Methods and devices for calibrating and measuring with an aptamer biosensor

Abstract

A method for obtaining a fitted calibration curve of a sensor modified with an aptamer as bioreceptor, the sensor configured to measure concentration of analyte in a sample, comprises; performing puls

Description

Field of the invention The present disclosure relates to aptamer biosensors, the methods of their use, and specifically their calibration. Background The electrochemical aptamer-based sensor principle is based on the coupling of target-induced conformational changes of an aptamer bioreceptor with electrochemical detection of the resulting altered charge transfer rate between the redox molecule and electrode surface. The sensor is constituted by the supporting substrate and the modified surface layer. The supporting substrate is produced from conducting materials including but not limited to gold, silver, carbon, conducting polymer or metal particle. The quality and composition of the substrate material can affect the sensor output significantly. The electrochemical methods allow an output signal to be obtained, which becomes the input from which the analyte concentration can be calculated. The dependence of signal on analyte concentration can be represented by a calibration curve that exists for each sensor. The most commonly used calibration method is a process by which blank solution and a series of standard target solutions are tested to set up a curve of concentration versus the signal change. For example: in order to obtain the calibration curve of the sensor, the first step is to test the standard blank solution (the same solution as the target sample but not containing target), then obtain the initial blank signal, Ao. The second step is to test a series of standard target solutions (1, 2, 3, 4, 5...), and then equally get a series of target signals At (Ai, Az, A3 ... An). The third step is to calculate the signal change between each target solution and initial signal, At - Ao or At / Ao-1. Using the experimental data, the calibration curve of signal change versus the target concentration, as shown in FIG. 1, can be determined. Thereafter, once the signal change is output from the electrochemical method, the analyte concentration can be calculated according to the calibration curve. In theory, then, to obtain the signal change, there are at least two necessary parameters needed to be measured or known, one is Ao, also called blank signal, another one is target signal, called At. However, in the process of practical mass production, it is impossible to establish a standard calibration curve for each individual sensor and measure the Ao for each sensor. While theoretically, ideal sensors of the same type have the same performance because they use the same design, materials, modification and production technology and can share one unified calibration curve, the reality is that sensor production cannot absolutely avoid the existence of electrode-to-electrode variations. Small variations can have significant influence on the accuracy of the test result if the same calibration curve is used for all sensors, even when manufactured in the same batch. For instance, FIG. 2 shows the output of a square wave voltammetry (SWV) test on two sensors of the same class that have been fabricated in as close to an identical manner as possible with current manufacturing techniques and tested in the same environmental conditions with the same target concentration, sample solution and electronic device. Voltammogram peak height is almost 50% higher in Test Strip 2 when compared to Test Strip 1, and the potential at which the peak occurs is different. The curve for Test Strip 2 is higher than Test Strip 1 because current measurements are higher across all potentials for Test Strip 2. Hence, in order to improve result accuracy, there are two methods often considered, one is to reduce the variations between electrodes by improving the production technique as much as possible, the other is to do blank test for each individual sensor before target detection. Given that electrode-to-electrode variations are impossible to completely remove during manufacturing, what is currently practiced in commercial settings is activation of a first blank or control test that is stored within a separate reservoir and / or separate sensor within the device. The disadvantage of this method is that extra space within the device is required for a blank solution and / or extra electrodes and increased manufacturing and test strip costs. Furthermore, a point-of-use control test reduces the scalability of the product, particularly because the user must conduct or wait for the blank test to complete, increasing the possibility of user error and overall increasing the complexity of the user experience. A blank solution test makes single test, disposable applications infeasible. Another solution is for calibration with a blank or control solution to be tested by the cartridge manufacture before shipping. However, conducting the blank test at the factory increases production costs and does not necessarily result in accuracy, due to shelf life decay and the significant possibility of very different conditions at the point of use. Thus, calibration methods are typically applied shortly before sensor use. One calibration method known in the industry is a ’calibration-free’ method proposed by Plaxco et al., WO 2018 / 223024 A2 ("Plaxco"). Plaxco’s innovation is based on the discovery that for some aptamers a particular frequency called the non-response frequency exists whereby when utilizing the electrochemical method at the non-response frequency, the difference of signal from aptamer in bound and unbound states is in fact zero. This means that the faradic discharge current is the same when the aptamer is bound and unbound and that measured signal is independent of concentration of target. This phenomenon helps calibration of an aptamer electrochemical sensor because the sensor can be tested once at the non-response frequency and secondly at a frequency to maximize difference of signal from aptamer in bound and unbound states. The change of signal directly from target addition can be calculated in-situ without the need to first conduct a blank test on test strips in the manufacturing process. The method relies upon the assumption that the aptamer’s characteristic non-response frequency, the frequency at which measured current signal is independent of target concentration remains at one constant frequency and can be determined with accuracy. In reality, experimentation shows that the non-response frequency of the aptamer significantly shifts across a large frequency range (50Hz-100Hz as shown in FIG. 3) and is not completely independent of target concentration. Therefore, each test strip must be tested multiple times to first identify the non-response frequency, rendering the method no better than the original blank test method it seeks to replace and so it is useless in large scale commercial applications. Essentially, Plaxco’s method involves experimentally determining the dose response curve of signal change versus target concentration. For any E-AB sensor this curve will be nonlinear, however the Calibration-Free method requires identification of a narrow section of the curve such that a linear line of best fit can be applied to it and utilized as the calibration equation used in future tests of unknown target concentrations. Fitting a linear curve to a non-linear dataset will mean that the error in estimated concentration values is quite large and so only a narrow range of concentrations can be measured to produce an estimated concentration accurate to within 20% - the largest acceptable total system error for any commercial diagnostic product - of the target concentration. This range of concentrations that the sensor can measure is typically within one order of magnitude of the dissociation constant Kd (which is determined in the Calibration-Free method by globally fitting their calibration equation to a dataset of multiple sensors of the same class). WO2020 / 104934 describes another calibration method referred to as a “self-parative” calibration method. This “self-parative” method is a method of measuring