1 00:00:00,240 --> 00:00:02,600 Speaker 1: Up next The Truth with Lisa both part of the 2 00:00:04,800 --> 00:00:08,680 Speaker 1: Welcome Back to the Truth with Lisa Booth. Iver Meden. 3 00:00:09,039 --> 00:00:11,440 Speaker 1: You've heard about it in the news. Some say it's 4 00:00:11,480 --> 00:00:14,880 Speaker 1: a game changer for treating COVID. Others say it's a 5 00:00:14,880 --> 00:00:18,040 Speaker 1: bunch of nonsense. So who's right? If you've been watching 6 00:00:18,040 --> 00:00:20,400 Speaker 1: CNN or reading the New York Times and you probably 7 00:00:20,440 --> 00:00:23,119 Speaker 1: think Iver macden is just a horse de wurmer that 8 00:00:23,200 --> 00:00:26,200 Speaker 1: a few crazy doctors and conservatives think can treat COVID. 9 00:00:27,200 --> 00:00:30,160 Speaker 1: That's the message that the media and the medical establishment 10 00:00:30,200 --> 00:00:32,519 Speaker 1: want you to hear, want you to believe. But the 11 00:00:32,560 --> 00:00:36,280 Speaker 1: truth is their substantial evidence showing Iver meckden is actually 12 00:00:36,360 --> 00:00:40,040 Speaker 1: quite effective at treating COVID. Dr Pierre Corey has poured 13 00:00:40,080 --> 00:00:42,000 Speaker 1: through all the evidence and is one of the world's 14 00:00:42,080 --> 00:00:45,120 Speaker 1: leading proponents of Iver macden as a treatment for COVID, 15 00:00:45,200 --> 00:00:47,600 Speaker 1: and he's someone who should take seriously. Dr Corey is 16 00:00:47,640 --> 00:00:50,040 Speaker 1: the president and a founding member of the front Line 17 00:00:50,120 --> 00:00:53,959 Speaker 1: COVID nineteen Critical Care Alliance. Dr Corey leat I see 18 00:00:54,040 --> 00:00:56,880 Speaker 1: us in multiple COVID hotspots, including New York City at 19 00:00:56,920 --> 00:00:58,920 Speaker 1: the height of the pandemic, and during that time he 20 00:00:59,000 --> 00:01:02,880 Speaker 1: also co authored several influential papers on the virus. Previously, 21 00:01:03,000 --> 00:01:05,280 Speaker 1: Dr Corey was the chief of Critical Care Service and 22 00:01:05,280 --> 00:01:08,240 Speaker 1: the medical director at the Trauma and Life Support Center 23 00:01:08,280 --> 00:01:10,800 Speaker 1: at the University of Wisconsin, and before that, he was 24 00:01:10,840 --> 00:01:13,600 Speaker 1: a physician at Beth Israel Medical Center in New York. 25 00:01:13,760 --> 00:01:16,200 Speaker 1: He's also a pioneer in the use of ultrasound by 26 00:01:16,240 --> 00:01:19,520 Speaker 1: physicians and the diagnosis and treatment of critically ill patients, 27 00:01:19,520 --> 00:01:22,320 Speaker 1: and has won numerous teaching awards in every hospital that 28 00:01:22,360 --> 00:01:24,520 Speaker 1: he has worked. But these days he has been a 29 00:01:24,560 --> 00:01:28,000 Speaker 1: passionate advocate for Ivor mecden and a fierce critic of 30 00:01:28,040 --> 00:01:31,280 Speaker 1: the medical establishment, which he believes is more focused on 31 00:01:31,319 --> 00:01:34,080 Speaker 1: other priorities than ensuring the health of the public. Today 32 00:01:34,120 --> 00:01:44,279 Speaker 1: we get to the truth of Ivor macdona. Dr Pierre Corey, 33 00:01:44,440 --> 00:01:46,400 Speaker 1: you know, thank you so much for coming on the show. 34 00:01:46,440 --> 00:01:49,880 Speaker 1: The Truth Quickly sa booth Is podcast actually started during 35 00:01:49,920 --> 00:01:51,920 Speaker 1: COVID because I felt like the truth wasn't getting out 36 00:01:51,920 --> 00:01:53,880 Speaker 1: there about things like lockdown and a lot of the 37 00:01:53,920 --> 00:01:56,760 Speaker 1: data and information. So I'm really excited to talk to 38 00:01:56,800 --> 00:01:59,920 Speaker 1: you about Iver macdon Yeah, I appreciate it. I'm glad 39 00:01:59,920 --> 00:02:01,800 Speaker 1: to have the opportunity to do that. You told a 40 00:02:01,920 --> 00:02:06,520 Speaker 1: Senate committee last December that iver mectin is effectively a 41 00:02:06,560 --> 00:02:10,720 Speaker 1: miracle drug against COVID nineteen. What is it? Yeah, so 42 00:02:10,840 --> 00:02:14,720 Speaker 1: iver mactin is um it's an old medicine. I guess 43 00:02:14,760 --> 00:02:17,519 Speaker 1: not very old. It is very pretty much discovered in 44 00:02:17,600 --> 00:02:21,560 Speaker 1: late seventies and first manufactured and distributed in the eighties 45 00:02:21,560 --> 00:02:26,000 Speaker 1: and nine eighties. It's an anti parasite drug. And the 46 00:02:26,040 --> 00:02:30,720 Speaker 1: discoverers of iver mecton actually won the Nobel Prize because 47 00:02:31,120 --> 00:02:36,160 Speaker 1: that drug actually transformed the global health status of hundreds 48 00:02:36,160 --> 00:02:40,600 Speaker 1: of millions of people that suffered from parasitic diseases. One 49 00:02:40,639 --> 00:02:45,399 Speaker 1: of them was called river blindness, where men, not only men, 50 00:02:45,480 --> 00:02:48,079 Speaker 1: but adults in a lot of communities in Africa were 51 00:02:48,120 --> 00:02:50,520 Speaker 1: blind by the age of forty from this parasite. And 52 00:02:50,560 --> 00:02:54,320 Speaker 1: so it essentially restored the site of of countless people 53 00:02:54,800 --> 00:02:58,919 Speaker 1: across Africa and Asia and even South America. And so 54 00:02:59,120 --> 00:03:02,960 Speaker 1: it's a really important drug historically UM and the who 55 00:03:03,120 --> 00:03:07,240 Speaker 1: actually has distributed across continents hundreds of millions of doses. 56 00:03:07,320 --> 00:03:10,880 Speaker 1: In fact, four billion doses have been used in humans 57 00:03:10,919 --> 00:03:13,480 Speaker 1: over the last four decades. So it's a really uh 58 00:03:13,680 --> 00:03:17,400 Speaker 1: well known and famous drug. The discoverers won the Nobel 59 00:03:17,440 --> 00:03:20,720 Speaker 1: Prize for it. And so that's what that's what it 60 00:03:20,720 --> 00:03:24,360 Speaker 1: originally was discovered to do. But over about ten years 61 00:03:24,360 --> 00:03:26,440 Speaker 1: ago it was discovered in the lab that it was 62 00:03:26,520 --> 00:03:29,360 Speaker 1: really effective against a number of viruses and so like 63 00:03:29,520 --> 00:03:33,160 Speaker 1: ZEKEA and west nil and denge and even HIV and influenza. 64 00:03:33,720 --> 00:03:35,560 Speaker 1: It was showing that, you know, at the bench it 65 00:03:35,600 --> 00:03:37,320 Speaker 1: was it was it was showing that it could stop 66 00:03:37,400 --> 00:03:41,160 Speaker 1: viral replication of a number of viruses similar to coronavirus. 67 00:03:41,200 --> 00:03:45,040 Speaker 1: So how in terms of using it to combat COVID nineteen, 68 00:03:45,440 --> 00:03:47,920 Speaker 1: you know, how is it best used against it? Well, 69 00:03:47,960 --> 00:03:49,880 Speaker 1: there's a number of ways you can use it. Actually, 70 00:03:49,960 --> 00:03:52,840 Speaker 1: I would say there's there's sort of four phases you 71 00:03:52,880 --> 00:03:55,000 Speaker 1: could use it in. And that's what makes it sort 72 00:03:55,040 --> 00:03:59,040 Speaker 1: of just this incredible drug. And as my colleague, UM 73 00:03:59,400 --> 00:04:03,600 Speaker 1: and sort of mentor who first actually identified that we needed. 74 00:04:03,600 --> 00:04:05,000 Speaker 1: You know, I'm part of a group, I'm part of 75 00:04:05,040 --> 00:04:09,040 Speaker 1: an organization. We're just five experts in medicine, highly published, 76 00:04:09,200 --> 00:04:12,920 Speaker 1: very credible. Um. We all have had many contributions to 77 00:04:12,960 --> 00:04:16,640 Speaker 1: medicine and are all actually very well known prior to 78 00:04:16,680 --> 00:04:20,880 Speaker 1: the pandemic. For individual contributions, and you know, we've been 79 00:04:20,920 --> 00:04:23,760 Speaker 1: researching everything COVID since it started. I mean we just 80 00:04:23,920 --> 00:04:26,720 Speaker 1: did nothing but read papers, exchange papers, and we were 81 00:04:26,720 --> 00:04:29,560 Speaker 1: looking at all the therapeutics, all of the trials, and 82 00:04:30,200 --> 00:04:33,640 Speaker 1: we you know Paul Paul Marrack, he he identified AVERMIC 83 00:04:33,760 --> 00:04:38,400 Speaker 1: and as as looking just really good. Uh, probably early October, 84 00:04:38,400 --> 00:04:41,760 Speaker 1: about a year ago, and we started looking into it 85 00:04:41,800 --> 00:04:43,880 Speaker 1: as a group, and I wrote a review paper with 86 00:04:43,960 --> 00:04:46,880 Speaker 1: the group, and we immersed ourselves in all of the 87 00:04:46,920 --> 00:04:52,839 Speaker 1: trials data and we just were overwhelmed. And so um, 88 00:04:52,960 --> 00:04:55,640 Speaker 1: we that that's what led to my testimony where I 89 00:04:55,680 --> 00:04:59,440 Speaker 1: really spoke very forcefully about the critical need to use 90 00:04:59,480 --> 00:05:01,320 Speaker 1: it and so would you use it in COVID? Well, 91 00:05:02,080 --> 00:05:06,560 Speaker 1: the strongest data is in prevention. It's actually this weekly 92 00:05:06,800 --> 00:05:10,760 Speaker 1: potent preventative. So if you take it regularly, um, your 93 00:05:10,800 --> 00:05:14,200 Speaker 1: chances of getting COVID are drastically reduced, especially if you 94 00:05:14,240 --> 00:05:16,880 Speaker 1: take it, you know, like weekly. Some of the trials 95 00:05:16,880 --> 00:05:19,440 Speaker 1: are showing near hundred percent protection, some are showing a 96 00:05:19,760 --> 00:05:23,880 Speaker 1: percent protection or well over ninety. Um. Some trials where 97 00:05:23,920 --> 00:05:27,080 Speaker 1: they take healthcare workers once a month or even showing 98 00:05:27,080 --> 00:05:31,839 Speaker 1: you seventy protection. So that's the prevention data. Then you 99 00:05:31,880 --> 00:05:35,159 Speaker 1: have early treatment, which is different. You wouldn't just take 100 00:05:35,200 --> 00:05:37,240 Speaker 1: one dose, you would take you know, a higher dose 101 00:05:37,320 --> 00:05:40,200 Speaker 1: for some days in a row to three five days. 102 00:05:40,240 --> 00:05:43,000 Speaker 1: We now advocate for five days um. And you can 103 00:05:43,080 --> 00:05:45,760 Speaker 1: use it early treatment, and especially the earlier you start, 104 00:05:45,839 --> 00:05:49,080 Speaker 1: people turn around immediately. Um. They really start to feel 105 00:05:49,120 --> 00:05:52,120 Speaker 1: better very quickly. And we just see these consistent, amazing 106 00:05:52,160 --> 00:05:54,839 Speaker 1: responses with it, and that that's been described by lots 107 00:05:54,839 --> 00:05:57,880 Speaker 1: of doctors around the world. And then later on in 108 00:05:57,960 --> 00:06:01,560 Speaker 1: hospital you have to use much higher doses, less effective 109 00:06:01,560 --> 00:06:04,400 Speaker 1: as a single agent there, but we use combinations of 110 00:06:04,440 --> 00:06:06,640 Speaker 1: therapies in the hospital and that's you know, one of 111 00:06:06,680 --> 00:06:09,680 Speaker 1: them in the in the protocol. And then the last, 112 00:06:09,800 --> 00:06:13,839 Speaker 1: which is incredible, is in long haul covid um. That's 113 00:06:13,880 --> 00:06:16,800 Speaker 1: been just incredible story. Like we use it in long 114 00:06:16,839 --> 00:06:20,200 Speaker 1: haulers and boy do they respond. We've had so many 115 00:06:20,279 --> 00:06:22,680 Speaker 1: people who are like I've had a number of patients 116 00:06:22,680 --> 00:06:25,719 Speaker 1: who are effectively disabled with long haul just couldn't function, 117 00:06:26,279 --> 00:06:30,360 Speaker 1: Fatigue all the time, dizzy, high heart rates, sweaty, like 118 00:06:30,400 --> 00:06:32,960 Speaker 1: all these odd like sort of what we call autonomic 119 00:06:33,040 --> 00:06:37,719 Speaker 1: symptoms that went away with ivermectin. On the challenge with 120 00:06:37,839 --> 00:06:41,240 Speaker 1: long haul is that we're finding that they actually need 121 00:06:41,279 --> 00:06:43,960 Speaker 1: to stay on it, so that most of my patients 122 00:06:44,000 --> 00:06:47,000 Speaker 1: with long haul they take two to three doses a week, 123 00:06:47,080 --> 00:06:49,080 Speaker 1: and if they miss a dose or try to stop 124 00:06:49,160 --> 00:06:50,720 Speaker 1: or try to wing them off, a lot of their 125 00:06:50,720 --> 00:06:53,560 Speaker 1: symptoms come back. And so we still haven't figure out 126 00:06:53,560 --> 00:06:56,400 Speaker 1: how to cure it, but we're definitely managing it really well. 127 00:06:56,880 --> 00:06:59,920 Speaker 1: And how safe is it. It's one of the safest 128 00:07:00,120 --> 00:07:03,800 Speaker 1: drugs known to man. So don't believe everything you read this. 129 00:07:04,279 --> 00:07:06,720 Speaker 1: You know, I'm just gonna speak frankly because I'm really done. 130 00:07:06,760 --> 00:07:08,159 Speaker 1: I've been doing this for a year and I'm just 131 00:07:08,160 --> 00:07:11,800 Speaker 1: gonna tell you what I'm seeing and just the absurdity. 132 00:07:11,960 --> 00:07:17,200 Speaker 1: But this controversy around iver mecton, I need to be clear, 133 00:07:17,320 --> 00:07:22,800 Speaker 1: it's not controversy. It's corruption masquerading as controversy. It is 134 00:07:22,880 --> 00:07:27,360 Speaker 1: what happens when you have a repurpose drug that's threatens 135 00:07:27,440 --> 00:07:30,240 Speaker 1: the financial interests of the pharmaceutical industry. They've been doing 136 00:07:30,280 --> 00:07:33,400 Speaker 1: this for years. Iver Macton is not unique at all. 137 00:07:34,160 --> 00:07:37,040 Speaker 1: I mean, Mettan is a repurpose drug, and the phone 138 00:07:37,080 --> 00:07:39,720 Speaker 1: student industry has been at war with repurpose drugs for 139 00:07:39,840 --> 00:07:43,240 Speaker 1: decades and so when you see all of this stuff 140 00:07:43,280 --> 00:07:46,800 Speaker 1: that they put out and in trying to inject you know, 141 00:07:46,880 --> 00:07:50,160 Speaker 1: controversy or doubt or distorting or suppressing the data around 142 00:07:50,160 --> 00:07:53,280 Speaker 1: iver mectin, it's just part of a playbook of those 143 00:07:53,320 --> 00:07:55,760 Speaker 1: who have, you know, deep financial interest in making sure 144 00:07:55,760 --> 00:07:59,320 Speaker 1: that iver mectin is not recognized as an effective therapeutic. 