If you haven't seen my past videos on acne, I had it so bad in high school and college I didn't want to leave the house. Once I took Accutane in my early 20s, it cleared up my skin but then gave me a slew of health issues that ultimately caused me to know everything I know now about diet and nutrition. Plus, the acne ended up coming back despite taking Accutane twice. The past videos I've done on acne are similar to how dermatologists skin doctors treat acne. They suppress it, temporarily fixing it as opposed to addressing the root cause. Unlike the videos I have been doing lately on the whiteboard where I read studies, research, this is based off of my 10 years of personal experience with acne from that dermatologist destroying my liver to the carnivore diet and now to a point where my skin is finally clear, basically eating anything I want, even McDonald's and the burger reviews I've been doing. So as we just said, modern treatments are suppressing acne, topical ointments burning your skin, oral antibiotics destroying your gut bacteria, Accutane replacing all of your liver stores with retinoic acid. The problem is entirely on the inside, your gut microbiome and your liver. So a topical acne treatment or face wash is never truly going to fix your acne and the current solutions internally just make things worse long term and there are some horror stories from Accutane, Crohn's, Colitis, people getting their intestines taken out. Modern dietary changes do help and are much closer to what the actual cause of acne is. But most people aren't educated enough to make drastic enough changes and no one really knows what an effective probiotic is. What drives me crazy about everything else is doctors don't get nutritional training in schooling and if they did, it was probably just standard American diet nonsense. This because someone is an MD or a physician should not give them any credibility whatsoever unless you enjoy them making money with big pharma off of your poor health. In order to treat the root cause of what is causing acne, you need to remove the negatives and add the positives. The negatives are toxins and imbalances in the body whereas the positives are things to help remove those toxins as well as get your body to balance. Basically, not being poisoned, however, someone like me, since I was a kid, it might take many many years to fix the problem. And I don't have a great memory because of Accutane and Benadryl and all this stuff I took when I was younger frying my brain, however, I remember specific conversations I had with that dermatologist asking him, hey, does diet matter? How come when I remove dairy, the acne goes away? And anything logical, the guy just disregarded it and said no, no, no, it's 100% genetic and just kept prescribing me stuff. Most people will have so many toxins in their water and food, the agrochemicals, herbicides, pesticides, fungicides, antibiotics, heavy metals, halogens like chlorine and chlorine, hormones specifically estrogen that is difficult to pinpoint what is causing the acne. And some of you might be wondering, well, if everyone in America is getting poisoned, how come some people have the acne and some people don't? Generally speaking, when people are breastfed, when they grew up with a healthy microbiome, they're more tolerant of these negatives. Your body is trying to detox all of that stuff. And sometimes the way it does that is the easiest access point, which might be the skin on your face as it's thinner than most parts of your body. The reason your body will detox through your skin instead of the bowel movement, sweat or urine is a combination of not being hydrated and the toxic load being excessive. Yes, it's possible that your breakout was caused by not drinking enough water. So instead of the toxins moving through, they just kind of sat in your stomach and went out through the skin as opposed to being flushed out. But it's also very likely that there is just too much poison in your body that's going out every which way it can. So everything you consume in your body for the most part is going to be processed by the liver and it can also be stored in the liver. I've heard people saying that the liver is a filter, but that doesn't make any logical sense. When people eat liver, there's vitamins, there's minerals, there's stuff stored in the liver. Your liver is the exact same.  One thing to also understand is that the body has limited detox capacities.  It's going to take years and years and years and years of following a proper diet to fix the issue. And it becomes really, really bad with Candida. Candida is one of the many yeasts that populate our gut microbiome. There's different strains. Yeasts and bacteria are symbiotic. They live happily together under normal circumstances. When there's an excess of toxins, Candida overgrows, essentially working as a sponge to soak up those toxins and prevent them from being reabsorbed. And some people say, oh, well, Candida is just from too much sugar in the diet.  When the toxic load becomes very, very high and Candida is persistent over taking your entire microbiome, it can branch out through the gut lining to all other parts of your body, expelling toxins through the skin. So when people have really, really, really horrendous bad acne, it's very, very fungal. And that's that component from Candida. Another example can be from something like dairy, which is very high in calcium. Some people don't tolerate calcium well for one reason or another, usually not enough magnesium. And the body starts expelling the calcium through the skin. Even raw, grass-fed dairy can give some people extreme breakouts. So just to explain that again, if you're born with a healthy gut microbiome, usually Candida is kept in check. And then some extreme circumstance where it's high levels of radiation exposure, you take an antibiotic, a poor diet for a very long period of time. There's going to be some type of catalyst that needs to disrupt your gut microbiome, typically being a toxic load, dysbiosis, same meaning as imbalanced gut bacteria. And Candida being protective against toxins means that the yeast is going to grow, hold those toxins, and hopefully be expelled in the bowel movement. If the Candida wasn't sucking up those toxins, where do you think they're going? They're going to get reabsorbed back in the stomach, which is not good. That's even worse than the Candida. Granted, you're not going to feel so good at any step along this process. So calcium is a very popular non-fungal possibility. And what I mean by that is, yeah, in bad cases of acne, Candida will typically be present. But in addition, drinking the raw dairy might be bad. If you have excess iron stored in your liver from iron fortified foods, if it's in a multivitamin, vitamin A in excess is stored in the liver. All vitamins are stored in the liver. B vitamins, vitamin D, vitamin K2, liver stores many different vitamins. So if you have excesses or imbalances of certain ones, you're going to have an issue. So it gets very complicated and difficult, you know, based on your past diet, while you're eating now, trying to pin the pieces of the puzzle together and be healthy.   It's just sometimes those on their own can just cause acne breakouts. In most cases, incorporating a probiotic that works will just entirely prevent acne. That's because the bacteria in the probiotic will keep the Candida in check. It will keep gut motility high. It will digest food, push the toxins out. The problem is, if you don't fix the diet and just take a probiotic, it's still kind of like the suppressing thing. We still need to address the root cause and fix it long term. However, in regards to the acne and clearing up your face, probiotic is the number one thing. The diet I've been following over the past year or so has allowed me to keep my skin incredibly clear while restoring my liver health. Most importantly, removing those negatives, using water kefir as a probiotic to keep the biome in check, and slowly adding supplements to fix past nutrient imbalances. But you really have to understand that you can take a probiotic, have completely clear skin, and the diet is separate, vice versa as well. You can be on a diet that's fixing the root cause and have terrible breakouts because you're not taking the probiotic. So checklists of things I do that keep my skin clear as of right now, hydrate with spring or mineral water in the morning and throughout the first hour or two of the day. We also did a liver detox smoothie that's very hydrating. Two, drink probiotic water kefir and consume water kefir grains with every meal to digest food, keep the candida in check. Three, keep the diet as clean as possible. Lean grass would be for protein, organic, rice, white potatoes for starch, small amount of coconut oil and certain nut fats for dessert, and maybe I'll have an apple with the meal as well for some more sugar and calories. Washing your face, brushing your teeth, keeping your pillowcase clean, things I spoke about in the past has seemed insignificant as long as I keep those first few things in check. You cannot brush your teeth for days, a week or two. You won't get cavities and you won't break out if the probiotic and the diet isn't checked. It's about what's going on inside your body, not the outside, and this goes above and beyond acne.  And I drew the little sun here with kind of like a recycle sign because the sun will clear up your skin and use it up, essentially. So if you need to recycle tissue quickly and push the scar tissue out, the sun tanning beds are excellent, excellent, excellent for that. And I think that's a big component that definitely fits in here because you want to be doing it. But technically speaking is a vitamin D deficiency you're going to cause. I mean, there are so many more components to this that I haven't spoken about today. I'm just talking about the most important ones. Of course, you know, if you guys go to like organ supplements.com where I have almost a dozen different mineral supplements, it becomes incredibly, incredibly complicated. You could have a selenium deficiency, it could be molybdenum, zinc, magnesium, it could be a dozen different things, but this is the very first step. And then once you have the diet in check, once you're taking a probiotic, once you're getting some sun, once your skin is cleared up, you get a hair mineral analysis. You start trying to put a few things here and there. You don't want to do everything all at once, which is what some people end up doing. FrankieSphereAgeFoods.com is listed there because I sell water key for grains. I sell water key for as a probiotic. It's, as I've said many, many times in basically all of my day of eating videos, the most important, significant thing that I've incorporated into my diet to improve my health. Because you're a living being. Your gut bacteria is living as well. If it's dead, I mean you're going to feel dead. And when your liver's really messed up and your gut's messed up, from past diets, whatever it may be, and you're able to replicate a healthy one with water key for grains, it really is the biggest game changer.  I wear my protective clothing and pants from Wi-Fi shielding every day. If you guys watch, you know, a few dozen, 100, 200 videos on my channel, you'll have an excellent understanding of nutrition and how to apply all of this stuff. So if you guys do want kind of like a catered one-on-one approach where I can guide you through this, you can go to FrankieSphereAgeFoods.com and schedule a consultation. But as I just said, all of the information is available freely on my YouTube channel. You just have to piece it together. You can go to FrankieSphereAgeFoods.com to check out all of my businesses, including Frankie Syringe Meat, Frankie Syringe Foods, Oregon Supplements, all that stuff. But if you guys could drop a like on the video, leave me a comment down below. Subscribe so that YouTube can unsubscribe you next week and be sure to check that notification bell so they don't notify you of my videos. And guys, I could really go on and on for probably over an hour on this. It's just, I get a little discouraged when I come up with like these creative things like remove the negatives, add the positives, things that are very basic. Everything would have been said before, but then other people say them without giving me credit. So, you know how it is. Some people deserve to be lined up in front of a ditch, but maybe in the next life we'll do that.


