Alerta

HOUSING FOR AN INTEGRATED GLUCOSE MONITORING SYSTEM

Absstract of: US2025352094A1

An integrated glucose monitoring system comprising a glucometer and mobile device housed in a housing. The glucometer includes a measurement component to receive a blood glucose test strip containing a sample of blood of an individual, and a port to transmit the blood glucose measurement to a mobile device. The mobile device communicatively and physically coupled to the glucometer. The mobile device includes a port configured to couple with the glucometer, a communication component to receive the blood glucose measurement from glucometer, a power management component to transmit power to the glucometer, and another port to receive power from a power source to power the mobile device. The housing is to house the glucometer and the mobile device to form a single solitary glucose monitoring system.

SYSTEMS AND METHODS FOR CONTINUOUS GLUCOSE MONITORING OUTCOME PREDICTIONS

Absstract of: AU2024252061A1

Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, and determining, using the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels.

NON-INVASIVE MONITORING SYSTEM

Absstract of: AU2025259892A1

(57) Abstract: A wearable device for non-invasive monitoring of the presence, amount, and/or concentration of an analyte in a sample from a user of the device is described. The analyte is selected to be indicative of or related to a physiological status of a user. Relevant physiological status include hypoglycemia, infection, respiratory infection, urinary infection, gastrointestinal infection, obesity, diabetes, type I diabetes and type II diabetes. (57) Abstract: A wearable device for non-invasive monitoring of the presence, amount, and/or concentration of an analyte in a sample from a user of the device is described. The analyte is selected to be indicative of or related to a physiological status of a user. Relevant physiological status include hypoglycemia, infection, respiratory infection, urinary infection, gastrointestinal infection, obesity, diabetes, type I diabetes and type II diabetes. ct c t

System, device and method of dynamic glucose profile response to physiological parameters

Absstract of: AU2025259998A1

Method, device and system for providing consistent and reliable glucose response information to physiological changes and/or activities is provided to improve glycemic control and health management. Method, device and system for providing consistent and reliable glucose response information to physiological changes and/or activities is provided to improve glycemic control and health management. ov o v

Apparatus for Optimizing A Patient's Insulin Dosage Regimen

Absstract of: US2025356983A1

An apparatus for optimizing a patient's insulin dosage regimen over time, comprising: at least a first computer-readable memory for storing data inputs corresponding at least to one or more components in a patient's present insulin dosage regimen and the patient's blood-glucose-level measurements determined at a plurality of times; a processor operatively connected to the at least first computer-readable memory, the processor programmed at least to determine from the data inputs corresponding to the patient's blood-glucose-level measurements determined at a plurality of times whether and by how much to vary at least one of the one or more components of the patient's present insulin dosage regimen in order to maintain the patient's future blood-glucose-level measurements within a predefined range; and a display operative to display information corresponding to at least the patient's present insulin dosage regimen.

COMPOSITIONS AND METHODS FOR TREATING POSTURAL TACHYCARDIA SYNDROME

Absstract of: US2025353905A1

The present invention provides compositions and methods for treating Postural orthostatic tachycardia syndrome (POTS). In some embodiments, the POTS is associated with hypermobility spectrum disorders/hypermobile Ehlers-Danlos syndrome (HSD/hEDS). In various embodiments, the invention relates to administering a composition comprising an effective amount of a molecular antagonist of Glucose-dependent Insulinotropic Polpeptide (GIP) to a patient in need thereof. In various embodiments, the molecular antagonist can be provided in a form for convenient self-administration upon the onset of symptoms or to prevent or reduce postprandial POTS symptoms, or in other embodiments, is administered at a set frequency.

MODELING TARGETS OF CONTINUOUS GLUCOSE MONITORING METRICS FOR GLYCEMIC CONTROL

Absstract of: US2025352092A1

Disclosed herein are techniques for blood glucose management. In one example, a processor-implemented method includes receiving an input of a target value of a first continuous glucose monitoring (CGM) metric, estimating a target value of at least a second CGM metric that corresponds to the target value of the first CGM metric, and providing the estimated target value of at least the second CGM metric to a user. In some examples, the processor-implemented method also includes determining that the estimated target value of at least the second CGM metric meets a predetermined criterion, and configuring an insulin delivery system based on the target value of the first CGM metric.

MEDICAL DEVICE DATA PROCESSING AND COMMUNICATION METHODS AND SYSTEMS

Absstract of: EP4649888A2

Methods and devices to monitor an analyte in body fluid are provided. Embodiments include continuous or discrete acquisition of analyte related data from a transcutaneously positioned in vivo analyte sensor automatically or upon request from a user. The detection and/or monitoring of glucose levels or other analytes, such as lactate, oxygen, AIC, or the like, in certain individuals is vitally important to their health. For example, the monitoring of glucose is particularly important to individuals with diabetes.

