Calibration system and method for assay systems

Abstract

Systems and methods for calibrating a sample assay for an analyte are provided that are based on calibration curve fitting using a modified four-parameter logistic regression fitting equation (modified 4PL) with a modified Hill slope that depends on a function of the inverse of the analyte amount or a function of the natural logarithm of the inverse of the analyte amount.

Description

[Technical Field]

[0001] This disclosure relates to methods for calibrating and using assay systems and devices. Specifically, this disclosure relates to methods and systems using improved calibration models. [Background technology]

[0002] Various assay devices and formats are available for performing assays and assay measurements. The generation of quantitative sample assay results based on assay signals can be achieved by calibrating the assay using appropriate calibration standards. The embodiments described herein provide improved calibration models and techniques for generating sample assay results with improved accuracy. [Overview of the project]

[0003] In this embodiment, an assay calibration method is provided. This method involves performing multiple calibration assays on multiple calibration samples, each having a defined amount of analyte and different amounts of analyte, on an assay system to obtain multiple calibration assay signal values; generating a calibration dataset by at least one processing unit, which includes multiple quantity values ​​according to a defined amount and multiple calibration assay signal values ​​corresponding to the multiple calibration samples; and selecting a calibration model equation by at least one processing unit that relates the defined amount to the multiple calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation, and the modified Hills slope depends on a function of the quantity values. The process includes: identifying fitting parameters to fit a calibration model equation to a calibration dataset using at least one processing unit; generating a calibration model including the calibration model equation and fitting parameters using at least one processing unit; performing at least one sample assay on at least one test sample on the assay system to obtain a sample assay signal value; generating a sample assay dataset including at least one sample assay signal value using at least one processing unit; and obtaining a sample volume value determined according to the calibration model and sample assay signal value using at least one processing unit.

[0004] In a further embodiment, an assay system is provided. The assay system includes at least one memory unit, at least one processing unit programmed according to instructions on the at least one memory unit, and at least one assay system component configured to be controlled by the at least one processing unit. At least one processing unit is configured to control at least one assay system component to perform multiple calibration assays on multiple calibration samples, each having a defined amount of analyte and different amounts of analyte, to obtain multiple calibration assay signal values; to generate a calibration dataset including multiple quantity values ​​according to a defined amount and multiple calibration assay signal values ​​corresponding to multiple calibration samples; to select a calibration model equation that associates a defined amount with multiple calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation and the modified Hills slope depends on a function of the quantity values; to identify fitted parameters that fit the calibration model equation to the calibration dataset; to generate a calibration model including the calibration model equation and fitted parameters; and to control at least one assay system component to perform at least one sample assay on at least one test sample to obtain a sample assay signal value; to generate a sample assay dataset including at least one sample assay signal value; and to obtain a sample quantity value determined according to the calibration model and the sample assay signal value.

[0005] In a further embodiment, one or more non-temporary computer-readable media are provided. The one or more non-temporary computer-readable media stores instructions, and when an instruction is executed by at least one processing unit, at least one processing unit, via control of an assay system, performs multiple calibration assays on a plurality of calibration samples, each having a defined amount of analyte and a calibration sample having different amounts of analyte, to obtain a plurality of calibration assay signal values; generates a calibration dataset, each having a plurality of quantity values ​​according to a defined amount and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples; and selects a calibration model equation that associates the defined amount with the plurality of calibration assay signal values, wherein the calibration model equation is modified The system performs the following steps: select a corrected four-parameter logistic regression fitted equation in which the modified hill slope depends as a function of the quantity value; identify fitted parameters to fit the calibration model equation to the calibration dataset; generate a calibration model including the calibration model equation and fitted parameters; perform at least one sample assay on at least one test sample via the control of the assay system to obtain a sample assay signal value; generate a sample assay dataset including at least one sample assay signal value; and obtain a sample quantity value determined according to the calibration model and sample assay signal value.

[0006] In a further embodiment, an assay calibration method is provided. This method includes: performing multiple calibration assays on multiple calibration samples, each having a defined amount of analyte and having different amounts of analytes, on an assay system to obtain multiple calibration assay signal values; generating a calibration dataset by at least one processing unit, which includes multiple quantity values ​​according to a defined amount and multiple calibration assay signal values ​​corresponding to the multiple calibration samples; selecting a calibration model equation by at least one processing unit that associates the defined amount with the multiple calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation, and the modified Hill Slope depends on a function of the quantity values; identifying fitted parameters by at least one processing unit to fit the calibration model equation to the calibration dataset; generating a calibration model by at least one processing unit, which includes the calibration model equation and fitted parameters; and storing the calibration model by at least one processing unit.

[0007] In a further embodiment, an assay calibration method is provided. The assay calibration method includes obtaining a calibration model, comprising a calibration model equation and fitting parameters, by at least one processing unit, wherein the calibration model equation is a modified four-parameter logistic regression fitting equation, the modified Hillslope depends on a function of the value of the quantity, and the fitting parameters fit the calibration model equation to a calibration dataset, which includes a plurality of quantity values ​​according to a defined quantity and a plurality of calibration assay signal values ​​corresponding to a plurality of calibration samples; performing at least one sample assay on at least one test sample on the assay system to obtain a sample assay signal value; generating a sample assay dataset containing at least one sample assay signal value by at least one processing unit; and determining a value of the sample quantity determined according to the calibration model and the sample assay signal value by at least one processing unit.

[0008] In a further embodiment, one or more non-temporary computer-readable media are provided. The one or more non-temporary computer-readable media stores instructions, and when an instruction is executed by at least one processing unit, at least one processing unit obtains a calibration dataset, which includes the results of assay measurements on a plurality of calibration samples, each having a defined amount of analyte and a calibration sample having different amounts of analyte, wherein the calibration dataset includes a plurality of quantitative values ​​according to the defined amount and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples, and selects a calibration model equation that associates the defined amount with the plurality of calibration assay signal values, wherein the calibration model equation is modified The process involves selecting a 4-parameter logistic regression fitted equation in which the modified hill slope depends as a function of the quantity value, identifying fitted parameters to fit the calibration model equation to the calibration dataset, generating a calibration model including the calibration model equation and fitted parameters, obtaining a sample assay dataset including the results of assay measurements for at least one test sample, wherein the test dataset includes at least one sample assay signal value corresponding to at least one test sample, and determining the sample quantity value according to the calibration model and sample assay signal value.

[0009] In a further embodiment, a computer implementation method is provided that is performed by a system comprising at least one memory unit and at least one processing unit programmed according to instructions on the at least one memory unit. The method comprises: obtaining a calibration model comprising a calibration model equation and fitting parameters, wherein the calibration model equation is a modified four-parameter logistic regression fitting equation relating a defined quantity to a plurality of calibration assay signal values, the modified Hillslope is a function of the quantity values, and the fitting parameters are such that the calibration model equation is fitted to a calibration dataset comprising a plurality of quantity values ​​according to a defined quantity and a plurality of calibration assay signal values ​​corresponding to a plurality of calibration samples; obtaining a sample assay dataset comprising the results of a sample assay measurement for at least one test sample, wherein the sample assay dataset comprises at least one sample assay signal value corresponding to at least one test sample; and determining the value of a sample quantity according to the calibration model and at least one sample assay signal value, by at least one processing unit. [Brief explanation of the drawing]

[0010] [Figure 1] This shows an assay system environment consistent with the embodiments described herein. [Figure 2A-2D] An assay device consistent with the embodiments described herein is shown. [Figure 3A-3C] An assay device consistent with the embodiments described herein is shown. [Figure 4] An assay device consistent with the embodiments described herein is shown. [Figure 5] The features of the assay device consistent with the embodiments described herein are shown. [Figure 6] A computing system consistent with the embodiments described herein is shown. [Figure 7]A four-parameter logistic regression fitted calibration curve consistent with the embodiments described herein is shown. [Figure 8] A modified four-parameter logistic regression fitted calibration curve consistent with the embodiments described herein is shown. [Figure 9] An embodiment of a four-parameter logistic regression fitted calibration curve consistent with the embodiments described herein is shown. [Figure 10] An embodiment of a four-parameter logistic regression fitted calibration curve consistent with the embodiments described herein is shown. [Figure 11] An embodiment of a four-parameter logistic regression fitted calibration curve consistent with the embodiments described herein is shown. [Figure 12] An embodiment of a four-parameter logistic regression fitted calibration curve consistent with the embodiments described herein is shown. [Figure 13] An embodiment of a four-parameter logistic regression fitted calibration curve consistent with the embodiments described herein is shown. [Figure 14] The recovery rates of different calibration-adjustable equations applied to representative assays according to embodiments of this specification are shown. [Figure 15] The recovery rates of several calibration curve points with respect to the signal-to-background ratio for different calibration model equations are shown according to embodiments of this specification. [Figure 16] This embodiment describes the process of calibrating an assay system and applying calibration values. [Modes for carrying out the invention]

[0011] This disclosure provides a system and computer implementation method for calibrating assay systems and assay media and applying calibration models to sample assay data. As discussed herein, the calibration methods and techniques provide accurate calibration models for calibrating assay systems and assay media using a modified four-parameter logistic regression fitted equation. The calibration methods and techniques discussed herein can be applied to a variety of assay systems, devices, and media by using a comprehensive assay system environment.

[0012] The following disclosure includes a description of acquired data, which includes multiple data points. In embodiments, multiple data points may be acquired according to similar parameters, procedures, methods, etc., and these multiple data points may be aggregated together as needed (e.g., as mean, weighted mean, geometric mean, median, etc.). In embodiments, aggregation of multiple data points may include filtering outlier data points, e.g., highest, lowest, and / or extreme values. Various parts of this disclosure may refer to a single method of aggregating (e.g., averaging) multiple data points, but those skilled in the art will understand that additional or different aggregation techniques described herein may be similarly applicable.

[0013] FIG. 1 shows an assay system environment 100 that conforms to an embodiment of this specification. The assay system environment 100 includes one or more assay devices 101, one or more local assay computing systems 102, one or more data storage devices 106, and one or more network computing systems 104. Further, the various devices of the system can be connected by a closed network 103 and / or an open network 105. As described herein, the various components of the assay system environment 100 can be connected via wired and / or wireless communication links. However, the components of the assay system environment need not be connected via a continuous communication link. The assay system environment 100 can include computing systems 102 and 104 and assay devices 101 (also referred to as assay systems) that are connected only intermittently. The assay system environment 100 can further include computing systems 102 and 104 and assay devices 101 that do not have a wired or wireless connection to other components within the assay system environment 100. In such cases, data transfer can be achieved, for example, via the physical transfer of a storage medium.

[0014] Assay devices 101 consistent with this disclosure include a variety of assay devices and / or forms. Assay devices may include an assay module having various components (assay system components), such as assay plates, cartridges, multi-well assay plates, reaction vessels, test tubes, cuvettes, flow cells, assay tips, and lateral flow devices, which are added as the assay progresses or pre-introduced into the wells, chambers, or assay areas of the assay module. These devices can be used with a variety of assay approaches to measure the presence, quantity, or activity of a target analyte. These include, but are not limited to, binding assays that measure the binding of a target to a specific binding partner, enzyme assays that measure the enzymatic activity of a target or the chemical transformation of a target by an enzyme, chemical assays that measure the chemical reaction of a target, and assays that measure the physical properties of a target (such as light absorbance). Within each of these groups, a variety of assay forms are known and can be used by the device. In the case of binding assays, the forms that can be used include, but are not limited to, direct binding assays, sandwich binding assays, and competitive binding assays. The conjugation assay may utilize a variety of different classes of conjugation reagents, including but not limited to antibodies (i.e., immunoassays), nucleic acids (i.e., hybridization assays), biological receptor / ligand pairs, haptens, and other reagents characterized by their ability to bind or selectively bind to a target analyte. Exemplary assay devices and forms are described below herein.

[0015] A variety of solid phases, including conventional solid phases from binding assay techniques, are suitable for use in the binding assay method of this embodiment. The solid phase can be made from a variety of different materials, including polymers (e.g., polystyrene and polypropylene), ceramics, glass, composite materials (e.g., carbon-polymer composite materials such as carbon-based inks). Suitable solid phases include the surfaces of macroscopic objects such as assay containers (e.g., test tubes, cuvettes, flow cells, cartridges, wells of multiwell plates), slides, assay chips (such as those used for measuring gene or protein chips), pins or probes, beads, filtration media, lateral flow media (e.g., filtration membranes used in lateral flow test strips).

[0016] Suitable solid phases also include particles commonly used in other types of particle-based assays (including but not limited to colloids or beads), such as magnetic, polypropylene, and latex particles, materials commonly used in solid-phase synthesis, such as polystyrene and polyacrylamide particles, and materials commonly used in chromatography applications, such as silica, alumina, polyacrylamide, and polystyrene. The material can also be a fiber such as carbon fibril. The microparticles can be abiotic or, alternatively, can include biological entities such as cells, viruses, bacteria.

[0017] The particles used in this method can be composed of any material suitable for attachment to one or more binding partners and / or labels and can be collected, for example, by centrifugation, gravity, filtration, or magnetic collection. A variety of different types of particles that can adhere to binding reagents are commercially available for use in binding assays. These include non-magnetic particles and particles containing magnetizable materials that allow the particles to be collected in a magnetic field. In one embodiment, the particles are composed of a conductive and / or semiconductive material, such as colloidal gold particles.

[0018] The fine particles can have a variety of sizes and shapes. For example, but not limited to, the fine particles may be between 5 nanometers and 100 micrometers in size. Preferably, the fine particles have a size between 20 nm and 10 micrometers. The particles may be spherical, rectangular, rod-shaped, or have an irregular shape.

[0019] The particles used in this method may be coded to enable the identification of specific particles or subpopulations of particles within a mixture of particles. The use of such coded particles has been used to enable the multiplexing of assays that use particles as solid-phase supports for binding assays. In one approach, particles are manufactured to contain one or more fluorescent dyes, and specific populations of particles are identified based on the intensity and / or relative intensity of fluorescence emission at one or more wavelengths. This approach has been used in the Luminex xMAP system (see, for example, U.S. Patent No. 6,939,720) and the Becton Dickinson Cytometric Bead Array system. Alternatively, particles may be coded by differences in other physical properties such as size, shape, or embedded optical patterns.

[0020] The methods of the embodiments can be used in conjunction with various methods for measuring the amount of analytes, specifically, various methods for measuring the amount of analytes bound to a solid phase. Techniques that can be used include, but are not limited to, techniques known in the art, such as cell culture assays, binding assays (including agglutination tests, immunoassays, nucleic acid hybridization assays, etc.), enzyme assays, and colorimetric assays. Other suitable techniques will be readily apparent to those skilled in the art. Some measurement techniques allow measurement to be performed by visual inspection, while others require, or benefit from, the use of instruments to perform the measurement.

[0021] Methods for measuring the quantity of an analyte include, but are not limited to, label-free techniques, which include: i) techniques for measuring the change in mass or refractive index on a surface after the analyte has bonded to the surface (e.g., surface acoustic wave techniques, surface plasmon resonance sensors, polarization analysis techniques, etc.); ii) mass spectrometry techniques (including techniques such as MALDI and SELDI that can measure analytes on a surface); iii) chromatography or electrophoresis techniques; and iv) fluorescence techniques (which may be based on the inherent fluorescence of the analyte).

[0022] Methods for measuring the quantity of an analyte also include techniques for measuring the analyte by detecting a label that may be directly or indirectly attached to the analyte (e.g., by using a labeled binding partner of the analyte). Suitable labels include those that are directly visualized (e.g., particles that can be visually observed, labels that produce measurable signals such as light scattering, optical absorption, fluorescence, chemiluminescence, electrochemiluminescence, radioactivity, and magnetic fields). Usable labels also include enzymes or other chemical reactants that have chemical activity resulting in measurable signals such as light scattering, absorbance, and fluorescence. The use of enzymes as labels is well established in enzyme-linked immunosorbent assays, also known as ELISA, enzyme immunoassay, or EIA. In the ELISA format, an unknown amount of antigen is attached to a surface, and then a specific antibody is washed on the surface to allow it to bind to the antigen. This antibody is bound to an enzyme, and in the final step, a substance is added that the enzyme converts into a product that provides a detectable signal change. The formation of the product may be detectable in terms of measurable properties such as absorbance, fluorescence, chemiluminescence, and light scattering, for example, due to differences from the substrate. Certain (but not all) measurement methods that can be used in the solid-phase bonding method according to the embodiment may benefit from, or require, a cleaning step to remove unbonded components (e.g., labels) from the solid phase. Therefore, the method of the embodiment may include such a cleaning step.

[0023] The assay methods disclosed herein can be performed manually, using automated techniques, or both. Automated techniques may be partially automated, for example, by one or more modular instruments or by a fully integrated automated instrument. Examples of automated systems are described and presented in the jointly owned International Patent Application Publications 2018 / 017156 and 2017 / 015636, International Patent Application Publication 2016 / 164477, and International Patent Application Publication 2021 / 231935, each of which is incorporated herein by reference in whole.

[0024] The assay device 101 may include an automated system (modular and fully integrated) on which the method described herein can be performed, and may include the following automated subsystems: a computer subsystem which may include hardware (e.g., personal computer, laptop, hardware processor, disk, keyboard, display, printer), software (e.g., driver, driver controller, and data analyzer processes), and a database; a liquid processing subsystem, e.g., sample and reagent processing, e.g., a robotic pipetting head, syringe, agitator, ultrasonic mixer, magnetic mixer; a sample, reagent, and consumable storage and processing subsystem, e.g., a robotic manipulator, tube or cap or foil penetration device, cap removal device, conveying devices such as linear and circular conveyors and robotic manipulators, tube rack, plate carrier, trough carrier, pipette Advanced carriers, plate shakers; centrifuges, assay reaction subsystems, e.g., fluid-based and consumable-based (tubing and multiwell plates, etc.); container and consumable washing subsystems, e.g., plate washing devices; magnetic separator or magnetic particle concentration subsystems, e.g., flow cells, tubes, and plate types; cell and particle detection, classification, and separation subsystems, e.g., flow cytometers and coulter counters; detection subsystems such as colorimetric, nephro, fluorescence, and ECL detectors; temperature control subsystems, e.g., air treatment, air cooling, air heating, fans, blowers, and water tanks; waste subsystems, e.g., liquid and solid waste containers; globally unique identifier (GUI) detection subsystems, e.g., 1D and 2D barcode scanners such as flatbed and wand types; sample identifier detection subsystems, e.g., 1D and 2D barcode scanners such as flatbed and wand types. Analytical subsystems, such as high-performance liquid chromatography (HPLC), high-performance protein liquid chromatography (FPLC), and mass spectrometers, can also be modularized or fully integrated. An automated system consistent with the embodiments described herein may be controlled and / or managed by the user interface manager 622, as described below.

[0025] A system or module that performs sample identification and preparation can be combined with (or linked, adjacent to, or robotically connected to) a system or module that performs assays, detection, or both. Multiple modular systems of the same type can be combined to increase throughput. Modular systems can be combined with modules that perform other types of analysis, such as chemical analysis, biochemical analysis, and nucleic acid analysis.

[0026] Automated systems enable batch, sequential random access, and point-of-care workflows, as well as single, medium, and high sample throughput.

[0027] An automated system consistent with the embodiments herein may include, for example, one or more of the following devices: plate sealers (e.g., Zymark), plate washers (e.g., BioTek, TECAN), reagent dispensers and / or automated pipetting stations and / or liquid handling stations (e.g., TECAN, Zymark, Labsystems, Beckman, Hamilton), incubators (e.g., Zymark), plate shakers (e.g., Q. Instruments, Inheco, Thermo Fisher Scientific), compound libraries or sample storage and / or compound and / or sample retrieval modules. One or more of these devices may be coupled to the apparatus via a robotic assembly, thereby enabling the entire assay process to be performed automatically. According to alternative embodiments, containers (e.g., plates) are moved manually between the apparatus and various devices (e.g., stacks of plates).

[0028] The automated system may be configured to perform one or more of the following functions: (a) moving consumables such as plates into, within, and outside the detection subsystem; (b) moving consumables between other subsystems; (c) storing consumables; (d) processing samples and reagents (e.g., adapted to mix reagents and / or introduce reagents into consumables); (e) shaking consumables (e.g., to mix reagents and / or to increase reaction rates); (f) washing consumables (e.g., washing plates and / or performing assay washing steps (e.g., well aspiration)); and (g) measuring assay signals (e.g., ECL signals) in flow cells or consumables such as tubes or plates. The automated system may be configured to process individual tubes placed on racks, multi-well plates such as 96 or 384-well plates.

[0029] In the embodiment, the automated system is fully automated, modular, and computerized, performing quantitative and qualitative in vitro tests on a wide range of analytes, including photometric assays, ion-selective electrode measurements, and / or electrochemiluminescence (ECL) assays. In the embodiment, the automated system includes the following hardware units: a control unit, a core unit, and at least one analytical module.

[0030] In the embodiment, the control unit, which may be a local assay computing system 102 and / or a network computing system 104, uses a graphical user interface to control all instrument functions and consists of a reading device such as a monitor, input devices such as a keyboard and mouse, and a personal computer using, for example, a Windows operating system. In the embodiment, the core unit consists of several components that manage the transport of samples to each assigned analysis module. The actual configuration of the core unit depends on the configuration of the analysis modules, which can be configured by those skilled in the art using methods known in the art. In the embodiment, the core unit includes, as its main components, at least a sampling unit and one rack rotor. A conveyor line and a second rack rotor are possible extensions. Several other core unit components include a sample rack loader / unloader, ports, barcode readers (for racks and samples), water supply, and system interface ports. In the embodiment, the analysis module performs an ECL assay and includes a reagent area, a measurement area, a consumables area, and a pre-wash area.

[0031] The methods of the present invention can be applied in singleplex or multiplex forms in which multiple assay measurements are performed on a single sample. Multiplex measurements that can be used in the present invention include, but are not limited to, i) multiplex measurements involving the use of multiple sensors, ii) multiplex measurements using individual assay domains on a surface (e.g., arrays) that can be identified based on their position on the surface, iii) multiplex measurements involving the use of reagents coated on particles that can be identified based on particle characteristics such as size, shape, and color, and iv) multiplex measurements that generate an assay signal that can be identified based on optical properties (e.g., absorbance or emission spectrum) based on the temporal characteristics of the assay signal (e.g., time, frequency, or phase of the signal).

[0032] Embodiments disclosed herein include methods for detecting and counting individual detection complexes. In embodiments, the surface comprises multiple binding domains, and each analyte forms a complex in different binding domains of the multiple binding domains. In embodiments, the surface is a particle. In embodiments, the surface is a bead. In embodiments, the surface is a plate. In embodiments, the surface is a well in a multiwell array. In embodiments, the surface includes an electrode. In embodiments, the electrode is a carbon ink electrode. In embodiments, each binding domain of each analyte of one or more additional analytes is on a separate surface, and the surface is a bead in a bead array. In embodiments, each binding domain of each analyte of one or more additional analytes is on a single surface, and the binding domains form elements of a capture reagent array on the surface. In embodiments, the surface includes an electrode, and the detection step of the method includes applying a potential to the electrode and measuring electrochemiluminescence. In embodiments, applying a potential to the electrode generates an electrochemiluminescence signal.

[0033] In one embodiment, the surface contains multiple capture reagents for one or more analytes present in the sample, and the multiple capture reagents are distributed across multiple degradable binding regions located on the surface. Under the conditions used to perform and analyze the measurement, a “degradable binding region” is a minimal surface region associated with an individual binding event that can be degraded and distinguished from another region where additional individual binding events are occurring. Thus, the method comprises binding one or more analytes to one or more capture reagents on the surface, determining the presence or absence of analytes in multiple degradable binding regions on the surface, and identifying the number of degradable binding regions containing the analyte of interest and / or the number of domains that do not contain the analyte.

[0034] Resolvable coupling regions can be optically matched holistically or partially; that is, each individual resolvable coupling region can be optically matched individually, and / or an entire surface containing multiple resolvable coupling regions can be imaged, and one or more pixels or groups of pixels in that image can be mapped to individual resolvable coupling regions. Resolvable coupling regions can also be microparticles within multiple microparticles. Resolvable coupling regions exhibiting changes in their optical characteristics can be identified by conventional optical detection systems. Depending on the detected species (e.g., type of fluorescent entity) and operating wavelength, optical filters designed for specific wavelengths can be used for optical matching of resolvable coupling regions. In embodiments where optical matching is used, the system may include two or more light sources and / or multiple filters to adjust the wavelength and / or intensity of the light sources. In some embodiments, light signals from multiple resolvable coupling regions are captured using a CCD camera. Other non-limiting examples of camera imaging systems that can be used for imaging include, as is known to those skilled in the art, charge injection devices (CIDs), complementary metal-oxide-semiconductor (CMO) devices, scientific CMOS (sCMOS) devices, and time-delay integration (TDI) devices. In some embodiments, a scanning mirror system coupled with a photodiode or photomultiplier tube (PMT) can be used for imaging.

[0035] In embodiments, the binding of each analyte to its corresponding capture reagent is performed in parallel by bringing one or more surfaces into contact with a single liquid volume containing multiple analytes. In embodiments, the multiple analytes include an analyte and one or more additional analytes. In embodiments, each step of the method is performed in parallel for each analyte. In embodiments, the method is a simultaneous multiplex assay. Multiplex measurements of analytes on surfaces are described herein and should also be referred to, for example, U.S. Patents 10,201,812, 7,842,246, and 6,977,722, which are incorporated herein by reference in their entirety.

[0036] In certain embodiments, the method of the present invention can be used in a multiplex form by binding multiple different analytes to multiple capture reagents for those analytes, and the capture analytes are immobilized on coded beads such that coding identifies the capture reagent (and analyte target) for a particular bead. The method may further include counting the number of beads having the bound analytes (using the detection approach described herein).

[0037] Alternatively or additionally, the capture reagent can be directly or indirectly bound to different individual binding domains on one or more solid phases, such that individual assay signals are generated on each binding domain and measured from each binding domain, for example, in the case of a binding array where the binding domains are individual array elements, or in the case of a set of beads where the binding domains are individual beads. If the capture reagents for different analytes are immobilized on different binding domains, the different analytes bound to those domains can be measured individually. In one example of such an embodiment, the binding domains are prepared by immobilizing individual domains of the capture reagent that bind to the analyte of interest on one or more surfaces. Optionally, this surface may partially define the boundaries of one or more containers (e.g., flow cells, wells, cuvettes, etc.) that hold or through which the sample passes. In a preferred embodiment, the individual binding domains are formed on an electrode for use in an electrochemical assay or an electrochemiluminescence assay. Multiplex measurements of surface analytes containing multiple binding domains using electrochemiluminescence are used in Meso Scale Diagnostics, LLC, MULTI-ARRAY®, and the SECTOR® imaging system line of its products (see, for example, U.S. Patents 10,201,812, 7,842,246, and 6,977,722, which are incorporated herein by reference in their entirety).

[0038] Furthermore, the capture reagent can be directly or indirectly bound to the electrode surface, which may include different individual binding domains as described above. The electrode surface may be a component of a multiwell plate and / or flow cell. The electrode may include conductive materials, such as metals, e.g., gold, silver, platinum, nickel, steel, iridium, copper, aluminum, and conductive materials. They may also include oxide-coated metals, e.g., aluminum coated with aluminum oxide. The electrode may include a working electrode and a counter electrode, e.g., a metal counter electrode and a carbon working electrode, which can be made of the same or different materials. In one particular embodiment, the electrode includes carbon-based materials such as carbon, carbon black, graphite carbon, carbon nanotubes, carbon fibrils, graphite, graphene, carbon fibers, and mixtures thereof. In one embodiment, the electrode includes elemental carbon, e.g., graphite, carbon black, carbon nanotubes, etc. Advantageously, they may include conductive carbon-polymer composite materials, conductive particles dispersed in a matrix (e.g., carbon ink, carbon paste, metal ink, graphene ink), and / or conductive polymers. One particular embodiment of the present invention is an assay module, preferably a multiwell plate, having electrodes (e.g., a working electrode and / or a counter electrode) comprising carbon, for example, a carbon layer and / or a screen-printed layer of carbon ink.

[0039] In the embodiment, each binding domain includes a complement of the target reagent that can bind to the complement of the target reagent, and each anchor reagent and capture reagent includes an additional binding reagent that can bind to the binding reagent, and the method further includes immobilizing the capture reagent and anchor reagent in each binding domain by: (1) binding the capture reagent and anchor reagent to the complement of the target reagent connected to the binding reagent via the additional binding reagent; and (2) binding the product of step (1) to a binding domain containing the complement of the target reagent, wherein (i) each binding domain includes a different complement of the target reagent, and (ii) each complement of the target reagent selectively binds to one of the target reagents.

[0040] Therefore, in the embodiment, the surface contains a complement of the target reagent, the target reagent is connected to the linking reagent, and each of the capture reagent and anchor reagent contains an additional linking reagent. Therefore, in the embodiment, the complement of the target reagent on the surface is bound to the target reagent, the target reagent is connected to the linking reagent, and the linking reagent is bound to the additional linking reagent on the capture reagent and anchor reagent.

[0041] In embodiments, the linking reagent has multiple binding sites for additional linking reagents, and the immobilization of the capture reagent and anchor reagent further comprises (i) linking the capture reagent and anchor reagent to a target reagent connected to the linking reagent via the additional linking reagent, and linking the product to a binding domain containing a complement of the target reagent, wherein (i) each binding domain contains a complement of a different target reagent, and (ii) each complement of the target reagent selectively binds to one of the target reagents. For example, if the target agent is an oligonucleotide, the linking reagent is streptavidin, and the additional linking reagent is biotin, the biotin-labeled oligonucleotide can bind to the first of the four biotin-binding sites of streptavidin to form a target reagent connected to the linking reagent. The biotin-labeled capture reagent (i.e., the capture reagent linked to the additional linking reagent) can then bind to the remaining biotin-binding sites on streptavidin to link the target agent to the capture reagent.

[0042] Exemplary target reagents and their complements are described herein. In embodiments, the target reagent and its complement are two members of a binding partner selected from avidin-biotin, streptavidin-biotin, antibody-hapten, antibody-antigen, antibody-epitope tag, nucleic acid-complementary nucleic acid, aptamer-aptamer target, and receptor-ligand. In embodiments, the target reagent is biotin and its complement is streptavidin. In embodiments, the linking reagent and additional linking reagent pair are different binding partners from the target reagent and its complement pair. In embodiments, the linking reagent is avidin or streptavidin and the additional linking reagent is biotin. In embodiments, the target reagent and its complement are complementary oligonucleotides.

[0043] In embodiments, the methods of the present invention are applied in singleplex or multiplex forms in which multiple assay measurements are performed on a single sample. Multiplex measurements that can be used in the present invention include, but are not limited to, i) multiplex measurements using multiple sensors, ii) multiplex measurements using individual assay domains on a surface (e.g., arrays) that can be identified based on their position on the surface, iii) multiplex measurements using reagents coated on particles that can be identified based on particle characteristics such as size, shape, and color, iv) multiplex measurements that generate assay signals that can be identified based on optical properties (e.g., absorbance or emission spectrum), or v) multiplex measurements based on the temporal characteristics of the assay signal (e.g., time, frequency, or phase of the signal). Exemplary assay formats include V-PLEX (www.mesoscale.com / en / products_and_services / assay_kits / v-plex) and U-PLEX (www.mesoscale.com / en / products_and_services / assay_kits / u-plex_gateway), as well as U.S. Patent Nos. 10,201,812 and 10,189,023, each of which is incorporated herein by reference in its entirety. Additional super-sensitivity assay formats are described in U.S. Provisional Application No. 62 / 812,928 filed March 1, 2019, U.S. Provisional Application No. 62 / 866,512 filed June 25, 2019, International Patent Publication No. 2020 / 0180645, and International Patent Publication No. 2020 / 227016, each of which is incorporated herein by reference in its entirety.

[0044] Exemplary plate readers include the MESO Sector S600 and MESO QUICKPLEX SQ120, for which therapies are available from Meso Scale Diagnostics, LLC, and these plate readers are described in U.S. Patent No. 6,977,722, U.S. Provisional Patent Application No. 62 / 874,828, titled "Assay Apparatuses, Methods and Reagents" by Krivoy et al., filed July 16, 2019, and International Patent Publication No. 2021 / 011630, each of which is incorporated herein by reference in whole.

[0045] Returning to Figure 1, in one embodiment, the local assay computing system 102 includes a computing system placed alongside the assay device 101 and configured to operate the assay device 101. The local assay computing system 102 may be a dedicated computing system provided integrally or separately from the assay device 101, and / or a general-purpose computing system provided integrally or separately from the assay device 101. The local assay computing system 102 may consist of software for operating the assay device 101 and for performing analysis of data captured by the assay device 101. The local assay computing system 102 may be configured as a server (e.g., having one or more server blades, processors, etc.), a personal computer (e.g., a desktop computer, a laptop computer, etc.), a smartphone, a tablet computing device, and / or other devices that can be programmed to interface with the assay device 101. In one embodiment, some or all of the functions of the local assay computing system 102 may be performed as part of a cloud computing platform.

[0046] In one embodiment, the network computing system 104 includes a computing system located remotely from the assay device 101. The network computing system 104 may be connected to one or more assay devices 101, one or more local assay computing systems 102, and / or one or more data storage devices 106 via a closed network 103 and / or an open network 105. In an embodiment, the network computing system 104 may be disconnected from any network. The network computing system 104 may include software for operating the assay device 101 and / or for receiving and analyzing data captured by the assay device 101. The network computing system 104 may be configured as a server (e.g., having one or more server blades, processors, etc.), a personal computer (e.g., a desktop computer, a laptop computer, etc.), a smartphone, a tablet computing device, and / or other devices that can be programmed to interface with the assay device 101 and / or to receive and manipulate data generated by the assay device 101. In one embodiment, some or all of the functions of the local assay computing system 102 may be performed as part of a cloud computing platform.

[0047] In one embodiment, one or more data storage devices 106 include devices and systems such as servers and hard drives configured for data storage. One or more data storage devices 106 are accessible by a local assay computing system 102 and a network computing system 104. Data storage devices 106 may include any type of computer-readable storage medium and / or computer-readable storage device. Such computer-readable storage medium or device may be configured to store data and provide access to the data. Examples of computer-readable storage mediums or devices include, but are not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof, such as computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), solid state drive (SSD), or memory stick.

[0048] All devices within the assay system environment 100 may be connected to a network via wired or wireless links. In embodiments, one or more devices may be connected to each other via one or more closed networks 103 and / or open networks 105. The closed network 103 includes any type of private network, whether ad-hoc or stable. The open network 105, consistent with embodiments herein, includes all types of public networks, such as the Internet. Devices in the assay system environment 100 may be connected via any combination of multiple networks. In embodiments, some devices in the assay system environment 100 may not be connected to other devices in the system and may exchange data only through the physical movement of storage media.

[0049] Networks consistent with the embodiments described herein may be connected via wired or wireless links. Wired links may include digital subscriber lines (DSL), coaxial cable lines, or fiber optic lines. Wireless links may include Bluetooth®, Bluetooth Low Energy (BLE), ANT / ANT+, ZigBee, Z-Wave, Thread, Wi-Fi®, Worldwide Interoperability for Microwave Access (WiMAX®), mobile WiMAX®, WiMAX®-Advanced, NFC, SigFox, LoRa, Random Phase Multiple Access (RPMA), Weightless-N / P / W, infrared channels, or satellite bands. Wireless links may also include any cellular network standard for communication between mobile devices, including standards that qualify as 2G, 3G, 4G, or 5G. Wireless standards may use various channel access methods, e.g., FDMA, TDMA, CDMA, or SDMA. In some embodiments, different types of data may be transmitted via different links and standards. In other embodiments, the same type of data may be transmitted over different links and standards. Network communication may be performed over any preferred protocol, including, for example, http, TCP / IP, UDP, Ethernet, ATM, etc.

[0050] The network can be any type and / or form of network. The geographical range of the network can vary greatly, and the network can be a body area network (BAN), personal area network (PAN), local area network (LAN), e.g., intranet, metropolitan area network (MAN), wide area network (WAN), or the internet. The network topology can be any form, e.g., including point-to-point, bus, star, ring, mesh, or tree. The network may be any such network topology known to those skilled in the art that can support the operations described herein. The network may utilize different layers or stacks of technologies and protocols, including, for example, Ethernet protocol, Internet Protocol Suite (TCP / IP), ATM (Asynchronous Transfer Mode) technology, SONET (Synchronous Optical Networking) protocol, or SDH (Synchronous Digital Layer) protocol. The TCP / IP Internet Protocol Suite may include an application layer, transport layer, internet layer (e.g., including IPv4 and IPv4), or link layer. The network can be a type of broadcast network, communication network, data communication network, or computer network.

[0051] The network environment shown in Figure 1 represents an exemplary embodiment of an assay system environment 100 configured to network and connect multiple assay devices. Although shown as being connected via networks 103 and 105, any suitable set of individual network connections may be used to enable the various devices and systems associated with the assay system environment 100 to communicate appropriately.

[0052] In the embodiment, any aspect of the assay system environment 100 that provides data storage and / or computing capabilities may be provided by a cloud computing solution.

[0053] Figures 2–5 show specific embodiments of assay device 101 consistent with the embodiments herein. The methods of these embodiments can be used in conjunction with various assay devices 101 and / or forms as described above. Although specific assay devices are depicted and described, the methods and systems described herein can be used with any suitable assay device, system, or method.

[0054] Assay devices consistent with the embodiments herein may be used, for example, to perform and read out assays in multiwell plate format having one or more of the following desirable attributes: (i) high sensitivity, (ii) wide dynamic range, (iii) small size and light weight, (iv) array-based multiplexing capability, (v) automated operation, and (vi) ability to process multiple plates. The devices and methods may be used with a variety of assay detection techniques, including but not limited to techniques for measuring one or more detectable signals. Some embodiments are suitable for electrochemiluminescence measurements and, specifically, are suitable for use with multiwell plates (and assay methods using these plates) having integrated electrodes, such as those described in U.S. Patent No. 7,842,246 by Wohlstadter et al., U.S. Patent No. 7,807,448 by Glezer et al., and U.S. Patent No. 10,281,678 by Chamberlin et al., each of which is incorporated herein by reference in whole.

[0055] In one embodiment, assay device 101 is provided for performing a luminescence assay in a multiwell plate. For example, an embodiment of assay device 101 comprises a photodetection subsystem and a plate handling subsystem, the plate handling subsystem including a light-shielding container that provides a light-free environment in which luminescence measurements can be performed. This light-shielding container includes a housing and a removable drawer located within the housing. The housing also includes a housing top having one or more plate introduction apertures from which plates can be lowered (manually or mechanically) onto a plate translation stage or removed within the drawer. A sliding light-shielding door within the housing is used to seal the plate introduction apertures from ambient light before performing luminescence measurements. The housing further includes a detection aperture, which is coupled to a photodetector mounted on the housing top and one or more plate stackers mounted on the housing top above the plate introduction apertures, the plate stackers being configured to receive or deliver plates into the removable drawer. The removable drawer includes a plate translation stage, which translates the plate horizontally within the drawer to a zone in the apparatus where a specific assay process and / or detection step is performed. The removable drawer also includes one or more plate lifters, which are positioned below one or more plate introduction apertures, and which are positioned below the plate translation stage, which is positioned below the detection aperture, and above the plate lifters on the plate lift platform.

[0056] The assay device 101 may also include a photodetector mounted on a detection aperture on the top of the housing (e.g., via a light-shielding connector or baffle). In certain embodiments, the photodetector is an imaging photodetector such as a CCD camera and may also include a lens. This photodetector may also be a conventional photodetector such as a photodiode, avalanche photodiode, or photomultiplier tube. A suitable photodetector may also include an array of such photodetectors. Possible photodetectors to be used may also include imaging systems such as CCD and CMOS cameras. This photodetector may also include lenses, light guides, etc., for directing, focusing, and / or imaging light onto the detector. In certain specific embodiments, the imaging system is used to image the luminescence from an array of binding domains in one or more wells of an assay plate, and the assay device reports the luminescence value of the luminescence emitted from the individual elements of the array. The photodetector is mounted on the top of the housing, which is equipped with a light-shielding seal. Additional components of this apparatus include plate contacts for making electrical contact with the plate and supplying electrical energy to electrodes in a well positioned below the photodetector (for example, to induce ECL).

[0057] Further elements of the assay device 101 are shown in Figures 2A and 2B, which show front and rear views of the assay device 101 with an aesthetic cover, and in Figures 2C and 2D, which show corresponding front and rear views of the assay device without a cover, respectively. As shown in the figures, for example in Figure 2D, the apparatus includes a photodetection subsystem 210 and a plate handling subsystem 220. Further detailed diagrams are provided in Figures 3A-3B. The plate handling subsystem 220 includes a light-shielding container 230, which comprises a housing 331 having a housing top 332, a bottom 333, a front 334, and a rear 335. The housing also includes a plurality of alignment functions, and the housing includes a removable drawer front and is adapted to receive a removable drawer 340 (shown in Figure 4) consisting of a single cast element.

[0058] Referring to Figure 3A, the upper housing 332 also includes one or more plate introduction (and exit) apertures 336 and 337, respectively, through which plates are lowered (manually or mechanically) to or removed from a plate translation stage. A sliding light-shielding door (shown as 339 in Figure 3C) is used to seal the plate introduction apertures 336, 337 from ambient light before performing luminescence measurements. Furthermore, the upper housing also includes an identifier controller for reading and processing data stored in identifiers on the plates.

[0059] In embodiments, the assay device 101 includes functions such as an identifier controller for automatic identification of sample plates. In one embodiment, the identifier controller is a barcode reader (338) mounted on an aperture in the upper part of the housing via a light-shielding seal, where the barcode reader is configured to read barcodes on plates placed on a plate translation stage in the housing. In a preferred embodiment, the barcodes on the plates are read when the plates are lowered into the drawer. In alternative or additional embodiments, the plates have identifiers such as EEPROM or RFID, and the upper part of the housing and / or the drawer includes an identifier controller suitable for communicating with each of these identifiers. In further embodiments, the identifier controller may be provided separately from the device. In this embodiment, information stored in an identifier attached to one plate or associated with one plate or a set of plates is transferred to the device via a computer and / or network attached to the device and / or manually entered via the user interface of the computer and / or network. In this regard, U.S. Patent Publication No. 2011 / 0022331 and U.S. Patent No. 8,770,471 are referenced, and their disclosures are incorporated herein by reference.

[0060] In some cases, the plate handling subsystem 220 further includes one or more plate stackers mounted on the upper housing 332 above the plate introduction apertures 336, 337, the plate stackers being configured to receive or deliver plates to a plate lifter. The plate handling subsystem optionally includes heating and / or cooling mechanisms (e.g., resistance heaters, fans, heat sinks, or thermoelectric heaters / coolers) for maintaining the subsystem's temperature under desired conditions. It may also include humidity control mechanisms (e.g., humidifiers and / or dehumidifiers, or drying chambers for maintaining the subsystem's humidity under desired conditions).

[0061] Figure 5 shows a plate carriage 522 with a multiwell plate 520. The plate carriage 522 supports the multiwell plate 520 (or other consumables configured for use in assay device 101). The plate carriage 522 is configured to support the multiwell plate 520 within assay device 101. Multiwell plates 520 (also known as microtita plates or microplates) consistent with the embodiments herein can take on a variety of forms, sizes, and shapes. For convenience, several standards have emerged for measuring instruments used to process samples for high-throughput assays. Multiwell assay plates are typically manufactured in standard sizes and shapes and have a standard arrangement of wells. Examples of well arrangements include those found in 96-well plates (12 × 8 well arrays), 384-well plates (24 × 16 well arrays), and 1536-well plates (48 × 32 well arrays). However, the methods and systems discussed herein are not limited to any particular plate or assay format.

[0062] The assay device 101 is configured to perform both calibration assays and sample assays as described herein. As described herein, a calibration assay includes an assay performed on a calibration sample having a defined amount of analyte. The calibration sample may include calibration samples having different amounts of analyte. Multiple calibration assay signal values ​​are acquired by the assay device 101 in response to different, known, amounts or concentrations of analyte during the calibration assay. After performing the calibration assay, the assay device 101 and / or the local assay computing system 102 associated with the assay device 101 may store the calibration data (i.e., information representing known amounts of analyte and corresponding calibration assay signal values) in any suitable storage device associated with the assay system environment 100. In embodiments, multiple calibration assay signal values ​​may be acquired to correspond to a single known amount of analyte; that is, the calibration sample may be measured multiple times.

[0063] As used herein, "quantity" may refer to the total amount of analyte in a sample, expressed in units of quantity such as weight or volume, moles, or the number of molecules. "Quantity" may also refer to the concentration of the analyte. "Quantity" may also refer to the amount of chemical, biological, or catalytic activity associated with the analyte. "Quantity" may be given in absolute terms or in units relative to a reference sample or material.

[0064] The assay device 101 is further configured to perform a sample assay on one or more test samples, each having an unknown amount of analyte, as described herein. Performing a sample assay on a test sample generates a sample assay signal value. The sample assay signal value indicates the unknown amount of analyte associated with it. In embodiments, multiple sample assay signal values ​​may be obtained to correspond to the unknown amount of analyte in a single test sample; that is, the test sample may be measured multiple times.

[0065] Figure 6 shows an embodiment of a computer system consistent with the embodiments described herein. Computing system 602 is an example of a local assay computing system 102 and a network computing system 104. For example, computing system 602 may include a server, a personal computer, a smartphone, and / or a tablet computing device. Additionally, the functions of computing system 602 may be performed via a cloud computing platform.

[0066] The computing system 602 may include one or more processors 610 (for convenience, both processing units 610 and processors 610 are used synonymously herein), one or more storage devices 630, and / or other components. In other embodiments, the functions of the processor may be performed by hardware (e.g., by using application-specific integrated circuits ("ASICs"), programmable gate arrays ("PGAs"), field-programmable gate arrays ("FPGAs"), etc.), or by any combination of hardware and software. The storage devices 630 include any type of non-temporary computer-readable storage medium and / or non-temporary computer-readable storage device. Such computer-readable storage medium or device may store computer-readable program instructions for causing the processor to perform one or more methodologies described herein. Examples of computer-readable storage media or devices include, but are not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof, such as computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), memory sticks, and the like.

[0067] The processor 610 is programmed by one or more computer program instructions stored in the storage device 630 and executable by the processor 610. For example, the processor 610 is programmed by the protocol manager 612, the network manager 614, the data manager 616, the calibration adjustment manager 618, the analysis manager 620, and the user interface manager 622. It will be understood that the functions of the various managers described herein are representative and not limited. Additionally, the storage device 630 may function as a data storage device 106 to provide data storage to the assay system environment 100. For convenience as used herein, the various “managers” are actually described as performing an operation when the manager programmed the processor 610 (and thus the computing system 602) to perform that operation.

[0068] The protocol manager 612 is a software protocol (e.g., a software module or library) that can operate on the computing system 602. The protocol manager 612 is configured to provide one or more control signals to one or more assay devices 101. The control signals provided by the protocol manager 612 are configured to provide the instructions necessary to operate one or more assay devices 101. The control signals may specify one or more assay protocols to be executed by one or more assay devices. Using the control signals provided by the protocol manager 612, any process that the assay devices 101 described herein are capable of initiating and / or controlling can be initiated and / or controlled.

[0069] In the embodiment, the protocol manager 612 may further operate to receive data collected during the operation of one or more assay devices 101. Such data may include, for example, calibration assay data and sample assay data. The received data may then be processed or stored by the data manager 616.

[0070] The protocol manager 612 is configured to operate to control one or more assay devices 101 to perform a calibration assay. The assay device 101, controlled by the protocol manager 612, can obtain calibration assay measurements for multiple calibration samples (e.g., calibration samples stored as calibrators in a multiwell plate) having a defined amount of analyte. The multiple calibration samples may contain different amounts of analyte. The protocol manager 612 operates to determine the calibration assay signal values ​​corresponding to the multiple calibration samples. The protocol manager 612 is configured to perform a calibration assay to determine one or more calibration datasets. The calibration dataset contains information relating multiple amounts of values ​​to the corresponding multiple calibration assay signal values.

[0071] The protocol manager 612 is further configured to operate to control one or more assay devices 101 to perform a sample assay. The assay device 101, controlled by the protocol manager 612, can obtain sample assay measurements for multiple test samples (e.g., test samples placed in a multiwell plate) having an unknown amount of analyte. The protocol manager 612 operates to determine the sample assay signal values ​​corresponding to the multiple test samples. The protocol manager 612 is configured to perform a sample assay to determine one or more sample assay datasets. The sample assay dataset may include information relating the sample assay signal values ​​to sample identification data. The sample identification data may include any suitable data for identifying the test sample, such as the plate location.

[0072] The network manager 614 is a software protocol (e.g., a software module or library) that can run on the computing system 602. The network manager 614 is configured to establish network communication between networks 103, 105, assay device 101, data storage device 106, and / or any other devices within the assay system environment 100 in Figure 1. The established communication paths can utilize any suitable network transfer protocol to provide one-way or two-way data transfer. The network manager 614 can establish as many network communications as necessary to communicate with various elements of the assay system environment 100.

[0073] The network manager 614 facilitates the transmission and reception of sample assay data, calibration assay data (also known as calibration assay information), sample assay and calibration assay protocols, calibration models, and any other information, and / or synchronizes with the operation of the assay system environment 100.

[0074] The data manager 616 is a software protocol or software module that can operate on the computing system 602. The data manager 616 is configured to access assay data, such as sample assay data and calibration assay data, from one or more assay devices 101 in the assay system environment 100. The assay data may include, for example, sample assay datasets and calibration datasets that can be acquired in near real time, may be archived data, and / or data extracts, as well as process information and process parameter information, and any other information or data generated by or stored in the assay device 101. The data manager 616 is further configured to access one or more data storage devices 106, a local assay computer system 102, and / or a network computer system 104, and to store and / or receive assay data stored in any or all of these. In a further embodiment, the data manager 616 is configured to access various removable physical storage media that can store assay data.

[0075] The data manager 616 may provide data to the user via the user interface manager 622. In embodiments, the data manager 616 may be further configured to provide the user with access tools for managing and manipulating assay data (also called assay system data). For example, the data manager 616 may be configured to generate reports, compare them with assay system data, reference assay system data, and enter assay system data into a database. In embodiments, the data manager 616 may provide data retention capabilities. The data manager 616 may be further configured to receive and store any and all data collected and / or used within the assay system environment 100.

[0076] The calibration fitting manager 618 is a software protocol (e.g., a software module or library) that operates on the computing system 602. The calibration fitting manager 618 is configured to access one or all of the data storage systems of the assay system environment 100 described herein in order to obtain the dataset necessary to perform calibration fitting operations. Specifically, the calibration fitting manager 618 is configured to obtain calibration datasets and generate calibration models. The calibration models include at least calibration model equations and associated fitting parameters.

[0077] As discussed above, a calibration dataset includes the results of calibration assay measurements performed on multiple calibration samples containing known amounts of analytes. Calibration samples may include those having a defined amount of analyte and those having different amounts of analytes. As discussed herein, the defined amount may be defined absolutely or relatively with respect to a reference standard or sample. A calibration dataset includes values ​​for multiple quantities and multiple assay signal values ​​corresponding to the values ​​for multiple quantities. If multiple calibration assay signal values ​​are obtained so that they correspond to a single known quantity value, they may be treated as individual data points or aggregated together to be treated as a single data point (e.g., mean, weighted mean, geometric mean, median, etc.). Calibration assay signal values ​​may be, for example, values ​​for quantities measured by assay device 101.

[0078] The calibration fitting manager 618 is configured to fit the dependency of assay signal values ​​to the values ​​of multiple quantities for the calibration model equation. The calibration model equation is used to model the analyte detection response. The calibration model equation is selected for a particular application according to various characteristics. For example, a particular calibration model equation may be selected for ease of use, overall accuracy or fit, accuracy or fit over a specific area, and other characteristics. The calibration model equation is used to generate estimates of results and does not represent natural laws or phenomena.

[0079] In an embodiment, the calibration model equation used to generate a calibration fit includes a modified four-parameter logistic regression fit equation. A general four-parameter logistic fit (4PL) equation can be expressed as follows. [Number] In the 4PL equation, x represents the value of the assay amount, y represents the assay signal value, and A, B, C, and D represent fit parameters.

[0080] As shown in FIG. 7, the 4PL equation generates a sigmoid, or sigmoid, curve 700. The fit parameters define the characteristics of the curve. Parameter A represents the asymptote of the curve. B represents the other asymptote of the curve. C represents the amount (e.g., analyte amount) that generates a signal midway between A and B and represents the inflection point of the sigmoid curve. Finally, D represents the Hill slope of the curve and is related to the order of the function that describes the curve at amount C or, in the logarithmic plot shown in FIG. 7, the steepness of the curve at amount C. In the example represented in FIG. 7, A = 100, B = 1,000,000, C = 100,000, and D = 1.

[0081] In an example such as that shown in FIG. 7, when D > 0, A represents the asymptotic value of y at low values of x, i.e., the value that the assay signal value y approaches as the value of the assay amount x decreases to values << C, and B represents the asymptotic value of y at high values of x, i.e., the value that the assay signal value y approaches as the value of the assay amount x increases to values >> C. Alternatively, when D < 0, A represents the asymptotic value of y at high values of x and B represents the asymptotic value of y at low values of x.

[0082] In an embodiment, the first equation used as the calibration model equation is a modified four-parameter logistic regression fit (referred to herein as "4PL+") equation, and the constant value Hill slope of the 4PL fit is modified by making it variable according to a function of the amount of analyte. In Equation 1A, which is the first 4PL+ equation, the function of the amount is the logarithmic function of the amount. The first 4PL+ equation, Equation 1A, is

number

[0083] Reverse the 4PL+ formula (formula 1A),

number

number

[0084] For non-zero values ​​of D and E, equation 1A typically produces a maximum or minimum value of y that is not sigmoid over all possible positive values ​​of x. For positive values ​​of E, and for very small and very large positive values ​​of x, the equation asymptotically approaches B, but has a maximum or minimum value if y approaches A. For negative values ​​of E, and for very small and very large positive values ​​of x, the equation asymptotically approaches A, but has a maximum or minimum value if y approaches B. In both of these cases, there exists a range of x values ​​that produces a graph of y where y is sigmoid or nearly sigmoid, with a shape in which the y values ​​approach A and B at the end of the graph. Therefore, when determining the parameters A, B, C, D, and E for calibration fit, the parameters may be selected such that the assay signal y in equation 1A is nearly sigmoid and changes monotonically over a range of interest x, e.g., the expected range of quantity x. In embodiments, the parameters of Equation 1A may be selected such that the assay signal y is approximately sigmoid and changes monotonically over a specified dynamic range of the assay for x, e.g., at least between the lower limit and the upper limit of quantification. In embodiments, the parameters are set such that over a specified dynamic range of x, the ratio of the absolute value of E*ln(C / x) to the absolute value of D is less than 1, less than 0.5, or less than 0.2. In embodiments, the parameters are set such that over a specified dynamic range of x, the ratio of -E*ln(C / x) to D is less than 1, less than 0.5, or less than 0.2.

[0085] In the embodiment, the second equation used as the calibration model equation is a modified four-parameter logistic regression fit (referred to herein as "4PL+") equation, in which the constant-value Hill slope of the 4PL fit is modified by making it variable according to a function of quantity x. In the second 4PL+ equation, the function quantity is proportional to the reciprocal of quantity x. The second 4PL+ equation, Equation 2A, is:

number

number

[0086] It is understood that all equations discussed herein can be expressed in an unlimited number of alternative but mathematically equivalent forms without altering the essential properties of the equations. Defining one form of fitting parameters essentially defines other forms of fitting parameters, for example, through formulas relating to two sets of parameters. For example, the general 4PL equation

number

number

number

[0087] Figures 8-14 illustrate the characteristics of Equation 1A across a range of quantities as different fitting parameters change.

[0088] Figure 8 shows a comparison of Equation 1A of the general 4PL and 4PL+ equations when calibrating a human MDC assay performed on an ECL imaging instrument. The ECL signal values ​​shown in the graph were averaged across five plates, with four replicates per plate performed in different wells. As shown in Figure 8, the 4PL+ fitted line 801 shows a better fit to the assay signal value 803 compared to the 4PL fitted line 802. Specifically, the 4PL fitted line 802 shows greater inaccuracy at low and high values. Table 1 below shows exemplary values ​​for the parameters of the 4PL+ and 4PL equations for generating fitted lines 801 and 802. [Table 1]

[0089] Figure 9 shows the effect of changing the value of E on the 4PL+ equation, equation 1A. Figure 9 shows the 4PL+ equation over a range of quantities for positive, negative, and zero values ​​of E. When E is zero (fit line 901), the 4PL+ equation is reduced to the general 4PL equation, as discussed above. Positive values ​​of E (e.g., fit line 903) result in lower signal values ​​for the 4PL+ equation with less quantity. Negative values ​​of E (e.g., fit line 902) result in higher signal values ​​for the 4PL+ equation with less quantity. Table 2 below shows exemplary values ​​for the parameters of the 4PL+ equation to generate fit lines 901, 902, and 903. [Table 2]

[0090] Figure 10 shows the effect of changing the value of A on the 4PL+ equation, equation 1A. Adjusting A in the 4PL+ equation changes the lower limit value, similar to the 4PL equation. Fitted line 1001 shows the 4PL+ equation with value A=100, fitted line 1002 is generated with value A=500, and fitted line 1003 is generated with value A=20. Table 3 below shows exemplary values ​​for the parameters of the 4PL+ equation to generate fitted lines 1001, 1002, and 1003. [Table 3]

[0091] Figure 11 shows the effect of changing the value of B on the 4PL+ equation, equation 1A. Adjusting B in the 4PL+ equation changes the upper limit value, similar to the 4PL equation. Fitted line 1101 shows the 4PL+ equation with value B = 1,000,000, fitted line 1102 is generated with value B = 5,000,000, and fitted line 1103 is generated with value B = 200,000. Table 4 below shows exemplary values ​​for the parameters of the 4PL+ equation to generate fitted lines 1101, 1102, and 1103. [Table 4]

[0092] Figure 12 shows the effect of changing the value of C on the 4PL+ equation, equation 1A. Adjusting C in the 4PL+ equation changes the value of the midpoint of the quantity, similar to the 4PL equation. Fitted line 1201 shows the 4PL+ equation with value C = 100,000, fitted line 1202 is generated with value C = 500,000, and fitted line 1203 is generated with value C = 20,000. Table 5 below shows exemplary values ​​for the parameters of the 4PL+ equation to generate fitted lines 1201, 1202, and 1203. [Table 5]

[0093] Figure 13 shows the effect of changing the value of D on the 4PL+ equation, equation 1A. Adjusting D in the 4PL+ equation changes the value of the slope of the equation, just as it does with the 4PL equation. Fitted line 1301 shows the 4PL+ equation with value D=1, fitted line 1302 is generated with value D=1.2, and fitted line 1303 is generated with value D=0.8. Table 6 below shows exemplary values ​​for the parameters of the 4PL+ equation to generate fitted lines 1301, 1302, and 1303. [Table 6]

[0094] In further embodiments, unrestricted or unuppercase modified 4PL equations may be selected as calibration model equations. These unrestricted or unuppercase modified 4PL equations may be selected for use in assays that do not have an upper asymptote and do not have a plateau for signal values ​​at high analyte values. An unuppercase modified 4PL equation consistent with the embodiments herein includes Equation 3A, which is an unuppercase modified 4-parameter logistic regression fitted equation, where the modified Hill slope is variable according to the logarithmic function of the quantity. Equation 3A is,

number

number

[0095] Figures 8–13 illustrate how Equation 1A of the 4PL+ equation in this disclosure differs from the general 4PL equation and how parameter modifications alter the calibration curve. These differences may offer advantages in calibration accuracy. In embodiments, due to its ability to vary the modified Hill Slope value quantitatively, the 4PL+ equation provides a closer calibration fit to a particular assay.

[0096] Figure 14 shows the recovery rates of several calibration curve points against the signal-to-background ratio of different calibration model equations for a human MDC assay performed on an ECL imaging instrument similar to that described herein. For each calibration curve point, the ECL signal value was averaged across results on five plates, with four replicates per plate in different wells. The calibration model equations shown in Figure 14 are Equation 1A of the 4PL+ equation, the general 4PL equation, and the general 5PL equation.

number

[0097] Figure 15 shows the recovery of several calibration curve points against the signal-to-background ratio for different calibration model equations for three assays performed on a Magpix® instrument (Luminex Corporation). For each calibration curve point, the central fluorescence intensity (MFI) signal value was averaged over two iterations. The calibration model equations shown in Figure 15 are Equation 1A of the 4PL+ equation, the general 4PL equation, and the general 5PL equation.

number

[0098] Returning to Figure 6, the calibration fitting manager 618 operates to determine the values ​​for the fitting parameters of equations 1A, 2A, 3A, and / or 4A of the 4PL+ equations based on the calibration dataset. The calibration dataset includes calibration assay signal values ​​corresponding to known assay quantity values. Any suitable curve fitting method may be used to determine the fitting parameters of the selected model equations, including, for example, the Levenberg-Marquardt algorithm, the Gauss-Newton algorithm, and the gradient descent algorithm.

[0099] In the embodiment, identifying values ​​for fitting parameters involves determining the values ​​of each fitting parameter that minimize the mean squared error or sum of squared errors between the calibration dataset and the values ​​of the fitting parameters, as obtained from the calibration model. In the embodiment, the curve fitting method is used after weighting the x or y values. In the embodiment, this is also called relative weighting, 1 / y 2 After applying the weighting, a curve fitting method can be used to minimize the mean squared error or sum of squared errors. 1 / y 2 The weighting adjusts the curve fit by considering larger signal values ​​and larger variances at larger quantities.

[0100] The conformance parameters are determined according to calibration assay data obtained through one or more calibration assays performed by one or more assay devices 101 in Figure 1. During operation, the calibration conformance manager 618 in Figure 6 may acquire calibration assay data when it is generated by one or more assay devices 101 (e.g., acquire calibration assay data directly from one or more assay devices 101). In a further embodiment, the calibration conformance manager 618 may access calibration data stored to perform calibration conformance operations (e.g., calibration data stored on a data storage device 106).

[0101] Model equations and fitting parameters are collectively referred to herein as calibration models. These can be stored as calibration information in any suitable data storage location within the assay system environment 100. This calibration information may include one or more calibration models (selected model equations and one or more fitting parameters or sets of fitting parameter values ​​associated with each of the selected model equations), and / or calibration assay data. The calibration information may be stored in such a manner that it is associated with the assay medium (e.g., a multiwell plate) to which the calibration information pertains. The calibration information may be further associated with the assay device 101 used to perform the calibration assay that yielded the calibration information. Thus, any system or device within the assay system environment 100 that accesses the calibrated assay medium can retrieve or access the stored calibration information.

[0102] The analysis manager 620 is a software protocol (e.g., a software module or library) that operates on the computing system 602 and is configured to access any or all of the data storage systems of the assay system environment 100 described herein to obtain the sample assay dataset necessary to perform the assay analysis operation. Specifically, the analysis manager 620 is configured to obtain the sample assay dataset and determine the sample volume value.

[0103] The analysis manager 620 is configured to acquire one or more sample assay datasets. The sample assay dataset may be acquired from any storage location within the assay system environment 100 (e.g., data storage device 106) and / or directly from the assay device 101 when acquired. The sample assay dataset includes the results of assay measurements for at least one test sample. The assay measurement includes at least one sample assay signal value corresponding to at least one test sample. In embodiments, the sample assay dataset may include any number of assay measurements for any number of test samples, including multiple sample assay signal values ​​per test sample.

[0104] The analysis manager 620 is further configured to determine (e.g., select) calibration information associated with the sample assay dataset. Calibration information associated with the sample assay dataset (e.g., calibration information associated with the multiwell plate from which the sample assay dataset was acquired) can be retrieved from any suitable storage location in the assay system environment 100.

[0105] Based on the acquired sample assay test data (e.g., one or more assay signal values) and calibration model (which may include the selected model equation and the respective values ​​of the associated fitting parameters), the analysis manager is configured to determine one or more sample volumes or volume values. Appropriate values ​​for the fitting parameters and assay signal values ​​are input into the selected model equation, which can then be solved for the sample volume values. In embodiments, the equation is solved numerically to determine the sample volume values ​​by, for example, using one of many algorithms known in the art to solve the equation. (Some short, non-restrictive lists of the many algorithms that can be used include the bisection method, secant method, Newton's method, Steffensen method, and Brent's method.) In embodiments, the selected model equation is inverted to facilitate the determination of sample volume values. The analysis manager 620 is configured to perform sample volume value calculations for any and all sample assay signal values ​​stored in the sample assay dataset.

[0106] The User Interface Manager 622 is a software protocol (e.g., a software module or library) that operates on the computing system 602. The User Interface Manager 622 is configured to provide a user interface to enable user interaction with the computing system 602. The User Interface Manager 622 is configured to receive input from any user input source, including, but not limited to, a touchscreen, keyboard, mouse, controller, joystick, or voice control. The User Interface Manager 622 is configured to provide a user interface such as a text-based user interface, a graphical user interface, or any other preferred user interface. In embodiments, the User Interface Manager 622 may be configured to provide a “methodical user interface” (MUI), as described in U.S. Patent Application No. 16 / 513,526, issued U.S. Patent No. 10,936,163 on March 2, 2021, the entire MUI is incorporated herein by reference. The User Interface Manager 622 is configured to provide such user interface services via one or more clients or computing systems 602 using a Network Manager 614. The user interface manager 622 can be configured to provide different user interface services depending on the type of client device. For example, a laptop or desktop computer may be provided with a user interface that includes a complete suite of interface options, while a smartphone or tablet may be provided with a user interface limited to status updates.

[0107] The user interface manager 622 is configured to provide a user authentication service. Users may be authenticated, for example, through a password, biometric scan (retinal scan, fingerprint, voiceprint, facial recognition, etc.), key card, token access, and any other preferred means of user authentication. The user authentication service may be provided to control access to one or more assay devices 101.

[0108] In some embodiments, one or more users may be provided with full access to all functions, process information, and / or production information of the assay system environment 100. One or more users may be provided with limited access to the functions, process information, and / or production information of the assay system environment 100. One or more users may be provided with full access to limited parts of the assay system environment 100. In some embodiments, one or more users may be provided with "read-only" access that allows them to view process information, production information, etc., but does not allow them to make any adjustments to process parameters. Furthermore, one or more users may be provided with full or limited access to archived data. Access control may be determined according to user identification, user function, user job identification, and any other preferred criteria.

[0109] In the embodiment, the user interface manager 622 may provide one or more users with access to any or all process and / or production information relating to one or more elements of the assay system environment 100. The user interface manager 622 may allow users to perform various tasks on one or more devices of the assay system environment. For example, the user interface manager 622 may allow users to directly adjust or control one or more protocol parameters.

[0110] As discussed herein, various managers can be implemented by any combination of computing devices in the assay system environment 100. In embodiments, this distributed nature allows the operation of the protocol manager 612, the calibration adjustment manager 618, and the analysis manager 620 at separate locations. Thus, an engineer operating the assay device 101 via the local assay computing system 102 can store the collected calibration assay data and sample assay data in any storage device associated with the assay system environment 100. The calibration adjustment manager 618 can then be operated on the calibration data, and the analysis manager 620 can then be operated on sample assay data from any other computer system within the assay system environment 100.

[0111] Accordingly, various workflow scenarios can be achieved by the system described herein. For example, a customer entity may receive a multiwell sample plate from a distributor and perform all necessary calibration, assay, and analysis steps. In another example, a customer entity may receive a multiwell sample plate with the calibration step already completed. Thus, in addition to receiving the sample plate, the customer entity may receive relevant calibration information. In yet another example, the calibration assay and sample assay steps may be performed by a first party, while a second party performs the calibration fit and sample volume value determination steps. In yet another example, a customer entity may perform the calibration assay and sample assay steps and receive calibration information from a second party based on the calibration assay dataset. The above are merely examples and do not limit the scope of this disclosure. The use of the assay system environment 100 enables the operation of any of the steps described herein in any preferred location.

[0112] Figure 16 is a flowchart of process 1600 for calibrating an assay and applying the calibration to sample assay data. Process 1600 is performed on one or more computer systems having one or more physical processors programmed with computer program instructions that cause the computer system to perform the method when performed by one or more physical processors. In embodiments, process 1600 is performed via one or more computing systems 602 associated with the assay system environment 100. Computing system 602 represents an example of a combination of hardware and software configured to perform process 1600, but the implementation of process 1600 is not limited to the combination of hardware and software of computing system 602. Additional details regarding each of the operations of the method can be understood by following the description of computing system 602 as described above.

[0113] In operation 1602 of process 1600, the computing system (e.g., 602) acquires a calibration dataset. Acquisition of the calibration dataset may be performed, for example, by the calibration conformance manager 618 associated with the assay system environment 100, and / or by the calibration conformance manager 618 in conjunction with the protocol manager 612 associated with the assay system environment 100. Acquisition of the calibration dataset may include performing a calibration assay to generate a calibration dataset using one or more assay devices 101 associated with the calibration system environment 100. Acquisition of the calibration dataset may, alternatively or additionally, include retrieving the calibration dataset from any storage location (e.g., data storage device 106) associated with the assay system environment 100. The calibration dataset includes multiple quantities of values ​​based on defined quantities of values ​​of multiple calibration samples, and includes multiple assay signal values ​​corresponding to multiple calibration samples.

[0114] In operation 1604 or process 1600, the computing system determines the fitting parameters of the model calibration equation. Determining the fitting parameters of the model calibration equation may be performed, for example, by any calibration fitting manager 618 associated with the assay system environment. Determining the fitting parameters may include selecting a calibration model equation and fitting the parameters to the calibration model equation. Fitting the parameters to the calibration model equation includes fitting the dependence of multiple assay signal values ​​from the calibration dataset to the values ​​of multiple quantities in the calibration dataset into the calibration model equation. The selected calibration model equation and associated fitting parameters constitute the calibration model. For example, the calibration fitting manager 618 may determine the values ​​of each of the above fitting parameters (e.g., A, B, C, D) that minimize the amount of error between the value of the analyte predicted or estimated by the calibration model and the value of the known analyte of the calibration sample.

[0115] In operation 1606 of process 1600, the computing system may acquire a sample assay dataset. