1 00:00:00,280 --> 00:00:02,840 Speaker 1: Brought to you by the reinvented two thousand twelve camera. 2 00:00:03,160 --> 00:00:07,640 Speaker 1: It's ready. Are you welcome to Stuff you Should Know 3 00:00:08,240 --> 00:00:11,960 Speaker 1: from how Stuff Works dot Com. Join Josh and Chuck, 4 00:00:12,200 --> 00:00:14,440 Speaker 1: the guys who bring you Stuff you Should Know, as 5 00:00:14,440 --> 00:00:16,479 Speaker 1: they take a trip around the world to help you 6 00:00:16,520 --> 00:00:19,520 Speaker 1: get smarter in a topsy turv economy. Check out the 7 00:00:19,560 --> 00:00:22,240 Speaker 1: all new super Stuffed Guide to the Economy from how 8 00:00:22,280 --> 00:00:27,640 Speaker 1: Stuff Works dot Com, available now exclusively on iTunes. Hey, 9 00:00:27,680 --> 00:00:30,440 Speaker 1: and welcome to the podcast Whoop Whoop. That's the side 10 00:00:30,440 --> 00:00:33,760 Speaker 1: of the police. I'm Josh Clark, Chuck Bryant's with me. 11 00:00:33,840 --> 00:00:37,080 Speaker 1: Boogie Down Productions, Old school rap. That's right, took nice one, 12 00:00:37,240 --> 00:00:40,479 Speaker 1: good call. I love it. Yeah. So um, how are 13 00:00:40,479 --> 00:00:44,640 Speaker 1: you doing. I'm well, sir, Yeah, I'm doing really well. Chuck. Yeah, 14 00:00:44,640 --> 00:00:48,200 Speaker 1: thank you for asking. Thanks. Do it look healthy from 15 00:00:48,200 --> 00:00:51,360 Speaker 1: the from the nose up, I've noticed that my base 16 00:00:51,479 --> 00:00:55,400 Speaker 1: is becoming increasingly resemblant to a catcher's mit, an old 17 00:00:55,440 --> 00:00:59,120 Speaker 1: catcher's met and not a compass, no, like a very 18 00:00:59,240 --> 00:01:02,040 Speaker 1: round catchers it a perfectly round catcher man. How about that, 19 00:01:02,120 --> 00:01:04,280 Speaker 1: like one of those catchers mits from like the early days. 20 00:01:04,440 --> 00:01:07,320 Speaker 1: Maybe my full name could be compass head catcherst it's 21 00:01:07,319 --> 00:01:09,880 Speaker 1: a little cumbersome. So, Chuck, have you ever met a cat? 22 00:01:10,520 --> 00:01:12,880 Speaker 1: I've got two cats, so you have? Oh yeah, you've 23 00:01:12,920 --> 00:01:18,040 Speaker 1: got the Whiz and Lebron. That's pretty close. Actually, I'm 24 00:01:18,080 --> 00:01:21,360 Speaker 1: impressed the Wizard and Laurn Lauren. Okay, yeah, I just 25 00:01:21,400 --> 00:01:23,679 Speaker 1: added a b right, and I would not name my 26 00:01:23,720 --> 00:01:27,920 Speaker 1: cat after Lebron James currently killing the Atlanta Hawks. Yeah, 27 00:01:27,959 --> 00:01:31,200 Speaker 1: he's good though, and a good guy here. Yeah. Sure, so, Chuck, 28 00:01:31,200 --> 00:01:34,679 Speaker 1: have you ever met a cat that could predict death? No? 29 00:01:35,400 --> 00:01:38,040 Speaker 1: But I love this story. It's a good one. Do 30 00:01:38,080 --> 00:01:40,440 Speaker 1: you mean the story of Oscar? Yeah? I remember what happened. 31 00:01:40,680 --> 00:01:42,520 Speaker 1: Oh really? Oh yeah, I remember reading about it. I've 32 00:01:42,520 --> 00:01:44,760 Speaker 1: got to pay more attention. Yeah. My wife and I 33 00:01:44,800 --> 00:01:47,680 Speaker 1: share these animal stories with each other, gotcha. Okay, like 34 00:01:48,000 --> 00:01:51,200 Speaker 1: Christian the lion who was taken to Kenyan the seventies, 35 00:01:51,280 --> 00:01:53,160 Speaker 1: don't think there was not a tear or two shed? 36 00:01:53,240 --> 00:01:56,320 Speaker 1: And I agreed Brian Alsehold, especially the one, the version 37 00:01:56,360 --> 00:01:58,880 Speaker 1: of it that was put to Aerosmith's Um. I don't 38 00:01:58,920 --> 00:02:01,840 Speaker 1: want to uh, don't want to miss a thing. Don't 39 00:02:01,840 --> 00:02:05,040 Speaker 1: want to miss a thing? Yes, exactly, nice Chuck, Okay, well. 40 00:02:05,080 --> 00:02:08,600 Speaker 1: Back in two thousand seven, the New England Journal of Medicine, 41 00:02:08,800 --> 00:02:12,840 Speaker 1: which is not exactly known for its sensational journalism UM 42 00:02:12,880 --> 00:02:17,800 Speaker 1: published a story about a cat named Oscar. And Oscar 43 00:02:18,040 --> 00:02:21,240 Speaker 1: was a resident at a Rhode Island nursing home, Yeah, 44 00:02:21,280 --> 00:02:25,120 Speaker 1: the Steerhouse Nursing and Rehab Center. And basically he was 45 00:02:25,200 --> 00:02:28,280 Speaker 1: just a normal cat. Aloof kind of was like, Hey, 46 00:02:28,360 --> 00:02:30,760 Speaker 1: you're old and I'm staying away from