Abstract: A computer implemented method for determining a dosage of semaglutide for administering to a patient for the treatment of obesity includes receiving patient data relating to a patient, wherein the patient data includes patient weight data; processing the patient data with a dosage calculator to determine the dosage of semaglutide for administering to the patient; and administering the dosage.

Abstract: A computer implemented method for determining a dosage of semaglutide for administering to a patient for the treatment of obesity includes receiving patient data relating to a patient, wherein the patient data includes patient weight data; processing the patient data with a dosage calculator to determine the dosage of semaglutide for administering to the patient; and administering the dosage.

Abstract: A computer implemented method for determining a dosage of dabigatran for administering to a patient, the method comprising: receiving patient data relating to a patient, wherein the patient data includes a kidney function metric; processing the patient data with a dosage calculator to determine the dosage of dabigatran for administering to the patient, wherein the dosage calculator is derived from a plasma level prediction model; and indicating the dosage.

Abstract: A method for administering a dosage of Cagrisema to a patient for the treatment of obesity, the method comprising: administering a dosage of Cagrisema to the patient, wherein the dosage of Cagrisema is determined by: receiving patient data relating to a patient, wherein the patient data includes patient weight data; and processing the patient data including the patient weight data with a dosage calculator to determine the dosage of Cagrisema for administering to the patient wherein the dosage calculator is derived from a pharmacokinetic, PK, model.

Abstract: A computer implemented method for determining a dosage of edoxaban for administering to a patient, the method comprising: receiving patient data relating to a patient, wherein the patient data includes a kidney function metric; processing the patient data with a dosage calculator to determine the dosage of edoxaban for administering to the patient, wherein the dosage calculator is derived from a plasma level prediction model; and indicating the dosage.

Abstract: A computer implemented method for determining a dosage of rivaroxaban for administering to a patient, the method comprising: receiving patient data relating to a patient, wherein the patient data includes a kidney function metric; processing the patient data with a dosage calculator to determine the dosage of rivaroxaban for administering to the patient, wherein the dosage calculator is derived from a plasma level prediction model; and indicating the dosage.

Abstract: A method for administering a dosage of Apixaban to a patient for the treatment or prevention of thrombosis, the method comprising: administering a dosage of Apixaban to the patient, wherein the dosage of apixaban is determined by: receiving patient data relating to a patient, wherein the patient data includes a kidney function metric of the patient; and processing, using one or more processors, the patient data with a dosage calculator to determine the dosage of Apixaban for the patient, wherein the dosage calculator is derived from a plasma level prediction model that predicts Apixaban drug plasma levels, and the dosage calculator determines the dosage for the patient based in part on the kidney function metric of the patient.

Abstract: A method for administering a dosage of Apixaban to a patient for the treatment or prevention of thrombosis, the method comprising: administering a dosage of Apixaban to the patient, wherein the dosage of apixaban is determined by: receiving patient data relating to a patient, wherein the patient data includes a kidney function metric of the patient; and processing, using one or more processors, the patient data with a dosage calculator to determine the dosage of Apixaban for the patient, wherein the dosage calculator is derived from a plasma level prediction model that predicts Apixaban drug plasma levels, and the dosage calculator determines the dosage for the patient based in part on the kidney function metric of the patient.

Abstract: A method for administering a dosage of Cagrisema to a patient for the treatment of obesity, the method comprising: administering a dosage of Cagrisema to the patient, wherein the dosage of Cagrisema is determined by: receiving patient data relating to a patient, wherein the patient data includes patient weight data; and processing the patient data including the patient weight data with a dosage calculator to determine the dosage of Cagrisema for administering to the patient wherein the dosage calculator is derived from a pharmacokinetic, PK, model.

Abstract: A computer implemented method for determining a dosage of semaglutide for administering to a patient for the treatment of obesity includes receiving patient data relating to a patient, wherein the patient data includes patient weight data; processing the patient data with a dosage calculator to determine the dosage of semaglutide for administering to the patient; and administering the dosage.