145 00:07:59,400 --> 00:08:04,120 Speaker 1: And so you know, these concerns on safety is just bizarre. 146 00:08:04,400 --> 00:08:07,680 Speaker 1: I mean it's not only bizarre, it's actually just absolutely false. 147 00:08:08,440 --> 00:08:11,800 Speaker 1: In the w h O documents themselves, their guidelines for 148 00:08:11,880 --> 00:08:17,080 Speaker 1: treating parasitic diseases, they repeatedly state that ibra mactin the 149 00:08:17,200 --> 00:08:20,760 Speaker 1: side effects are generally minor and transient. One of the 150 00:08:20,800 --> 00:08:23,440 Speaker 1: world experts who did a safety review of ira mactin 151 00:08:24,000 --> 00:08:28,400 Speaker 1: said that severe side effects are unequivocally and exceedingly rare. 152 00:08:29,440 --> 00:08:31,880 Speaker 1: It's not toxic to the liver, it's not toxic to 153 00:08:31,920 --> 00:08:35,400 Speaker 1: the kidneys, it's not toxic to the lungs, and like 154 00:08:35,440 --> 00:08:39,520 Speaker 1: I said, it's distributed across continents to people, old, young, infirm, 155 00:08:39,760 --> 00:08:44,040 Speaker 1: more abidities for decades, and we have tons of safety 156 00:08:44,080 --> 00:08:46,760 Speaker 1: data now in COVID even at very high doses and 157 00:08:46,840 --> 00:08:50,760 Speaker 1: for extended durations and so there's not one lack of 158 00:08:50,800 --> 00:08:53,360 Speaker 1: a safety there's not one signal that shows that it's 159 00:08:53,400 --> 00:08:58,000 Speaker 1: not safe. And so even in overdoses, So in the 160 00:08:58,120 --> 00:09:03,760 Speaker 1: safety review, there's acts not one documented accepted instance of 161 00:09:03,760 --> 00:09:07,719 Speaker 1: a death directly caused by ivermecne, even in massive overdoses. 162 00:09:08,400 --> 00:09:11,240 Speaker 1: So massive overdoses, people have gotten sick, they go to 163 00:09:11,280 --> 00:09:13,800 Speaker 1: the hospital and with just supportive care, they're they're better 164 00:09:13,840 --> 00:09:15,760 Speaker 1: in two to three days. But those are like you 165 00:09:15,760 --> 00:09:17,640 Speaker 1: can count them on one hand the amount of times 166 00:09:17,679 --> 00:09:22,720 Speaker 1: that's been reported. And so the safety is just unparalleled, 167 00:09:22,840 --> 00:09:26,440 Speaker 1: absolutely unparalleled. Well, and isn't problem part of the problem 168 00:09:26,480 --> 00:09:28,599 Speaker 1: as you see the media and the f DA, you know, 169 00:09:28,640 --> 00:09:30,920 Speaker 1: they're trying to label it as a horsety warmer because 170 00:09:30,920 --> 00:09:33,640 Speaker 1: it does have purposes for both livestock and for humans, 171 00:09:33,640 --> 00:09:35,880 Speaker 1: But it isn't part of the challenges. You know, you 172 00:09:35,960 --> 00:09:39,520 Speaker 1: have people taking dosages that they shouldn't be taking because 173 00:09:39,520 --> 00:09:42,480 Speaker 1: obviously a dosage meant for a cow or horse that 174 00:09:42,600 --> 00:09:45,320 Speaker 1: weighs like a thousand pounds or you know, more than 175 00:09:45,360 --> 00:09:47,439 Speaker 1: a ton is not going to be healthy for a 176 00:09:47,520 --> 00:09:50,280 Speaker 1: human being, right, And so isn't that part of the 177 00:09:50,440 --> 00:09:53,240 Speaker 1: challenge there or what what is that? So the challenges 178 00:09:53,400 --> 00:09:57,920 Speaker 1: this I would reframe that, which is that without guidance 179 00:09:58,520 --> 00:10:01,040 Speaker 1: from the health agents, then you're not going to get 180 00:10:01,080 --> 00:10:04,040 Speaker 1: that right. So, so the health agents are firmly opposed 181 00:10:04,040 --> 00:10:08,480 Speaker 1: to iver mac them. Um Again, I'm sorry, but we 182 00:10:08,480 --> 00:10:11,120 Speaker 1: we are seeing one of the grossest and most absurd 183 00:10:11,160 --> 00:10:15,400 Speaker 1: examples of regulatory capture and history. Right. So those agencies, 184 00:10:15,400 --> 00:10:20,520 Speaker 1: what I call the alphabet agencies, are literally acting under 185 00:10:20,559 --> 00:10:25,400 Speaker 1: the sole intent and guidance of the pharmaceutical industry. It's 186 00:10:25,440 --> 00:10:28,640 Speaker 1: absolutely clear there's abundant amounts of evidence. And it's not 187 00:10:28,720 --> 00:10:32,880 Speaker 1: just around iver mactin, it's around remdessevere, it's around the vaccines. 188 00:10:33,000 --> 00:10:36,960 Speaker 1: And so to ask for guidance from them on two 189 00:10:36,960 --> 00:10:40,200 Speaker 1: people and how to or providers, how to prescribe and 190 00:10:40,200 --> 00:10:42,439 Speaker 1: how to dose it, they're not giving it. So you're 191 00:10:42,480 --> 00:10:46,720 Speaker 1: creating a situation were Unfortunately, people who you know, have 192 00:10:46,840 --> 00:10:49,719 Speaker 1: followed the data, they follow credible scientists like myself and 193 00:10:49,760 --> 00:10:53,560 Speaker 1: my organization. They understand its efficacy and they hear the 194 00:10:53,559 --> 00:10:55,760 Speaker 1: reports from around the world and they want to use it. 195 00:10:55,840 --> 00:10:59,680 Speaker 1: And so unfortunately they're they're having to self prescribe. Now 196 00:11:00,200 --> 00:11:03,719 Speaker 1: those reports of overdoses where people are filling e rs. 197 00:11:03,880 --> 00:11:09,000 Speaker 1: You understand at least those were completely false, like totally false, 198 00:11:09,040 --> 00:11:12,080 Speaker 1: and I've been debunked, And so no one's filling up 199 00:11:12,120 --> 00:11:15,720 Speaker 1: e rs with overdoses. The calls to the poison Control Center, 200 00:11:15,840 --> 00:11:19,240 Speaker 1: I the vast majority of people asking questions because they 201 00:11:19,280 --> 00:11:22,440 Speaker 1: were forced to take animal veterinary products. Is no one's 202 00:11:22,440 --> 00:11:25,120 Speaker 1: going to prescribe it, or a very few doctors are prescribing. 203 00:11:26,040 --> 00:11:29,120 Speaker 1: So it's just an unfortunate situation of a war on 204 00:11:29,160 --> 00:11:32,960 Speaker 1: a very safe, old, cheap and repurpose drug, and so 205 00:11:33,000 --> 00:11:35,040 Speaker 1: it's not being made available and there's not good guidance 206 00:11:35,080 --> 00:11:37,480 Speaker 1: on on how to use them. So I think these 207 00:11:37,520 --> 00:11:42,080 Speaker 1: people are unfortunately having the self prescribed. They're gonna make mistakes, 208 00:11:42,160 --> 00:11:44,480 Speaker 1: But I gotta tell you it has such a wide 209 00:11:44,520 --> 00:11:47,960 Speaker 1: margin of safety around dosing that I mean every time 210 00:11:47,960 --> 00:11:51,120 Speaker 1: I read an article about people overdosing, I mean I laugh, 211 00:11:51,840 --> 00:11:54,440 Speaker 1: I mean literally that it's very hard to do that. 212 00:11:55,040 --> 00:11:57,400 Speaker 1: And just because you take a horse paste, I mean 213 00:11:57,720 --> 00:12:00,880 Speaker 1: it clearly says on the box, you know that this 214 00:12:00,880 --> 00:12:03,319 Speaker 1: this much for a thousand pound horse, this much for 215 00:12:03,360 --> 00:12:06,000 Speaker 1: a two and fifty pound So just because you're taking 216 00:12:06,000 --> 00:12:07,880 Speaker 1: a horse space doesn't mean that you're going to overdose 217 00:12:08,080 --> 00:12:14,120 Speaker 1: and so UM. Now again I cannot advocate for veterinary products. UM. 218 00:12:14,240 --> 00:12:16,360 Speaker 1: I feel bad for those people who resort to that. 219 00:12:16,440 --> 00:12:18,840 Speaker 1: But you know, it's like a colleague said, you know, 220 00:12:18,880 --> 00:12:21,360 Speaker 1: it's like someone who's dying of first who's forced to 221 00:12:21,800 --> 00:12:23,920 Speaker 1: drink out of a muddy creek. It's it's a truly 222 00:12:24,000 --> 00:12:28,839 Speaker 1: unfortunate situation. And our organization has been working tiresly trying 223 00:12:28,880 --> 00:12:31,120 Speaker 1: to get the agency is trying to get someone to 224 00:12:31,240 --> 00:12:35,400 Speaker 1: provide guidance to providers, at least a weak recommendation, some recommendation, 225 00:12:35,800 --> 00:12:37,800 Speaker 1: and they refuse to do so. Why are they so 226 00:12:37,920 --> 00:12:40,120 Speaker 1: against it? I mean, as you mentioned the alphabet agencies. 227 00:12:40,160 --> 00:12:42,120 Speaker 1: You've got the f d A, the ni AGE, the 228 00:12:42,160 --> 00:12:46,360 Speaker 1: World Health Organization, the Journal, American Medical Association. You all 229 00:12:46,400 --> 00:12:50,959 Speaker 1: against iver MACTAM for usage to combat COVID. Why are 230 00:12:51,000 --> 00:12:54,760 Speaker 1: they so against it? So there's many reasons. And again 231 00:12:54,800 --> 00:12:58,200 Speaker 1: I hate sounding like a conspiracy theorist, but you know 232 00:12:58,240 --> 00:13:01,040 Speaker 1: when I said regulatory caps R, the w h O 233 00:13:01,200 --> 00:13:03,880 Speaker 1: has been well documented to be under the influence of 234 00:13:03,880 --> 00:13:10,440 Speaker 1: the pharmaceutical industry and or philanthropy organizations which are very 235 00:13:10,520 --> 00:13:15,000 Speaker 1: heavily vaccine influenced, and so it's viewed as an opponent 236 00:13:15,040 --> 00:13:20,160 Speaker 1: of vaccine policy. That's one is that the one one 237 00:13:20,280 --> 00:13:22,760 Speaker 1: argument is that in the eu A, so the Emergency 238 00:13:22,800 --> 00:13:27,559 Speaker 1: Youth Authorization for these vaccines, it's dependent on the fact 239 00:13:27,840 --> 00:13:31,040 Speaker 1: that there is no effective treatment for the disease. Because 240 00:13:31,080 --> 00:13:32,880 Speaker 1: if you have an effect to treatment for the disease, 241 00:13:34,200 --> 00:13:36,440 Speaker 1: if you played by the rules, the eu AS could 242 00:13:36,480 --> 00:13:40,040 Speaker 1: would have to be rescinded for the vaccines. Right, So 243 00:13:40,080 --> 00:13:42,920 Speaker 1: there's there's that that's a very clear and almost legal 244 00:13:43,280 --> 00:13:47,000 Speaker 1: incentive to ensure that iver mectant is not recognized. So 245 00:13:47,080 --> 00:13:49,560 Speaker 1: that would be one. The second is the list of 246 00:13:49,640 --> 00:13:54,559 Speaker 1: financial interests that would be really kind of smashed if 247 00:13:54,600 --> 00:13:58,000 Speaker 1: iver mettan was widely used and adopted is a really 248 00:13:58,000 --> 00:14:02,960 Speaker 1: long list, right, so number one, um number one is 249 00:14:03,280 --> 00:14:06,320 Speaker 1: just something like remdesse of her. I mean, we're seeing 250 00:14:06,360 --> 00:14:08,679 Speaker 1: health industries around the world that are using this in 251 00:14:08,800 --> 00:14:14,040 Speaker 1: early testing treat programs like well over seventy of people 252 00:14:14,040 --> 00:14:19,240 Speaker 1: are avoiding hospital and so you would literally decrease hospitalizations 253 00:14:19,240 --> 00:14:21,080 Speaker 1: on the order and that's the minimum of what would 254 00:14:21,080 --> 00:14:26,880 Speaker 1: be capable of and so the appetite remdesse of her 255 00:14:26,920 --> 00:14:30,240 Speaker 1: would dry up. There are competing oral anti virals that 256 00:14:30,280 --> 00:14:35,240 Speaker 1: are in the pipeline from Mark and Fiser, and they 257 00:14:35,240 --> 00:14:37,400 Speaker 1: want to bring those to market, and that would be 258 00:14:37,440 --> 00:14:40,200 Speaker 1: a huge bonanza for them in a pandemic to bring 259 00:14:40,200 --> 00:14:43,640 Speaker 1: an oral anti viral for early treatment. If iver Magnan 260 00:14:43,760 --> 00:14:46,320 Speaker 1: is in that market, I mean, what happens to that 261 00:14:46,320 --> 00:14:50,400 Speaker 1: that they have no market for that? And so again 262 00:14:50,440 --> 00:14:52,960 Speaker 1: I'm just stating it's I've had a front row seat 263 00:14:53,000 --> 00:14:56,160 Speaker 1: to this. I've seen the attacks. I've seen the distortions, 264 00:14:56,600 --> 00:15:01,760 Speaker 1: the misrepresentations, the the false the false statements that are 265 00:15:01,760 --> 00:15:04,920 Speaker 1: coming out of those agencies. It's it's it's just been. 266 00:15:04,960 --> 00:15:07,760 Speaker 1: It's been a horror show, to be honest, and I've 267 00:15:07,800 --> 00:15:10,000 Speaker 1: had an education of a lifetime. I'll never be the 268 00:15:10,040 --> 00:15:14,240 Speaker 1: same again. And I just my only hopes is that 269 00:15:14,320 --> 00:15:18,320 Speaker 1: when this story is written and the history books are written, 270 00:15:18,360 --> 00:15:20,360 Speaker 1: and hopefully within a year or two when this comes out, 271 00:15:21,200 --> 00:15:23,640 Speaker 1: is that we we we stopped this system. I mean, 272 00:15:23,640 --> 00:15:27,640 Speaker 1: we're in a system where we literally are driven. It's 273 00:15:27,720 --> 00:15:31,520 Speaker 1: basically run on four profit medicines. There's no appetite and 274 00:15:31,520 --> 00:15:35,680 Speaker 1: there's no pathway for nonprofit medicines to make it, and 275 00:15:35,680 --> 00:15:38,200 Speaker 1: and it's killing people and that's why I hope this 276 00:15:38,280 --> 00:15:40,640 Speaker 1: system blows up. Well, I mean, I would love to 277 00:15:40,640 --> 00:15:42,240 Speaker 1: see that. I don't trust any of these people. And 278 00:15:42,520 --> 00:15:44,880 Speaker 1: you talk about the profit driven aspect of it, I mean, 279 00:15:44,920 --> 00:15:47,760 Speaker 1: you look at the COVID vaccine as Viser's top seller alone. 