the concentration of an analyte in sample presented to a sensor modified with an aptamer as bioreceptor, the sensor belonging to a sensor class, the method comprising the steps of: applying a pulsed voltammetric electrochemical method at a first frequency to obtain a first current value; applying the electrochemical method at a second frequency to obtain a second current value, wherein the first frequency and the second frequency generate a first and a second current response, respectively, when the electrochemical method is applied to any sensor of the sensor class that is modified with the aptamer, and said first and second current response are non-zero and not equal; calculating the concentration of the analyte according to an equation specified in WO 2020 / 104934. Summary of the invention Aspects of the invention are as set out in the independent claims and optional features are set out in the dependent claims. Aspects of the invention may be provided in conjunction with each other and features of one aspect may be applied to other aspects. In a first aspect, there is provided a method for obtaining a fitted calibration curve of a sensor modified with an aptamer as bioreceptor, the sensor configured to measure a concentration of an analyte in sample, the method comprising the steps of: performing a pulsed voltametric electrochemical method that gives an electrical current response at a plurality of different concentrations of analyte in sample at a first and second frequency for each concentration of the plurality of concentrations; calculating a ratio of the peak electrical current response at the first and second frequencies for each concentration of analyte; generating empirical calibration data from the calculated ratios; fitting a theoretical calibration curve to at least a portion of the empirical calibration data to obtain the fitted calibration curve; and accepting the fitted calibration curve based on determining whether the correlation between at least the portion of the empirical calibration data and the fitted calibration curve is above a selected threshold. This method allows a fitting of a calibration curve that may be used by the same or other sensors of a same sensor class in order to measure concentrations of analyte in samples without any further in-situ or lab set-up or preparation. Optionally, the first and second frequencies are more than a selected threshold frequency difference apart. It may be advantageous to have the first and second frequencies more than 50Hz apart as this will create a larger ratio of current response. However, it will be understood that in some examples there is no specific rule dictating the required separation between the two frequencies; the choice of frequencies may depend entirely on the behaviour of the aptamer. Ideally, two frequencies at which the aptamer exhibits distinct signal responses across the target concentration range may be selected. This ensures that the ratiometric output follows sigmoidal curves. For instance, if the aptamer shows the same incremental change in response to increasing target concentration for both frequencies, the ratiometric output remains constant across the entire dynamic range, which is undesirable. Since aptamers often have similar responses at closely spaced frequencies, this separation between the dual frequencies may be preferred. Optionally, neither the first nor second frequency is a non-response frequency. If either of the first or second frequency are non-response frequencies, this will skew the ratio of current responses towards the responsive frequency, and if both frequencies are nonresponse frequencies, there will be no useful information output. Optionally, the pulsed voltametric electrochemical method is square wave voltammetry or differential pulse voltammetry. These are both techniques that will allow the method to work, where preferably square wave voltammetry is used. Optionally, performing the pulsed voltametric electrochemical method at the first and second frequency for each concentration comprises performing the pulsed voltametric electrochemical method at the first and second frequency for each concentration at a same temperature. This may advantageously mean that none of the results are skewed by any temperature dependence. Optionally, the temperature is one or more of an ambient temperature, a temperature of the sensor, and a temperature of the sample. Each of these temperatures may cause the current response to increase or decrease. Optionally, performing the pulsed voltametric electrochemical method at the two frequencies for each concentration comprises performing the pulsed voltametric electrochemical method for each frequency and concentration with the same sample matrix. As the sample matrix type can cause the current response of the sensor to change for the same analyte concentration, it is important to keep the sample matrix the same across all calibration measurements for the same calibration curve. Optionally, the theoretical calibration curve is defined by the equation: wherein, S is a ratio of the electrical current response at the same concentration of analyte in sample at the first and second frequencies, So is a ratio of the electrical current response at the same concentration of analyte in sample at the first and second frequencies where the concentration of the analyte in sample is 0, Smax is a ratio of the electrical current response at the same concentration of analyte in sample at the first and second frequencies where the concentration of the analyte in sample is fully saturated, Kd is a dissociation constant for the sensor modified with the aptamer, [T] is the concentration of the analyte in sample, and h is a Hill coefficient. This equation has been found to very closely model and fit to experimental data from sensors modified with an aptamer as a bioreceptor, thus making it an attractive option for a calibration equation. Optionally, the ratios S, So, and Smax are all calculated by dividing the electrical current response at the first frequency by the electrical current response at the second frequency. Alternatively, the ratios S, So, and Smax are all calculated by dividing the electrical current response at the second frequency by the electrical current response at the first frequency. Having the above ratios all calculated in the same way rather than randomly means that when the fitted calibration curve is used to measure concentrations, no other processing of the equation is required. Optionally, the constant Kd is determined from the fitted calibration curve. Optionally, the constant h is determined from the fitted calibration curve. These may be useful constants to determine to use as a ‘sanity check’ that the calibration curve is still correct. Optionally, the theoretical calibration curve is in the form of a sigmoidal curve when the ratio of the electrical current response at a plurality of concentrations of analyte in sample at the first and second frequency is plotted against the concentrations of analyte in sample. This means that any mathematical expression may be used, so long as it models the sensor output ratios sufficiently for the desired accuracy of the fitted calibration curve. Optionally, determining whether the correlation between the calculated ratios and the fitted calibration curve is above a selected threshold comprises performing a regression analysis between the calculated ratios and fitted calibration curve to obtain a regression coefficient, and wherein the selected threshold is a selected threshold value for the regression coefficient. This allows the fitted calibration curve to always meet a selected threshold which may be set for particular applications, sensors, or other factors. Optionally, fitting a theoretical calibration curve to the calculated ratios comprises performing an iterative method to determine constants of the theoretical calibration curve. Advantageously, using an iterative method means that the calibration curve can be further and further fitted to the real data. Optionally, the iterative method may be any combination of a known iterative method such as least-squares, gradient descent, or any other known linear or non-linear regression method. These are all