A computer-implemented method for providing a real-time estimate of glycosylated hemoglobin (HbA1c) of a patient from a self-monitoring blood glucose (SMBG) measurement, and tracking changes in average glycemia of said patient over time is disclosed. The method includes the steps of; a computer computing a surrogate fasting measurement based on SMBG data received from the patient; a computer computing a glycation value using the said surrogate fasting measurement in a predetermined glycation equation; a computer outputting said glycation value as an initial estimate of HbA1c upon initialization of tracking of said patient's average glycemia; a computer updating said glycation value by using an updated SMBG value in said predetermined glycation equation, said updated SMBG value being based on a subsequent computed surrogate fasting measurement; and a computer computing an updated estimate of HbA1c using said initial estimate of HbA1c and said updated glycation value in a predetermined HbA1c estimation equation.

A computer-implemented method for providing a real-time estimate of glycosylated hemoglobin (HbA1c) of a patient from a self-monitoring blood glucose (SMBG) measurement, and tracking changes in average glycemia of said patient over time is disclosed. The method includes the steps of; a computer computing a surrogate fasting measurement based on SMBG data received from the patient; a computer computing a glycation value using the said surrogate fasting measurement in a predetermined glycation equation; a computer outputting said glycation value as an initial estimate of HbA1c upon initialization of tracking of said patient's average glycemia; a computer updating said glycation value by using an updated SMBG value in said predetermined glycation equation, said updated SMBG value being based on a subsequent computed surrogate fasting measurement; and a computer computing an updated estimate of HbA1c using said initial estimate of HbA1c and said updated glycation value in a predetermined HbA1c estimation equation.

A non-invasive analyte sensor that includes first and second non-invasive analyte sensor assemblies each of which can emit transmit signals that are in a radio or microwave frequency range of the electromagnetic spectrum into a target and can detect responses resulting from emission of the transmit signals into the target.

A method of measuring hematocrit is provided. The method for measuring hematocrit includes the following steps. A test strip is provided. The test strip includes a reaction region and a pair of electrodes disposed in the reaction region. A whole blood sample is entered to the reaction region. After the whole blood sample enters the reaction region, a plurality of sets of square wave voltages are intermittently applied to the pair of electrodes based on a square wave voltammetry method to obtain a plurality of feedbacks related to hematocrit. An interval between two adjacent sets of square wave voltages ranges from 0.1 seconds to 4 seconds. A feedback of an n-th set of square wave voltages is obtained to calculate a hematocrit value of the whole blood sample and n is a positive integer greater than 1. A hematocrit value is calculated according to the feedback.

A method of measuring hematocrit is provided. The method for measuring hematocrit includes the following steps. A test strip is provided. The test strip includes a reaction region and a pair of electrodes disposed in the reaction region. A whole blood sample is entered to the reaction region. After the whole blood sample enters the reaction region, a plurality of sets of square wave voltages are intermittently applied to the pair of electrodes based on a square wave voltammetry method to obtain a plurality of feedbacks related to hematocrit. An interval between two adjacent sets of square wave voltages ranges from 0.1 seconds to 4 seconds. A feedback of an n-th set of square wave voltages is obtained to calculate a hematocrit value of the whole blood sample and n is a positive integer greater than 1. A hematocrit value is calculated according to the feedback.

The present invention relates to a method for diagnosing Sézary syndrome or mycosis fungoides in a subject. The present invention further relates to a method for determining the frequency of Sézary signature cells and/or mycosis fungoides cells in a sample. Further, the present invention relates to a computer-implemented method comprising a classifier algorithm to determine the frequency of Sézary signature cells and/or mycosis fungoides cells. In addition the present invention relates to a panel of biomarkers that can be used for the diagnosis of Sézary syndrome or mycosis fungoides.

This present disclosure relates to the use of syncytiotrophoblast-derived microvesicles for diagnosing and/or monitoring a subject with preeclampsia. Accordingly, this disclosure provides for methods for isolating, purifying, and/or detecting syncytiotrophoblast-derived microvesicles from a biological fluid of a pregnant subject. The present disclosure also provides kits for diagnosing a subject with preeclampsia, where the kit contains reagents useful for isolating, purifying, and/or identifying the syncytiotrophoblast-derived microvesicles in a biological sample and for detecting one or more biomarkers present on the surface of or within the syncytiotrophoblast-derived microvesicles.

Machine learning analysis for classifying agglutination of fluid samples. A method includes scanning a unique scannable code printed on a test card, wherein the test card comprises a negative control fluid sample, a positive control fluid sample, and a test fluid sample. The method includes capturing an image of the test card and providing the image of the test card to a machine learning algorithm configured to assess agglutination of the test fluid sample based on the image. The method includes receiving from the machine learning algorithm one or more of a qualitative analysis or a quantitative analysis of the agglutination of the test fluid sample.

This invention provides a method that enables determining the fibrinogen concentration in plasma of a sample. The method comprises: computing the fibrinogen concentration in whole blood of the sample using magnetic particles; computing the waveform-based hematocrit value based on the peak value of the movement signal of the magnetic particles; subjecting the fibrinogen concentration in whole blood to hematocrit correction using the waveform-based hematocrit value; and computing the fibrinogen concentration in plasma of the sample.

This invention provides a method that enables determining the fibrinogen concentration in plasma of a sample. The method comprises: computing the fibrinogen concentration in whole blood of the sample using magnetic particles; computing the waveform-based hematocrit value based on the peak value of the movement signal of the magnetic particles; subjecting the fibrinogen concentration in whole blood to hematocrit correction using the waveform-based hematocrit value; and computing the fibrinogen concentration in plasma of the sample.