1 00:00:00,080 --> 00:00:06,080 Speaker 1: M. This is Mesters in Business with Very Renaults on 2 00:00:06,200 --> 00:00:10,920 Speaker 1: Bluebird Radio. This week on the podcast, I have an 3 00:00:10,920 --> 00:00:14,720 Speaker 1: extra special guest. His name is Dr Charles Strom, and 4 00:00:14,800 --> 00:00:18,840 Speaker 1: he is the CEO and co founder of Liquid Diagnostics, 5 00:00:19,239 --> 00:00:25,040 Speaker 1: an advanced testing company. He has several decades of experience 6 00:00:25,440 --> 00:00:30,320 Speaker 1: in the field of genetic testing. He ran Quest Diagnostics 7 00:00:30,440 --> 00:00:34,559 Speaker 1: Labs for sixteen years, and we really just began to 8 00:00:34,600 --> 00:00:37,640 Speaker 1: scratch the surface of his mark. I didn't get to 9 00:00:37,680 --> 00:00:40,720 Speaker 1: the sixty Minutes episode he appeared on, or or his 10 00:00:40,800 --> 00:00:45,680 Speaker 1: appearances on Oprah, but we did talk about COVID testing 11 00:00:45,760 --> 00:00:49,840 Speaker 1: and why we're not looking at antibodies. Dr Strom thinks 12 00:00:49,880 --> 00:00:52,280 Speaker 1: we should be. If you want to decide whether you 13 00:00:52,320 --> 00:00:55,600 Speaker 1: need a booster or a second booster, wouldn't it be 14 00:00:55,600 --> 00:00:58,520 Speaker 1: helpful to know if you're actually at a high level 15 00:00:58,560 --> 00:01:01,280 Speaker 1: of antibodies or a low level of anybodies. When we 16 00:01:01,320 --> 00:01:05,440 Speaker 1: talk about that early detection for certain types of lung 17 00:01:05,480 --> 00:01:09,720 Speaker 1: cancers and how the world of genetics is just rapidly 18 00:01:09,920 --> 00:01:15,520 Speaker 1: changing the way we not only detect potentially dangerous diseases, 19 00:01:15,560 --> 00:01:19,320 Speaker 1: but some of the treatments we do, it's really quite fascinating. So, 20 00:01:19,400 --> 00:01:23,800 Speaker 1: with no further ado, my conversation with Liquid Diagnostics Dr 21 00:01:23,920 --> 00:01:30,520 Speaker 1: Charles Strom. This is Mesters in Business with very renaults 22 00:01:31,000 --> 00:01:36,280 Speaker 1: on Bloomberg Radio. I'm Barry Hults. You're listening to Masters 23 00:01:36,280 --> 00:01:39,360 Speaker 1: in Business on Bloomberg Radio. My special guest this week 24 00:01:39,520 --> 00:01:43,520 Speaker 1: is Dr Charles Strom. He is the CEO and co 25 00:01:43,720 --> 00:01:47,920 Speaker 1: founder of Liquid Diagnostics. Dr Strom has pioneered the use 26 00:01:47,960 --> 00:01:53,720 Speaker 1: of DNA testing for forensic and paternity applications before joining 27 00:01:54,160 --> 00:01:58,800 Speaker 1: Quest Diagnostics, where he was the medical director for genetic testing. 28 00:01:59,680 --> 00:02:02,600 Speaker 1: His work has led him to appearances on such shows 29 00:02:02,760 --> 00:02:08,639 Speaker 1: as Sixty Minutes and Oprah. Dr buck Strom, Welcome to Bloomberg. 30 00:02:09,040 --> 00:02:12,079 Speaker 1: Thank you for having me, Baron my pleasure. So let's 31 00:02:12,080 --> 00:02:15,600 Speaker 1: start a little bit with your educational background. You graduate 32 00:02:15,680 --> 00:02:19,680 Speaker 1: University of Chicago with both a PhD in biology and 33 00:02:19,800 --> 00:02:25,200 Speaker 1: a medical degree. Was the plan always to work in genetics. Yes, 34 00:02:25,400 --> 00:02:27,960 Speaker 1: from the time I was in seventh grade, I knew 35 00:02:28,000 --> 00:02:32,400 Speaker 1: I wanted to be a scientist, and as an undergraduate 36 00:02:32,720 --> 00:02:39,799 Speaker 1: I became interested in prenatal diagnosis in particular. And when 37 00:02:39,840 --> 00:02:44,120 Speaker 1: I was an undergraduate, I did research and found that 38 00:02:44,240 --> 00:02:47,440 Speaker 1: one of the centers to do that research was at 39 00:02:47,520 --> 00:02:51,320 Speaker 1: University of Chicago. In one of my early mentors. Albert 40 00:02:51,320 --> 00:02:56,280 Speaker 1: Dorfman had published paper on prenatal diagnosis for Hunter syndrome. 41 00:02:57,000 --> 00:03:01,080 Speaker 1: So I actually sent him a letter. Uh, typed it 42 00:03:01,080 --> 00:03:05,000 Speaker 1: out on my Smith Corona electric typewriter, send it to him, 43 00:03:05,360 --> 00:03:08,200 Speaker 1: and lo and behold. A month later, I got a 44 00:03:08,200 --> 00:03:11,480 Speaker 1: packet of information saying how would you like to come 45 00:03:11,520 --> 00:03:15,639 Speaker 1: work on my lab over the summer, and uh, that 46 00:03:15,760 --> 00:03:19,720 Speaker 1: led to my entering an m D pH d program 47 00:03:20,000 --> 00:03:22,680 Speaker 1: that was called the Medical Scientist Training Program. Was a 48 00:03:22,680 --> 00:03:28,400 Speaker 1: federally funded program paid for my tuition and gave me 49 00:03:28,720 --> 00:03:32,440 Speaker 1: a living stipend as a six year program turned my 50 00:03:32,600 --> 00:03:35,080 Speaker 1: m D and PhD. So yeah, I was always my 51 00:03:35,200 --> 00:03:38,280 Speaker 1: plan to be a medical scientist. And you worked under 52 00:03:38,520 --> 00:03:44,320 Speaker 1: biochemical geneticist William Nihan, who's kind of legendary in that field. 53 00:03:44,360 --> 00:03:47,000 Speaker 1: Tell us a little bit about working with Dr Nihan 54 00:03:47,120 --> 00:03:49,280 Speaker 1: and what you learned from him and what that experience 55 00:03:49,320 --> 00:03:52,760 Speaker 1: was like, Yeah, well that was fabulous. So again this 56 00:03:52,960 --> 00:03:57,080 Speaker 1: was a cold call. I started out in between my 57 00:03:57,160 --> 00:04:02,400 Speaker 1: freshman and stophmore year of college, and um, my advisor 58 00:04:02,480 --> 00:04:08,320 Speaker 1: and the master of my college was a scientist named 59 00:04:08,360 --> 00:04:12,520 Speaker 1: Richard Goldslee, and UH said, Hey, I have a buddy, 60 00:04:12,920 --> 00:04:17,160 Speaker 1: Dr William Ihan out in San Diego. Uh, maybe I 61 00:04:17,200 --> 00:04:19,560 Speaker 1: could send a letter and you could go out and 62 00:04:19,600 --> 00:04:22,239 Speaker 1: work for him over the summer between your freshman sophomore 63 00:04:22,279 --> 00:04:24,919 Speaker 1: year in college. And it was like, you know, somebody 64 00:04:24,960 --> 00:04:27,480 Speaker 1: asking me if if I wanted to work for the pope, 65 00:04:27,960 --> 00:04:31,080 Speaker 1: and I said, yes, sure of course I do. And 66 00:04:31,400 --> 00:04:34,240 Speaker 1: same thing. He welcomed me. He had me in the 67 00:04:34,320 --> 00:04:40,400 Speaker 1: laboratory and he and and his partner Larry Sweetman got 68 00:04:40,440 --> 00:04:45,839 Speaker 1: me hooked on biochemical genetics. And then after I you know, 69 00:04:46,360 --> 00:04:51,479 Speaker 1: went to medical school got my MDI got my PhD. Uh. 70 00:04:51,520 --> 00:04:54,320 Speaker 1: The obvious choice for me to do a residency was 71 00:04:54,440 --> 00:04:57,120 Speaker 1: at University of California, San Diego, where Dr and I 72 00:04:57,200 --> 00:05:00,920 Speaker 1: had had become the chairman of the department. So that 73 00:05:01,080 --> 00:05:03,679 Speaker 1: was just the no brainers. So I ended up doing 74 00:05:03,680 --> 00:05:06,839 Speaker 1: my residency there and for the three years of my 75 00:05:06,960 --> 00:05:11,080 Speaker 1: residency and fellowship, I worked with William Nihan, who has 76 00:05:11,160 --> 00:05:15,640 Speaker 1: an encyclopedic knowledge of biochemical genetics, and it was just 77 00:05:15,720 --> 00:05:18,760 Speaker 1: a fabulous experience for me. Yeah, I can imagine. So 78 00:05:18,880 --> 00:05:22,480 Speaker 1: tell us about some of the grants to pursue genetics 79 00:05:22,480 --> 00:05:25,680 Speaker 1: of growth disorders that you were working on at the 80 00:05:25,760 --> 00:05:30,400 Speaker 1: University of Chicago. They seem really quite fascinating. So from 81 00:05:30,400 --> 00:05:33,760 Speaker 1: a very early age, I was interested in developmental biology, 82 00:05:33,839 --> 00:05:38,760 Speaker 1: which is the science studying the mechanisms by which we 83 00:05:38,920 --> 00:05:44,360 Speaker 1: go from UM an embryo in which all cells are 84 00:05:44,360 --> 00:05:49,080 Speaker 1: identical UH, to an adult where we have hundreds of 85 00:05:49,200 --> 00:05:54,400 Speaker 1: difference of specialized cells. And Albert Dorfan my mentor University 86 00:05:54,400 --> 00:05:59,920 Speaker 1: of Chicago, was working on the differentiation of cartilage in chickens. UH. 87 00:06:00,080 --> 00:06:03,359 Speaker 1: So I was cutting off them buds from nine dozen 88 00:06:03,440 --> 00:06:06,640 Speaker 1: chickens a week UH and growing up in tissue culture 89 00:06:06,839 --> 00:06:11,200 Speaker 1: and UH they would differentiate into mature contraslits, into mature 90 00:06:11,279 --> 00:06:15,800 Speaker 1: cartage cells and tissue culture. And so I worked on that. 91 00:06:16,040 --> 00:06:21,400 Speaker 1: And then when that was all before DNA sequencing DNA 92 00:06:21,480 --> 00:06:29,480 Speaker 1: analysis was available. And then when cloning started, gene cloning UM, 93 00:06:29,520 --> 00:06:32,960 Speaker 1: I got a grant to UH to clone the gene 94 00:06:33,040 --> 00:06:37,160 Speaker 1: for humans cartless specific collagen and to see how that 95 00:06:37,200 --> 00:06:41,560 Speaker 1: got turned on during development. It was very exciting. So 96 00:06:41,600 --> 00:06:47,280 Speaker 1: how does that lead to pioneering DNA testing for forensic 97 00:06:47,320 --> 00:06:52,320 Speaker 1: and paternity applications? So I've always been what I call 98 00:06:52,720 --> 00:06:56,719 Speaker 1: an applied scientist. You know, the scientists out there really 99 00:06:57,400 --> 00:07:01,400 Speaker 1: come in two forms. One is the basic scientists, the 100 00:07:01,480 --> 00:07:06,440 Speaker 1: person that really wants to delve incredibly deeply, UH into 101 00:07:06,720 --> 00:07:10,040 Speaker 1: one particular problem. UH. There used to be a thing 102 00:07:10,080 --> 00:07:15,480 Speaker 1: about the medical practitioners that the general practitioner knows nothing 103 00:07:15,520 --> 00:07:19,320 Speaker 1: about everything, that the specialist knows everything about nothing, and 104 00:07:19,360 --> 00:07:22,200 Speaker 1: the pathologist knows everything about everything, but it's too late 105 00:07:22,240 --> 00:07:27,840 Speaker 1: to do any good. So the basic scientists delves very 106 00:07:27,880 --> 00:07:32,520 Speaker 1: deeply into a single subject. I always was more interested 107 00:07:32,600 --> 00:07:35,080 Speaker 1: in how are we going to use these developments to 108 00:07:35,120 --> 00:07:40,440 Speaker 1: help people, in particular the medical aspects. Now they would 109 00:07:40,480 --> 00:07:43,400 Speaker 1: call it translational medicine, but how are we going to 110 00:07:43,520 --> 00:07:46,480 Speaker 1: take what we learned at the lab bench and put 111 00:07:46,560 --> 00:07:50,360 Speaker 1: it into practice? So I was the professor at University 112 00:07:50,400 --> 00:07:56,080 Speaker 1: of Chicago. DNA testing for forensics was just in its infancy. 113 00:07:56,400 --> 00:07:59,880 Speaker 1: There was the Blooding I don't know if you remember that, 114 00:08:00,240 --> 00:08:04,480 Speaker 1: where an entire village was genotyped UH in England to 115 00:08:04,640 --> 00:08:09,480 Speaker 1: find a rapist, and forensics DNA had not yet been 116 00:08:09,520 --> 00:08:13,840 Speaker 1: admitted into courts in UH. In Illinois, I was approached 117 00:08:13,960 --> 00:08:20,160 Speaker 1: by several different prosecutors who had very difficult cases and 118 00:08:20,320 --> 00:08:24,480 Speaker 1: asked if I could you know, if I could do 119 00:08:24,640 --> 00:08:29,360 Speaker 1: DNA testing to support their cases, and being in academics 120 00:08:29,360 --> 00:08:33,440 Speaker 1: and having some academic freedom, I I said yes and 121 00:08:33,640 --> 00:08:38,640 Speaker 1: did the did some DNA testing and UH in UH 122 00:08:38,960 --> 00:08:42,360 Speaker 1: in legal in Illinois. I don't know if this is 123 00:08:42,679 --> 00:08:45,720 Speaker 1: around the United States. There's something called a fry hearing 124 00:08:46,240 --> 00:08:51,120 Speaker 1: where before UH laboratory evidence can be introduced in court, 125 00:08:51,200 --> 00:08:54,720 Speaker 1: it has to pass a certain amount of standards, um, 126 00:08:55,400 --> 00:08:59,920 Speaker 1: whether it's generally accepted in the scientific community, whether it's reliable, 127 00:09:00,920 --> 00:09:04,800 Speaker 1: those sorts of things. And so I participated in several 128 00:09:04,840 --> 00:09:10,000 Speaker 1: pry hearings in Illinois to allow the admission of DNA 129 00:09:10,080 --> 00:09:14,200 Speaker 1: testing and forensics. And my famous case, the one I 130 00:09:14,360 --> 00:09:20,560 Speaker 1: published about, was a gentleman who had actually murdered his 131 00:09:20,640 --> 00:09:27,679 Speaker 1: wife and then burned her body to UH near completion 132 00:09:28,240 --> 00:09:31,720 Speaker 1: in the steel drum in his garage. UH. Then he 133 00:09:32,320 --> 00:09:37,120 Speaker 1: went to the police station and decided to confess, and 134 00:09:37,160 --> 00:09:40,520 Speaker 1: then when he got an attorney, he withdrew his confession. 135 00:09:41,200 --> 00:09:44,839 Speaker 1: So the prosecutors kind of knew that he had done it, 136 00:09:45,200 --> 00:09:49,320 Speaker 1: but I had no way. There was nobody to h 137 00:09:49,480 --> 00:09:52,440 Speaker 1: to be identified. So we were able to actually to 138 00:09:52,559 --> 00:09:57,079 Speaker 1: identify his wife from the charred remains in UH the 139 00:09:57,240 --> 00:10:00,920 Speaker 1: steel drum. UH and you know, the pry evidence was 140 00:10:00,960 --> 00:10:06,120 Speaker 1: accepted and he was convicted. So that was basically my 141 00:10:06,120 --> 00:10:09,199 Speaker 1: my moment in the sun in forensics, and I never 142 00:10:09,200 --> 00:10:14,440 Speaker 1: really did anything after that. Quite fascinating. So you work 143 00:10:14,480 --> 00:10:16,480 Speaker 1: at QUESTS for a couple of years where you're head 144 00:10:16,520 --> 00:10:21,040 Speaker 1: of the research labs, and eventually, um you're working with 145 00:10:21,160 --> 00:10:26,560 Speaker 1: Dr David Wang tell us about Imperial technology and what 146 00:10:26,760 --> 00:10:31,040 Speaker 1: Dr Wang had created. So I had worked in QUEST 147 00:10:31,080 --> 00:10:35,840 Speaker 1: Diagnostics or sixteen years basically running all the genetic laboratories. 