Matt Henriksen

Yeah, I feel like my wife's on the same boat there.

But it's interesting, though, because it just makes you think that you have this – we'll dive deeper into kind of the market opportunities and the patient pools that can benefit from these AIDs.

But just the fact that it just – when we go into that, knowing that the type 1 –

the patients that can benefit from this, it's not a static number.

It's an ever-evolving number, which is I always find interesting with how the disease progresses with some of these patients.

Yeah, and let's maybe take a step back about how insulin used to be delivered to these patients.

You know, because as you said, it's been a 20-plus year journey for, you know, the technology that's now in the market.

But walk us through, for these patients who need insulin, what were the steps that they had beforehand and then kind of has progressed to now what we're calling, you know, the AIDs?

Yeah, and it's interesting because from your perspective, it sounds like

It wasn't the smooth delivery of insulin every five minutes.

That was the game factor.

It was when was the right time to deliver the insulin in the first place.

Yeah, and maybe that's, you know, we can jump into the islet pump.

How is that designed to kind of, you know, have that dual mandate of one accuracy and being able to deliver the right insulin at the right time, but also that ease of use factor?

Interesting.

And I was about to ask, kind of dive in a little bit deeper into that carb counting, because that is an important input for some of these other pumps.

I was going to ask how your algorithm is able to avoid requiring those carb counting.

But it sounds like it takes, it's a few days of learning the patient's behavior.

So is it kind of, your algorithm is being able to use kind of that big data machine learning capabilities to be able to

Maybe the same thing as we're talking about, understanding the patient and the impact of the carbs before the patient even realizes it.