you. For the 47 00:02:30,760 --> 00:02:33,560 Speaker 1: most part. It was a home. It is a home 48 00:02:33,639 --> 00:02:36,639 Speaker 1: for people with advanced dementia, and I get the impression 49 00:02:36,680 --> 00:02:38,640 Speaker 1: that it has a bit of a hospice vibe to 50 00:02:38,720 --> 00:02:41,880 Speaker 1: it here there um so Oscar, as I said, it's 51 00:02:41,880 --> 00:02:46,079 Speaker 1: generally aloof except when you're about to die all of 52 00:02:46,120 --> 00:02:48,520 Speaker 1: a sudden. If you're laying in bed and Oscar comes 53 00:02:48,560 --> 00:02:50,480 Speaker 1: over to you and sits down next to your bed 54 00:02:50,480 --> 00:02:52,600 Speaker 1: and starts hanging out with you, you got a couple 55 00:02:52,600 --> 00:02:56,120 Speaker 1: of hours left. And uh, Oscar is actually pretty good 56 00:02:56,120 --> 00:03:00,560 Speaker 1: at predicting death. Uh, there's at least twenty five cases 57 00:03:00,919 --> 00:03:04,960 Speaker 1: where he accurately predicted the death of a person in 58 00:03:05,040 --> 00:03:09,080 Speaker 1: the Steer Nursing Home and Josher's there was a rumor 59 00:03:09,120 --> 00:03:11,400 Speaker 1: there is a rumor on the internet going around that 60 00:03:11,760 --> 00:03:17,120 Speaker 1: Oscar met an untimely death and there was a mysterious 61 00:03:17,200 --> 00:03:21,520 Speaker 1: dented bedpan found near his lifeless cat body. Not so, 62 00:03:21,720 --> 00:03:25,280 Speaker 1: because we just called. We called the Steer Nursing Home 63 00:03:25,280 --> 00:03:27,799 Speaker 1: to find out there were there were It was kind 64 00:03:27,800 --> 00:03:30,440 Speaker 1: of vague. There were a couple of reputable news sources 65 00:03:30,600 --> 00:03:34,200 Speaker 1: e g. The Savannah Morning News that carried that story, 66 00:03:34,240 --> 00:03:36,480 Speaker 1: but they all appeared to be the same. So Chuck 67 00:03:36,520 --> 00:03:38,640 Speaker 1: and I, being the internet sluice that we are, just 68 00:03:38,680 --> 00:03:41,280 Speaker 1: picked up the phone and did it the old fashioned way, 69 00:03:41,600 --> 00:03:44,000 Speaker 1: and we called the nursing home. And Oscar is still 70 00:03:44,040 --> 00:03:47,120 Speaker 1: alive and well and in the nursing home, apparently predicting 71 00:03:47,160 --> 00:03:49,600 Speaker 1: death as well. Right, although she said that she was 72 00:03:49,920 --> 00:03:52,160 Speaker 1: she was looking at Oscar. I wonder if she met 73 00:03:52,160 --> 00:03:56,000 Speaker 1: Oscar's stuffed body on the counter of the check in 74 00:03:56,120 --> 00:03:58,520 Speaker 1: on that floor. But how hilarious is that that Oscar 75 00:03:58,560 --> 00:04:01,480 Speaker 1: would be murdered because patients didn't like him predicting their 76 00:04:01,480 --> 00:04:04,600 Speaker 1: deaths anymore. So not true. Oscars alive and well. I 77 00:04:04,640 --> 00:04:06,560 Speaker 1: must say when I said my wife and I really 78 00:04:06,560 --> 00:04:08,680 Speaker 1: loved the story. We didn't see it as a as 79 00:04:08,720 --> 00:04:12,680 Speaker 1: a maccab predictor of death kind of thing. Like Josh Bannett, 80 00:04:12,720 --> 00:04:16,320 Speaker 1: we saw it as a comforting thing that an animal provides. 81 00:04:16,920 --> 00:04:18,560 Speaker 1: Was trying to comfort these people. So that's the way 82 00:04:18,600 --> 00:04:23,159 Speaker 1: I took it. Is it? There are other theories? Okay? Alright, 83 00:04:23,200 --> 00:04:25,840 Speaker 1: so Chuck, how could a cat possibly predict death? Right? 84 00:04:26,080 --> 00:04:30,240 Speaker 1: They can smell it? Perhaps smelling is probably the likeliest answer. Yes. 85 00:04:30,680 --> 00:04:32,720 Speaker 1: You know, when you're sick, like when you have the flu, 86 00:04:33,320 --> 00:04:35,599 Speaker 1: you don't smell quite right, you know what I mean? 87 00:04:35,800 --> 00:04:39,880 Speaker 1: You smell sick like your breath is messed up, there's 88 00:04:39,920 --> 00:04:43,680 Speaker 1: some gunk coming out of your pores. I when I 89 00:04:43,720 --> 00:04:47,200 Speaker 1: was sick over the course of the last like eight podcast, UM, 90 00:04:47,279 --> 00:04:51,120 Speaker 1: I was waking up with literally my eyelids pasted shut 91 00:04:51,600 --> 00:04:53,880 Speaker 1: from gunk that was coming out. And don't think that 92 00:04:53,960 --> 00:04:58,360 Speaker 1: didn't smell, pal You want to know more swine flu? 93 00:04:58,640 --> 00:05:01,839 Speaker 1: I want to get back