148 00:10:36,559 --> 00:10:42,720 Speaker 1: And after I left, I took a temporary position to 149 00:10:42,760 --> 00:10:46,800 Speaker 1: be the director of the molecular pathology laboratories at u 150 00:10:46,840 --> 00:10:50,040 Speaker 1: c l A. This was because of they were trying 151 00:10:50,040 --> 00:10:53,679 Speaker 1: to recruit a permanent director. I was in semi retirement 152 00:10:53,920 --> 00:10:57,160 Speaker 1: and so you know, I took a temporary job working 153 00:10:57,160 --> 00:10:58,760 Speaker 1: out for u C l A for a couple of 154 00:10:58,800 --> 00:11:02,760 Speaker 1: days a week. Then one day my boss calls me 155 00:11:02,800 --> 00:11:06,240 Speaker 1: in and she says, um, buck, we have a problem. 156 00:11:06,280 --> 00:11:10,199 Speaker 1: There is a dentist in the dental school by the 157 00:11:10,320 --> 00:11:14,400 Speaker 1: name of David Wong who has just gotten a grant, 158 00:11:15,320 --> 00:11:18,600 Speaker 1: and part of the grant was that our laboratory would 159 00:11:18,640 --> 00:11:22,240 Speaker 1: validate the test. As a laboratory developed test so it 160 00:11:22,240 --> 00:11:26,600 Speaker 1: could be offered clinically. Um, the pathologist who had co 161 00:11:26,720 --> 00:11:31,440 Speaker 1: written that Grant had left the left the institution, and 162 00:11:31,480 --> 00:11:33,320 Speaker 1: so she says, you've got to go up and see 163 00:11:33,320 --> 00:11:36,600 Speaker 1: what's going on and see what we can do. So 164 00:11:37,000 --> 00:11:39,600 Speaker 1: I take the elevator up to the seventh floor the 165 00:11:39,640 --> 00:11:44,160 Speaker 1: Basic Science Building at U C l A. And I 166 00:11:44,200 --> 00:11:47,000 Speaker 1: go up to meet with Dr Long and it was like, 167 00:11:47,360 --> 00:11:51,160 Speaker 1: oh my god, that's the guy, because I had met 168 00:11:51,280 --> 00:11:54,960 Speaker 1: him about ten years previously when he had given a 169 00:11:55,000 --> 00:11:59,120 Speaker 1: talk at Quest Diagnostic and he had dedicated his life 170 00:11:59,280 --> 00:12:03,680 Speaker 1: to saliva based diagnostics. And when he gave a talk, 171 00:12:03,880 --> 00:12:06,840 Speaker 1: I was blown away and said to myself and came 172 00:12:06,840 --> 00:12:08,880 Speaker 1: home and said to my wife, you know, this guy's 173 00:12:08,880 --> 00:12:12,360 Speaker 1: a visionary. And we had lunch afterwards and we had 174 00:12:12,360 --> 00:12:15,439 Speaker 1: a wonderful talk and it was like you know, a 175 00:12:15,559 --> 00:12:18,520 Speaker 1: rom com. We saw each other in the hallway and 176 00:12:18,559 --> 00:12:22,440 Speaker 1: it was Bucked. It was David Uh and he said, 177 00:12:22,520 --> 00:12:25,640 Speaker 1: let me show you what I got here. And Um. 178 00:12:25,720 --> 00:12:28,800 Speaker 1: He had developed a platform which at that time was 179 00:12:28,880 --> 00:12:33,840 Speaker 1: called e Firm we now call it Imperial, which could 180 00:12:34,160 --> 00:12:39,040 Speaker 1: do UH diagnostics of anti biomolecule including d n A, 181 00:12:39,320 --> 00:12:45,679 Speaker 1: including antibodies including protein on saliva UH as an open 182 00:12:45,720 --> 00:12:50,839 Speaker 1: platform UH, and he had used this UH to actually 183 00:12:51,520 --> 00:12:57,000 Speaker 1: UH demonstrate that he could UH detect circulating tumor DNA 184 00:12:57,360 --> 00:13:00,400 Speaker 1: in patients with early stage lung cancer, something that had 185 00:13:00,520 --> 00:13:05,280 Speaker 1: never been done before successfully. UM and I looked at 186 00:13:05,280 --> 00:13:08,520 Speaker 1: this data and it knocked my socks off, and I said, 187 00:13:08,679 --> 00:13:11,360 Speaker 1: you know, baby, I want to work with you here. 188 00:13:11,800 --> 00:13:17,720 Speaker 1: So it began a wonderful collaboration UM and I became 189 00:13:17,760 --> 00:13:21,240 Speaker 1: blown away by the potential of this platform. The problem 190 00:13:21,360 --> 00:13:24,200 Speaker 1: is that Dr Long is an academic. He had no 191 00:13:24,320 --> 00:13:27,240 Speaker 1: idea of how to commercialize anything. I had come from 192 00:13:27,320 --> 00:13:31,920 Speaker 1: sixteen years in the diagnostic industry, so my expertise was 193 00:13:33,720 --> 00:13:36,480 Speaker 1: it was complimentary to his. I knew how to make 194 00:13:36,679 --> 00:13:40,160 Speaker 1: essays that could be used hundreds of thousands of times 195 00:13:40,640 --> 00:13:45,320 Speaker 1: and give accurate results. So I was very excited. But 196 00:13:45,440 --> 00:13:49,160 Speaker 1: in order to commercialize the intellectual property has to be 197 00:13:49,240 --> 00:13:52,480 Speaker 1: in order. There has to be an organization, there has 198 00:13:52,520 --> 00:13:56,760 Speaker 1: to be funding. And then I reached out to friends 199 00:13:56,760 --> 00:14:00,240 Speaker 1: of mine who were also UH leaders in their deal 200 00:14:00,960 --> 00:14:07,000 Speaker 1: Bob AGNERN, who was an executive in Amaco for many years. 201 00:14:07,000 --> 00:14:11,800 Speaker 1: I was a lawyer and ran businesses for Amaco, Jeff Weisberg, 202 00:14:11,880 --> 00:14:16,719 Speaker 1: who started uh Atina Diagnostics, one of the major neurology 203 00:14:16,760 --> 00:14:21,080 Speaker 1: diagnostics companies and was a financial guy. And my friend 204 00:14:21,200 --> 00:14:25,040 Speaker 1: Rich Bender, who was a medical oncologist. All of these 205 00:14:25,080 --> 00:14:28,000 Speaker 1: people I knew from from past life. Bob, where I 206 00:14:28,040 --> 00:14:32,200 Speaker 1: knew from from little e Um, and the other two 207 00:14:32,240 --> 00:14:35,520 Speaker 1: I admit a quest diagnosis and full disclosure. I met 208 00:14:35,560 --> 00:14:39,520 Speaker 1: you through Bob Agdern, who is my brother UM, and 209 00:14:39,600 --> 00:14:41,680 Speaker 1: I was so intrigued by the work you guys have 210 00:14:41,760 --> 00:14:44,560 Speaker 1: been doing. We've been talking about this for a couple 211 00:14:44,560 --> 00:14:47,440 Speaker 1: of years. Before we move on to COVID, I have 212 00:14:47,640 --> 00:14:52,320 Speaker 1: to you know, you kinda buried the lead about the 213 00:14:52,400 --> 00:14:59,360 Speaker 1: lung cancer. The key thing about those early indicators is 214 00:14:59,400 --> 00:15:02,640 Speaker 1: that this is very difficult to diagnose, and if you 215 00:15:02,760 --> 00:15:06,600 Speaker 1: catch it early, it's very treatable, and if you catch 216 00:15:06,640 --> 00:15:09,320 Speaker 1: it late, it tends to have a very bad outcome. 217 00:15:09,400 --> 00:15:13,200 Speaker 1: Is that a fair way to describe it. Absolutely, About 218 00:15:13,240 --> 00:15:16,920 Speaker 1: eight lung cancer now is diagnosed. That stage is three 219 00:15:16,920 --> 00:15:20,480 Speaker 1: to four where it's not curable. Um, you know, you 220 00:15:20,520 --> 00:15:24,000 Speaker 1: can be treated, it can prolong your life, but basically 221 00:15:24,080 --> 00:15:27,160 Speaker 1: you're going to die. A lung cancer stage one and two, 222 00:15:27,200 --> 00:15:29,600 Speaker 1: which is what we call early stage lung cancer. It 223 00:15:29,760 --> 00:15:34,440 Speaker 1: is still potentially curable with both surgery and chemotherapy or 224 00:15:34,440 --> 00:15:38,400 Speaker 1: a combination of both, and that's why it's so important 225 00:15:38,520 --> 00:15:42,000 Speaker 1: to diagnose this early. But eight percent of the time 226 00:15:42,120 --> 00:15:46,200 Speaker 1: it's not. There is a screening test now that's available, 227 00:15:46,240 --> 00:15:49,800 Speaker 1: which is a spiral CT scan for people who have 228 00:15:50,040 --> 00:15:56,360 Speaker 1: long histories of smoking and UM. The problem with with 229 00:15:56,720 --> 00:15:59,800 Speaker 1: spiral CT scanning is that you get these things called 230 00:16:00,120 --> 00:16:03,720 Speaker 1: terminate nodules. So some people have you do the CT 231 00:16:03,920 --> 00:16:06,040 Speaker 1: scan and it's, oh, this has got to be cancer. 232 00:16:06,160 --> 00:16:09,240 Speaker 1: Sometimes you do the CT scan and it's negative. But 233 00:16:09,360 --> 00:16:11,840 Speaker 1: about thirty of the time you do the CT scan 234 00:16:11,960 --> 00:16:14,560 Speaker 1: and there's something there, but you don't know whether it's 235 00:16:14,600 --> 00:16:17,880 Speaker 1: cancer or not. UH. And we have an NIH funded 236 00:16:17,920 --> 00:16:21,600 Speaker 1: study to use our platform to look at these patients 237 00:16:22,520 --> 00:16:28,120 Speaker 1: within determinate nodules using either saliva or plasma or both 238 00:16:28,600 --> 00:16:32,360 Speaker 1: to see if we can inform the decision about who 239 00:16:32,440 --> 00:16:36,080 Speaker 1: needs a biopsy UH and who doesn't need a biopsy, 240 00:16:36,440 --> 00:16:39,880 Speaker 1: and how the results have been so far, we're in 241 00:16:39,960 --> 00:16:42,160 Speaker 1: the middle of it. We have not yet done any 242 00:16:42,400 --> 00:16:46,560 Speaker 1: of the data analysis we're right now. We're collecting, we're 243 00:16:46,600 --> 00:16:50,280 Speaker 1: collecting samples and they'll be analyzed actually next year. So 244 00:16:50,320 --> 00:16:53,960 Speaker 1: I can't tell you how it's going, uh, but we're 245 00:16:54,000 --> 00:16:57,440 Speaker 1: hoping that it's it's going to give us positive results. 246 00:16:57,520 --> 00:17:00,640 Speaker 1: So that was the original plan. When you form liquid 247 00:17:00,680 --> 00:17:05,320 Speaker 1: diagnostics and then you know COVID and the pandemic starts 248 00:17:05,320 --> 00:17:09,080 Speaker 1: and we go into lockdown, how did you guys pivot 249 00:17:09,600 --> 00:17:13,000 Speaker 1: to using this technology to either detect COVID or look 250 00:17:13,000 --> 00:17:19,399 Speaker 1: at antibodies or both. So interesting story. So, Uh, doctor 251 00:17:19,720 --> 00:17:25,200 Speaker 1: Wong is a dentist and he had an incredible interest 252 00:17:25,359 --> 00:17:30,000 Speaker 1: in a disease called Schogrin syndrome. Schogrin syndrome is an 253 00:17:30,040 --> 00:17:33,520 Speaker 1: autoimmune disease where the body attacks itself and it causes 254 00:17:33,680 --> 00:17:37,600 Speaker 1: dry mouth and dry eyes. Uh. There's actually four million 255 00:17:37,680 --> 00:17:41,200 Speaker 1: people who present to their physicians every year with that complaint, 256 00:17:41,240 --> 00:17:45,119 Speaker 1: either dry mouth, dry eyes or false um. It was 257 00:17:45,280 --> 00:17:48,400 Speaker 1: known that some of these patients who present with dry 258 00:17:48,480 --> 00:17:52,280 Speaker 1: mouth and dry eyes actually have a disorder called chogrin syndrome, 259 00:17:52,800 --> 00:17:57,320 Speaker 1: which is where the salivary glands make anna where antibodies 260 00:17:57,359 --> 00:18:01,840 Speaker 1: are made that attack the salivary glands. UM. The the 261 00:18:02,160 --> 00:18:06,240 Speaker 1: diagnosis of chagrin syndrome was incredibly difficult because the blood 262 00:18:06,240 --> 00:18:11,680 Speaker 1: based antibodies, UH, we're not particularly sensitive or specific for 263 00:18:11,760 --> 00:18:15,560 Speaker 1: the disease, so people often had to have biopsies UH, 264 00:18:15,560 --> 00:18:17,960 Speaker 1: and most people didn't want to have a biopsy of 265 00:18:18,000 --> 00:18:22,359 Speaker 1: a salivary gland. UH. So he began to use our platform, 266 00:18:22,440 --> 00:18:27,240 Speaker 1: Imperial platform, to look for antibody in saliva. And it 267 00:18:27,320 --> 00:18:30,639 Speaker 1: turns out that that is a much better way of 268 00:18:30,680 --> 00:18:34,919 Speaker 1: diagnosing chagrin syndrome than in blood. So we knew that 269 00:18:35,000 --> 00:18:38,800 Speaker 1: we could use this platform for antibodies. So I remember 270 00:18:38,840 --> 00:18:41,600 Speaker 1: it was mid February and the beginning of the pandemic, 271 00:18:41,680 --> 00:18:45,480 Speaker 1: and I said to our group at Liquid Diagnostics, you know, 272 00:18:45,520 --> 00:18:48,200 Speaker 1: I think we could use this to measure covid antibody. 273 00:18:48,760 --> 00:18:51,240 Speaker 1: And I remember Bob said, well, you know, how much 274 00:18:51,320 --> 00:18:53,439 Speaker 1: is that going to cost? And they said, well, you know, 275 00:18:53,520 --> 00:18:56,200 Speaker 1: maybe you know, five or ten thousand dollars to buy 276 00:18:56,200 --> 00:18:58,919 Speaker 1: the re agents or things, um, and the rest they 277 00:18:58,960 --> 00:19:01,800 Speaker 1: say is history. We were able to make a saliva 278 00:19:01,880 --> 00:19:07,160 Speaker 1: based diagnostic, which is quantitative, which is very different from 279 00:19:07,200 --> 00:19:12,520 Speaker 1: almost all other antibody measuring tests available that can measure 280 00:19:12,880 --> 00:19:16,680 Speaker 1: your unoglobulin g R I d T levels to U 281 00:19:17,640 --> 00:19:20,159 Speaker 1: Sorrow's c O G two which is the virus that 282 00:19:20,320 --> 00:19:23,919 Speaker 1: causes UH COVID nineteen. So so let me interrupt you 283 00:19:23,920 --> 00:19:26,600 Speaker 1: and just translate that into English for a second. Most 284 00:19:26,640 --> 00:19:29,760 Speaker 1: of the tests, either the rapid test or the PCR 285 00:19:29,840 --> 00:19:32,560 Speaker 1: test is going to give you thumbs up thumbs downy, 286 00:19:32,560 --> 00:19:35,600 Speaker 1: either you're showing this or you don't. You're able to 287 00:19:35,680 --> 00:19:40,800 Speaker 1: do a measurement that quantifies shows you your levels of COVID. 