Absstract of: US2022248965A1

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Absstract of: US2022254500A1

Computer-implemented systems and methods for automated diagnosis of diabetic retinopathy apply machine learning techniques to clinical and demographic data combined with optical coherence tomography and optical coherence tomography angiography image data to diagnose and grade diabetic retinopathy.

Absstract of: US2022248986A1

An example medical device includes an optical sensor, processing circuitry, an antenna, and a power source. The optical sensor includes a light source; a reference optical beacon having a first fluorophore that emits a first fluorescence proportional to a first concentration of a substance proximate the beacon; a test optical beacon having a reagent substrate that reacts with an analyte to produce the substance and a second fluorophore that emits a second fluorescence proportional to a second concentration of the substance proximate the test beacon; and a photodetector to detect the first and second fluorescence. The processing circuitry determines a difference between the first and second fluorescence, which is indicative of the concentration of the analyte. The antenna and power source enable the medical device to operate completely within a biological system for continuous analyte monitoring.

Absstract of: US2022248984A1

The present disclosure relates to noninvasive methods, devices, and systems for measuring various blood constituents or analytes, such as glucose. In an embodiment, a light source comprises LEDs and super-luminescent LEDs. The light source emits light at at least wavelengths of about 1610 nm, about 1640 nm, and about 1665 nm. In an embodiment, the detector comprises a plurality of photodetectors arranged in a special geometry comprising one of a substantially linear substantially equal spaced geometry, a substantially linear substantially non-equal spaced geometry, and a substantially grid geometry.

Absstract of: US2022248990A1

The present invention relates to a glucose detecting complex and a contact lens-type sensor comprising the same for detecting glucose in tears. The contact lens-type sensor for detecting glucose according to the present invention includes a complex in which glucose oxidase is coupled to cerium oxide nanoparticles. Such a configuration allows the visualization of changes in glucose concentrations and the quantitative measured of glucose in a simpler and more economical way. In addition, glucose concentrations can be monitored in real time through a non-invasive method by measuring the concentration of glucose in tears rather than in blood in comparison with the conventional blood glucose measurement method. Therefore, the present invention can be widely applied in the technical field for the early diagnosis and prevention of diabetes.

Absstract of: US2022249773A1

Disclosed are techniques, a system and devices that enable the setting of an upper boundary constraint that may be a multiple of a total daily dosage setting for a liquid drug being administered to a user to control a condition, such as type 1 or type 2 diabetes mellitus. An automatic drug delivery algorithm may be configured to obtain a glucose control metric. A controller executing the automatic drug delivery algorithm may ascertain, based on the glucose control metric, an upper boundary constraint for the liquid drug that limits an amount of a dose of the liquid drug that may be delivered by the automatic drug delivery system or components thereof.

Absstract of: US2022249767A1

A method for treating a patient with a neurodegenerative disease including performing Ketamine Infusion Therapy on the patient, performing hyperbaric oxygen therapy on the patient, intranasally infusing plasma into the patient, and intranasally infusing diluted insulin into the patient. Performing hyperbaric oxygen therapy on the patient may include placing the patient into a hyperbaric chamber, sealing and pressurizing the hyperbaric chamber, and pumping oxygen into the hyperbaric chamber while it is pressurized. Before intranasally infusing plasma into the patient, the method may include drawing blood from the patient and centrifugally extracting the plasma from the drawn blood, such as platelet rich plasma (PRP). Before drawing blood from the patient, the method may include intravenously injecting the patient with supplements, such as a combination of magnesium, calcium, B-vitamins, Vitamin C, nicotinamide adenine dinucleotide (NAD+), and Glutathione.

Absstract of: US2022249779A1

Systems, devices and methods are provided for determining a medication dose for a patient or user. The dose determination can account for recent and/or historical analyte levels of the patient or user. The dose determination can also take into account other information about the patient or user, such as physiological information, dietary information, activity, and/or behavior. Many different dose determination embodiments are set forth, pertaining to a wide array of different aspects of the system or environment in which the embodiments can be implemented. Systems, devices and methods are provided for displaying information related to glucose levels, including a time in range display and a graph of analyte levels containing an identification of a pattern type of a segment of the day.

Absstract of: US2022254473A1

An aspect of the invention provides a method and a product for determining a modification of the therapy management by using a processor unit which retrieves few data related to the blood glucose measurement performed over a predetermined time period; retrieve the medication delivery parameter executed by the delivery device over said predetermined time period; retrieve from the memory data associated to the CIR of the patient; and determine a modification to the therapy based on at least a part of the retrieved data.

Absstract of: US2022248989A1

A method of managing insulin includes receiving blood glucose measurements on a computing device from a glucometer. The blood glucose measurements are separated by a time interval. The method includes determining, by the computing device, an insulin dose rate based on the blood glucose measurements and determining a blood glucose drop rate based on the blood glucose measurements and the time interval. The method also includes determining a blood glucose percentage drop based on the blood glucose measurements. The method includes decreasing the time interval between blood glucose measurements by the glucometer when the blood glucose drop rate is greater than a threshold drop rate, and decreasing the time interval between blood glucose measurements by the glucometer when the blood glucose percentage drop is greater than a threshold percentage drop.

Absstract of: US2022248988A1

Improved digital interfaces, graphical user interfaces, and alarms for analyte monitoring systems are provided. For example, disclosed herein are various embodiments of methods, systems, and interfaces for signal loss condition determination, Time-in-Ranges interfaces, GMI metrics, urgent low glucose alarms, alarm suppression features, alarm setup interfaces, and alarm unavailability detection features. In addition, various embodiments of interfaces for alarm logging and compatibility checking of an analyte monitoring software application are described. Also, various embodiments of interface enhancements are described, including an enhanced visibility mode, a voice accessibility mode, additional interfaces relating to user privacy, as well as caregiver alarms, among other embodiments.