to the cats smelling death? Okay, 94 00:05:01,880 --> 00:05:05,560 Speaker 1: So smelling is a pretty obvious way. Apparently, as UM 95 00:05:05,680 --> 00:05:08,839 Speaker 1: people's organs begin to shut down or fail. Uh that 96 00:05:09,000 --> 00:05:13,400 Speaker 1: there's the hypothesis that, um, this would omit certain smells, 97 00:05:13,720 --> 00:05:16,719 Speaker 1: certain chemicals that humans cannot smell. What's that called? When 98 00:05:16,800 --> 00:05:26,680 Speaker 1: a cell commits suicide. Uh sellicide at auto license r 99 00:05:26,720 --> 00:05:31,560 Speaker 1: mortist baby. Um, so okay, the I guess the the 100 00:05:31,560 --> 00:05:35,400 Speaker 1: the senses that as these cells begin to cannibalize themselves 101 00:05:35,400 --> 00:05:37,960 Speaker 1: and break down and all their contents are at least 102 00:05:38,000 --> 00:05:41,560 Speaker 1: it starts to admit a smell that might attract Oscar. Right. 103 00:05:42,480 --> 00:05:45,000 Speaker 1: But the cool thing is, as you said, uh, he 104 00:05:45,279 --> 00:05:48,200 Speaker 1: it's not like he just goes in points like this, 105 00:05:48,240 --> 00:05:51,080 Speaker 1: one's next and then leaves. He hangs out until the 106 00:05:51,080 --> 00:05:54,240 Speaker 1: person is dead and then he leaves. Yeah, that's that's 107 00:05:54,360 --> 00:05:56,200 Speaker 1: what I took. Is that's the comforting part to me, 108 00:05:56,720 --> 00:05:59,160 Speaker 1: and comforting or you know, you wonder if you could 109 00:05:59,160 --> 00:06:01,640 Speaker 1: get the cat that lead you've got a second chance 110 00:06:01,720 --> 00:06:03,520 Speaker 1: or something not true at all. I wonder if people 111 00:06:03,520 --> 00:06:05,719 Speaker 1: have ever tried to bargain with the cat, like, dude, 112 00:06:06,040 --> 00:06:08,640 Speaker 1: I'll totally get you more friskies if you just get 113 00:06:08,680 --> 00:06:11,000 Speaker 1: out of here, or if one of the people in 114 00:06:11,040 --> 00:06:12,520 Speaker 1: the home that didn't like one of the other people 115 00:06:12,600 --> 00:06:15,719 Speaker 1: left a little trail of kibble, yeah, just to screw 116 00:06:15,800 --> 00:06:19,040 Speaker 1: with the their fellow patient nursing home hygiene. It makes 117 00:06:19,040 --> 00:06:21,600 Speaker 1: you wonder, Chuck, is it possible that Oscar the cat 118 00:06:21,839 --> 00:06:24,919 Speaker 1: is in fact the Grim Reaper. Uh, he is not Josh. 119 00:06:25,600 --> 00:06:27,679 Speaker 1: But that's just one example of what we're talking about, 120 00:06:27,680 --> 00:06:30,839 Speaker 1: which is animals having a sixth sense. Oh what a 121 00:06:30,920 --> 00:06:34,680 Speaker 1: sixth CeNSE? Well said Chuck. You we already decided that 122 00:06:34,760 --> 00:06:37,599 Speaker 1: Chuck was going to say six cents and not me 123 00:06:37,720 --> 00:06:41,719 Speaker 1: because I can't say yeah, thanks for the impression of 124 00:06:41,760 --> 00:06:45,760 Speaker 1: each other. I have a speech impediment, it turns out. So, uh, 125 00:06:46,160 --> 00:06:48,919 Speaker 1: should we talk about dogs next? Well? Yeah, one of 126 00:06:48,920 --> 00:06:51,760 Speaker 1: the one of the things that's fascinating about oscars. Cats 127 00:06:51,760 --> 00:06:54,400 Speaker 1: aren't They're not they don't do that. They're not empathetic, 128 00:06:54,400 --> 00:06:56,320 Speaker 1: they're not supposed to do that. They're not known for 129 00:06:56,360 --> 00:07:02,599 Speaker 1: being dogs are right, Um? Dogs tend to be very happy, 130 00:07:02,720 --> 00:07:06,680 Speaker 1: loving creatures, and so it would make more sense if 131 00:07:06,680 --> 00:07:10,080 Speaker 1: Oscar was a dog. Sure, because dogs there's all kinds 132 00:07:10,080 --> 00:07:15,320 Speaker 1: of anecdotal stories, uh about dogs detecting cancer by smelling, 133 00:07:15,960 --> 00:07:20,040 Speaker 1: and not just anecdotal. My friend, well, Mr Stack guy, 134 00:07:20,320 --> 00:07:23,240 Speaker 1: I happen to have a study right here, you're killing me. 135 00:07:23,600 --> 00:07:27,480 Speaker 1: I have a two thousand six study right um, And 136 00:07:27,560 --> 00:07:30,600 Speaker 1: I have no idea where it's from, but it seems 137 00:07:30,600 --> 00:07:33,240 Speaker 1: a little shaky to me quite too. It's from Science 138 00:07:33,320 --> 00:07:37,000 Speaker 1: Daily pal. They don't printice anything. UM. It was a 139 00:07:37,000 --> 00:07:39,880 Speaker 1: two thousand six study where they