Researchers at UVA recently published a study called Design and Validation of an Open-Source Closed-Loop Testbed for Artificial Pancreas Systems. They say what they’ve set up here is quote – a valid tool that can be used by the research community to demonstrate the effectiveness of different control algorithms and safety features for APS, automated pancreas systems.
This week, you’ll hear from Xugui Zho and Homa Alemzadeh, two of the researchers on this study.
Link to the study:
This podcast is not intended as medical advice. If you have those kinds of questions, please contact your health care provider.
Episode Transcription Below (or coming soon!)
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Stacey Simms 0:00
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This is Diabetes Connections with Stacey Simms.
Welcome to another week of the show. I’m so glad to have you here. You know, we aim to educate and inspire about diabetes with the focus on people who use insulin. I’m your host, Stacey Simms
DIY. Do It Yourself pump and CGM systems have been around for almost 10 years now. Some of them probably more than that. But you know, publicly the we are not waiting movement and the attention that it got within the diabetes community just about 10 years, which is really hard for me to believe at least, it has contributed not only to quality of life in ways that are very hard to argue for many, many people, but it has also contributed to the commercial systems to pushing these ahead in ways that are, I think, pretty easy to pinpoint. You can look at them and say yeah, you know, that person did this, which then wound up at that company, that commercial company, which is now in front of the FDA for approval, that sort of thing. But the open source method has created ways of testing and iterating that otherwise would really be impossible, or at least closed off and research facilities, right.
I recently saw something that caught my eye. And while I’m still not 100% sure I understand it, I knew this audience would want to hear about him. Researchers at UVA published a study called design and validation of an open source closed loop testbed for artificial pancreas systems. They say what they have set up here is, quote, a valid tool that can be used by the research community to demonstrate the effectiveness of different control algorithms and safety features for APS automated pancreas systems.
In this interview, you’re going to hear from Xugui Zho and Homa Alemzadeh, two of the researchers on this study, quick note, English is not their first language. And I know that can be difficult. This is Audio, you want to understand it quickly. So I apologize in advance, I will tell you that I will make a transcript available for this episode as soon as possible, hopefully, you know, as it’s going live, if not very shortly thereafter. But to me, it was more important to speak to these really great folks. And let’s face it, their English is a lot better than my Chinese or Farsi. So I hope the information is the priority here. And this podcast is not intended as medical advice. If you have those kinds of questions, please contact your health care provider.
All right, my interview with Homa and Xugui in just a moment, but first Diabetes Connections is brought to you by Afrezza. And one of the most frustrating parts of mealtime insulin can be the need to pre bolus I’ve seen my son of course forget to do it or hesitate because he’s not confident about meal timing in a restaurant you know when he’s out and about Afrezza is unique because it is the only Ultra rapid acting inhaled insulin available. Once you breathe Afrezza into your lungs using the inhaler. Insulin appears in your bloodstream in less than a minute, and it may start reducing blood sugar in about 12 minutes. A phrase that allows you to inhale your insulin right when food arrives even unexpectedly so you can be spontaneous but still in control without the need for injections at mealtime. Find out more and see if Afrezza is right for you. Go to diabetes dash connections.com and click on the Afrezza logo. Afrezza can cause serious side effects including sudden lung problems and low potassium and is not for patients with chronic lung disease such as asthma or COPD or for patients allergic to insulin. Tell your doctor if you’ve ever smoked ever had kidney or liver problems a history of lung cancer or if you’re pregnant or breastfeeding. Most common side effects are low blood sugar, cough and sore throat severe low blood sugar can be fatal. Do not replace long acting insulin with Afrezza is not for us to treat diabetic ketoacidosis please see full prescribing information including box warning medication guide and instructions for use on Afrezza.com/safety.
Xugui and Homa. Thank you so much for spending some time with me. I am so interested to see what we’re going to find out today. Thanks for being here.
Homa Alemzadeh 4:12
Hello, thanks for having us today. We’re very excited to talk to you today.