280 00:15:47,880 --> 00:15:50,560 Speaker 1: You know they're going to make over thirty three billion 281 00:15:50,600 --> 00:15:53,560 Speaker 1: dollars from the two doors regimen by itself, not even 282 00:15:53,560 --> 00:15:55,840 Speaker 1: including the additional amount of money they're going to make 283 00:15:56,120 --> 00:15:58,720 Speaker 1: from boosters. And then you know how many boosters they 284 00:15:58,720 --> 00:16:01,480 Speaker 1: actually end up we're seeing people to get. So that's 285 00:16:01,680 --> 00:16:04,520 Speaker 1: so much money involved here. So what's interesting is that 286 00:16:04,640 --> 00:16:11,120 Speaker 1: other countries have used like India ends Yeah, talk about India. Yeah, So, Lisa, 287 00:16:11,160 --> 00:16:15,640 Speaker 1: you just mentioned right, this this endless, this endless horizon 288 00:16:15,840 --> 00:16:19,560 Speaker 1: of these just astronomical profits that standing to be gained 289 00:16:19,680 --> 00:16:26,920 Speaker 1: from from a market of of just endless vaccines and boosters. Right. Meanwhile, right, 290 00:16:27,040 --> 00:16:30,840 Speaker 1: one of the most incredible stories, and I think one 291 00:16:30,840 --> 00:16:34,760 Speaker 1: of the most major public health achievements in history, was 292 00:16:34,840 --> 00:16:38,200 Speaker 1: just realized in the state of Utar Pradeshian India. Right. 293 00:16:38,240 --> 00:16:41,400 Speaker 1: So it's a state in northern India has a population 294 00:16:41,560 --> 00:16:45,720 Speaker 1: of two hundred and forty one million people, which is 295 00:16:45,760 --> 00:16:48,680 Speaker 1: basically like a country two thirds of size the United States. 296 00:16:48,680 --> 00:16:50,240 Speaker 1: I think it would be like the seventh or eighth 297 00:16:50,280 --> 00:16:54,000 Speaker 1: largest country if it was a country. And that state 298 00:16:54,080 --> 00:16:59,880 Speaker 1: is unique because they adopted iver macton. They first started 299 00:17:00,000 --> 00:17:03,600 Speaker 1: a relaxing healthcare workers with hydroxy cork in a year ago. 300 00:17:04,240 --> 00:17:07,040 Speaker 1: Then they did a study where they started giving healthcare 301 00:17:07,040 --> 00:17:10,080 Speaker 1: workers I mean, and they noticed that almost none of 302 00:17:10,080 --> 00:17:13,560 Speaker 1: the healthcare workers were getting sick. And so that state 303 00:17:13,720 --> 00:17:16,240 Speaker 1: then put it into a policy throughout the state and 304 00:17:16,280 --> 00:17:19,560 Speaker 1: they not only started doing prevention of healthcare of all 305 00:17:19,600 --> 00:17:22,920 Speaker 1: healthcare workers, but they started using it in early treatment, 306 00:17:23,400 --> 00:17:26,560 Speaker 1: and they started using in prevention of household contacts. And 307 00:17:26,600 --> 00:17:29,879 Speaker 1: what they did is it's such an incredible story of 308 00:17:29,920 --> 00:17:32,840 Speaker 1: what they did. But they had also a massive public 309 00:17:32,880 --> 00:17:37,360 Speaker 1: health contact tracing. They had seventy thousand healthcare workers all 310 00:17:37,480 --> 00:17:40,600 Speaker 1: on ivermectin and they went to all of the household 311 00:17:40,680 --> 00:17:44,320 Speaker 1: so they did contact tracing and surveillance every household. Every 312 00:17:44,359 --> 00:17:47,240 Speaker 1: positive test, they visited the house, they gave ivermectin and 313 00:17:47,240 --> 00:17:50,399 Speaker 1: treatment to the person who's sick. They gave it to 314 00:17:50,880 --> 00:17:56,760 Speaker 1: um the house the household contacts and using that strategy, 315 00:17:56,800 --> 00:17:59,640 Speaker 1: they did incredibly well in the fall, and they did. 316 00:18:00,000 --> 00:18:02,479 Speaker 1: They had some of the best numbers in the world 317 00:18:03,040 --> 00:18:07,960 Speaker 1: until April May, when that huge crisis overwhelmed India. And 318 00:18:08,000 --> 00:18:10,080 Speaker 1: what happened in Utar Pradesh is they had about three 319 00:18:10,119 --> 00:18:14,120 Speaker 1: million migrant workers who worked in cities around India who 320 00:18:14,200 --> 00:18:18,000 Speaker 1: all fled the impending lockdowns um and they fled back 321 00:18:18,040 --> 00:18:20,879 Speaker 1: to Uttar Pradesh. So there was this huge like surge 322 00:18:21,119 --> 00:18:24,560 Speaker 1: of cases um. But what Utarparadesh did was it's just 323 00:18:24,600 --> 00:18:26,720 Speaker 1: so smart, like what they did should be the playbook 324 00:18:26,760 --> 00:18:28,640 Speaker 1: for the world. But they went to all the train 325 00:18:28,760 --> 00:18:31,639 Speaker 1: stations and bus stations and airports and they tested, they treated, 326 00:18:31,680 --> 00:18:36,679 Speaker 1: they followed, and their cliff like drop was just impressive. 327 00:18:36,720 --> 00:18:39,760 Speaker 1: So that this huge meteoric rise and then a sudden 328 00:18:39,800 --> 00:18:43,199 Speaker 1: drop because they knew how to extinguish this surge. Okay, 329 00:18:43,359 --> 00:18:46,720 Speaker 1: let's talk about what they achieved. Since then, they continued 330 00:18:46,880 --> 00:18:50,600 Speaker 1: on with that policy and with their program. And in 331 00:18:50,640 --> 00:18:52,920 Speaker 1: the last two weeks, we've been waiting for this. I've 332 00:18:52,920 --> 00:18:55,199 Speaker 1: been waiting for granular data because we can see the 333 00:18:55,200 --> 00:18:58,960 Speaker 1: epidemiologic data. But in the last two weeks, finally the 334 00:18:59,040 --> 00:19:02,800 Speaker 1: health officials Butar Pradesh are now coming out. They're sharing 335 00:19:02,960 --> 00:19:06,119 Speaker 1: really granular data and they're doing interviews and basically what 336 00:19:06,240 --> 00:19:11,760 Speaker 1: happened in that state is they've effectively eradicated COVID. So 337 00:19:12,040 --> 00:19:16,160 Speaker 1: in the last week, there was an article last week 338 00:19:16,200 --> 00:19:18,960 Speaker 1: that showed the prior week, of the two hundred and 339 00:19:18,960 --> 00:19:22,719 Speaker 1: twenty six thousand tests that were done in the previous 340 00:19:22,720 --> 00:19:28,119 Speaker 1: actually twenty four hours, only eleven were positive, which is 341 00:19:28,160 --> 00:19:30,600 Speaker 1: like a positive rate of like point oh four which 342 00:19:30,640 --> 00:19:34,480 Speaker 1: is indescribable and effectively zero. And then in the prior 343 00:19:34,520 --> 00:19:37,240 Speaker 1: two weeks they had done two and a half million 344 00:19:37,440 --> 00:19:41,679 Speaker 1: tests and only two hundred one were positive, which is 345 00:19:41,720 --> 00:19:46,439 Speaker 1: like a point oh seven percent, effectively zero. And they 346 00:19:46,520 --> 00:19:49,680 Speaker 1: have like out of seven five districts in that state, 347 00:19:49,680 --> 00:19:52,280 Speaker 1: of two or forty one million people, I think sixty 348 00:19:52,320 --> 00:19:56,119 Speaker 1: five of them have no active cases of COVID. And 349 00:19:56,200 --> 00:19:59,240 Speaker 1: so so when you talk about that endless mill of 350 00:19:59,320 --> 00:20:01,800 Speaker 1: Monday to be made from vaccines, you're talking about a 351 00:20:01,920 --> 00:20:04,160 Speaker 1: huge portion of the globe, which is two forty one 352 00:20:04,160 --> 00:20:07,840 Speaker 1: million people who have eradicated COVID. And you know what 353 00:20:07,880 --> 00:20:14,200 Speaker 1: their vaccination rate is, Lisa five fully vaccinated. Such a racket, 354 00:20:14,680 --> 00:20:17,119 Speaker 1: So they did not do it with the vaccines. And 355 00:20:17,160 --> 00:20:20,360 Speaker 1: now can you understand why there's so much opposition. Let's 356 00:20:20,359 --> 00:20:22,680 Speaker 1: take a quick commercial break and then back with Dr 357 00:20:22,760 --> 00:20:30,399 Speaker 1: Corey on the other side. So you've compared what's happening 358 00:20:30,440 --> 00:20:33,440 Speaker 1: today with Iver Macton to Dr Fauci and the and 359 00:20:33,560 --> 00:20:36,280 Speaker 1: I age not recognizing the efficacy of backdroom. I think 360 00:20:36,280 --> 00:20:39,159 Speaker 1: I'm pronouncing it right for aids in the nies. What 361 00:20:39,200 --> 00:20:41,280 Speaker 1: are the parallels there for the folks at home who 362 00:20:41,320 --> 00:20:45,119 Speaker 1: are sort of unaware of that. So the parallel, the 363 00:20:45,520 --> 00:20:50,919 Speaker 1: closest parallel is during the HIV epidemic. You know smart 364 00:20:51,000 --> 00:20:53,840 Speaker 1: doctors I call him frontline doctors who are actually seeing 365 00:20:54,000 --> 00:20:56,879 Speaker 1: HIV and treating them. You know that they started to 366 00:20:56,920 --> 00:20:59,200 Speaker 1: see that many of the young and mostly young men 367 00:20:59,200 --> 00:21:01,560 Speaker 1: in the beginning, right, were dying of this pneumonia, right, 368 00:21:01,560 --> 00:21:05,480 Speaker 1: which everyone knows is pc pneumonia, which is actually a fungus. 369 00:21:05,520 --> 00:21:09,840 Speaker 1: And they knew, I mean, it's this is like, I mean, 370 00:21:09,920 --> 00:21:13,919 Speaker 1: it's just so straightforward. But like those doctors knew that 371 00:21:13,960 --> 00:21:17,320 Speaker 1: there was a lot of literature that when Pete, when 372 00:21:17,359 --> 00:21:20,919 Speaker 1: pc P pneumonia occurred in leukemia patients who are severely 373 00:21:20,960 --> 00:21:26,680 Speaker 1: admit suppressed, it was really successfully treated with bactroom, right, 374 00:21:27,480 --> 00:21:31,520 Speaker 1: and so it wasn't a stretch to use bathroom in 375 00:21:31,560 --> 00:21:34,400 Speaker 1: these young men dying of AIDS and those that were 376 00:21:34,520 --> 00:21:38,439 Speaker 1: using bathroom saw that it worked, and so they really 377 00:21:38,480 --> 00:21:41,159 Speaker 1: they went to the NIH and they were saying, please 378 00:21:41,240 --> 00:21:45,280 Speaker 1: provide guidance, like you should prove or recommend backtroom for 379 00:21:45,280 --> 00:21:49,439 Speaker 1: the treatment of HIV and PC pneumonia. And what did 380 00:21:49,480 --> 00:21:54,800 Speaker 1: they do, Lisa. They did not do that. They refused 381 00:21:54,800 --> 00:21:58,760 Speaker 1: to provide guidance or recommendations on a repurpose drug for 382 00:21:58,800 --> 00:22:01,879 Speaker 1: the treatment of this deadly pneumonia that was killing uh, 383 00:22:02,359 --> 00:22:05,600 Speaker 1: you know young gay or HIV in factors they weren't 384 00:22:05,600 --> 00:22:07,920 Speaker 1: all gay, right that women start to get HIV very 385 00:22:07,920 --> 00:22:10,679 Speaker 1: soon after that, and so you know, there's thousands of 386 00:22:10,680 --> 00:22:14,080 Speaker 1: people who died because of lack of guidance around using 387 00:22:14,080 --> 00:22:17,720 Speaker 1: this cheap and repurpose drug BACKTROM. And the thoughts were 388 00:22:17,760 --> 00:22:20,439 Speaker 1: that they were testing some other for profit drugs and 389 00:22:20,480 --> 00:22:23,080 Speaker 1: they were looking for new HIV anti virals, and at 390 00:22:23,080 --> 00:22:26,400 Speaker 1: one point some ludicrous public health fish actually said, oh, 391 00:22:26,480 --> 00:22:29,560 Speaker 1: now that we have a zy t you won't need 392 00:22:29,600 --> 00:22:32,000 Speaker 1: backtroom for for p JP because they thought they had 393 00:22:32,040 --> 00:22:34,160 Speaker 1: a cure for it. I mean, the whole thing is crazy. 394 00:22:34,240 --> 00:22:37,520 Speaker 1: But the point is, frontline docks knew what was working. 395 00:22:37,760 --> 00:22:40,080 Speaker 1: They didn't need a big randomized control trial. They knew 396 00:22:40,080 --> 00:22:44,120 Speaker 1: something was effective. Um, they knew the mechanisms, and yet 397 00:22:44,240 --> 00:22:47,280 Speaker 1: you didn't get the agencies listening to them and following 398 00:22:47,320 --> 00:22:50,919 Speaker 1: their guidance. Iver Mecton is the same frontline docks have 399 00:22:51,080 --> 00:22:54,280 Speaker 1: long known in this pandemic, from as early as last year, 400 00:22:54,960 --> 00:22:57,160 Speaker 1: um in March and April. Those who started using it, 401 00:22:57,480 --> 00:23:01,360 Speaker 1: they've known that this was wickedly effective as this virus, 402 00:23:01,400 --> 00:23:03,639 Speaker 1: and that number of docs who have understood that is 403 00:23:03,720 --> 00:23:07,919 Speaker 1: increasing and increasing and increasing, and in this country it's increasing. 404 00:23:08,520 --> 00:23:12,479 Speaker 1: So sort of put things in context. What I call farmageddon, right, 405 00:23:12,480 --> 00:23:15,520 Speaker 1: so what's today today is like September or twenty two 406 00:23:15,760 --> 00:23:18,680 Speaker 1: or something. You know, farm agedting started like two and 407 00:23:18,720 --> 00:23:20,400 Speaker 1: a half weeks ago, and it's it's what I call 408 00:23:20,520 --> 00:23:23,919 Speaker 1: this just insane battle against iber macta, which hit the 409 00:23:23,960 --> 00:23:26,520 Speaker 1: media and the late night shows and all of those 410 00:23:26,560 --> 00:23:30,400 Speaker 1: horse paste articles and all of these attacks right as 411 00:23:30,440 --> 00:23:32,879 Speaker 1: this you know, people eating horse paste and it's an 412 00:23:32,920 --> 00:23:36,399 Speaker 1: animal drug and all of this insane stuff. You know, 413 00:23:36,440 --> 00:23:40,520 Speaker 1: what triggered that. What triggered that was that the prescriptions 414 00:23:40,560 --> 00:23:43,720 Speaker 1: for iver mectin in this country, we're going through the 415 00:23:43,840 --> 00:23:49,040 Speaker 1: roof doctors and patients were learning that this is highly 416 00:23:49,080 --> 00:23:53,399 Speaker 1: effective against COVID, and so what happened is, you know, 417 00:23:54,040 --> 00:23:56,480 Speaker 1: now now we're an all out war and it's really 418 00:23:56,480 --> 00:23:58,760 Speaker 1: a war on people and the doctors who know that 419 00:23:58,800 --> 00:24:01,800 Speaker 1: there's an effective cheap drugs. You know, cheapness is by 420 00:24:01,840 --> 00:24:06,000 Speaker 1: the way, it costs six cents for a twelve milligram 421 00:24:06,000 --> 00:24:10,440 Speaker 1: tablet to make six cents, and so you know that's 422 00:24:10,480 --> 00:24:13,920 Speaker 1: the parallel is that again they want to they want 423 00:24:14,000 --> 00:24:17,880 Speaker 1: to have some four for profit drugs to treat this illness. 424 00:24:18,359 --> 00:24:20,600 Speaker 1: They want to make room for the for profit drugs. 425 00:24:20,600 --> 00:24:23,639 Speaker 1: And if you if you if aver metin uh, you know, 426 00:24:23,720 --> 00:24:27,199 Speaker 1: gets thrown in, you're not gonna you're gonna dry up 427 00:24:27,520 --> 00:24:30,000 Speaker 1: anything for all those other drugs. Well, and it's also 428 00:24:30,000 --> 00:24:32,400 Speaker 1: a war on the truth as well. I mean, YouTube 429 00:24:32,440 --> 00:24:35,359 Speaker 1: has taken down some of your videos. Facebook has blocked 430 00:24:35,400 --> 00:24:39,520 Speaker 1: some of your content as well. I mean, what's your 431 00:24:39,560 --> 00:24:43,200 Speaker 1: response to that censorship at this point? Unsurprised? I mean, 432 00:24:43,200 --> 00:24:46,159 Speaker 1: I totally understand why they're doing it. I mean, it's 433 00:24:46,560 --> 00:24:49,040 Speaker 1: all of the actions are with one goal. Is they 434 00:24:49,160 --> 00:24:53,639 Speaker 1: really need to suppress iver mactin again, you know, going 435 00:24:53,680 --> 00:24:56,439 Speaker 1: back to the same thing. The opposition iver mectin is 436 00:24:56,480 --> 00:25:01,280 Speaker 1: so vast, so deep, so wide ranging that it I mean, 437 00:25:01,320 --> 00:25:04,600 Speaker 1: it's this little cheap repurpose drug and it's getting you know, 438 00:25:05,160 --> 00:25:07,280 Speaker 1: part of us. Like we kind of chuckled because we're 439 00:25:07,280 --> 00:25:10,000 Speaker 1: like when we see the bazookas that they're bringing out, Like, 440 00:25:10,000 --> 00:25:12,480 Speaker 1: like I said, this form again, Like we know it's 441 00:25:12,520 --> 00:25:16,320 Speaker 1: because because it works, right. If it didn't work, do 442 00:25:16,359 --> 00:25:18,680 Speaker 1: you think they'd have to do all this. No, we 443 00:25:18,760 --> 00:25:22,119 Speaker 1: wouldn't care because it doesn't work. Right. They know it works. 444 00:25:22,359 --> 00:25:24,439 Speaker 1: That's why you're seeing a war on it. What's been 445 00:25:24,480 --> 00:25:27,000 Speaker 1: the impact of all this on you? For for because 446 00:25:27,040 --> 00:25:28,879 Speaker 1: as you mentioned, you know front log doctors of the 447 00:25:28,880 --> 00:25:31,119 Speaker 1: people we should be listening. You've been on the front 448 00:25:31,160 --> 00:25:34,400 Speaker 1: lines fighting COVID and I see unit units. What's been 449 00:25:34,400 --> 00:25:38,240 Speaker 1: this response on you just professionally and personally as you've 450 00:25:38,320 --> 00:25:42,240 Speaker 1: kind of been demonized by people in some of your peers, Like, 451 00:25:42,320 --> 00:25:50,600 Speaker 1: what's the cost for speaking out? The personal cost on me? Um? 452 00:25:50,720 --> 00:25:57,320 Speaker 1: Who you know, I'm exhausted. I'm just exhausted all the time. Um, 453 00:25:57,440 --> 00:26:03,920 Speaker 1: I you know, it's it's it's been really infuriating. It's said, 454 00:26:04,080 --> 00:26:07,440 Speaker 1: it's distress, and it's a lot of negative things. But 455 00:26:07,440 --> 00:26:10,720 Speaker 1: at the same time, you know, I've gotten to build 456 00:26:10,760 --> 00:26:14,920 Speaker 1: like a network of colleagues and relationships of like minded 457 00:26:14,960 --> 00:26:18,480 Speaker 1: doctors who know the truth and are fighting for the truth. 458 00:26:18,520 --> 00:26:20,639 Speaker 1: And so like my organization right which is called the 459 00:26:20,640 --> 00:26:24,320 Speaker 1: Frontline COVID nineteen Critical Caroliniance or the fl c c C. 460 00:26:25,359 --> 00:26:27,680 Speaker 1: You know, there are little f l c c c 461 00:26:28,000 --> 00:26:31,360 Speaker 1: s all over the world, like Canada has one, UK 462 00:26:31,600 --> 00:26:34,719 Speaker 1: has one, and like I know, all the doctors and 463 00:26:34,760 --> 00:26:37,560 Speaker 1: scientists and all those organizations who understand the truth, they're 464 00:26:37,600 --> 00:26:40,679 Speaker 1: all fighting against the regulatory agencies and in all of 465 00:26:40,720 --> 00:26:43,840 Speaker 1: their countries. And so the relationships that I've built, the 466 00:26:43,840 --> 00:26:47,800 Speaker 1: amount that I've learned has been like really really satisfying. 467 00:26:48,680 --> 00:26:52,080 Speaker 1: And then most importantly is like despite all of the attacks, 468 00:26:52,160 --> 00:26:55,439 Speaker 1: they said, generally people that were like whose lives are saved, 469 00:26:55,440 --> 00:26:58,240 Speaker 1: how many people who like turn around on the dime? 470 00:26:58,800 --> 00:27:01,560 Speaker 1: I mean, how many people have aid hospitalization? I mean 471 00:27:01,640 --> 00:27:04,600 Speaker 1: it's literally hundreds of thousands, not millions, around the world. 472 00:27:04,600 --> 00:27:07,640 Speaker 1: And I even can't get my head around that. Um, 473 00:27:07,720 --> 00:27:09,840 Speaker 1: And that's why we do what we do, and and 474 00:27:09,840 --> 00:27:12,320 Speaker 1: we know, you know, you know, like a friend told 475 00:27:12,320 --> 00:27:13,720 Speaker 1: me last week, and I really like to stay and 476 00:27:13,800 --> 00:27:16,560 Speaker 1: he said, there's only three things that are guaranteed to 477 00:27:16,560 --> 00:27:20,399 Speaker 1: come out, the sun, the moon, and the truth. And 478 00:27:21,520 --> 00:27:24,760 Speaker 1: the problem with the truth in this respect is that, Um, 479 00:27:24,800 --> 00:27:27,560 Speaker 1: it's taking a while for this truth to come out, 480 00:27:27,600 --> 00:27:32,680 Speaker 1: but it's coming. I mean, this, this um Udoburdust story 481 00:27:32,760 --> 00:27:35,359 Speaker 1: cannot be kept on the wraps for too long. And 482 00:27:35,480 --> 00:27:39,400 Speaker 1: there's been similar stories like Mexico City emptied their hospitals 483 00:27:39,480 --> 00:27:42,600 Speaker 1: last winter with an early test and treatment program. That 484 00:27:42,680 --> 00:27:45,920 Speaker 1: paper is out, um, I mean, and like I said, 485 00:27:45,960 --> 00:27:49,600 Speaker 1: the prescriptions in this country, despite from again of two 486 00:27:49,640 --> 00:27:53,000 Speaker 1: and a half weeks of attacks on it, are increasing, 487 00:27:53,280 --> 00:27:55,480 Speaker 1: you know, like that the people are understanding that there 488 00:27:55,520 --> 00:27:58,320 Speaker 1: is an effective drug, and they're you know, the doctors 489 00:27:58,320 --> 00:28:02,919 Speaker 1: are now understanding and so um, I'm really encouraged, but 490 00:28:03,200 --> 00:28:06,520 Speaker 1: the attacks are really tiresome, and the lies, the constant 491 00:28:06,600 --> 00:28:11,520 Speaker 1: lies everywhere and misrepresentations and the implications of that is 492 00:28:12,040 --> 00:28:15,480 Speaker 1: I'm somewhat numb to it now because like I've used 493 00:28:15,480 --> 00:28:17,240 Speaker 1: to the fact that people are going to be dying 494 00:28:17,640 --> 00:28:19,840 Speaker 1: and they're going to continue to die as a result 495 00:28:19,880 --> 00:28:22,920 Speaker 1: of this suppression of a scientific truth, which is this 496 00:28:22,960 --> 00:28:26,000 Speaker 1: is a highly effective medicine COVID. It's also just all 497 00:28:26,040 --> 00:28:29,440 Speaker 1: bizarre because if you think the interest was saving lives, 498 00:28:29,520 --> 00:28:31,439 Speaker 1: you would want and all of the above approach right 499 00:28:31,440 --> 00:28:34,200 Speaker 1: to try to use, you know, to to use everything 500 00:28:34,280 --> 00:28:37,040 Speaker 1: in the fight against COVID, and instead it's like you 501 00:28:37,119 --> 00:28:40,040 Speaker 1: take the vaccine or else. So it's it's it's it's 502 00:28:40,120 --> 00:28:42,080 Speaker 1: very bizarre. I don't know if you know who Brett 503 00:28:42,080 --> 00:28:46,040 Speaker 1: Weinstart is, but he's this evolutionary biologist and I've gotten 504 00:28:46,040 --> 00:28:47,800 Speaker 1: to be friends with him and I did a podcast 505 00:28:47,840 --> 00:28:50,479 Speaker 1: with him. But you know, he talks about like when 506 00:28:50,560 --> 00:28:53,360 Speaker 1: you look at the anomalies, you know, the the baron 507 00:28:53,440 --> 00:28:56,880 Speaker 1: sees the abnormality, abnormal sort of actions that are being taken. 508 00:28:57,560 --> 00:29:00,320 Speaker 1: You know, you really have to wonder like, what's what's 509 00:29:00,360 --> 00:29:02,960 Speaker 1: going on here? Right, So the fact that they don't 510 00:29:02,960 --> 00:29:05,720 Speaker 1: adopt and they're fighting against an early treatment which we 511 00:29:05,840 --> 00:29:09,200 Speaker 1: know should be paired with the vaccines, right, it's not 512 00:29:09,240 --> 00:29:11,720 Speaker 1: necessarily an enemy. You can use it, you know, in 513 00:29:11,800 --> 00:29:14,440 Speaker 1: all hands on deck approach and really try to, you know, 514 00:29:14,520 --> 00:29:18,360 Speaker 1: go after this pandemic with everything you have. That's one abnormality. 515 00:29:18,680 --> 00:29:22,080 Speaker 1: The other one that's really bizarre, is this is this 516 00:29:22,280 --> 00:29:27,880 Speaker 1: overwhelming obsession with vaccinating and vaccinating those who had the illness, right, 517 00:29:27,960 --> 00:29:31,840 Speaker 1: and so it's just there's some things that aren't making sense, right, 518 00:29:31,880 --> 00:29:35,520 Speaker 1: and so it you know, and then you have to 519 00:29:35,520 --> 00:29:38,760 Speaker 1: wonder what drives those behaviors. And I gotta tell you 520 00:29:38,720 --> 00:29:40,920 Speaker 1: it comes back to the same thing that these agencies 521 00:29:40,920 --> 00:29:43,760 Speaker 1: are captured. They're not acting in the best interest, that 522 00:29:43,840 --> 00:29:47,080 Speaker 1: the public health of the citizens is not primary. And 523 00:29:47,120 --> 00:29:48,720 Speaker 1: I have to tell you something. I went into this 524 00:29:48,800 --> 00:29:53,000 Speaker 1: pandemic actually trusting that that was their primary goal and 525 00:29:53,040 --> 00:29:54,800 Speaker 1: that's their only interest. And I'll tell you this, I 526 00:29:54,840 --> 00:29:58,920 Speaker 1: think many people who work in those agencies actually do 527 00:29:59,040 --> 00:30:01,880 Speaker 1: believe that, and those are their careers and that is 528 00:30:01,920 --> 00:30:05,160 Speaker 1: their goal. But they're not the leaders. And I think 529 00:30:05,160 --> 00:30:07,800 Speaker 1: to become, you know, on the top of those agencies 530 00:30:07,800 --> 00:30:11,760 Speaker 1: and actually direct to make the final decisions, you don't 531 00:30:11,840 --> 00:30:14,600 Speaker 1: get there by doing the right thing and saying the 532 00:30:14,680 --> 00:30:17,720 Speaker 1: right thing. I think you only get there if you 533 00:30:17,760 --> 00:30:20,720 Speaker 1: know how to play well with the pharmaceutical companies. And and 534 00:30:20,560 --> 00:30:23,120 Speaker 1: and that's that's the tragedy is I don't want to 535 00:30:23,160 --> 00:30:26,200 Speaker 1: impugne you know, all of the fine people who work 536 00:30:26,240 --> 00:30:29,760 Speaker 1: in those agencies. But I will tell you the ultimate 537 00:30:29,800 --> 00:30:33,920 Speaker 1: direction of those agencies are certainly not influenced of the 538 00:30:34,000 --> 00:30:37,360 Speaker 1: public health as as the primary goal. It doesn't make 539 00:30:37,360 --> 00:30:40,160 Speaker 1: sense that the behaviors do not line up to suggest that. Well. 540 00:30:40,200 --> 00:30:42,200 Speaker 1: It also, you know, a lot of this seems political 541 00:30:42,200 --> 00:30:44,320 Speaker 1: in the sense of, you know, you had the White 542 00:30:44,320 --> 00:30:47,680 Speaker 1: House pushed boosters before the FDA had you even voted 543 00:30:48,120 --> 00:30:51,000 Speaker 1: or given approval to the booster shots, and then you 544 00:30:51,040 --> 00:30:54,480 Speaker 1: had to top two top people. The FDI stepped down 545 00:30:54,800 --> 00:30:56,800 Speaker 1: or f d A rather step down because of that. 546 00:30:56,840 --> 00:30:59,240 Speaker 1: So it seems like it's more, you know, Joe Biden 547 00:30:59,280 --> 00:31:02,480 Speaker 1: wants to hit ex percentage of Americans have been vaccinated 548 00:31:02,520 --> 00:31:05,880 Speaker 1: for political purposes verse, you know, versus is this really 549 00:31:05,880 --> 00:31:08,560 Speaker 1: in the best interest of all I've just been like 550 00:31:08,720 --> 00:31:12,120 Speaker 1: lamenting for months, like where are the whistleblowers? I mean, 551 00:31:13,040 --> 00:31:15,320 Speaker 1: you know, there's some of them have come out and 552 00:31:15,360 --> 00:31:17,280 Speaker 1: you know there there is you know, one that came 553 00:31:17,280 --> 00:31:19,520 Speaker 1: out in Organ, Arkansas. They have a lawyer and there's 554 00:31:19,520 --> 00:31:22,160 Speaker 1: a lawsuit there. You know, the f d A, the 555 00:31:22,160 --> 00:31:26,080 Speaker 1: two FDA officials you know resigning. I mean, I think 556 00:31:26,120 --> 00:31:28,640 Speaker 1: that's almost almost like blowing a whistle that you know 557 00:31:28,760 --> 00:31:32,680 Speaker 1: things are not right. I mean, UM, I just think 558 00:31:32,720 --> 00:31:35,760 Speaker 1: we need more UM. And that's the other said truth 559 00:31:35,880 --> 00:31:38,400 Speaker 1: is that you know, I spoke out. I've left two jobs. 560 00:31:38,720 --> 00:31:40,800 Speaker 1: One I resigned from the other one I was essentially 561 00:31:40,800 --> 00:31:43,480 Speaker 1: forced to resign because they were going to really just 562 00:31:43,520 --> 00:31:46,120 Speaker 1: take away all of my First Amendment rights. And I said, 563 00:31:46,120 --> 00:31:48,680 Speaker 1: I'm not gonna, not gonna subject myself to that. But 564 00:31:49,480 --> 00:31:51,600 Speaker 1: you know, what I've learned is I don't want to 565 00:31:51,600 --> 00:31:54,120 Speaker 1: put people down this, but very few people are willing 566 00:31:54,120 --> 00:31:57,680 Speaker 1: to walk away from a job and their livelihoods. And 567 00:31:57,680 --> 00:32:01,400 Speaker 1: and I just find that said, I know, there's a 568 00:32:01,400 --> 00:32:03,440 Speaker 1: lot of people who know the truth in those agencies, 569 00:32:03,480 --> 00:32:06,280 Speaker 1: and and very few are coming out. They don't want 570 00:32:06,320 --> 00:32:08,040 Speaker 1: to blow up their careers. And I get that. So 571 00:32:08,160 --> 00:32:11,280 Speaker 1: you got COVID. Were you taking ivermectin at the time 572 00:32:11,440 --> 00:32:16,000 Speaker 1: or did you use it? Yeah? Yeah, So here's the thing, right, 573 00:32:16,040 --> 00:32:18,080 Speaker 1: So I've been attacked for that as well. Oh, this 574 00:32:18,320 --> 00:32:20,680 Speaker 1: jerk you know is saying that it's preventive against COVID. 