took eight six patients 140 00:07:39,880 --> 00:07:42,840 Speaker 1: with cancer with lung cancer and thirty one with breast 141 00:07:42,840 --> 00:07:46,480 Speaker 1: cancer and these were confirmed cancer cases. And then they 142 00:07:46,560 --> 00:07:50,280 Speaker 1: used a control sample of eighty three healthy people, right, 143 00:07:50,600 --> 00:07:54,280 Speaker 1: And they actually took breath samples from these people and 144 00:07:54,280 --> 00:07:57,640 Speaker 1: and seal them in special tubes, and then they exposed 145 00:07:57,640 --> 00:08:00,920 Speaker 1: them to these dogs. They had dog sniff the different 146 00:08:00,920 --> 00:08:09,280 Speaker 1: samples and UM with a eight to seven percent UM accuracy. 147 00:08:09,320 --> 00:08:12,840 Speaker 1: Thank you. Uh, these dogs could pick out people with cancer. 148 00:08:13,000 --> 00:08:17,680 Speaker 1: Really wow. So clearly there's some smells that we humans 149 00:08:18,360 --> 00:08:21,440 Speaker 1: aren't aware of, aren't cognizant of, you know, because we 150 00:08:21,560 --> 00:08:25,280 Speaker 1: like to smoke and eat cheeseburgers and things like that. UM, 151 00:08:25,320 --> 00:08:28,240 Speaker 1: that that animals can sense, which would explain why you 152 00:08:28,280 --> 00:08:31,720 Speaker 1: could detect cancer. And there's another study that showed that 153 00:08:31,880 --> 00:08:37,520 Speaker 1: UM dogs could detect bladder cancer in urine. So there 154 00:08:37,559 --> 00:08:40,200 Speaker 1: you have that one to thank you, and uh that 155 00:08:40,400 --> 00:08:42,640 Speaker 1: you know that makes sense to me because animals certainly 156 00:08:42,720 --> 00:08:47,560 Speaker 1: have different hearing capacities than we do. UM. High pitched 157 00:08:47,559 --> 00:08:51,560 Speaker 1: sounds like dog whistles, we can't hear humans typically here 158 00:08:51,600 --> 00:08:55,440 Speaker 1: between twenty and twenty thousand hurts. And elephants too can 159 00:08:55,480 --> 00:08:58,880 Speaker 1: hear between sixteen and twelve thousand, and cattle can go 160 00:08:58,960 --> 00:09:01,320 Speaker 1: all the way up to forty thou And so that's 161 00:09:01,320 --> 00:09:04,280 Speaker 1: why when animals are said to predict weather and earthquakes 162 00:09:04,320 --> 00:09:07,880 Speaker 1: and things like that, that and bear barometric pressure changes. 163 00:09:07,920 --> 00:09:10,600 Speaker 1: They pick up on these things when humans don't. So 164 00:09:10,640 --> 00:09:12,520 Speaker 1: it's not exactly that they have a sixth sense, but 165 00:09:13,000 --> 00:09:16,439 Speaker 1: they used the five senses are more heightened than humans are. 166 00:09:16,679 --> 00:09:19,200 Speaker 1: That's interesting that you say cattle can hear better than 167 00:09:19,240 --> 00:09:23,160 Speaker 1: anybody else. What was that? Well, because I was reading 168 00:09:23,160 --> 00:09:26,560 Speaker 1: in this article it mentions the two four tsunami and 169 00:09:26,600 --> 00:09:30,120 Speaker 1: how there were so few animal carcasses found because so 170 00:09:30,160 --> 00:09:33,760 Speaker 1: many animals acted strangely and basically headed to higher ground 171 00:09:34,280 --> 00:09:37,000 Speaker 1: before the tsunami hit. The animals that they found the 172 00:09:37,040 --> 00:09:39,640 Speaker 1: most of her cattle. So maybe they know and they're 173 00:09:39,679 --> 00:09:41,400 Speaker 1: just like, I don't have a whole lot to live for, 174 00:09:41,559 --> 00:09:44,320 Speaker 1: I'm just gonna let death take me now. Or maybe they, 175 00:09:44,679 --> 00:09:47,280 Speaker 1: you know, had a harder time getting out of there 176 00:09:47,760 --> 00:09:49,840 Speaker 1: than heading the higher crown. I don't know. Yeah, that's 177 00:09:49,840 --> 00:09:52,600 Speaker 1: just one theory. So okay, So Oscar maybe in the 178 00:09:52,679 --> 00:09:55,000 Speaker 1: league of his own by predicting death. But yeah, there 179 00:09:55,080 --> 00:09:59,520 Speaker 1: there is tons of anecdotal evidence that animals, um especially 180 00:09:59,600 --> 00:10:05,840 Speaker 1: dag can sense illness. Right. Um, there was there was 181 00:10:05,880 --> 00:10:09,760 Speaker 1: a chihuahua that a woman in England owned. Who um 182 00:10:10,040 --> 00:10:13,240 Speaker 1: said that her dog detected a breast cancer. I thought 183 00:10:13,240 --> 00:10:17,400 Speaker 1: you're gonna say, taco bell, No awful. It was a 184 00:10:17,480 --> 00:10:21,520 Speaker 1: detective breast cancer three different times in her which sucks. 