288 00:19:40,840 --> 00:19:44,640 Speaker 1: Anybody's am I saying that? Right? That's correct. They'll very 289 00:19:44,880 --> 00:19:49,760 Speaker 1: PCR doesn't measure antibodies. PCR measures viruses. But yes, most 290 00:19:49,880 --> 00:19:52,880 Speaker 1: most of the well all of the home tests are qualitative. 291 00:19:52,960 --> 00:19:56,120 Speaker 1: They're not quantitative, which means they tell you positive or negative. 292 00:19:56,400 --> 00:19:59,920 Speaker 1: The laboratory tests are what's called semi quantitative. They give 293 00:20:00,000 --> 00:20:02,879 Speaker 1: you a number that's pretty meaningless. It says, you know, 294 00:20:03,119 --> 00:20:06,040 Speaker 1: three point one or three point two, and you don't 295 00:20:06,040 --> 00:20:08,680 Speaker 1: really know what to do about it. Our test actually 296 00:20:08,720 --> 00:20:12,560 Speaker 1: gives you the level of your antibody, and then we 297 00:20:12,680 --> 00:20:16,760 Speaker 1: also tell you how you stand with respect to you know, 298 00:20:16,880 --> 00:20:20,199 Speaker 1: several thousand samples that we have from individuals who have 299 00:20:20,240 --> 00:20:23,640 Speaker 1: been vaccinated. So they'll say, Barry, your level is four 300 00:20:23,720 --> 00:20:26,680 Speaker 1: point two. An anagrams per m L when you say 301 00:20:26,760 --> 00:20:29,119 Speaker 1: what does that mean? Then we tell you you're in 302 00:20:29,160 --> 00:20:33,520 Speaker 1: the eightieth percentile for all patients who have been vaccinated 303 00:20:34,040 --> 00:20:37,959 Speaker 1: against COVID, so you know you've got good, healthy levels. 304 00:20:38,000 --> 00:20:40,440 Speaker 1: On the other hand, you could get a level that says, 305 00:20:40,560 --> 00:20:43,000 Speaker 1: you know, it's ten managrams per m L and this 306 00:20:43,119 --> 00:20:46,120 Speaker 1: is at the tenth percentile, which means you know that 307 00:20:46,200 --> 00:20:49,920 Speaker 1: you are you're low on the scale. UH. The other 308 00:20:50,240 --> 00:20:53,119 Speaker 1: beauty of this test is because it's saliva base, you 309 00:20:53,160 --> 00:20:56,560 Speaker 1: don't have to have your blood drawn, and it's relatively inexpensive. 310 00:20:56,760 --> 00:21:00,119 Speaker 1: You can have multiple tests. So, for example, we have 311 00:21:00,240 --> 00:21:03,760 Speaker 1: a clinical trial going which I'm a participant, where we 312 00:21:04,040 --> 00:21:09,119 Speaker 1: looked at people's levels every two weeks UH for for 313 00:21:09,240 --> 00:21:12,359 Speaker 1: six months. And when we looked at that, we could 314 00:21:12,359 --> 00:21:16,040 Speaker 1: see that that most people's levels went up after their 315 00:21:16,080 --> 00:21:19,639 Speaker 1: second vaccination, but then they slowly came down so that 316 00:21:19,760 --> 00:21:21,880 Speaker 1: by from four to six months they were almost back 317 00:21:21,920 --> 00:21:25,000 Speaker 1: down the baseline, which would mean that we could have 318 00:21:25,080 --> 00:21:27,720 Speaker 1: probably predicted that you're going to need a booster after 319 00:21:27,800 --> 00:21:31,080 Speaker 1: six months. That that sounds like it's really useful given 320 00:21:31,200 --> 00:21:34,400 Speaker 1: that there's been a pretty big push to not only 321 00:21:34,400 --> 00:21:37,000 Speaker 1: get people to get boosted, but then to get a 322 00:21:37,080 --> 00:21:41,679 Speaker 1: second booster. So I'm vaccinated, I'm boosted. I would like 323 00:21:41,800 --> 00:21:45,480 Speaker 1: to know if I should get a booster now heading 324 00:21:45,480 --> 00:21:49,040 Speaker 1: into the summer or in the fall, when I usually 325 00:21:49,080 --> 00:21:52,720 Speaker 1: get my flu shot, because that's when when we move indoors. 326 00:21:52,760 --> 00:21:55,920 Speaker 1: These viruses seem to be spread around the most in 327 00:21:56,240 --> 00:22:00,600 Speaker 1: at least in the cooler areas of the country. Yeah. Right, 328 00:22:00,640 --> 00:22:03,119 Speaker 1: that's a great point. So you know, the issue is, 329 00:22:03,680 --> 00:22:08,640 Speaker 1: you know, I know now that after my third booster, 330 00:22:08,920 --> 00:22:12,920 Speaker 1: the third shot, so the first booster, that my levels 331 00:22:13,040 --> 00:22:16,520 Speaker 1: now eight months out are the same as they were, 332 00:22:17,160 --> 00:22:21,119 Speaker 1: uh two weeks after my third booster. So I don't 333 00:22:21,200 --> 00:22:24,600 Speaker 1: feel that at this moment I need a fourth booster. 334 00:22:25,640 --> 00:22:28,840 Speaker 1: And you know, there's no dated to say that that's 335 00:22:28,920 --> 00:22:32,480 Speaker 1: good or bad. Unfortunately, FDA says that a person of 336 00:22:32,600 --> 00:22:36,640 Speaker 1: my age could get a fourth shot if I wanted, 337 00:22:37,240 --> 00:22:40,639 Speaker 1: But there's no reasonable way for me to make that 338 00:22:40,720 --> 00:22:43,879 Speaker 1: decision right now. A lot of my friends that said 339 00:22:44,000 --> 00:22:47,520 Speaker 1: I'm going to take I'm going to take the fourth shot. Um. 340 00:22:47,680 --> 00:22:50,400 Speaker 1: Your point is is well taken that if you take 341 00:22:50,440 --> 00:22:53,560 Speaker 1: the fourth shot, who knows if you're gonna be able 342 00:22:53,600 --> 00:22:57,840 Speaker 1: to get a tip shot. UM or when? So you know, 343 00:22:57,880 --> 00:23:01,399 Speaker 1: we have that luxury of those who have participated in 344 00:23:01,440 --> 00:23:05,359 Speaker 1: our trial of knowing that our levels are stable over time. 345 00:23:06,119 --> 00:23:10,200 Speaker 1: Um again, you know this is this is the personal 346 00:23:10,240 --> 00:23:13,880 Speaker 1: decision that I'm making, you know, with myself and my position, 347 00:23:14,440 --> 00:23:17,040 Speaker 1: and I can't say that you know that there's a 348 00:23:17,040 --> 00:23:20,680 Speaker 1: recommendation about this sort of thing, But this is the 349 00:23:20,760 --> 00:23:23,520 Speaker 1: kind of data that we need. The beauty of our 350 00:23:23,680 --> 00:23:27,920 Speaker 1: tests is that we could actually get the data that 351 00:23:28,040 --> 00:23:31,399 Speaker 1: would inform these kinds of decisions. So we could look 352 00:23:31,520 --> 00:23:35,440 Speaker 1: at a whole bunch of people, say, you know, everybody 353 00:23:35,560 --> 00:23:39,679 Speaker 1: in a city, or everybody in a large company, and 354 00:23:39,800 --> 00:23:44,840 Speaker 1: we could test people every month for their quantitative antibody levels, 355 00:23:44,960 --> 00:23:47,640 Speaker 1: and then we can follow them and see who gets COVID, 356 00:23:47,720 --> 00:23:51,000 Speaker 1: who doesn't get COVID, who goes into the hospital, who 357 00:23:51,000 --> 00:23:55,080 Speaker 1: gets long COVID, you know, who dies, and then correlate 358 00:23:55,200 --> 00:23:58,640 Speaker 1: that with our antibody levels and see if our hypothesis 359 00:23:58,680 --> 00:24:02,159 Speaker 1: are correct. The problem is, as far as I know, 360 00:24:02,280 --> 00:24:06,719 Speaker 1: nobody is doing these sorts of tests because the blood 361 00:24:06,720 --> 00:24:10,320 Speaker 1: tests are only semi quantitive at quantitative At the moment, 362 00:24:10,359 --> 00:24:14,560 Speaker 1: the quantitative tests are expensive to do, and this study 363 00:24:14,560 --> 00:24:18,000 Speaker 1: would be very expensive to perform. So it frustrates me. 364 00:24:18,200 --> 00:24:21,040 Speaker 1: Is I believe we have a tool we published on 365 00:24:21,200 --> 00:24:25,800 Speaker 1: this Imperaview Publications where we could do these sorts of studies. 366 00:24:25,840 --> 00:24:29,159 Speaker 1: We could get the information because COVID is not going away. 367 00:24:29,240 --> 00:24:32,399 Speaker 1: That's the one thing that's sure. Uh this is going 368 00:24:32,440 --> 00:24:35,520 Speaker 1: to be part of our lives for the foreseeable future, 369 00:24:36,119 --> 00:24:39,119 Speaker 1: and we need to start getting information that will allow 370 00:24:39,240 --> 00:24:44,159 Speaker 1: physicians and people to make informed decisions about things like vaccines. 371 00:24:44,240 --> 00:24:47,920 Speaker 1: For example, what if your vaccine level, your your antibody 372 00:24:48,000 --> 00:24:51,760 Speaker 1: level is very low, and you've already gotten your four 373 00:24:52,480 --> 00:24:55,080 Speaker 1: uh M R and a booster as well. Now there's 374 00:24:55,119 --> 00:24:58,240 Speaker 1: gonna be a new vaccine this summer. I hear that's 375 00:24:58,280 --> 00:25:02,800 Speaker 1: based on the old technology of antagine technology. So maybe 376 00:25:02,840 --> 00:25:05,480 Speaker 1: that would be someone who would want to get that 377 00:25:05,680 --> 00:25:09,600 Speaker 1: vaccine because they're not responding very well to the m 378 00:25:09,760 --> 00:25:12,919 Speaker 1: RNA vaccines. Now, one of the issues, you know, in 379 00:25:13,040 --> 00:25:17,480 Speaker 1: public health, everybody is treated like they're the same, and 380 00:25:17,720 --> 00:25:22,040 Speaker 1: what we're finding in terms of antibody production and antibody 381 00:25:22,080 --> 00:25:26,880 Speaker 1: affinity is that everybody is not the same. Um. For example, 382 00:25:26,920 --> 00:25:30,679 Speaker 1: with all macron, some people's i g G antibodies that 383 00:25:30,760 --> 00:25:36,680 Speaker 1: were made with the the original visor M dinner vaccines. 384 00:25:37,000 --> 00:25:42,720 Speaker 1: They cross react, you know, nearly, so that the antibodies 385 00:25:42,760 --> 00:25:45,560 Speaker 1: that these people make are just as good against A 386 00:25:45,680 --> 00:25:49,800 Speaker 1: macron as they were against the original virus. On the 387 00:25:49,840 --> 00:25:54,000 Speaker 1: other hand, some of the people their antibodies have less 388 00:25:54,000 --> 00:25:58,480 Speaker 1: than fifty of the affinity. Uh, then thanking for the 389 00:25:58,520 --> 00:26:02,240 Speaker 1: wild type. So sort of uh. And we're able to 390 00:26:02,320 --> 00:26:05,200 Speaker 1: make that assett because it's an open platform. We can 391 00:26:05,240 --> 00:26:09,520 Speaker 1: make an asset for O macron within weeks of when 392 00:26:09,560 --> 00:26:13,800 Speaker 1: O macron is first identifying. So I think that these 393 00:26:13,840 --> 00:26:18,239 Speaker 1: sorts of these sorts of studies could really help inform 394 00:26:18,280 --> 00:26:21,280 Speaker 1: on what's going on. Some of the you know, critics 395 00:26:21,280 --> 00:26:24,640 Speaker 1: of antibody testing say, well, we don't want people doing 396 00:26:24,760 --> 00:26:29,080 Speaker 1: risky behavior because they know they have antibodies. My response 397 00:26:29,119 --> 00:26:31,520 Speaker 1: to that is, well, if you don't you have antibody 398 00:26:31,720 --> 00:26:34,280 Speaker 1: and you've been vaccinated, you should feel free to do 399 00:26:34,359 --> 00:26:37,760 Speaker 1: everything that the CDC says a vaccinated person should do. 400 00:26:38,600 --> 00:26:40,840 Speaker 1: But I'm looking at the flip side. What if your 401 00:26:40,880 --> 00:26:45,320 Speaker 1: antibodies are low, Uh, then maybe you should not do 402 00:26:45,640 --> 00:26:49,280 Speaker 1: everything that a vaccinated person could do, or you should 403 00:26:49,320 --> 00:26:52,240 Speaker 1: and you should talk to your doctor about maybe doing something. 404 00:26:53,160 --> 00:26:58,560 Speaker 1: Either a booster with the same vaccine or a different vaccine, uh, 405 00:26:58,680 --> 00:27:02,480 Speaker 1: to try to get those levels up. So again, there's 406 00:27:02,520 --> 00:27:05,840 Speaker 1: not enough data to make any real recommendations at the moment. 407 00:27:06,359 --> 00:27:09,560 Speaker 1: And I would like to, you know, I would like 408 00:27:09,720 --> 00:27:14,200 Speaker 1: people to think about using our tests to um to 409 00:27:14,200 --> 00:27:17,840 Speaker 1: either do the research or or to make their own 410 00:27:17,840 --> 00:27:22,720 Speaker 1: informed decisions. So you mentioned the c d C, Um, 411 00:27:22,760 --> 00:27:27,240 Speaker 1: what are they doing about the entire space of anybody's? 412 00:27:27,359 --> 00:27:30,760 Speaker 1: Is this something that they're just not paying attention to 413 00:27:31,240 --> 00:27:34,960 Speaker 1: do they really think people with high anybody's are going 414 00:27:35,040 --> 00:27:37,440 Speaker 1: to go out and be reckless? What does the CDC 415 00:27:37,640 --> 00:27:42,960 Speaker 1: say about knowing what your anybody levels are? Yeah, well, 416 00:27:43,160 --> 00:27:48,720 Speaker 1: the CDC and FDA boats have made public statements that 417 00:27:48,840 --> 00:27:52,400 Speaker 1: they don't think that measuring antibody levels have any role 418 00:27:53,160 --> 00:27:57,000 Speaker 1: in the pandemic. Uh. And you know, I can see 419 00:27:57,000 --> 00:28:00,000 Speaker 1: the point, you know, to them, as I said in public, 420 00:28:00,080 --> 00:28:05,400 Speaker 1: tell everybody is a human being and everybody is the same. Uh. 421 00:28:05,480 --> 00:28:08,480 Speaker 1: So you know that's been their position, and we're gonna 422 00:28:08,600 --> 00:28:12,199 Speaker 1: we're gonna make recommendations, you know, for everybody, and and 423 00:28:12,280 --> 00:28:16,480 Speaker 1: it will work for most people. So the issue about 424 00:28:16,520 --> 00:28:18,679 Speaker 1: whether or not to be vaccinated or not, that's a 425 00:28:18,720 --> 00:28:22,960 Speaker 1: political issue about whether you can force vaccinations on people. 