MODELING TARGETS OF CONTINUOUS GLUCOSE MONITORING METRICS FOR GLYCEMIC CONTROL

Absstract of: EP4651146A1

Disclosed herein are techniques for blood glucose management. In one example, a processor-implemented method includes receiving an input of a target value of a first continuous glucose monitoring (CGM) metric, estimating a target value of at least a second CGM metric that corresponds to the target value of the first CGM metric, and providing the estimated target value of at least the second CGM metric to a user. In some examples, the processor-implemented method also includes determining that the estimated target value of at least the second CGM metric meets a predetermined criterion, and configuring an insulin delivery system based on the target value of the first CGM metric.

System and method for non-invasive blood glucose monitoring

Absstract of: US12471808B1

A system and method for continuous glucose monitoring (CGM) of blood in a blood vessel of a patient using a non-invasive sensor composed of a patch antenna operating in the Industrial, Scientific and Medical (ISM) Radio band (5.725 GHz-5.875 GHz). The device determines the blood glucose concentration of the blood in the blood vessel based on the measured shift of the resonant frequency of the non-invasive antenna patch sensor. A radio frequency (RF) synthesizer is used to drive the patch antenna with a fraction of its output coupled to both the antenna and receiver through a directional coupler. In this approach both the transmitted (FWD) and received (REV) power are processed, by demodulating logarithmic amplifiers, which convert the RF signals to corresponding voltages for downstream processing. The resulting voltages are then fed into a microcontroller and the measured shift in resonant frequency is converted to a real-time glucose concentration.

MANAGEMENT PROCESS FOR INSULIN THERAPY USING REINFORCEMENT LEARNING AND THERAPY ESCALATION PATHWAYS

Absstract of: AU2024252324A1

A method of therapy escalation for patients with diabetes includes receiving glucose data of a user from an in vivo glucose monitoring device, receiving first therapy information of a first therapy, wherein the first therapy includes basal insulin, calculating one or more glucose metrics based on the received glucose data, titrating a dose of the basal insulin based on the one or more glucose metrics, and determining overbasalization based on one or more of the glucose data and the first therapy information. Advantageously the system can regularly monitor glucose control of a user, detect overbasalization, provide frequent therapy intervention and adjustment, decrease a duration of intervention, and increase user adherence, outcomes, and satisfaction.

COMBINATION SODIUM-GLUCOSE COTRANSPORTER INHIBITOR (SGLTI) INSULIN THERAPY FOR GLYCEMIC CONTROL IN TYPE 1 DIABETES

Absstract of: US2025345512A1

Provided are a method, system, and computer-readable medium for optimizing glycemic control of a diabetic subject having Type 1 diabetes through co-administration of sodium-glucose cotransporter inhibitors (SGLTi) and insulin. Such co-administration can be effected by, for example, regulating one or more administration reactions in view of analyses of continuous glucose monitoring (CGM) data that can be indicative of at least the potential for one or more glycemic events including hypoglycemia and hyperglycemia. The aforementioned regulation can occur according to a balancing of insulin infusion and provisioning of SGLTi so as to avoid the occurrence of either of such events while, at the same time, not promoting an instance of diabetic ketoacidosis (DKA).

NONINVASIVE OPTICAL SYSTEM AND METHOD FOR MEASURING HEMOGLOBIN A1C AND OTHER BLOOD ANALYTES

Absstract of: US2025344969A1

A noninvasive system and method estimate hemoglobin A1c, glucose, lipids, and other blood analytes without requiring a blood sample. The portable device uses diffuse reflectance spectroscopy and optical sensing across various wavelengths to collect data from the user's skin, interstitial fluid, saliva, sweat, tear fluid, or exhaled air. Machine learning algorithms analyze the optical data to estimate blood analyte levels, which are displayed on the device or synced with a software application. The system securely transmits data to the user's electronic health record for integration and remote monitoring. The invention encompasses FDA-approved, CE-marked, and non-FDA-approved devices, including key components, digital health features, and various medical applications.