reasonable options to use depending on the calibration equation in use, whether it is a linear or non-linear equation. Optionally, the iterative method comprises the steps of: a. varying a constant of the theoretical calibration curve between predetermined limits b. determining what value for that constant gives the best correlation between the calculated ratios and the fitted calibration curve c. repeating steps (a) and (b) for each constant at least once until the correlation between the calculated ratios and the fitted calibration curve does not increase by a second selected threshold amount for each repetition of steps (a) and (b). This method can be used to fine tune a single variable at a time, and repeat for each variable as many times as needed until the improvements are negligible. In a second aspect there is provided a method of measuring a concentration of an analyte in sample presented to a sensor modified with an aptamer as bioreceptor, the sensor belonging to a sensor class, the method comprising the steps of: performing a pulsed voltametric electrochemical method that gives an electrical current response at a first and second frequency for the concentration of analyte in sample; calculating a ratio of the peak electrical current response at the first and second frequencies; determining the concentration of the analyte using the calculated ratio and empirical calibration data of the sensor of the sensor class. This method allows the measurement of concentrations of analyte in samples with only using a calibration curve without any further in-situ or lab setup or preparation. Optionally, determining the concentration of the analyte using the calculated ratio and empirical calibration data of the sensor of the sensor class comprises obtaining a fitted calibration curve using at least a portion of the empirical calibration data by the method of the first aspect. Optionally, the calibration curve is a plurality of calculated ratios of peak electrical current response at the first and second frequencies. Advantageously, this means that no mathematical modelling or fitting is required, and determining the unknown concentration of the analyte may be done by interpolating between at least the two closest known calculated ratios, wherein this interpolation may be linear or non-linear. Optionally, the calibration curve is a selected calibration curve from a plurality of calibration curves wherein the selected calibration curve is selected based on an ambient temperature, and a temperature of the sample, and a temperature of the sensor. Advantageously, this means that a calibration curve can be used that most fits the conditions of the measurement being performed. Optionally, determining the concentration of the analyte using the calculated ratio and empirical calibration data of the sensor of the sensor class comprises interpolating the calculated ratio between at least a first empirical calibration data point and a second empirical calibration data point, wherein the first empirical calibration data point is the closest empirical calibration data point lower than the calculated ratio and the second empirical calibration data point is the closest empirical calibration data point higher than the calculated ratio. Advantageously, this means that the process of determining the concentration of the analyte is very simple. Optionally, determining the concentration of the analyte using the calculated ratio and empirical calibration data of the sensor of the sensor class comprises using only the fitted calibration curve if the calculated ratio is in a threshold range. This allows for preferential use of the empirical calibration data or calibration curve, which may allow for a more accurate measurement of the concentration. In a third aspect there is provided a calibration device comprising a sensor, a processor, a computer readable medium, the computer readable medium storing code that when executed, performs the steps of: performing a pulsed voltametric electrochemical method that gives an electrical current response at a plurality of different concentrations of analyte in sample at a first and second frequency for each concentration; calculating a ratio of the peak electrical current response at the two frequencies for each concentration of analyte; fitting a theoretical calibration curve to the calculated ratios to obtain the fitted calibration curve; and accepting the fitted calibration curve based on determining whether the correlation between the calculated ratios and the fitted calibration curve is above a selected threshold. This device allows a fitting of a calibration curve that may be used by the same or other sensors of a same sensor class in order to measure concentrations of analyte in samples without any further in-situ or lab set-up or preparation. Optionally, the calibration device is also configured to perform the method of the first aspect. In a fourth aspect there is provided a measuring device comprising a sensor, a computer readable medium, the computer readable medium storing code that when executed, perform the steps of: performing a pulsed voltametric electrochemical method at a first and second frequency that gives an electrical current response; calculating a ratio of the peak electrical current response at the first and second frequency; determining the concentration of the analyte using the calculated ratio and a calibration curve of the sensor of the sensor class. This device allows the measurement of concentrations of analyte in samples with only using a calibration curve without any further in-situ or lab set-up or preparation. Optionally, the measuring device is also configured to perform the method of the second aspect. Optionally, the device of the third and fourth aspect comprises a temperature sensor. This allows the devices to determine a temperature that can be used as specified above. Optionally, the sensor of the third and fourth aspect is at least one of: printed circuit board, PCB, electrodes, screen-printed electrodes, and electrodes fabricated by sputtering. The inventors have identified that any ratios of ifl / if2 for any concentration and any two selected frequencies remains constant across all sensors of a sensor class, regardless of the sensor-to-sensor variations that are expected to occur during manufacturing. The only caveat to this is that the frequencies selected cannot be the same, or a non-response frequency (i.e., a frequency where the sensor output does not vary with concentration). Therefore, a calibration curve for a sensor of a sensor class can be determined during development of a new sensor class or during manufacturing, and can be used when testing in situ when the target solution is measured at the two selected frequencies. Of course, the calibration curve should be determined using a suitable number of tests and having a variance between the fitted calibration curve and experimental points below a certain threshold (i.e., the accuracy of the calibration curve is above a certain threshold). This threshold may be selected depending on the type of test, the application the test will be used for, and various other factors. Thus, once the calibration curve has been determined and fitted to the initial tests, any sensor can be used without any additional tests on samples or blanks. Drawings Embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings, in which: Fig. 1 illustrates several graphs showing signal change of different sensors plotted against target concentration. Fig. 2 is a graph of the output when the electrochemical method is performed on two aptamer test strips that have been fabricated in as close a manner as possible with current manufacturing techniques. Fig. 3 is a graph of voltammetry peak height change when the electrochemical method is performed at each frequency for different target concentrations (OmM to 200mM), showing the "non-response frequency" of Plaxco spanning frequencies from 100 to 180 Hz. Fig. 4 is an exemplary sensor of a sensor class. Fig. 5 is a graph illustrating sensor output ratios when the sensors are used at different ambient temperatures. Fig. 6 is a graph illustrating how sensor output ratios vary between two different solutions containing the same concentration