Abstract: Compositions and methods for treating diabetes are disclosed. In particular, the invention relates to co-formulations of amylin analogues with insulin analogues for treatment of diabetes.

Abstract: Techniques for controlling an insulin pump include determining values for parameters selected from a group including a first prediction time horizon, a predicted glucose threshold (Goff) for turning the insulin pump off, a maximum shut off time within a time window, and duration of the time window. A safety rule is determined based on the maximum shut off time within the duration. Glucose readings are collected up to a current time. An expected current glucose value G and glucose temporal rate of change are determined based only on the glucose readings and a Kalman filter configured for noisy glucose readings. A glucose level (Gh1) is predicted for a future time that is the prediction time horizon after the current time. A command is issued to shut off the insulin pump if it is determined both that Gh1 is less than Goff and that the safety rule is satisfied.

Abstract: Techniques for controlling an insulin pump include determining values for parameters selected from a group including a first prediction time horizon, a predicted glucose threshold (Goff) for turning the insulin pump off, a maximum shut off time within a time window, and duration of the time window. A safety rule is determined based on the maximum shut off time within the duration. Glucose readings are collected up to a current time. An expected current glucose value G and glucose temporal rate of change are determined based only on the glucose readings and a Kalman filter configured for noisy glucose readings. A glucose level (Gh1) is predicted for a future time that is the prediction time horizon after the current time. A command is issued to shut off the insulin pump if it is determined both that Gh1 is less than Goff and that the safety rule is satisfied.

Abstract: A system for predicting hypoglycemia based on continuous blood glucose monitor values is provided. The hypoglycemia detection algorithm is a set of individual alarms that are combined through a voting system into one combined alarm. The system could have five components and an overall voting algorithm that produces a binary alarm outcome depending on the number of constituent algorithms that report an alarm. A controller system automatically shuts off the insulin pump when pending or real hypoglycemia has been reached. The algorithms operate in a closed loop and automatically take action when the subject is asleep.

Abstract: A real-time controller operating as an artificial pancreas uses a Kalman control algorithm to control glucose level of a patient in real time. The real-time controller receives an estimate of the patient glucose level and a reference glucose level. The estimate of the patient glucose level can be provided by an optimal estimator implemented using a linearized Kalman filter. The estimated glucose level and the reference glucose level are processed by the Kalman control algorithm to determine a control command in real time. The Kalman control algorithm has a dynamic process forced by the control command a cost function determining a relative level of control. The control command is provided to a dispenser which secretes insulin or glucagon in response to the control command to correct a relatively high glucose level or a relatively low glucose level.

Abstract: A real-time glucose estimator uses a linearized Kalman filter to determine a best estimate of glucose level in real time. The real-time glucose estimator receives at least one measurement corresponding to glucose level. The measurement can be obtained with one or more sensors and is provided to the linearized Kalman filter in real time. The linearized Kalman filter has dynamic models and executes a recursive routine to determine the best estimate of glucose level based upon the measurement. Additional information can be provided to the linearized Kalman filter for initialization, configuration, and the like. Outputs of the linearized Kalman filter can be provided to a patient health monitor for display or for statistical testing to determine status of the real-time glucose estimator. The real-time glucose estimator can be implemented using a software algorithm.

Abstract: A real-time controller operating as an artificial pancreas uses a Kalman control algorithm to control glucose level of a patient in real time. The real-time controller receives an estimate of the patient glucose level and a reference glucose level. The estimate of the patient glucose level can be provided by an optimal estimator implemented using a linearized Kalman filter. The estimated glucose level and the reference glucose level are processed by the Kalman control algorithm to determine a control command in real time. The Kalman control algorithm has a dynamic process forced by the control command a cost function determining a relative level of control. The control command is provided to a dispenser which secretes insulin or glucagon in response to the control command to correct a relatively high glucose level or a relatively low glucose level.

Abstract: A real-time controller operating as an artificial pancreas uses a Kalman control algorithm to control glucose level of a patient in real time. The real-time controller receives an estimate of the patient glucose level and a reference glucose level. The estimate of the patient glucose level can be provided by an optimal estimator implemented using a linearized Kalman filter. The estimated glucose level and the reference glucose level are processed by the Kalman control algorithm to determine a control command in real time. The Kalman control algorithm has a dynamic process forced by the control command a cost function determining a relative level of control. The control command is provided to a dispenser which secretes insulin or glucagon in response to the control command to correct a relatively high glucose level or a relatively low glucose level.

Abstract: A real-time glucose estimator uses a linearized Kalman filter to determine a best estimate of glucose level in real time. The real-time glucose estimator receives at least one measurement corresponding to glucose level. The measurement can be obtained with one or more sensors and is provided to the linearized Kalman filter in real time. The linearized Kalman filter has dynamic models and executes a recursive routine to determine the best estimate of glucose level based upon the measurement. Additional information can be provided to the linearized Kalman filter for initialization, configuration, and the like. Outputs of the linearized Kalman filter can be provided to a patient health monitor for display or for statistical testing to determine status of the real-time glucose estimator. The real-time glucose estimator can be implemented using a software algorithm.