What's up everyone it's your girl Kenzie here from late in sports cards and I'm ripping a couple of personals here for Jeffrey H We've got three twenty two twenty three Panini prison basketball. Hobby boxes. All right. Best of luck Here we go, Jeff The rookie of David Roddy Grizzly Pulsar is gonna be Gary Trent Jr. for the wraps Wesley Goings purple to 99 it's gonna be CJ McCollum pelicans a mellow fearless silver Moses moody Jovich rookie Tim Duncan Silver luck of the lottery is gonna be Jaden Ivy Edwards hyper Franz Vacher Monte Graham Ewing and Pippin Jr. Rookie Feeling a gold here feeling gold coming up Silver firework Zion Chrismatic jaw that's been being silver Jalen Williams Brooks True gold on a black and gold Those are set the black and golds Zion we've got a Jordan Clarkson hyper Sohan rookie base more junior Ray Allen Ruby is a Reggie bullet Lottery Jalen Williams silver Marvin Bagley third Chandler rookie Watson and Desmond Bain Silver fireworks Treyway first auto up is gonna be a key on Ellis on the rookie sensational signatures Key on Ellis Shaq hyper It's Carter Davis and Weber Blue ice coming up. It's gonna be a rookie of Josh Pinot for the T wolves. Thanks a little off-color match there 125 Chrismatic LeBron silver stress Jalen Williams rookie base Clark and Diabatte For one more auto here out of your first box Jeff Ruby wave of Patty Mills Nets LeBron widescreen hyper Jonathan Isaac Warren Ben Wallace and Barclay G&G so nice cards coming out this morning so far. Yeah, Jalen Williams luck a lottery silver. Well, there's Sillard Sillard silver Vince Carter Sorry, Easton rookie base Robi and kid Silver fearless Shaq next auto up is gonna be a Christian brawn for the Nuggets on these silver signatures worth the auto there Christian brawn for the Nuggets. How about a LeBron pole saw that's sick Like that. I haven't seen many of those at all They slow brawn random rookie base Stottemire and a Keegan Murray rookie base back box one here Jeff Chrismatic Tatum silver Chris Boucher Danny green Roddy rookie and a valentine's All right, so that will do it for box one box to box number two See that gold What is gold LeBron? I bet that would look sick with the Lakers uni Got Steve Nash on the Ruby Like Bajie lottery pole saw our Christian Coloco rookie for the Raptors Wesley McGowan's Wilkins. We got a mojo here for you Jeff Mojo is a Franz Wagner for the magic that's sick one out of 25 Nice second-year Franz Heck yeah about an hour an hour and a half right now Sam But an hour and a half currently fireworks Luca silver Wendell Carter, Jr. Yovitch glam Prismatic silver Anthony Edwards Lillard. We've got hyper Josh Richardson hip and junior Steve Nash Silver widescreen John Morant's prismatic Yoke it's silver trade Jones Jalen Williams rookie Sohan rookie base Watching some college bowling over here Realist Ray young Jonathan Isaac hyper When the more junior Ray Allen and Chandler Ruby way Tyler hero on the color match for the heat fireworks curry silver Donovan Mitchell Watson vane and Kirk Silver fireworks of Lamello ball luck with a lottery chat silver a Bajie for the jazz nice Roby kid and a random rookie base nice pack there Silver luck at the lottery of Johnny Davis Wizards Next auto up we have Clippers is gonna be a Musa Diabatae rookie sensational signatures on Diabatae Randall Pulsar Stottemeyer a Keegan Murray rookie base and Danny green from prismatic Lillard silver Roddy Valenkunis and Baldwin jr. Silver widescreen of Luka Donchich next auto We've got a Josh Minot for the tools on this sensational on this signatures Rikki Otto He walks Scotty Barnes hyper Davis Weber and a Jalen Williams rookie base got an orange waves gonna be out of 60 And it is a Joe Ingalls for the Bucks that one is 13 out of 60 there Walkie KD prismatic silver roseaer Clark Diabatae rookie and war last pack box to your Jeff Ruby wave Evan Mobley calves widescreen KD hyper Danny green Ben Wallace Barclay and Aunt Tari Eason rookie base All right something big needs to come out of this third box here Come on. This card's organized real quick. I box three your Jeff Box three. What's up Jason? We have some immaculate WWE and Q a little bit of ways though Wahoos with some wrestling Ruby way roseaer the third Sohan on the luck of the lottery Bruce Brown silver McDumbett and I bow chance First auto up. We got red precious to chew it for the Raptors to 199 To that's to 99. Sorry, and we've got a spurs. It is gonna be sensational signatures auto Doug McDermott Dermot there for the spurs Yang on the hyper Jaden Hardy rookie Peyton and Dalen Terry rookie that orange to 49 Show me Rook have a pan Wallace for the Pistons that one is 36 of 49 Okay, fireworks silver Aten Shaq Williams and Richardson. It's up Luke Right. Wahoos get to see and chat man. Good luck on your box Ruby, I don't assume move for the bolts fearless LeBron James Cole Anthony hyper Christie Nambar and Liddell Silver widescreen Luka Donchich got prismatic Lamella ball got silver Dirk Tray Jones Igbaji rookie base and Kendall Brown who Doug McDermott to 199 last auto up It's gonna be a Keegan Murray. There you go. Nice hit there Jeffrey on these silver sensational signatures rookie auto nice Murray Get this one in a mag for you Jeff. Heck. Yeah, Coloco on the Pulls our rookie lost my train of thought there Durin and jenoblee Chad not from us. I don't believe I think only one person has hit it over on like all of drip Silver connington Matherin rookie base Tyus Jones Caleb Houston Kyle's pulled a black and gold, but it wasn't a black or a true gold Fortunately, so that did not count Ruby way valentine's they're hard pulls Alec Burks on the hyper stocked in shade on sharp rookie base and Ingram Definitely hard pulls got red power. It's gonna be a Devon but sell for the spurs 17 to 75 there the cell Cosmatic veal silver uber a jr. I've got shat rookie dang and AI red to 299 Tim Duncan widescreen Lillard hyper Austin Reeves And not Mollins and Lofton jr. Silver lottery Johnny Davis lottery durin silver jaden ivy.  Thanks as always Jeff


I've edited my very re-edited I should say my very first YouTube video and I'm bringing it to you here next I've cleaned it up just a little bit added an intro and an ending and I hope you enjoy it the channel was started by accident for those of you who don't know I was trying to show how to do a technique to a teacher of mine and the only place I could figure out how to load the video was YouTube so there you have it now I have a YouTube channel all right enjoy the video I'll be back so I do my monoprints so first of all I went to an art salvage company near me in San Francisco California and they had this sort of acrylic box I cut it apart thinking I was gonna make stencils out it out of it turns out it's great for monoprinting for especially what we're gonna do now you could go to the art supply office supply store and this is just an inexpensive binder it's clear enough to do what we're gonna do you could cut the covers off and use them I'm gonna use my acrylic sheet I used before one side of this has a texture and one side is smooth so I want the smooth side up that's where I want the paint and I have my picture it's a different one than the one we've been using in class I've put it in a clear sheet protector so that I tend to be a messy painter so I don't want to get paint on the original print in case I want to use it again so I have it in a sheet protector so I can wipe it off easily and I'm gonna lay my clear sheet on top of that you have an assortment of pallet knives and small brushes and of course our handy-dandy rollers I've picked a color of paint that I like in this case this green and then white and black I also am going to use a glaze acrylic glaze so when you're doing monoprints you want a thinner paint you want to slow down the drying time and glaze does that it also makes the paint a little bit translucent which I like so okay we're gonna mix make three little piles of paint we're gonna mix each one with a good amount of glaze we're gonna end up with a medium value a light value and a dark value so I want to add white to one pile and a little bit of black to the other pile not too much and we will mix it up with a pallet knife this is our medium we didn't do anything to the medium you can see that better this is gonna be our dark I want to mix it fairly well if it's you know not completely mixed not gonna hurt anything and it'll just add more interest to the painting the idea is to create a suggestion of the figure as Pauline's always saying happy accidents are okay okay so we're gonna start with our small roller and we're gonna take our medium value paint and we're gonna just sort of outlining suggest the basic figure with the medium value not too much paint if you get too much on there you can wipe it off and again our sheet of watercolor paper and we are gonna take this clear acrylic sheet and lay it over the top large roller and set that aside you don't have to wipe this off every time you do a print I would suggest maybe doing a couple of prints sometimes when I do these the first one comes out kind of dark and that first one here that looks a little dark to me it may end up turning out okay but we'll see but I'm gonna print it again yeah that one's I think that one's better so now we're gonna not wipe any paint off we're gonna go back over our figure and we are gonna use a brush and our light value of paint you can use a brush or one of the small palette knives depends on what I'm doing hold it down with one hand while you're doing your painting in your highlights that way if you need to you can lift it up a little bit I come in here with the palette knife just making suggestions see what happens let's take our one that we like just this one we left the darker green paint on there so we can sort of line those up and just eyeball it which is why we didn't wipe it off set it aside and we're gonna take our other one and we're gonna use up some of the excess paint don't ever waste your paint I started doing this with glazes because I didn't want to go to the art supply and buy ink every time I go to the art supply I spend way too much money okay so now we're gonna go in and we're going to do some of the dark shadows and the hair just make sure I like to use the palette knife but make sure you don't get too much on