426 00:28:23,480 --> 00:28:28,480 Speaker 1: I think a more interesting question is that, certainly internationally, Uh, 427 00:28:28,520 --> 00:28:34,000 Speaker 1: there is the problem with vaccine card counterfeiting, right, so 428 00:28:34,040 --> 00:28:37,800 Speaker 1: that there are people who have not been vaccinated who present, 429 00:28:38,360 --> 00:28:41,080 Speaker 1: you know, and you know what your vaccine card looks like. 430 00:28:41,120 --> 00:28:45,800 Speaker 1: I mean, how difficult would that be to cornerway? Uh. Yeah, 431 00:28:45,880 --> 00:28:49,120 Speaker 1: it's it's ridiculous. And there's no centralized database, you know, 432 00:28:49,480 --> 00:28:53,440 Speaker 1: in this modern age. That's ridiculous. The fact and when 433 00:28:53,480 --> 00:28:56,360 Speaker 1: I went to uh, the tennis tournament out in the desert, 434 00:28:56,440 --> 00:28:59,440 Speaker 1: the b NB Puribous Open, Uh, they made a big 435 00:28:59,480 --> 00:29:02,480 Speaker 1: deal of how everyone would be vaccinated. And there was 436 00:29:02,520 --> 00:29:05,440 Speaker 1: an app. An the app you know, proved that you 437 00:29:05,480 --> 00:29:07,680 Speaker 1: were vaccinated. But the way the app proved you were 438 00:29:07,800 --> 00:29:10,600 Speaker 1: vaccinated and you took a picture of your vaccine card, 439 00:29:11,040 --> 00:29:14,560 Speaker 1: you took a picture of your your driver's license, and 440 00:29:14,600 --> 00:29:17,680 Speaker 1: they you know, validated that you've been vaccinated. Well that's 441 00:29:17,760 --> 00:29:21,240 Speaker 1: not really because if I had a fake vaccine card, 442 00:29:21,880 --> 00:29:26,040 Speaker 1: that would not establish anything. Again, with a saliva based 443 00:29:26,080 --> 00:29:29,840 Speaker 1: quantitative test, you could actually make sure that people had 444 00:29:29,880 --> 00:29:34,719 Speaker 1: antibodies who you're hiring again in Florida, that would be 445 00:29:34,880 --> 00:29:38,760 Speaker 1: legal most likely, But as I said, that's a political decision, 446 00:29:38,800 --> 00:29:42,400 Speaker 1: that's not a medical decision. And the saliva test seems 447 00:29:42,440 --> 00:29:47,040 Speaker 1: to be far less invasive than the swab. How does 448 00:29:47,080 --> 00:29:50,000 Speaker 1: it compare in terms of the time for the turnaround 449 00:29:50,520 --> 00:29:54,840 Speaker 1: and the cost relative to other forms of testing. The 450 00:29:54,920 --> 00:29:57,600 Speaker 1: cost to do the test is similar to what you'd 451 00:29:57,680 --> 00:29:59,880 Speaker 1: have in a blood test. But the thing about alo 452 00:30:00,000 --> 00:30:04,120 Speaker 1: a test is that there is a cost associated withdrawing 453 00:30:04,160 --> 00:30:08,280 Speaker 1: the blood. People don't really calculate that that in the 454 00:30:08,400 --> 00:30:13,760 Speaker 1: ease of testing is amazing. You just put plastic wand 455 00:30:13,920 --> 00:30:15,800 Speaker 1: with a sponge on the end of it into your 456 00:30:15,800 --> 00:30:18,840 Speaker 1: mouth between your cheek and gums for two minutes. Uh. 457 00:30:18,840 --> 00:30:20,720 Speaker 1: So it can be done in the office, it can 458 00:30:20,720 --> 00:30:23,000 Speaker 1: be done at all. That can be done in a 459 00:30:23,120 --> 00:30:26,840 Speaker 1: nurse's office, and then it can be mailed in using 460 00:30:26,840 --> 00:30:33,600 Speaker 1: the appropriate biohazard containers. So cost is is low. Um, 461 00:30:33,640 --> 00:30:37,040 Speaker 1: obviously we're a company where you know there will be 462 00:30:37,160 --> 00:30:42,520 Speaker 1: some markup, but certainly the cost is reasonable and you know, 463 00:30:42,600 --> 00:30:45,680 Speaker 1: we feel that people may want to know. Let's talk 464 00:30:45,720 --> 00:30:47,920 Speaker 1: a little bit about the work you did as med 465 00:30:48,000 --> 00:30:52,720 Speaker 1: director at QUEST. They're a big fortune. What sort of 466 00:30:52,720 --> 00:30:55,040 Speaker 1: work do they do and tell us a little bit 467 00:30:55,040 --> 00:30:58,920 Speaker 1: about your role there. Okay. I arrived at QUEST in 468 00:30:59,040 --> 00:31:01,800 Speaker 1: a year two thousand. It was, as you said, a 469 00:31:01,880 --> 00:31:06,479 Speaker 1: large commercial laboratory, actually the largest libortary in the United States, 470 00:31:06,440 --> 00:31:11,520 Speaker 1: and I believe ester was true, and they were just 471 00:31:11,720 --> 00:31:15,600 Speaker 1: beginning to do DNA tests. And when I got there 472 00:31:15,600 --> 00:31:19,640 Speaker 1: in two thousand, they were using technologies that you know, 473 00:31:19,760 --> 00:31:22,400 Speaker 1: I had been using at the University of Chicago that 474 00:31:22,440 --> 00:31:25,800 Speaker 1: we're really designed you know, to do ten or twenty 475 00:31:25,840 --> 00:31:28,080 Speaker 1: tests at the time. They were not designed to do 476 00:31:28,240 --> 00:31:31,160 Speaker 1: thousands of tests at the time. And so when I 477 00:31:31,200 --> 00:31:35,320 Speaker 1: got there, I made it my business to try to 478 00:31:35,360 --> 00:31:38,640 Speaker 1: find other ways of doing this test thing that would 479 00:31:38,680 --> 00:31:44,600 Speaker 1: be you know, one high throughput, two extremely high accuracy, 480 00:31:44,800 --> 00:31:49,760 Speaker 1: and three cost efficient. Because Quest Diagnostics was a business 481 00:31:50,160 --> 00:31:53,560 Speaker 1: and we were able to do that. We found initially 482 00:31:53,680 --> 00:31:58,280 Speaker 1: what was interesting is that we invented something called a 483 00:31:58,320 --> 00:32:02,440 Speaker 1: one thousand sample comparison and that before we would introduce 484 00:32:02,480 --> 00:32:05,640 Speaker 1: a new platform, we would look at a thousand samples 485 00:32:06,120 --> 00:32:09,760 Speaker 1: with the old technology and the new technology. Uh, if 486 00:32:09,800 --> 00:32:13,040 Speaker 1: there were any discrepancies, we would resolve that discrepancy with 487 00:32:13,080 --> 00:32:16,720 Speaker 1: a third technology to see what we were doing, which 488 00:32:16,720 --> 00:32:18,960 Speaker 1: would be the best platform and because a lot of 489 00:32:19,000 --> 00:32:21,760 Speaker 1: people were using a hundred samples. Well, what we found 490 00:32:21,880 --> 00:32:24,400 Speaker 1: is a lot of times for the hundred samples there 491 00:32:24,440 --> 00:32:27,360 Speaker 1: was complete agreement, but as you got to a thousand samples, 492 00:32:27,360 --> 00:32:31,320 Speaker 1: there would be three, four or five discrepancies between the 493 00:32:31,360 --> 00:32:35,000 Speaker 1: two platforms. No one had ever shown that before, and 494 00:32:35,040 --> 00:32:37,720 Speaker 1: we were able to show actually that the old technology 495 00:32:37,840 --> 00:32:40,680 Speaker 1: was not as good as the new technology. And so 496 00:32:40,800 --> 00:32:43,480 Speaker 1: with a lot of confidence and we published about this, 497 00:32:43,560 --> 00:32:46,040 Speaker 1: we were able to move from the older technologies to 498 00:32:46,080 --> 00:32:51,520 Speaker 1: the newer technologies. Then we were able to to start 499 00:32:51,680 --> 00:32:56,040 Speaker 1: really doing high through foot high quality testing, and then 500 00:32:56,080 --> 00:33:00,160 Speaker 1: we just started increasing our menu. Uh So, because a 501 00:33:00,200 --> 00:33:03,360 Speaker 1: lot of people when I was practicing genetics, a lot 502 00:33:03,400 --> 00:33:06,680 Speaker 1: of the frustration was that people couldn't get the genetic 503 00:33:06,720 --> 00:33:09,600 Speaker 1: tests that I wanted them to get because often these 504 00:33:09,640 --> 00:33:14,560 Speaker 1: tests were done in specialty laboratories, They were expensive laboratories 505 00:33:14,600 --> 00:33:17,920 Speaker 1: did not have a relationship with the insurance companies, and 506 00:33:18,040 --> 00:33:20,360 Speaker 1: so basically people had to either pay out of their 507 00:33:20,400 --> 00:33:24,240 Speaker 1: pocket or not have the tests. And it was very, very, 508 00:33:24,320 --> 00:33:26,440 Speaker 1: very frustrating. I remember there would be people who had 509 00:33:26,520 --> 00:33:29,400 Speaker 1: drive their portion into my office and I'd say, you know, 510 00:33:29,480 --> 00:33:32,760 Speaker 1: you really need to have cystic fibrosis carrier testing and 511 00:33:32,800 --> 00:33:36,800 Speaker 1: they say, does insurance coverment? And I'd say, well, let's 512 00:33:36,880 --> 00:33:40,760 Speaker 1: check and know your insurance doesn't cover it. And they say, well, 513 00:33:40,800 --> 00:33:42,880 Speaker 1: then I don't want to have it. And you know, 514 00:33:42,920 --> 00:33:46,400 Speaker 1: I felt like shaking them, saying, you know, get the tests. 515 00:33:47,080 --> 00:33:48,480 Speaker 1: You know. One of the reasons I went the Quest 516 00:33:48,520 --> 00:33:52,000 Speaker 1: Diagnostics because of the Quest Diagnostics had relationships with all 517 00:33:52,040 --> 00:33:55,960 Speaker 1: the major insurance companies, and so what I wanted to 518 00:33:56,000 --> 00:33:58,880 Speaker 1: do is make these tests available to the general public. 519 00:33:59,520 --> 00:34:02,200 Speaker 1: And I very proud that I was able to accomplish that. 520 00:34:02,440 --> 00:34:06,000 Speaker 1: And we moved to sequencing, and we moved to uh, 521 00:34:06,080 --> 00:34:09,560 Speaker 1: you know, all the major platforms, and uh. It was 522 00:34:09,600 --> 00:34:14,480 Speaker 1: a great experience. I I learned pathologists. You know, in general, 523 00:34:14,560 --> 00:34:18,000 Speaker 1: it's interesting there there have been wars between pathologists and 524 00:34:18,040 --> 00:34:23,160 Speaker 1: geneticist because pathologists feel that they own the rights to 525 00:34:23,280 --> 00:34:28,280 Speaker 1: all testing that's done on humans. Uh. Geneticists said, hey, 526 00:34:28,440 --> 00:34:31,240 Speaker 1: you guys don't know how to do the specialized things 527 00:34:31,280 --> 00:34:35,160 Speaker 1: that we do. And so every hospital had this kind 528 00:34:35,200 --> 00:34:39,480 Speaker 1: of give and take between who is going to do carriatypes, 529 00:34:39,560 --> 00:34:42,880 Speaker 1: kicking of chromosomes, who is going to do DNA testing? 530 00:34:43,000 --> 00:34:45,160 Speaker 1: Was it going to be the pathology department wasn't going 531 00:34:45,200 --> 00:34:48,120 Speaker 1: to be the genetics department. And when I got the 532 00:34:48,200 --> 00:34:53,560 Speaker 1: Quest Diagnostic, which is the pathology company, I learned from them. 533 00:34:53,600 --> 00:34:58,480 Speaker 1: I learned about quality assurance, quality control, how to what 534 00:34:58,680 --> 00:35:04,359 Speaker 1: you have to do to UH to do hundreds of 535 00:35:04,400 --> 00:35:07,880 Speaker 1: thousands of tests in an accurate way, and how you 536 00:35:08,000 --> 00:35:11,560 Speaker 1: need to have methods in place to make sure that 537 00:35:11,640 --> 00:35:15,080 Speaker 1: nothing has gone wrong. So for me, it was an 538 00:35:15,080 --> 00:35:17,719 Speaker 1: eye opening experience. And the last thing I learned was 539 00:35:17,760 --> 00:35:20,319 Speaker 1: that this is a business. How do you make a 540 00:35:20,360 --> 00:35:25,279 Speaker 1: business decision? How do you try to balance health of 541 00:35:25,400 --> 00:35:29,279 Speaker 1: the nation versus business? For example, what if I want 542 00:35:29,320 --> 00:35:33,200 Speaker 1: to do a test that you know won't make a profit, 543 00:35:33,640 --> 00:35:36,279 Speaker 1: but that could help people. How are we going to 544 00:35:36,400 --> 00:35:40,640 Speaker 1: make those decisions? Do we make those decisions those kinds 545 00:35:40,680 --> 00:35:45,520 Speaker 1: of very difficult situations? You know? I learned a lot. 546 00:35:45,960 --> 00:35:48,640 Speaker 1: Let's stick with the issue of of both the test 547 00:35:48,719 --> 00:35:54,000 Speaker 1: menu and the cost benefit analysis of these testings. I 548 00:35:54,120 --> 00:35:59,960 Speaker 1: have to imagine that cystic fibrosis is an expensive, complix 549 00:36:00,040 --> 00:36:04,760 Speaker 1: headed disease to test, Isn't it in the insurer's interest 550 00:36:05,000 --> 00:36:08,880 Speaker 1: to anyone who is indicated to test for this to 551 00:36:09,080 --> 00:36:13,000 Speaker 1: pay for that rather than you know, a later stage 552 00:36:13,120 --> 00:36:17,000 Speaker 1: treatment after it's going to be further developed, more complicated, 553 00:36:17,000 --> 00:36:20,680 Speaker 1: and more expensive to treat. But one of the great 554 00:36:20,719 --> 00:36:26,600 Speaker 1: ironies of modern medicine and healthcare is informatics. And I've 555 00:36:26,600 --> 00:36:32,080 Speaker 1: had discussions with with insurers about about aspects like this, 556 00:36:33,000 --> 00:36:37,440 Speaker 1: and some insurers will say, well, we know that people 557 00:36:37,560 --> 00:36:41,919 Speaker 1: change insurance companies every two and a half to three years, 558 00:36:42,000 --> 00:36:46,400 Speaker 1: so why should I do this task if it's going 559 00:36:46,440 --> 00:36:50,279 Speaker 1: to prevent a heart attack in a patient five or 560 00:36:50,280 --> 00:36:54,759 Speaker 1: ten years down the line, which is incredibly shortsighted, I 561 00:36:54,840 --> 00:36:57,319 Speaker 1: have to say, and not all insurance companies have this 562 00:36:57,440 --> 00:37:01,160 Speaker 1: kind of attitude. But I would also say that in 563 00:37:01,239 --> 00:37:04,160 Speaker 1: publicly traded companies, one of the things that I've seen 564 00:37:04,320 --> 00:37:08,560 Speaker 1: is they're pretty myopic. They're looking at the next quarterly 565 00:37:08,600 --> 00:37:13,520 Speaker 1: earnings report, they're looking at the stock price. UH, they're 566 00:37:13,600 --> 00:37:18,440 Speaker 1: not necessarily looking at the long term. And in this country, 567 00:37:18,480 --> 00:37:23,480 Speaker 1: insurance companies are for the most part profit. UH, they're 568 00:37:23,520 --> 00:37:29,319 Speaker 1: not nonprofit, and they have to deliver value to their shareholders, 569 00:37:29,360 --> 00:37:33,520 Speaker 1: and so sometimes they make short sighted decisions. In the 570 00:37:33,600 --> 00:37:37,080 Speaker 1: early days of DNA tests that the real problem was 571 00:37:37,120 --> 00:37:40,480 Speaker 1: that the insurance companies didn't have relationships with companies that 572 00:37:40,560 --> 00:37:44,400 Speaker 1: did it, and those UH tests were very expensive, so 573 00:37:44,440 --> 00:37:46,839 Speaker 1: it's easier for them to say this is research, we're 574 00:37:46,840 --> 00:37:49,839 Speaker 1: not going to cover it. In terms of system fibrosis, 575 00:37:49,880 --> 00:37:53,440 Speaker 1: the Emertant College of Etcentrics and Gynecology and the Emertant 576 00:37:53,440 --> 00:37:57,400 Speaker 1: College of Medical Genetics both came to a to a 577 00:37:57,480 --> 00:38:01,480 Speaker 1: recommendation that you know, every want at certain races should 578 00:38:01,560 --> 00:38:06,160 Speaker 1: be tested for assistic fibrosis carrier status when the woman 579 00:38:06,200 --> 00:38:10,200 Speaker 1: became pregnant, when we knew, and and we had been 580 00:38:10,200 --> 00:38:13,520 Speaker 1: given fair warning for that when I was a Quest Diagnostics, 581 00:38:13,520 --> 00:38:16,879 Speaker 1: so we knew that volume was going to increase, and 582 00:38:16,920 --> 00:38:19,640 Speaker 1: the business people in Quest Diagnostics knew that it would 583 00:38:19,680 --> 00:38:23,960 Speaker 1: become profitable because insurers would have a difficult time saying 584 00:38:23,960 --> 00:38:28,239 Speaker 1: its research if the professional societies had recommended it. So 585 00:38:28,400 --> 00:38:31,600 Speaker 1: that was kind of a no brainer decision. Some of 586 00:38:31,640 --> 00:38:34,040 Speaker 1: the other decisions that we had to make were not 587 00:38:34,160 --> 00:38:39,359 Speaker 1: so easy. Pardon my naivete and asking this, but if 588 00:38:39,400 --> 00:38:43,239 Speaker 1: people are changing insurers every two and a half three years, 589 00:38:44,360 --> 00:38:48,560 Speaker 1: then the flip side of we don't want to test 590 00:38:48,560 --> 00:38:51,600 Speaker 1: because this person is going to end up elsewhere is 591 00:38:51,680 --> 00:38:54,600 Speaker 1: what about the person who wasn't tested? Five years ago, 592 00:38:54,640 --> 00:38:58,680 Speaker 1: who shows up as you're insured and has that expensive 593 00:38:58,680 --> 00:39:02,879 Speaker 1: heart attack. Wouldn't you want a uniform approach across all 594 00:39:02,960 --> 00:39:08,080 Speaker 1: the insurers so that the preventative, less expensive treatment and 595 00:39:08,160 --> 00:39:12,640 Speaker 1: testing was taking place before. Yeah, this guy is leaving 596 00:39:12,719 --> 00:39:15,880 Speaker 1: your insurance company, but someone else who wasn't tested is 597 00:39:15,920 --> 00:39:18,680 Speaker 1: going to end up at your company. It seems like 598 00:39:19,320 --> 00:39:24,440 Speaker 1: the better approach would be to agree on a uniform 599 00:39:24,560 --> 00:39:32,759 Speaker 1: testing process. Verry, it's so logical, you don't think I 600 00:39:32,960 --> 00:39:37,279 Speaker 1: didn't scream that. But the problem is two things about that. Is, 601 00:39:37,320 --> 00:39:40,800 Speaker 1: first of all, if all the insurance companies are gonna 602 00:39:40,840 --> 00:39:44,920 Speaker 1: get together and decide that they're gonna do something like that, 603 00:39:44,920 --> 00:39:48,680 Speaker 1: that would probably be considered collusion. What if it comes 604 00:39:48,680 --> 00:39:52,200 Speaker 1: from the medical community, or the research community, or or 605 00:39:52,680 --> 00:39:56,360 Speaker 1: god forbid, actual legislation that says you should have to 606 00:39:56,400 --> 00:40:01,200 Speaker 1: pay for these sort of testing. Yeah. Well, interestingly enough, 607 00:40:01,520 --> 00:40:07,000 Speaker 1: just because the professional organization say that this is standard 608 00:40:07,080 --> 00:40:09,360 Speaker 1: of care and should be done, does not mean that 609 00:40:09,400 --> 00:40:14,520 Speaker 1: insurance companies will pay for it. Basically, insurance companies role 610 00:40:14,640 --> 00:40:17,480 Speaker 1: in life is to not pay for things. Our new 611 00:40:17,600 --> 00:40:21,279 Speaker 1: CEO of Quest Diagnostics you used to say, you know 612 00:40:21,320 --> 00:40:23,920 Speaker 1: what other business do you have where you give your 613 00:40:23,960 --> 00:40:26,920 Speaker 1: services a way for free and then you hope and 614 00:40:26,960 --> 00:40:29,279 Speaker 1: pray that you're going to get paid for it. And 615 00:40:29,360 --> 00:40:32,000 Speaker 1: that's what lab testing is all about. The test is 616 00:40:32,040 --> 00:40:34,880 Speaker 1: sent in, we send out the results, and then we 617 00:40:34,920 --> 00:40:37,520 Speaker 1: hope that insurance is going to reimburse us for those 618 00:40:38,160 --> 00:40:41,920 Speaker 1: It's not a great system. Um, you can be denied 619 00:40:42,120 --> 00:40:45,760 Speaker 1: for a whole bunch of reasons because the ordering physician 620 00:40:45,840 --> 00:40:50,720 Speaker 1: put the wrong diagnosis code. Even though a person needed 621 00:40:50,719 --> 00:40:55,600 Speaker 1: the test, the test was sent, the test was pre authorized, 622 00:40:56,280 --> 00:41:00,279 Speaker 1: and the test was performed, a result was given, and 623 00:41:00,320 --> 00:41:02,920 Speaker 1: then all of a sudden you're told you're not going 624 00:41:02,960 --> 00:41:05,839 Speaker 1: to get paid for this because the doctor coded this 625 00:41:05,880 --> 00:41:09,000 Speaker 1: as a routine office visit and not as a office 626 00:41:09,080 --> 00:41:13,759 Speaker 1: visit because there was a breast lump found. So you know, 627 00:41:13,920 --> 00:41:19,120 Speaker 1: there is a huge part of the industry which you 628 00:41:19,160 --> 00:41:21,920 Speaker 1: know basically has to take into account to fact that 629 00:41:22,000 --> 00:41:25,399 Speaker 1: you're not going to get paid for a certain percentage 630 00:41:25,440 --> 00:41:29,160 Speaker 1: of what you do, and they're actually when I was 631 00:41:29,239 --> 00:41:33,200 Speaker 1: a quest there were, um, there were people who are 632 00:41:33,239 --> 00:41:37,640 Speaker 1: trying to work on just improving the percentage because you know, 633 00:41:37,680 --> 00:41:40,120 Speaker 1: you didn't have to do any more testing if you 634 00:41:40,120 --> 00:41:44,279 Speaker 1: could improve your percentage of reimbursement, you know, from to 635 00:41:46,000 --> 00:41:49,600 Speaker 1: or whatever it was. And so you know, obviously in 636 00:41:49,600 --> 00:41:53,280 Speaker 1: a single payer system you don't have those kinds of issues. 637 00:41:53,320 --> 00:41:57,560 Speaker 1: You can make those decisions easily. Uh. And that's you know, 638 00:41:57,640 --> 00:42:01,560 Speaker 1: in in Canada, that's it's a much easier thing to do. 639 00:42:02,440 --> 00:42:05,560 Speaker 1: You can simply say the public health and system is 640 00:42:05,600 --> 00:42:07,840 Speaker 1: going to be paying for this testing, and then pretty 641 00:42:07,920 --> 00:42:11,759 Speaker 1: much everybody gets it covered and paid for. Here you 642 00:42:11,800 --> 00:42:14,200 Speaker 1: could say, yeah, I think we need that everybody should 643 00:42:14,200 --> 00:42:17,879 Speaker 1: be paid for this testing, but insurance companies don't have 644 00:42:17,960 --> 00:42:21,840 Speaker 1: to Listen, let's talk a little bit about the work 645 00:42:21,920 --> 00:42:25,319 Speaker 1: you're doing at the children's hospital. Tell me the sort 646 00:42:25,320 --> 00:42:27,919 Speaker 1: of patients you focus on and what do you try 647 00:42:27,960 --> 00:42:31,120 Speaker 1: and do for them? So very you know, I was 648 00:42:31,160 --> 00:42:34,440 Speaker 1: in semi retirement and I got a call from a 649 00:42:34,600 --> 00:42:38,760 Speaker 1: children's hospital Los Angeles saying, you know, we have such 650 00:42:38,840 --> 00:42:42,240 Speaker 1: a backlog of patients that need to see clinical geneticists, 651 00:42:42,520 --> 00:42:46,719 Speaker 1: and especially my subspecialty, which is biochemical genetics. Back from 652 00:42:46,719 --> 00:42:50,719 Speaker 1: the days with with William nine hand, you know, could 653 00:42:50,760 --> 00:42:55,239 Speaker 1: you please, you know, come work for us at least 654 00:42:55,320 --> 00:42:59,400 Speaker 1: part time. And this was actually right before the pandemic 655 00:42:59,840 --> 00:43:02,960 Speaker 1: and Dr Randolph, the chair in of the department, is 656 00:43:03,000 --> 00:43:07,400 Speaker 1: such a wonderful woman, you know that. I said, yes, um, 657 00:43:07,440 --> 00:43:09,960 Speaker 1: because you know, if somebody asked you to help out, 658 00:43:10,080 --> 00:43:13,480 Speaker 1: you help out. And I was really dreading it because 659 00:43:13,520 --> 00:43:15,120 Speaker 1: I was going to have to drive up to Los 660 00:43:15,120 --> 00:43:18,120 Speaker 1: Angeles and I live in the South Orange County. And 661 00:43:18,160 --> 00:43:21,160 Speaker 1: then COVID hit. And one of the interesting things that 662 00:43:21,520 --> 00:43:25,160 Speaker 1: happened with with the COVID epidemic, there's been you know, 663 00:43:25,560 --> 00:43:27,959 Speaker 1: can you say, have there been any positive things? Well, 664 00:43:28,160 --> 00:43:30,279 Speaker 1: one of the positive things that's actually we now have 665 00:43:30,520 --> 00:43:35,440 Speaker 1: mRNA vaccines, where before COVID they asked me it was 666 00:43:35,520 --> 00:43:37,560 Speaker 1: it was going to be five to seven years before 667 00:43:37,560 --> 00:43:41,160 Speaker 1: we had mRNA vaccines. But the other thing interesting thing 668 00:43:41,160 --> 00:43:47,800 Speaker 1: that's happened is that telemedicine has become reimbursable two reasonable levels. 669 00:43:47,840 --> 00:43:50,319 Speaker 1: So when COVID hit, I said, you know, I'm of 670 00:43:50,360 --> 00:43:54,560 Speaker 1: an age I don't really feel comfortable driving up and uh, 671 00:43:54,719 --> 00:43:57,200 Speaker 1: you know, working in a hospital. And they said, wow, 672 00:43:57,239 --> 00:44:01,320 Speaker 1: would you see patients, uh remotely by tele medicine? And 673 00:44:01,600 --> 00:44:06,919 Speaker 1: I said sure, And it's surprisingly good. You know, yes, 674 00:44:07,160 --> 00:44:11,640 Speaker 1: I cannot touch patients, but I can see patients. And 675 00:44:11,800 --> 00:44:18,160 Speaker 1: I've been seeing patients UM in clinical genetics that very tremendously. 676 00:44:18,640 --> 00:44:22,800 Speaker 1: Most states have what's called newborn screening. Newborn screening is 677 00:44:22,840 --> 00:44:29,040 Speaker 1: one of the most amazing phenomenon for disease identification and 678 00:44:29,120 --> 00:44:32,200 Speaker 1: early treatment that nobody knows about. It's the heel stick 679 00:44:32,280 --> 00:44:36,160 Speaker 1: that all your children, grandchildren and great grandchildren have when 680 00:44:36,160 --> 00:44:40,880 Speaker 1: they're born. And this is analyzed in California for about 681 00:44:40,920 --> 00:44:43,960 Speaker 1: fifty different what we call inborn errors in the tables, 682 00:44:44,719 --> 00:44:48,680 Speaker 1: and so these children are identified, and these children need 683 00:44:48,800 --> 00:44:53,040 Speaker 1: to be cared for by physicians who know how to 684 00:44:53,120 --> 00:44:57,880 Speaker 1: care for these children with these extremely rare genetic diseases. 685 00:44:58,480 --> 00:45:01,799 Speaker 1: But it's been phenomenal. For example, there's a disease called 686 00:45:01,840 --> 00:45:06,680 Speaker 1: who terrag aciduria type one where every patient I ever 687 00:45:06,719 --> 00:45:10,879 Speaker 1: saw back when I was working with Dr Nihan was horribly, 688 00:45:11,080 --> 00:45:16,880 Speaker 1: horribly brain damage. Uh. These kids were almost in vegetative states. 689 00:45:17,600 --> 00:45:20,520 Speaker 1: I now have a child in my practice who is 690 00:45:20,600 --> 00:45:25,120 Speaker 1: identified by newborn screening, who is placed on a specialized diet, 691 00:45:25,760 --> 00:45:29,600 Speaker 1: is now three years old and completely normal. Every time 692 00:45:29,640 --> 00:45:34,279 Speaker 1: I see this kid, I want to scream, how wonderful. Uh. 693 00:45:34,400 --> 00:45:38,480 Speaker 1: Newborn screening is started out with general keaton nuria. Again, 694 00:45:38,600 --> 00:45:43,080 Speaker 1: these are children who would have been horribly horribly mentally deficient, 695 00:45:43,160 --> 00:45:47,160 Speaker 1: deficient who are put on specialized diets and they're normal. 