Abstract: In an aspect of the present invention, a fluoropolymer represented by formula 1 is provided. The fluoropolymer provided in one aspect of the present invention can obtain low surface energy and high light transmittance, and thus can be applied to various applications requiring such properties. In particular, when the fluoropolymer is coated on glass, the surface energy can be lowered to 19 mN/m or less and the light transmittance can be increased by 2% or more. At the same time, it has the effect of exhibiting excellent pencil hardness. In addition, the fluoropolymer provided in one aspect of the present invention can be applied as a surface coating and membrane material of various products due to its high solubility in general organic solvents, despite having very low surface energy during coating.

Abstract: Disclosed are a system, methods and computer-readable medium products that provide safety constraints for an insulin-delivery management program. Various examples provide safety constraints for a control algorithm-based drug delivery system that provides automatic delivery of a drug based on sensor input. Glucose measurement values may be received at regular time intervals from a sensor. A processor may predict future glucose values based on prior glucose measurement values. The safety constraints assist in safe operation of the drug delivery system during various operational scenarios. In some examples, predicted future glucose values may be used to implement safety constraints that mitigate under-delivery or over-delivery of the drug while not overly burdening the user of the drug delivery system and without sacrificing performance of the drug delivery system. Other safety constraints are also disclosed.

Abstract: Exemplary embodiments calculate a user's progress towards maximum adapted system performance when utilizing a drug delivery device such as an insulin pump and display this progress to the user. Further embodiments calculate potential changes to a particular user's interaction with the system given a history of glucose and insulin deliveries and provide guidance to that user. This can be used, for example, to change how the user splits up their basal and bolus doses to improve metrics such as time-in-range, incidences of hyper- or hypo-glycemia, time in hyper- or hypo-glycemia, mean glucose, etc. It may also be used to speed the above-described adaptation process by suggesting changes that the user can make to achieve stability more quickly.

Abstract: A model-based control scheme consisting of either a proportional-integral-derivative (IMC-PID) controller or a model predictive controller (MPC), with an insulin feedback (IFB) scheme personalized based on a priori subject characteristics and comprising a lower order control-relevant model to obtain PID or MPC controller for artificial pancreas (AP) applications.

Abstract: Disclosed are techniques and a device operable to determine a total amount of insulin delivered to the user over a predetermined time period. The total amount of insulin includes a total basal dosage delivered in basal dosages and a total bolus dosage delivered in bolus dosages over the predetermined time period. A proportion of the total amount of insulin delivered to the user provided via the total basal dosage amount over the predetermined time period is calculated. In response determining the proportion of the total amount of insulin attributed to the total basal dosage amount of insulin exceeds a threshold, an average basal dosage to be delivered within a subsequent time period that is approximately equal to the threshold may be determined. An instruction may be generated and output to deliver a modified basal dosage that substantially maintains the average basal dosage over the subsequent time period.

Abstract: Disclosed herein are systems and methods for the delivery of a co-formulation of insulin and a second drug, such as GLP-1, using an automated insulin delivery system. In a first embodiment, a dose of insulin is calculated by a medication delivery algorithm and a reduction factor is applied to account for the effect of second drug on the user's daily insulin requirement. In a second embodiment of the invention, a total amount of the second drug administered to the user during the past 24 hours is used to modify the correction factor and the insulin-to-carbohydrate ratio used by the medication delivery algorithm to cause a reduction in the insulin delivered to the user to account for the effect of the administration of the second drug portion of the co-formulation.

Abstract: A drug delivery system including a memory storing programming code operable to control delivery of a drug, a user interface operable to accept an input indicating a perceived glucose state of a user, and a processor operable to execute the programming code. When executed, the programming code causes the processor to: receive a perceived state signal from the user interface indicating the perceived glucose state of the user, evaluate the perceived glucose state of the user indicated by the perceived state signal using a glucose measurement value corresponding in time to when the perceived state signal was received, determine an adjustment to a drug delivery algorithm, and utilize the adjustment in a determination of a drug delivery dosage to be administered to a user.