185 00:10:21,520 --> 00:10:23,920 Speaker 1: She had breast cancer three different times. Better chihuahua was like, 186 00:10:24,000 --> 00:10:26,839 Speaker 1: you got breast cancer? Hunt? Interesting? And uh. There was 187 00:10:26,920 --> 00:10:30,640 Speaker 1: a person with a Dalmatian. I don't know where he 188 00:10:30,760 --> 00:10:34,880 Speaker 1: or she was from, but um, the dalmatian kept smelling 189 00:10:34,920 --> 00:10:39,080 Speaker 1: this freckle on the on the owner's arm and it 190 00:10:39,160 --> 00:10:43,040 Speaker 1: turned out skin cancer. Isn't that weird? I believe it? Man? 191 00:10:44,120 --> 00:10:46,839 Speaker 1: Why not? Like I always say, why not? I mean, 192 00:10:46,840 --> 00:10:50,240 Speaker 1: what do we know as humans? I mean, who knows? 193 00:10:50,240 --> 00:10:52,439 Speaker 1: Maybe animals can totally smell these things. Should we go 194 00:10:52,480 --> 00:10:55,760 Speaker 1: to epilepsy? I think we should. I think we totally should. 195 00:10:55,760 --> 00:10:58,920 Speaker 1: This is something that's it's kind of controversial. Um. One 196 00:10:58,920 --> 00:11:01,600 Speaker 1: of the great failings of my opinion of science is 197 00:11:01,640 --> 00:11:05,720 Speaker 1: that if it can't readily explain something immediately, then it's 198 00:11:05,800 --> 00:11:09,280 Speaker 1: just poop poo's it. But it's looking more and more 199 00:11:09,400 --> 00:11:12,160 Speaker 1: like which is a scientific term for discredit. I think, yeah, 200 00:11:12,760 --> 00:11:15,839 Speaker 1: poo poo, um, it is looking more and more like 201 00:11:16,240 --> 00:11:19,839 Speaker 1: dogs can sense epilepsy. Now, I think one of the 202 00:11:19,880 --> 00:11:22,920 Speaker 1: misleading things in these two articles can animals predict death? 203 00:11:23,320 --> 00:11:25,800 Speaker 1: And can a dog really predict a seizure is the 204 00:11:25,880 --> 00:11:29,480 Speaker 1: use of the word predict. There is no prediction. I 205 00:11:29,559 --> 00:11:33,559 Speaker 1: was reading this awesome article on CNN and it was 206 00:11:33,559 --> 00:11:38,480 Speaker 1: about a woman named Klie Johnson and she has um 207 00:11:38,720 --> 00:11:42,680 Speaker 1: epilepsy and cerebral palsy, so she has a seizure. She 208 00:11:42,880 --> 00:11:46,160 Speaker 1: is in trouble. She she wrote, She's in a wheelchair 209 00:11:46,200 --> 00:11:47,760 Speaker 1: and she has to wear a helmet all the time 210 00:11:47,800 --> 00:11:52,199 Speaker 1: because of this UM. And she actually recently got a 211 00:11:52,200 --> 00:11:57,520 Speaker 1: a dog that is a UM seizure sensing dog, epilepsy 212 00:11:57,520 --> 00:12:00,880 Speaker 1: trained seizure alert dog. That's what it's called. Yeah um. Now, 213 00:12:00,880 --> 00:12:04,480 Speaker 1: there's all sorts of seizure response dogs. This is established fact. 214 00:12:05,000 --> 00:12:08,560 Speaker 1: Dogs can be trained to um basically go get help, 215 00:12:09,520 --> 00:12:13,000 Speaker 1: bring food or a blanket. UM. Some lay on top 216 00:12:13,080 --> 00:12:15,680 Speaker 1: of their owners while they're having caesars to like to 217 00:12:15,800 --> 00:12:18,120 Speaker 1: keep um. Yeah, to keep from any kind of uh, 218 00:12:18,600 --> 00:12:22,920 Speaker 1: further injury or right. Um. This is different though totally different, 219 00:12:23,320 --> 00:12:26,760 Speaker 1: because that's a response. This is an alert. Um. She 220 00:12:26,920 --> 00:12:29,160 Speaker 1: has a dog that she got actually chucked from up 221 00:12:29,200 --> 00:12:32,120 Speaker 1: the street. In alfred and Georgia, there's a group called 222 00:12:32,160 --> 00:12:36,320 Speaker 1: Canine Assistance. Yeah and um. In the last few years 223 00:12:36,320 --> 00:12:40,720 Speaker 1: they've trained a hundred seizure dogs. Um. And they actually 224 00:12:40,760 --> 00:12:44,480 Speaker 1: this is the cool part. Caesar dogs um tend to 225 00:12:44,520 --> 00:12:47,079 Speaker 1: be one of the more expensive dogs, like ten g 226 00:12:48,520 --> 00:12:51,400 Speaker 1: twenty grand for a dog to train and keep it 227 00:12:51,480 --> 00:12:56,760 Speaker 1: healthy and fed over its lifetime. Right, yes, that's for 228 00:12:56,840 --> 00:13:00,000 Speaker 1: the lifetime. Yeah, veterinary care, that kind of think. That's 229 00:13:00,160 --> 00:13:03,880 Speaker 1: way more than the average dog. Um. The cool thing 230 00:13:04,080 --> 00:13:07,480 Speaker 1: about Canine Assistance is that they the people who get 231 00:13:07,520 --> 00:13:10,720 Speaker 1: their dogs kidding for free and they actually fly the 232 00:13:10,760 --> 00:13:15,120 Speaker 1: people there their dogs recipients out to Atlanta to hang 233 00:13:15,120 --> 00:13:16,600 Speaker 1: out with the dog for two weeks and they pay 234 00:13:16,640 --> 00:13:20,240 Speaker 1: for everything that pay for air fair um, lodging, food, 235 00:13:20,320 --> 00:13:23,960 Speaker 1: the whole shebang. Um. And they actually also pay for 236 00:13:24,080 --> 00:13:26,640 Speaker 1: the dogs veterinary and food bills for the rest of 237 00:13:26,640 --> 00:13:30,920 Speaker 1: its life. Yeah. That is a great organization. It's a 238 00:13:30,960 --> 00:13:33,920 Speaker 1: canine assistance out of Alpharetta, Georgia, and they are doing 239 00:13:33,960 --> 00:13:37,080 Speaker 1: some good in the world. Um. But anyway, there in 240 00:13:37,120 --> 00:13:42,600 Speaker 1: this article there were two funny well one was terribly ironic, 241 00:13:42,640 --> 00:13:46,120 Speaker 1: the other was kind of funny. UM. The the the 242 00:13:46,320 --> 00:13:51,079 Speaker 1: researcher a neurologist who is poo pooing the concept that 243 00:13:51,440 --> 00:13:58,440 Speaker 1: UM dogs can predict seizures. His name is Dr Gregory Berkley. Barkley, 244 00:13:59,080 --> 00:14:00,480 Speaker 1: you give me this look, and I was like, I'm 245 00:14:00,520 --> 00:14:03,120 Speaker 1: missing something here, and he said, and I actually agree 246 00:14:03,160 --> 00:14:05,720 Speaker 1: with him. He points out that the dogs can't predict 247 00:14:05,760 --> 00:14:08,720 Speaker 1: seizures UM, but that it's actually responding to an earlier 248 00:14:08,800 --> 00:14:11,559 Speaker 1: stage of the seizure before the patient is aware that 249 00:14:11,600 --> 00:14:14,079 Speaker 1: the caesuar is going on. Okay, like an eye movement 250 00:14:14,240 --> 00:14:18,400 Speaker 1: or dilation, possibly a smell, something that the patient's not 251 00:14:18,520 --> 00:14:21,440 Speaker 1: aware of. And the big problem with seizures is that 252 00:14:21,600 --> 00:14:23,960 Speaker 1: if you're driving a car, yeah, and you have a seizure, 253 00:14:24,840 --> 00:14:28,040 Speaker 1: so long, right, And I know that the ones who 254 00:14:28,080 --> 00:14:29,640 Speaker 1: are good at this, the dogs are good at this, 255 00:14:29,680 --> 00:14:33,240 Speaker 1: can predict anywhere. But I mean sometimes it's like thirty seconds, 256 00:14:33,240 --> 00:14:35,160 Speaker 1: which is enough time to pull a car over. But 257 00:14:35,320 --> 00:14:37,280 Speaker 1: this one lady said that she gets about a thirty 258 00:14:37,320 --> 00:14:39,960 Speaker 1: to forty five minute heads up from her her dog, 259 00:14:40,280 --> 00:14:43,560 Speaker 1: so did MS Johnson. She gets anywhere from twenty to 260 00:14:43,640 --> 00:14:46,040 Speaker 1: forty minutes. And this this dog that she just got 261 00:14:46,400 --> 00:14:51,480 Speaker 1: last year named ben Um, he's actually her second. Here's 262 00:14:51,520 --> 00:14:54,600 Speaker 1: the here's the horribly ironic thing. Um. She had another 263 00:14:54,680 --> 00:14:59,160 Speaker 1: great seizure dog for twelve years named McKeever, who actually 264 00:14:59,640 --> 00:15:03,160 Speaker 1: helps through her roughest time. She was having many more 265 00:15:03,200 --> 00:15:07,560 Speaker 1: seizures I think, uh, I think about ten a week maybe, 266 00:15:07,880 --> 00:15:10,360 Speaker 1: and it's actually gone down since then. But he was 267 00:15:10,440 --> 00:15:13,520 Speaker 1: really working over time. She had him for twelve years 268 00:15:13,600 --> 00:15:16,280 Speaker 1: until two thousand and seven when he died after having 269 00:15:16,320 --> 00:15:20,200 Speaker 1: his own seizure. Boy, isn't that awful? Yeah, this podcast 270 00:15:20,240 --> 00:15:22,520 Speaker 1: officially became one that my wife will not listen to. 271 00:15:22,640 --> 00:15:24,600 Speaker 1: I will steer her away from this. Yeah, dogs having 272 00:15:24,640 --> 00:15:28,560 Speaker 1: seizures is kind of sad. Yeah, but what an irony is? 273 00:15:28,680 --> 00:15:32,040 Speaker 1: So it is possible for a dog to uh Again, 274 00:15:32,040 --> 00:15:34,080 Speaker 1: we shouldn't use the word predict the seizure, and they 275 00:15:34,080 --> 00:15:36,160 Speaker 1: don't necessarily have to be trained, right, Chuck? Aren't