there hold it down good with one hand so now if you get some paint where you don't want it to be like I just did take a rag I believe in happy accidents but that would have been too big of a happy accident okay we're gonna suggest there everywhere you see some shadows in the picture it doesn't have to be perfect we're not going for perfection you're just suggesting the figure that's good let's see maybe a little more this is a tough one let's see what happens I think I want to do this one first because I think it might be a little bit dark see so again we're gonna line up sort of that paint that's in the background not bad I do think that's a little dark though so we're gonna go we're not gonna add any more paint over this one that's what we want I like that one okay now now we're gonna mix up one more color so I like to for the outline use a golden fluid acrylic it's easier to use I'll mix a little bit of it I'll mix a little bit of it on a spare acrylic sheet glaze to slow down the drying time so it doesn't dry before I get it all on there like a good nice long liner brush this one mix it up our picture back now is where we want to wipe off some of that extra paint so I just use a rag paper towel napkin it's not dry on there you'll be able to wipe it off if you wanted you could make a couple more prints there's probably enough paint on there to do a couple more now you're gonna just outline the figure holding down one corner make sure you get all the important parts make sure you also don't stick your hand in the one of the outlines I've done that I don't like this line right here it's kind of thick and going there a little less paint you know is add more take it away as harder all right here we go let's see what we get let's take our favorite one of the two first moment of truth see what we have shall we yep I knew I was gonna like that one so there we go there's our figure let's see if there's enough paint on it to do that one there is that one's not bad either if I grab my pad of paper I always have extra paper around like most of you I'm sure there's that paint on there to squeeze out one more it's faint so I don't ever waste my paint I always have extra paper around so I can just make some random marks and use it my paint later on this will become another painting a collage or something but I always have something to use up the extra paint like this did you enjoy that I hope you did that was a lot of fun wasn't it we were in my old studio space which used to be out in the garage which had its good points and its bad points it was really cold the winter and really hot in the summer and really dirty and dusty which wasn't good for my asthma but on the plus side I could make a mess I could be free I could be splattery nobody cared it was a lot of fun I missed that part of it all right that's it right now I hope you enjoyed this I'm gonna do one or two other old videos oldies but goodies and I hope you enjoy that and don't forget the most important thing go out and have a great day and do something nice for yourself because you deserve it don't forget that all right I'll see you later bye


Dude, what is this, man? Why is there a moon? Dude, what are the stars? Dude, he looks intimidating, bro. I'm guessing it's one of the higher-ups, if the highest level. This place isn't as big as I expected. Oh, they said the leader was Franky. Is this Frankie?  Like, so the argument already kind of falls flat. Dude, it's huge, though. Taking the Navy! I mean, how are you guys any better, stealing from them? You need to have an excuse for that.  Barefoot, too! Buddy Skip Leg Day! Oh my God! Go shopping? That's it?  Okay, I see why he skipped Leg Day. Oh, this is Frankie! So he's like the leader of them. This is Timothy Thub. I was picturing, like, I don't know why, like a council and whatnot. Let's just throw out Frankie and the Frankie family. Hey, whoa, whoa, whoa, whoa. Sling shot is... All reliable. Damn, as if they haven't eaten them up enough. This is so sad. At least he took a stand for himself. He's still trying to fight. Where did those doors even go? This place doesn't look that big. Is he leaving the house? He's gonna run into Luffy and them soon. This is so sad. They're right there, bro! Oh, Nami is strapped. Oh my gosh. She's picking out weapons? Oh, she's defending the money, I see. The rest of the money. Oh, they're looking for Usopp. Oh, no. No, Usopp's like missing to them. Yeah, yeah, I was just about to say he's not Yuzoro. A blood trail. Yeah, buddy just got up and ran. Is that Luffy? He can't swim, guys. He can't swim. Guys, he can't swim. Where did he come from? He's mutating. Oh my gosh, he was trying to be like Kaku. You can't be serious. Go roll them, bro! Frankie and all. I do not care. They hurt my boy. They hurt my boy. I was meeting with the official. Who's he there for? You don't get to know. Wait, there's a wanted Niko Robin poster behind them. They're really looking for her.  Has it or not? Iceberg, I want to trust you, bro. Come on. Don't be suspicious. This is a corgi. That is his name. It's the dog. I would like to know the same. The mouse? What is going on here? What's happening at Water 7? Oh my god, did they find Usopp? Yo, y'all got to stick up for your boy. Oh my gosh, they just left him out here?  Oh my god. He's just crying.  Literally, just mass murder. I don't care anymore. You stand up for your boy Usopp. Even Luffy's serious. You don't hurt him like that. Let's go! Let's go! Yes! Massive up! I don't think Frankie's there anymore. My god, we're just at the intercut too. This is feeling like the walk to Arlong Park now. Oh, they're so clueless. So unaware. Oh shoot. This is Arlong Park. Arlong Park walk 2.0, bro. They have no idea. I don't think there's a single one of these guys that can stand up to Luffy and all them. I don't think I'm overestimating Luffy and all them either. They are fighting with the passion of a thousand warriors, bro, for Usopp. Oh my god. Dude, they're huge.  Wait, perspective. Oh my gosh. God, he let him out too. You all look so goofy right now. Oh my god. He's got a hundred mil bounty for a reason. I'll just say that now.  Dude, you guys don't stand a chance. I'm sorry. Oh, brother. Oh, brother. You can put as much armor on as you want. Clueless. So clueless, man. Oh my god. Luffy, don't murder him too fast, bro. But he's not hitting him. Battleship cannon catch that? Just punch him in the face. What is he doing? He's going to start cracking it or something. What was that? He combined them in the one? Battleship cannon? It's too late. Too late for talking. I'm sorry. But I'm not. He's going to absorb all? Or Zoro will just, you know, cut through them. So clean with it. Like they're nothing. He cut through steel, bro. This don't matter. Wow, they're so done. There's no one that can... Oh, iron. Iron's nothing, bro. Just cut their cannon. Literally, none of you stand a chance. You're nothing. Well, you don't mess with them, man. You deserve this. You kicked my boy on the ground. Stole his money. You don't let a single survivor escape. Sanji. Not a single one. Tell it to him. Talk that. This feels so good. Oh my gosh. It's like revitalizing. Oh, you're trapped. Why can't they use the back door? Everybody gets a bite of them. Oh my god, he's going to ram them? Okay, choppers literally going to murder them. Wait a second. What? Damage is done, man. Give us the money back, but we're still going to hurt you. You're going to go ask the boss man for it? Really? It's not always just about the money.   That was the baby. You did too much already. Damn. Oh my god. How do you think your bones remain? Wait, is that actually what they say? There's no way you're actually still trying to fight back.  This shoots out randomness? Moon Knight style?  The windmill style? Oh my god. Dude, they have like square afros? Oh, he lacks so much critical information. Oh, he's taking the train. He's going to be gone, gone. They won't know where to find him. I thought he was just going to go down the street. Dude, the entire house is destroyed. Oh my god. They were not joking.  Oh my gosh.  Can't do anything if you already spent it, yeah. You go find him. Good work, Chopper. Good work. That's my boy. That's my boy. I love him so much. Yeah, this is still the whole thing with Robin. There's already so many loose ends with this. The thing with iceberg too? Icebergs connected to Robin? Then Usob doesn't even know about the ship. Well, do I have to wait? I wonder if it has to do with the fact that Usob got so hurt because they told them that like a good ship captain, a good captain would want to basically change it out here now, especially with the advice they're giving them. And seeing how horribly hurt Usob got, Luffy doesn't want to run into any instance where the ship failing or the ship being destroyed causes any of the crew to get hurt.  That's why they took so much, they took it out on this house. Dude, seeing that house explode was so satisfying. My God. They wasted no time, which I love. But this is very interesting that he made his decision right here. And I think that's why. I'm not sure. But man, I don't want to say goodbye to Mary. Usob's definitely not going to want to. Like I say, it's been here from the very beginning. They got it from freaking Syrup Village, man. That feels like a lifetime ago. I just, I don't know what could replace that, you know, that feeling of like home in a sort of way. Like, yeah, you know, everything about Mary feels like home. But guys, that's been it from episode 233 and 234 over One Piece Watch. So do drop us up on the channel if you are new. And I'll catch you guys in the next one. Peace, peace.