Xugui Zho 4:17
Yeah, thank you
Stacey Simms 4:18
you, before we dive in, and I will be the first to say I’m not exactly sure what I’m asking because I reached out to these folks after seeing a study that I can only understand a bit. But I think this is so important to our community Homa when we’re talking about this to be clear, we’re talking about some Do It Yourself stuff to write open source everything like that. Yeah,
Homa Alemzadeh 4:40
so actually, one of the things that is really important for the researcher community and I guess beyond that also like that patients are interested in like medical devices are using is the aspect of being open source. So because as a researcher who has been in this area of medical devices in general, it’s very hard to get your hands on kind of the actual design on the side. For those using biomedical devices, especially if you want to develop a new algorithm or new software, those are not available to the especially researchers in academia unless we’re working with the industry or the company. So in general for kind of any medical device, but also for APS, having this sort of open source software is really, really important. And I guess you have heard about open APS project, that is actually do it yourself kind of APS controller is a big project, people are kind of using it actually dissolve the patient, we use the open API’s because we want to have the ability to kind of change the self or kind of verify validated and also, like integrated with realistic glucose simulators to mimic what happens in real world. So with commercial device owners, you’re actually kind of inside the company working with a company specific company, you probably don’t have that kind of access, right? And this is a huge help for other researchers, because they do lots of other researchers in the community who want to work out very novel techniques, but they cannot test it, because they don’t have access to those software. Yep. So that’s the motivation for using got
Stacey Simms 6:02
it. This study that, you know, that I had asked about, it says design validation of an open source closed loop testbed, does that mean that you designed a way to test these open source closed loop systems, and that that can be replicated the future like any new open source that is developed? And you get the information form? You kind of put through this? I mean, I guess I’m asking you, what does testbed mean? Or does it mean that you tested this system? The one, you know, the all the information you have? And now you’re done, and it’s time to move on to something else?
Homa Alemzadeh 6:35
Yeah, that’s a good question. So actually, I think, I think I asked about if this is generalizable. So I think that we are presenting a way to create a closed loop testbed and the components of this test, but that we have or not something I mean, part of it is actually what we develop, but some of them are actually already available, like open APS, the simulators that we actually modified. So we are proposing a way of creating this closed loop test that’s that you can actually make the real operation of again, I as I mentioned, for the closed loop, it’s important that you have the patient simulator, giving sensor measurements, simulated sensor measurements to the controller, and then the controller giving like inserting values. And this is actually a principle that we also follow in other medical devices we actually work on, like, for example, surgical robots. I mean, I don’t know if you’re familiar with them is another kind of case study we have in our lab. And we have simulators for. And it’s not just medical devices. They’re also like kind of like for autonomous vehicles, or like self driving cars, these days, people are testing them on the road. But many companies are also looking into this kind of closed loop simulation in house so that you don’t kind of kill people in the road, right. So you have a kind of simulated, so you have a simulated environment of a kind of a realistic environment and the controller of a car and everything that you can have to be as realistic as possible to test it. So what is key here is that we have designed a way of building this sort of closed loop simulation. That’s the design part. And then we also show how we can validate it to make sure it’s as realistic as possible. And then by validation, we mean that we compare it to real data from clinical trials, we show that we can actually replicate that in simulation. And then we also show that we can compare different controllers, or we can come we can actually integrate any different kind of patient simulator we want to have. So if someone wants actually to kind of now test a new controller or a new global simulator or patient simulator, they can just actually plug it in into our platform. So this is our open source available on GitHub. And if other researchers want to kind of experiment with it, or like they want to add a new controller, or a new glucose simulator or like a meal simulator, they can do that. I think it’s just the beginning for maybe other things we can do in the future. That’s
Stacey Simms 8:46
fabulous. Yeah, I mean, it’s so interesting. How do you measure accuracy on something like this? Is it about blood glucose in a certain range? How do you check it what do you check it against?
