before. And that really brings us back to the 334 00:20:30,040 --> 00:20:33,840 Speaker 2: need to have diagnostic tests that could help us detect 335 00:20:33,960 --> 00:20:37,159 Speaker 2: and diagnose Parkinson's before we get to that point. The 336 00:20:37,200 --> 00:20:40,000 Speaker 2: other thing to keep in mind is that typically ten 337 00:20:40,040 --> 00:20:42,800 Speaker 2: to twenty years even before they have these movement symptoms, 338 00:20:42,840 --> 00:20:46,520 Speaker 2: a lot of these patients with Parkinson's report symptoms that 339 00:20:46,560 --> 00:20:49,440 Speaker 2: are outside of the brain. They have gastro intestinal problems, 340 00:20:49,480 --> 00:20:53,080 Speaker 2: they have constipation, they have mood changes, and so this 341 00:20:53,200 --> 00:20:55,280 Speaker 2: is telling us that there are things that are happening 342 00:20:55,359 --> 00:20:58,480 Speaker 2: way in advance of the presentation of the tremor. However, 343 00:20:58,560 --> 00:21:01,320 Speaker 2: we haven't yet become skilled enough to be able to 344 00:21:01,320 --> 00:21:02,679 Speaker 2: detect it at that stage. 345 00:21:03,119 --> 00:21:08,240 Speaker 1: It sounds to me like Parkinson's is a wider part 346 00:21:08,359 --> 00:21:12,320 Speaker 1: of your neurological system, and that Alzheimer's tends to be 347 00:21:12,359 --> 00:21:16,360 Speaker 1: focused in the brain. I mean, is that a reasonably accurate, 348 00:21:16,600 --> 00:21:17,960 Speaker 1: simple minded way of putting it. 349 00:21:18,920 --> 00:21:21,120 Speaker 2: I think that's fairly reasonable. The tria, do you think 350 00:21:21,160 --> 00:21:22,320 Speaker 2: that's an accurate way to put it? 351 00:21:23,000 --> 00:21:25,760 Speaker 4: I don't call myself a Parkinson's disease expert, but I 352 00:21:25,800 --> 00:21:29,360 Speaker 4: think the point is trying to make is that there 353 00:21:29,359 --> 00:21:32,119 Speaker 4: are these physical observations that you can make on this 354 00:21:32,240 --> 00:21:35,120 Speaker 4: patient who has Parkinson's disease, in terms of, let's say, 355 00:21:35,200 --> 00:21:37,720 Speaker 4: the way they walk, the way they hold hands, the 356 00:21:37,760 --> 00:21:41,000 Speaker 4: way they speak. I think those are more apparent, and 357 00:21:41,080 --> 00:21:43,320 Speaker 4: I think it's fair to assume that you know, those 358 00:21:43,359 --> 00:21:47,280 Speaker 4: are things that are kind of outside the brain, that 359 00:21:47,359 --> 00:21:50,320 Speaker 4: are kind of manifested. Then in the case of Alzheimer's, 360 00:21:50,359 --> 00:21:54,160 Speaker 4: it's basically the memory symptoms that I think pretty apparent. 361 00:21:54,640 --> 00:21:57,960 Speaker 4: So I think that's a fair way to describe, and. 362 00:21:57,680 --> 00:22:03,720 Speaker 1: In both cases, get the inaccurate early diagnostic system is 363 00:22:03,760 --> 00:22:05,639 Speaker 1: a key part of trying to figure out how to 364 00:22:05,640 --> 00:22:08,199 Speaker 1: get ahead of the disease, because if I understand the 365 00:22:08,240 --> 00:22:11,720 Speaker 1: two of you, there's a very high value to an 366 00:22:11,800 --> 00:22:15,960 Speaker 1: early intervention and being able to minimize the progression of 367 00:22:16,000 --> 00:22:16,480 Speaker 1: the disease. 368 00:22:16,560 --> 00:22:19,040 Speaker 2: In both cases, that's absolutely right. 369 00:22:19,400 --> 00:22:23,480 Speaker 1: There's a whole issue about dopamine signaling in the brain. 370 00:22:24,040 --> 00:22:26,440 Speaker 1: One of you has to walk me through the whole 371 00:22:26,480 --> 00:22:30,000 Speaker 1: concept of dopamine signaling and what the correlation is. 372 00:22:31,000 --> 00:22:33,639 Speaker 2: Absolutely So, let's say a person is riding a bicycle. 373 00:22:34,000 --> 00:22:37,320 Speaker 2: There are certain neurons that are sending signals to different 374 00:22:37,359 --> 00:22:39,320 Speaker 2: parts of the brain that tell the person to make 375 00:22:39,359 --> 00:22:42,600 Speaker 2: this continuous movement. Now, on the other hand, when somebody 376 00:22:42,720 --> 00:22:44,920 Speaker 2: is reaching for a mug of coffee, that we call 377 00:22:44,960 --> 00:22:48,239 Speaker 2: that intentional movement. So the person reaches out and their 378 00:22:48,280 --> 00:22:50,760 Speaker 2: brain is sending a signal that their hand has to 379 00:22:50,800 --> 00:22:54,200 Speaker 2: go towards this target and make this one very specific motion. 380 00:22:54,720 --> 00:22:58,600 Speaker 2: That's a slightly different circuit. And so when a dopamine 381 00:22:58,640 --> 00:23:02,000 Speaker 2: neuron sends that signal to release dopamine, we're signaling for 382 00:23:02,119 --> 00:23:04,320 Speaker 2: one of two different kinds of movement, at least in 383 00:23:04,320 --> 00:23:08,160 Speaker 2: the context of Parkinson's disease. And it seems that in Parkinson's, 384 00:23:08,240 --> 00:23:11,800 Speaker 2: it's the neurons that are sending the signals to help 385 00:23:11,840 --> 00:23:14,720 Speaker 2: a person initiate in complete a movement like reaching for 386 00:23:14,760 --> 00:23:17,840 Speaker 2: amuga coffee or picking up their pen to make their signature. 