It's a great privilege to now introduce the next panel. This is a panel that's now happened year on year for the last five years, reflecting the trends in our industry around data, digital, and technology. It's an incredible group of panelists that I'll let our wonderful moderator, Najat Khan, introduce. Just briefly, Najat Khan is a figurehead and a leader in the field. She's the chief data and science officer at Janssen. And she also leads the strategy and operations group. And one of the things that I've noticed about Najat as I've been on the circuit with her in various places is how much energy she brings and how patient focused she is. So Najat, with that said, I will hand it over to you. Thank you so much for the very kind introduction. Thank you, Karin, for having us all here. As Angie was saying, we've had this panel for a few years now, and it's really a testament to the fact that we are moving from ideas and concepts of what we can do with data science to real impact that's happening. Now, the advent of General Data AI has certainly helped propel a lot of what's been happening as dinner table conversations, which is fantastic. But we're going to focus on health care today. And my esteemed panelists here have actually looked at it from many different angles, from industry to academia, and then also from all the way from discovery, the inception of understanding what's driving a disease, which we frankly have a lot more to do and work on. The work is definitely not done to clinical trial execution and then ensuring that there's access to these really important medicines. And why is that important? Because we serve our patients and every single day there are patients that are waiting. And if we can improve the probability of success that something becomes a medicine and make it better and faster, I think we have all served our core purpose. With that, what I will do is just set the context up. My philosophy would be a lot of the times people have asked, is it hyper-hope? And it's neither. It's just real. And to be competitive, and this is what we need to do. The question is, how do we do it really well? How do we collaborate even better to ensure that we have impact at scale? So with that, I'm going to start with my first question. And it's for each of the panelists here. A couple of minutes to answer. First, please share sort of an example where you have solved a significant problem or are solving a significant problem that would help make a medicine, transformational medicine for patients. So it can be a discovery, development, you pick. And then another example that we're not working on yet today, because it's not prime time, but you anticipate will be prime time in the next few years. I'm going to start with Alice. Hi, thank you. So I'm Alice, I'm the CEO and co-founder of Virginomics. We're using AI to develop new drugs for these kind of huge unmet needs, like ALS Parkinson's disease. And in my view, one of the reasons I started the company was because I think one of the biggest limitations right now, especially in applications of AI is really the input data. We've seen huge advances in AI over the last 5, 10 years. But the fact of the matter is that the cost of R&D still continues to increase. And so what's the gap here? And I think what I see is that it's really doable to generate large quantities of data from cell models. But ultimately, cell models aren't predictive of success in humans. And so I believe that in biology, the biggest problem is really a missing data problem. So what we've been doing is instead of starting with cell models, we actually go directly to humans. We have a massive collection of human brains, human tissue from patients that have directly passed away from disease. And this has allowed us to quickly identify new mechanisms in ALS and also a new target in ALS that we discovered entirely from a platform and developed a clinical trials in just four years. So that's actually a new treatment for ALS that's currently in clinical trials. It's in a phase one trial based entirely on human data and based using our AI algorithms. And I think that's just such a promising example of how we can address this fundamental challenge of using AI, not just to test more drugs faster, but really to fundamentally think about how can we increase clinical success? Because even an incremental change there would have massive implications on drug development writ large. And I think our fundamental belief is really the sophistication of your algorithms and AI on its own is insufficient if you don't have the right data to train and learn from. So that's kind of our hope at verge is that by really building the right datasets that are truly predictive, we can get to better outcomes not just faster, not just more, but higher quality outcomes. That's such a great point, Alice. I mean, it starts with understanding what's driving the disease. So your point around novel insights is going to change the way is so, so important. All right, great. We'll go to Anne next. Thank you, Najat. And I'm Anne Hetherington. I'm head of the Data Sciences Institute and within our own data, Takeda. And it's an honor to be here with my fellow panelists. So I guess the example I'd like to give is going back to patient centricity, which is there are many diseases where it's actually really hard to really understand the disease and monitor the disease and the patient. If you think about the brain and the intestine, it's quite hard to get inside. And I know a couple of people in this panel are actually helping us in this space as well. And so with Takeda, we're developing digital biomarkers and tools to help us get inside these organs. So for instance, for Celiac disease, we are using an old technology, a Pil-Cam. But what we've done is we use that to photograph and create video of thousands of thousands of frames along the length of the intestine and then use AI algorithms to read those frames, understand the extent of the impact of Celiac disease along the whole length of the intestine. And therefore we can monitor the impact of any subsequent treatment on those patients and really help us really connect the pathology of the intestine to the Celiac symptoms that patients generally have to talk about. The other organ we're getting inside is the brain and particularly in sleep disorders where we're using novel AI ML techniques to monitor EEG outputs to really help look at sleep staging and diagnosis of narcolepsy and thinking about wakefulness, quality of wakefulness during the day and then sleep staging at night. And we've a group of data scientists and statisticians that are developing multiple different algorithms here to allow us to understand how disorders like narcolepsy really impact patients. And so a bit like Alice described, changes in how we get at medicines, we want to really understand our patients and really develop drugs that impact their disease through a deep understanding of their disease. Thank you, Anne. It's about how you actually connected the pieces around understanding what's driving the disease, but how do we measure it to determine are the outcomes getting better or even stratifying patients bringing precision medicine to reality for all diseases, right? So now I'm going to go to Dina because I know Dina does some great cutting edge work in terms of not just digital endpoints, but how to bring, you know, vision medicine and the patient centricity with devices and solutions that are much, much easier for patients and allow decentralized trials. So Dina, over to you. Yeah, thanks, Nisha. So Dina Kachabi, a professor at MIT and also the president and CEO of Emerald Innovations. So Nisha, at the end of the morning, actually I asked Joaquin about basically why clinical trials still like 90% of the drugs don't make it. And I think that is very, very important question that we should ask. And this is where I think AI and data science, digital technology can help a lot. So I really very much like what both Ann and Alice were saying. Particularly Alice mentioned something about going to the human, understanding diseases and target in the humans. And then you take this, these new drugs to the clinical trial. And now what we need is particularly in diseases that are complex, like in neurology, like ALS, like Parkinson's, Alzheimer's, we need to be able to decide very quickly that we are on the right path or not, that we are seeing and creating through digital biomarker. I think here play very, very important role. Can we get very early on, very quickly, insights whether these drugs are able to provide statistical significance between the arm of the drug and the alternative arms. And this is actually where we have proven recently to publication in Nature Medicine and Science Translational Medicine. The example that we use is Parkinson's, but I think what we did extends beyond Parkinson's to other diseases like Alzheimer's, ALS, and others. But basically that we can use our data to very quickly reduce the sample size by an order of magnitude to reduce the time of the trial and quickly be able to give statistical significance on differentiating the difference arm of in Parkinson's clinical trials. So I think we need to move to the human as soon as possible. And this is what we are seeing increasingly. And we need to be able to provide statistical significance on optimizing the portfolio even before thinking about like, okay, taking it to the FDA because today only 10% goes to the FDA. Yeah. And that's such an important point because if you can make that go-no-go decision with the right methodology earlier on, you really change the trajectory in terms of the probability of success. So Dina, that's a small sample size. Exactly. Smaller, more smarter, efficient trials. All right. Next, I'm gonna go to Junaid.  