Methods Of Treating Diabetes

Absstract of: AU2025248709A1

Described herein are doses and dosing regimens comprising determining and administering doses of long-acting insulin receptor agonists suitable for once-weekly dosing, such as Weekly Basal Insulin-Fc (BIF). Described herein are doses and dosing regimens comprising determining and administering doses of long-acting insulin receptor agonists suitable for once-weekly dosing, such as Weekly Basal Insulin-Fc (BIF). ct c t

GLUCOSE MEASUREMENT PREDICTIONS USING STACKED MACHINE LEARNING MODELS

Absstract of: US2025344967A1

Glucose measurement and glucose-impacting event prediction using a stack of machine learning models is described. A CGM platform includes stacked machine learning models, such that an output generated by one of the machine learning models can be provided as input to another one of the machine learning models. The multiple machine learning models include at least one model trained to generate a glucose measurement prediction and another model trained to generate an event prediction, for an upcoming time interval. Each of the stacked machine learning models is configured to generate its respective output when provided as input at least one of glucose measurements provided by a CGM system worn by the user or additional data describing user behavior or other aspects that impact a person's glucose in the future. Predictions may then be output, such as via communication and/or display of a notification about the corresponding prediction.

OPTIONAL SENSOR CALIBRATION IN CONTINUOUS GLUCOSE MONITORING

Absstract of: US2025344973A1

A method for optional external calibration of a calibration-free glucose sensor uses values of measured working electrode current (Isig) and EIS data to calculate a final sensor glucose (SG) value. Counter electrode voltage (Vcntr) may also be used as an input. Raw Isig and Vcntr values may be preprocessed, and low-pass filtering, averaging, and/or feature generation may be applied. SG values may be generated using one or more models for predicting SG calculations. When an external blood glucose (BG) value is available, the BG value may also be used in calculating the SG values. A SG variance estimate may be calculated for each predicted SG value and modulated, with the modulated SG values then fused to generate a fused SG. A Kalman filter, as well as error detection logic, may be applied to the fused SG value to obtain a final SG, which is then displayed to the user.

SYSTEMS, DEVICES AND METHODS FOR AUTOMATED INSULIN DELIVERY DEVICE INCLUDING VOICE-COIL ACTUATION

Absstract of: WO2025233918A1

The present disclosure is directed to automated-insulin-delivery (AID) systems, devices and methods. Specifically, in some aspects, an AID device/patch includes a housing having an adhesive tape, an insulin pump including a single lumen cannula, a glucose sensor including a flat probe, a processor, and an automated-insulin-delivery algorithm (AIDA). In some embodiments, the housing is configured to be adhered the skin of a user via the adhesive tape and house the insulin pump, glucose sensor, and AIDA, the probe at least partially resides within the cannula, and the AIDA is configured as computer instructions operating on the processor causing the processor to control insulin delivery by the insulin pump according to glucose levels determined in subcutaneous tissue based on signals received from the glucose sensor.

CONTINUOUS ANALYTE MONITORING SYSTEM WITH MICRONEEDLE ARRAY

Absstract of: EP4647772A2

Described herein are variations of an analyte monitoring system, including an analyte monitoring device. For example, an analyte monitoring device may include an implantable microneedle array for use in measuring one or more analytes (e.g., glucose), such as in a continuous manner. The microneedle array may include, for example, at least one microneedle including a tapered distal portion having an insulated distal apex, and an electrode on a surface of the tapered distal portion located proximal to the insulated distal apex. At least some of the microneedles may be electrically isolated such that one or more electrodes is individually addressable.

SYSTEMS AND METHODS FOR MEDICATION DOSING AND TITRATION

Absstract of: US2025342932A1

Dose guidance systems and methods for titrating medication doses are described. The dose guidance system may receive glucose data from a continuous glucose monitor and may receive medication data related to medication administered by the user. The dose guidance system may initialize dose guidance parameters, recommend medication doses, titrate medication doses, and provide alerts based on the glucose data and medication data.

NON-INVASIVE PREDICTION OF INSULIN RESISTANCE USING SENSOR DATA

Absstract of: WO2025231209A1

A computing system, a computer implemented method, and a wearable computing device to predict insulin resistance in a wearer of the wearable computing device without requiring additional invasive testing other than test data that may already be available, although not required, is provided. For instance, a machine-learned model is trained to predict insulin resistance in the wearer of the wearable computing device based at least in part on non-invasive biometric data associated with the wearer. Then, one or more non-transitory computer-readable media cause the computing system to perform operations via one or more processors. The operations include receiving the non-invasive biometric data from one or more sensors associated with the wearable computing device; and implementing the machine-learned model to determine if the non-invasive biometric data associated with the wearer is indicative of insulin resistance.

