Mayo Foundation for Medical Education and Research

Arizona State University

Universitá Degli Studi Di Padova

Yogish C Kudva

Claudio Cobelli

Ayan Banerjee

Andre Terzic

Yasuhiro Ikeda

Michele Schiavon

Chiara Dalla Man

Tayaramma Thatava

Ananda Basu

The present disclosure discloses a data science driven system and associated methodology framework for mining relevant knowledge from input/output traces of a closed-loop control system for glycemic control to aid in actionable feedback for Type 1 diabetes management. The framework takes Continuous Glucose Monitor (CGM) records and insulin pump records from a closed loop control (CLC) system and interprets the CGM records and insulin pump data to provide medically useful information, wherein the closed-loop control system is operable for operation in an 'auto mode' (AP) and a “manual mode” (SAP).

Systems and methods for a data driven framework for actionable feedback in insulin management for type 1 diabetic subjects on artificial pancreas

This document provides methods and materials related to differentiating iPS cells into glucose-responsive, insulin-secreting progeny. For example, methods and material for using indolactam V (ILV) and glucagon like peptide-1 (GLP-1) to produce glucose-responsive, insulin-secreting progeny from iPS cells are provided.

Differentiating induced pluripotent stem cells into glucose-responsive, insulin-secreting progeny

In a method of determining insulin sensitivity in a patient, glucose level is sensed continuously. A first area under the curve representing the glucose level over time is calculated. An amount of insulin that has been administered to the patient is sensed. An estimation of insulin on board the patient is calculated based on the glucose level and the amount of insulin administered to the patient. A second area under the curve representing the insulin on board over time is calculated. Patient data indicative of at least one patient physical parameter is received. Information indicative of amount of glucose ingested by the patient during a meal is received. An insulin sensitivity output indicative of ability of insulin to stimulate glucose utilization and inhibit glucose production in the patient based on the first and second area under the curve, the patient data and the meal information is generated.

Estimation of insulin sensitivity from CGM and subcutaneous insulin delivery in type 1 diabetes

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Rochester, MN, United States of America

Yogish C Kudva