387 00:23:18,320 --> 00:23:21,240 Speaker 2: These are the neurons that seem to be primarily affected. 388 00:23:21,920 --> 00:23:24,439 Speaker 2: And it turns out that when we started this study, 389 00:23:24,640 --> 00:23:27,439 Speaker 2: for the past fifty to one hundred years, people have 390 00:23:27,600 --> 00:23:30,440 Speaker 2: known that the neurons in the brain that are signaling 391 00:23:30,480 --> 00:23:33,439 Speaker 2: movement are the ones that are affected in Parkinson's. But 392 00:23:33,840 --> 00:23:36,520 Speaker 2: in the past ten to fifteen years, a number of 393 00:23:36,560 --> 00:23:40,560 Speaker 2: researchers have also found similar markers. Actually, the same markers 394 00:23:40,560 --> 00:23:43,040 Speaker 2: that are on these neurons in the brain are also 395 00:23:43,160 --> 00:23:46,040 Speaker 2: found on immune cells that are these are immune cells 396 00:23:46,080 --> 00:23:49,120 Speaker 2: white blood cells that are circulating in our blood. And 397 00:23:49,280 --> 00:23:52,680 Speaker 2: so when we started about the study about seven years ago, 398 00:23:53,080 --> 00:23:55,320 Speaker 2: we asked a very simple question. We said, when somebody 399 00:23:55,320 --> 00:23:58,159 Speaker 2: has Parkinson's, we know that these markers are changing in 400 00:23:58,200 --> 00:24:01,960 Speaker 2: the brain because these neurons are dying. So then we said, okay, 401 00:24:02,000 --> 00:24:04,600 Speaker 2: so is there a change in the immune system. Are 402 00:24:04,600 --> 00:24:07,280 Speaker 2: these markers also changing in the immune system And that 403 00:24:07,320 --> 00:24:11,159 Speaker 2: answer ended up being a resounding yes. So what this 404 00:24:11,320 --> 00:24:13,760 Speaker 2: really opens the door to is this is something that 405 00:24:13,800 --> 00:24:16,480 Speaker 2: you alluded to and that Vejia also alluded to earlier. 406 00:24:16,880 --> 00:24:19,720 Speaker 2: If there were a test that could be cheaply and 407 00:24:19,800 --> 00:24:23,760 Speaker 2: widely administered, say a blood test that could help detect 408 00:24:23,800 --> 00:24:26,080 Speaker 2: Parkinson's disease early. This could be something that could be 409 00:24:26,119 --> 00:24:30,920 Speaker 2: administered by a patient's primary care physician versus somebody who's 410 00:24:30,920 --> 00:24:33,600 Speaker 2: a specialist that a patient might have to travel to see. 411 00:24:33,920 --> 00:24:37,160 Speaker 2: And so this really opens the door to potentially bringing 412 00:24:37,280 --> 00:24:39,639 Speaker 2: us closer to the point where somebody could get a 413 00:24:39,640 --> 00:24:43,439 Speaker 2: diagnosis of Parkinson's much much earlier than we could currently do. 414 00:24:44,320 --> 00:24:48,480 Speaker 1: You're currently working with a diagnosis based on a blood 415 00:24:48,520 --> 00:24:52,520 Speaker 1: test that seems to have like ninety six percent accuracy. 416 00:24:53,359 --> 00:24:55,800 Speaker 2: That's right. So we seem to have at least the 417 00:24:55,840 --> 00:24:59,919 Speaker 2: same accuracy as a clinician as a neurologist who make 418 00:25:00,240 --> 00:25:03,280 Speaker 2: diagnosis in the clinic based on their movement, symptoms and 419 00:25:03,320 --> 00:25:06,560 Speaker 2: their response to a medication. So we seem to be 420 00:25:06,760 --> 00:25:09,920 Speaker 2: right on par with the diagnostic accuracy of a neurologist. 421 00:25:10,480 --> 00:25:12,680 Speaker 2: And it's sort of my dream in the long term 422 00:25:12,800 --> 00:25:15,120 Speaker 2: that this would be one of the tests that maybe 423 00:25:15,119 --> 00:25:17,360 Speaker 2: people would get included in their blood work once they 424 00:25:17,359 --> 00:25:21,280 Speaker 2: turned fifty as a routine monitoring system, and if something 425 00:25:21,320 --> 00:25:23,800 Speaker 2: concerning pops up in a blood test like that, the 426 00:25:23,840 --> 00:25:26,399 Speaker 2: physician could say, Hey, you know, mister Jones, maybe you 427 00:25:26,440 --> 00:25:28,480 Speaker 2: should go see a neurologist to have somebody you could 428 00:25:28,520 --> 00:25:31,119 Speaker 2: talk with. So it would really open the door to 429 00:25:31,160 --> 00:25:34,280 Speaker 2: potentially getting people treated and evaluated much earlier than we 430 00:25:34,320 --> 00:25:35,160 Speaker 2: could do right now. 431 00:25:35,640 --> 00:25:38,480 Speaker 1: That's a very large jump from the way we used 432 00:25:38,480 --> 00:25:39,000 Speaker 1: to look at it. 433 00:25:39,119 --> 00:25:41,800 Speaker 2: Right Absolutely, the way we used to look at it 434 00:25:41,840 --> 00:25:45,440 Speaker 2: was when the movement symptoms became debilitating and people didn't 435 00:25:45,480 --> 00:25:47,840 Speaker 2: really have another option other than to see a specialist 436 00:25:47,920 --> 00:25:50,240 Speaker 2: and seek help. That's typically when a person would