1
00:00:00,520 --> 00:00:03,520
Speaker 1: Okat a team. It's it's the project. Doctor.

2
00:00:03,560 --> 00:00:06,920
Speaker 2: Bill has been away. He's been surfing. He's been No,

3
00:00:07,000 --> 00:00:09,119
Speaker 2: he hasn't. I don't know where he's been, but he's

4
00:00:09,119 --> 00:00:12,440
Speaker 2: been trying to avoid me, let's be honest, and somehow

5
00:00:13,760 --> 00:00:15,560
Speaker 2: I secured him back on the show for.

6
00:00:15,560 --> 00:00:17,759
Speaker 1: You, and he's doing it for you, not me. So

7
00:00:18,320 --> 00:00:19,600
Speaker 1: welcome back, man. Where you been?

8
00:00:20,800 --> 00:00:23,360
Speaker 3: Hey, it's great to be back. I'm glad that you

9
00:00:24,400 --> 00:00:28,320
Speaker 3: didn't get to you know, bored without me. Well, you

10
00:00:28,400 --> 00:00:31,680
Speaker 3: have just been trying to absorb some family time this

11
00:00:31,760 --> 00:00:34,480
Speaker 3: summer and I had a few conferences doing some traveling,

12
00:00:34,880 --> 00:00:36,879
Speaker 3: so it was a little tough to get our schedules

13
00:00:36,880 --> 00:00:41,240
Speaker 3: to aligne. But it's fantastic to be welcomed back, and

14
00:00:41,320 --> 00:00:43,360
Speaker 3: I hope to do a couple more shows before the

15
00:00:43,479 --> 00:00:43,960
Speaker 3: end of the year.

16
00:00:44,440 --> 00:00:45,159
Speaker 1: Yeah, let's do that.

17
00:00:45,840 --> 00:00:48,920
Speaker 2: What's your favorite thing to do when you're not being productive,

18
00:00:48,960 --> 00:00:51,960
Speaker 2: when you're being unproductive, when you don't care about crossing

19
00:00:51,960 --> 00:00:54,880
Speaker 2: the t's, dotting the eyes or ticking the boxes, what's

20
00:00:54,920 --> 00:00:57,280
Speaker 2: your favorite thing to do in terms of just chilling

21
00:00:57,280 --> 00:00:57,760
Speaker 2: out for you?

22
00:00:59,160 --> 00:01:01,160
Speaker 3: I guess I would have to say something like just

23
00:01:01,200 --> 00:01:03,800
Speaker 3: going to the beach where there is a bar right

24
00:01:03,840 --> 00:01:07,440
Speaker 3: next to the shore, just grab a cold one and

25
00:01:07,680 --> 00:01:09,800
Speaker 3: stare out into the sea.

26
00:01:10,360 --> 00:01:13,559
Speaker 2: And think about what is Are you on any level

27
00:01:14,200 --> 00:01:19,960
Speaker 2: philosophical or like we all know you very logical, rational, scientific,

28
00:01:20,000 --> 00:01:21,720
Speaker 2: But is there a part of you that's just like,

29
00:01:21,880 --> 00:01:23,160
Speaker 2: what's it all about?

30
00:01:23,959 --> 00:01:25,959
Speaker 3: Well, it depends how many beers I have.

31
00:01:27,840 --> 00:01:31,120
Speaker 2: As a correlation, as one goes up, the other goes up.

32
00:01:31,840 --> 00:01:36,679
Speaker 3: That's exactly right. But yeah, for the sake of argument, yeah,

33
00:01:36,720 --> 00:01:38,759
Speaker 3: you do start to think about some of those things.

34
00:01:38,760 --> 00:01:41,200
Speaker 3: But basically what I try to do when I'm in

35
00:01:41,240 --> 00:01:45,160
Speaker 3: that environment is keep my mind clear. Yeah, you know,

36
00:01:45,240 --> 00:01:48,240
Speaker 3: like most people, my default brain just runs in all

37
00:01:48,280 --> 00:01:50,760
Speaker 3: sorts of different directions all the time, and that gets

38
00:01:50,800 --> 00:01:53,720
Speaker 3: a little exhausting. So it's nice just to be able

39
00:01:53,760 --> 00:01:58,000
Speaker 3: to make yourself aware of the present moment, the beauty

40
00:01:58,000 --> 00:02:00,680
Speaker 3: of your surroundings, you know, the piece, and and just

41
00:02:00,760 --> 00:02:04,200
Speaker 3: kind of be in the present as they say, be

42
00:02:04,280 --> 00:02:05,600
Speaker 3: here now kind of thing.

43
00:02:06,280 --> 00:02:09,240
Speaker 2: I love that there's a difference between busy, chaos and

44
00:02:09,440 --> 00:02:16,560
Speaker 2: busy okay, because sometimes busy is stressful, anxiety producing, unenjoyable,

45
00:02:16,560 --> 00:02:20,760
Speaker 2: and sometimes busy is like creative and fun, right sure.

46
00:02:20,600 --> 00:02:23,480
Speaker 3: Sure, And I think John Lennon had a great quote like,

47
00:02:24,080 --> 00:02:26,840
Speaker 3: you know, life is what happens, you know, while you're

48
00:02:26,880 --> 00:02:29,800
Speaker 3: making all these plans. Yeah, so it's really nice to

49
00:02:29,800 --> 00:02:32,440
Speaker 3: step back from all of the I got to do this,

50
00:02:32,520 --> 00:02:34,560
Speaker 3: I got to do that, I'm worrying about this, and

51
00:02:34,720 --> 00:02:38,639
Speaker 3: just actually experience your existence.

52
00:02:39,639 --> 00:02:40,399
Speaker 1: Yes, so true.

53
00:02:40,480 --> 00:02:44,639
Speaker 2: It's like life happens despite me, Like life was here

54
00:02:44,680 --> 00:02:46,560
Speaker 2: before me. Life's going to be here. It depends how

55
00:02:46,560 --> 00:02:51,160
Speaker 2: we're defining life, I guess. But the human experience of life,

56
00:02:51,200 --> 00:02:52,960
Speaker 2: it's like it ain't going anywhere.

57
00:02:53,000 --> 00:02:54,200
Speaker 1: You know, we'll come and go.

58
00:02:54,280 --> 00:02:58,880
Speaker 2: But do you get with your and I know we're

59
00:02:58,880 --> 00:03:00,560
Speaker 2: going to have a particular chat this is not it,

60
00:03:00,600 --> 00:03:01,280
Speaker 2: but we'll get there.

61
00:03:01,360 --> 00:03:03,400
Speaker 1: Do you with your workload.

62
00:03:03,240 --> 00:03:08,240
Speaker 2: And your responsibilities, do you get particularly stressed or are

63
00:03:08,320 --> 00:03:10,920
Speaker 2: you not really that personality top.

64
00:03:12,000 --> 00:03:14,440
Speaker 3: Well, I think, as most of your audience knows, I'm

65
00:03:14,440 --> 00:03:19,560
Speaker 3: a scientist, so that can be a very stressful job

66
00:03:19,600 --> 00:03:23,399
Speaker 3: because you always need to acquire funding to support your

67
00:03:23,440 --> 00:03:25,840
Speaker 3: science and the team that is doing the work in

68
00:03:25,880 --> 00:03:30,160
Speaker 3: the lab. But I can't imagine doing anything else, Craig.

69
00:03:30,320 --> 00:03:34,080
Speaker 3: I mean, it's like being an explorer. It's like being

70
00:03:34,160 --> 00:03:38,120
Speaker 3: paid to solve mysteries. And puzzles, and it's a job

71
00:03:38,280 --> 00:03:40,880
Speaker 3: where no two days are the same. And you know,

72
00:03:41,040 --> 00:03:45,280
Speaker 3: monotony does not exist in my line of work because

73
00:03:45,320 --> 00:03:48,119
Speaker 3: we are at the edge of human knowledge and we're

74
00:03:48,120 --> 00:03:54,360
Speaker 3: trying to push that frontier forward. And there's no greater

75
00:03:54,800 --> 00:03:58,440
Speaker 3: task I can think of to do on a daily basis,

76
00:03:58,480 --> 00:04:02,960
Speaker 3: except maybe being a podcast aster like you that would

77
00:04:03,160 --> 00:04:07,680
Speaker 3: have such rewards. You know, because even though I'm getting

78
00:04:07,720 --> 00:04:12,080
Speaker 3: a kick out of, you know, exploring the unknown, I

79
00:04:12,160 --> 00:04:15,680
Speaker 3: work in biomedicine, so the things that we discover could

80
00:04:15,760 --> 00:04:20,360
Speaker 3: potentially down the road sometime lead to some very significant

81
00:04:20,760 --> 00:04:25,760
Speaker 3: and clinically relevant therapeutic that's going to help people. And

82
00:04:25,960 --> 00:04:27,679
Speaker 3: you know, if that doesn't get you out of bed

83
00:04:27,880 --> 00:04:30,400
Speaker 3: every morning, yes, I don't know what will.

84
00:04:31,279 --> 00:04:36,839
Speaker 2: Has there been anything significant over your research journey, your

85
00:04:36,880 --> 00:04:42,599
Speaker 2: creative investigating, your curiosity, or your curious career that you've

86
00:04:42,640 --> 00:04:45,200
Speaker 2: done a total one ID on something that you went, No,

87
00:04:45,320 --> 00:04:48,360
Speaker 2: this is true, this is this is how it works,

88
00:04:48,400 --> 00:04:51,680
Speaker 2: and then maybe a decade later, you went, doesn't really

89
00:04:51,680 --> 00:04:52,200
Speaker 2: work like that.

90
00:04:52,279 --> 00:04:54,520
Speaker 1: I just thought it did. Oh.

91
00:04:54,560 --> 00:04:59,719
Speaker 3: I think that's kind of the that's science in a nutshell.

92
00:05:00,040 --> 00:05:01,800
Speaker 3: I mean, it's like all these great things we do

93
00:05:01,920 --> 00:05:06,200
Speaker 3: discover you. With continued research, we find either nuances to it,

94
00:05:06,520 --> 00:05:09,920
Speaker 3: or sometimes we totally turn the idea on its head.