575 00:32:20,720 --> 00:32:24,080 Speaker 1: He got COVID. Listen, I am open and honest. So 576 00:32:24,160 --> 00:32:26,840 Speaker 1: what happened was, UM, we were doing once a week 577 00:32:27,160 --> 00:32:31,520 Speaker 1: of profil axis and UM, I actually got I was 578 00:32:31,560 --> 00:32:33,520 Speaker 1: like the eighth day I hadn't taken it, and I 579 00:32:33,560 --> 00:32:35,640 Speaker 1: got it like right around there. I was probably exposed 580 00:32:35,640 --> 00:32:37,760 Speaker 1: on day six or seven since my last dose. But 581 00:32:38,800 --> 00:32:41,400 Speaker 1: the same day that I got sick, I got my 582 00:32:41,520 --> 00:32:45,760 Speaker 1: first reports in like seven months of breakthroughs. And what 583 00:32:45,920 --> 00:32:48,920 Speaker 1: what the thing is is delta variant has two d 584 00:32:49,040 --> 00:32:53,040 Speaker 1: and fifty times the viral load of the prior variants. 585 00:32:53,080 --> 00:32:55,520 Speaker 1: I mean, it's got this huge viral burden. That's why 586 00:32:55,520 --> 00:32:59,000 Speaker 1: it's so wickedly transmissible. It's one of the reasons. And 587 00:32:59,560 --> 00:33:01,320 Speaker 1: you know, the tire the viral burden, the more the 588 00:33:01,400 --> 00:33:03,520 Speaker 1: higher the dose you need to combat it. And so 589 00:33:03,560 --> 00:33:06,320 Speaker 1: what we found is we needed to change our strategy. 590 00:33:06,360 --> 00:33:10,320 Speaker 1: And so although we had breakthroughs, we also found So 591 00:33:10,360 --> 00:33:12,760 Speaker 1: I have colleagues in Brazil who have been using profile 592 00:33:12,800 --> 00:33:15,120 Speaker 1: access and they say, UM, let me tell you a 593 00:33:15,200 --> 00:33:17,760 Speaker 1: really cool anecdote. So one of um one of our 594 00:33:18,280 --> 00:33:22,720 Speaker 1: newest members of the FLCCC is this incredible research or 595 00:33:22,720 --> 00:33:26,360 Speaker 1: clinician from Brazil's Flavio Kata Johnny, and he's done a 596 00:33:26,520 --> 00:33:29,600 Speaker 1: number of clinical trials on a bunch of different molecules 597 00:33:29,680 --> 00:33:33,280 Speaker 1: in um in, in COVID, he's made some really great discoveries, 598 00:33:33,320 --> 00:33:36,800 Speaker 1: but he led a medical mission. He left the capital. 599 00:33:36,800 --> 00:33:40,240 Speaker 1: They were doing the research and clinical missions throughout the Amazon, 600 00:33:41,240 --> 00:33:44,080 Speaker 1: and they were visiting city after city during the time 601 00:33:44,120 --> 00:33:46,280 Speaker 1: of what's called the Gamma variant. And if you've heard 602 00:33:46,320 --> 00:33:47,840 Speaker 1: of the gamma variant, you probably haven't heard of it 603 00:33:47,840 --> 00:33:50,240 Speaker 1: because it's really just been down in Brazil and in 604 00:33:50,320 --> 00:33:54,160 Speaker 1: parts of South America. But it's extremely violent, meaning it 605 00:33:54,240 --> 00:33:57,320 Speaker 1: moves fast, like from the first symptom to like wide 606 00:33:57,360 --> 00:34:00,680 Speaker 1: it out lungs and meeting hospital in high it's of oxygen. 607 00:34:00,760 --> 00:34:04,840 Speaker 1: Sometimes it's two to three days, and so it's really 608 00:34:05,080 --> 00:34:07,200 Speaker 1: a wicked one. And they were doing very well with 609 00:34:07,280 --> 00:34:11,400 Speaker 1: combination therapies, and then when gamma came, they started really 610 00:34:11,400 --> 00:34:14,000 Speaker 1: losing patients and they had to learn and you know, 611 00:34:14,040 --> 00:34:16,600 Speaker 1: they had figured out some different treatment strategy. But here's 612 00:34:16,600 --> 00:34:19,960 Speaker 1: the thing. They're traveling through the Amazon and they're literally 613 00:34:20,040 --> 00:34:24,000 Speaker 1: seeing cities and hospitals under collapse. City after city that 614 00:34:24,080 --> 00:34:27,759 Speaker 1: they visit, you know, running out of oxygen, full of 615 00:34:28,640 --> 00:34:32,640 Speaker 1: hospitals of capacity, many people dying. And then they visit 616 00:34:32,760 --> 00:34:35,239 Speaker 1: this city called Kari I think it's c O A 617 00:34:35,560 --> 00:34:40,120 Speaker 1: r I. And they get there, and they see that 618 00:34:40,200 --> 00:34:44,560 Speaker 1: the hospital is like not overwhelmed, it's not that crazy, 619 00:34:44,640 --> 00:34:47,080 Speaker 1: and it's very different from all the other cities they visited. 620 00:34:47,080 --> 00:34:50,040 Speaker 1: And so he's talking to the health minister all of 621 00:34:50,040 --> 00:34:53,040 Speaker 1: that city and she's being a little evasive, and finally 622 00:34:53,120 --> 00:34:57,400 Speaker 1: she like admits to him that for many, many weeks 623 00:34:57,560 --> 00:35:00,360 Speaker 1: they had been distributing iv mect into the city's popular lation, 624 00:35:00,880 --> 00:35:04,000 Speaker 1: not only in prevention, but in treatment. And what was 625 00:35:04,080 --> 00:35:07,759 Speaker 1: interesting is many people were taking it in prevention. There 626 00:35:07,800 --> 00:35:09,600 Speaker 1: was still a lot of cases. There was still a 627 00:35:09,640 --> 00:35:12,560 Speaker 1: lot of cases there, but they were all generally mild 628 00:35:12,600 --> 00:35:15,600 Speaker 1: and very few needed the hospital. So almost uniformly they 629 00:35:15,600 --> 00:35:19,239 Speaker 1: would avoid hospital if you were on. I met them beforehand, 630 00:35:19,239 --> 00:35:21,799 Speaker 1: so it's not like that they didn't see cases. And 631 00:35:21,880 --> 00:35:25,200 Speaker 1: that was even a wilder variant than delta. And also 632 00:35:25,239 --> 00:35:27,080 Speaker 1: they weren't act They were taking it like every seven 633 00:35:27,120 --> 00:35:29,440 Speaker 1: to ten days and not a very big dose. And 634 00:35:29,520 --> 00:35:33,120 Speaker 1: so I'm just saying that that, like, to get sick 635 00:35:33,360 --> 00:35:37,480 Speaker 1: while you're on ivermectin can happen, but it's generally mild, 636 00:35:37,800 --> 00:35:41,720 Speaker 1: and so it's still quite preventative, so it avoids severe disease. 637 00:35:41,760 --> 00:35:44,759 Speaker 1: And so um, when I got it. I have to 638 00:35:44,800 --> 00:35:47,120 Speaker 1: tell you that, you know, I wasn't going to hide 639 00:35:47,160 --> 00:35:49,920 Speaker 1: that fact. I thought it was I had a moral 640 00:35:49,960 --> 00:35:52,279 Speaker 1: and ethical responsibility to say, you know what I was 641 00:35:52,360 --> 00:35:55,480 Speaker 1: on prevention and I got sick. And what we were 642 00:35:55,560 --> 00:35:57,560 Speaker 1: taking from this is we need a higher dose or 643 00:35:57,640 --> 00:36:00,320 Speaker 1: higher frequency, and so we we now moved our protical 644 00:36:00,400 --> 00:36:04,240 Speaker 1: to take twice a week for prevention. And so anyway, 645 00:36:04,280 --> 00:36:06,879 Speaker 1: that's my story on on prevention and with these new 646 00:36:07,120 --> 00:36:10,759 Speaker 1: more violent variants quick break more and ivermectin. After the 647 00:36:10,760 --> 00:36:18,400 Speaker 1: commercial break you mentioned, I want to talk to you 648 00:36:18,400 --> 00:36:21,359 Speaker 1: about it. The hospital and i CU capacity, So that's 649 00:36:21,400 --> 00:36:24,680 Speaker 1: been a big reference point throughout the entire you know, 650 00:36:24,760 --> 00:36:27,920 Speaker 1: pandemic with COVID is talking about hospitals and i C 651 00:36:28,040 --> 00:36:31,120 Speaker 1: units across the country recent capacity. But don't I mean, 652 00:36:31,800 --> 00:36:33,480 Speaker 1: I guess where I struggle to find the truth on 653 00:36:33,520 --> 00:36:37,320 Speaker 1: this is don't most hospitals and IC units operate almost 654 00:36:37,400 --> 00:36:42,799 Speaker 1: New York capacity for resource purposes even before COVID. Yeah, like, 655 00:36:43,239 --> 00:36:45,759 Speaker 1: I guess how much of that story is true. Let 656 00:36:45,760 --> 00:36:47,839 Speaker 1: me talk about what happened in New York last year. 657 00:36:47,920 --> 00:36:51,040 Speaker 1: So when New York at its first surge, that literally 658 00:36:51,360 --> 00:36:55,960 Speaker 1: was hospitals overwhelmed, and it was something I'll never forget. 659 00:36:56,000 --> 00:36:58,759 Speaker 1: So I used to be the critical care service chief 660 00:36:58,800 --> 00:37:01,000 Speaker 1: and the director of the I see it University of Wisconsin. 661 00:37:01,040 --> 00:37:04,319 Speaker 1: But I'm a New Yorker, and I actually resigned from 662 00:37:04,320 --> 00:37:06,719 Speaker 1: the University Wisconsin to go back to New York because 663 00:37:06,719 --> 00:37:10,680 Speaker 1: they were they were just getting crushed and they needed intensivenests. Um. 664 00:37:10,800 --> 00:37:13,240 Speaker 1: And I went back and what I saw was literally 665 00:37:14,120 --> 00:37:17,680 Speaker 1: hospitals way over capacity. UM. You know, there was one 666 00:37:17,719 --> 00:37:20,520 Speaker 1: system in New York that going into that surge, they 667 00:37:20,560 --> 00:37:23,759 Speaker 1: had n operational I c you beds, and in two 668 00:37:23,760 --> 00:37:26,680 Speaker 1: and a half weeks they had to create three, D 669 00:37:26,880 --> 00:37:31,000 Speaker 1: and fifty And you don't have enough. I see specialists, 670 00:37:31,040 --> 00:37:32,840 Speaker 1: you don't have enough. I see nurses and say, they 671 00:37:32,840 --> 00:37:34,799 Speaker 1: are all sorts of doctors and nurses who are an 672 00:37:34,880 --> 00:37:37,759 Speaker 1: unfamiliar with critical care having to manage I see you 673 00:37:37,800 --> 00:37:41,280 Speaker 1: bed So that was clearly a point and a surge 674 00:37:41,840 --> 00:37:46,399 Speaker 1: that overwhelmed systems. Now what is going on now at 675 00:37:46,400 --> 00:37:51,440 Speaker 1: the delta variant? So um, since that early time, different 676 00:37:51,480 --> 00:37:55,279 Speaker 1: hospitals now know how to scale capacity a little bit UM. 677 00:37:55,360 --> 00:37:57,680 Speaker 1: And so for instance, a hospital that I worked at, 678 00:37:57,719 --> 00:38:01,200 Speaker 1: now they built a dedicated that I see you for COVID, 679 00:38:01,680 --> 00:38:03,880 Speaker 1: and we were pretty full in July. We had a 680 00:38:04,000 --> 00:38:07,640 Speaker 1: lull in August um where we actually emptied that COVID 681 00:38:07,680 --> 00:38:08,960 Speaker 1: I see you, and so we only had a few 682 00:38:08,960 --> 00:38:11,239 Speaker 1: other patients in the main I see you. And now 683 00:38:11,280 --> 00:38:14,839 Speaker 1: that one's full again, but we're managing it. We're not overwhelmed. 684 00:38:14,920 --> 00:38:17,719 Speaker 1: But there's some capacity that was able to be absorbed. 685 00:38:18,160 --> 00:38:19,680 Speaker 1: And so I think a lot of hospitals able to 686 00:38:19,719 --> 00:38:22,440 Speaker 1: absorb some of the excess capacity through the new search. 687 00:38:22,480 --> 00:38:24,440 Speaker 1: Because this is not our first rodeo now right, We've 688 00:38:24,480 --> 00:38:27,359 Speaker 1: been doing this for a while. But I do have 689 00:38:27,440 --> 00:38:30,799 Speaker 1: colleagues like for instance, in Tennessee, like in August and 690 00:38:30,880 --> 00:38:33,880 Speaker 1: Joy in August, they literally were overwhelmed. They said they 691 00:38:33,880 --> 00:38:35,799 Speaker 1: had no more I see you beds. They you know, 692 00:38:35,800 --> 00:38:37,960 Speaker 1: they were you know, many many kids were going to 693 00:38:37,960 --> 00:38:39,839 Speaker 1: the hospital. Like the things that we're hearing from close 694 00:38:39,920 --> 00:38:44,520 Speaker 1: colleagues were really really bad. Um. But now that's lessening, right, 695 00:38:44,560 --> 00:38:48,239 Speaker 1: So they're like surges happening and then they recede, and 696 00:38:48,239 --> 00:38:50,960 Speaker 1: I think some hospitals know how to absorb or now 697 00:38:51,000 --> 00:38:53,920 Speaker 1: scale a little bit to capacity. But to your other question, 698 00:38:54,000 --> 00:38:58,200 Speaker 1: which is, you know, in normal times, don't we usually 699 00:38:58,239 --> 00:39:01,080 Speaker 1: have full I cus And here's the interesting part about that. 700 00:39:01,200 --> 00:39:04,680 Speaker 1: So as a physician, one of my core responsibilities to 701 00:39:04,719 --> 00:39:07,600 Speaker 1: decide who needs I See you or not. When we 702 00:39:07,719 --> 00:39:10,799 Speaker 1: have empty beds, I'm allowed to be a little bit 703 00:39:10,840 --> 00:39:13,560 Speaker 1: more liberal. So if I go see a patient and 704 00:39:13,600 --> 00:39:17,560 Speaker 1: they're kind of sick, I'm somewhat worried about them. You know, 705 00:39:17,640 --> 00:39:19,200 Speaker 1: I'll put them in the I See you. If I 706 00:39:19,200 --> 00:39:22,200 Speaker 1: have a lot of capacity, I'm just an abundance of caution. 