696 00:45:48,120 --> 00:45:50,759 Speaker 1: So these are the kind of kids I see. I 697 00:45:50,840 --> 00:45:55,319 Speaker 1: also see children who have autism, children who have other 698 00:45:55,480 --> 00:45:59,400 Speaker 1: forms of birth defects. You know, now we can get 699 00:46:00,160 --> 00:46:05,319 Speaker 1: specialized DNA sequencing tests for these children UH to identify 700 00:46:05,440 --> 00:46:10,080 Speaker 1: their disorders and perhaps treat Now we have these been 701 00:46:10,160 --> 00:46:14,759 Speaker 1: called the whole x own sequence, which allows where the 702 00:46:14,840 --> 00:46:19,000 Speaker 1: laboratory basically looks at every gene UH known in the 703 00:46:19,120 --> 00:46:23,400 Speaker 1: body and compares that with both parents to see about 704 00:46:23,400 --> 00:46:25,880 Speaker 1: whether or not a child has a disease. Well, I 705 00:46:25,920 --> 00:46:30,320 Speaker 1: did a test like that on a child that was hypotonic, 706 00:46:30,400 --> 00:46:35,400 Speaker 1: who couldn't walk, he was eighteen months old, had spastic movements, 707 00:46:35,400 --> 00:46:38,400 Speaker 1: had been diagnosed with cerebral palsy. I did that test. 708 00:46:38,440 --> 00:46:42,760 Speaker 1: It turned out he had a treatable inborn error metabolism 709 00:46:43,040 --> 00:46:46,880 Speaker 1: called congenital disorder of like constellation, and we started to 710 00:46:46,920 --> 00:46:50,840 Speaker 1: treat him and he's getting better. So you know, it's 711 00:46:50,880 --> 00:46:53,800 Speaker 1: these kinds of things were used to be very very rare. 712 00:46:54,239 --> 00:46:58,000 Speaker 1: It's almost like a revival meeting are becoming you know, 713 00:46:58,200 --> 00:47:01,400 Speaker 1: pretty common. But in order to do that, you have 714 00:47:01,600 --> 00:47:04,400 Speaker 1: to be able to get the testing done. And that's 715 00:47:04,400 --> 00:47:08,879 Speaker 1: the great frustration you had mentioned previously that the insurers 716 00:47:09,040 --> 00:47:13,200 Speaker 1: are sometimes none too keen about paying for some of 717 00:47:13,200 --> 00:47:17,239 Speaker 1: these um screening tests or preliminary tests. The heel stick 718 00:47:17,360 --> 00:47:20,399 Speaker 1: is that best practice was that mandated by law? How 719 00:47:20,400 --> 00:47:22,960 Speaker 1: did that come about? And and what sort of headaches 720 00:47:23,000 --> 00:47:25,800 Speaker 1: do you run into when you want to test in 721 00:47:25,920 --> 00:47:30,839 Speaker 1: The insurers says, um, we're not interested. Yeah, well, there's 722 00:47:31,120 --> 00:47:33,400 Speaker 1: there are two questions in there. The first is newborn 723 00:47:33,520 --> 00:47:38,400 Speaker 1: screening is legislatively mandated in all fifty days, and the 724 00:47:38,440 --> 00:47:42,319 Speaker 1: beauty of the legislative mandate is that the follow up 725 00:47:42,520 --> 00:47:46,520 Speaker 1: is covered. So these children who you know, test posit 726 00:47:46,600 --> 00:47:50,080 Speaker 1: for newborn screening, their treatments are covered. Any follow up 727 00:47:50,120 --> 00:47:53,960 Speaker 1: genetic testing is covered. So that's at least in California, 728 00:47:54,000 --> 00:47:56,879 Speaker 1: is a great system. When people that kids that fall 729 00:47:56,960 --> 00:47:59,120 Speaker 1: through the cracks are the kids that don't have one 730 00:47:59,120 --> 00:48:01,520 Speaker 1: of the diseases that is screened for in the newborn 731 00:48:01,560 --> 00:48:06,040 Speaker 1: screening program. And these are children who for example, Medical 732 00:48:06,280 --> 00:48:12,160 Speaker 1: which is the state sponsored health insurance, doesn't cover whole 733 00:48:12,200 --> 00:48:16,680 Speaker 1: excellent sequence. So the kids who are covered by medical 734 00:48:17,480 --> 00:48:23,160 Speaker 1: can't have the task which might identify a treatable cause 735 00:48:23,400 --> 00:48:27,719 Speaker 1: of their illness, and that is extraordinarily frustrating. And sometimes 736 00:48:27,760 --> 00:48:31,080 Speaker 1: even private insurance will say, you know, I don't want 737 00:48:31,080 --> 00:48:33,399 Speaker 1: to cover this test, even though you know, I want 738 00:48:33,440 --> 00:48:36,560 Speaker 1: to scream at them, this kid needs this test. And 739 00:48:36,640 --> 00:48:40,120 Speaker 1: that's the greatest frustration in medicine right now, at least 740 00:48:40,160 --> 00:48:44,480 Speaker 1: in genetics, uh, for me, is not being able to 741 00:48:44,520 --> 00:48:48,120 Speaker 1: get the tests I need for my patients. Um. And 742 00:48:48,200 --> 00:48:53,200 Speaker 1: again that's because there's no uniformity and insurance coverage for 743 00:48:53,360 --> 00:48:56,880 Speaker 1: these sorts of genetic testing. I can see the you know, 744 00:48:57,040 --> 00:48:59,600 Speaker 1: the position of the insurance companies are these are expensive 745 00:48:59,640 --> 00:49:03,680 Speaker 1: tests and um, you know again error they want to 746 00:49:03,680 --> 00:49:06,960 Speaker 1: be profitable, and if they their fear is that if 747 00:49:07,000 --> 00:49:10,600 Speaker 1: they start having to pay for these very expensive tests, 748 00:49:10,719 --> 00:49:12,839 Speaker 1: that's going to eat into their profits. So I mean, 749 00:49:13,000 --> 00:49:16,600 Speaker 1: I do understand it, but it is extremely frustrating for 750 00:49:16,600 --> 00:49:20,879 Speaker 1: our practitioner. I can imagine and you sort of see 751 00:49:20,920 --> 00:49:26,480 Speaker 1: the medical industry, both the practice and the commercialization from 752 00:49:26,680 --> 00:49:31,000 Speaker 1: both ends of the business, both as a doctor who 753 00:49:31,120 --> 00:49:35,760 Speaker 1: is a practitioner and someone who's working in what's essentially 754 00:49:35,800 --> 00:49:39,680 Speaker 1: a biotech start up looking at it from the complete 755 00:49:39,680 --> 00:49:43,160 Speaker 1: opposite end. How do we get this through the CDC, 756 00:49:43,440 --> 00:49:45,480 Speaker 1: through an I H, through f DA, How do we 757 00:49:45,520 --> 00:49:48,080 Speaker 1: get this approved? How do we get insurance to start 758 00:49:48,080 --> 00:49:50,200 Speaker 1: paying for this? How do we get practitioners to start 759 00:49:50,320 --> 00:49:54,160 Speaker 1: using it? How does that sort of unique perspective of 760 00:49:54,200 --> 00:49:58,200 Speaker 1: seeing both ends of the elephant affect how you view 761 00:49:58,239 --> 00:50:02,799 Speaker 1: the practice of medicine and the United States. Wow, what 762 00:50:02,880 --> 00:50:06,520 Speaker 1: a question. We could probably talk for an hour about that, 763 00:50:06,719 --> 00:50:10,160 Speaker 1: I think. Um, you know, the short answer is it 764 00:50:10,280 --> 00:50:17,000 Speaker 1: has become amazingly complicated to introduce anything new in medicine. 765 00:50:17,800 --> 00:50:21,759 Speaker 1: So back in the day, somebody would find something, they 766 00:50:21,800 --> 00:50:25,840 Speaker 1: would publish it, and then if it was good, it 767 00:50:25,880 --> 00:50:29,440 Speaker 1: would be reproduced, and then everybody would do it, and 768 00:50:29,800 --> 00:50:35,160 Speaker 1: medicine progressed that way. Nowadays it's completely different. So you 769 00:50:35,280 --> 00:50:39,920 Speaker 1: make a discovery, you talk to the Technology Transfer office 770 00:50:39,960 --> 00:50:45,640 Speaker 1: at your university, they patented, uh. Then you spin off 771 00:50:45,880 --> 00:50:50,000 Speaker 1: a biotech company. Then you have to get venture capital 772 00:50:50,040 --> 00:50:53,759 Speaker 1: funding for your biotech funding company. Then you shop it 773 00:50:53,840 --> 00:50:57,200 Speaker 1: around and then nobody trust what you're doing because you're 774 00:50:57,200 --> 00:51:00,280 Speaker 1: a private company. And then you have to get people 775 00:51:00,320 --> 00:51:05,960 Speaker 1: to will interact with the governmental players, people who will 776 00:51:06,040 --> 00:51:09,880 Speaker 1: interact with private payers. People who will work on the 777 00:51:09,960 --> 00:51:15,480 Speaker 1: CPT codes. It is amazing complex process. I have a talk, 778 00:51:15,560 --> 00:51:18,200 Speaker 1: actually a power point that I can and I talk 779 00:51:18,280 --> 00:51:22,759 Speaker 1: about somebody who invents the best test. I call it TBT. 780 00:51:23,520 --> 00:51:27,200 Speaker 1: So somebody invents a test that can use your blood 781 00:51:27,440 --> 00:51:31,719 Speaker 1: and decide with sensitivity and specificity whether or not you've 782 00:51:31,719 --> 00:51:36,160 Speaker 1: got prostate cancer, for example, and I I lead the 783 00:51:36,239 --> 00:51:40,080 Speaker 1: people through the person who invents that test to the 784 00:51:40,160 --> 00:51:43,319 Speaker 1: point where a quest diagnostics is no, no, thank you, 785 00:51:43,360 --> 00:51:47,800 Speaker 1: we don't want this test. And it is perfectly plausible. 786 00:51:48,239 --> 00:51:52,160 Speaker 1: And that's because the whether or not a test will 787 00:51:52,200 --> 00:51:58,759 Speaker 1: be profitable depends on so many different interchangeable parts, and 788 00:51:58,880 --> 00:52:02,600 Speaker 1: if the parts don't all fit together correctly, it won't 789 00:52:02,640 --> 00:52:07,440 Speaker 1: be a profitable test. So that's the way the industry 790 00:52:07,680 --> 00:52:12,560 Speaker 1: is now. It's frustrating as a to be a physician 791 00:52:13,160 --> 00:52:17,000 Speaker 1: in the system can get extremely frustrating because of course 792 00:52:17,000 --> 00:52:20,680 Speaker 1: we feel we know everything so that if I say, 793 00:52:20,840 --> 00:52:23,719 Speaker 1: if I say it must be so, it must be so. 794 00:52:24,320 --> 00:52:28,000 Speaker 1: But seriously, uh it can. It can be extremely frustrating. 795 00:52:28,040 --> 00:52:30,560 Speaker 1: And my problem is that I have started a company. 796 00:52:31,120 --> 00:52:34,279 Speaker 1: I believe I have a game changing technology, but the 797 00:52:34,400 --> 00:52:38,840 Speaker 1: chances of it actually changing the game are pretty small, 798 00:52:39,600 --> 00:52:42,680 Speaker 1: and one of the problems that my company has is, 799 00:52:42,800 --> 00:52:46,160 Speaker 1: you know, we're underfunded. I don't have the ability to 800 00:52:46,239 --> 00:52:48,640 Speaker 1: go out and hire a marketer, or to hire a 801 00:52:48,640 --> 00:52:52,239 Speaker 1: sale at salesforce, to hire people, uh, to deal with 802 00:52:52,320 --> 00:52:55,759 Speaker 1: insurance companies. So I felt if I built a better 803 00:52:55,840 --> 00:52:58,439 Speaker 1: mouse trap, that the world would come to my door. 804 00:52:58,560 --> 00:53:01,960 Speaker 1: But that has not happened. And so now you know, 805 00:53:02,040 --> 00:53:04,279 Speaker 1: I'm sitting trying to figure out what we're going to 806 00:53:04,400 --> 00:53:07,760 Speaker 1: do with this technology. You know, I know it's good, 807 00:53:07,920 --> 00:53:10,160 Speaker 1: I know it works. You know, I just need to 808 00:53:10,200 --> 00:53:12,640 Speaker 1: figure out how to do it from a business standpoint. 809 00:53:12,760 --> 00:53:17,000 Speaker 1: So that's been my frustration. So we've seen over the 810 00:53:17,080 --> 00:53:21,759 Speaker 1: years a lot of large either pharma or diagnostic companies 811 00:53:22,320 --> 00:53:26,400 Speaker 1: go through a series of acquisitions and roll ups and mergers. 812 00:53:26,880 --> 00:53:31,000 Speaker 1: It seems like scale is something that's really significant in 813 00:53:31,040 --> 00:53:35,280 Speaker 1: this space. Is that just a function of how unique 814 00:53:35,320 --> 00:53:39,120 Speaker 1: and somewhat backwards the U S system is. Between the 815 00:53:39,200 --> 00:53:43,520 Speaker 1: hospitals and the insurers and the practitioners, everybody seems to 816 00:53:43,520 --> 00:53:47,680 Speaker 1: be operating at a cross purpose, to say nothing of 817 00:53:47,719 --> 00:53:51,400 Speaker 1: the patient and the outcome of their visits. Is this 818 00:53:51,480 --> 00:53:55,160 Speaker 1: a uniquely American problem? Or do we see other issues 819 00:53:55,200 --> 00:54:00,560 Speaker 1: like this elsewhere. What happens here is evolutionary and the 820 00:54:00,600 --> 00:54:04,400 Speaker 1: way we evolved, of course, you know, the way evolution 821 00:54:04,440 --> 00:54:08,440 Speaker 1: occurs as with natural selection. So we're in a completely 822 00:54:08,520 --> 00:54:11,960 Speaker 1: capitalist system here in the United States. And the way 823 00:54:12,000 --> 00:54:16,279 Speaker 1: the laboratory industry evolved is it started out with I 824 00:54:16,320 --> 00:54:19,000 Speaker 1: guess you'd call a mom and pop started out that 825 00:54:19,080 --> 00:54:23,680 Speaker 1: every hospital had a laboratory. That laboratory was run by 826 00:54:23,800 --> 00:54:27,680 Speaker 1: the local pathologists. They drove the fanciest cars I can 827 00:54:27,719 --> 00:54:30,600 Speaker 1: tell you. You You know, they were charging two and three 828 00:54:31,440 --> 00:54:34,200 Speaker 1: for tests that cost them two or three dollars to run, 829 00:54:35,200 --> 00:54:39,520 Speaker 1: and they were happy. The insurance industry didn't know any better. 830 00:54:39,640 --> 00:54:45,240 Speaker 1: They were reasonably happy, and then a revolution occurred. Revolution 831 00:54:45,280 --> 00:54:49,839 Speaker 1: occurred firstly with a laboratory called net path that decided 832 00:54:49,920 --> 00:54:52,799 Speaker 1: that they were going to be a commercial laboratory. They 833 00:54:52,840 --> 00:54:56,080 Speaker 1: were going to compete with the mom and pop local pathologists, 834 00:54:56,520 --> 00:55:01,000 Speaker 1: and so they started buying up laboratories. Then Corning, who 835 00:55:01,160 --> 00:55:05,640 Speaker 1: was making Corning wear but also fiber Optics, was also 836 00:55:05,760 --> 00:55:10,759 Speaker 1: making laboratory flasks in pirates. They were making graduated cylinders, 837 00:55:10,760 --> 00:55:14,080 Speaker 1: they're making flasks. So they decided that they were going 838 00:55:14,200 --> 00:55:19,560 Speaker 1: to diversify and get into the laboratory industry, and they 839 00:55:19,600 --> 00:55:22,759 Speaker 1: spun off and they started with Corning Clinical Labs and 840 00:55:22,760 --> 00:55:27,200 Speaker 1: then they spun it off as Quest Diagnostics. Quest Diagnostics 841 00:55:27,200 --> 00:55:31,680 Speaker 1: with their original CEO, who was a visionary, decided that 842 00:55:31,719 --> 00:55:34,920 Speaker 1: he was going to consolid try to consolidate the laboratory 843 00:55:34,640 --> 00:55:37,960 Speaker 1: and industry, so he bought met Death, he bought other 844 00:55:38,080 --> 00:55:42,680 Speaker 1: laboratories UH and basically got to a point where they 845 00:55:42,680 --> 00:55:46,960 Speaker 1: were close to eight percent of the total laboratory market share. 