of analyte. Fig. 7 is a graph illustrating sensor output ratios with a fitted calibration curve, highlighting a range of concentrations that are measurable with standard techniques. Fig. 8 is an exemplary device suitable for use in implementing one or more methods of the present disclosure. Fig. 9 is a flow diagram of determining a fitted calibration curve for a sensor. Fig. 10 is a flow diagram of a measurement process with a sensor utilising a calibration curve. Specific description Embodiments of the disclosure relate to a method for obtaining empirical calibration data and a fitted calibration curve of a sensor. In particular, embodiments of the disclosure enable a better fitted calibration curve for a sensor to be obtained that is suitable for use over a wider range of concentrations than can be obtained by conventional methods as discussed above. Further, a sensor using the empirical calibration data or fitted calibration curve will have no additional set-up or preparation required for use in situ. The method is particularly relevant when applied to a sensor modified with an aptamer as bioreceptor. Aptamers are oligonucleotides or peptide molecules that bind to a specific target molecule with high affinity to target molecules based on three-dimensional conformations that interact with the complementary target molecules. The more target present in a sample, the more aptamers bind and the larger the average measured signal (faradic discharge current) across the electrode surface when an electrochemical method is applied. The electrochemical method is preferably a pulsed voltammetry method such as square wave voltammetry (SWV) or differential pulse voltammetry (DPV. In these methods, the current response is measured when potential is applied across the working, reference and counter electrodes of the sensor. Upon binding, some aptamers undergo conformational change. The aptamers for target analyte may be developed using SELEX (Systematic Evolution of Ligands by Exponential enrichment) method, where they are selected for high binding affinity and conformational change of the aptamer whereby it folds (or opens wider) after binding to target. Examples of suitable aptamers for adenosine triphosphate and tobramycin are found in the literature at Liu et al., "Achieving Reproducible Performance of Electrochemical, Folding Aptamer-Based Sensors on Microelectrodes: Challenges and Prospects," Analytical Chemistry 2014, 86, 11417-11424. The aptamer sequence may be further modified to produce a greater structure change by: 1) truncating the aptamer to make it shorter by conducting experiments to approximate the region on the aptamer that binds to target and then removing DNA bases that are not in the binding region; and 2) mismatching bases in the aptamer’s structure to make it less stable whereby when the aptamer binds to target the conformational change is more significant. The process of developing the aptamers may require several rounds of testing with the target to ascertain whether the aptamer shows signal change due to structure change. Aptamers displaying the conformational change are further modified with a redox-active molecule, such as methylene blue (MB) or ferrocene (Fc), on the free end. An aptamer with redox-active molecule at the free end will therefore change the proximity of the redox-active molecule to the substrate depending upon whether it is in a bound state or unbound state. This results in faradic discharge that can be sampled using the electrochemical method. A sensor class is defined herein as the set of sensors having sufficiently common design characteristics such that all sensors of the selected design will respond similarly when utilized within a selected set of selected assay conditions. For instance, sensors belonging to the same sensor class will have identical electrode structure, substrate composition, aptamers, aptamer composition, type of redox reporter, and will be fabricated according to the same process. As one example, a representative instance of a sensor 31 in a sensor class is depicted in FIG. 4. This sensor class represents all sensors having a reference electrode 310, a counter electrode 311, and working electrodes 312 through 318 positioned as shown. Sensor 31 is a multiplex sensor that can have seven different aptamers each immobilized on a different working electrode, but the aptamers and their location as assigned to each working electrode is identical across the sensor class. The only variations within a sensor class are the variations that manifest due to differences in manufacturing and environmental conditions, but all sensors in the same sensor class are manufactured according to the same process. In the case of a bioreceptor with reversible binding, a known formula for the reversible reaction is: Ka Equation 1: [A] + [T] [C] Kd Where [A] is the concentration of the unbound bioreceptor, [T] is the concentration of the target analyte in solution, [C] is the concentration of the target that is bound to the bioreceptor, Ka is the association constant, and Kd is the dissociation constant. The dissociation constant can also be defined as: Equation 2: Kd = And a fraction 0 defines the fraction of the analyte that is bound to the bioreceptor: Equation 3: 0 = M+ [C] Combining Equations 2 and 3 provides the following new definition for 0: Equation 4: 0 = ....................-..........-.................... in The ideal form of Equation 4 is presented in Equation 5: Equation 5: 0 = [r] = Kd+ [T]h + i mh Where h is a Hill co-efficient that can be worked out empirically and is different for different reactions. As this definition of 0 will be used to determine a calibration curve, taking the ideal form from Equation 5 is preferable as this allows some element of fine tuning the calibration curve to fit experimental data or empirical calibration data. As 0 is defined as the fraction of the analyte that is bound to the bioreceptor, it may also be expressed in terms of the sensor output from an electrochemical method, such as Square Wave Voltammetry (SWV): Equation 6: 0 = . 1 ~ " ^max lmin Where i is the SWV peak output current from the sensor when measuring the concentration of analyte in solution, imin is the SWV peak output current from the sensor when there is no analyte in solution, and imax is the SWV peak output current from the sensor when the solution is saturated with analyte. In order to make Equation 6 more robust to electrode-to-electrode variations, SWV can be performed at two frequencies at the various analyte concentrations to produce Equation 7: Equation 7: 0 = -—.....-............... 1 c C ^max ~ Where S is ifl / if2, So is imin fl / imln f2, and Smax is imax fl / imax f2. In other words, these are the ratiometric outputs from the sensor when performing SWV at two frequencies fl and / 2. It should also be noted that it is not important whether fl is of a higher or lower frequency than / 2. Equating Equations 5 and 7 yields Equations 8 and 9: Equation 8: Equation 9: Equation 8 provides a theoretical calibration curve that can be fitted to experimental data or empirical calibration data obtained by SWV. This would require measuring So and Smax, and then various measurements S at various concentrations between no analyte in solution to saturated analyte in solution. Preferably, when obtaining the experimental data, the concentrations of analyte in solution would provide evenly distributed S values. It may also be preferable to obtain measurements at concentrations over the whole dynamic range of the sensor through serial dilution. It may also be preferable to have at least 15 S values distributed over the whole dynamic range of the sensor. It may also be preferable to have at least three S' values in each decade, where a decade is a change in S' value by a factor of 10. In other words, it may be preferable to have at least three S' values between ratiometric outputs of 1 and 10. This is achievable using serial half dilution, or more particularly, serial half logarithmic dilution. Subsequently, the sensor's peak current at two selected frequencies is measured. The ratiometric output is then calculated by dividing the peak current values corresponding to these frequencies. As the sensor outputs are also