Thanks, Nijia. Always difficult to follow such clever people who've come up with all the right answers before you. So I'm Junaid Bajo, I'm a physician by training background and the chief medical scientist at Microsoft Research. So as I thought about your question Nijia, I think it's interesting how we think about both the hope and the hype and how do we make it real? There's been so many potential options for what we could do, so many papers that have been written, but I think it still requires us collectively to take responsibility to how we think about real world impact as a consequence of some of these tools, technologies. And there's huge opportunity across the entire farmer value chain, right? From discovery to development all the way to commercialization and what we need to exist in the real world. From a data perspective, I think we recognize the unlocked potential that we have across all types of data. So what we have in electronic medical records, what we have in social determinants of health are genomic profiles, our individual behaviors and others, which is beginning to allow us to unlock novel digital biomarkers in addition to the biological markers that we already consider and have access to today. And the things that excite me on the discovery side is when you look at ecosystem players and others who are doing some really novel work in optimizing small molecule drug candidates today and bringing that actually into the fore. So you look at the work that generate biomedicines are doing or you look at the work that excite are doing and you think what needs to be true and how do we accelerate some of that work and bring that to life. And they work through a partnership mechanism with other partners of course as well. And the work that we're doing at Microsoft is truly to think about what do we do and what must we do to empower the chemists and organizations, the trialists and organizations and colleagues across the farmer value chain to unlock the potential that they have with their data to ultimately deliver value to the beneficiaries, the patients, public and society for who need faster, cheaper, more specific medicines in the future. Great, Junaid, I mean, end to end, how do you enable to do this better? Now, moving on to Shastri. Shastri, tell us a little bit about your perspective. The end-to-end platform, the ability to do all of these great examples that Dina and Alice and Junaid mentioned at scale in a reliable, transparent and understandable way is super important. So we'd love to hear your perspectives on that. Yeah, absolutely. If I can also answer the question about where we've seen the successes and where we see the opportunities, if we've been talking about data and digital and analytics for more than a decade in life sciences in a pretty serious way. And I think the biggest areas that we've now recorded successes across the industry are in the use of real world data for regulatory approvals as well as in decentralized clinical trials and then in the area of clinical operations around identifying new sites and investigators to be able to put the sites in. And then the biggest opportunity is something that we've talked about in this panel as well as the previous panels is this concept that I call around digital biology, which is how do you collect all of this high resolution multimodal data around the patient to be able to drive new insights be it around disease progression and sub-characterization of disease or simply identifying drugs that won't work in the patient so that you have a better chance of success around putting the patient on the right drug. Now to your question, Najat, about platforms and the platform story, we are still on that path a decade in around building these big robust scalable platforms that are reliable. And the analogy I draw is around electricity. When electricity first came in as a utility, it took about 20 years for electricity to be truly integrated into the operations of the industry. And because everything had to change, the way the plants were laid out, the way that workers were trained around the use of electricity as well as drawing this boundary between do you have a power plant in-house or do you actually go by the utility from someone outside? And I think we're under a similar journey around AI about how do we start to build these platforms at scale and how do we partner? What standard should we be adopting as well as how do we ensure that we have robust and reliable data that we're able to use for training our algorithms because that's really what drives the downstream use. And it's also iterative. All of us as an industry are trying to build the plane while we're flying it. So it's not like the platform is built and then we do a big bang launch and then everyone starts to use it. We're driving this continuous innovation where we continue to tweak what the platforms look like and then people in the big farm organizations as well as biotechs are guiding us around here are these cases to prioritize and here is what we wanted to in-house which is what we want to do outside. So I think we're in the second decade of our journey around the broad based adoption of these technologies and the drug development process. I believe the processes will fundamentally change at the end of this decade and it's gonna be a lot of co-creation and partnership as we continue to go along this journey of building these platforms at scale. Super helpful Shashri. And just reflecting before going to the next question on the answers here, I mean, you all have shared examples starting from Target ID to denied dimension sort of the de novo design that's happening small molecule and biologics to digital endpoints for stratification monitoring outcomes Shashri to the clinical trials piece and RWE in terms of evidence generation. So there's really value across the board platforms are not built perfectly yet but we are getting there and it's totally gonna take collaboration. No one person is gonna be able to do it on their own. I guess the question for me is, when you think about unlocking that value end to end there are still some with any new innovation or any new approach challenges that exist whether it's the data quality, the data integration whether it's the deployment of some of these new approaches because the ecosystem has to also mature while you're trying to do it you have to do it all at the same time and then there's the good old cultural change. So I'd love to just get perspectives from a couple of you of what do you think will it take to really unlock this value because if not, we're not doing the right thing for our patients. So I'll start with Anne first just your perspective from what you've, you're seeing at Takeda but many industries before that many pharma companies before that and then I'll also go to Dina from the academic perspective and being on the scientific advisory boards and just be a rounded awesome person in the space that's innovating and pioneering your perspective as well what is it gonna take? So starting with Anne first. So I think it can take many things and I actually considered which answer I give for this. Like many people here I could give multiple answers but I think the answer I'm gonna give is within a company and I'll speak, I could talk externally but for this answer I'll talk internally within a company we need to really take our responsibility for data very, very seriously and we need to take control of our data in terms of its governance, its quality, its storage and its accessibility and so within Takeda we have done two things actually that are really leaning into that notion. The first one is, is that we've brought our technology and data partners into the business of R&D and we have brought the disciplines that really think about how we use data in clinical trials like our statisticians our clinical pharmacologists to sit beside our technologists our data scientists our data architects or data engineers and so by bringing the technologists and the business experts together we're really galvanizing everybody to really think about the business of R&D and how do we develop drugs with a greater insights to data to get them to patients faster. So that's one big area that we have done. The second big change we've made is that we're halfway through a build phase for our clinical trials where we are really taking control of our clinical trial data and we're really taking a quality first automation a first mindset for this. So we're thinking about how do we ingest our data no matter where it comes from? You know, it could be digital tools as I mentioned before it could be ePros it could be those captured at sites. How do we ingest that data? How do we derive its quality? How do we make sure by the end of the trial that we're ready just to push a button at our database lock because our data is high quality? And by doing this in a very repeated way we're just where our goal is to have high quality efficiency in our system and then through good governance enable appropriate access to that data for both primary and secondary use. So they're two things that we're doing internally to really move us along this path within R&D. Excellent. Andina? Yeah. So I would say, okay, so there are many challenges of course but I wanna pick one challenge because I think it's probably the most fundamental challenge is the culture, I think. And you said that I'm in academia and not just in academia I'm a computer scientist also. So take it from someone who's outside your industry and looking at it but for the last six years I've been interacting I mean I'm on this tab with Janssen and work with many of you guys. Looking at it from the outside from a computer scientist perspective and I really love the industry. I can see, like I work with the scientists I work with the R&D people, crisis aside and I said different question but the industry has a level of the scientists the chemists, the biologists the people who develop drugs have immense, immense ability to innovate on the drug design, on the chemistry, on the biology but they are really conservative when it comes to digital when it comes to the thing that they don't really know and therefore they don't trust. And as a result like naturally and like everything that we are not so comfortable was because we don't understand it that well there is a huge gap there that they are way more willing to accept things like genetic drugs or gene therapy, cell therapy stuff like that because it's closer to that understanding when it comes to digital AI it's this black box and there is the cultural gap I think is one of the biggest challenges and pharma is by nature a conservative industry particularly when it comes I think to digital is even a bigger conservatism that you can see in the history like even the electronic PROs we still don't see them everywhere it's just like sometimes we have clinical trials with paperwork and you ask why like why we are still in 2023 have that. So I think there is a need to all understand and all accept a bit that you have to take some challenges of course you have to be very, very careful when it comes to patients and people's lives but you have to be more open to digital and AI and digital health and look at it and work with those concepts and adopt them in your operation much more. Yeah, I mean both of your answers super insightful and I mean if you don't have the data platforms hard to do at skill and Dean of the cultural aspect