707 00:39:23,040 --> 00:39:25,759 Speaker 1: But if we're really full, and I go see a 708 00:39:25,800 --> 00:39:29,040 Speaker 1: patient sometimes on the regular medical wards, even if they 709 00:39:29,040 --> 00:39:31,759 Speaker 1: look kind of quite ill, you know, sometimes I don't 710 00:39:31,800 --> 00:39:35,080 Speaker 1: take them. And so you can see what I'm saying. 711 00:39:35,080 --> 00:39:37,879 Speaker 1: So it's like what happens in COVID is like we 712 00:39:37,880 --> 00:39:41,560 Speaker 1: were managing increasingly, Like at for instance, in New York, 713 00:39:42,000 --> 00:39:45,680 Speaker 1: the acuity level on the regular hospital floors that we're managing, 714 00:39:45,760 --> 00:39:50,239 Speaker 1: we're we're light years beyond what UM I had seen 715 00:39:50,280 --> 00:39:53,040 Speaker 1: in my career. We were leaving very sick patients out 716 00:39:53,080 --> 00:39:57,160 Speaker 1: of the I see you, um and so again. And 717 00:39:57,239 --> 00:39:59,239 Speaker 1: in fact, you know, I teach medicine. A lot of 718 00:39:59,280 --> 00:40:01,759 Speaker 1: my trainees in my specialty, I was telling them, I'm 719 00:40:01,800 --> 00:40:05,040 Speaker 1: like what you're learning here is not what I learned. 720 00:40:05,120 --> 00:40:07,239 Speaker 1: I said, you know, and they understood. I said, we 721 00:40:07,320 --> 00:40:10,360 Speaker 1: usually do not leave these kind of severely ill patients 722 00:40:10,360 --> 00:40:13,440 Speaker 1: and regular medical wards. And so so that's the point 723 00:40:13,520 --> 00:40:15,600 Speaker 1: you don't see, like on the ground level, we're making 724 00:40:15,640 --> 00:40:20,600 Speaker 1: clinical decisions according to capacity. And so I don't know, 725 00:40:20,680 --> 00:40:22,560 Speaker 1: is that a party answer to your question, it's it's 726 00:40:22,560 --> 00:40:25,840 Speaker 1: a complicated one. Yeah, so it's it's nuanced. Is basically 727 00:40:26,160 --> 00:40:29,279 Speaker 1: it's nuance, very nuanced. Yeah, let's take a break and 728 00:40:29,320 --> 00:40:36,840 Speaker 1: then back to Dr Corey. COVID vaccines. They're the fastest 729 00:40:36,920 --> 00:40:40,200 Speaker 1: vaccines ever created, improved ever. You know previously, I think 730 00:40:40,200 --> 00:40:43,319 Speaker 1: the vaccine the fastest vaccine to go from development to 731 00:40:43,360 --> 00:40:46,000 Speaker 1: deployment was the Mom's vaccine in the nineteen sixties. That 732 00:40:46,120 --> 00:40:49,200 Speaker 1: took four years. I mean, you get a vaccine through 733 00:40:49,200 --> 00:40:52,640 Speaker 1: the approval process without cutting corners that quickly. I can 734 00:40:52,680 --> 00:40:56,359 Speaker 1: just say it's on an unprecedented speed. And you know, 735 00:40:56,920 --> 00:41:00,880 Speaker 1: with medicineism, anything with science, with speed, you raise the 736 00:41:00,960 --> 00:41:05,200 Speaker 1: risk of making errors. And and that's all I'm gonna say. 737 00:41:05,239 --> 00:41:07,960 Speaker 1: I'm not a vaccine expert, but yes, I think your 738 00:41:07,960 --> 00:41:12,680 Speaker 1: statement is true. It's extremely fast and Um, you know, 739 00:41:13,160 --> 00:41:16,600 Speaker 1: what I actually believe is that speed in which they 740 00:41:16,680 --> 00:41:20,160 Speaker 1: developed and rolled them out might have been reasonable in 741 00:41:20,200 --> 00:41:21,880 Speaker 1: the fog of war, right, like we were in a 742 00:41:21,920 --> 00:41:28,080 Speaker 1: really tough time, especially last winter. Um. But I also 743 00:41:28,239 --> 00:41:31,760 Speaker 1: think that, you know, with time, you need to continue 744 00:41:31,800 --> 00:41:34,440 Speaker 1: to collect the data on efficacy and safety and that 745 00:41:34,480 --> 00:41:37,920 Speaker 1: should be transparent. And my only issue with the vaccines 746 00:41:37,960 --> 00:41:40,640 Speaker 1: and the data is I just don't find the data transparent. 747 00:41:40,680 --> 00:41:44,160 Speaker 1: They're not sharing it. It's all in newspaper articles, and 748 00:41:44,400 --> 00:41:47,680 Speaker 1: it's very unsatisfied to someone who you know, I I 749 00:41:47,760 --> 00:41:50,120 Speaker 1: like to look and analyze data, and so do my colleagues, 750 00:41:50,120 --> 00:41:53,320 Speaker 1: and were just it's we see a lot of talk 751 00:41:53,480 --> 00:41:55,400 Speaker 1: of the data, but we don't actually see the data, 752 00:41:55,520 --> 00:41:58,600 Speaker 1: and so that's my concern. But I think that's that's 753 00:41:58,640 --> 00:42:00,279 Speaker 1: the key, is that you need to contin need to 754 00:42:00,280 --> 00:42:02,319 Speaker 1: look at data. I mean, they did a rush last year, 755 00:42:02,360 --> 00:42:05,200 Speaker 1: but um, you know, continue to look at data, but 756 00:42:05,239 --> 00:42:08,040 Speaker 1: provide the data, that's the other thing. So that's all 757 00:42:08,080 --> 00:42:10,279 Speaker 1: I'll say about that. Well, and to your point, you know, 758 00:42:10,320 --> 00:42:12,840 Speaker 1: I support right to try, So I support trying to 759 00:42:12,880 --> 00:42:16,520 Speaker 1: get the vaccine to market under emergency use authorization for 760 00:42:16,640 --> 00:42:18,759 Speaker 1: you know, an eighty five year old who could die 761 00:42:18,800 --> 00:42:22,400 Speaker 1: if they get COVID. But now we're forcing the vaccine 762 00:42:22,600 --> 00:42:25,960 Speaker 1: on you know, so many people around the country who 763 00:42:26,040 --> 00:42:29,520 Speaker 1: probably don't need the vaccine, and also only one of 764 00:42:29,520 --> 00:42:31,800 Speaker 1: them has even been approved, and then that approval process 765 00:42:31,840 --> 00:42:35,360 Speaker 1: was incredibly fast. Yet we're forcing Americans to get the vaccine. 766 00:42:35,360 --> 00:42:37,080 Speaker 1: It's just insane to me. And to your point about 767 00:42:37,440 --> 00:42:41,600 Speaker 1: the transparency regarding you know, deaths and and vaccine injury, 768 00:42:41,600 --> 00:42:43,480 Speaker 1: I mean, like they I know, there's you know, people 769 00:42:43,480 --> 00:42:45,760 Speaker 1: try to condemn Varius. However, the CDC and the government 770 00:42:45,840 --> 00:42:47,879 Speaker 1: uses it as an early reporting system, so it does 771 00:42:47,920 --> 00:42:50,600 Speaker 1: have benefit. And then it is also a good comparison 772 00:42:50,640 --> 00:42:53,600 Speaker 1: tool to look at deaths and injury from COVID vaccines 773 00:42:53,719 --> 00:42:56,160 Speaker 1: versus other ones, because if you think the information is 774 00:42:56,160 --> 00:42:59,040 Speaker 1: skewed for COVID, it would be skewed for everything. So 775 00:42:59,160 --> 00:43:03,200 Speaker 1: we've seen you know, seven thousand reports of death from 776 00:43:03,200 --> 00:43:06,719 Speaker 1: the COVID vaccine. Again, it's soef reporting. The information has 777 00:43:06,800 --> 00:43:09,520 Speaker 1: not been you know, entirely examined, so you have to 778 00:43:09,560 --> 00:43:12,000 Speaker 1: take it with a grain assault. But you know, we've 779 00:43:12,040 --> 00:43:14,759 Speaker 1: also seen recent studies showing heart problems are a much 780 00:43:14,760 --> 00:43:19,040 Speaker 1: bigger risk than previously thought, you know, how much vaccine 781 00:43:19,080 --> 00:43:22,399 Speaker 1: injury are are using in the hospital, in the ICU 782 00:43:22,760 --> 00:43:25,120 Speaker 1: or some of your colleagues. You know, that's a hard 783 00:43:25,200 --> 00:43:28,600 Speaker 1: number from from a one person perspective, And but I 784 00:43:28,719 --> 00:43:33,560 Speaker 1: certainly have seen a number of um very severe blood 785 00:43:33,560 --> 00:43:37,799 Speaker 1: clots so that have occurred within the weeks after a vaccine. 786 00:43:38,000 --> 00:43:40,279 Speaker 1: And then you know what I've been concerned about is 787 00:43:40,320 --> 00:43:43,200 Speaker 1: I've had a number of cases in the last month 788 00:43:43,280 --> 00:43:46,920 Speaker 1: or two where an elderly patient like came in with 789 00:43:47,040 --> 00:43:49,640 Speaker 1: like a pneumonia, which is very common many people diet 790 00:43:49,719 --> 00:43:54,200 Speaker 1: inder theives with pneumonia and or sepsis, And so they 791 00:43:54,239 --> 00:43:57,440 Speaker 1: came in with these conditions that are rather normal for 792 00:43:57,560 --> 00:43:59,759 Speaker 1: me to take care of in the elderly, but the 793 00:44:00,040 --> 00:44:02,880 Speaker 1: families would like spontaneously tell me that he wasn't the 794 00:44:02,960 --> 00:44:06,080 Speaker 1: same since the vaccine, or like he he seemed to be, 795 00:44:06,239 --> 00:44:09,239 Speaker 1: you know, dwindling or not not the same in his health, 796 00:44:09,280 --> 00:44:12,719 Speaker 1: seemed to suffer. And you know, I just found that 797 00:44:12,800 --> 00:44:15,960 Speaker 1: concerning that the families would would notice that that they 798 00:44:16,000 --> 00:44:19,399 Speaker 1: saw people so like they didn't die of a vaccine injury, 799 00:44:19,440 --> 00:44:22,719 Speaker 1: but it seemed like something predisposed them to have the 800 00:44:22,760 --> 00:44:26,080 Speaker 1: illness that brought them before me. And again I can't 801 00:44:26,080 --> 00:44:28,560 Speaker 1: say how common that is, but I've I've definitely seen 802 00:44:28,640 --> 00:44:31,240 Speaker 1: cases of that, so you know, asking an individual doctor 803 00:44:31,280 --> 00:44:33,879 Speaker 1: and then you know as an outpatient. I've definitely had 804 00:44:33,920 --> 00:44:38,680 Speaker 1: people in my circle and uh you know through friends 805 00:44:38,680 --> 00:44:41,520 Speaker 1: and family network who have who have definitely come to 806 00:44:41,560 --> 00:44:44,080 Speaker 1: me with um, you know, problems after vaccines. But again 807 00:44:44,120 --> 00:44:48,239 Speaker 1: that's hard to quantity or put into context. I mean 808 00:44:48,280 --> 00:44:50,279 Speaker 1: I've had I went to go get an antibody test. 809 00:44:50,320 --> 00:44:52,160 Speaker 1: I won't say where, but I had two of the 810 00:44:52,239 --> 00:44:53,960 Speaker 1: nurses I talked to so that they were saying an 811 00:44:53,960 --> 00:44:57,520 Speaker 1: increase in hard injury from the vaccine, particularly at young people. 812 00:44:57,840 --> 00:44:59,319 Speaker 1: So I just I just don't think. I just don't 813 00:44:59,320 --> 00:45:01,279 Speaker 1: think that we're being told the truth about all this. 814 00:45:01,320 --> 00:45:04,520 Speaker 1: And I'm not I'm not anti the COVID vaccine. I'm not. 815 00:45:04,680 --> 00:45:06,839 Speaker 1: I'm not I'm not for or against anything. I'm just 816 00:45:06,880 --> 00:45:09,399 Speaker 1: for the truth. And I just don't feel like we're 817 00:45:09,560 --> 00:45:11,160 Speaker 1: yeah for data and the truth, and I just don't 818 00:45:11,160 --> 00:45:13,080 Speaker 1: feel like we're getting it from people. And I certainly 819 00:45:13,200 --> 00:45:16,880 Speaker 1: certainly don't think anyone should be mandated for sake of 820 00:45:16,920 --> 00:45:20,279 Speaker 1: having a job to get something that one you know, 821 00:45:20,640 --> 00:45:23,759 Speaker 1: three of the vaccines or no, two of the three 822 00:45:23,800 --> 00:45:26,239 Speaker 1: aren't even FDA proved, and then the other has been 823 00:45:26,320 --> 00:45:27,880 Speaker 1: rushed through, and then we have no one. You know, 824 00:45:27,920 --> 00:45:30,400 Speaker 1: we're not really getting the real truth about potential injury. 825 00:45:31,160 --> 00:45:33,360 Speaker 1: It just should not be mandated on anyone. I just 826 00:45:33,400 --> 00:45:38,280 Speaker 1: think that's disgusting. And my concerns is that all the data, 827 00:45:38,640 --> 00:45:44,440 Speaker 1: whatever data is being shared um is actually artificial, because 828 00:45:44,640 --> 00:45:48,319 Speaker 1: all the data on efficacy and safety and everything, it's 829 00:45:48,400 --> 00:45:53,080 Speaker 1: artificial because you're not including effective early treatment options in 830 00:45:53,080 --> 00:45:57,840 Speaker 1: the equation. So so like if people could get treatment 831 00:45:57,920 --> 00:46:01,360 Speaker 1: with you know, early treatment, the effects of the vaccine 832 00:46:01,360 --> 00:46:04,280 Speaker 1: would be much less impressive, you know, like the story 833 00:46:04,320 --> 00:46:06,520 Speaker 1: I told you whether you know, even though there were cases, 834 00:46:06,680 --> 00:46:10,600 Speaker 1: very few went to hospital, and so, you know, I 835 00:46:10,719 --> 00:46:12,920 Speaker 1: just feel like we're not getting a full picture of 836 00:46:13,239 --> 00:46:15,600 Speaker 1: the way in which you can address this illness. So 837 00:46:15,880 --> 00:46:20,200 Speaker 1: this this maniacal singular focus on vaccines, you know, as 838 00:46:20,320 --> 00:46:22,960 Speaker 1: as the only way to end the pandemic. It's ignoring 839 00:46:23,000 --> 00:46:25,280 Speaker 1: the fact that there are other options that we're not using. 840 00:46:25,320 --> 00:46:28,360 Speaker 1: And so um again, I'm for early treatment. And you know, 841 00:46:28,400 --> 00:46:29,719 Speaker 1: the other thing I want to tell you say is 842 00:46:29,760 --> 00:46:34,080 Speaker 1: that what's fascinating as a physician in this pandemic is 843 00:46:34,400 --> 00:46:37,160 Speaker 1: I've remectin is not the only thing that works early on. 844 00:46:37,680 --> 00:46:40,799 Speaker 1: There's a number of other compounds and molecules that are 845 00:46:40,840 --> 00:46:44,080 Speaker 1: really effective early on. There's actually another anti parasite drug 846 00:46:44,120 --> 00:46:47,960 Speaker 1: which is highly effected called nittas oxinide um. There are 847 00:46:48,040 --> 00:46:51,839 Speaker 1: anti viral nasal drops and mouth washes that you can 848 00:46:51,880 --> 00:46:54,480 Speaker 1: do because all the viral burden is actually in the 849 00:46:54,520 --> 00:46:58,040 Speaker 1: nose and pharynx and like throat, and you can actually 850 00:46:58,120 --> 00:47:02,399 Speaker 1: kind of sanitize or sterilize those areas with varicidal which 851 00:47:02,480 --> 00:47:08,000 Speaker 1: is like virus killing solutions, and that alters trajectory incredibly. 