846 00:55:47,040 --> 00:55:50,319 Speaker 1: But it's still a very fragmented market. You have the 847 00:55:50,480 --> 00:55:55,400 Speaker 1: huge players, the lab corps, the Quest bioreference people, but 848 00:55:55,680 --> 00:55:59,680 Speaker 1: still the majority of laboratory testing is done by in 849 00:55:59,760 --> 00:56:03,279 Speaker 1: the dual hospitals. So then how did individual hospitals No 850 00:56:03,360 --> 00:56:05,800 Speaker 1: longer could they's charge two hun or fifty dollars for 851 00:56:05,960 --> 00:56:09,200 Speaker 1: tests that they uh then only took them four hours 852 00:56:09,239 --> 00:56:11,680 Speaker 1: to make. So they had to come down with pricing. 853 00:56:12,120 --> 00:56:17,200 Speaker 1: And so now hospitals are working with among themselves. So 854 00:56:17,280 --> 00:56:21,440 Speaker 1: now you have hospital chains buying up other hospitals running 855 00:56:21,440 --> 00:56:25,239 Speaker 1: the laboratories from the central laboratory. So you have that 856 00:56:25,360 --> 00:56:29,960 Speaker 1: going on, and then insurance companies love that because now 857 00:56:30,000 --> 00:56:33,600 Speaker 1: there's competition. So they can say, well, I can get 858 00:56:33,640 --> 00:56:36,640 Speaker 1: this from Quest Diagnostic, or should I tell you this? 859 00:56:37,440 --> 00:56:40,640 Speaker 1: And then you all know the story about United Healthcare, 860 00:56:40,719 --> 00:56:43,279 Speaker 1: and they went from quests the lab corps and now 861 00:56:43,320 --> 00:56:46,440 Speaker 1: they're in both. But insurance companies began to wield an 862 00:56:46,440 --> 00:56:50,760 Speaker 1: increasing amount of power over healthcare and they still wield 863 00:56:50,840 --> 00:56:54,279 Speaker 1: that amazing kind of power because in many ways, your 864 00:56:54,320 --> 00:56:58,880 Speaker 1: insurance company decides what tests your doctor can order and 865 00:56:59,040 --> 00:57:02,680 Speaker 1: from what laboratory. Right in the early days of when 866 00:57:02,719 --> 00:57:06,279 Speaker 1: pap smears went to something called thin prep, you know, 867 00:57:06,320 --> 00:57:10,680 Speaker 1: there was no question that the thin prep was better 868 00:57:11,200 --> 00:57:15,319 Speaker 1: in terms of of what it could do. But in 869 00:57:15,560 --> 00:57:18,360 Speaker 1: when patients would come to our clinic, there was a 870 00:57:18,400 --> 00:57:21,280 Speaker 1: big boltin board saying if the patient had this insurance, 871 00:57:21,320 --> 00:57:24,000 Speaker 1: they could get thin Prep. But the patient had that insurance, 872 00:57:24,000 --> 00:57:27,520 Speaker 1: they could only get a regular PAP smear. So what 873 00:57:27,560 --> 00:57:31,960 Speaker 1: people don't understand is that their insurance companies in many 874 00:57:32,000 --> 00:57:35,680 Speaker 1: ways are determining what they you know, what kind of 875 00:57:35,720 --> 00:57:38,480 Speaker 1: testing they can have, what kind of medical care they 876 00:57:38,480 --> 00:57:41,240 Speaker 1: will get. And most people don't pay any attention to that. 877 00:57:41,520 --> 00:57:44,960 Speaker 1: They don't pay any attention to whether or not holds 878 00:57:45,080 --> 00:57:48,320 Speaker 1: excellent coverage. Is you know, is covered by their insurance 879 00:57:48,920 --> 00:57:52,000 Speaker 1: until they have a child that has autism, or until 880 00:57:52,040 --> 00:57:54,520 Speaker 1: they have a child that, uh, you know, that has 881 00:57:54,600 --> 00:57:57,520 Speaker 1: developmental delay, and now all of a sudden, their geneticists 882 00:57:57,560 --> 00:58:01,400 Speaker 1: wants to order that test and their insurance company doesn't come. 883 00:58:02,080 --> 00:58:04,640 Speaker 1: But one of the problems is that you would really 884 00:58:04,640 --> 00:58:09,800 Speaker 1: want an informed consumer, But in healthcare are consumers are 885 00:58:09,800 --> 00:58:12,880 Speaker 1: not informed? You know, you look at these things when 886 00:58:12,880 --> 00:58:16,400 Speaker 1: there's open enrollment, and mostly everybody's looking at what the 887 00:58:16,480 --> 00:58:19,840 Speaker 1: code pay is, what this is, what that is. And 888 00:58:19,920 --> 00:58:24,240 Speaker 1: it's not reasonable for people to understand whether or not, 889 00:58:24,360 --> 00:58:26,720 Speaker 1: you know, they can have a cardiac authorization, or whether 890 00:58:26,800 --> 00:58:29,640 Speaker 1: or not they can have a treadmill for certain indications, 891 00:58:29,680 --> 00:58:31,800 Speaker 1: you know, because you don't know what the future is 892 00:58:31,840 --> 00:58:35,960 Speaker 1: going to hold. So the paradigm of, you know, an 893 00:58:35,960 --> 00:58:42,400 Speaker 1: informed consumer in a capitalist system with free enterprise, I 894 00:58:42,440 --> 00:58:48,440 Speaker 1: think doesn't work very well for healthcare. But the centralized systems, 895 00:58:48,480 --> 00:58:50,919 Speaker 1: you know, are not that great in some places too. 896 00:58:50,960 --> 00:58:53,680 Speaker 1: I mean, everybody points to Canada as being the best 897 00:58:54,400 --> 00:58:58,120 Speaker 1: a good example of a single party pair, but I 898 00:58:58,200 --> 00:59:01,760 Speaker 1: know a physician in Canada and he needed his wisdom 899 00:59:01,800 --> 00:59:04,520 Speaker 1: teeth out, and he had waited two and a half 900 00:59:04,600 --> 00:59:07,400 Speaker 1: years to have his wisdom team, and a lot of 901 00:59:07,400 --> 00:59:11,200 Speaker 1: people in Canada actually drive over to Buffalo to have 902 00:59:11,320 --> 00:59:14,480 Speaker 1: CT scans because you know, the whole city of Toronto 903 00:59:14,600 --> 00:59:17,880 Speaker 1: has two CT scanners or something and something. You know 904 00:59:18,120 --> 00:59:22,680 Speaker 1: there there's a limited number of you know, CT scanners 905 00:59:22,760 --> 00:59:26,000 Speaker 1: for population, and so there's a long waiting list for 906 00:59:26,040 --> 00:59:29,040 Speaker 1: those kinds of things. So it's not like single party 907 00:59:29,080 --> 00:59:32,240 Speaker 1: payer is the panacea. Then if you ask, how are 908 00:59:32,280 --> 00:59:34,840 Speaker 1: you going to fix the system as it currently exists, 909 00:59:34,880 --> 00:59:37,800 Speaker 1: it's a nightmare and I have no idea of how 910 00:59:37,840 --> 00:59:40,760 Speaker 1: I would fix it. So I know I only have 911 00:59:40,880 --> 00:59:46,240 Speaker 1: you for a couple more minutes. Let's jump from our 912 00:59:46,280 --> 00:59:50,600 Speaker 1: medical discussion to our favorite questions that we ask all 913 00:59:50,640 --> 00:59:53,480 Speaker 1: of our guests. I wanted to start with something like, 914 00:59:53,760 --> 00:59:57,000 Speaker 1: tell us what you've been streaming over the past couple 915 00:59:57,080 --> 01:00:00,760 Speaker 1: of years. What what has kept you entertained during the 916 01:00:00,760 --> 01:00:05,600 Speaker 1: pandemic lockdown? Well, yeah, I stream a lot. I guess 917 01:00:05,600 --> 01:00:09,240 Speaker 1: I can also combine, and so I during the pandemic 918 01:00:09,480 --> 01:00:15,880 Speaker 1: started reading Michael connelly novels Ronymous Bos The Detective. He's 919 01:00:15,920 --> 01:00:19,800 Speaker 1: written over twenty novels, and of course then I streamed 920 01:00:19,800 --> 01:00:23,040 Speaker 1: a Lincoln lawyer which is also from Michael Connolly, and 921 01:00:23,200 --> 01:00:26,560 Speaker 1: I will be streaming The Bush Legacy right now. I'm 922 01:00:26,560 --> 01:00:30,040 Speaker 1: watching Judy Queen of Jerusalem, which is an incredibly interesting 923 01:00:30,120 --> 01:00:36,680 Speaker 1: Israeli film about the early days of in Jerusalem. Watching 924 01:00:36,760 --> 01:00:40,640 Speaker 1: gas Lit with Martha Martha Mitchell Uh and a real 925 01:00:40,720 --> 01:00:43,240 Speaker 1: cool one is Servant of the People. I don't know 926 01:00:43,280 --> 01:00:47,480 Speaker 1: if you've been seeing that. Barry Uh, that's uh the 927 01:00:47,480 --> 01:00:51,520 Speaker 1: President of Ukraine, his original comedy show. Do you know 928 01:00:51,560 --> 01:00:53,800 Speaker 1: about that? You know, I've I've heard all about it, 929 01:00:53,800 --> 01:01:00,200 Speaker 1: and it's supposed to be tremendous, um fantastic. Yeah, I mean, 930 01:01:00,360 --> 01:01:02,960 Speaker 1: and first of all, it's you talk about art imitating 931 01:01:03,000 --> 01:01:06,400 Speaker 1: life and life imitating art. I mean, you know, he's 932 01:01:06,440 --> 01:01:09,760 Speaker 1: President of Ukraine, and you know, the whole the whole 933 01:01:09,840 --> 01:01:13,240 Speaker 1: TV series is based on, you know, him going off 934 01:01:13,240 --> 01:01:15,840 Speaker 1: on a rant about the corruption and government and getting 935 01:01:15,840 --> 01:01:20,280 Speaker 1: elected to be President of Ukraine. I would highly recommend that. 936 01:01:20,280 --> 01:01:22,520 Speaker 1: That's on my let's on my list. Let's talk a 937 01:01:22,600 --> 01:01:26,040 Speaker 1: little bit about some of your mentors we mentioned them earlier. 938 01:01:26,200 --> 01:01:30,040 Speaker 1: Tell us who helped to shape your career. You know, 939 01:01:30,200 --> 01:01:34,160 Speaker 1: I was very lucky that whenever I needed somebody, they 940 01:01:34,200 --> 01:01:37,000 Speaker 1: were there. The first one was, of course Albert Dorfman, 941 01:01:37,000 --> 01:01:40,720 Speaker 1: who was my doctor advisor. He was an m D, PhD. 942 01:01:40,840 --> 01:01:46,000 Speaker 1: He worked on on inborn eraism, metabolism, and he taught 943 01:01:46,040 --> 01:01:49,800 Speaker 1: me one very important thing. He called me in one 944 01:01:49,880 --> 01:01:53,280 Speaker 1: day and he said, you know, when I designed an 945 01:01:53,320 --> 01:01:57,800 Speaker 1: experiment and I think I know what the results should be, 946 01:01:58,440 --> 01:02:03,360 Speaker 1: and I get that result, I don't trust it. You know, 947 01:02:03,400 --> 01:02:07,600 Speaker 1: what he was basically saying is that that science and 948 01:02:07,680 --> 01:02:13,760 Speaker 1: discovery is about what you're not expecting. Just as Alexander 949 01:02:13,840 --> 01:02:20,160 Speaker 1: Fleming discovered penicillan not looking for penicillin. He discovered penicillin 950 01:02:20,520 --> 01:02:24,840 Speaker 1: because of an accident that mold started growing on his plates. 951 01:02:24,960 --> 01:02:29,040 Speaker 1: And we've lost that in science. I'm afraid we've lost that. 952 01:02:29,520 --> 01:02:33,280 Speaker 1: You know. Right now science has managed it as a business. 953 01:02:33,600 --> 01:02:37,000 Speaker 1: You know, we're gonna make a vaccine. We're gonna do 954 01:02:37,080 --> 01:02:39,320 Speaker 1: this step, this step, this steps, this step, and make 955 01:02:39,360 --> 01:02:42,960 Speaker 1: a vaccine. No one is saying, let's look at how 956 01:02:43,000 --> 01:02:45,880 Speaker 1: he anybodies are formed. Let's look at what's going on 957 01:02:46,040 --> 01:02:49,920 Speaker 1: and see if there's anything anomalous, something that we don't understand. 958 01:02:50,520 --> 01:02:54,080 Speaker 1: My next mentor was Dr Sam Specter. Dr Sam Specter, 959 01:02:54,280 --> 01:02:56,600 Speaker 1: I guess you could call you know, was one of 960 01:02:56,600 --> 01:03:00,320 Speaker 1: the fathers of modern pediatrics. He worked with Benjamin's Bock 961 01:03:00,440 --> 01:03:05,320 Speaker 1: writing h writing the famous book on childcare um and 962 01:03:05,400 --> 01:03:08,680 Speaker 1: I was fortunate enough to have him as a professor 963 01:03:08,760 --> 01:03:11,760 Speaker 1: at the University of Chicago Medical School, and then he 964 01:03:11,840 --> 01:03:15,160 Speaker 1: moved to University of California, San Diego. So when I 965 01:03:15,200 --> 01:03:17,640 Speaker 1: went to do my residency, he was there for me too. 966 01:03:18,400 --> 01:03:21,880 Speaker 1: And what he taught me is that the best way 967 01:03:21,880 --> 01:03:25,600 Speaker 1: to be a pediatrician is to be with the child. 968 01:03:26,320 --> 01:03:29,720 Speaker 1: He said, I want you to go hold babies. You know, 969 01:03:29,800 --> 01:03:32,400 Speaker 1: if you if you're not an older brother and older sister, 970 01:03:32,560 --> 01:03:35,720 Speaker 1: hold babies, walking around with babies, see how they feel. 971 01:03:36,560 --> 01:03:40,000 Speaker 1: And you can tell simply by being with a baby, 972 01:03:40,080 --> 01:03:43,480 Speaker 1: by holding a child, whether this is a child who's 973 01:03:43,520 --> 01:03:46,760 Speaker 1: just fussy and can be discharged, where this is someone 974 01:03:46,800 --> 01:03:50,280 Speaker 1: who is seriously ill. And those were the days afford 975 01:03:50,280 --> 01:03:54,160 Speaker 1: the meningitis vaccine, and we were really concerned about meningitis. 976 01:03:55,040 --> 01:03:57,600 Speaker 1: Then my boss and my chairman of my department I 977 01:03:57,680 --> 01:04:01,920 Speaker 1: was at a community hospital in Chicago's name was John Barton, 978 01:04:02,640 --> 01:04:05,920 Speaker 1: and he was a cowboy, and he taught me two things. 979 01:04:06,000 --> 01:04:10,080 Speaker 1: He first taught me that to be a leader, you 980 01:04:10,200 --> 01:04:13,240 Speaker 1: have to want what's best for your people more than 981 01:04:13,280 --> 01:04:18,400 Speaker 1: you want what's best for you. He got such joy 982 01:04:18,560 --> 01:04:23,520 Speaker 1: in our successes, and he did everything possible so that 983 01:04:23,560 --> 01:04:27,240 Speaker 1: we could be successful, even though sometimes that made him 984 01:04:27,320 --> 01:04:31,800 Speaker 1: unpopular with the management. And the last one was William 985 01:04:31,920 --> 01:04:37,960 Speaker 1: Nihan uh And and Bill taught me that you have 986 01:04:38,160 --> 01:04:41,840 Speaker 1: to know the basic science if you're going to treat patients. 