it's a really good insight because on one hand I would just say that pharma companies it's a highly risky business 10% success rate, I mean these are risk takers I mean I would say but it's a new discipline I'm very innovative, it's amazing I really like working with pharma because actually the level of innovation and the mentality I work with scientists in the pharma industry. Yeah, so I mean but I was gonna say that I do think representing pharma today like it is a very high risk is high tolerance for risk and innovation but it's a new discipline that's merging and coming together and the sooner it can be adopted I couldn't agree with you more I think the better off we will all be you know Alice I would love your perspectives on this too you know just from a company from a biotech tech bio I don't know what's the perfect term these days but really looking at how you figure out the right targets drivers of disease in a different way and also the culture and mindset must be different so how are you pulling that together both the science medicine and the data science aspect? Yeah thanks I think it's a really interesting time and you know on the topic of tech versus biotech startup versus pharma I think that you know we're entering this new era I'm a believer we're entering a new tech driven era we're just increasingly technology is advancing at just an unprecedented rate the cycles are getting compressed and when this happens you have to really think about what is the right culture to leverage all of these advantages I like I read a quote once from the founder of Genentech that said you know I always maintain the best attribute we had was naivete and I think in times when the playbook has yet to be written and where the technological challenges can be daunting oftentimes innovation may be even more important than experience right and naivete can actually be an asset so I think we're starting to see actually this new breed of CEOs that don't look like what other CEOs have looked like in the past where you know being very nimble and very quick about how you make decisions you know in the field that we work in AI and genomics you know if it takes you months to make a decision the landscape looks entirely different we've seen that with you know chat GPT and generative AI in months and I think it's important to think about how are we evolving our culture I've heard we have had a very different culture than I would say the vast majority of pharma companies and biotechs we have a huge emphasis for example on emotional vulnerability where we actually we start meeting by naming our emotions we meditate for 10 minutes every day and I think that this reflects a larger shift in the generations now that are entering the workforce because I think that there's a shift from a leadership style of pounding your fists on the table saying we're crushing it every day to one where people really want us the authenticity they want to be brought along they don't want the leader that has it all figured out and I think that you know our workforce being very heavy on engineering and AI is tends to be a younger kind of population and that's you know been really key for us to really attract and retain these folks especially when we live in a neighborhood when Google and Facebook are you know constantly approaching our employees and offering them packages of four times what they're making now? Yeah no I mean I think it's a couldn't agree more it's a new new breed of talent that you know I've used the word bilingual before but understand both disciplines I think it's a balance you know but I'm biased but you know we have to get on board and really figure out how to do it effectively thank you for sharing Alan's you know one thing is you know we hear about the data the platform the cultural change and the change in the type of leadership as you mentioned Alice right like all of that is changing but it's in the midst of that I think one thing that concerns people quite a bit is around you know how are we using AI for good you know how are we using it data science in a responsible way in an ethical way in a transparent way because for centuries any new innovation can do a lot of good and the reverse can also happen so the guardrails are really important and the understanding of it and how you apply it is important so with that I'd love to pose the question starting with Junai at first just you know tell us a little bit about what does responsible AI mean to you and then you know what are the aspects to really think about around the data the algorithms, development, deployment and so forth Thank you for the opportunity so the exam question that I focus on at Microsoft is how might we transform the practice of medicine with trusted reliable human-centered AI so it's not just about AI but it's all the precursor words I said that are really really important around the trusted nature of what we do the reliability of things it has to work but within that there's an ethical equitable and responsible component to it and so there's a conversation and an ethos that many communities in the AI space have been thinking about for a long time we have a set of principles and standards within Microsoft that we call the responsible AI principles that we try and adhere to and we think about both the development of tools and products in addition to then the deployment of these tools and products and so we think about the importance of fairness of reliability and safety of privacy and security of inclusiveness of transparency and of accountability and there are checks and balances and a governance process that we have to go through in throughout the course of the research development cycle through to product development cycle and then to the deployment cycle and then what's probably required on the receiving end if we've built what we have how does that look within some of our partner organizations how equipped do they feel around the checks and balances that we have put into the checks and balances that they'll probably have to put in on their side and I think it's an uneven taking thought at the moment where the tech companies have their principles and approach but actually I'm not sure how many farmer companies have a set of responsible AI governance processes in their organizations today that they're willing to adhere, adopt or combine with and then you extend it out and then the farmer companies are probably thinking so what are the regulators thinking about this space and how do they feel about responsible AI and if we were to submit something to a regulator of your choice even if we've adopted these, is this good enough and I sometimes wonder whether everybody's waiting for somebody else to do something first somebody's willing to go to the FDA first or the MHRA first or EMEA first and waiting for them to give permission on the approach but everybody's learning this at pace and scale so we've spoken about generative AI I'm sure if we had this conversation last year we wouldn't even mention generative AI in this dialogue today but now it's in the hands of everybody 100 million people adopting it within six weeks it's democratized access to these tools, techniques and solutions and it will be affecting all aspects of our life as a general purpose technology what do we need to do to ensure the safe deployment of these from healthcare and life science purposes within the organizations that you represent and how we do it from a technology side and work hand in hand to do it in a safe reliable manner I think is the challenge before us collectively The challenge and a huge opportunity so I'm just gonna ask one question lightning round I'm gonna start with Shastri 30 seconds who's gonna be, we have folks from the tech background or tech companies focusing on the space pharma big and small and we also of course have tech bio the new age or biotech tech bio companies we're gonna be the winners in the next five years who's actually gonna make a significant dent in what we consider as valuable impact using data science and making medicines Shastri first but 30 seconds All of them the space is just so big I don't think there's gonna be any single winner that takes all we're gonna see tremendous progress across all of these players and hopefully as a community A, we learn from each other to be able to move the ball forward a lot faster and share best practices and then secondly to build on what Jeanette said I really hope that as an industry we come out with good AI practices just as we have good manufacturing practices good clinical practices we need to come out with good AI practices that allow us to be able to promote and use ethical use of AI because the training data sets actually amplify the disparities in healthcare and the lack of diversity of clinical trials and we just can't keep amplifying the problem we need to figure out how we break it Love it Anne? So in my mind if the winner isn't the patient we can all pack up our bags and go home Seriously That's the best So either we directly get good drugs to patients faster by speeding along the development and discovery cycle or we indirectly make it easier to take away the operational components and things but no matter what way we slice it if the patient's not winning we're not doing our job Alice? I totally agree but I also love being controversial on panels because I think it makes it more interesting I will say that for any fast-moving technology obviously from a biased perspective that ultimately I think that smaller companies are more well-equipped to just move more quickly and capitalize initially on those technologies you know many pharma companies are so talented but I think just structurally there are a lot of challenges that misalign incentives both on the financial side the bottom line and what's really needed to really see a highly risky technology and innovation through so I think if it follows history I think my prediction is that the biggest initial advances will be made on the startup side when there's clear kind of de-risking then pharma companies will come in and collaborate more broadly but I think a lot of the initial innovation will be made from startups as a startup founder myself Great Dina? So I don't think that there is a sector that's going to be a winner or a loser I think actually all of