affected by both temperature and solution type (i.e. water, commercial serum, blood, etc.), these variables may also be measured and recorded. To this end a sensor of a sensor class may have temperature sensors to measure the temperature of the sensor itself, the solution, and ambient temperature. The skilled person will appreciate that there may be more options of places to measure temperature, and the listed areas are simply by way of example. Equation 8 is a model equation for a sigmoidal response of the ratiometric output of the sensor measuring concentration. It will be appreciated by the skilled person that while this equation is used here, it is possible that another mathematical equation may also model the response similarly or even better. Equation 9 is simply Equation 8 rearranged to obtain a value of the concentration of the analyte in solution from ratiometric output of the sensor. In other words, Equation 9 would be used at the point of use with the relevant constants determined from the relevant fitted calibration curve. As Equation 8 has some dependence on various temperatures and type of solution, it may also be beneficial to obtain a plurality of calibration curves and empirical calibration data for the same sensor under these different measured conditions. The various calibration curves and empirical calibration data could then be stored somewhere accessible by the sensor at the point of use, where the sensor can then determine which calibration curve or set of empirical calibration data to apply to obtain the most accurate results. By way of example these calibration curves and empirical calibration data could be stored in memory in the sensor device itself or in cloud / server storage that the sensor device may be able to access. A simple approach to this issue of varying temperatures and / or solution types would be to calculate the calibration curve or empirical calibration data under the circumstances that the sensor is expected to be used under (for example at room temperature). As well as obtaining fitted calibration curves with the equations discussed above, the ratiometric outputs may also be used directly. The ratiometric outputs for the entire range of may be collated and stored in memory in the sensor device or otherwise as empirical calibration data. In order for empirical calibration data to be used effectively, the density of ratiometric outputs over concentration may need to be over a certain threshold. For example, it may be preferable to have at least 15 ratiometric outputs distributed over the whole dynamic range of the sensor, in some cases this may be done using serial dilution. It may also be preferable to have at least three data points in each decade, where a decade is a change in concentration by a factor of 10. In other words, it may be preferable to have at least three ratiometric outputs between analyte concentrations of 1mg / L and 10mg / L. This is achievable using serial half dilution, or more particularly, serial half logarithmic dilution. However, the empirical calibration data and calibration curves are not incompatible with each other. For example, all or part of the empirical calibration data may be used to generate a calibration curve by mathematical modelling and curve fitting. Additionally, a calibration curve may be used in combination with empirical calibration data, where the measuring of a concentration may take an average of the concentration estimated by using empirical calibration data and a calibration curve. In particular, the empirical calibration data can be used by measuring an unknown concentration, and interpolating (linearly or non-linearly) between at least the two surrounding data points of the empirical calibration curve. Similarly, it may also be applicable to take a number of surrounding data points and generate a local calibration curve around the unknown concentration measurement data. In some cases, using a calibration curve instead of empirical calibration data, or vice versa, may be preferable. The preferential use of a calibration curve or empirical calibration data may be where a measured concentration is within or outside of a threshold range, or above or below a threshold. For example, if a calibration curve has only been calculated based on empirical calibration data for half of the dynamic range of the sensor, the empirical calibration data may be more accurate outside the range on which the calibration curve was fitted to, and therefore the empirical calibration data should be used to obtain a concentration value. However, if the measured value is within the range of the calibration curve, it may be preferable to take an average of the value obtained by calibration curve and the value obtained by empirical calibration data. The interpolation process may involve determining which two empirical calibration data points the unknown concentration ratiometric output lies between, and taking an average of the two empirical calibration data points. This average may just be the mean of the two empirical calibration data points, or the average may be a weighted average towards the empirical calibration data point that the unknown concentration ratiometric output is closest to. As large amounts of ratiometric outputs and data in general may be generated in the process of obtaining empirical calibration data or a calibration curve, it may be advantageous to process this data in an efficient manner. Particularly, if each sensor output may generate its own data file or data files, then after what could be hundreds or even thousands of data files are generated, this creates a problem of handling such large amounts of data spread across what would be hundreds or even thousands of separate files. Therefore, it may be beneficial to verify that these files are of a proper file type, to collate the key data from each file into a separate key data file, either as the original data is generated, in set intervals, or even after all the data is created. Such key data may include a device name, date, time, tag, concentration, electrodes, start voltage, frequencies, amplification, and other key data that may be calculated from the data files includes means, maximums, minimums, standard deviations, and relative standard deviations of each data file. Then, the key data file may then be analysed and stored to create a calibration curve or empirical calibration data. Particularly, a method may involve determining what data from a file generated from a sensor measurement is key data, and generating a key data file with key data from one or more files generated from sensor measurements. Additionally, the key data file may be processed to produce empirical calibration data and a calibration curve. Fig. 5 displays four data sets 503, 504, 505, 506 taken at different ambient temperatures plotting the normalised ratiometric outputs 501 for various concentrations of analyte 502. Particularly, there appears to be a significant difference in output when the concentration to be measured is larger, for example between data sets 503 and 505. Therefore, unless the in-situ use of the sensor is done in the same conditions as the calibration curve, there is a need to account for temperature, without compromising on accuracy over the entire calibration curve. As discussed above, these temperature effects could be accounted for by obtaining various calibration curves for many different conditions. Alternatively, or in combination with the use of various calibration curves, the variations in temperature or other conditions can be accounted for analytically. This may be by mathematical relationships, such as interpolating between known calibration curves at different conditions, for example interpolating between two calibration curves taken at ambient temperatures of 15 and 30 degrees centigrade. For example, the sensor device may be configured to determine a temperature (such as the ambient temperature), and further select an appropriate calibration curve based on the determination. Fig. 6 displays a relationship of sensor output ratios in patient blood 601 to sensor output ratios in commercial serum 602. There is a clear linear relationship 603 between the two conditions which generally appear to be quite well