987 01:04:42,040 --> 01:04:45,760 Speaker 1: For example, he could give a lecture on diarrhea where 988 01:04:46,200 --> 01:04:50,040 Speaker 1: you learned about what causes the diarrhea, not just how 989 01:04:50,080 --> 01:04:53,400 Speaker 1: to treat the diarrhea. And so those are my mentors, 990 01:04:53,440 --> 01:04:56,120 Speaker 1: and I thank god that I had. Sounds like a 991 01:04:56,200 --> 01:04:58,880 Speaker 1: hack of a list. Let's talk about books. You mentioned 992 01:04:58,880 --> 01:05:02,520 Speaker 1: some already. Tell us what you've been reading lately and 993 01:05:02,560 --> 01:05:06,440 Speaker 1: what are some of your favorites. Okay, well, my favorite 994 01:05:06,440 --> 01:05:10,240 Speaker 1: book is Field of Dreams. My father was a baseball catcher, 995 01:05:10,360 --> 01:05:13,360 Speaker 1: and it's one of the only books I've ever cried 996 01:05:13,480 --> 01:05:16,640 Speaker 1: while reading. I think it's a better book than it 997 01:05:16,760 --> 01:05:19,320 Speaker 1: is a movie, but I love the movie. Also, there's 998 01:05:19,360 --> 01:05:23,160 Speaker 1: also a fabulous book called The Goldbug Variations by Richard Powers. 999 01:05:23,640 --> 01:05:26,240 Speaker 1: I don't know if you've heard of it, but he 1000 01:05:26,360 --> 01:05:31,120 Speaker 1: combines genetics, music, and a couple of love stories together. 1001 01:05:31,760 --> 01:05:33,919 Speaker 1: More recently, as I told you, I've been reading all 1002 01:05:34,000 --> 01:05:38,800 Speaker 1: the detective Runo Sposh novels by Michael Connolly, and that's 1003 01:05:38,800 --> 01:05:42,480 Speaker 1: what I do for recreation. Sounds like fun. Our final 1004 01:05:42,560 --> 01:05:46,560 Speaker 1: two questions starting with what sort of advice would you 1005 01:05:46,600 --> 01:05:49,320 Speaker 1: give a recent college grad who was interested in a 1006 01:05:49,440 --> 01:05:54,720 Speaker 1: career in either medicine or genetics. Well, I would say 1007 01:05:54,760 --> 01:05:58,160 Speaker 1: that there's been a sea change from in just the 1008 01:05:58,200 --> 01:06:02,160 Speaker 1: past ten years in genetics. Uh, and it's going to 1009 01:06:02,200 --> 01:06:06,680 Speaker 1: be in medicine too, And that is you need to understand. Informatic. 1010 01:06:07,680 --> 01:06:12,400 Speaker 1: When I was even a quest diagnostics, my expertise and 1011 01:06:12,560 --> 01:06:16,280 Speaker 1: what we call wet work, it was in making essays, 1012 01:06:16,720 --> 01:06:20,560 Speaker 1: you know, making methods to detect things and doing it 1013 01:06:20,600 --> 01:06:23,400 Speaker 1: in a better way. We talked about that a little earlier. 1014 01:06:23,960 --> 01:06:28,200 Speaker 1: Now pretty much everything goes on the DNA sequencer, on 1015 01:06:28,280 --> 01:06:32,360 Speaker 1: the next generation sequencers, and so the wet work is 1016 01:06:32,480 --> 01:06:36,880 Speaker 1: almost irrelevant. But what isn't irrelevant is the analysis of 1017 01:06:37,000 --> 01:06:40,280 Speaker 1: the tremendous, the humongous amount of data that comes off 1018 01:06:40,320 --> 01:06:44,400 Speaker 1: those sequencers. And so I would say to someone who 1019 01:06:44,520 --> 01:06:46,960 Speaker 1: wants to go into genetic you have to get a 1020 01:06:47,040 --> 01:06:50,440 Speaker 1: handle on the informatics. Whether or not you need to be, 1021 01:06:50,880 --> 01:06:53,040 Speaker 1: you know, a computer major or whether you're not to 1022 01:06:53,200 --> 01:06:55,520 Speaker 1: need to be a programmer that I don't know, but 1023 01:06:56,160 --> 01:06:59,600 Speaker 1: you need to be able to because the computer folks 1024 01:07:00,120 --> 01:07:03,760 Speaker 1: know the medicine and you need to know where the 1025 01:07:03,800 --> 01:07:09,080 Speaker 1: weaknesses are in the computer algorithms or else you're going 1026 01:07:09,120 --> 01:07:12,480 Speaker 1: to start, you know, being let off on blind alley. 1027 01:07:12,600 --> 01:07:15,760 Speaker 1: So that would be my advice to anyone who's getting 1028 01:07:15,760 --> 01:07:20,880 Speaker 1: into modern medicine is to understand the informatics, Understand how 1029 01:07:20,920 --> 01:07:26,160 Speaker 1: these algorithms work, understand where their strengths are, where their weaknesses, 1030 01:07:26,240 --> 01:07:30,560 Speaker 1: or even become involved in the analysis because it's incredibly powerful. 1031 01:07:31,120 --> 01:07:36,240 Speaker 1: I mean, there's an algorithm that basically looks at sales 1032 01:07:36,360 --> 01:07:43,000 Speaker 1: of kleenex in um in pharmacies that predicts blue epidemics 1033 01:07:43,040 --> 01:07:47,280 Speaker 1: better than anything else. It's the same kind of algorithm 1034 01:07:47,360 --> 01:07:49,600 Speaker 1: that they use to map the craters of the moon. 1035 01:07:50,240 --> 01:07:52,640 Speaker 1: So you know, this is you know, we live in 1036 01:07:52,720 --> 01:07:58,080 Speaker 1: an age where basically privacy has gone. But the up 1037 01:07:58,160 --> 01:08:00,840 Speaker 1: the other side of it is there is so much 1038 01:08:00,960 --> 01:08:04,960 Speaker 1: data out there that could be used for good. You know, 1039 01:08:05,000 --> 01:08:08,040 Speaker 1: people are always worried about how it could be used 1040 01:08:08,040 --> 01:08:11,360 Speaker 1: for the bad. But you know, people are listening to 1041 01:08:11,400 --> 01:08:14,720 Speaker 1: our phone conversations. They're paying attention to what we buy. 1042 01:08:14,880 --> 01:08:17,599 Speaker 1: You know, that's the negative part. But on the other hand, 1043 01:08:18,080 --> 01:08:20,760 Speaker 1: I just turned on my computer and on Google there 1044 01:08:20,880 --> 01:08:23,360 Speaker 1: was something that I wanted, you know. It was like, 1045 01:08:23,720 --> 01:08:27,200 Speaker 1: I think, how did you know? How did the algorithm 1046 01:08:27,280 --> 01:08:29,600 Speaker 1: know that this would be something that I would be 1047 01:08:29,640 --> 01:08:33,240 Speaker 1: looking for because it wasn't obvious. Uh and yet there 1048 01:08:33,240 --> 01:08:36,320 Speaker 1: it was. So it can be used for good um 1049 01:08:36,360 --> 01:08:39,960 Speaker 1: as well as for for bad and uh So I 1050 01:08:40,000 --> 01:08:43,920 Speaker 1: think that that, yes, there there is reason for concerns 1051 01:08:43,920 --> 01:08:46,519 Speaker 1: about privacy. I would also say that the kids today 1052 01:08:47,000 --> 01:08:49,960 Speaker 1: they don't care about privacy, right They put everything on 1053 01:08:49,960 --> 01:08:53,639 Speaker 1: Facebook as soon as it happened, So maybe we're moving 1054 01:08:53,640 --> 01:08:58,240 Speaker 1: into a different era. Quite interesting. And our final question, 1055 01:08:58,960 --> 01:09:01,559 Speaker 1: what do you know about the world of genetics and 1056 01:09:01,600 --> 01:09:05,759 Speaker 1: testing in medicine today that you wish you knew forty 1057 01:09:05,840 --> 01:09:10,240 Speaker 1: years ago when you were first getting started. I guess 1058 01:09:10,240 --> 01:09:12,320 Speaker 1: what I'd say is one of the most important things 1059 01:09:12,320 --> 01:09:16,800 Speaker 1: that I've learned is unintended consequences. So I lived through 1060 01:09:16,960 --> 01:09:23,200 Speaker 1: the original Medicare guidance when the diagnostic related groups were formed, 1061 01:09:23,560 --> 01:09:27,479 Speaker 1: So this was in probably the seventies or eighties, probably 1062 01:09:27,479 --> 01:09:32,120 Speaker 1: the eighties, and basically the way that medicine was reimbursed 1063 01:09:32,240 --> 01:09:40,200 Speaker 1: was changed inalterably. So hospitals were paid not by what 1064 01:09:40,360 --> 01:09:43,680 Speaker 1: was done to a patient or for a patient. They 1065 01:09:43,680 --> 01:09:47,519 Speaker 1: were paid a single amount based on the diagnosis of 1066 01:09:47,640 --> 01:09:51,920 Speaker 1: that patient when they entered the hospital. So you would 1067 01:09:51,960 --> 01:09:55,080 Speaker 1: get the same amount of money for admitting a patients 1068 01:09:55,160 --> 01:10:01,240 Speaker 1: with down syndrome for pneumonia, whether or not did two 1069 01:10:01,320 --> 01:10:04,200 Speaker 1: hundred thousand dollars worth of work on them or whether 1070 01:10:04,240 --> 01:10:07,320 Speaker 1: it did twenty dollars worth of work on him. So 1071 01:10:07,520 --> 01:10:11,400 Speaker 1: that changed medicine incredibly. And you say, well, you know 1072 01:10:11,479 --> 01:10:14,679 Speaker 1: Medicare was Medicare, but then you know the insurance company 1073 01:10:14,760 --> 01:10:19,200 Speaker 1: used Medicare as a model, and that that irremically altered 1074 01:10:19,200 --> 01:10:21,920 Speaker 1: the way medicine was practiced. The other thing about those 1075 01:10:21,960 --> 01:10:27,360 Speaker 1: Medicare regulations is they had better reimbursement for procedures. So 1076 01:10:27,479 --> 01:10:32,440 Speaker 1: specialties which did a lot of procedures, colon oscarpies, cardiac 1077 01:10:32,520 --> 01:10:38,639 Speaker 1: cathorizations became more powerful because the reimbursement was better and 1078 01:10:38,920 --> 01:10:43,160 Speaker 1: basically you could make more money in that era of 1079 01:10:43,280 --> 01:10:50,200 Speaker 1: the general practitioners, the pediatricians, you know, all got less reimbursement, 1080 01:10:50,320 --> 01:10:52,519 Speaker 1: and it became harder for them to make a living. 1081 01:10:53,360 --> 01:10:56,200 Speaker 1: Then all of a sudden, somebody says, well, these primary 1082 01:10:56,280 --> 01:10:59,160 Speaker 1: care people are not doing well. So then they changed 1083 01:10:59,400 --> 01:11:04,960 Speaker 1: reimburse sent to favor primary care, and that, again, you know, 1084 01:11:05,120 --> 01:11:08,679 Speaker 1: changes the equation. So I guess what I would say 1085 01:11:08,840 --> 01:11:12,800 Speaker 1: is be careful when you legislate anything that has to 1086 01:11:12,840 --> 01:11:16,759 Speaker 1: do with medicine. I don't know if I would become 1087 01:11:17,000 --> 01:11:23,000 Speaker 1: a physician if I were uh a young person today. Um, 1088 01:11:23,120 --> 01:11:25,960 Speaker 1: it's much harder. So, you know, let me tell you 1089 01:11:26,000 --> 01:11:28,320 Speaker 1: what you know. One way that costs is being controlled 1090 01:11:28,320 --> 01:11:32,759 Speaker 1: in medicine is with scheduling. So our hospital was purchased 1091 01:11:32,760 --> 01:11:37,120 Speaker 1: by another hospital, they introduce a scheduling program. Well, I 1092 01:11:37,240 --> 01:11:39,839 Speaker 1: noticed that they were going to schedule me fifteen minutes 1093 01:11:39,880 --> 01:11:42,040 Speaker 1: to see every patient. And I said, wait a second, 1094 01:11:42,400 --> 01:11:45,160 Speaker 1: I'm a geneticist. I can spend an hour with the patient. 1095 01:11:45,280 --> 01:11:47,799 Speaker 1: I can spend an hour and a half of the patients. 1096 01:11:47,800 --> 01:11:50,679 Speaker 1: They said, Tosh, They said, you know, if you do that, 1097 01:11:50,800 --> 01:11:53,479 Speaker 1: then your patients are gonna be waiting in the waiting room. 1098 01:11:53,600 --> 01:11:56,960 Speaker 1: They're not going to be happy. So, you know, a 1099 01:11:57,160 --> 01:12:02,080 Speaker 1: simple thing like a scheduling program from someone who you 1100 01:12:02,080 --> 01:12:04,960 Speaker 1: know has done an analysis and says that you know, 1101 01:12:05,040 --> 01:12:08,280 Speaker 1: we want doctors to see patients every fifteen minutes and 1102 01:12:08,320 --> 01:12:10,920 Speaker 1: get a ten minute break for coffee and and that 1103 01:12:11,080 --> 01:12:14,920 Speaker 1: sort of thing has made you know, being a doctor, 1104 01:12:15,640 --> 01:12:20,800 Speaker 1: being a physician less enjoyable, You have less freedom, Your 1105 01:12:20,800 --> 01:12:24,960 Speaker 1: people are feeling more like they're they're just employees, uh 1106 01:12:25,000 --> 01:12:29,400 Speaker 1: than they have a vocation. Really quite interesting. Thanks back 1107 01:12:29,479 --> 01:12:31,920 Speaker 1: for being so generous with your time. We have been 1108 01:12:31,960 --> 01:12:35,320 Speaker 1: speaking with Dr buck Strom. He is the CEO and 1109 01:12:35,479 --> 01:12:41,040 Speaker 1: founder of Liquid Diagnostics. If you enjoy this conversation, well, 1110 01:12:41,120 --> 01:12:43,120 Speaker 1: be sure and check out any of the previous four 1111 01:12:43,160 --> 01:12:47,960 Speaker 1: hundred such discussions we've add. You can find those at iTunes, Spotify, 1112 01:12:48,200 --> 01:12:51,960 Speaker 1: or wherever you regularly get your podcasts. We love your comments, 1113 01:12:51,960 --> 01:12:56,240 Speaker 1: feedback and suggestions right to us at m IB podcast 1114 01:12:56,400 --> 01:12:59,960 Speaker 1: at Bloomberg dot net. Sign up from my daily reading 1115 01:13:00,080 --> 01:13:03,120 Speaker 1: list at Ridolts dot com. Follow me on Twitter at 1116 01:13:03,200 --> 01:13:06,000 Speaker 1: rid Halts. I would be remiss if I did not 1117 01:13:06,160 --> 01:13:09,040 Speaker 1: thank our crack team who helps put these conversations together 1118 01:13:09,640 --> 01:13:14,519 Speaker 1: each week. Mohammed Rumaui is my audio engineer. Paris Wald 1119 01:13:14,680 --> 01:13:19,160 Speaker 1: is my producer. Sean Russo is my director of research ATKO. 1120 01:13:19,240 --> 01:13:23,639 Speaker 1: Valberon is our project manager. I'm Barry Rihalts. You've been 1121 01:13:23,680 --> 01:13:28,600 Speaker 1: listening to Master Some Business on Bloomberg Radio.