852 00:47:08,040 --> 00:47:11,640 Speaker 1: There's a number of studies showing that the hospitalization rates 853 00:47:11,680 --> 00:47:14,560 Speaker 1: if you do regular like povid on iodine nasal drops 854 00:47:15,080 --> 00:47:18,359 Speaker 1: um with with these versible mouthwashes, I mean, they're like 855 00:47:18,400 --> 00:47:22,520 Speaker 1: twenty times less than if you didn't. And so there's 856 00:47:22,560 --> 00:47:25,000 Speaker 1: there's just a bunch of approaches. And now now we 857 00:47:25,080 --> 00:47:28,400 Speaker 1: have like new medicines that suppress androgen activity, which is 858 00:47:28,440 --> 00:47:32,319 Speaker 1: like testosterone because what we recognize that covid is men 859 00:47:32,400 --> 00:47:35,520 Speaker 1: fare a lot worse at almost every age group um. 860 00:47:35,600 --> 00:47:37,799 Speaker 1: In fact, men between the ages of forty to forty 861 00:47:37,880 --> 00:47:40,720 Speaker 1: nine or six times more likely to die than women 862 00:47:41,239 --> 00:47:43,879 Speaker 1: of COVID, and between thirty and fifty they're like two 863 00:47:43,920 --> 00:47:46,480 Speaker 1: to three times more likely to be hospitalized. And the 864 00:47:46,520 --> 00:47:50,600 Speaker 1: reason why that is is that testosterone and its derivatives 865 00:47:50,680 --> 00:47:54,839 Speaker 1: actually drive an enzyme which allows the virus to enter, 866 00:47:54,880 --> 00:47:57,719 Speaker 1: and that's why men do worse. And so there are 867 00:47:57,760 --> 00:48:01,359 Speaker 1: these incredible trials coming out of Brazil old in other 868 00:48:01,440 --> 00:48:05,480 Speaker 1: areas showing that if you use medicines which suppress the phosterone, 869 00:48:05,800 --> 00:48:09,440 Speaker 1: the patients do incredibly well, even in women. And so 870 00:48:09,520 --> 00:48:11,120 Speaker 1: I just want to make sure that like we use 871 00:48:11,160 --> 00:48:13,960 Speaker 1: the combination of therapies. Our protocols are on our website 872 00:48:14,600 --> 00:48:17,319 Speaker 1: um F L c CC dot MET in case your 873 00:48:17,480 --> 00:48:20,120 Speaker 1: audience is interested in looking at our treatment protocols. But 874 00:48:20,160 --> 00:48:23,880 Speaker 1: they're they're highly evidence based and highly effective um and 875 00:48:23,920 --> 00:48:25,960 Speaker 1: we learned from a network of colleagues who have done 876 00:48:25,960 --> 00:48:30,040 Speaker 1: research and have gained clinical experience, and so, you know, 877 00:48:30,080 --> 00:48:32,840 Speaker 1: I just want to point out early tearing right now. Today, 878 00:48:32,880 --> 00:48:35,399 Speaker 1: the NAH does not have an early treatment option. They 879 00:48:35,400 --> 00:48:38,759 Speaker 1: don't even recommend vitamin D even though their their own 880 00:48:38,840 --> 00:48:42,520 Speaker 1: data over decades shows that vitamin D is uh, you know, 881 00:48:42,640 --> 00:48:46,080 Speaker 1: vitamin D deficiency is highly common in the U S population, 882 00:48:46,560 --> 00:48:51,279 Speaker 1: especially in the poor uh and disadvantage in minority populations, 883 00:48:51,280 --> 00:48:53,640 Speaker 1: and so they don't even recommend vitamin D. It's it's 884 00:48:53,680 --> 00:48:58,439 Speaker 1: really again another incredible anomaly of how they're approaching this well. 885 00:48:58,560 --> 00:49:01,960 Speaker 1: And another reason em against the vaccine mandates is because 886 00:49:03,239 --> 00:49:06,800 Speaker 1: COVID impacts different groups of people. There's such a disparity 887 00:49:06,840 --> 00:49:09,760 Speaker 1: and the way it impacts people, you know, young versus old. 888 00:49:09,920 --> 00:49:11,959 Speaker 1: You know, you start to get over the age of eight, 889 00:49:12,080 --> 00:49:13,880 Speaker 1: it starts to get you know, a lot more danger 890 00:49:14,040 --> 00:49:16,799 Speaker 1: is if you're even my age three six ninety nine 891 00:49:16,880 --> 00:49:23,360 Speaker 1: point nine seven percent chance of surviving different I tried. 892 00:49:23,560 --> 00:49:26,360 Speaker 1: I tried, Dr Corey. So take us through what you know, 893 00:49:26,440 --> 00:49:29,000 Speaker 1: especially from your experiences. What are the higher risk groups 894 00:49:29,000 --> 00:49:32,280 Speaker 1: of people you know, who should be concerned, who less concerned? 895 00:49:32,320 --> 00:49:34,440 Speaker 1: You know, take us through some of the different you 896 00:49:34,440 --> 00:49:38,880 Speaker 1: know the risk calculation here. So number one age is 897 00:49:38,920 --> 00:49:41,600 Speaker 1: what you mentioned, So we know with every ten years 898 00:49:41,600 --> 00:49:45,360 Speaker 1: of age um it's a linear sort of plot on 899 00:49:45,400 --> 00:49:48,160 Speaker 1: the graph, like a diagonally rising one. Like with every 900 00:49:48,280 --> 00:49:51,880 Speaker 1: dec sile or ten years of age, the mortality increases. 901 00:49:52,320 --> 00:49:56,279 Speaker 1: So definitely you don't want to be older and get 902 00:49:56,280 --> 00:49:58,800 Speaker 1: this disease. The older you are, the worst you'll fair. 903 00:49:58,880 --> 00:50:04,160 Speaker 1: That's number one, flat out. Number two is obesity, UM, 904 00:50:04,200 --> 00:50:06,480 Speaker 1: and you know, the more overweight and obese you are, 905 00:50:06,640 --> 00:50:10,879 Speaker 1: you're going to do worse. Number three, UM is diabetes, 906 00:50:11,880 --> 00:50:14,640 Speaker 1: and you know those are diabetes which is actually causes 907 00:50:14,680 --> 00:50:18,359 Speaker 1: the form of immuno suppression, they do worse. And so 908 00:50:18,520 --> 00:50:21,640 Speaker 1: it's really obesity, which are obese is an you know, 909 00:50:21,960 --> 00:50:24,520 Speaker 1: endemic in society and at least the US society and 910 00:50:24,560 --> 00:50:28,160 Speaker 1: many others. UM. Diabetes type one or two is very 911 00:50:28,200 --> 00:50:31,120 Speaker 1: common UM. And then obviously age. But those those are 912 00:50:31,200 --> 00:50:34,080 Speaker 1: kind of the three the three ones that you sort 913 00:50:34,080 --> 00:50:36,840 Speaker 1: of that I worry about, Like when I see someone 914 00:50:36,880 --> 00:50:40,239 Speaker 1: really overweight with diabetes just coming with COVID or an 915 00:50:40,280 --> 00:50:42,800 Speaker 1: elderly patient, you know, I know I'm going to have 916 00:50:42,840 --> 00:50:45,319 Speaker 1: a rougher time and may not succeed at saving them, 917 00:50:45,600 --> 00:50:47,480 Speaker 1: which is why you know, we should be kind of 918 00:50:47,480 --> 00:50:49,600 Speaker 1: looking at the totality of all this and trying to 919 00:50:49,600 --> 00:50:51,839 Speaker 1: figure out the best ways to both mitigate and then 920 00:50:51,840 --> 00:50:54,320 Speaker 1: also to potentially save lives for people who get COVID. 921 00:50:54,560 --> 00:50:57,520 Speaker 1: You know, we're also saying breakthrough cases with the vaccine 922 00:50:57,640 --> 00:51:00,360 Speaker 1: is that something that's prevalent in the ice US in 923 00:51:00,719 --> 00:51:03,359 Speaker 1: hospitals right now, or or people showing up with or 924 00:51:03,400 --> 00:51:06,279 Speaker 1: with breakooth cases or that's like another thing that I've 925 00:51:06,320 --> 00:51:10,080 Speaker 1: been bemoaning. The data on that is they're not sharing 926 00:51:10,120 --> 00:51:12,680 Speaker 1: that data. So you know, we have officials in the 927 00:51:12,760 --> 00:51:16,719 Speaker 1: CDC who've been running around saying that the people in 928 00:51:16,760 --> 00:51:19,839 Speaker 1: hospital or vaccinated. That's not true. You know, they had 929 00:51:19,960 --> 00:51:23,880 Speaker 1: data coming out of CDC that as of June, of 930 00:51:23,880 --> 00:51:28,560 Speaker 1: the people in hospital, uh, we're vaccinated, right, and so UM, 931 00:51:28,600 --> 00:51:31,440 Speaker 1: we know those numbers are higher in Israel. It's sixty 932 00:51:32,000 --> 00:51:35,560 Speaker 1: of people in hospital have been double vaccinated. UM. A 933 00:51:35,640 --> 00:51:38,839 Speaker 1: lot of my colleagues in the i c U over 934 00:51:38,880 --> 00:51:41,640 Speaker 1: the last couple of months, they do say that almost 935 00:51:41,680 --> 00:51:44,879 Speaker 1: everyone is unvaccinated. But that's changing and we know why 936 00:51:44,960 --> 00:51:46,960 Speaker 1: that's changing, and it has to do with the timing 937 00:51:46,960 --> 00:51:49,320 Speaker 1: of the vaccine. So Israel was the fastest out of 938 00:51:49,320 --> 00:51:52,680 Speaker 1: the gate, and they're starting to see waning efficacy, right, 939 00:51:53,160 --> 00:51:55,920 Speaker 1: and so I'm starting to see double vaccinated in the 940 00:51:55,960 --> 00:51:58,359 Speaker 1: i c Now. I just had a patient last week, UM, 941 00:51:58,640 --> 00:52:02,640 Speaker 1: double vaccinated, very sick in the I S you and so, UM, 942 00:52:02,719 --> 00:52:05,839 Speaker 1: they do seem the data seems to suggest that you're 943 00:52:05,920 --> 00:52:09,000 Speaker 1: much less likely to get severe disease, but it's not 944 00:52:09,080 --> 00:52:12,320 Speaker 1: a guarantee. And that's the other thing. That's why early 945 00:52:12,400 --> 00:52:15,000 Speaker 1: treatment matters. These you know, all of these people who 946 00:52:15,000 --> 00:52:17,240 Speaker 1: have done the right thing, they've shown up for their shots, 947 00:52:17,239 --> 00:52:20,920 Speaker 1: they've socially distanced and masked, and now they're getting sick 948 00:52:21,600 --> 00:52:24,920 Speaker 1: and we're not giving them an option for treatment. I mean, 949 00:52:24,960 --> 00:52:29,120 Speaker 1: it's really it's unconscionable. You know, the vaccinated amy non 950 00:52:29,200 --> 00:52:31,920 Speaker 1: vaccinated will need treatment. And that's what you know, Florida 951 00:52:31,960 --> 00:52:35,160 Speaker 1: has said as well, because they've been pushing the monoquoto 952 00:52:35,200 --> 00:52:37,920 Speaker 1: antibody treatments and they're saying they've seen I think I 953 00:52:37,960 --> 00:52:40,680 Speaker 1: believe one of the tweets I saw, you know, I 954 00:52:40,719 --> 00:52:42,480 Speaker 1: believe this is true because this is just going off 955 00:52:42,480 --> 00:52:44,400 Speaker 1: the top of my head. I think it was almost 956 00:52:44,560 --> 00:52:48,680 Speaker 1: over where individuals were vaccinated who are still getting sick 957 00:52:48,719 --> 00:52:51,840 Speaker 1: and they needed the monoclonal antibodies. Because again, it's just 958 00:52:51,880 --> 00:52:55,040 Speaker 1: sort of this weird situation that we're in where it's 959 00:52:55,080 --> 00:52:58,040 Speaker 1: like they were originally trying to deny and push back 960 00:52:58,080 --> 00:53:01,160 Speaker 1: against monoclonal antibodies because they just want people to go 961 00:53:01,200 --> 00:53:03,000 Speaker 1: get vaccinated. You have to get vaccinate. You have to 962 00:53:03,000 --> 00:53:05,759 Speaker 1: get vaccinated. But but what's not being part of that 963 00:53:05,800 --> 00:53:09,200 Speaker 1: conversation is what what about the people who get vaccinated 964 00:53:09,200 --> 00:53:11,680 Speaker 1: and then still get really sick and neither life to 965 00:53:11,719 --> 00:53:14,759 Speaker 1: be saved by you know, either the monoquote antibodies or 966 00:53:14,800 --> 00:53:16,680 Speaker 1: you're saying ever macton or some of these other things. 967 00:53:16,920 --> 00:53:18,440 Speaker 1: So it's like it's just it's just like it just 968 00:53:18,480 --> 00:53:22,040 Speaker 1: blows my mind because there's just no rationale or any 969 00:53:22,080 --> 00:53:25,920 Speaker 1: common sense anymore whatsoever. Again, you you're pointing out all 970 00:53:25,960 --> 00:53:28,400 Speaker 1: of these things that just don't make rational sense. So 971 00:53:28,680 --> 00:53:32,000 Speaker 1: and there's a lot of us right in society, smart 972 00:53:32,040 --> 00:53:35,040 Speaker 1: people pay attention, and a lot of us have scratching 973 00:53:35,080 --> 00:53:36,879 Speaker 1: their heads. And that's why when I when you see 974 00:53:36,920 --> 00:53:41,360 Speaker 1: these behaviors which are inconsistent with sound medical principles and 975 00:53:41,200 --> 00:53:44,280 Speaker 1: in fact seemed to violate them, right like this rush 976 00:53:44,320 --> 00:53:47,880 Speaker 1: to vaccinate people who have already had the disease and 977 00:53:48,040 --> 00:53:51,959 Speaker 1: mandate it even if you've had the illness and have antibodies. 978 00:53:52,719 --> 00:53:55,120 Speaker 1: I mean, when have we ever done that in history? 979 00:53:55,280 --> 00:53:57,319 Speaker 1: And so they're making up this new rule and it's 980 00:53:57,560 --> 00:54:00,920 Speaker 1: it's it's bizarre, and so you're pointing out a lot 981 00:54:00,960 --> 00:54:02,960 Speaker 1: of them, but but this early treatment one is the 982 00:54:03,000 --> 00:54:07,080 Speaker 1: one that's actually causing the loss of life. That the 983 00:54:07,120 --> 00:54:11,080 Speaker 1: continued suppression of early treatment options, which I maintain is 984 00:54:11,120 --> 00:54:13,920 Speaker 1: being done until these new orl anti viwals can be 985 00:54:14,000 --> 00:54:17,120 Speaker 1: rolled out by the big former companies. For every day 986 00:54:17,160 --> 00:54:19,120 Speaker 1: that they continue to do that, we're going to lose 987 00:54:19,160 --> 00:54:23,160 Speaker 1: a lot of people. And and they better hope those 988 00:54:23,200 --> 00:54:25,799 Speaker 1: oil anti virals work, because I gotta tell you the 989 00:54:25,880 --> 00:54:29,000 Speaker 1: one from Murk, it's called Moment Pure of Your already 990 00:54:29,000 --> 00:54:31,920 Speaker 1: failed in the hospital. They tried it in the hospital, 991 00:54:31,960 --> 00:54:34,160 Speaker 1: and that those trials failed, they're actually trying it now 992 00:54:34,200 --> 00:54:38,640 Speaker 1: as an outpatient. And again I don't care about what 993 00:54:38,680 --> 00:54:40,960 Speaker 1: that shows because we already have a highly effective drug 994 00:54:41,040 --> 00:54:43,720 Speaker 1: and I remectin. But um, that's what I think has happening. 