Xugui Zho 8:57
You know, we build or CO to Samsung lab consists of basically two pathways or simulators which trying to mimic the patient dynamics, basically the glucose dynamics, and another party to the control algorithm. So we wants to validate that the simulators were integrated into these platform can accurately represent the BG trajectories that has been recorded in the clinical trial. So we can compel validate for accuracy for the product, particle codes, values, and then we want to also compare or validate whether the country operates and we’ve integrated into this taxpayers can make their reasonable decisions on the output of the entrance notice. So basically giving the same input of the bg values which you can measure by the BG sensors, it can output the exact or same the amount of the incidental details with the clinical or records, which can come from the commercial insulin pumps. So basically, we compound the outputs of the simulators and their controllers with real commercial products in the clinical trial,
Stacey Simms 10:13
this might be a very dumb question I may have misunderstood. Are you looking at actual people? Or are you looking at their results? And then comparing those against other results? In other words, do you? Do you have people in your lab? No,
Xugui Zho 10:27
we do not have people in our lab. So we took our public available data set, which was collected by the UVA Diabetes Center. So they have 168 diabetes patients. So Lee, well, their insulin palms, they will their glucose sensors. So they have a lot of data, Dell. So we know, the big values of rotation at a different time in the day, and what contractions are, the insulin pump has been output to the patient. So we can take that this data for our study to do the validations here,
Stacey Simms 11:01
what did you find? Do these open source of DIY systems that they hold up?
Homa Alemzadeh 11:06
Yeah, so actually, I think there are two questions there, if I can step back a little bit to kind of clarify something. Yeah. So I wanted to explain something. So will you ask about like, how do you test this system? So I think in general, like I think a regular part would be like, there will be some testing in the kind of in house in the company that they developed this sort of algorithms, whatever algorithm they’re developing for control, like, APS controller, but also, as you know, they do clinical trials with real patients, right. But as a kind of like, if you want to think of like how we can mimic the real kind of scenario that you have a POS device with a patient in the loop like it’s actually working during a day, the best setup would be if you have a closed loop system, by closed loop, we mean like that, you have the software that is providing insulin Ray and kind of values to a simulated patient. And then that simulated patient kind of glucose values change. And then you have events simulations for meal activity and other kinds of factors. And then the measurements of the glucose from this simulated patient goes to this software, the controller, and then you kind of have this going on. So basically, you can run these sorts of tests, whether simulator for hours, and that those hours might be actually much faster than real kind of hours in the real world. And I’m trying to test try to test how this software behaves with different patient profiles. So the real benefit is that you don’t have real patients, you have virtual patients. So the risk of harm is less, you don’t need to kind of recruit patients, and also the cost is less, right, you don’t need to recruit patients, there is no risk. But the real challenge is that how you can have patients simulated patients that are realistic or representative of actual patients in the ward, right, because we don’t want to have like very toy examples, like we want to be as realistic as possible. I think the contribution of this paper work is that we are trying to look at the real data from clinical trials. So we have this data from real patients 168 patients that actually were mentioned. And then we have this glucose simulator that we kind of upgraded with estimating the parameters or like the patient characteristics from their data. So assume that I have like, say, five different patients from a clinical trial study, and I have the glucose traces and insulin values from a pump, then we have a method that we can actually reverse engineer the patient parameters like the kind of the representing a profile of the patient, that is kind of use for modeling, how is there a kind of glucose sensitivity or insulin kind of consumption, and so on. And then these parameters are given to our simulator. And then our simulator with any software, any control software like open APS or any other one that we can have even companies or other like researchers can work together in a kind of simulated closed loop simulation, meaning that as if like a patient is using a pump, and then we can estimate, if the pump is doing what is expected to do, then again, we can compare it to what is done in kind of a clinical trial, we can compare different controllers. And this actually is a huge help, again, saves the cost actually helps the researchers to evaluate different controllers and so on. And then your question that you asked Is that what we found? So we tried two different kinds of controllers for now the open APS controller, and there is a controller that I think, is used by many other studies called basal bodies, and also by the UVA kind of study, they use the same controller. So we try to compare them in terms of what kind of Insulet values they generate for the same patient glucose, and also if they’re actually false, what I mean, we didn’t talk about that yet. But if say, unexpected behavior, like kind of like noise in the environment that might affect the functionality of the pump or the controller, which of them is more robust. So what happens actually in those scenarios?
Stacey Simms 14:43
Well, let me ask you to follow up on that. Talk to me about the noise or the false readings when it’s kind of like when things go wrong, right, because it’s not perfect.