Abstract: A method of generating a dosage calculator for determining a dosage of a drug for administering to a patient, the method comprising: receiving population data comprising real population data, and/or simulated population data calculated using a pharmacokinetic-pharmacodynamic, PKPD, model for the drug; and training a machine learning dosage calculator using the population data.

Abstract: A method for treating obesity in a patient, the method comprising administering a dose of a GLP1 agonist to the patient in a treatment regimen effective to manage the patient's satiety and thereby effect a reduction in the patient's weight; and, in conjunction with the administration of the dose of GLP1 agonist, engaging in a directed digital therapeutic program that manages patient weight loss and the treatment regimen for the GLP1 agonist with the objective of achieving a predetermined target weight for the patient, wherein the patient achieves the target weight.

Abstract: The present invention relates to a system and devices for monitoring medical conditions of patients. More particularly, the present invention relates to the monitoring of the behaviours and relevant diagnostics data relating to patients with or at risk of type 2 diabetes, and its associated cardiovascular disease and/or weight management, including using in-vitro diagnostics (IVD) devices.

Abstract: The present invention relates to a self monitoring device for attachment to the body, which incorporates real-time and correlated measurement of body movement and physiological analytes of metabolism (e.g. glucose) that can be modulated by physical activity. The system enables real time and personalized modulation of the selected physiological analyte(s) through appropriately selected cued activities and other evidence based clinical guidance dependent on previous and current analyte levels correlated with previous and current patterns of movement. The system also allows for personalization of the outputs with regular calibration to the user.

Abstract: The present invention relates to a self monitoring device for attachment to the body, which incorporates real-time and correlated measurement of body movement and physiological analytes of metabolism (e.g. glucose) that can be modulated by physical activity. The system enables real time and personalised modulation of the selected physiological analyte(s) through appropriately selected cued activities and other evidence based clinical guidance dependent on previous and current analyte levels correlated with previous and current patterns of movement. The system also allows for personalisation of the outputs with regular calibration to the user.

Abstract: A test kit for determining the presence of one or more analytes in a fluid sample, including an assay device together with a reading device which engages with the assay device and wherein precisely located engagement of the assay device with the reading device is essential for accurate reading of the assay result, where precisely located engagement of the assay device with the reading device causes a ‘lock-and-key’ interaction between the assay device and reading initiation means of the reading device. Preferably the reading initiation means includes switch actuating means including at least one fixed projecting portion and at least one displaceable projecting portion, and a contact portion of the assay device casing includes a recess shaped to accommodate the fixed projecting portion of the switch actuating means but not the displaceable projecting portion.

Abstract: A fertility determining system employing hormone level measuring apparatus on selected days in a menstrual cycle to determine the levels of analytes, i.e., estrone-3-glucuronide (“E3G”) and luteinizing hormone (“LH”), in early morning urine. The fertility/infertility signalling algorithm relies upon data characterizing the user earlier in that menstrual cycle, and also upon her analyte pattern in past recent cycles. At a point in a menstrual cycle which is user-dependent, the early morning E3G urine level is compared against the lower E3G level earlier in the cycle. The beginning of the fertile period is signalled when a sufficient E3G increase occurs. The end of the fertile period (and, correspondingly, the reestablishment of a period requiring no contraception) is signalled a period after the LH surge. In the absence of an LH surge in any subject, the end of the fertile phase is signalled a period of time following the peak of the E3G urinary metabolite of estradiol.

Abstract: The present invention relates to a disposable electrochemical sensor which can measure redox species in a liquid sample

Abstract: The present invention provides a disposable electrochemical sensor which can measure redox species in a liquid sample

Abstract: In an apparatus and method for obtaining an indication of energy expenditure by a mammal during exercise, one or more movement transducers (1) each output a respective movement signal related to physical movement. A frequency analysis (3) is performed on at least one of the movement signals to obtain a frequency analysis result. Classification means (5) determines from the frequency analysis result, what class of physical movement is involved in the exercise. Selection means (7) selects a form of calculation according to a class determined by the classification means. A form of calculation selected by the selection means is applied (9) to at least one of the movement signals so as to obtain the energy expenditure indication.