them will have winners and we will have losers and what I think is going to be very important is the role of leaders because there are going to be many big decisions that have high stakes and people are going to make different decisions companies are going to make the right decisions are going to be the winners and companies who are going to be taken by the inertia they might find themselves irrelevant And Junaid, before we go to Q&A bring us home I should have gone before Dina I think it is leadership but it's leadership with a long term view so not just about short termism I think it's sustained leadership and leadership that is not just willing to make the investment but go on the change curve and change and transform business processes within their organizations to enable and unlock the value of these technologies moving forward I couldn't agree more I think the leadership aspect and leaning in day in and day out when it gets hard and actually ensuring we keep the talented folks inside and outside they are empowered so they can actually have an equal share of voice and make a difference I think that's going to be what helps us all Thank you to everyone Now we have a few minutes left and we have a couple of Q&A questions I'll start with Chris Benko Chris, take it away Nice to see you all Thanks, Najaf We have really great representation on this panel as we often do We've got some really well capitalized technology companies pharma companies and other early stage companies represented Right now the capital markets are really tough and I know that we're seeing a lot of earlier stage companies with technology capabilities not have the capital required for their companies to be going concerns or to generate all the clinical evidence they might need and my observation in this field in the last 10 years is that very often people have two out of the three capabilities but you need them all You might have technology and capital but you don't have the clinical development capability or you might have the clinical capabilities like pharma you don't understand the technology So my question back to you all is are resources really allocated in the right place right now across the industry or if you had the opportunity to squeeze the balloon if you will and shift some resources from one place to the other where might you do that in order to make sure that promising innovations actually make their way all the way through to patients and their use at scale I can start with again I want to put in controversial views out there So I would say one area where I think resources are not as democratized as they could be really is in venture capital right now I think a lot of the classic formation of biotech companies is concentrated within very few VCs they go out to academic groups they decide what is promising and then they start these companies from within the VCs oftentimes perverse incentives to flip them and take them public right and I think that's what's led to a lot of the challenges in the bubble that we see right now I think that the field is changing I think we're moving more and more towards like a tech-like model where there's more organic company formation that are coming directly from the scientists the postdocs, the grad schools, out of grad school that are started because there's someone that's passionate about the problem on the idea and I think that overall democratization of company formation dollars into the hands of many rather than the hands of few will lead to better innovation because that will lead to supply demand like what are the ideas that are actually needed by the field rather than just what people think can be profitable in the next five years If I can jump in Chris to answer your question I think just given the resource crunch there is gonna be a greater need for what I'd call translational tech organizations which is how do you work backward from the patient and take all of this amazing innovation that's happening in biology and technology and turn that into treatments that get to patients faster I think we have a lot of innovation that's happening in pockets nobody is really taking a step back and saying how do you string all of this together to be able to fundamentally change the patient outcomes? Sorry, Anne, you were saying something. Yeah, no, actually a similar but different answer actually I think tech companies are really good at the ideas and pitching the ideas and starting there the piece that's missing for me is the scalability for clinical trials and for testing So that piece, if the tech companies were able to have more resources and more understanding of that scalability piece that's so important for us and Pharma and the rigor piece, so scalability and rigor I think that if we could bolster that in the tech side I think that would be really helpful. Thank you, Anne. All right, I'm gonna go. Thank you, Chris, for the question. It's a great question. Deepa, are you on? Deepa, can you hear us? All right, thankfully, I know Deepa's questions. I'm gonna try my best to ask the question. So one of the things that Deepa had mentioned is we talk a lot about the various different data sets and I think Shostri you and others mentioned a little bit around EHR clinical omics data and so forth. Connectivity of the various data sets is really important to be able to do this better at scale. And of course, there's a lot of questions around using it in a way that doesn't add to any bias. So just a couple of answers from the panelists here in terms of how do you see that evolution happening from a multimodal data perspective to answer some of these really, really important questions. I'll throw out two controversial perspectives. The first one is the reason we've hidden behind large data sets of large N numbers is because we want to make up for errors in data. If we had really well curated data for small subpopulations, I think we should be able to get to really good insights a lot faster. So I would like to see a shift away from this big rush we have to get the largest number of data sets to actually get a really clean meticulously curated data sets that are multimodal as well as high resolution that allow us to get insights faster. And then secondly, I think back to my concept of good AI practices, how do you actually make sure that these declining data sets are truly representative of the world so that these algorithms that come out are explainable and actually come out with something that we can rely on is gonna be a big thing that we have to solve for. And you know, there's a part of it that came up and maybe I'll just ask Dina and Alice to comment around perspective data too, right? In terms of generating data that's actually really comprehensive, doesn't have the missingness and is curated the way you need for the question you need to answer because a lot of real world data doesn't have that today and then having really good methods to solve for it. So maybe Dina, do you wanna comment on that and then Alice too? Sure, yeah, so I do agree and actually I work as AI model, that's my bread and butter and I do agree that actually it's very important to like a smaller data set that is right data set is much better in achieving the goal than a bigger data set that is noisy and has missing and wrong information. I particularly think that one of the data that we are missing much of the data that we have is Snapchat like medical record or genetic data. And one of the things that I think it's really important is to be able to have dynamic data that's tracking disease and symptoms and response to medications. Yeah, I think a big learning we've had at Verge is that single data types on their own are not particularly helpful but really looking at where signal converges across multiple data types is where you get the smoking gun of disease pathogenesis, large-scale genetic data sets being a great example of this, I got one in thinking we would find lots of new therapeutic targets, instead we found hundreds of variants each with a small risk of disease and now the missing question is how do these different genetic hits actually tie together? I think the nice thing about prospective data is that it gives you an opportunity to iterate using the models that you've developed. So there's always gonna be learnings on where the gaps are in the data and if you don't have an ability to actually address those gaps quickly, then that can be a big disadvantage versus prospective, let's say you, we discover that certain disease stage or longitudinal data is really important that allows you to alter how you're collecting the data to fill the gap quickly. So with that, great answers from everyone, just wanna say a big thank you to all the panelists. If I reflect back on each year we've had this panel and we go back even two, three years ago, there's been such a progression to see from impact use cases and just a level of sophistication around what works versus not and the collaboration that's increasingly happening to figure it out. So much more to do again for the patients that we all serve. Thank you so much everyone for your time and perspectives today. Back to Andy and Karun. Could we have the pulse light please? Data science panel really terrific and really appreciated the focus on culture as much as the focus on technical. So for audience participation, the question that follows this panel is the following, where are digital technologies likely to make their greatest impact on drug development in the near term? A, digital endpoints, B, digital therapeutics, C, patient recruitment, D, trial decentralization and E, real world evidence collection and interpretation. So I don't think there's a right answer there but we'll see where the audience feels the friends will take us in the near term. So again, please contribute to the polls and once more, thanks to the panel. Could you bring up the results please? Surprise actually, a clear winner actually in terms of the audience. So almost half of you feel that the greatest applications are gonna be in real world evidence generation and interpretation. So really, really terrific. An area that's taking off. And of course as we heard earlier from our FDA commissioner, Rob Califf, an area that's primed for excellence and really to contribute. So terrific, thank you very much.