Absstract of: WO2022169721A1

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Absstract of: WO2022169149A1

The present invention relates to a system for calculating the risk of diabetes incidence, the system comprising: an analysis server which includes one or more computers, calculates the severity of metabolic syndrome on the basis of input metabolic syndrome incidence factor values for metabolic syndrome incidence factors, and calculates the risk of diabetes incidence from the calculated severity of metabolic syndrome, on the basis of a statistical technique; and a client connected to the analysis server, wherein the client receives, as inputs, the input metabolic syndrome incidence factor values, provides the same to the analysis server, receives the risk of diabetes incidence from the analysis server, and outputs the same, and the metabolic syndrome incidence factors may include fasting blood sugar, systolic blood pressure, waist girth, high-density lipoprotein, and triglycerides.

Absstract of: US2022254472A1

The technology described herein relates to control models for artificial pancreas systems, including insulin injections in people with diabetes. The methods provided herein allow for a modular and personalized intervention for the treatment of diabetes using an iterative learning controller (ILC). The ILC allows for long-acting insulin doses to be computationally applied to track a basal glucose concentration reference, a run-to-run (R2R) control policy to update the treatment plan, that progressively meets the recommended glycemic targets.

Absstract of: EP4040443A1

One or more embodiments of the disclosure relate, generally, to a diabetes management system. The diabetes management system may include a computing device that is configured to receive data relating to prior insulin use of a user and calculate a sliding scale glucose correction based, at least in part, on the data relating to prior insulin use.

Absstract of: WO2021063695A1

The present invention relates a device and a method for noninvasively determining an analyte in blood, in particular for noninvasively determining an analyte in capillary blood.

Absstract of: US2021101905A1

The present disclosure describes glucose analogs that are transported on the sodium dependent glucose transporters (SGLTs). These compounds may be useful in a variety of disorders such as, for example, cancer, heart disease, neurological disorders, diabetes, and atherosclerosis. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present invention.

Absstract of: EP4039293A1

Disclosed are techniques, a system and devices that enable the setting of an upper boundary constraint that may be a multiple of a total daily dosage setting for a liquid drug being administered to a user to control a condition, such as type 1 or type 2 diabetes mellitus. An automatic drug delivery algorithm may be configured to obtain a glucose control metric. A controller executing the automatic drug delivery algorithm may ascertain, based on the glucose control metric, an upper boundary constraint for the liquid drug that limits an amount of a dose of the liquid drug that may be delivered by the automatic drug delivery system or components thereof.

Absstract of: WO2022164737A1

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.

Absstract of: WO2022164981A1

Embodiments of the invention provide amperometric analyte sensors having optimized elements such as interference rejection membranes, and associated architectures, as well as methods for making and using such sensors. The interference rejection membranes are made by photopolymerisation of a reaction mixture comprising a monomer such as methacrylate, a crosslinker such as ethylene glycole or silane and a diazo photoinitiator. While embodiments of the innovation can be used in a variety of contexts, typical embodiments of the invention include glucose sensors used in the management of diabetes.

Absstract of: US2022240818A1

Methods, systems, and devices for modeling a relationship between glucose sensitivity and a sensor electrical property are described herein. More particularly, the methods, systems, and devices describe partitioning an input signal feature space relating glucose sensitivity and a sensor electrical property into subspaces and training a model for each subspace. For example, the subspace models may form a mosaic of models, for which the output is more accurate than a single model.

Absstract of: WO2022165136A1

Methods, systems, and devices for modeling a relationship between glucose sensitivity and a sensor electrical property are described herein. More particularly, the methods, systems, and devices describe partitioning an input signal feature space relating glucose sensitivity and a sensor electrical property into subspaces and training a model for each subspace. For example, the subspace models may form a mosaic of models, for which the output is more accurate than a single model.

Absstract of: WO2022164738A1

Disclosed herein are systems and methods for the delivery of insulin and pramlintide using an automated insulin delivery system. In a first embodiment, a drug delivery system is configured to deliver independent doses of insulin and pramlintide. The system monitors the user's blood glucose level and determines when a meal is been ingested and, in response, delivers the dose of pramlintide which, in turn alters the required delivery of insulin. In the second embodiment, the drug delivery system is configured to deliver a co-formulation of insulin and pramlintide as basal doses. The total amount of pramlintide delivered in a most recent pre-determine period of time, for example, 24 hours, is used to alter the aggressiveness of the algorithm which determines the basal doses of the co-formulation.

Absstract of: US2022241501A1

Disclosed herein are techniques related to glucose level management without carbohydrate counting. The techniques may involve obtaining contextual information for a meal, predicting amounts of glucose to be absorbed into a bloodstream of the patient over a duration of time due to consumption of the meal based on the contextual information for the meal, determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose, and affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.

Absstract of: US2022240864A1

According to various embodiments, a machine-learning based system for diabetes analysis is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs and demographic data from a user interface. The processors are further configured to train at least one neural network based on a grow-and-prune paradigm to generate at least one diabetes inference model. The neural network grows at least one of connections and neurons based on gradient information and prunes away at least one of connections and neurons based on magnitude information. The processors are also configured to output a diabetes-based decision by inputting the received physiological data and demographic data into the generated diabetes inference model.