correlated also as seen in 603. This again highlights the difference in sensor output depending on the conditions that the sensor may be used in. For example, the sensor’s calibration curve may be calculated from testing on a commercial serum, but at the point of use the sensor may be testing a blood sample from a patient. Here there already appears to be a strong positive correlation 603 between the two sample types (601, 602). Therefore, an analytical relationship (603) between the solution that the sensor was calibrated on, and other expected solutions that the sensor will be used to test (such as blood). Such relationships could be used in the sensor either by default (i.e. the calibration curve is calculated on commercial serum and altered by the relevant relationship between commercial serum and blood) or some method of selecting the type of sample that is being tested. This selection method may be manual, or automatic by the sensor device detecting what type of solution the sample is. For example, the sensor device may be configured to determine the type of solution that the sample is in, and further select an appropriate calibration curve based on the determination. In practice, at least one sensor of a sensor class may be tested before mass production of said sensor of the sensor class to obtain ratiometric sensor outputs, and these results will be fitted to Equation 8 or another suitable equation to determine the constants (in the case of Equation 8 these constants are Kd and h), or simply stored to be used as empirical calibration data. Preferably, at least one sensor from each batch of producing said sensors will also be tested to verify the fit of a calibration curve or empirical calibration data, whichever is in use. If the mass production is a continuous process, then a sensor may be tested periodically instead to ensure the sensors still remain within the sensor class. The method of fitting the ratiometric sensor outputs or empirical calibration data to a theoretical calibration curve may utilise known iterative methods such as the least squares or gradient descent (also known as steepest descent) methods. As the ratiometric sensor outputs 701 graphed against concentration 702 produce a sigmoidal curve 704 as shown in Fig. 7. Therefore, another theoretical calibration curve that could be used as an alternative to equation 8 will still have to match the data. In the case of fitting equation 8 to the sigmoidal results, it may be beneficial or more computationally efficient to utilise an iterative method that attempts to obtain the best fit of the theoretical calibration curve to the sigmoidal results by varying only one constant at a time between predetermined limits. In other words, the value Kd will be varied until the calibration curve fits the best (where Kd is within the predetermined limits) for an arbitrary h value. Once the best Kd value is found, then the best h value will be iteratively determined in the same way. This cycle can go back and forth until the fit does not improve more than a certain number cycle to cycle. Another type of iterative method that may be used would be a Levenberg-Marquardt method of minimizing a mathematical cost function. The Levenberg-Marquardt method iteratively updates the parameters of a model function and checks for convergence. The method may stop when the change in the parameter values become small or reach a set threshold, indicating that the curve has been fitted. Checking the fit between the theoretical calibration curve and the sigmoidal data may be performed by regression analysis between the calculated ratios and fitted calibration curve to obtain a regression coefficient. Any analytical, graphical, or other method to give an indication of the quality of the fit between the sigmoidal data and the calibration curve may be used. In the case of obtaining a regression coefficient, an example of a suitable threshold may be 0.99. Therefore, in this example the minimum improvement of the regression coefficient that needs to be reached to continue the iterative method for fitting the sigmoidal data to the calibration curve may be 0.01. If the calibration curve does not satisfy the threshold of 0.99 after the iterative method is completed, the iterative method may be re-run with wider limits or smaller step size. For example, as both Kd and h must be positive numbers, then if the best possible value for Kd or h is at their current upper limit, the new limits will be the same range as previous, but starting from the previous upper limit. Alternatively, if the best value is within the current limits, the step size may be reduced to find a more precise value. Fig. 8 is a block diagram of a computer system 1200 suitable for implementing one or more embodiments of the present disclosure, including for example any of the methods described above with reference to Figs. 1 to 7. The computer system 800 includes a bus 812 or other communication mechanism for communicating information data, signals, and information between various components of the computer system 800. The components include an input / output (I / O) component 804 that processes a user (i.e., sender, recipient, service provider) action, such as selecting keys from a keypad / keyboard, selecting one or more buttons or links, etc., and sends a corresponding signal to the bus 812. It may also include a camera for obtaining image data. The I / O component 804 also includes sensor 830. Sensor 830 may be configured as a sensor modified with an aptamer as bioreceptor, the sensor configured to measure a concentration of an analyte in sample. The I / O component 804 may also include an output component, such as a display 802 and a cursor control 808 (such as a keyboard, keypad, mouse, etc.). The display 802 may be configured to present a login page for logging into a user account, and is configured to display a user interface such as the user interface described above with reference to Figs. 1 to 8 and 14. An optional audio input / output component 806 may also be included to allow a user to use voice for inputting information by converting audio signals. The audio I / O component 806 may allow the user to hear audio. A transceiver or network interface 820 transmits and receives signals between the computer system 800 and other devices, such as another user device, a merchant server, or a service provider server via network 822. In one embodiment, the transmission is wireless, although other transmission mediums and methods may also be suitable. A processor 814, which can be a micro-controller, digital signal processor (DSP), or other processing component, processes these various signals, such as for display on the computer system 800 or transmission to other devices via a communication link 824. The processor 814 may also control transmission of information, such as cookies or IP addresses, to other devices. The components of the computer system 800 also include a system memory component 810 (e.g., RAM), a static storage component 816 (e.g., ROM), and / or a disk drive 818 (e.g., a solid-state drive, a hard drive). The computer system 800 performs specific operations by the processor 814 and other components by executing one or more sequences of instructions contained in the system memory component 810. Fig. 9 is a flow diagram of a method 900 for obtaining a fitted calibration curve of a sensor modified with an aptamer as bioreceptor. The method 900 starts with step 902 of performing a pulsed voltametric electrochemical method that gives an electrical current response at a plurality of different concentrations of analyte in sample at a first and second frequency for each concentration of the plurality of concentrations. The pulsed voltametric electrochemical method may be square wave voltammetry or differential pulse voltammetry. This step establishes a range of current responses over a range of known concentrations of analyte in solution at the same two frequencies for each concentration. These current responses are then used in step 904 in calculating a ratio of the peak electrical current response at the first and second frequencies for each concentration of analyte. This calculation is preferably done uniformly for each current response (i.e. all current