Homa Alemzadeh 14:51
Yeah. So that’s the focus of what we do in our group because as I said, we work on safety and security of medical devices. And I don’t know if you’re familiar with like, FDA databases like basically, FDA has different ways of like collecting information on when things go wrong with different devices. And many of our research in different medical devices, including IPS is motivated by real data from FDA. Sir, these databases on recalls of medical devices, or actually adverse event reports that are submitted by the patient’s doctors are users of devices, and just basically go to the FDA databases internally. And a portion of that is actually publicly available. So I don’t know if any, I mean, everyone knows about it. But actually, you can go to FDA website, and then search for a second kind of device name or manufacturer. And then it shows you the records that were issued, and also a subset of adverse events, basically, when somebody was using a device and something went wrong. So we have in our research, before we have developed tools that we can, this data is huge. So if you want to manually look for it, you want you might not be able, you might not be able to get all of the data,
Stacey Simms 15:55
clearly understand the abstract that I got.
Homa Alemzadeh 15:58
Yeah. No, no, but there are millions of records for just a single device if you search for it. And it’s basically impossible to read it manually, right. So what we have done in our research before is that we have developed tools that we can automatically mined the FDA website, basically the databases, and pull out statistics on different types of devices. So if you turn to, for example, I want to know how many recalls I have for like, say, a surgical robot, or insulin pump or some other medical device or two can actually give you some estimates on death, I mean estimates because things change all the time, like the FDA website gets updated. So yeah, this was one of the kind of main works that we have done in our group. And we keep updating that so and that actually has motivated a lot of our research. So for APS, for example, if you look at the paper, we looked exactly on like these databases to find out how many records or what type of records were reported for glucose monitors for insulin pumps, and we couldn’t find much on APS controllers, because they’re very new. So they’re just kind of the first one just got approved recently. And they’re not many reports on those. But I mean for actual pumps. And the monitors themselves are lots of kind of, I mean, not limiting the number of records and rough compared to other devices, and not many, but number of records and adverse events. And then we look into this report, and then try to find out as much as we can, because the reports are also very high level abstract, they don’t share all the details. I mean, if you really want to know you need to probably dig into like the company records or like talk to FDA, but based on what we are publicly available, we try to understand what was the cause of it, what kind of defect it was. And some of these are like software bugs, like for example, the control, or the software that was written for that controller had a bug in it, that caused say the pump to do something weird, or like the pump itself had a bug or like a malfunction. And then if you look at the paper, we list some examples of those. There were like a total of I think 50, around 50 recalls for insulin pumps, and then 50 for glucose monitors in the years we looked at like 2000 12,021. And then we try to see okay, can we somehow simulate the effect of these defects also in our simulator, so say I have now this controller and this patient simulator, they’re working together as like kind of a mutual patient with a pump. And then can I somehow simulate the effect of having one of these defects suddenly happening. So as soon as the patient is using a pump, and suddenly like the pump shuts off, because there is a software bug or like the value goes to something very large, because there is a bug in one of the algorithms that was coded. And then we try to see how the controller behaves when these faults happen, because some of the controllers, for example, open API is that we have studied, it has actually safety mechanisms in place to detect some of these things. So if it sees that say that glucose, I mean, the insulin value is going to be very high, or like the glucose is something, nobody tries to kind of fix it right. And not everything is fixed. Not all the kind of safety or security issues that might be there might be addressed. But some of them are address. One of the main motivations of this work is to study the effect of these kind of unexpected events or defects that might happen. And to see how resilient or robust are these different controllers against those events. And this is very helpful again, for not probably like for the patient, but for maybe in forming the kind of the companies or like the developers to know, hey, we have this kind of similar kind of bug that was reported not for your device, but for another device in FDA database. If you have it, then you might get in trouble. Right. So it will be good to kind of fix it. Right. So it’s like kind of learning from past failures to fix the future devices.
Stacey Simms 19:32
So yeah, yeah. And let me just ask Google your home well, you can jump in if this is something you can answer to should wait. After listening to Homer say all of that, is this the kind of thing where you will share your findings with all the companies? Is this something that they seem to be interested in? Or is this more theoretical?