go 437 00:25:50,280 --> 00:25:53,119 Speaker 2: into the clinic and see their specialists. This is definitely 438 00:25:53,200 --> 00:25:56,359 Speaker 2: a big jump, and currently the studies that we have 439 00:25:56,480 --> 00:25:59,560 Speaker 2: underway with the support of the Trust hopefully are going 440 00:25:59,560 --> 00:26:02,199 Speaker 2: to lead us to be able to diagnose Parkinson's or 441 00:26:02,240 --> 00:26:05,600 Speaker 2: detect Parkinson's even earlier than I'm currently dreaming of. So 442 00:26:06,280 --> 00:26:09,480 Speaker 2: in theory, if we could detect Parkinson's before a lot 443 00:26:09,520 --> 00:26:12,199 Speaker 2: of the dopamine neurons are lost. There's a chance that 444 00:26:12,240 --> 00:26:15,600 Speaker 2: we could intervene at that early stage and maybe prevent 445 00:26:15,640 --> 00:26:17,840 Speaker 2: the disease from ever getting to the point where we 446 00:26:17,920 --> 00:26:20,000 Speaker 2: currently see it at the clinic. And of course, what 447 00:26:20,080 --> 00:26:23,000 Speaker 2: I'm thinking decades into the future, hope by the time 448 00:26:23,040 --> 00:26:25,560 Speaker 2: my career comes to an end, we're definitely at that stage. 449 00:26:25,680 --> 00:26:28,959 Speaker 1: It's conceivable if it's a blood based test and you're 450 00:26:29,000 --> 00:26:33,040 Speaker 1: getting a diagnostic off of looking for certain specific traces, 451 00:26:33,680 --> 00:26:36,119 Speaker 1: then you could literally begin to find people at a 452 00:26:36,240 --> 00:26:40,840 Speaker 1: very early stage. That would change dramatically our whole capacity. 453 00:26:40,880 --> 00:26:43,120 Speaker 1: I mean you could be talking twenty or thirty year 454 00:26:43,160 --> 00:26:46,840 Speaker 1: difference in being able to intervene. Is that a reasonable statement? 455 00:26:47,680 --> 00:26:51,400 Speaker 2: Yes, absolutely, because all the evidence right now in people 456 00:26:51,600 --> 00:26:55,400 Speaker 2: and in studies in animals and cells and dishes suggests 457 00:26:55,440 --> 00:26:58,120 Speaker 2: that the changes that are happening in Parkinson's are starting 458 00:26:58,160 --> 00:27:01,119 Speaker 2: decades before we're able to detected in the clinics. So, 459 00:27:01,560 --> 00:27:03,439 Speaker 2: as you say, if we had a way to detect 460 00:27:03,480 --> 00:27:07,040 Speaker 2: this via a blood test ten twenty thirty years in advance, 461 00:27:07,680 --> 00:27:10,080 Speaker 2: that would be the time press to start intervening and 462 00:27:10,119 --> 00:27:12,919 Speaker 2: hopefully slow or even stop the progression of the disease. 463 00:27:12,960 --> 00:27:15,320 Speaker 2: Maybe we could prevent it from ever getting to the 464 00:27:15,320 --> 00:27:34,080 Speaker 2: point where we know that somebody has Parkinson's. 465 00:27:36,080 --> 00:27:38,080 Speaker 1: Let me switch that in to a totally different approach 466 00:27:38,160 --> 00:27:41,439 Speaker 1: that Jaya is doing, and that's your focus on using 467 00:27:42,160 --> 00:27:47,600 Speaker 1: artificial intelligence to really create an opportunity to have a 468 00:27:47,680 --> 00:27:50,840 Speaker 1: much more sophisticated analytical framework. I want to go back 469 00:27:50,880 --> 00:27:54,440 Speaker 1: to the basics here. Explain to us the difference between 470 00:27:54,600 --> 00:27:58,880 Speaker 1: just artificial intelligence and just mass computational analysis. 471 00:27:59,400 --> 00:28:02,840 Speaker 4: Yeah, I want to start by actually talking a little 472 00:28:02,880 --> 00:28:06,320 Speaker 4: bit more about the point that you mentioned, and I 473 00:28:06,359 --> 00:28:08,239 Speaker 4: want to add one more thing to it, which is 474 00:28:09,200 --> 00:28:12,199 Speaker 4: in this country and probably around the world, there is 475 00:28:12,240 --> 00:28:16,679 Speaker 4: actually a shortage of expertise. So we really have a 476 00:28:16,680 --> 00:28:19,800 Speaker 4: shortage of neurologists who have the right skill set to 477 00:28:20,640 --> 00:28:24,399 Speaker 4: make a diagnosis, whether it's Alzheimer's or Parkinson's. And this 478 00:28:24,520 --> 00:28:27,040 Speaker 4: is actually declining, and in fact, there were some recent 479 00:28:27,080 --> 00:28:31,359 Speaker 4: papers that actually mentioned about the fact that this is 480 00:28:31,440 --> 00:28:34,560 Speaker 4: going to worsen in the next decade or so. And 481 00:28:34,600 --> 00:28:37,760 Speaker 4: the reason is because not many people really want to 482 00:28:37,800 --> 00:28:40,480 Speaker 4: be neurologists. So if you go to a medical school, 483 00:28:40,600 --> 00:28:42,920 Speaker 4: most of them want to become an orthopedic surgeons or 484 00:28:42,960 --> 00:28:47,320 Speaker 4: international cardiologists. Because they make probably twice more money or 485 00:28:47,360 --> 00:28:51,600 Speaker 4: three times more money than a neurologist. Plus neurologist profession 486 00:28:51,680 --> 00:28:54,840 Speaker 4: is a very hard job because you have to sit 487 00:28:54,920 --> 00:28:57,040 Speaker 4: in front of the patient, work with the patient, and 488 00:28:57,080 --> 00:29:00,240 Speaker 4: then sort of take care of them. I think there 489 00:29:00,280 --> 00:29:02,640 Speaker 4: is that part which I really want to add, because 490 00:29:03,240 --> 00:29:05,480 Speaker 4: the reason we want to build these tools, the reason 491 00:29:05,520 --> 00:29:07,920 Speaker 4: we want to use AI to sort of come up 492 00:29:07,920 --> 00:29:10,640 Speaker 4: with these technologies is to sort of address that need, 493 00:29:10,680 --> 00:29:14,040 Speaker 4: that shortage of expertise, and one day we are hoping 494 00:29:14,080 --> 00:29:17,080 Speaker 4: that these tools can be assistem in terms of making 495 00:29:17,120 --> 00:29:21,040 Speaker 4: diagnosis and sort of increasing the efficiency of patient care. So, 496 00:29:21,080 --> 00:29:24,000 Speaker 4: in terms of AI broadly, the way I see it 497 00:29:24,040 --> 00:29:27,640 Speaker 4: is that a simple computation analysis is going to basically 498 00:29:27,680 --> 00:29:30,920 Speaker 4: look at the data that is out there in terms 499 00:29:30,920 --> 00:29:33,600 Speaker 4: of looking at the information that's on the data, But 500 00:29:33,720 --> 00:29:36,400 Speaker 4: I think AI has the ability to sort of learn 501 00:29:36,480 --> 00:29:40,040 Speaker 4: from the data, and once it learns from the data, 502 00:29:40,440 --> 00:29:43,440 Speaker 4: it would then have the capacity to sort of make 503 00:29:43,480 --> 00:29:47,240 Speaker 4: predictions on a new instance or a new case which 504 00:29:47,240 --> 00:29:49,520 Speaker 4: it has not seen before. So I think that's the 505 00:29:49,560 --> 00:29:52,520 Speaker 4: advantage of using AI, And imagine in the context of 506 00:29:52,560 --> 00:29:55,960 Speaker 4: this dementia due to Alzheimer's cases. What we did was 507 00:29:56,040 --> 00:30:00,160 Speaker 4: we basically took data from thousands and thousands of of 508 00:30:00,240 --> 00:30:04,200 Speaker 4: patients around the world. We fed all this information to 509 00:30:04,280 --> 00:30:06,840 Speaker 4: this AI model. And when I say data, I'm talking 510 00:30:06,880 --> 00:30:10,520 Speaker 4: about all kinds of things that a doctor collects in 511 00:30:10,560 --> 00:30:16,960 Speaker 4: a routin clinical setting, which is demographics, medications, MRIs, neuropsych tests, 512 00:30:17,000 --> 00:30:20,160 Speaker 4: and other bedside cognitive tests. So this kind of information 513 00:30:20,240 --> 00:30:22,680 Speaker 4: is fed into this AI model coming from all these 514 00:30:22,720 --> 00:30:25,880 Speaker 4: tens of thousands of patients, and this algorithm sort of 515 00:30:25,960 --> 00:30:29,600 Speaker 4: learns the pattern from all this data, and then now 516 00:30:29,640 --> 00:30:31,880 Speaker 4: it's in a position to sort of predict on a 517 00:30:31,920 --> 00:30:34,240 Speaker 4: new case. Look tomorrow, imagined, there is a new patient 518 00:30:34,280 --> 00:30:37,440 Speaker 4: walking in and the doctor is actually seeing this patient 519 00:30:37,480 --> 00:30:40,160 Speaker 4: and trying to collect information on this new patient. And 520 00:30:40,200 --> 00:30:43,000 Speaker 4: at that point, if you plug this AI model, this 521 00:30:43,120 --> 00:30:45,520 Speaker 4: AI model will be able to sort of learn from 522 00:30:45,560 --> 00:30:47,880 Speaker 4: all the things that it has seen before and sort 523 00:30:47,880 --> 00:30:50,400 Speaker 4: of in for what actually is happening on this specific 524 00:30:50,440 --> 00:30:52,920 Speaker 4: patient who's of interest. So that I think has this 525 00:30:53,520 --> 00:30:56,600 Speaker 4: ability for AI to learn from the data and make 526 00:30:56,760 --> 00:31:00,960 Speaker 4: a new inference, I think is the key. 527 00:31:00,120 --> 00:31:04,640 Speaker 1: You talk about using artificial intelligence, what you're really suggesting 528 00:31:04,760 --> 00:31:09,520 Speaker 1: is that you will presently have an ability to sort 529 00:31:09,520 --> 00:31:14,840 Speaker 1: of autonomously evaluate each patient against a huge database of patients, 530 00:31:15,480 --> 00:31:19,560 Speaker 1: and that over time the analytical tool doing that will 531 00:31:19,600 --> 00:31:23,640 Speaker 1: continuously learn and evolve to become more accurate at an 532 00:31:23,640 --> 00:31:28,080 Speaker 1: earlier stage. And so in one way, you're both making 533 00:31:28,160 --> 00:31:33,520 Speaker 1: it easier to be a neurologist, and you are enabling 534 00:31:33,560 --> 00:31:36,640 Speaker 1: a neurologists to deal with vastly more cases than they 535 00:31:36,680 --> 00:31:41,240 Speaker 1: could if they were still back using Cree artificial intelligence capabilities. 536 00:31:41,960 --> 00:31:44,760 Speaker 4: Absolutely, you are spot on. In fact, I think that's 537 00:31:44,800 --> 00:31:46,840 Speaker 4: kind of really the goal for us, because we want 538 00:31:46,880 --> 00:31:50,360 Speaker 4: to create tools that can be assistive to the neurologist. 539 00:31:50,400 --> 00:31:52,360 Speaker 4: If we don't want to replace the neurologists, we want 540 00:31:52,400 --> 00:31:56,280 Speaker 4: to assist them because they want to be as efficient 541 00:31:56,280 --> 00:31:58,960 Speaker 4: as possible because there is a huge lot of patients 542 00:31:59,040 --> 00:32:02,000 Speaker 4: we're waiting in line for their appointments, so we want 543 00:32:02,040 --> 00:32:03,840 Speaker 4: to make sure it'll be increase the efficiency in these 544 00:32:03,880 --> 00:32:06,840 Speaker 4: practices so that they can do things in a shorter 545 00:32:06,920 --> 00:32:07,680 Speaker 4: frame of time. 546 00:32:08,160 --> 00:32:11,480 Speaker 1: Both of you are passionate. Both of you are deeply 547 00:32:11,520 --> 00:32:15,120 Speaker 1: immersed in what you're doing for young people who are 548 00:32:15,280 --> 00:32:17,400 Speaker 1: kind of thinking about what they want to do with 549 00:32:17,440 --> 00:32:21,560 Speaker 1: their lives. What would you tell them about your own experiences? 550 00:32:22,440 --> 00:32:25,440 Speaker 1: Is it fulfilling, is it fun? Is it exciting? How 551 00:32:25,440 --> 00:32:28,680 Speaker 1: would you describe how you got to be here in science? 552 00:32:29,960 --> 00:32:32,800 Speaker 2: So that's actually a multifold answer, but I'm going to 553 00:32:32,800 --> 00:32:35,160 Speaker 2: try and make it short. I got to be in 554 00:32:35,200 --> 00:32:39,400 Speaker 2: science because people throughout my life teachers, primarily starting from 555 00:32:39,440 --> 00:32:44,280 Speaker 2: elementary school onwards, have been extraordinarily supportive and they recognized 556 00:32:44,280 --> 00:32:47,240 Speaker 2: my early interest in science and nurtured it. But what 557 00:32:47,320 --> 00:32:49,920 Speaker 2: really helped me turn it into a career was to 558 00:32:49,960 --> 00:32:53,200 Speaker 2: find a focus something that I was particularly passionate about. 559 00:32:53,840 --> 00:32:57,360 Speaker 2: And really it's the interactions with the patients that really 560 00:32:57,480 --> 00:33:00,960 Speaker 2: drives me forward because I'm seeing these individuals who are 561 00:33:01,000 --> 00:33:04,200 Speaker 2: suffering with this disease that, to be honest, they are 562 00:33:04,240 --> 00:33:07,760 Speaker 2: seeing as a progressive decline throughout their lives. And when 563 00:33:07,760 --> 00:33:09,880 Speaker 2: I see these patients on a day to day basis 564 00:33:10,000 --> 00:33:13,640 Speaker 2: here at the FIXEL Institute, I can't help but want 565 00:33:13,800 --> 00:33:16,600 Speaker 2: desperately to find a way for them to move forward. 566 00:33:17,000 --> 00:33:19,080 Speaker 2: Whether or not it's going to help me personally with 567 00:33:19,160 --> 00:33:22,040 Speaker 2: my own health is completely irrelevant, because there's this whole 568 00:33:22,040 --> 00:33:25,560 Speaker 2: world of people out there who are looking for answers 569 00:33:25,560 --> 00:33:28,400 Speaker 2: who are looking for help. So when it comes down 570 00:33:28,520 --> 00:33:31,560 Speaker 2: to a fifteen eighteen hour day for me of research, 571 00:33:32,040 --> 00:33:34,400 Speaker 2: I really don't bout an eyelid at doing it because I, 572 00:33:34,600 --> 00:33:38,040 Speaker 2: number one, recognize the need. Number Two, I'm completely passionate 573 00:33:38,040 --> 00:33:40,760 Speaker 2: about the topic and the area that I'm working in, 574 00:33:40,880 --> 00:33:44,080 Speaker 2: that is Parkinson's disease and dopemine signaling both in the 575 00:33:44,080 --> 00:33:46,640 Speaker 2: brain and outside of the brain, and the immune system. 576 00:33:46,960 --> 00:33:51,080 Speaker 2: And then finally there's this obvious unmet need and as 577 00:33:51,080 --> 00:33:53,280 Speaker 2: a human being, as an empathetic human being, it's really 578 00:33:53,320 --> 00:33:54,480 Speaker 2: impossible to ignore that. 579 00:33:55,000 --> 00:33:57,200 Speaker 1: I realized when I was looking at your biography, you 580 00:33:57,320 --> 00:34:00,120 Speaker 1: actually get into this in part by self diagnosis. 581 00:34:01,720 --> 00:34:05,280 Speaker 2: So it turned out throughout my childhood and early adulthood 582 00:34:05,720 --> 00:34:09,120 Speaker 2: it turned out that I had autoimmune disease and that 583 00:34:09,200 --> 00:34:12,600 Speaker 2: went undiagnosed, actually misdiagnosed as a number of different things 584 00:34:12,640 --> 00:34:15,360 Speaker 2: over the course of my life until I took a 585 00:34:15,400 --> 00:34:18,960 Speaker 2: step back, took my own notes, kept detailed records, and 586 00:34:19,040 --> 00:34:22,440 Speaker 2: recognized the patterns that led me to find a specialist 587 00:34:22,520 --> 00:34:25,800 Speaker 2: that could help me obtain a correct diagnosis and treatment. 