Abstract: A model-based control scheme consisting of either a proportional-integral-derivative (IMC-PID) controller or a model predictive controller (MPC), with an insulin feedback (IFB) scheme personalized based on a priori subject characteristics and comprising a lower order control-relevant model to obtain PID or MPC controller for artificial pancreas (AP) applications.

Abstract: Exemplary embodiments provide an approach to predicting meal and/or exercise events for an insulin delivery system that otherwise does not otherwise identify such events. The insulin delivery system may use a model of glucose insulin interactions that projects estimated future glucose values based on the history of glucose values and insulin deliveries for the user. The predictions of meal events and/or exercise events may be based on residuals between actual glucose values and predicted glucose values. The exemplary embodiments may calculate a rate of change of the residuals over a period of time and compare the rate of change to thresholds to determine whether there likely has been a meal event or an exercise event. The insulin delivery system may then take measures to account for the meal or exercise by the user.

Abstract: Disclosed are a system, methods and computer-readable medium products that provide safety constraints for an insulin-delivery management program. Various examples provide safety constraints for a control algorithm-based drug delivery system that provides automatic delivery of a drug based on sensor input. Glucose measurement values may be received at regular time intervals from a sensor. A processor may predict future glucose values based on prior glucose measurement values. The safety constraints assist in safe operation of the drug delivery system during various operational scenarios. In some examples, predicted future glucose values may be used to implement safety constraints that mitigate under-delivery or over-delivery of the drug while not overly burdening the user of the drug delivery system and without sacrificing performance of the drug delivery system. Other safety constraints are also disclosed.

Abstract: The exemplary embodiments attempt to identify impending hypoglycemia and/or hyperglycemia and take measures to prevent the hypoglycemia or hyperglycemia. Exemplary embodiments may provide a drug delivery system for delivering insulin and glucagon as needed by a user of the drug delivery system. The drug delivery system may deploy a control system that controls the automated delivery of insulin and glucagon to a patient by the drug delivery system. The control system seeks among other goals to avoid the user experiencing hypoglycemia or hyperglycemia. The control system may employ a clinical decision support algorithm as is described below to control delivery of insulin and glucagon to reduce the risk of hypoglycemia or hyperglycemia and to provide alerts to the user when needed. The control system assesses whether the drug delivery system can respond enough to avoid hypoglycemia or hyperglycemia and generates alerts when manual action is needed to avoid hypoglycemia or hyperglycemia.

Abstract: Disclosed are techniques to establish initial settings for an automatic insulin delivery device. An adjusted total daily insulin (TDI) factor usable to calculate a TDI dosage may be determined. The adjusted TDI factor may be a TDI per unit of a physical characteristic of the user (e.g., weight) times a reduction factor. The adjusted TDI factor may be compared to a maximum algorithm delivery threshold. Based on the comparison result, the application or algorithm may set a TDI dosage and output a control signal. Blood glucose measurement values may be collected from a sensor over a period of time. A level of glycated hemoglobin of the blood may be determined based on the obtained blood glucose measurement values. In response to the level of glycated hemoglobin, the set TDI dosage may be modified. A subsequent control signal including the modified TDI dosage may be output to actuate delivery of insulin.

Abstract: Disclosed are processes and techniques implementable by a drug delivery system to maintain optimal drug delivery for a patient according to a treatment plan. The disclosed techniques enable a new drug delivery device that is exchanged for a previous drug delivery device to operate using analyte-based drug delivery control immediately during initialization instead of having to wait for a warm-up period. For example, a drug delivery device may include a processor and a memory storing instructions that, when executed by the processor, operate the drug delivery device to receive a present analyte measurement value from an analyte sensor during an initialization of the drug delivery device, receive backfill values measured by the analyte sensor prior to the initialization, calculate a dosage of a drug using the present analyte measurement value and the backfill values, and deliver the dosage of the drug. Other embodiments are described.

Abstract: Disclosed are a device, system, methods and computer-readable medium products that provide an updated insulin-to-carbohydrate ratio and an updated total daily insulin. The described processes may be used for periodic updating of the insulin-to-carbohydrate ratio and the total daily insulin. The insulin-to-carbohydrate ratio and/or the total may be used in the calculation of new doses of insulin that a drug delivery device may be commanded to deliver to a user.

Abstract: A wearable drug delivery device, techniques, and computer-readable media that provide an application that implements a diabetes treatment plan for a user are described. The drug delivery device may include a controller operable to direct operation of the wearable drug delivery device. The controller may provide a selectable activity mode of operation for the user. Operation of the drug delivery device in the activity mode of operation may reduce a likelihood of hypoglycemia during times of increased insulin sensitivity for the user and may reduce a likelihood of hyperglycemia during times of increased insulin requirements for the user. The activity mode of operation may be manually activated by the user or may be activated automatically by the controller. The controller may automatically activate the activity mode of operation based on a detected activity level of the user and/or a detected location of the user.

Abstract: Exemplary embodiments provide an approach to predicting meal and/or exercise events for an insulin delivery system that otherwise does not otherwise identify such events. The insulin delivery system may use a model of glucose insulin interactions that projects estimated future glucose values based on a history of glucose values and insulin deliveries for a user. The predictions of meal events and/or exercise events may be based on residuals between actual glucose values and predicted glucose values. The exemplary embodiments may calculate a rate of change of the residuals over a period of time and compare the rate of change to thresholds to determine whether there likely has been a meal event or an exercise event. The insulin delivery system may then take measures to account for the meal event or the exercise event by the user.

Abstract: Compensation may be provided for the varying accuracy levels of a sensor over time. As a result of the compensation, the medicament delivery device may perform better. The sensor may provide input to a medicament delivery device, and the input may be used to determine a dose of medicament to be delivered to a user by the medicament delivery device. A degree of inaccuracy of sensor values may be determined based on when in the lifetime a sensor value is generated. Glucose level values read by a glucose monitor, such as a CGM, may be directly modified before being used by an automated insulin delivery (AID) control of an insulin delivery device. The compensation for the inaccuracy of the glucose level values from the glucose monitor instead may be achieved by modifying a weight coefficient of a glucose cost component of a cost function in one example.

Abstract: Disclosed are a device, a computer-readable medium, and techniques that provide an onboarding process and an adaptivity process for a drug delivery device. A processor executing an onboarding process determines whether a history of delivered insulin to a user meets certain sufficiency requirements. The onboarding process enables a processor to cause the drug delivery device to administer doses of insulin to a user according to an initial total daily insulin dose calculation that is determined based on the sufficiency of the insulin delivery history. The initial total daily insulin may be adapted according to the adaptivity process as new insulin delivery is collected. The insulin delivery history, when sufficient, may be used to set total daily insulin dosages that enable automated insulin delivery upon replacement of a drug delivery device. The adaptivity process may be implemented to modify an initial insulin delivery doses to provide adapted insulin delivery doses.

Abstract: The exemplary embodiments provide medicament delivery devices that use cost functions in their control systems to determine medicament dosages. The cost function may have a medicament cost component and a performance cost component. The exemplary embodiments may use cost functions having medicament cost components that scale asymmetrically for different ranges of inputs (i.e., different candidate medicament dosages). The variance in scaling for different input ranges provides added flexibility to tailor the medicament cost component to the user and thus provide better management of medicament delivery to the user and better conformance to a performance target. The exemplary embodiments may use a cost function that has a medicament cost component (such as an insulin cost component) of zero for candidate dosages for a range of candidate dosages (e.g., below a reference dosage).

Abstract: The exemplary embodiments attempt to identify impending hypoglycemia and/or hyperglycemia and take measures to prevent the hypoglycemia or hyperglycemia. Exemplary embodiments may provide a drug delivery system for delivering insulin and glucagon as needed by a user of the drug delivery system. The drug delivery system may deploy a control system that controls the automated delivery of insulin and glucagon to a patient by the drug delivery system. The control system seeks among other goals to avoid the user experiencing hypoglycemia or hyperglycemia. The control system may employ a clinical decision support algorithm as is described below to control delivery of insulin and glucagon to reduce the risk of hypoglycemia or hyperglycemia and to provide alerts to the user when needed. The control system assesses whether the drug delivery system can respond enough to avoid hypoglycemia or hyperglycemia and generates alerts when manual action is needed to avoid hypoglycemia or hyperglycemia.