Facial hair update! No, that's nice. Hello! Welcome to Kill It With Fire! Heatwave!  They're gross. They're coarse. They get everywhere. So today, we will cancel the apocalypse!  You, uh... You, you know. Ooh, inspect. Interesting. Open. What if I want to open the door? And not the drawer? Is this the first time? Oh! Oh!  Oh! Oh! Oh!  Where did it go? Take the clipboard. Okay. First blood. Kill a spider. Spiderman? I see it. I see it. Yeah! Ha-ha! Ha-ha! Die!  Get out of my home! They're everywhere. I can hear them. Get the tracker.  I hear them. Spider tracker? This. The spider tracker. Find the spiders. I see it. I see it. Get over here. Get over here. Get over here.  Where are you? Oh God! Oh God! Don't eat me! Don't eat me! Don't eat me! Dude, where are these fuckers coming from? They're coming out of the trees!  You fast. You fast as hell, but not faster than my clipboard. Ha. Where are you? Where are you? I can hear you. I can hear you. I see you. Ah! Filthy. Filthy.  You're not going to over shit? Ah! Ah! Ah! Where is he? I don't see him anymore. I don't know. I see him! I see him! Get out of my home!  Where he go? I could use the spider tracker, couldn't I? I don't know how the spider tracker works. To be honest. There! There! One more. I hear him. I hear him! Where is this motherfucker? Where is he? You cannot escape me! You cannot escape me, little bug! Now you've died. All right. Give me something better.  Ha ha ha ha! Cannot escape me! Get out of my home! Oh, you think? You think you can escape? Two of you! There's two of you!  I hear him still. One of them is around here. I can smell you. Stench. That's stench. I know it so well. It's the stench of a spoter. Isn't it? The stench of a spoter. Ah! Locked in the drawer! Are you? No.  A gun? My dude, this doesn't seem like something that I should metal with, but I'm going to is the thing.  Where is he? Ah, right there. Well, too bad for you. The exit is unlocked. But I just need... Oh, can I unlock it now? I still need two more, don't I? A gift! Let me try this gun out. Are you at? I know you're in here somewhere. You can't trick me. You think you can, you can't. I'm smarter than you.  I'm leaving. I better not see you here ever again, spoter. All right? If I do, this bullet's going right between your chompers, all right? Oh, equipment. Fire. Oh my god. Is that a big old flamethrower? It is. Oh, this is just equipment that I've unlocked. Okay, let's play. Let's go to close encounters. Ooh, we still have things, too. All right. Where is it? A white spider? A white, a white spider? Get out of my home! Ah! Ah! I don't have it! Just die already. Just die already. Die, thank you. And now, with one shot, I will end- Oh, he's already dead. Okay. What do we have around here? Oh! Oh! It's high noon. Oh, get out. Seems awfully quiet in here. And it is quiet.  Cigarettes. Nope, those were bullets. Cigarettes. All right, what do we got in here? Can I shoot the lock off of this? Is that something that I can do? I don't think so. I don't want to waste my bullets because I have so few of them. Oh no. Is my greatest fear about to come to life? Oh, please. God, do not even think about it. Big red spider! Oh! Oh no! Everything must burn! Everything must burn! I hate it. I hate it. I've never hated anything more. Why did they make this? So awful. Uh, what if- That does work. Okay, cool. Baby. Yes. Oh god. Reload. All in one go. It's a shame you had to go like that. All right, I need one more- one more thing and then I can open whatever that is. Hello? Hello? You've got something for me, I? Well, I've got something for you too. Dude, a streaming setup? Hell yeah. In my bed? In my bed, dude? It makes everything go slow. That's cool. And a key. Interesting. We should probably utilize the energy drink for as long as we have it. Kill all of its babies. Kill all of its babies. Kill all of its babies.  Where did it go? Oh god, it had- it had children. It definitely had babies, right? It totes did. I'm hearing things. There it is. Come on. Where is it? Where is it? Bye bye. Oh no. Oh no. Oh no. I don't have enough equipment. They're going to be everywhere now. You want me some ammo? Whatever I possibly can. Okay. I don't have ammo of any kind. Come on. I can predict your movements. Yes.  What is this? Discover a hidden room. I forgot about my objectives. What doth the key go? What is this? Hmm. Does gamer boy have a secret key? Or a secret room? Secrets of the hidden temple? But where would the secret be? Taking out all the books. It's the secret. Oh baby. We got money. We got bombs. I can get this. What is this? Is this the big flamethrower? Let's go to the next stage. If there is another stage? Let's do one more. I've got six things. Do they explode right away? They don't. Listen. Sometimes you gotta fight fire with fire. Now, let's blow this popsicle stand. Did I do anything? Everything is on fire. I do like that. Oh yes. Fire, aren't you? Are you gonna birth things? Not too shabby. Not too shabby. Killed everybody in here. Alright. Like that. Oh, like that. Oh, like that. Blow my way. I didn't even ever see me coming. Hate that. I wish this game was never made. I'm actually having a lot of fun. This is a good time. Alright. So, the thing here is guys, is that we have. Alright. Alright. Whoopsie daisies. Didn't even mean to hit that that soon. I thought that maybe I would pick it up. But it's actually fine. Cause they're all dying now. They're all going to little spoter heaven. That is exactly what I want for them to go to spoter. Are you jump?   Okay, okay, okay. He's dying. He's dying. He's on fire. I like that. I like it when they catch on fire. It makes me feel nice. Okay, let's get this stuff real quick. We put the tools back? Why we put tool back? Do we get something cool if we put the tools back? Very interesting. Molotov cocktails. Yep. You love to see it. You love to see it. To see it. That's going to be a treat. Oh my god. Oh boy. Listen, that's what happens when you goof with me, baby. Now what? I organized the tools. Do I get anything cool? No. I don't get anything cool for organizing the tools. We, oh, we can leave. Hell yeah. Hell yeah. Let's just destroy everything. We've done it, gamers. We've done it. Mission complete. We killed all the spiders in the whole world. All except for Tomahawk and Spider-Man. All right. Well, this is Kill it with Fire. I like this game a lot. It's really fun. Again, it's early access right now. Coming August 13th. There we go. So pretty soon. Anyway, if you want to check it out, I'll leave it down in the description down below. Thank you guys so much for watching. Hope you enjoyed it. If you did, make sure to slap that like button right in the face. And I will see you guys in the next video. Love you all. Stay cranky. Bye.