995 00:54:43,719 --> 00:54:45,560 Speaker 1: They're waiting for those drugs to come in and save 996 00:54:45,640 --> 00:54:48,960 Speaker 1: the day. But while they do that, I mean incalculable 997 00:54:48,960 --> 00:54:52,400 Speaker 1: loss of life and morbidity, even and even if you survived. 998 00:54:52,440 --> 00:54:54,400 Speaker 1: The poor people with long haul COVID. I don't know 999 00:54:54,400 --> 00:54:57,879 Speaker 1: if you have friends or family, but it's miserable long haul. 1000 00:54:57,960 --> 00:55:00,279 Speaker 1: I mean that that's a whole other epidemic and itself 1001 00:55:00,400 --> 00:55:02,360 Speaker 1: and so and by the way, we have a protocol 1002 00:55:02,400 --> 00:55:05,560 Speaker 1: for that. How frequent is long haul in terms of 1003 00:55:05,800 --> 00:55:08,200 Speaker 1: you know, the people who get COVID you know, the 1004 00:55:08,200 --> 00:55:11,760 Speaker 1: the the incidents ranges, but anywhere from ten to fifty 1005 00:55:12,560 --> 00:55:17,040 Speaker 1: the general somewhere around thirty. That maybe a little high. 1006 00:55:17,120 --> 00:55:20,279 Speaker 1: But um, that's what we're seeing from, you know, which 1007 00:55:20,320 --> 00:55:24,279 Speaker 1: is lingering effects of some amount. Um. I were really 1008 00:55:24,320 --> 00:55:26,400 Speaker 1: about the more severe ones. You know, I've had like 1009 00:55:26,480 --> 00:55:29,879 Speaker 1: young people who like can't go back to work, twenty 1010 00:55:29,960 --> 00:55:33,719 Speaker 1: nine year old who's literally incapacitated, um, just with so 1011 00:55:33,800 --> 00:55:37,280 Speaker 1: much fatigue and dizziness and just feels unwell all the time. 1012 00:55:37,320 --> 00:55:40,280 Speaker 1: And and he's really sad because he's a very functional, 1013 00:55:40,400 --> 00:55:43,640 Speaker 1: very active guy, and he just Um, you know, that's 1014 00:55:43,680 --> 00:55:46,280 Speaker 1: one case. But you know, I've had others. Now, he 1015 00:55:46,320 --> 00:55:49,279 Speaker 1: we've done good work with him. He's actually been my 1016 00:55:49,440 --> 00:55:53,000 Speaker 1: least satisfactory case, because I've had numbers of other cases 1017 00:55:53,080 --> 00:55:56,000 Speaker 1: where on our protocol which is on our websites called 1018 00:55:56,040 --> 00:55:59,279 Speaker 1: I Recover, which is sent in around IV mectin and 1019 00:55:59,360 --> 00:56:03,600 Speaker 1: some other medicines. UM, we had just incredible responses. And 1020 00:56:03,640 --> 00:56:06,480 Speaker 1: also that protocol is for those who are vaccine injured. 1021 00:56:06,480 --> 00:56:11,400 Speaker 1: We have tremendous responses in vaccine injury because you know, 1022 00:56:11,800 --> 00:56:15,080 Speaker 1: i've remectin right. One of the thoughts of why it's 1023 00:56:15,120 --> 00:56:18,600 Speaker 1: so effective is that it's it's it's a drug that's 1024 00:56:18,600 --> 00:56:22,040 Speaker 1: sought to tightly bind to the spike protein, and that's 1025 00:56:22,040 --> 00:56:25,000 Speaker 1: why it prevents entry. So if it binds, it can't 1026 00:56:25,120 --> 00:56:27,480 Speaker 1: enter the cell, it can't replicate, and that's why it's 1027 00:56:27,480 --> 00:56:31,680 Speaker 1: a good prevention. And because the vaccines, right, they tell 1028 00:56:31,719 --> 00:56:35,440 Speaker 1: the self to make spike protein, i've mectan actually binds 1029 00:56:35,440 --> 00:56:37,160 Speaker 1: to the spike protein, and so what we think is 1030 00:56:37,200 --> 00:56:39,600 Speaker 1: happening in the vaccine injury is that the spike protein 1031 00:56:39,719 --> 00:56:42,880 Speaker 1: is leaving the tissue of the arm and circulating and 1032 00:56:42,960 --> 00:56:46,399 Speaker 1: causing all of these you know, other symptoms. And if 1033 00:56:46,400 --> 00:56:49,319 Speaker 1: you give them ivermectin, they really respond. In fact, some 1034 00:56:49,360 --> 00:56:52,640 Speaker 1: of them A little satisfying clinical experiences has been treating 1035 00:56:53,239 --> 00:56:56,960 Speaker 1: patients who really felt unwell after the vaccine. And so um, 1036 00:56:57,080 --> 00:57:00,560 Speaker 1: I think your your audience and you know, anyone out 1037 00:57:00,560 --> 00:57:02,799 Speaker 1: there has longhould, they should go to our website and 1038 00:57:02,840 --> 00:57:05,120 Speaker 1: look at their protocol for that well. And I think too, 1039 00:57:05,200 --> 00:57:07,279 Speaker 1: you know, just for the you know, what sort of 1040 00:57:07,360 --> 00:57:10,520 Speaker 1: underscores how dumb their public health officials are and the 1041 00:57:10,560 --> 00:57:13,200 Speaker 1: people in charge, is how much they're undermining their own 1042 00:57:13,239 --> 00:57:17,280 Speaker 1: message with vaccines, because if you're essentially saying unvaccinated people 1043 00:57:17,360 --> 00:57:20,040 Speaker 1: or the enemy, you have to fear them while simultaneously 1044 00:57:20,040 --> 00:57:22,560 Speaker 1: telling us how great the vaccines are and somehow prevents 1045 00:57:22,600 --> 00:57:27,000 Speaker 1: them from severe illness. That that doesn't that doesn't really correlate, right, 1046 00:57:27,040 --> 00:57:29,200 Speaker 1: or that that doesn't really square right. You can't you can't. 1047 00:57:29,240 --> 00:57:31,400 Speaker 1: You can't say unvaccinated people or the enemy, and you 1048 00:57:31,440 --> 00:57:34,640 Speaker 1: have to fear them while also saying somehow vaccines are 1049 00:57:34,640 --> 00:57:37,160 Speaker 1: going to protect and save lives, and that just doesn't 1050 00:57:37,480 --> 00:57:42,440 Speaker 1: that doesn't track. So it's uh, it's so, it's you know, 1051 00:57:43,080 --> 00:57:46,080 Speaker 1: so for any anything else we're missing in this conversation 1052 00:57:46,240 --> 00:57:48,840 Speaker 1: that you want the folks listening to to know, No, 1053 00:57:50,520 --> 00:57:52,280 Speaker 1: I just want to give, like you know, I don't 1054 00:57:52,280 --> 00:57:54,160 Speaker 1: want to sound too hold to it, really a message 1055 00:57:54,160 --> 00:57:58,520 Speaker 1: of hope because you know, like I said, a stark 1056 00:57:58,720 --> 00:58:01,640 Speaker 1: achievement in public health has been realized in Utar Pradesh. 1057 00:58:01,720 --> 00:58:04,200 Speaker 1: They should be the model for the world. Um, just 1058 00:58:04,280 --> 00:58:07,680 Speaker 1: like Mexico City's Department of Health I M s S. 1059 00:58:08,360 --> 00:58:10,160 Speaker 1: They also could be a model for the world. I mean, 1060 00:58:10,200 --> 00:58:12,320 Speaker 1: we know how to solve this pandemic. So that's the 1061 00:58:12,440 --> 00:58:16,480 Speaker 1: positive message. UM. The tragedy is that we live in 1062 00:58:16,520 --> 00:58:21,080 Speaker 1: a very capitalistic societyist run on on profit motives and 1063 00:58:21,080 --> 00:58:25,720 Speaker 1: and unfortunately we have agencies that are captured regulatory capture 1064 00:58:25,800 --> 00:58:29,439 Speaker 1: by those with financial interests and and and that's why 1065 00:58:29,520 --> 00:58:32,760 Speaker 1: you're seeing the US have such a tough time with 1066 00:58:32,800 --> 00:58:36,240 Speaker 1: this pandemic. I mean, we're getting hammered here and and 1067 00:58:36,760 --> 00:58:39,760 Speaker 1: I you know, my organization, although we're a group of 1068 00:58:39,800 --> 00:58:43,720 Speaker 1: doctors and researchers, UM, we've had to learn to do grassroots, 1069 00:58:44,480 --> 00:58:47,560 Speaker 1: meaning you know, our normal dissemination of our knowledge was 1070 00:58:47,600 --> 00:58:51,360 Speaker 1: not working. Lecturing and publishing papers. We've published a dozen 1071 00:58:52,120 --> 00:58:54,880 Speaker 1: actually two dozen if you count the group. It just 1072 00:58:55,000 --> 00:58:58,240 Speaker 1: wasn't registering. And so we found that this was a 1073 00:58:58,320 --> 00:59:01,160 Speaker 1: life saving medicine, and so we tried to bring it, 1074 00:59:01,760 --> 00:59:04,120 Speaker 1: you know, with a website and press conference and I 1075 00:59:04,160 --> 00:59:06,920 Speaker 1: gave that testimony which luckily went viral and they got 1076 00:59:06,960 --> 00:59:09,520 Speaker 1: an important message out and we've continued to try to 1077 00:59:09,520 --> 00:59:12,520 Speaker 1: deliver that message. And so the message is good. There 1078 00:59:12,560 --> 00:59:16,760 Speaker 1: is a solution. There are treatments, UM, and UH, you've 1079 00:59:16,760 --> 00:59:18,960 Speaker 1: got to convince your doctors to learn about them and 1080 00:59:19,040 --> 00:59:21,440 Speaker 1: use them. Um. And it's working. Like I said, the 1081 00:59:21,440 --> 00:59:24,800 Speaker 1: prescriptions are going up, and so the early treatment message 1082 00:59:24,800 --> 00:59:27,000 Speaker 1: is getting out there. It's just it's really painful to 1083 00:59:27,040 --> 00:59:29,720 Speaker 1: see how slow it is and how much resistance to 1084 00:59:29,920 --> 00:59:31,600 Speaker 1: it is, which is which is going to be a 1085 00:59:31,720 --> 00:59:35,920 Speaker 1: historic humanitarian crisis. That was that that history will not 1086 00:59:36,080 --> 00:59:38,640 Speaker 1: be kind to these actions that you outline, Lisa, They 1087 00:59:38,640 --> 00:59:41,680 Speaker 1: will not be kind and resistance in terms of I 1088 00:59:41,720 --> 00:59:44,480 Speaker 1: mean there have been lawsuits of people suing on behalf 1089 00:59:44,480 --> 00:59:49,040 Speaker 1: of family members, suing hospitals who won't provide iver met 1090 00:59:49,040 --> 00:59:51,640 Speaker 1: done and things like that. So to the point where 1091 00:59:51,640 --> 00:59:54,240 Speaker 1: it's become you know, people have taken legal action to 1092 00:59:54,280 --> 00:59:56,560 Speaker 1: try to because they weren't able to get the prescription 1093 00:59:56,640 --> 00:59:59,200 Speaker 1: or they weren't able to get it. So you've mentioned 1094 00:59:59,360 --> 01:00:02,400 Speaker 1: uh and you're president founding member of the nonprofit called 1095 01:00:02,440 --> 01:00:06,640 Speaker 1: Frontline COVID nineteen Critical Carolines. You've mentioned it throughout the show. Again, 1096 01:00:06,680 --> 01:00:09,680 Speaker 1: where can people go to find this information to support 1097 01:00:09,720 --> 01:00:11,840 Speaker 1: you to read some of the work. Yeah, So it's 1098 01:00:12,400 --> 01:00:16,520 Speaker 1: f l c CC dot net UM and you know 1099 01:00:16,560 --> 01:00:19,320 Speaker 1: it was originally founded by Professor Paul Marrick, who was 1100 01:00:19,360 --> 01:00:21,920 Speaker 1: my dear friend and colleague and mentor of mine. And 1101 01:00:22,520 --> 01:00:24,680 Speaker 1: you know, he was tasked, you know, asked a year ago, 1102 01:00:24,800 --> 01:00:26,919 Speaker 1: you know, why don't you put together a protocols. He's 1103 01:00:26,960 --> 01:00:31,480 Speaker 1: famous for his sepsis protocols UM and he's a giant 1104 01:00:31,480 --> 01:00:34,880 Speaker 1: in medicine. He's the most published practicing intensivist in the 1105 01:00:34,880 --> 01:00:38,080 Speaker 1: world and the history of critical care medicine, and and 1106 01:00:38,120 --> 01:00:40,120 Speaker 1: all of us are well published and very well known. 1107 01:00:40,160 --> 01:00:43,480 Speaker 1: And we got together and we just have consumed everything COVID, 1108 01:00:43,800 --> 01:00:45,880 Speaker 1: and all we've tried to do is put together as 1109 01:00:45,920 --> 01:00:49,440 Speaker 1: effective treatment protocols we have, we can, and we have, 1110 01:00:49,800 --> 01:00:54,160 Speaker 1: and but our message and our expertise is being attacked 1111 01:00:54,320 --> 01:00:57,120 Speaker 1: and suppressed and it's it's said, but we'll get there. 1112 01:00:57,760 --> 01:01:00,200 Speaker 1: F l c cc dot net that I mentioned, So 1113 01:01:00,360 --> 01:01:04,120 Speaker 1: f l ccc dot net is the website. Dr Corey. 1114 01:01:04,160 --> 01:01:08,040 Speaker 1: I appreciate your time. This was a fascinating, fascinating discussion. Yes, 1115 01:01:08,480 --> 01:01:10,920 Speaker 1: so thank you. I really appreciate the opportunity to share 1116 01:01:11,040 --> 01:01:13,560 Speaker 1: them what people really need to hear, and so I 1117 01:01:13,600 --> 01:01:24,640 Speaker 1: thank you for that. I want to bank Dr pire 1118 01:01:24,680 --> 01:01:28,280 Speaker 1: Corey again at for such a fascinating and informative interview, 1119 01:01:28,960 --> 01:01:30,560 Speaker 1: and I want to thank you guys at home so 1120 01:01:30,720 --> 01:01:33,560 Speaker 1: much for listening. If you enjoyed today's show, please leave 1121 01:01:33,640 --> 01:01:36,400 Speaker 1: us a review and rate us five stars and Apple Podcasts. 1122 01:01:36,760 --> 01:01:39,200 Speaker 1: You can also find me on Twitter, Facebook and Instagram 1123 01:01:39,200 --> 01:01:42,640 Speaker 1: and at least and rebooth. Special thanks to our producer 1124 01:01:42,720 --> 01:01:45,240 Speaker 1: John Cassio, writer Aaron Kleegman, and he also does a 1125 01:01:45,400 --> 01:01:49,400 Speaker 1: research and executive producers Debbie Myers and Speaker New Gingridge, 1126 01:01:49,840 --> 01:01:53,600 Speaker 1: all part of the Gingridge three sixty network and team