Xugui Zho 19:47
Yeah, so we would like to share this the findings way we found in the research to the arm community, but we happened so we just published a paper and the conference. So we have in the like update or the issue will find for sale control algorithms or for the stadium to their company yet.
Stacey Simms 20:06
So then the next question I would ask and again, shoot where you can answer this is. So what is next? Are you continuing with this study? Is there another one you’re turning your attention to?
Xugui Zho 20:14
Yeah, so let our staff the project going on in your group. So let’s well, then we’re keep to update these testbed. So the current one we integrated can, so they only consider you know, a single mirrors in the day. So we want to consider make them more realistic. Like say we have three meals a day, or we have also want to consider the different activities, also the HDMI inputs. So yeah, we wanted to make this testbed more realistic, and look closer to their real implementation with a real patient, you
Stacey Simms 20:51
would UVA, where my understanding is, this is where the type zero algorithm was developed, which became what we now know as control IQ, which is what my son uses. Are you all working? I don’t know if I should ask Kenneth this home? Are you sharing this information with them? Did you compare anything with control IQ in this study?
Homa Alemzadeh 21:09
No. Have you actually shared this with them? So we have been in touch maybe a century like crime for sure, kind of a paper and then try to kind of start some discussion. But we don’t we have not really looked at their specific software, because I think that’s kind of closed, they cannot share it with public. Yeah, just like I’ll say that in some of the things that we’re doing, kind of the things are publicly okay to kind of discuss our progress on another project that we’re also working on safety, like we have kind of we collaborate with one person who actually, again, they’re not disclosing anything that is kind of internal information, but they kind of it has experienced working, she has experience working with them. So but yeah, we have just recently contacted, and I’m trying to kind of see if we can find a way to kind of work together. The problem is, as researchers, we always want to publish papers. And again, we open source publicly available, but then the companies have a different model, right? So from my experience working with some other medical device companies, usually we can we sign NDA, and then things become like you cannot publish it. Right. So then yeah, so it’s kind of a trade off between these two aspects, right?
Stacey Simms 22:12
I don’t know if you can answer this. So I’ll ask it very carefully. And I’d like to ask both of you shoot, we’ll start with you. Open APS is one of a few Do It Yourself closed loop or artificial pancreas systems? We’ve got a few different names for them. Now. I’m curious if you think just from what you’ve seen in your studies here, are they safe? We’ve had lots of studies published, they were just published in the New England Journal of Medicine that says yes, these are safe. You know, they’ve been around for quite some time. But there’s a lot of people in the diabetes community who worry about trying them. I don’t know if you can answer that. I didn’t tell you what was going to ask. And I hate to put you on the spot. But just from what you’ve seen in these studies, what do you think?
Xugui Zho 22:49
Yeah, that’s a really tough question. And I should be really careful, because I don’t want I don’t want to scale the, you know, that IBD community. So I will say this is a really an advanced control algorithm, it is really an advanced algorithm that can, you know, do even better than most of the commercial insulin pumps, but you know, even their product with remote strict design and validation, they ask the one or 2x, Tandem forwards or sound, you know, malicious attacks. So that has to stop potential safety issues. But I think most the time they are safe.
Homa Alemzadeh 23:26
Just one thing that like when people talk about safety, it’s not an absolute kind of thing is like, actually, I just have a book in front of me about safety of autonomous vehicles. Like it’s the same kind of concept like you hear about like self driving cars, this this. So basically is about like how safe it is. Right? So it’s like, also a question of how you can measure it like how you can quantify safety, right? That’s also kind of something that researchers are asking a lot is this, but like, yeah, so what should we mention is actually true. What we’re talking here is about like, especially with the devices that are at fast speeds are on the market with FDA approval processes and everything. We’re talking about very rare events, things that basically I’m telling you about, like 50 cases over like millions of devices are on the market, right? So it’s like these are rare things that happen but if they happen and they affect lots of devices, right, so if a single defect is found, they need to replace all those devices. Millions of them are like upgrade. Yeah, I think we can so generally they’re safe especially with open APS, we found that actually, they have as I said, we analyze the software also using some tools we found that actually have some do have some safety mechanisms in place for some of the issues that might happen. But with any software, even like the most Atlantis was the things that use every day there are actually false and more importantly, security attacks that might actually target them, but they’re very rare. But what do you want to make sure if those rare things happen also patients are safe and we can prevent them from happening.