95
00:05:10,800 --> 00:05:13,560
Speaker 3: In my own field as a parasitologist working on this

96
00:05:13,680 --> 00:05:19,880
Speaker 3: brain parasite toxoplasma GANDHII, the overriding themes when I was

97
00:05:19,960 --> 00:05:22,760
Speaker 3: a young graduate student was that this was a fairly

98
00:05:23,240 --> 00:05:26,200
Speaker 3: benign parasite. Yeah, it gets in the brain, but it

99
00:05:26,200 --> 00:05:29,960
Speaker 3: doesn't seem to be doing anything as long as you're healthy.

100
00:05:30,080 --> 00:05:33,320
Speaker 3: It becomes a significant problem only when you're immune compromised,

101
00:05:33,320 --> 00:05:36,920
Speaker 3: and in that case it can potentially kill you. So

102
00:05:37,000 --> 00:05:39,440
Speaker 3: that was the dogma when I was in graduate school.

103
00:05:40,200 --> 00:05:44,080
Speaker 3: But as of maybe ten fifteen years ago, new studies

104
00:05:44,080 --> 00:05:47,200
Speaker 3: are coming out suggesting that even in healthy people, this

105
00:05:47,320 --> 00:05:51,120
Speaker 3: brain parasite is maybe not benign, and it could be

106
00:05:51,200 --> 00:05:57,200
Speaker 3: causing predisposition to schizophrenia or other neuropsychoses. And that was

107
00:05:57,200 --> 00:06:01,400
Speaker 3: something we really didn't appreciate when I was learning about

108
00:06:01,400 --> 00:06:04,400
Speaker 3: this parasite in the beginning. So all sorts of I mean,

109
00:06:04,480 --> 00:06:06,880
Speaker 3: that's not like turning something on its head, per se,

110
00:06:07,040 --> 00:06:10,159
Speaker 3: but it's learning something brand new that no one really

111
00:06:10,200 --> 00:06:13,440
Speaker 3: gave a second thought to, and that's really exciting for

112
00:06:13,560 --> 00:06:17,000
Speaker 3: me and my laboratory got engaged in some of those

113
00:06:17,040 --> 00:06:20,719
Speaker 3: studies to show that the neuroinflammatory component of the disease

114
00:06:20,839 --> 00:06:24,760
Speaker 3: is probably the root cause of some of these behavioral

115
00:06:24,880 --> 00:06:27,720
Speaker 3: changes that we see in infected animals and humans, and

116
00:06:28,080 --> 00:06:31,640
Speaker 3: that was one of the most rewarding findings that has

117
00:06:31,760 --> 00:06:35,200
Speaker 3: come out of my laboratory.

118
00:06:35,279 --> 00:06:38,120
Speaker 2: What I mean, I'm sure it's far and wide, but

119
00:06:38,880 --> 00:06:42,000
Speaker 2: in a nutshell, what is the most effective treatment for

120
00:06:42,080 --> 00:06:45,200
Speaker 2: that inflammation in the Brian Is it just an antibiotic,

121
00:06:45,320 --> 00:06:46,760
Speaker 2: is it a string of things?

122
00:06:46,920 --> 00:06:48,200
Speaker 1: Or am I off the mark?

123
00:06:49,200 --> 00:06:53,880
Speaker 3: No, You're not off the mark. Neuroinflammation is linked to

124
00:06:54,440 --> 00:06:59,080
Speaker 3: a variety of different diseases. So there are some therapeutics

125
00:06:59,080 --> 00:07:01,159
Speaker 3: that are trying to be developed where they can do

126
00:07:01,320 --> 00:07:07,559
Speaker 3: like a targeted you know, quelling of that neuroinflammation, because

127
00:07:07,560 --> 00:07:12,080
Speaker 3: it's always a catch twenty two. If that neuroinflammation is

128
00:07:12,160 --> 00:07:15,520
Speaker 3: caused by an infectious agent and you get rid of it,

129
00:07:15,560 --> 00:07:19,920
Speaker 3: there's a risk that the infection could roar back, maybe

130
00:07:19,960 --> 00:07:23,960
Speaker 3: in a very dangerous way, or some unrelated infection. You know,

131
00:07:24,000 --> 00:07:26,600
Speaker 3: you could be susceptible to that. It's to catch twenty two.

132
00:07:26,680 --> 00:07:30,080
Speaker 3: It's some of these immunotherapies that try to dampen the

133
00:07:30,120 --> 00:07:34,080
Speaker 3: immune system, and people who have allergies or autoimmune disease.

134
00:07:34,840 --> 00:07:38,080
Speaker 3: You can do that and alleviate those symptoms, but you

135
00:07:38,160 --> 00:07:43,080
Speaker 3: make those individuals more vulnerable to infection because their immune

136
00:07:43,120 --> 00:07:45,520
Speaker 3: system is modestly depressed.

137
00:07:46,120 --> 00:07:49,440
Speaker 2: Yes, yes, I didn't plan to tell you this, but

138
00:07:49,480 --> 00:07:51,880
Speaker 2: I'm going to tell you this because it's vaguely in

139
00:07:51,920 --> 00:07:55,280
Speaker 2: the ballpark. But the last month or so, I've felt rubbish, right,

140
00:07:55,560 --> 00:07:58,720
Speaker 2: And I try to not let people know that because

141
00:07:58,720 --> 00:08:02,280
Speaker 2: I don't think not because I don't want people to know,

142
00:08:02,360 --> 00:08:03,800
Speaker 2: but because I don't want to turn up and do

143
00:08:03,880 --> 00:08:05,800
Speaker 2: my job and say, by the way, I feel trash.

144
00:08:05,800 --> 00:08:08,080
Speaker 2: So you're going to get a three out of ten podcast, right,

145
00:08:08,200 --> 00:08:10,360
Speaker 2: or whatever it is, whatever it is the thing that

146
00:08:10,400 --> 00:08:14,480
Speaker 2: I'm doing. But I've had this, just this, you know,

147
00:08:14,640 --> 00:08:18,200
Speaker 2: onslaught of weird and different symptoms. And I thought I

148
00:08:18,200 --> 00:08:20,640
Speaker 2: had diabetes because I was thirsty all the time, and

149
00:08:21,440 --> 00:08:24,040
Speaker 2: which I don't eat rubbish. I eate two meals a day.

150
00:08:24,040 --> 00:08:26,200
Speaker 2: I don't drink beer, I don't drink soft drink. You know,

151
00:08:26,360 --> 00:08:29,160
Speaker 2: like that, from a behavioral point of view, the chance

152
00:08:29,240 --> 00:08:32,959
Speaker 2: of me and from a physiological point of view, fit

153
00:08:33,040 --> 00:08:34,199
Speaker 2: I'm strong, I'm healthy, I'm lean.

154
00:08:34,679 --> 00:08:35,640
Speaker 1: It doesn't make sense.

155
00:08:35,679 --> 00:08:37,800
Speaker 2: But my dad had diabetes and I went, well, mate,

156
00:08:37,880 --> 00:08:40,240
Speaker 2: or has diabetes. So I'm like, well, maybe this is

157
00:08:40,280 --> 00:08:43,360
Speaker 2: genetic and this is me now so, and then a

158
00:08:43,400 --> 00:08:45,720
Speaker 2: whole bunch of other things like weeing a bunch and

159
00:08:45,760 --> 00:08:48,120
Speaker 2: I'm like, oh, maybe I've got prostate issues and all

160
00:08:48,120 --> 00:08:49,120
Speaker 2: these old man things.

161
00:08:49,120 --> 00:08:52,480
Speaker 1: I'm like, really, I don't know. Anyway, went had all

162
00:08:52,520 --> 00:08:54,559
Speaker 1: of these tests done and all of that.

163
00:08:54,600 --> 00:08:56,760
Speaker 2: Good, like, you're not a diabetic, you're not even nearly

164
00:08:56,760 --> 00:08:59,760
Speaker 2: a diabetic, you don't have prostate issues, your PSA is low,

165
00:09:00,040 --> 00:09:03,880
Speaker 2: don't have like your cholesterol's low, your blood pressure is low.

166
00:09:05,000 --> 00:09:07,080
Speaker 2: And then they said but and then I had to

167
00:09:07,080 --> 00:09:09,240
Speaker 2: have a CT scan on my head and they went, well,

168
00:09:09,240 --> 00:09:10,040
Speaker 2: you've got.

169
00:09:10,360 --> 00:09:12,600
Speaker 1: Basically the whole right side of your face.

170
00:09:12,679 --> 00:09:16,120
Speaker 2: The sinuses are blocked and I was getting constant blocked

171
00:09:16,160 --> 00:09:19,000
Speaker 2: years right and you've got an infection in your head

172
00:09:19,000 --> 00:09:20,240
Speaker 2: that you probably had for months.

173
00:09:20,240 --> 00:09:21,920
Speaker 1: And I'm like, could that explain all this?

174
00:09:22,040 --> 00:09:24,560
Speaker 2: And they go yes, I go because I feel like

175
00:09:24,800 --> 00:09:30,000
Speaker 2: dog shit, and they went, well, that could be it.

176
00:09:30,120 --> 00:09:33,520
Speaker 1: And then so I have for the last six months.

177
00:09:33,559 --> 00:09:34,400
Speaker 1: Doc had.

178
00:09:36,240 --> 00:09:39,079
Speaker 2: Every day of every day, not one day without it

179
00:09:39,160 --> 00:09:42,240
Speaker 2: where my ears would block, and sometimes it be one ear,

180
00:09:42,320 --> 00:09:46,640
Speaker 2: sometimes it'd be two. And I did a speaking gigla

181
00:09:46,640 --> 00:09:49,920
Speaker 2: not last week, the week before. Yeah, so about nine

182
00:09:49,960 --> 00:09:53,040
Speaker 2: days ago, ten days ago, and both of my ears blocked.

183
00:09:53,080 --> 00:09:55,199
Speaker 2: Before the presentation, I had to talk for two and

184
00:09:55,240 --> 00:09:59,240
Speaker 2: a half hours with two totally blocked years.

185
00:10:00,960 --> 00:10:04,160
Speaker 1: And you can't. I can't hear anyone. I can't. My

186
00:10:04,280 --> 00:10:07,040
Speaker 1: voice is reverberating in my head. It is so loud

187
00:10:07,679 --> 00:10:09,800
Speaker 1: that if I say, go to the toilet.