some 276 00:15:36,200 --> 00:15:38,920 Speaker 1: of them just the household pets that are picking up 277 00:15:38,920 --> 00:15:41,400 Speaker 1: on this just from living around people with epilepsy. Right, 278 00:15:41,440 --> 00:15:44,000 Speaker 1: And I think they've also decided that it's uh not 279 00:15:44,080 --> 00:15:46,640 Speaker 1: breed specific either, so it's not I don't think they 280 00:15:46,680 --> 00:15:50,000 Speaker 1: found any specific breed has been any better than the next. Right, 281 00:15:50,160 --> 00:15:53,080 Speaker 1: Is that true? Uh? Yeah, I remember. I think it's 282 00:15:53,320 --> 00:15:55,560 Speaker 1: the impression I have is that it's more exposure to 283 00:15:55,800 --> 00:15:59,040 Speaker 1: epilepsy than anything else, and looking for signs and queues, 284 00:15:59,360 --> 00:16:01,680 Speaker 1: and then the second stages learning to not be afraid 285 00:16:02,440 --> 00:16:05,440 Speaker 1: of what happens when the owner's eyes roll back ahead 286 00:16:05,480 --> 00:16:07,520 Speaker 1: and they start trembling, and then they alert. They all 287 00:16:07,520 --> 00:16:09,800 Speaker 1: have their different ways of alerting. Some poet them, some 288 00:16:10,160 --> 00:16:13,880 Speaker 1: lick their hand. Uh. Some I think walk around in 289 00:16:13,960 --> 00:16:17,240 Speaker 1: circles or make a close eye contact. So it's pretty cool. 290 00:16:17,280 --> 00:16:19,920 Speaker 1: They have their different messages they'll send the owner. Yeah 291 00:16:20,200 --> 00:16:23,160 Speaker 1: you got anything else? No, I don't think so, Josh. 292 00:16:23,280 --> 00:16:25,000 Speaker 1: I would like to say, UM. One of the things 293 00:16:25,040 --> 00:16:29,520 Speaker 1: that I have read in researching this was that, UM, people, 294 00:16:29,760 --> 00:16:33,000 Speaker 1: since it's not proven, and since if it if it 295 00:16:33,080 --> 00:16:36,080 Speaker 1: does work, if a dog can sense a seizure early 296 00:16:36,120 --> 00:16:40,120 Speaker 1: on just from being around someone with epilepsy. UM, I 297 00:16:40,280 --> 00:16:42,480 Speaker 1: read over and over again that people are kind of 298 00:16:42,520 --> 00:16:46,800 Speaker 1: warned from staying away from um dog breeders or trainers 299 00:16:47,160 --> 00:16:50,760 Speaker 1: that charge you like twenty grand for a dog UM, 300 00:16:50,920 --> 00:16:54,080 Speaker 1: especially with with groups like Canine Assistants out there doing 301 00:16:54,280 --> 00:16:56,360 Speaker 1: lots of good. Yeah, and they can never guarantee to 302 00:16:56,520 --> 00:16:58,720 Speaker 1: That's the other takeaway I had is that doctors say 303 00:16:58,760 --> 00:17:00,840 Speaker 1: that this can be you can be a good thing, 304 00:17:00,880 --> 00:17:03,440 Speaker 1: but it's certainly not a fail safe and you should 305 00:17:03,520 --> 00:17:07,080 Speaker 1: never like rely on this as your only means of 306 00:17:07,880 --> 00:17:10,440 Speaker 1: helping yourself out if you have. But at the end, 307 00:17:10,520 --> 00:17:13,720 Speaker 1: even if the dogs hit and they feel pretty good 308 00:17:13,760 --> 00:17:15,800 Speaker 1: and they can and they also doctors said, they do 309 00:17:15,920 --> 00:17:19,760 Speaker 1: provide the companionship and the and all the other good things. Ultimately, 310 00:17:19,840 --> 00:17:21,480 Speaker 1: you have a dog, and how can you go wrong 311 00:17:21,520 --> 00:17:23,600 Speaker 1: when you got a dog? Exactly? My dogs the only 312 00:17:23,600 --> 00:17:27,720 Speaker 1: thing they can predict is five o'clock dinner time. And 313 00:17:27,840 --> 00:17:32,240 Speaker 1: so where am I? What does that leave me? Um? 314 00:17:32,280 --> 00:17:35,200 Speaker 1: Feeding the dogs? But feeding the dogs, it's their world 315 00:17:35,240 --> 00:17:36,680 Speaker 1: and I'm just living in it. If you want to 316 00:17:36,760 --> 00:17:41,440 Speaker 1: learn more about animals and their sixth senses? How is 317 00:17:41,480 --> 00:17:45,800 Speaker 1: that type in animals and predict in the search part 318 00:17:45,800 --> 00:17:49,320 Speaker 1: how stuff works dot com? And uh, that leaves us 319 00:17:49,359 --> 00:17:53,480 Speaker 1: with only one possibility, the possibility that it is time 320 00:17:53,520 --> 00:18:00,320 Speaker 1: for listener mail. It is Josh. This is part two 321 00:18:00,520 --> 00:18:05,480 Speaker 1: of high fructose Corn Syrup Replies. Seriously, dude, I'm telling 322 