responses at the first frequency divided by all current responses at the second frequency) to avoid any potential calculation overhead in later steps or usage of the calibration curve. Then these ratios are used in step 906 in fitting a theoretical calibration curve to the calculated ratios to obtain the fitted calibration curve, before the step 908 of accepting the fitted calibration curve based on determining whether the correlation between the calculated ratios and the fitted calibration curve is above a selected threshold. Particularly, step 906 may be an iterative process of fitting the calculated ratios to a theoretical calibration curve. Fig. 10 is a flow diagram of a method of measuring a concentration of an analyte in sample presented to a sensor modified with an aptamer as bioreceptor. The method 1000 starts with step 1002 performing a pulsed voltametric electrochemical method that gives an electrical current response at a first and second frequency for the concentration of analyte in sample. The pulsed voltametric electrochemical method may be square wave voltammetry or differential pulse voltammetry, and preferably be the same electrochemical method used in obtaining the fitted calibration curve as in step 902 above. This step 1002 provides two current responses for the unknown concentration which are used in step 1004 in calculating a ratio of the peak electrical current response at the first and second frequencies. This calculated ratio should preferably be calculated in the same manner as calculated above in step 904. This ratio can then be used in step 1006 in determining the concentration of the analyte using the calculated ratio and a calibration curve of the sensor of the sensor class. It will also be understood that the modules may be implemented in software or hardware, for example as dedicated circuitry. For example, the modules may be implemented as part of a computer system. The computer system may include a bus or other communication mechanism for communicating information data, signals, and information between various components of the computer system. The components may include an input / output (I / O) component that processes a user (i.e., sender, recipient, service provider) action, such as selecting keys from a keypad / keyboard, selecting one or more buttons or links, etc., and sends a corresponding signal to the bus. The I / O component may also include an output component, such as a display and a cursor control (such as a keyboard, keypad, mouse, etc.). A transceiver or network interface may transmit and receive signals between the computer system and other devices, such as another user device, a merchant server, or a service provider server via a network. In one embodiment, the transmission is wireless, although other transmission mediums and methods may also be suitable. A processor, which can be a micro-controller, digital signal processor (DSP), or other processing component, processes these various signals, such as for display on the computer system or transmission to other devices via a communication link. The processor may also control transmission of information, such as cookies or IP addresses, to other devices. The components of the computer system may also include a system memory component (e.g., RAM), a static storage component (e.g., ROM), and / or a disk drive (e.g., a solid-state drive, a hard drive). The computer system performs specific operations by the processor and other components by executing one or more sequences of instructions contained in the system memory component. Logic may be encoded in a computer readable medium, which may refer to any medium that participates in providing instructions to a processor for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. In various implementations, non-volatile media includes optical or magnetic disks, volatile media includes dynamic memory, such as a system memory component, and transmission media includes coaxial cables, copper wire, and fibre optics. In one embodiment, the logic is encoded in non-transitory computer readable medium. In one example, transmission media may take the form of acoustic or light waves, such as those generated during radio wave, optical, and infrared data communications. Some common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer is adapted to read. In various embodiments of the present disclosure, execution of instruction sequences to practice the present disclosure may be performed by a computer system. In various other embodiments of the present disclosure, a plurality of computer systems 600 coupled by a communication link to a network (e.g., such as a LAN, WLAN, PTSN, and / or various other wired or wireless networks, including telecommunications, mobile, and cellular phone networks) may perform instruction sequences to practice the present disclosure in coordination with one another. It will also be understood that aspects of the present disclosure may be implemented using hardware, software, or combinations of hardware and software. Also, where applicable, the various hardware components and / or software components set forth herein may be combined into composite components comprising software, hardware, and / or both without departing from the spirit of the present disclosure. Where applicable, the various hardware components and / or software components set forth herein may be separated into subcomponents comprising software, hardware, or both without departing from the scope of the present disclosure. In addition, where applicable, it is contemplated that software components may be implemented as hardware components and vice-versa. Software in accordance with the present disclosure, such as program code and / or data, may be stored on one or more computer readable mediums. It is also contemplated that software identified herein may be implemented using one or more general purpose or specific purpose computers and / or computer systems, networked and / or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and / or separated into sub-steps to provide features described herein. The various features and steps described herein may be implemented as systems comprising one or more memories storing various information described herein and one or more processors coupled to the one or more memories and a network, wherein the one or more processors are operable to perform steps as described herein, as non-transitory machine-readable medium comprising a plurality of machine-readable instructions which, when executed by one or more processors, are adapted to cause the one or more processors to perform a method comprising steps described herein, and methods performed by one or more devices, such as a hardware processor, user device, server, and other devices described herein. Reference throughout this specification to “some embodiments,” or “an embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in some embodiment,” or “in an embodiment,” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. As utilized herein, terms “component,” “system,” “interface,” “unit” and the like are intended to refer to a computer-related entity, hardware, software (e.g., in execution), and / or firmware. For example, a component can be a processor, a process running on a processor, an object, an executable, a program, a storage device, and / or a computer. By way of illustration, an application running on a server and the server can be a component. One or more components can reside within a process, and a component can be localized on one computer and / or distributed between two or more computers. Further, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and / or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and / or across a network, e.g., the Internet, a local area network, a wide area network, etc. with other systems via the signal). It will be appreciated from the discussion above that the embodiments shown in the Figures are merely exemplary, and include features which may be generalised, removed or replaced as described herein and as set out in the claims. In the context of the present disclosure other examples and variations of the apparatus and methods described herein will be apparent to a person of skill in the art.