188
00:10:09,400 --> 00:10:14,040
Speaker 2: Where I'm in a quiet room, I can hear my

189
00:10:14,120 --> 00:10:17,520
Speaker 2: heartbeat in my head. I can just stand still and

190
00:10:17,559 --> 00:10:20,120
Speaker 2: I could tell you what my current heart rate is

191
00:10:20,800 --> 00:10:23,800
Speaker 2: just by looking at my watch and you know, doing

192
00:10:23,800 --> 00:10:27,400
Speaker 2: fifteen seconds counting the beats, multiplying before I can go

193
00:10:27,480 --> 00:10:29,640
Speaker 2: my heart rate sixty at the moment or whatever, right,

194
00:10:30,400 --> 00:10:35,679
Speaker 2: And it just makes communication and interaction and focusing almost impossible.

195
00:10:36,200 --> 00:10:37,120
Speaker 1: So I've had that forever.

196
00:10:37,480 --> 00:10:41,760
Speaker 3: Yeah, so I'm sorry you went through that ordeal. But

197
00:10:42,040 --> 00:10:43,600
Speaker 3: did you get legs amuntombiotics?

198
00:10:43,800 --> 00:10:46,920
Speaker 2: Yeah, so, I've got doxy cycling. I think that's what

199
00:10:46,960 --> 00:10:50,320
Speaker 2: it's called. Is that anyway? And so I started on

200
00:10:50,320 --> 00:10:53,920
Speaker 2: that I'm currently I think today was my fourth or

201
00:10:53,960 --> 00:10:58,240
Speaker 2: fifth and for the first time in six months, I've

202
00:10:58,280 --> 00:11:00,880
Speaker 2: had touchwood. I don't want to get ahead of myself,

203
00:11:00,920 --> 00:11:03,839
Speaker 2: but for about forty eight hours, I haven't had a

204
00:11:03,880 --> 00:11:05,000
Speaker 2: blocked year, which.

205
00:11:04,880 --> 00:11:06,640
Speaker 1: Is like a miracle.

206
00:11:06,720 --> 00:11:09,880
Speaker 2: And it's just like and it's this sounds weird and

207
00:11:09,920 --> 00:11:13,200
Speaker 2: it's but talking to you now is so awesome because

208
00:11:13,240 --> 00:11:16,040
Speaker 2: I can hear you clearly, my ear is not blocked.

209
00:11:16,360 --> 00:11:17,080
Speaker 1: And then it's me.

210
00:11:17,400 --> 00:11:18,720
Speaker 3: But just because you can hear.

211
00:11:19,160 --> 00:11:23,000
Speaker 2: No, you're definitely therapeutic, so with Jesus for doctor Bill.

212
00:11:23,960 --> 00:11:27,480
Speaker 2: But here's the thing though, when all this shit happens, right, so,

213
00:11:27,520 --> 00:11:30,400
Speaker 2: there's this physiological stuff that's gone on but also impacts

214
00:11:30,400 --> 00:11:33,559
Speaker 2: your brain because you've got inflammation, you know, right up there,

215
00:11:34,160 --> 00:11:38,120
Speaker 2: and then it then it affects your psychology. And then

216
00:11:38,240 --> 00:11:41,880
Speaker 2: I would start to get nervous ten fifteen minutes before

217
00:11:41,920 --> 00:11:46,079
Speaker 2: a gig, thinking please don't block, please don't block, please

218
00:11:46,080 --> 00:11:49,719
Speaker 2: don't block, and invariably they would block, and it's like.

219
00:11:49,720 --> 00:11:51,280
Speaker 1: I almost talk myself into it.

220
00:11:51,640 --> 00:11:56,560
Speaker 2: So anyway, yeah, that's that's where I'm at, and it's

221
00:11:57,080 --> 00:11:59,480
Speaker 2: it seems to be it seems to be on the end,

222
00:11:59,480 --> 00:12:02,760
Speaker 2: which is so good. It's so funny when one little

223
00:12:02,840 --> 00:12:06,680
Speaker 2: thing and it's well, seems little, but it can just

224
00:12:06,800 --> 00:12:12,400
Speaker 2: make such a big difference to everything from psychology to emotion,

225
00:12:12,559 --> 00:12:17,959
Speaker 2: to sociology, to physiology to energy to you know, function,

226
00:12:18,640 --> 00:12:22,000
Speaker 2: Like it's like your body is so finely tuned.

227
00:12:22,760 --> 00:12:26,560
Speaker 3: Yeah, it's almost it's hard to believe that. You know,

228
00:12:27,280 --> 00:12:32,280
Speaker 3: before nineteen thirties, antibiotics didn't exist. You know, people literally

229
00:12:32,679 --> 00:12:36,240
Speaker 3: died from a lot of these infections. And yeah, we

230
00:12:36,360 --> 00:12:39,040
Speaker 3: really take that group of drugs for granted, and they

231
00:12:39,120 --> 00:12:43,880
Speaker 3: really are small miracles, so imagine but continues working for

232
00:12:43,960 --> 00:12:45,920
Speaker 3: you and you make a full recovery soon.

233
00:12:46,120 --> 00:12:46,559
Speaker 1: Thanks sir.

234
00:12:47,360 --> 00:12:50,800
Speaker 2: Yeah, I yeah, it's like literally I thought about that.

235
00:12:50,840 --> 00:12:53,040
Speaker 2: I thought about somebody with this two hundred years ago,

236
00:12:53,080 --> 00:12:56,040
Speaker 2: what happened to them because they hadn't taken anything that

237
00:12:56,120 --> 00:12:58,880
Speaker 2: was going to kill the infection, or they didn't have

238
00:12:58,920 --> 00:13:01,319
Speaker 2: access to that, or they didn't know what killed And

239
00:13:01,800 --> 00:13:04,440
Speaker 2: you know, I'm like, maybe someone would have died probably,

240
00:13:04,520 --> 00:13:04,800
Speaker 2: you know.

241
00:13:04,960 --> 00:13:09,160
Speaker 3: So now, well there's a reason why life expectancy was

242
00:13:09,280 --> 00:13:14,079
Speaker 3: like forty years of age prior to the discovery of

243
00:13:14,120 --> 00:13:15,439
Speaker 3: the germ theory of disease.

244
00:13:16,080 --> 00:13:18,160
Speaker 1: Yeah, yeah, yeah, amazing.

245
00:13:18,600 --> 00:13:22,319
Speaker 2: Now speaking of research and speaking of breakthroughs and speaking

246
00:13:22,360 --> 00:13:28,800
Speaker 2: of potential treatments for something which is very common unfortunately

247
00:13:29,120 --> 00:13:35,600
Speaker 2: in our population, Alzheimer's is being or they're looking at

248
00:13:35,640 --> 00:13:38,679
Speaker 2: treating Alzheimer's with lithium, or they are doing that or

249
00:13:39,240 --> 00:13:40,920
Speaker 2: I didn't do a deep dive because I wanted you

250
00:13:40,960 --> 00:13:42,920
Speaker 2: to tell me. I just know that there's something interesting

251
00:13:42,960 --> 00:13:43,960
Speaker 2: happening in that space.

252
00:13:45,080 --> 00:13:48,920
Speaker 3: Yeah, there's been a very exciting study that came out

253
00:13:48,960 --> 00:13:51,080
Speaker 3: back in August, and I thought it would be really

254
00:13:51,120 --> 00:13:55,240
Speaker 3: wonderful to talk about it because Alzheimer's has touched so

255
00:13:55,360 --> 00:13:59,320
Speaker 3: many lives, either people developing it themselves or they have

256
00:13:59,400 --> 00:14:01,880
Speaker 3: a friend or a loved one who's suffering from it.

257
00:14:03,040 --> 00:14:07,280
Speaker 3: That and there's no cure, which makes the terror even

258
00:14:07,360 --> 00:14:12,920
Speaker 3: more amplified. But like you said, Alzheimer's it's one of

259
00:14:12,920 --> 00:14:17,640
Speaker 3: the most common forms of dementia and it's terrifying if

260
00:14:17,640 --> 00:14:23,120
Speaker 3: you've ever witnessed someone's succumbing to these symptoms of you know,

261
00:14:23,280 --> 00:14:29,960
Speaker 3: gradually losing their memory, gradually losing their cognitive abilities, their

262
00:14:30,040 --> 00:14:35,360
Speaker 3: capacity for reason and good decision making, developing paranoia, and

263
00:14:35,640 --> 00:14:39,280
Speaker 3: all sorts of horrible things as this disease progresses and

264
00:14:40,160 --> 00:14:44,640
Speaker 3: slowly erodes the brain. But we're talking about fifty seven

265
00:14:44,960 --> 00:14:48,960
Speaker 3: million people worldwide, which is a substantial number of people,

266
00:14:49,560 --> 00:14:51,920
Speaker 3: and as more people we were just talking about, like

267
00:14:52,000 --> 00:14:55,800
Speaker 3: the average life expectancy age, it's much higher these days,

268
00:14:56,280 --> 00:15:01,920
Speaker 3: and old age is a significant factor Alzheimer's. So as

269
00:15:02,040 --> 00:15:06,760
Speaker 3: our population continues to live into these advanced ages, Alzheimer's

270
00:15:06,800 --> 00:15:12,160
Speaker 3: is probably going to become even more common. So a

271
00:15:12,160 --> 00:15:16,240
Speaker 3: lot of researchers are doubling down on investing into trying

272
00:15:16,240 --> 00:15:20,120
Speaker 3: to find a new treatment for this terrible and poorly

273
00:15:20,200 --> 00:15:25,680
Speaker 3: understood disease. So in addition to the human toll that

274
00:15:25,760 --> 00:15:30,119
Speaker 3: it takes, there's a significant economic toll associated with Alzheimer's

275
00:15:30,200 --> 00:15:34,720
Speaker 3: disease too. So as I was researching this particular study

276
00:15:34,720 --> 00:15:36,320
Speaker 3: that came out in August because I want to do

277
00:15:36,440 --> 00:15:40,560
Speaker 3: a popular science article about it, I was struck to

278
00:15:40,720 --> 00:15:44,240
Speaker 3: find that in twenty nineteen and an estimate was made

279
00:15:44,560 --> 00:15:47,400
Speaker 3: on what the global cost of Alzheimer's is and it's

280
00:15:47,520 --> 00:15:52,280
Speaker 3: one point three trillion dollars in US money. Wow, So

281
00:15:52,680 --> 00:15:55,840
Speaker 3: it's just staggering. You can think about what that kind

282
00:15:55,880 --> 00:15:59,720
Speaker 3: of money could be going towards if it wasn't dealing

283
00:15:59,840 --> 00:16:03,120
Speaker 3: with the aftermath of Alzheimer's and what it does to people.