00:18:05,480 --> 00:18:08,800 Speaker 1: you these For some reason, corn brings out the smarts 323 00:18:09,359 --> 00:18:12,360 Speaker 1: because these people were awesome and it was not just 324 00:18:13,200 --> 00:18:17,600 Speaker 1: fluffy like so many fanmail. No, I'm just kidding, Wow, Chuck, 325 00:18:17,720 --> 00:18:21,200 Speaker 1: just kidding. Uh. This is from when in Los Angeles 326 00:18:21,760 --> 00:18:24,280 Speaker 1: and when says, uh, he wants to add a little 327 00:18:24,280 --> 00:18:27,320 Speaker 1: bit about what we said about HFCs. He said, you 328 00:18:27,320 --> 00:18:29,160 Speaker 1: said it was very cheap, and that's why it's used 329 00:18:29,160 --> 00:18:32,119 Speaker 1: to such extent. True enough, but you did not mention. 330 00:18:32,200 --> 00:18:33,720 Speaker 1: And we had a couple of people right in about 331 00:18:33,720 --> 00:18:37,240 Speaker 1: this is that the price of HFCs has kept artificially 332 00:18:37,240 --> 00:18:40,360 Speaker 1: low by the policies of the US government. US government 333 00:18:40,359 --> 00:18:42,840 Speaker 1: has placed a quota on sugar imports to the US 334 00:18:43,280 --> 00:18:46,480 Speaker 1: in order to protect the domestic sugar producers and sugars. 335 00:18:46,520 --> 00:18:50,560 Speaker 1: Case are called tariff rate quotas. So that provides for 336 00:18:50,600 --> 00:18:53,320 Speaker 1: a low tariff on certain quantity which is a quota amount, 337 00:18:53,600 --> 00:18:56,000 Speaker 1: and a higher tariff on any quantity above that level. 338 00:18:56,760 --> 00:18:59,560 Speaker 1: So this creates an artificial shortage of sugar that drives 339 00:18:59,600 --> 00:19:03,200 Speaker 1: up US prices and supports American sugar growers, but it 340 00:19:03,280 --> 00:19:05,919 Speaker 1: also makes sugar a very expensive product. Just to give 341 00:19:05,960 --> 00:19:07,879 Speaker 1: you an idea, last year, the price per pound of 342 00:19:07,920 --> 00:19:10,800 Speaker 1: sugar in the United States was about fifty five cents 343 00:19:11,359 --> 00:19:14,240 Speaker 1: and the world price was about eighteen cents. Wow. Is 344 00:19:14,320 --> 00:19:17,520 Speaker 1: that amazing? God, I wish I'd lived in Portugal. Uh. 345 00:19:17,600 --> 00:19:21,160 Speaker 1: In contrast, Josh HFCs runs about twenty five cents per pound. 346 00:19:21,400 --> 00:19:24,359 Speaker 1: What So it is no surprise that when President Reagan 347 00:19:24,440 --> 00:19:27,520 Speaker 1: drastically lowered the quota for sugar in the eighties, driving 348 00:19:27,600 --> 00:19:30,920 Speaker 1: up the domestic price way way up, the major soft 349 00:19:31,000 --> 00:19:33,560 Speaker 1: drink maker switch from sugar to high princ dos corn strups. 350 00:19:33,600 --> 00:19:35,879 Speaker 1: So there you have it, and thank yeah. If the 351 00:19:36,280 --> 00:19:39,520 Speaker 1: if the sugar market in the US was unrestricted, there 352 00:19:39,520 --> 00:19:43,119 Speaker 1: will be no economic consented for anyone to use HFCs, 353 00:19:43,240 --> 00:19:45,439 Speaker 1: and those quotas would be removed from the sugar market. 354 00:19:46,119 --> 00:19:49,119 Speaker 1: Is uh that they would be removed as pretty much 355 00:19:49,160 --> 00:19:52,439 Speaker 1: impossible now because there's too much money at stake. So 356 00:19:52,640 --> 00:19:55,000 Speaker 1: we had other people writing in about this and that's 357 00:19:55,040 --> 00:19:57,800 Speaker 1: it seems to be. It's going on. Yeah, So thank you. 358 00:19:57,840 --> 00:20:01,840 Speaker 1: When in Los Angeles you are super fan an awesome 359 00:20:02,160 --> 00:20:04,080 Speaker 1: field reporter will call you, hey, you know what, we 360 00:20:04,119 --> 00:20:06,000 Speaker 1: should start saying what we're going to do in the 361 00:20:06,040 --> 00:20:08,080 Speaker 1: future and let people tell us ahead of time. Then 362 00:20:08,119 --> 00:20:09,840 Speaker 1: we can work it into the podcast and give him 363 00:20:09,960 --> 00:20:12,119 Speaker 1: zero credit for it. That's true field reporters. We have 364 00:20:12,200 --> 00:20:16,240 Speaker 1: field reporters. Yeah, eye reporters. So if you wanted to 365 00:20:16,560 --> 00:20:18,840 Speaker 1: I report for us, or just say hi or be 366 00:20:18,920 --> 00:20:22,760 Speaker 1: like what up yo? Send us a an email to 367 00:20:23,040 --> 00:20:29,440 Speaker 1: Stuff Podcast at how stuff works dot com. 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