Stacey Simms 24:47
And then my last question isn’t I again should have asked this earlier on do either one of you have any kind of personal connection to diabetes and it’s fine if not, I just always like to ask
Homa Alemzadeh 24:55
myself why I had my grandmother having yet but yeah, That was the only connection with in general, as I said, my via the dailies and you get interested in it was because actually, in addition to be like the only gaming being at UVA is actually, I don’t know if you know about it, like artificial pancreas systems are one of the very first probably like autonomous medical devices that are actually being approved by FDA. By alternatives, I mean that there is a controller that basically you give input to it, but basically is making a decision about your kind of treatment autonomously and then giving that to a pump, and then the pump is actually kind of providing that to your body, right. So this notion of autonomous kind of control, or like treatment is not really there yet for medical domain. And the reason we really got interested in this was that because this is one of the maybe closest one to like, something like self driving car in medicine, right. The other type of software dies using like medicine data, like they’re using some sort of intelligence or like machine learning, they’re not really doing like, directly by giving it to a pump, they’re actually having patients or doctors in the loop to approve it and then provide the treatment. But here with fully autonomous, artificial pancreas systems is like really the first step towards going back to autonomy in medicine, which is also very safety critical, right? The reason we got interested in was like this very important to ensure safe and secure for the patients.
Stacey Simms 26:17
That’s great. Should we How about you?
Xugui Zho 26:19
Yeah, I think same there, maybe. So my current Mars sister was diagnosed with the diabetes. I don’t remember where maybe 20 years ago, or sometimes close to that been passed away maybe 10 years ago. So I know that this was really severe. Serious. Eunice. So I, I was hoping that she wouldn’t be able to use these kinds of the artificial pancreas system at that time. So that might be hard for to really sum the payments.
Stacey Simms 26:51
But it’s interesting, I really think it’s almost said I wasn’t thinking about it as one as a few are only medical autonomous systems. That’s it really is pretty exciting stuff. Yeah, we’ve
Homa Alemzadeh 27:01
also caused them to make sure we ensure safety, right? I always use self driving car. People relate to that weather. But that’s what we see in everyday life these days. They say, Oh, we’re going to have a car that drives. So it’s similar like in medicine, we don’t have it yet. And this is I think one of the few that is happening right now that is kind of adding much I mean, autonomy or like intelligence into like, controlling our body and like real world, everyday life.
Xugui Zho 27:29
Yeah. So I think one thing to mention, so these kids are in a bad Asian, but that’s about and you have love words. So have some safety words when trying to build our safety monitor to protect this of APS, you know, illicit and advanced system that we mentioned. And but that might be a sound safety, wonder abilities. So you overlap, we also call for a solution or approach to protect this openaps system to make it more safe.
Homa Alemzadeh 27:56
Yeah, actually, that was the main, also like the main starting point that motivated this test, to kind of how we can test those monitors. Now basically, if you have some safety checking in place, how do we know we are doing well, and we need other kinds of tests that are tested in within. Within that.
Stacey Simms 28:13
Thank you so much for joining me, I really appreciate you spending some time to explain it. Your patience was wonderful too, as well with me. So thank you so much, and I hope we can talk again soon.
You’re listening to Diabetes Connections with Stacey Simms.
More information and again, I will link to the transcript at diabetes connections.com. All of those links are in the show notes. You can read the study for yourself and learn more about what these researchers are trying to do. Up next, a little bit of a report from our endocrinologist what have been his recent appointments, and I want to talk about event stuff as well.
But first Diabetes Connections is brought to you by Dexcom. It’s pretty amazing to think back how we used to do blood sugar checks before share and follow. We actually had them on a timer, we would check doing a finger stick the same times every day at home and at school. Of course we’d add in if we needed to. But it is amazing to think about how much our diabetes management has changed with share and follow. Using the share and follow apps have really helped us talk less about diabetes, which I never thought would happen with a teenager. Benny loves that part to me talking about diabetes less. And that’s what’s so great about the Dexcom system. I think for the caregiver or the spouse or the friend, you can help the person with diabetes manage in the way that works for their individual situation. Internet connectivity is required to access Dexcom follow separate follow up required. learn more, go to diabetes connections.com and click on the Dexcom logo.