284
00:16:04,160 --> 00:16:08,840
Speaker 3: So just really underscores the urgency to come up with

285
00:16:08,880 --> 00:16:11,800
Speaker 3: new treatments. And that's why this study which came out

286
00:16:11,880 --> 00:16:17,680
Speaker 3: of Harvard Medical School led by a geneticist and neuroscientist

287
00:16:18,040 --> 00:16:22,600
Speaker 3: named Bruce Yank Nurse. So he and his team have

288
00:16:22,680 --> 00:16:26,080
Speaker 3: come up with this connection that hasn't been seen before

289
00:16:26,840 --> 00:16:33,120
Speaker 3: with lithium in the brain and the pathogenesis of Alzheimer's.

290
00:16:34,000 --> 00:16:38,520
Speaker 3: And the study was important for well several major reasons.

291
00:16:38,880 --> 00:16:42,760
Speaker 3: The first of which is no one really appreciated that

292
00:16:42,840 --> 00:16:47,280
Speaker 3: the element lithium had a major role in the function

293
00:16:47,400 --> 00:16:51,120
Speaker 3: of the brain. Yes, and this study kind of showed that,

294
00:16:51,200 --> 00:16:53,520
Speaker 3: and we can walk through some of the details why.

295
00:16:54,760 --> 00:16:58,680
Speaker 3: The second major feature is that it could lead to

296
00:16:58,720 --> 00:17:02,400
Speaker 3: new treatments. This is a not discovery, first time it

297
00:17:02,480 --> 00:17:06,119
Speaker 3: was ever seen, and if it does hold up, it

298
00:17:06,160 --> 00:17:10,080
Speaker 3: could lead to a very rapid progression of new treatments

299
00:17:10,359 --> 00:17:13,639
Speaker 3: for Alzheimer's there's very few at the moment. And it

300
00:17:13,680 --> 00:17:16,880
Speaker 3: can also lead to a new diagnostic tool because now

301
00:17:16,920 --> 00:17:20,680
Speaker 3: we know that you know, with lithium being connected to Alzheimer's.

302
00:17:20,680 --> 00:17:23,399
Speaker 3: You can measure that element in the blood and maybe

303
00:17:23,440 --> 00:17:28,520
Speaker 3: come up with a new screening tool to identify Alzheimer's

304
00:17:28,520 --> 00:17:32,959
Speaker 3: in people before the more dramatic symptoms kick in, and

305
00:17:33,000 --> 00:17:35,760
Speaker 3: that would give you the advantage of treating early and

306
00:17:35,840 --> 00:17:40,040
Speaker 3: maybe slowing progression of disease, because once it gets going,

307
00:17:40,080 --> 00:17:41,919
Speaker 3: there's really not much we can do about it.

308
00:17:42,440 --> 00:17:47,159
Speaker 2: Yeah, So so to have almost like a preemptive stroke

309
00:17:47,320 --> 00:17:51,879
Speaker 2: or a preventative measure before it kind of accelerates, so

310
00:17:52,040 --> 00:17:52,600
Speaker 2: tykes called.

311
00:17:53,760 --> 00:17:56,600
Speaker 3: Yeah, Yeah, there's drugs that kind of like put obstacles

312
00:17:56,640 --> 00:17:59,359
Speaker 3: in the in the way of the disease progression. And

313
00:17:59,480 --> 00:18:02,119
Speaker 3: it's not eliminated. It's not going to stop it, but

314
00:18:02,200 --> 00:18:05,200
Speaker 3: it will slow it down and maybe give it'll certainly

315
00:18:05,600 --> 00:18:08,240
Speaker 3: extend the quality of life and maybe even give these

316
00:18:08,240 --> 00:18:13,080
Speaker 3: individuals some extra extra years.

317
00:18:12,840 --> 00:18:15,760
Speaker 2: What's what's the mechanism? Like, do you know how it works?

318
00:18:15,840 --> 00:18:19,080
Speaker 2: Like what they did they come up with? Obviously it's

319
00:18:19,119 --> 00:18:22,360
Speaker 2: doing something good, or it's mitigating something or slowing something

320
00:18:22,480 --> 00:18:24,119
Speaker 2: or what did they find?

321
00:18:24,200 --> 00:18:24,360
Speaker 3: Then?

322
00:18:24,480 --> 00:18:25,800
Speaker 1: What is it that causes it?

323
00:18:27,119 --> 00:18:30,560
Speaker 3: Right? So I'll tell you a little bit about Alzheimer's first,

324
00:18:30,640 --> 00:18:33,280
Speaker 3: you can so you can understand the study a little better.

325
00:18:33,680 --> 00:18:36,600
Speaker 3: So this was a disease that was first described in

326
00:18:36,640 --> 00:18:40,040
Speaker 3: the early nineteen hundreds, but the trademark feature was that

327
00:18:40,080 --> 00:18:43,760
Speaker 3: in these people who were suffering from dementia, memory loss,

328
00:18:43,920 --> 00:18:48,480
Speaker 3: and basically, you know, even not being able to remember

329
00:18:48,560 --> 00:18:51,080
Speaker 3: their name or their loved ones or where they lived,

330
00:18:51,520 --> 00:18:57,600
Speaker 3: you know, very significant things. When these individuals, you know,

331
00:18:57,680 --> 00:19:03,439
Speaker 3: pass on, investigations of their brain started to commence. So

332
00:19:03,520 --> 00:19:07,480
Speaker 3: in the autopsies, you don't need to be a scientist

333
00:19:07,560 --> 00:19:11,760
Speaker 3: or doctor to identify which brain had Alzheimer's and which

334
00:19:11,800 --> 00:19:17,080
Speaker 3: brain did not. These brains look dramatically different. They're usually shrunken,

335
00:19:17,280 --> 00:19:21,920
Speaker 3: and they they're lacking some gray matter. But under the microscope,

336
00:19:21,960 --> 00:19:26,760
Speaker 3: what you will also see are these things called amyloid plaques,

337
00:19:26,920 --> 00:19:30,000
Speaker 3: and these are made of a protein called amyloid that

338
00:19:30,160 --> 00:19:35,560
Speaker 3: is not being processed correctly. They form these aggregates that

339
00:19:35,640 --> 00:19:38,680
Speaker 3: get in between the brain cells and kind of mess

340
00:19:38,760 --> 00:19:43,520
Speaker 3: up their communicating with one another. So it really disrupts

341
00:19:44,160 --> 00:19:47,919
Speaker 3: brain thought patterns, and that explains a lot of the

342
00:19:47,960 --> 00:19:53,119
Speaker 3: cognitive defects that we see in Alzheimer's patients. So knowing

343
00:19:53,200 --> 00:19:57,480
Speaker 3: that these plaques form is a big step forward. But

344
00:19:57,560 --> 00:20:01,679
Speaker 3: We've known that for almost I guess one hundred years now,

345
00:20:02,119 --> 00:20:05,639
Speaker 3: and we still haven't come up with really, you know,

346
00:20:06,480 --> 00:20:09,640
Speaker 3: decent treatments for them. We don't really have a way

347
00:20:09,680 --> 00:20:13,639
Speaker 3: to get rid of them. One of the older treatments

348
00:20:13,680 --> 00:20:19,040
Speaker 3: for Alzheimer's was cholinesterase inhibitors, and that modulates neurotransmitters in

349
00:20:19,080 --> 00:20:23,320
Speaker 3: the brain to try to preserve memory a little bit,

350
00:20:24,320 --> 00:20:28,240
Speaker 3: preserve learning ability and motor skills as well. But the

351
00:20:28,320 --> 00:20:31,840
Speaker 3: newer therapies are monoclonal antibodies, and these are directed to

352
00:20:32,080 --> 00:20:37,600
Speaker 3: the amyloid proteins themselves, so patients can be given these antibodies.

353
00:20:38,000 --> 00:20:41,639
Speaker 3: They will bind up the plaques and then sometimes the

354
00:20:41,680 --> 00:20:46,240
Speaker 3: immune system will help even you degrade the plaque to

355
00:20:46,320 --> 00:20:50,359
Speaker 3: a degree. And whether these are really effective on a

356
00:20:50,440 --> 00:20:53,959
Speaker 3: large scale is a little The data is a little mixed.

357
00:20:54,240 --> 00:20:57,240
Speaker 3: They do seem to respond better in some people than others.

358
00:20:58,160 --> 00:21:00,560
Speaker 3: But the bottom line, the upshot of at all is

359
00:21:00,600 --> 00:21:04,720
Speaker 3: that we really don't have excellent treatments for Alzheimer's and

360
00:21:04,760 --> 00:21:08,520
Speaker 3: these patients are really in need of a breakthrough discovery,

361
00:21:08,920 --> 00:21:13,320
Speaker 3: and this study linking lithium to it could be that answer.

362
00:21:14,320 --> 00:21:17,920
Speaker 2: How is it used like that I don't know. Do

363
00:21:18,000 --> 00:21:20,160
Speaker 2: they get in as a tablets, do they get injected?

364
00:21:21,080 --> 00:21:23,600
Speaker 2: How is lithium used as a treatment.

365
00:21:24,560 --> 00:21:27,160
Speaker 3: Oh well, that's I'm glad you brought that up, because

366
00:21:27,320 --> 00:21:31,480
Speaker 3: the study did not look at how lithium works in

367
00:21:31,600 --> 00:21:35,920
Speaker 3: human patients just yet. So this study was it did

368
00:21:36,000 --> 00:21:41,119
Speaker 3: involve looking at human brains and what was going on

369
00:21:41,200 --> 00:21:44,000
Speaker 3: with lithium, and I'll explain that in a minute. But

370
00:21:44,160 --> 00:21:48,600
Speaker 3: the actual proof of concept for the utility that lithium

371
00:21:48,680 --> 00:21:52,159
Speaker 3: might have as an Alzheimer's treatment was limited to mice

372
00:21:52,400 --> 00:21:56,360
Speaker 3: so far. You know, scientists have genetically engineered a mouse

373
00:21:56,840 --> 00:22:02,160
Speaker 3: to lack a certain gene that predisposes it to Alzheimer's.

374
00:22:02,520 --> 00:22:06,399
Speaker 3: So we got this wonderful mouse model of Alzheimer's. And

375
00:22:06,480 --> 00:22:10,080
Speaker 3: as these mice age, because they have this genetic mutation,

376
00:22:10,760 --> 00:22:13,840
Speaker 3: they start to develop the same amyloid plaques and the

377
00:22:13,880 --> 00:22:19,200
Speaker 3: same deficiencies in memory learning and so on. Right, right,

378
00:22:19,280 --> 00:22:23,040
Speaker 3: So let's let's get into this lithium part.

379
00:22:23,160 --> 00:22:26,119
Speaker 1: Sure, sure, sure, So it.

380
00:22:26,040 --> 00:22:30,240
Speaker 3: Was known through other research that these amyloid plaques they're

381
00:22:30,320 --> 00:22:34,199
Speaker 3: kind of sticky, and you know, I would hope that

382
00:22:34,240 --> 00:22:38,440
Speaker 3: your audience knows that metals are part of our diet,

383
00:22:38,600 --> 00:22:43,560
Speaker 3: you know, like calcium, iron, copper, you know, even like

384
00:22:43,640 --> 00:22:47,600
Speaker 3: sodium potassium. These these are metals, and a lot of people,

385
00:22:47,720 --> 00:22:49,639
Speaker 3: you know, they look at you funny if you say, like,

386
00:22:49,760 --> 00:22:53,400
Speaker 3: you know, you're eating metal in your diet, and that's

387
00:22:53,440 --> 00:22:57,160
Speaker 3: perfectly fine. You know, you have trace metals, trace elements

388
00:22:57,160 --> 00:23:00,920
Speaker 3: in your food that actually play a role in your biology.

389
00:23:01,240 --> 00:23:04,399
Speaker 3: A lot of enzymes in your cells require some of

390
00:23:04,440 --> 00:23:08,640
Speaker 3: these metals in order to function. And it turns out

391
00:23:08,680 --> 00:23:12,720
Speaker 3: that previous research was finding that these amyloid plaques could

392
00:23:12,800 --> 00:23:15,480
Speaker 3: bind some of the metals that are supposed to be

393
00:23:15,560 --> 00:23:20,840
Speaker 3: operating in your brain. So these scientists that Harvard wondered, well,

394
00:23:20,880 --> 00:23:23,440
Speaker 3: why don't we look at all the metals that people

395
00:23:23,520 --> 00:23:28,520
Speaker 3: would possibly ingest and see if there's a difference between

396
00:23:29,000 --> 00:23:33,600
Speaker 3: an Alzheimer's brain and a normal brain. OK. So they

397
00:23:33,640 --> 00:23:38,280
Speaker 3: tested like twenty seven different metals and there pretty much

398
00:23:38,400 --> 00:23:42,240
Speaker 3: wasn't any difference between a diseased brain and a normal brain,

399
00:23:42,800 --> 00:23:46,680
Speaker 3: with the exception of lithium. So it turns out that

400
00:23:46,800 --> 00:23:51,399
Speaker 3: people with Alzheimer's their brain had very little and possibly

401
00:23:51,520 --> 00:23:56,040
Speaker 3: no detectable lithium in it, whereas a normal brain did

402
00:23:56,080 --> 00:24:00,280
Speaker 3: have lithium, and that was a little surprising because it's

403
00:24:00,440 --> 00:24:05,520
Speaker 3: been underappreciated what exactly lithium does in our brain, despite

404
00:24:05,560 --> 00:24:09,240
Speaker 3: the fact that we've known for some time, and in

405
00:24:09,280 --> 00:24:14,879
Speaker 3: fact it's a clinically, it's a prescribed treatment. There's a

406
00:24:14,880 --> 00:24:18,679
Speaker 3: certain form of lithium that's given for bipolar disorder. You know,

407
00:24:18,720 --> 00:24:21,480
Speaker 3: you may be aware of this in your psychological studies

408
00:24:21,720 --> 00:24:27,240
Speaker 3: and your own research, but bipolar disorder can be mitigated

409
00:24:27,280 --> 00:24:32,920
Speaker 3: to a degree with high doses of lithium. And when

410
00:24:32,920 --> 00:24:37,040
Speaker 3: they saw that Alzheimer's brains were lacking lithium, I guess

411
00:24:37,040 --> 00:24:39,600
Speaker 3: the light bulb went off in their head and they're like, wait,

412
00:24:39,640 --> 00:24:43,199
Speaker 3: we could really be onto something here, because we know,

413
00:24:43,440 --> 00:24:48,439
Speaker 3: for example, these dietary metals play important roles in our biology,

414
00:24:48,480 --> 00:24:52,000
Speaker 3: including the cells that make up the brain, and if

415
00:24:52,040 --> 00:24:55,639
Speaker 3: they are suddenly without lithium, and it's important for something

416
00:24:55,640 --> 00:24:58,280
Speaker 3: in the brain, that could explain a lot of the

417
00:24:58,320 --> 00:25:04,000
Speaker 3: symptoms that these people are that these Alzheimer's patients are experiencing.

418
00:25:05,560 --> 00:25:08,320
Speaker 2: Okay, So for those of us who aren't scientists and

419
00:25:09,040 --> 00:25:12,800
Speaker 2: do research and understand the methodology and the protocol and

420
00:25:12,840 --> 00:25:16,679
Speaker 2: the steps. So when you make a I know this

421
00:25:16,760 --> 00:25:19,920
Speaker 2: is super fundamental for you. But when you make a

422
00:25:19,960 --> 00:25:21,919
Speaker 2: discovery like that, you go, oh, there seems to be

423
00:25:21,960 --> 00:25:25,320
Speaker 2: some kind of correlation between lithium, or more accurately, lack

424
00:25:25,359 --> 00:25:29,200
Speaker 2: of lithium and this cognitive decline through this thing called

425
00:25:29,200 --> 00:25:36,080
Speaker 2: Alzheimer's Right, So initially it must just be well, let's

426
00:25:36,160 --> 00:25:39,600
Speaker 2: try this, because you don't Let's try x amount of

427
00:25:39,640 --> 00:25:43,560
Speaker 2: whatever into this rat or mouse or whatever, and just

428
00:25:43,600 --> 00:25:46,720
Speaker 2: then see what that response is, because it must be.

429
00:25:47,400 --> 00:25:48,400
Speaker 1: High level guessing.

430
00:25:48,480 --> 00:25:51,119
Speaker 2: Really, and then you do something, you create an outcome,

431
00:25:51,160 --> 00:25:52,720
Speaker 2: and you go, all right, let's try this.

432
00:25:52,920 --> 00:25:54,440
Speaker 1: Is that essentially the process?

433
00:25:56,440 --> 00:25:59,159
Speaker 3: It can be, It can be. I mean there was

434
00:25:59,200 --> 00:26:05,760
Speaker 3: some precedent before this study. Metals were found in amyloid plaques. Okay,

435
00:26:06,359 --> 00:26:10,359
Speaker 3: so this group just said I guess they were sitting around,

436
00:26:10,480 --> 00:26:12,840
Speaker 3: maybe in lab meeting one day or at happy hour

437
00:26:12,960 --> 00:26:15,680
Speaker 3: at the pub, and just said, you know, maybe we

438
00:26:15,720 --> 00:26:17,760
Speaker 3: should look at all the metals that should be in

439
00:26:17,800 --> 00:26:20,400
Speaker 3: the brain and see if there's a difference. No one's

440
00:26:20,440 --> 00:26:22,439
Speaker 3: done that before, and this is one of the joys

441
00:26:22,440 --> 00:26:24,720
Speaker 3: of science that I was talking about earlier. Craig, You

442
00:26:24,800 --> 00:26:28,320
Speaker 3: just get this interesting question. No one's apparently done it before,

443
00:26:28,400 --> 00:26:31,960
Speaker 3: even though it seems kind of you know, simplistic in hindsight.

444
00:26:33,000 --> 00:26:36,560
Speaker 3: So let's go check it out. And that's part of

445
00:26:36,600 --> 00:26:40,000
Speaker 3: the fun of being in science. And lo and behold

446
00:26:40,400 --> 00:26:43,280
Speaker 3: what I said. They tested like twenty seven metals or something.

447
00:26:43,440 --> 00:26:45,960
Speaker 3: Twenty six of them were a bust. They didn't see

448
00:26:45,960 --> 00:26:49,440
Speaker 3: any difference. So you can imagine like testing metal after

449
00:26:49,480 --> 00:26:52,480
Speaker 3: metal and getting discouraged. But then bam, they find the

450
00:26:52,640 --> 00:26:57,800
Speaker 3: magic one, the magic one, and that just changes everything

451
00:26:57,840 --> 00:27:00,200
Speaker 3: and say, hey, we could really be onto something here.

452
00:27:01,119 --> 00:27:01,600
Speaker 1: The fact that.

453
00:27:02,240 --> 00:27:05,080
Speaker 3: Sorry, go ahead, you you got another question, Go ahead, no, no.

454
00:27:04,840 --> 00:27:06,240
Speaker 2: No, I was just gonna sign in the fact that

455
00:27:06,240 --> 00:27:09,840
Speaker 2: twenty six showed nothing is actually great news that only

456
00:27:09,880 --> 00:27:13,439
Speaker 2: one did, because that makes it abundantly clear which is

457
00:27:13,480 --> 00:27:16,399
Speaker 2: the thing that's having some kind of impact.

458
00:27:17,200 --> 00:27:20,360
Speaker 3: Yeah, exactly, or else they could have been you know,

459
00:27:20,640 --> 00:27:22,719
Speaker 3: this study could have been delayed for years as they

460
00:27:22,880 --> 00:27:25,639
Speaker 3: tried to dissect which metal was the most important or

461
00:27:25,720 --> 00:27:28,359
Speaker 3: are they working together? You're right, it could have gotten

462
00:27:28,400 --> 00:27:31,840
Speaker 3: a lot more complicated than what it currently seems to be.

463
00:27:33,880 --> 00:27:36,199
Speaker 3: So when they when they found this observation, and this

464
00:27:36,359 --> 00:27:39,679
Speaker 3: was in human brains, so this obviously has disease relevance

465
00:27:39,960 --> 00:27:43,280
Speaker 3: to humans themselves. But they took advantage of that mouse

466
00:27:43,320 --> 00:27:46,800
Speaker 3: model that I was describing earlier to see if it

467
00:27:46,920 --> 00:27:53,160
Speaker 3: also happened in mice. So people can, you know, mostly

468
00:27:53,280 --> 00:27:56,119
Speaker 3: get lithium from their diet. Okay, it's present in a

469
00:27:56,119 --> 00:27:59,239
Speaker 3: lot of fruits and vegetables, nuts and seeds, So our

470
00:27:59,280 --> 00:28:03,000
Speaker 3: brains do have a low level of lithium that is

471
00:28:03,040 --> 00:28:06,800
Speaker 3: doing something. You know, scientists aren't exactly sure what it's doing.

472
00:28:06,960 --> 00:28:09,639
Speaker 3: Is probably doing many things, but one thing's for sure.

473
00:28:09,960 --> 00:28:13,320
Speaker 3: If it's depleted, it looks like you're susceptible to Alzheimer's.

474
00:28:13,840 --> 00:28:16,880
Speaker 3: And that's exactly what happened in mice. So they took

475
00:28:16,920 --> 00:28:23,000
Speaker 3: these mouse model for Alzheimer's disease and they controlled their

476
00:28:23,040 --> 00:28:26,760
Speaker 3: diet so that they were ingesting little to know lithium

477
00:28:27,040 --> 00:28:30,520
Speaker 3: at all. And in those mice that were deprived of

478
00:28:30,600 --> 00:28:34,760
Speaker 3: lithium in their diet, they started having defects and learning

479
00:28:35,280 --> 00:28:38,440
Speaker 3: and long term memory, and lo and behold, when you

480
00:28:39,000 --> 00:28:43,120
Speaker 3: dissect their brain, they got these amyloid plaques. So just

481
00:28:43,120 --> 00:28:47,360
Speaker 3: by depriving those mice of lithium, they basically gave them

482
00:28:47,400 --> 00:28:48,880
Speaker 3: the symptoms of Alzheimer's.

483
00:28:50,640 --> 00:28:53,880
Speaker 2: Now, this is digressing, but I'm just interested. So when

484
00:28:53,880 --> 00:28:59,680
Speaker 2: we use mice, rats, whatever, we obviously there I guess,

485
00:28:59,800 --> 00:29:05,120
Speaker 2: much more easy to access than other types of brains.

486
00:29:06,120 --> 00:29:09,520
Speaker 2: Does it usually correlate quite strongly? Like what will happen

487
00:29:09,720 --> 00:29:13,160
Speaker 2: in the you know, the brain of a mouse or

488
00:29:13,160 --> 00:29:19,800
Speaker 2: a rat. Is it typically somewhat you know, closely associated

489
00:29:19,880 --> 00:29:23,240
Speaker 2: with similar responses in human brains?

490
00:29:23,280 --> 00:29:26,240
Speaker 1: Like is that why we use them? Because it's quite indicative.

491
00:29:27,320 --> 00:29:30,600
Speaker 3: Yeah, that's an excellent question. And you really do got

492
00:29:30,600 --> 00:29:32,560
Speaker 3: to answer that kind of question on a case by

493
00:29:32,600 --> 00:29:36,760
Speaker 3: case basis, because there are some models where they're really good,

494
00:29:37,040 --> 00:29:40,720
Speaker 3: the parallels are really strong, and then there's other ones

495
00:29:40,720 --> 00:29:44,480
Speaker 3: that are not. And there are there's no shortage of

496
00:29:44,640 --> 00:29:48,240
Speaker 3: drugs that have looked great in mouse models, but then

497
00:29:48,280 --> 00:29:51,800
Speaker 3: for some reason do not work in humans. And that's

498
00:29:51,840 --> 00:29:56,960
Speaker 3: probably due to different metabolism or different bioavailability or things

499
00:29:57,040 --> 00:29:59,600
Speaker 3: like that. And you do have to be particularly careful

500
00:29:59,600 --> 00:30:02,400
Speaker 3: when you're dealing with a disease like Alzheimer's because that's

501
00:30:02,400 --> 00:30:07,479
Speaker 3: affecting the brain. Yeah, lo and behold structurally, on a

502
00:30:07,560 --> 00:30:10,560
Speaker 3: more fundamental level, the rodent brain does have a lot

503
00:30:10,560 --> 00:30:14,680
Speaker 3: of similarities to the human brain. All mammalian brains do

504
00:30:14,720 --> 00:30:19,200
Speaker 3: have these similarities, but obviously we are far more cognitively

505
00:30:19,280 --> 00:30:23,800
Speaker 3: advanced than a rodent, so you do have to be

506
00:30:23,920 --> 00:30:28,240
Speaker 3: very careful when you're like drawing some comparisons between rodent

507
00:30:28,280 --> 00:30:33,000
Speaker 3: models and humans. But we can't ignore the power that

508
00:30:33,040 --> 00:30:36,600
Speaker 3: these model systems have, because we can't do a study

509
00:30:37,120 --> 00:30:43,440
Speaker 3: like lithium deprivation in humans without being deeply unethical. So

510
00:30:44,000 --> 00:30:48,000
Speaker 3: that's the advantage of these, you know, these mouse models,

511
00:30:48,880 --> 00:30:52,000
Speaker 3: and they're also very useful for the next question, which

512
00:30:52,440 --> 00:30:59,080
Speaker 3: which is, well, what if we give lithium back to

513
00:30:59,240 --> 00:31:03,400
Speaker 3: these mighty that are developing Alzheimer's and don't we don't

514
00:31:03,440 --> 00:31:05,320
Speaker 3: see a lot of lithium in their brain. What if

515
00:31:05,360 --> 00:31:08,640
Speaker 3: we give it back? What happens then? So I imagine

516
00:31:08,680 --> 00:31:11,800
Speaker 3: that was a really exciting question for that laboratory. And again,

517
00:31:11,840 --> 00:31:13,440
Speaker 3: you can't do that kind of stuff. You can't just

518
00:31:13,560 --> 00:31:16,280
Speaker 3: jump right into a human study and do that. You've

519
00:31:16,280 --> 00:31:18,560
Speaker 3: got to look and see what happens in the mouse model.

520
00:31:19,800 --> 00:31:24,360
Speaker 2: Old diseases are you know, or I think old disease,

521
00:31:24,440 --> 00:31:27,000
Speaker 2: you can correct me. But there's a can be a

522
00:31:27,040 --> 00:31:30,000
Speaker 2: genetic component, that can be a lifestyle component, you know,

523
00:31:30,160 --> 00:31:33,240
Speaker 2: like with heart disease, and with diabetes and with you know,

524
00:31:33,480 --> 00:31:37,560
Speaker 2: a myriad of diseases. What do we know about Alzheimer's

525
00:31:37,680 --> 00:31:42,240
Speaker 2: Is that purely genetic? Is it ninety percent genetic? Tempcent

526
00:31:42,680 --> 00:31:44,400
Speaker 2: lifestyle and behavior and nutrition?

527
00:31:44,720 --> 00:31:46,200
Speaker 1: Is it? What is it? Do you know?

528
00:31:48,200 --> 00:31:52,120
Speaker 3: It's it's It's unclear, and it's probably different for different people.

529
00:31:52,560 --> 00:31:52,760
Speaker 1: Yeah.

530
00:31:53,640 --> 00:31:56,720
Speaker 3: The motive, the current school of thought is that it's

531
00:31:56,760 --> 00:32:02,360
Speaker 3: a mixture between genetic and environmental influences. So there is

532
00:32:02,480 --> 00:32:08,520
Speaker 3: one particular gene that runs in families that seems to

533
00:32:08,720 --> 00:32:10,760
Speaker 3: when it's mutated in a certain way. It's called the

534
00:32:10,840 --> 00:32:15,000
Speaker 3: apoe four gene, and it's a strong risk factor because

535
00:32:15,000 --> 00:32:19,400
Speaker 3: when that gene has a certain variant, you are highly

536
00:32:19,400 --> 00:32:22,880
Speaker 3: predisposed to developing Alzheimer's, and in fact, you'll probably get

537
00:32:22,880 --> 00:32:25,960
Speaker 3: it at an earlier age than most people do, maybe

538
00:32:26,000 --> 00:32:29,160
Speaker 3: as early as in your forties, whereas most people develop

539
00:32:29,200 --> 00:32:33,640
Speaker 3: Alzheimer's in their sixties or later. So are there certainly

540
00:32:33,680 --> 00:32:38,280
Speaker 3: can be genetic susceptibilities, and then there's also environment. It's

541
00:32:38,360 --> 00:32:41,960
Speaker 3: well known that if you are a female, you're more

542
00:32:42,120 --> 00:32:47,360
Speaker 3: likely to get Alzheimer's. Age increases your risk in pre

543
00:32:47,440 --> 00:32:52,880
Speaker 3: existing conditions, including like diabetes and obesity can also increase risk.

544
00:32:53,440 --> 00:32:57,880
Speaker 3: So there's clearly some environmental aspects to it. And what

545
00:32:58,000 --> 00:33:00,680
Speaker 3: this new study just did, Craig, was suggest that there

546
00:33:00,760 --> 00:33:03,960
Speaker 3: might be a dietary aspect to it. People who don't

547
00:33:03,960 --> 00:33:08,600
Speaker 3: get enough lithium in their natural diet could perhaps be

548
00:33:08,680 --> 00:33:14,240
Speaker 3: predisposed to Alzheimer's, assuming the study turns out to be true.

549
00:33:15,360 --> 00:33:18,920
Speaker 2: And of course the exercise scientist wants to know people

550
00:33:18,960 --> 00:33:24,000
Speaker 2: who played or do play sports, you know, whether or

551
00:33:24,040 --> 00:33:27,840
Speaker 2: not it's soccer, or it's boxing, or it's MMA or

552
00:33:28,000 --> 00:33:33,200
Speaker 2: it's full contact whatever Australian rules gridiron who get a

553
00:33:33,240 --> 00:33:37,960
Speaker 2: lot of brain trauma and even traumatic brain injuries. I

554
00:33:38,160 --> 00:33:42,680
Speaker 2: would imagine, but perhaps incorrectly, that that predisposes them to

555
00:33:42,760 --> 00:33:45,400
Speaker 2: be more likely to get Alzheimer's.

556
00:33:46,280 --> 00:33:52,160
Speaker 3: Head trauma is another environmental risk factor. Yeah, absolutely, yeah, So.

557
00:33:53,720 --> 00:33:58,040
Speaker 2: Just to explain to us who don't really understand this, well,

558
00:33:58,200 --> 00:34:00,480
Speaker 2: I'm sure some of my listeners understand it better than me.

559
00:34:01,200 --> 00:34:04,240
Speaker 2: But let's say, all right, we figured this out with

560
00:34:05,560 --> 00:34:09,520
Speaker 2: We've got this clarity around the research that's come out

561
00:34:09,560 --> 00:34:14,120
Speaker 2: with mice, and this seems to be a clear indicator

562
00:34:14,280 --> 00:34:18,680
Speaker 2: of such and such. What would the timeline be if

563
00:34:18,719 --> 00:34:22,879
Speaker 2: everything went pretty well before this would be you know,

564
00:34:23,120 --> 00:34:25,160
Speaker 2: is it two years? Is it twenty years where this

565
00:34:25,200 --> 00:34:28,680
Speaker 2: would be potentially being used as a treatment in humans?

566
00:34:29,000 --> 00:34:31,319
Speaker 2: Like how long does this take? And I know it's

567
00:34:31,320 --> 00:34:33,080
Speaker 2: how long it's a bit of string, I get it.

568
00:34:34,000 --> 00:34:38,839
Speaker 2: But COVID only took ten minutes, So I mean, how

569
00:34:38,880 --> 00:34:42,760
Speaker 2: long does something like this come to you know, fruition

570
00:34:42,920 --> 00:34:45,320
Speaker 2: and be a recognized treatment.

571
00:34:46,040 --> 00:34:49,760
Speaker 3: Well, it could be something that advanswers quite quickly because

572
00:34:51,880 --> 00:34:55,040
Speaker 3: and the answer to that question actually might become a

573
00:34:55,080 --> 00:34:57,759
Speaker 3: little more clear as I talk about this second part

574
00:34:57,760 --> 00:35:01,640
Speaker 3: of this study, which was how do we possibly fix

575
00:35:01,760 --> 00:35:06,000
Speaker 3: this in the mice. What they were discovering was that

576
00:35:06,600 --> 00:35:11,000
Speaker 3: Alzheimer's brains didn't seem to have any lithium. But when

577
00:35:11,040 --> 00:35:13,400
Speaker 3: they looked more closely, what they found is that the

578
00:35:13,440 --> 00:35:17,600
Speaker 3: lithium was there, but it was trapped in these amyloid plaques,

579
00:35:17,640 --> 00:35:22,640
Speaker 3: these protein aggregations that were disrupting neuronal communication. They seemed

580
00:35:22,680 --> 00:35:25,759
Speaker 3: to sequester or gobble up the lithium that was in

581
00:35:25,800 --> 00:35:28,240
Speaker 3: the brain and kind of trap it into this entity

582
00:35:28,280 --> 00:35:31,640
Speaker 3: where it could no longer do its thing. So that's

583
00:35:31,680 --> 00:35:34,839
Speaker 3: where the lithium was going, so that creates a little

584
00:35:34,880 --> 00:35:36,960
Speaker 3: bit of a problem. If you were to give a

585
00:35:37,000 --> 00:35:40,520
Speaker 3: patient lithium, it would probably just get sucked into these

586
00:35:40,560 --> 00:35:45,200
Speaker 3: plaques and it would be useless. So these investigators had

587
00:35:45,239 --> 00:35:49,880
Speaker 3: to get really clever and they identified a different form

588
00:35:49,920 --> 00:35:52,960
Speaker 3: of lithium. It's actually a salt form which is a

589
00:35:53,000 --> 00:35:58,400
Speaker 3: slight chemical modification to the lithium. For the chemically inclined

590
00:35:58,440 --> 00:36:02,359
Speaker 3: people in your audience, it's called lithium or orotate, and

591
00:36:02,600 --> 00:36:06,279
Speaker 3: it's actually been suggested that it could be a better

592
00:36:06,360 --> 00:36:10,520
Speaker 3: alternative for the bipolar disorder condition that we talked about earlier,

593
00:36:10,520 --> 00:36:13,240
Speaker 3: because lithium, a form of lithium, is used to treat

594
00:36:13,280 --> 00:36:17,680
Speaker 3: that as well. The magical property about this salt form

595
00:36:17,719 --> 00:36:21,600
Speaker 3: of lithium, this lithium rotate, is that it appears to

596
00:36:21,680 --> 00:36:25,160
Speaker 3: do the things that lithium needs to do in the brain,

597
00:36:25,680 --> 00:36:29,920
Speaker 3: but it is immune from being sucked into those amyloid plaques.

598
00:36:30,000 --> 00:36:33,920
Speaker 3: It doesn't seem to get absorbed by the plaques. At

599
00:36:34,000 --> 00:36:37,120
Speaker 3: least that's what is seen in mice. And this is

600
00:36:37,160 --> 00:36:39,400
Speaker 3: great because it allows you to use it at a

601
00:36:39,520 --> 00:36:43,720
Speaker 3: very low dose. You don't have to overwhelm the body

602
00:36:43,760 --> 00:36:48,000
Speaker 3: with forms of lithium because that can get toxic, and

603
00:36:48,000 --> 00:36:51,440
Speaker 3: that's one of the major problems with treating bipolar disorder

604
00:36:51,480 --> 00:36:58,120
Speaker 3: with lithium is that the side effects can be really nasty.

605
00:36:58,200 --> 00:37:01,600
Speaker 3: But with this lithium rotate version, you can use a

606
00:37:01,680 --> 00:37:06,080
Speaker 3: very low dose. And the real proof in the putting

607
00:37:06,560 --> 00:37:11,920
Speaker 3: was that in these Alzheimer's mice that are genetically engineered

608
00:37:12,120 --> 00:37:15,879
Speaker 3: to develop Alzheimer's as they age, if they give this

609
00:37:16,040 --> 00:37:21,279
Speaker 3: lithium ooritate to the mice before they exhibit Alzheimer's symptoms,

610
00:37:21,800 --> 00:37:25,520
Speaker 3: they don't develop the disease, which is pretty striking as

611
00:37:25,560 --> 00:37:29,400
Speaker 3: it is, but they want one step further and said, Okay,

612
00:37:29,520 --> 00:37:34,200
Speaker 3: that's really cool, you know, really promising. But what I

613
00:37:34,239 --> 00:37:36,120
Speaker 3: think a lot of people would like to see is

614
00:37:36,160 --> 00:37:39,360
Speaker 3: what if you give lithium ooritate to mice that already

615
00:37:39,400 --> 00:37:44,120
Speaker 3: started developing symptoms of Alzheimer's. So this is why the

616
00:37:44,160 --> 00:37:49,160
Speaker 3: paper got so much publicity. This lithium ooritate in mice

617
00:37:49,200 --> 00:37:53,400
Speaker 3: that we're exhibiting Alzheimer's symptoms not only got rid of

618
00:37:53,440 --> 00:37:57,759
Speaker 3: the plaques, but it reversed the memory loss. And wow,

619
00:37:57,920 --> 00:38:02,279
Speaker 3: that is something that anyone who has had the misfortune

620
00:38:02,320 --> 00:38:05,839
Speaker 3: of witnessing a loved one suffer from Alzheimer's. That's their

621
00:38:05,920 --> 00:38:11,000
Speaker 3: dream come true. You could potentially restore the memory and

622
00:38:11,080 --> 00:38:16,600
Speaker 3: cognitive abilities in these patients with Alzheimer's, assuming this lithium

623
00:38:16,640 --> 00:38:20,200
Speaker 3: oriitate works the same way in people as it does

624
00:38:20,640 --> 00:38:24,600
Speaker 3: in this very promising mouse models. To me, that was

625
00:38:24,680 --> 00:38:30,120
Speaker 3: just like a home run story. Oh yeah, and that's

626
00:38:30,160 --> 00:38:33,320
Speaker 3: why I thought it would be worthwhile talking about today.

627
00:38:33,880 --> 00:38:36,920
Speaker 3: But by the same token, we certainly don't want anyone

628
00:38:36,960 --> 00:38:41,200
Speaker 3: in your audience running out and buying like a lithium supplement,

629
00:38:41,960 --> 00:38:46,360
Speaker 3: which are commercially available, but they're not regulated very well.

630
00:38:47,000 --> 00:38:49,839
Speaker 3: You can't be sure what you're getting. This is not

631
00:38:49,920 --> 00:38:53,120
Speaker 3: a treatment that is approved by the US FDA for

632
00:38:53,239 --> 00:38:57,440
Speaker 3: any medical condition. And like I said before, lithium can

633
00:38:57,480 --> 00:39:00,480
Speaker 3: be toxic. I mean it has caused death in some people.

634
00:39:00,800 --> 00:39:04,920
Speaker 3: So you've got to be extremely careful with any supplement

635
00:39:05,000 --> 00:39:10,200
Speaker 3: you ingest. So this study, as promising as it is,

636
00:39:10,239 --> 00:39:12,080
Speaker 3: you have to keep in mind it is limited to

637
00:39:12,600 --> 00:39:18,040
Speaker 3: mouse models so far, and like you said, there must

638
00:39:18,040 --> 00:39:23,440
Speaker 3: be a robust clinical trial conducted in people before we

639
00:39:23,520 --> 00:39:27,560
Speaker 3: get too excited, and safety profiles as well, because even

640
00:39:27,640 --> 00:39:30,280
Speaker 3: though it looks safe in the mice, this lithium oritate

641
00:39:30,400 --> 00:39:34,920
Speaker 3: could have some adverse effect in humans, We simply don't know. So,

642
00:39:35,040 --> 00:39:39,000
Speaker 3: circling back to your question, Craig, how long might we

643
00:39:39,080 --> 00:39:43,040
Speaker 3: see something? Well, it depends on how many hurdles we jump.

644
00:39:43,760 --> 00:39:45,880
Speaker 3: Is this lithium oritate safe in people?

645
00:39:46,360 --> 00:39:46,600
Speaker 1: Great?

646
00:39:46,680 --> 00:39:49,880
Speaker 3: And passes the first phase of clinical trials? Can it

647
00:39:49,920 --> 00:39:52,720
Speaker 3: actually help with Alzheimer's would be the next best question.

648
00:39:53,400 --> 00:39:57,440
Speaker 3: Maybe it does. How much do we use? You know,

649
00:39:57,520 --> 00:40:00,560
Speaker 3: are there adverse effects? These are all important questions that

650
00:40:00,600 --> 00:40:03,920
Speaker 3: have to be addressed. You know, is it more effective

651
00:40:03,960 --> 00:40:08,479
Speaker 3: than the monoclonal antibodies that are currently used to treat

652
00:40:08,520 --> 00:40:11,160
Speaker 3: Alzheimer's Because you don't want to deprive someone of taking

653
00:40:11,200 --> 00:40:15,200
Speaker 3: that medication which might help them, and risking taking a

654
00:40:15,280 --> 00:40:17,880
Speaker 3: medication which might do nothing or even make it worse.