588 00:34:26,400 --> 00:34:28,680 Speaker 2: And I certainly wouldn't want anybody else to go through 589 00:34:28,680 --> 00:34:29,520 Speaker 2: that if I could help. 590 00:34:29,400 --> 00:34:33,840 Speaker 1: It, so it really becomes a personal journey very much so. 591 00:34:34,560 --> 00:34:36,480 Speaker 1: But Joya, how did you get involved in all this? 592 00:34:37,560 --> 00:34:41,240 Speaker 4: I was born in India and the life in India 593 00:34:41,360 --> 00:34:43,640 Speaker 4: was different when I was growing up. I was very 594 00:34:43,680 --> 00:34:47,240 Speaker 4: fortunate to have a close knit family in my parents 595 00:34:47,320 --> 00:34:50,560 Speaker 4: as well as my grandmother inspired me to do science. 596 00:34:51,360 --> 00:34:55,280 Speaker 4: I think what I really want to do is to 597 00:34:55,600 --> 00:34:58,759 Speaker 4: have fun. In fact, I'm an associate professor here to 598 00:34:58,840 --> 00:35:01,360 Speaker 4: be you, and I keep telling my students and my 599 00:35:01,920 --> 00:35:04,719 Speaker 4: colleagues that this is one of the best jobs. And 600 00:35:04,800 --> 00:35:07,520 Speaker 4: the reason is because in my lab I have students 601 00:35:07,560 --> 00:35:11,120 Speaker 4: who are very passionate. They come from multiple disciplines like, 602 00:35:11,160 --> 00:35:14,879 Speaker 4: for instance, I have PhD students in computer science, MD 603 00:35:15,000 --> 00:35:18,879 Speaker 4: students who are going to be future doctors, post doctoral scholars, 604 00:35:19,160 --> 00:35:23,279 Speaker 4: and engineers. So all these people are literally working in 605 00:35:23,320 --> 00:35:26,920 Speaker 4: my lab and it's a joy, fun and enjoy to 606 00:35:27,040 --> 00:35:29,839 Speaker 4: work with them on a daily basis. Obviously, there are 607 00:35:29,920 --> 00:35:33,360 Speaker 4: many different problems in the world and I think, you know, 608 00:35:33,400 --> 00:35:35,239 Speaker 4: it's not fair for me to say that this is 609 00:35:35,560 --> 00:35:38,719 Speaker 4: the only thing that is more important. But overall, I 610 00:35:38,760 --> 00:35:42,759 Speaker 4: think I felt that the unmet need is a lot 611 00:35:42,840 --> 00:35:47,319 Speaker 4: higher in the neurological disorder realm because there are very 612 00:35:47,360 --> 00:35:50,960 Speaker 4: few therapies and brain is very very complex as opposed 613 00:35:50,960 --> 00:35:53,440 Speaker 4: to ingo, for example, cancer, you know, somebody has cancer, 614 00:35:53,520 --> 00:35:55,400 Speaker 4: there is a test, and there is a therapy. Potentially. 615 00:35:55,440 --> 00:35:58,160 Speaker 4: Of course, not every cancer is cured, but in the 616 00:35:58,160 --> 00:36:01,239 Speaker 4: context of the brain, there is a lot more that 617 00:36:01,320 --> 00:36:04,279 Speaker 4: we can learn and sort of resolve. So I think 618 00:36:04,320 --> 00:36:06,160 Speaker 4: that kind of really helps us to sort of really 619 00:36:06,200 --> 00:36:09,600 Speaker 4: think about it. But personally, I think this career is 620 00:36:09,760 --> 00:36:13,200 Speaker 4: so much fun, and I know we are literally doing 621 00:36:13,280 --> 00:36:16,279 Speaker 4: work at the cutting edge, and students in my lab 622 00:36:16,440 --> 00:36:19,120 Speaker 4: really come up with new ideas every day, so it's very, 623 00:36:19,200 --> 00:36:20,520 Speaker 4: very exciting to do this. 624 00:36:21,040 --> 00:36:23,920 Speaker 1: Clearly both of you are passionate. I'm gonna start sketch 625 00:36:23,920 --> 00:36:26,319 Speaker 1: you of you for a second to what extent how 626 00:36:26,360 --> 00:36:30,720 Speaker 1: important has the Karen Toffler Charitable Trust been in your research. 627 00:36:31,320 --> 00:36:34,120 Speaker 4: I keep shouting about this to everybody I know. This 628 00:36:34,200 --> 00:36:36,080 Speaker 4: has been an honor for me. I'm one of the 629 00:36:36,239 --> 00:36:41,520 Speaker 4: earlier Topler scholars, and when I met them and her colleagues, 630 00:36:41,840 --> 00:36:45,960 Speaker 4: it's so amazing to actually see them participate in our research, right, 631 00:36:46,000 --> 00:36:46,960 Speaker 4: that's kind of really unique. 632 00:36:46,960 --> 00:36:47,120 Speaker 1: You know. 633 00:36:47,160 --> 00:36:50,040 Speaker 4: I have gotten funding from a few other agencies but 634 00:36:50,400 --> 00:36:53,960 Speaker 4: I only submit my annual progress reports to them, whereas 635 00:36:54,000 --> 00:36:56,800 Speaker 4: in the context of the Topler Trust, it's very personal 636 00:36:56,880 --> 00:36:59,960 Speaker 4: because they are really invested not just in terms of 637 00:37:00,680 --> 00:37:03,520 Speaker 4: pushing the science, but also really helping us really think 638 00:37:03,560 --> 00:37:06,120 Speaker 4: about the next steps. Right, So DEV has connected me 639 00:37:06,200 --> 00:37:10,400 Speaker 4: to several other organizations and people several other organizations because 640 00:37:11,040 --> 00:37:12,680 Speaker 4: what we want to do is we want to build 641 00:37:12,719 --> 00:37:15,439 Speaker 4: tools that can hopefully be translated to the clinic one day, 642 00:37:15,600 --> 00:37:18,440 Speaker 4: which means we are not just thinking about science and papers. 643 00:37:18,719 --> 00:37:21,120 Speaker 4: We really want to build tech and then create companies. 644 00:37:21,600 --> 00:37:23,600 Speaker 4: We want to make sure company is a way to 645 00:37:23,600 --> 00:37:25,200 Speaker 4: sort of build a tech and then at the end 646 00:37:25,280 --> 00:37:27,719 Speaker 4: of the day that would reach the patient one day. 647 00:37:27,920 --> 00:37:30,400 Speaker 4: So DEV is actually also helping me to really think 648 00:37:30,440 --> 00:37:33,240 Speaker 4: about those elements as well. So Tofler Trust has helped 649 00:37:33,280 --> 00:37:36,760 Speaker 4: us above and beyond just doing science. It has actually 650 00:37:36,760 --> 00:37:39,600 Speaker 4: helped us to connect with more people, ask us the 651 00:37:39,640 --> 00:37:41,919 Speaker 4: right questions, and also think about the next step, which 652 00:37:41,920 --> 00:37:44,600 Speaker 4: is translation. Because one thing I learned from my format 653 00:37:44,640 --> 00:37:47,719 Speaker 4: advisor is that there is probably no drug or a 654 00:37:47,760 --> 00:37:50,480 Speaker 4: device in the market today that has not gone through 655 00:37:50,480 --> 00:37:52,920 Speaker 4: a company, because at the end of the day, company 656 00:37:52,960 --> 00:37:56,080 Speaker 4: is the thing that is taking science and then taking 657 00:37:56,120 --> 00:37:58,200 Speaker 4: it to the patients, and I think that's the journey 658 00:37:58,239 --> 00:37:59,920 Speaker 4: that I really want to take, and I have to 659 00:38:00,040 --> 00:38:01,120 Speaker 4: thank the Trust for that. 660 00:38:01,840 --> 00:38:04,000 Speaker 1: Hear how big a factor has the Trust been for you? 661 00:38:04,640 --> 00:38:08,000 Speaker 2: I would echo many of Jia's statements there. So the 662 00:38:08,000 --> 00:38:11,560 Speaker 2: Trust support has been I would say, irreplaceable. And this 663 00:38:11,719 --> 00:38:14,640 Speaker 2: is really why. So the research that we're conducting sits 664 00:38:14,640 --> 00:38:19,600 Speaker 2: at the intersection between two fields of science, neuroscience and immunology. 665 00:38:20,000 --> 00:38:22,360 Speaker 2: And historically, at least if you go back twenty or 666 00:38:22,360 --> 00:38:27,000 Speaker 2: more years, these two fields considered themselves completely separate and unrelated. 667 00:38:27,320 --> 00:38:28,920 Speaker 2: But it turns out the head is connected to the 668 00:38:28,960 --> 00:38:31,400 Speaker 2: rest of the body, and so the immune system and 669 00:38:31,480 --> 00:38:34,880 Speaker 2: the brain do in fact communicate, and specifically when we 670 00:38:34,920 --> 00:38:39,160 Speaker 2: get into the neuroscience and the biology of dopamine signaling 671 00:38:39,239 --> 00:38:42,719 Speaker 2: in the immune system. This has been a very difficult 672 00:38:42,760 --> 00:38:45,280 Speaker 2: gap to bridge because we're talking about two different fields 673 00:38:45,280 --> 00:38:48,680 Speaker 2: that really don't talk to one another. So support via 674 00:38:48,719 --> 00:38:52,960 Speaker 2: the traditional funding agencies, the NIH the other large foundations 675 00:38:53,480 --> 00:38:56,480 Speaker 2: has been difficult to come by, I would say, because 676 00:38:56,960 --> 00:38:59,759 Speaker 2: it really depends on the audience who happens to read 677 00:38:59,880 --> 00:39:03,520 Speaker 2: or review our research submissions, and if you have one 678 00:39:03,560 --> 00:39:05,279 Speaker 2: side of the camp versus the other side of the 679 00:39:05,320 --> 00:39:08,200 Speaker 2: camp reading it, it becomes very very hard to bridge 680 00:39:08,200 --> 00:39:12,319 Speaker 2: that gap, and the trust support has allowed us to 681 00:39:12,360 --> 00:39:16,040 Speaker 2: move forward such that we have data, we have information 682 00:39:16,200 --> 00:39:18,600 Speaker 2: that both sides of the aisle can understand, so to speak. 683 00:39:19,120 --> 00:39:21,680 Speaker 2: And so without the trust support, I don't think we 684 00:39:21,719 --> 00:39:22,440 Speaker 2: would have a chance. 685 00:39:23,280 --> 00:39:27,760 Speaker 1: Well, Denbra, given the remarkable role that the Karen Toffler 686 00:39:28,200 --> 00:39:32,040 Speaker 1: Charitable Trust and the Toughler Scholars are playing. As our 687 00:39:32,080 --> 00:39:36,440 Speaker 1: listeners hear this, what can they do to be involved 688 00:39:36,440 --> 00:39:39,800 Speaker 1: and to help you with the Karen Toffler Charitable. 689 00:39:39,400 --> 00:39:42,879 Speaker 3: Trust, engage with us, let us know that they're out 690 00:39:42,920 --> 00:39:45,480 Speaker 3: there and they want to engage with us. I think 691 00:39:45,560 --> 00:39:48,759 Speaker 3: there's lots of ways to support. We would like to 692 00:39:48,760 --> 00:39:53,280 Speaker 3: do more, and so of course financial support is very welcome. 693 00:39:53,440 --> 00:39:57,040 Speaker 3: And you know, as you mentioned earlier, all of us 694 00:39:57,360 --> 00:40:02,600 Speaker 3: know somebody or are personally touched by these diseases and 695 00:40:02,680 --> 00:40:06,719 Speaker 3: it's close to us. I lost my grandfather to dementia 696 00:40:06,880 --> 00:40:09,399 Speaker 3: and I lost my grandmother to ALUs so these things 697 00:40:09,440 --> 00:40:12,080 Speaker 3: are very very important to all of us. So for 698 00:40:12,160 --> 00:40:15,160 Speaker 3: those listeners out there, you know, follow up with us 699 00:40:15,200 --> 00:40:18,719 Speaker 3: on the website, follow up with me personally or with 700 00:40:18,800 --> 00:40:22,400 Speaker 3: the DDA or VJ and engage and then we can 701 00:40:22,440 --> 00:40:25,560 Speaker 3: see where we go from there. We give grants, but 702 00:40:25,760 --> 00:40:29,560 Speaker 3: as mentioned, we also love these people and we're building 703 00:40:29,880 --> 00:40:33,800 Speaker 3: these relationships for the long term for their professional careers. 704 00:40:33,840 --> 00:40:37,279 Speaker 3: We want them to be successful thirty forty years from 705 00:40:37,280 --> 00:40:40,120 Speaker 3: now and be able to say, hey, we were there 706 00:40:40,280 --> 00:40:44,600 Speaker 3: at the beginning. It's a very Alvin and Heidi Toffler 707 00:40:44,760 --> 00:40:48,279 Speaker 3: idea is let's change the world together and take this 708 00:40:48,400 --> 00:40:52,120 Speaker 3: journey together. We do things like we bring all of 709 00:40:52,160 --> 00:40:56,960 Speaker 3: our scholars together to have cross collaborative discussions about what 710 00:40:57,000 --> 00:40:59,879 Speaker 3: are you learning and what do you need? And we've 711 00:41:00,080 --> 00:41:04,759 Speaker 3: encourage them to even submit joint proposals back to us 712 00:41:04,800 --> 00:41:08,880 Speaker 3: for grants that are looking at As Adithia said, you know, 713 00:41:08,920 --> 00:41:12,640 Speaker 3: trying to bring these communities together that may never talk 714 00:41:12,680 --> 00:41:15,880 Speaker 3: to each other, because the breakthroughs are going to be 715 00:41:16,120 --> 00:41:20,320 Speaker 3: in those areas that are between the seams where lights 716 00:41:20,320 --> 00:41:23,520 Speaker 3: aren't being shown right now, and so we're trying to 717 00:41:23,560 --> 00:41:29,320 Speaker 3: shine those flashlights on those areas through networking and collaboration 718 00:41:29,840 --> 00:41:34,280 Speaker 3: and then funding. And we're very hopeful and we're very proud, 719 00:41:34,320 --> 00:41:37,240 Speaker 3: and I know Alvin and Heidi would be so proud 720 00:41:37,360 --> 00:41:39,719 Speaker 3: of each one of our scholars. They would just be 721 00:41:39,800 --> 00:41:42,680 Speaker 3: amazed at what they're doing and who they are as people. 722 00:41:43,080 --> 00:41:46,520 Speaker 3: So you can follow up on our website. The information 723 00:41:46,760 --> 00:41:50,959 Speaker 3: is there the Toffler Trust dot org, and we would 724 00:41:51,000 --> 00:41:54,000 Speaker 3: love to talk to somebody or anybody that wants to 725 00:41:54,040 --> 00:41:55,560 Speaker 3: engage well. 726 00:41:55,680 --> 00:41:59,400 Speaker 1: Debrah Alifia and I want to thank you for joining 727 00:41:59,400 --> 00:42:01,960 Speaker 1: me and for ae educating me. The work you're doing 728 00:42:02,080 --> 00:42:05,200 Speaker 1: is fascinating and I think people will find this to 729 00:42:05,239 --> 00:42:08,680 Speaker 1: be an amazing conversation. We will let everyone know that 730 00:42:08,719 --> 00:42:12,120 Speaker 1: they can learn more about the Karen Toafler Charitable Trust 731 00:42:12,640 --> 00:42:16,279 Speaker 1: in advancing Medical Research at TAFLA Trust dot Oregon. I 732 00:42:16,320 --> 00:42:19,120 Speaker 1: want to thank all three of you for taking time 733 00:42:19,160 --> 00:42:20,720 Speaker 1: today to help educate folks. 734 00:42:20,920 --> 00:42:21,799 Speaker 4: Thank you so much. 735 00:42:25,600 --> 00:42:29,680 Speaker 1: Thank you to my guest Deborah westfall, Aditya Gopinath and 736 00:42:30,280 --> 00:42:34,120 Speaker 1: Vijaya Koleachlama. You can learn more about the Karen Tafler 737 00:42:34,200 --> 00:42:38,000 Speaker 1: Charitable Trust on our show page at newtsworld dot com. 738 00:42:38,280 --> 00:42:41,720 Speaker 1: Newts World is produced by Gingrid three sixty and iHeartMedia. 739 00:42:42,239 --> 00:42:47,400 Speaker 1: Our executive producer is Guarnsey Slow. Our researcher is Rachel Peterson. 740 00:42:48,160 --> 00:42:50,960 Speaker 1: The artwork for The show was created by Steve Penley. 741 00:42:51,520 --> 00:42:54,759 Speaker 1: Special thanks to the team at Gingrich three sixty. If 742 00:42:54,760 --> 00:42:57,400 Speaker 1: you've been enjoying Newtsworld, I hope you'll go to Apple 743 00:42:57,440 --> 00:43:00,680 Speaker 1: Podcast and both rate us with five stars and give 744 00:43:00,760 --> 00:43:03,480 Speaker 1: us a review so others can learn what it's all about. 745 00:43:04,160 --> 00:43:06,600 Speaker 1: Right now, listeners of neuts World can sign up for 746 00:43:06,719 --> 00:43:11,520 Speaker 1: my three freeweekly columns at Ginglish three sixty dot com 747 00:43:11,560 --> 00:43:15,719 Speaker 1: slash newsletter. I'm Newt Gingrich. This is Neutsworld.