We had to reschedule our endocrinology visit that of course we set up like you probably do every three months well in advance and probably like where you live it’s really difficult if you have to reschedule because they’re always so busy. We got really lucky that because I didn’t know this apparently for years now, Dr. V our local endocrinologist has been having Monday night hours, he stays in the office till something like eight o’clock at night. So he can see patients who otherwise you know, are I have a hard time getting to the office during the day, which is fantastic. And we’ve always been a really early morning family, longtime listeners, if you’ve known us for a long time, you know, that I used to do super early morning radio and that kind of stuff. So it was never a problem for us to get down there early. But they had a Monday night opening just a couple of days after we had to reschedule. So it’s perfect.
We live about 45 minutes from this Endo. So we usually make it not a day of it, but at least a meal of it. We go and we have breakfast or lunch in these fun places that are down by the South Park Mall area. So Benny decided that he wanted to go to cow fish, which is this really fun. That’s like a cow fish sushi crossed with burgers. I think there’s one at Universal in Orlando, but it’s a Charlotte company that has them. I think they’ve expanded I think there’s one in Raleigh, but if you’ve got one near you, they’re really fun. So I posted this on social media, he had this ridiculous cheeseburger sushi thing. He said it was delicious. Of course, he ate all of it. I know most of us don’t like to have like a huge meal before you go to the end. know that’s supposed to be what you do after but at this point, whatever I mean, that he’s just doing his own thing, but a great visit.
And by that I don’t mean like numbers because I don’t share those, although he’s doing fine. I just mean, it’s, it’s just so great to have an endocrinologist that we’ve seen this whole time. Talk to us about what’s next. And we all realize sitting there that we have one more appointment, assuming we don’t reschedule it before Benny turns 18. And we’re really lucky that we get to stay with this endocrinologist. We don’t have to leave. You can see Benny all through college, and I think up to his early 20s. But it’s just such a milestone to think about. I’m gonna let that sink in a little bit later on when I’m ready, because I’m not ready to have an my youngest child be 18 years old. But man, we are getting close.
So I will change the subject to something much easier, which is to talk about my schedule. We do have a lot of fun stuff coming up in the next couple of weeks, I’m going to be traveling to healthy voices, which is a delayed from 2020 conference, I think I first went to healthy voices in 2017. This one this year has all the people that were accepted as speakers and attendees in 2020. So that’s in Philadelphia, the last weekend of this month and the first weekend of October. It’s always interesting, because it’s not just for diabetes advocates. I’m really interested to see who’s coming this time around and what kind of resources and teachings they’re going to do for us. So I will report back after that of a second weekend in October is friends for life in the Washington DC area. I am not able to go this year. But if you are going it’s always a terrific conference, I will link up more information. I think the room block has closed but you can certainly still attend if you’re local. So I will link that up as well in November. Looking ahead, I will not get too far ahead of myself. But I have some really fun stuff coming up. The book launch will be happening in November. I’ve got a local event here on the 15th. But before that, I’m going to Florida for Macy’s believers they have a great event the second weekend in November, I will link up more information about that. If you’re in the Port St. Lucie area or really anywhere within driving distance. They’re doing a really fun weekend to raise money for diabetes charities. And then of course, January will be moms night out. You guys. I’m so excited about this. If you haven’t registered for your spot, and you are interested in this conference, I’m telling you please register as soon as possible. We have a an event capacity for this one. And the response has been terrific. I think we’re going to hit it sooner than we thought. So please make sure to register for mom’s night out if this is something you are remotely interested in. And I really appreciate all the people I’ve heard from all over the country who want me to bring a moms Night Out event to them. Man, I would love to do that. So stay tuned.
Okay, thank you as always to my editor John Bukenas from audio editing solutions. Thank you so much for joining me. We will have a newscast later this week. So I will see you then. Until then I’m Stacey Simms, kind to yourself.
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