655
00:40:17,920 --> 00:40:21,280
Speaker 3: You know, that's a possibility too. We've seen some really

656
00:40:21,360 --> 00:40:25,720
Speaker 3: weird things happen in mice that don't hold up in humans.

657
00:40:26,440 --> 00:40:29,520
Speaker 2: Yeah, And also I guess over the long term, like

658
00:40:29,600 --> 00:40:31,640
Speaker 2: it might be great for a month or three months,

659
00:40:31,640 --> 00:40:34,560
Speaker 2: but maybe over two or three years we find out

660
00:40:34,640 --> 00:40:38,040
Speaker 2: something that's has a long term negative impact that we

661
00:40:38,080 --> 00:40:39,400
Speaker 2: didn't realize upfront.

662
00:40:41,320 --> 00:40:41,640
Speaker 1: That's it.

663
00:40:41,880 --> 00:40:45,240
Speaker 3: That's always a possibility too, and even if clinical trials

664
00:40:45,280 --> 00:40:47,840
Speaker 3: look good, as you start to introduce it into larger

665
00:40:47,880 --> 00:40:54,040
Speaker 3: patient populations, additional side effects may show themselves. But I think,

666
00:40:54,680 --> 00:40:58,120
Speaker 3: you know, anyone who has experienced with Alzheimer's is going

667
00:40:58,160 --> 00:41:01,200
Speaker 3: to tell you this is a devastating disease and we

668
00:41:01,280 --> 00:41:05,080
Speaker 3: really need some new treatments fast. So I think, given

669
00:41:05,200 --> 00:41:08,800
Speaker 3: the you know, the promise of this study, this type

670
00:41:08,800 --> 00:41:12,160
Speaker 3: of work is going to be fast tracked and we

671
00:41:12,200 --> 00:41:16,120
Speaker 3: should be hopefully getting answers sooner rather than later. But

672
00:41:16,200 --> 00:41:20,080
Speaker 3: I would I would fathom to guess that we're still

673
00:41:20,239 --> 00:41:24,760
Speaker 3: years away from this proof of concept to a bona

674
00:41:24,760 --> 00:41:28,760
Speaker 3: fide treatment. What may happen sooner, like we alluded to earlier,

675
00:41:28,880 --> 00:41:33,239
Speaker 3: is a new diagnostic tool to catch Alzheimer's sooner, maybe

676
00:41:33,280 --> 00:41:37,120
Speaker 3: even before symptoms develop, just by taking a blood sample

677
00:41:37,160 --> 00:41:42,319
Speaker 3: and seeing you're lithium levels lowering. If so, that could

678
00:41:42,400 --> 00:41:45,399
Speaker 3: be a sign that Alzheimer's is developing. We can put

679
00:41:45,440 --> 00:41:48,080
Speaker 3: you on one of these monoclonal antibodies and slow the

680
00:41:48,120 --> 00:41:52,280
Speaker 3: progression of that disease, buy you some valuable time until

681
00:41:52,280 --> 00:41:54,280
Speaker 3: maybe a new treatment like this comes along.

682
00:41:55,719 --> 00:41:59,359
Speaker 2: So good, so good, so exciting. The stuff that you do.

683
00:41:59,440 --> 00:42:02,280
Speaker 2: And I know this ain't your research, but your research

684
00:42:02,400 --> 00:42:04,279
Speaker 2: is exciting as well. But to be able to share

685
00:42:04,320 --> 00:42:07,560
Speaker 2: this kind of information with the world, that you can

686
00:42:07,600 --> 00:42:11,600
Speaker 2: make it accessible and understandable for us, and also especially

687
00:42:11,600 --> 00:42:16,160
Speaker 2: when it's something that is like, everybody knows somebody who's

688
00:42:16,320 --> 00:42:19,759
Speaker 2: affected by Alzheimer's, and of course it's even if it's

689
00:42:19,800 --> 00:42:23,200
Speaker 2: one member of a family, it's still affecting twenty people,

690
00:42:23,320 --> 00:42:27,560
Speaker 2: not one, because everybody's affected and everybody loves and cares

691
00:42:27,600 --> 00:42:30,960
Speaker 2: about and worries about and needs to care for that person.

692
00:42:31,000 --> 00:42:34,280
Speaker 2: And you know, so it's like many diseases, it's something

693
00:42:34,360 --> 00:42:37,120
Speaker 2: that even if it's only an individual within the group,

694
00:42:38,120 --> 00:42:42,719
Speaker 2: the group is affected, then everyone needs to you know,

695
00:42:42,800 --> 00:42:46,279
Speaker 2: be involved to it an extent. So I guess it'll

696
00:42:46,320 --> 00:42:49,680
Speaker 2: be fascinating to see how it unfolds and how it develops.

697
00:42:49,719 --> 00:42:52,520
Speaker 2: And you know, yeah, it will take a little while,

698
00:42:52,560 --> 00:42:56,280
Speaker 2: but you think, well, we've never had anything, so we're closer,

699
00:42:56,680 --> 00:43:00,920
Speaker 2: like we've had nothing that really really effective deals with it,

700
00:43:00,920 --> 00:43:07,319
Speaker 2: perhaps reverses it, perhaps make somebody from really cognitively, you know,

701
00:43:07,360 --> 00:43:09,920
Speaker 2: down the rabbit hole back to somewhere in the ballpark

702
00:43:09,960 --> 00:43:14,080
Speaker 2: of normal, Like what what an absolute scientific miracle that

703
00:43:14,120 --> 00:43:15,839
Speaker 2: would be for millions of.

704
00:43:15,840 --> 00:43:19,760
Speaker 1: People who are die thosed every year. And yeah, it's exciting.

705
00:43:19,760 --> 00:43:23,040
Speaker 3: You're literally giving someone their life back, and then you

706
00:43:23,080 --> 00:43:27,600
Speaker 3: know there's nothing you can't put a price on that. So,

707
00:43:27,719 --> 00:43:31,360
Speaker 3: while I wouldn't encourage anyone to go pursue lithium supplements

708
00:43:31,400 --> 00:43:36,920
Speaker 3: of any kind, eating a proper diet will ensure that

709
00:43:36,960 --> 00:43:39,799
Speaker 3: your brain is getting the lithium it needs. So good

710
00:43:39,840 --> 00:43:45,320
Speaker 3: sources of lithium include nuts, seeds, and beans, but honestly,

711
00:43:45,360 --> 00:43:48,560
Speaker 3: most grains, fruits, and vegetables are going to have trace

712
00:43:48,600 --> 00:43:51,359
Speaker 3: amounts of lithium and your body doesn't need much of it.

713
00:43:52,440 --> 00:43:55,880
Speaker 3: So if you're eating a healthy, you know, sensible diet,

714
00:43:56,080 --> 00:43:59,680
Speaker 3: you have no reason to believe that you need a supplement.

715
00:44:01,480 --> 00:44:03,640
Speaker 1: You're the guy. It's always good to chat with you.

716
00:44:04,320 --> 00:44:08,839
Speaker 2: We love you, and you deliver every time. So yeah,

717
00:44:08,960 --> 00:44:12,160
Speaker 2: thanks so much, as always, thanks so much, You're great.

718
00:44:12,200 --> 00:44:13,040
Speaker 1: We appreciate you.

719
00:44:13,200 --> 00:44:15,120
Speaker 3: Thank you. It's great to be back.

720
00:44:16,000 --> 00:44:18,240
Speaker 2: Tell people where they can find you, follow you, connect

721
00:44:18,280 --> 00:44:22,560
Speaker 2: with you. And for those people who need an amazing

722
00:44:22,760 --> 00:44:28,120
Speaker 2: corporate speaker, just contact me because I'm now his agent, and.

723
00:44:30,680 --> 00:44:33,560
Speaker 3: Will we negotiate ten percent here zero percent.

724
00:44:33,920 --> 00:44:37,720
Speaker 1: I'm going to manage you for free. Yeah.

725
00:44:37,800 --> 00:44:39,400
Speaker 2: If you want doctor Bill to come and do some

726
00:44:39,480 --> 00:44:42,239
Speaker 2: work with your team, email me and I will make

727
00:44:42,280 --> 00:44:44,680
Speaker 2: it happen. Other than that, how do they find you,

728
00:44:44,760 --> 00:44:46,319
Speaker 2: follow you and connect with you?

729
00:44:47,120 --> 00:44:50,040
Speaker 3: Yeah? People can. Like I said, I'm in the process

730
00:44:50,080 --> 00:44:53,799
Speaker 3: of writing up this story for Psychology Today. Hopefully you'll

731
00:44:53,800 --> 00:44:56,560
Speaker 3: come out in next week or two and you'll be

732
00:44:56,560 --> 00:45:00,879
Speaker 3: able to find that link at author Bill sullivan doc com,

733
00:45:00,920 --> 00:45:03,600
Speaker 3: along with all the other writing that I've done all

734
00:45:03,640 --> 00:45:08,719
Speaker 3: of my previous podcasts, including with Craig here and I

735
00:45:08,920 --> 00:45:11,880
Speaker 3: do book talks. I wrote, Please to meet me jeans,

736
00:45:11,960 --> 00:45:15,080
Speaker 3: germs and the curious forces that make us who we are.

737
00:45:15,320 --> 00:45:18,479
Speaker 3: If you're interested in human behavior, be more than happy

738
00:45:18,560 --> 00:45:20,879
Speaker 3: to come talk to your corporation or group about it.

739
00:45:21,400 --> 00:45:23,839
Speaker 2: One hundred percent so bypassed me. You don't know, make

740
00:45:23,920 --> 00:45:25,120
Speaker 2: I strike to his website.

741
00:45:25,480 --> 00:45:26,839
Speaker 3: I'm sorry, go talk.

742
00:45:26,960 --> 00:45:29,080
Speaker 1: No, definitely don't.

743
00:45:29,480 --> 00:45:33,040
Speaker 2: Definitely don't go to you, Michael Sagobie affair.

744
00:45:33,080 --> 00:45:35,319
Speaker 1: But it's always great to catch up with you. Thanks

745
00:45:35,360 --> 00:45:35,719
Speaker 1: so much.

746
00:45:36,480 --> 00:45:37,640
Speaker 3: Yeah, until next time,