A system, method, and computer readable media are provided for obtaining a first set of skin data from an image capture system including at least one ultraviolet (UV) image of a user's skin. Performing a correction on the skin data using a second set of skin data associated with the user. Quantifying a plurality of skin parameters of the user's skin based on the first skin data, including quantifying a bacterial load. Quantifying the bacterial load by applying a brightness filter to isolate portions of the at least one UV image containing fluorescence, applying a dust filter, identifying portions of the at least one UV image that contain fluorescence due to bacteria, and determining a quantity of bacterial load in the users skin. Determining, using a machine learning model, an output associated with a normal skin state of the user and a current skin state of the user.

In various implementations, computer-implemented method, such as one executed on a computer system or on instructions stored on computer-readable media may include: receiving from a user device associated with a user, a request to access one or more treatment plans for patient; identifying a treatment template for user, the treatment template representing treatment preferences of the user, the treatment template being expressed according to treatment domain-specific protocols; processing the treatment template with the treatment domain-specific protocols to convert one or more parts of the treatment template into one or more runtime elements that interactively display customized user interface elements related to the treatment plan, the customized user interface elements configured to provide one or more customized user interactions with the treatment plan in accordance with the treatment preferences of the user; and providing instructions to display the one or more runtime elements on the user device.

In various implementations, computer-implemented method, such as one executed on a computer system or on instructions stored on computer-readable media may include: receiving from a user device associated with a user, a request to access one or more treatment plans for patient; identifying a treatment template for user, the treatment template representing treatment preferences of the user, the treatment template being expressed according to treatment domain-specific protocols; processing the treatment template with the treatment domain-specific protocols to convert one or more parts of the treatment template into one or more runtime elements that interactively display customized user interface elements related to the treatment plan, the customized user interface elements configured to provide one or more customized user interactions with the treatment plan in accordance with the treatment preferences of the user; and providing instructions to display the one or more runtime elements on the user device.

Systems, methods, and computer-readable media determine a representative series using multiple time series of glucose readings. Systems may comprise one or more processors (“processing”) and one or more computer-readable data storages (CRDS) storing instructions that, when executed by processing, cause processing to perform steps of the method. Some embodiments include one or more CRDS storing instructions for the methods that may be executed by one or more processors. Methods may comprise one or more processors receiving multiple time series in which each of the time series comprises glucose readings associated with respective timestamps. The time series may be generated during respective time periods. The one or more processors may adaptively determine a gamma parameter and determine a barycenter using the multiple time series and the gamma parameter. The one or more processors may apply a smoothing function to the barycenter yielding the representative series.

The invention relates to a method for simulating the deformation of an IMD after implantation in a natural cavity, from a three-dimensional model of a wall of the cavity, comprising the steps of: determination of an intermediate deformation state of a numerical IMD, deformed as a function of a shape of the wall model while remaining included in said shape, calculation of a mechanical equilibrium state of the numerical IMD from the intermediate deformation state, comprising the calculation of mechanical stresses undergone by the numerical IMD in the intermediate deformation state which are a function of the mechanical behaviours of the numerical IMD and the wall model, and relaxation of said stresses, the behaviour of the wall model being taken as non-deformable rigid during the calculation of the mechanical equilibrium state, the mechanical behaviour of the numerical IMD, and/or the rest state of the numerical IMD, being different between the determination of the intermediate deformation state and the calculation of the mechanical equilibrium.

The invention relates to a method for simulating the deformation of an IMD after implantation in a natural cavity, from a three-dimensional model of a wall of the cavity, comprising the steps of: determination of an intermediate deformation state of a numerical IMD, deformed as a function of a shape of the wall model while remaining included in said shape, calculation of a mechanical equilibrium state of the numerical IMD from the intermediate deformation state, comprising the calculation of mechanical stresses undergone by the numerical IMD in the intermediate deformation state which are a function of the mechanical behaviours of the numerical IMD and the wall model, and relaxation of said stresses, the behaviour of the wall model being taken as non-deformable rigid during the calculation of the mechanical equilibrium state, the mechanical behaviour of the numerical IMD, and/or the rest state of the numerical IMD, being different between the determination of the intermediate deformation state and the calculation of the mechanical equilibrium.

A method includes obtaining training data for a plurality of patients of a patient population. The training data includes training blood glucose history data including treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose. The method also includes identifying, for each patient of the patient population, one or more optimum treatment doses of insulin from the treatment doses yielding favorable outcome attributes. The method also includes receiving patient-state information for the treated patient, determining a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with the patients of the patient population having training patient-state information similar to the patient-state information for the treated patient, and transmitting the next recommended treatment dose to a portable device associated with the treated patient.

A method includes obtaining training data for a plurality of patients of a patient population. The training data includes training blood glucose history data including treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose. The method also includes identifying, for each patient of the patient population, one or more optimum treatment doses of insulin from the treatment doses yielding favorable outcome attributes. The method also includes receiving patient-state information for the treated patient, determining a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with the patients of the patient population having training patient-state information similar to the patient-state information for the treated patient, and transmitting the next recommended treatment dose to a portable device associated with the treated patient.

The disclosure relates to a method for carrying out a quantitative polymerase chain reaction (qPCR) process, comprising the following steps:—cyclically carrying out qPCR cycles; —measuring the fluorescence for each qPCR cycle to obtain a qPCR curve of intensity values (I); —creating a probability density function (PDF) from the intensity values (I); —establishing a presence or absence of the DNA strand section to be detected depending on the presence of one or more features of the probability density function (PDF); —carrying out the qPCR process depending on the presence or absence of the DNA strand section to be detected.

The invention provide an assessment method and device for infectious disease transmission, computer equipment and storage medium. The method comprises: obtaining respective target track data corresponding to assessment objects within a preset area in a first time slice; determining a matching subarea to which each assessment object matches in the first time slice based on the target track data; taking at least one of the plurality of subareas as a target subarea, and assessing an assessment object within the target subarea based on an infectious disease model to determine a transmission trend of an infectious disease for the assessment object within the preset area in the first time slice; and taking a next time slice as the first time slice, and re-performing the above steps until end of the target time period to determine a transmission trend of the infectious disease among the assessment objects during the target time period.