Claims

1. A method for obtaining a fitted calibration curve of a sensor modified with an aptamer as bioreceptor, the sensor configured to measure a concentration of an analyte in sample, the method comprising the steps of:performing a pulsed voltametric electrochemical method that gives an electrical current response at a plurality of different concentrations of analyte in sample at a first and second frequency for each concentration of the plurality of concentrations;calculating a ratio of the peak electrical current response at the first and second frequencies for each concentration of analyte;generating empirical calibration data from the calculated ratios;fitting a theoretical calibration curve to at least a portion of the empirical calibration data to obtain the fitted calibration curve; andaccepting the fitted calibration curve based on determining whether the correlation between at least the portion of the empirical calibration data and the fitted calibration curve is above a selected threshold.

2. The method of claim 1, wherein the first and second frequencies are more than a selected threshold frequency difference apart.

3. The method of claims 1 or 2, wherein neither the first nor second frequency is a non-response frequency.

4. The method of any of the preceding claims, wherein the pulsed voltametric electrochemical method is square wave voltammetry or differential pulse voltammetry.

5. The method of any of the preceding claims, wherein performing the pulsed voltametric electrochemical method at the first and second frequencies for each concentration comprises performing the pulsed voltametric electrochemical method at the first and second frequency for each concentration at a same temperature.

6. The method of claim 5, wherein the temperature is one or more of an ambient temperature, a temperature of the sensor, and a temperature of the sample.

7. The method of any of the preceding claims, wherein performing the pulsed voltametric electrochemical method at the first and second frequencies for each concentration comprises performing the pulsed voltametric electrochemical method for each frequency and concentration with the same sample matrix.

8. The method of any of the preceding claims, wherein the theoretical calibration curve is defined by the equation: c _ c „ ~ . ^max u0S = So + —j7-----wherein, S is a ratio of the electrical current response at the same concentration of analyte in sample at the first and second frequency, So is a ratio of the electrical current response at the same concentration of analyte in sample at the first and second frequency where the concentration of the analyte in sample is 0, Smax is a ratio of the electrical current response at the same concentration of analyte in sample at the first and second frequency where the concentration of the analyte in sample is fully saturated, Kd is a dissociation constant for the sensor modified with the aptamer, [T] is the concentration of the analyte in sample, and h is a Hill coefficient.

9. The method of claim 8, wherein the ratios S, So, and Smax are all calculated by dividing the electrical current response at the first frequency by the electrical current response at the second frequency.

10. The method of claim 8, wherein the ratios S', So, and Smax are all calculated by dividing the electrical current response at the second frequency by the electrical current response at the first frequency.

11. The method of claims 8 to 10, wherein the constant Kd is determined from the fitted calibration curve.

12. The method of claims 8 to 10, wherein the constant h is determined from the fittedcalibration curve.

13. The method of any of the previous claims, wherein the theoretical calibration curve is in the form of a sigmoidal curve when the ratio of the electrical current response at the plurality of concentrations of analyte in sample at the first and second frequency is plotted against the concentrations of analyte in sample.

14. The method of any of the previous claims wherein determining whether the correlation between the calculated ratios and the fitted calibration curve is above a selected threshold comprises performing a regression analysis between the calculated ratios and fitted calibration curve to obtain a regression coefficient, and wherein the selected threshold is a selected threshold value for the regression coefficient.

15. The method of any of the previous claims wherein fitting a theoretical calibration curve to the calculated ratios comprises performing an iterative method to determine constants of the theoretical calibration curve.

16. The method of claim 15 wherein the iterative method is any combination of known iterative methods such as least-squares, gradient descent, or any other known linear or non-linear regression method.

17. The method of claims 15 or 16, wherein the iterative method comprises the steps of:varying a first constant of a plurality of constants of the theoretical calibration curve between predetermined limits;determining what value for that constant gives the best correlation between the calculated ratios and the fitted calibration curve; andrepeating steps (a) and (b) for each remaining constant of the plurality of constants at least once until the correlation between the calculated ratios and the fitted calibration curve does not increase by a second selected threshold amount for each repetition of steps (a) and (b).

18. A method of measuring a concentration of an analyte in a sample presented to a sensor modified with an aptamer as bioreceptor, the sensor belonging to a sensor class, the method comprising the steps of:performing a pulsed voltametric electrochemical method that gives an electrical current response at a first and second frequency for the concentration of analyte in the sample;calculating a ratio of the peak electrical current response at the first and second frequencies;determining the concentration of the analyte using the calculated ratio and empirical calibration data of the sensor of the sensor class.

19. The method of claim 18, wherein determining the concentration of the analyte using the calculated ratio and empirical calibration data of the sensor of the sensor class comprises obtaining a fitted calibration curve using at least a portion of the empirical calibration data by the method of claims 1 to 17.

20. The method of claim 19, wherein the fitted calibration curve is a selected calibration curve from a plurality of calibration curves, wherein the selected calibration curve is selected based on an ambient temperature, and a temperature of the sample, and a temperature of the sensor.

21. The method of any one of claims 18 to 20, wherein determining the concentration of the analyte using the calculated ratio and empirical calibration data of the sensor of the sensor class comprises determining interpolating the calculated ratio between at least a first empirical calibration data point and a second empirical calibration data point, wherein the first empirical calibration data point is the closest empirical calibration data point lower than the calculated ratio and the second empirical calibration data point is the closest empirical calibration data point higher than the calculated ratio.

22. The method of claim 21, wherein the determining the concentration of the analyte using the calculated ratio and empirical calibration data of the sensor of the sensor class comprises using only the fitted calibration curve if the calculated ratio is in a threshold range.

23. A calibration device comprising a sensor, a processor, and a computer readable medium, the computer readable medium storing code that when executed, performs thesteps of:performing a pulsed voltametric electrochemical method that gives an electrical current response at a plurality of different concentrations of analyte in sample at a first frequency and a second frequency for each concentration;calculating a ratio of the peak electrical current response at the each of the first and second frequencies for each concentration of analyte;fitting a theoretical calibration curve to the calculated ratios to obtain a fitted calibration curve; andaccepting the fitted calibration curve based on determining whether the correlation between the calculated ratios and the fitted calibration curve is above a selected threshold.

24. The calibration device of claim 23, wherein the calibration device is also configured to perform the method of any of claims 2-17.

25. A measuring device comprising a sensor, a computer readable medium, the computer readable medium storing code that when executed, perform the steps of:performing a pulsed voltametric electrochemical method at a first and second frequency that gives an electrical current response;calculating a ratio of the peak electrical current response at the first and second frequency;determining the concentration of the analyte using the calculated ratio and a calibration curve of the sensor of the sensor class.

26. The measuring device of claim 25, wherein the measuring device is also configured to perform the method of any of claims 18-22.

27. The calibration device of claims 23 and 24, wherein the device comprises a temperature sensor.

28. The measuring device of claims 25 and 26, wherein the device comprises a temperature sensor.

29. The calibration device of claims 23 and 24, wherein the sensor is at least one of:printed circuit board, PCB, electrodes, screen-printed electrodes, and electrodes fabricated by sputtering.

30. The measuring device of claims 25 and 26, wherein the sensor is at least one of:5 printed circuit board, PCB, electrodes, screen-printed electrodes, and electrodes fabricated by sputtering.

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