Parenteral Nutrition Formulation with Optimized Amino Acid and Glucose Content

Absstract of: AU2025252653A1

Abstract The present disclosure relates to a sterile medical product for parenteral nutrition comprising a polymeric container having at least a first and a second chamber which are sep- arated by a non-permanent peel seal, wherein the first chamber contains a composition of amino acids and optional- ly electrolytes, and wherein the second chamber contains a dextrose solution, and wherein the product is characterized by a high protein (nitrogen) content per volume. The recon- stituted solution is configured to be administered periph- erally or centrally for the treatment of patients suffering from malnutrition and/or having a need for increased uptake of amino acids. Abstract The present disclosure relates to a sterile medical product for parenteral nutrition comprising a polymeric container having at least a first and a second chamber which are sep- arated by a non-permanent peel seal, wherein the first chamber contains a composition of amino acids and optional- ly electrolytes, and wherein the second chamber contains a dextrose solution, and wherein the product is characterized by a high protein (nitrogen) content per volume. The recon- stituted solution is configured to be administered periph- erally or centrally for the treatment of patients suffering from malnutrition and/or having a need for increased uptake of amino acids. ct b s t r a c t c t h e p r e s e n t d i s c l o s u r e r e l a t e s t o a s t e r i l e m e d i c a l p r o d u c t f o r p a r e n t e r a l n

MILLIMETER RADAR FOR INTERROGATION, CLASSIFICATION AND LOCALIZATION OF TARGET OBJECTS HAVING A NON-LINEAR FREQUENCY DEPENDENT FREQUENCY RESPONSE, ENHANCED BY WIDEBAND CHAOS GENERATING MATERIAL (WCGM)

Absstract of: US2025341624A1

A system for millimeter RADAR object recognition and classification using sub-band frequency interference and resonance effects from primary targets signals and reflected signals from secondary target preferably in the form of Wideband Chaos Generating Material (WCGM) objects, preferably detecting frequency dependent absorbing material, frequency dependent resonance effects from the second and primary target objects, frequency signal resonance effects caused by water molecule dipole effects in different sugar solutions, impedance of material, shape of metamaterial, and interference effects due to combination of signal sources resulting in a wider range of transmitter and scanning frequency band for RADAR based interrogation of target objects. The RADAR system makes opportunistic use of traditionally seen problematic interference signals, as extra signal sources providing extended range and frequency bandwidth for frequency-based interrogation of target object signatures in a frequency-intensity plane, a frequency-polarization plane, and a frequency-phase shift plane for doppler effects.

BLOOD GLUCOSE ESTIMATION USING NEAR INFRARED LIGHT EMITTING DIODES

Absstract of: US2025339033A1

Near Infrared Spectroscopy is employed to non-invasively detect blood glucose concentrations, in a multi-sensing detection device. A multi-layered artificial neural network is used to assess these relationships of non-linear interference from human tissue, as well as differences among individuals, and accurately estimate blood glucose levels. Diffuse reflectance spectrum from the palm at six different wavelengths analyzed with a neural network, results in a correlation coefficient as high as 0.9216 when compared to a standard electrochemical glucose analysis test.

DEVICES, SYSTEMS AND METHODS FOR BLOOD GLUCOSE MONITORING

Absstract of: US2025339060A1

Devices, systems and methods for blood glucose monitoring. The device includes a light emitter, configured to emit light signals; a light receiver, configured to receive the reflected light signal; a controller, configured to operatively connect with the light emitter and the light receiver; and an enclosure. The light signal comprises a first light signal having a first wavelength of about 940 nm, a second light signal having a second wavelength of about 1350 nm, and/or a third light signal having a third wavelength of about 1500 nm, wherein the controller comprises an operating module, and further comprises or operatively connects with a data processing system comprising a machine learning module that analyzes the data signal to generate an output data. The devices, systems and methods are non-invasive and monitor blood glucose levels in real time with high accuracy.

ADAPTIVE UPDATE OF AUTOMATIC INSULIN DELIVERY (AID) CONTROL PARAMETERS

Nº publicación: US2025339617A1 06/11/2025

Applicant:

INSULET CORP [US]
INSULET CORPORATION

Absstract of: US2025339617A1

Exemplary embodiments may modify the cost function parameters based on current and projected mean outcomes in blood glucose level control performance. The exemplary embodiments may modify the weight coefficient R for the insulin cost so that the value of R is not fixed and is not based solely on clinical determined values. Exemplary embodiments may also adjust the cost function to address persistent low-level blood glucose level excursions for users. The exemplary embodiments may reduce the penalty of the insulin cost by the sum of the converted insulin cost of the glucose excursions above target for a period divided by a number of cycles of average insulin action time. The AID system reduces the insulin cost by the lack of insulin in previous cycles.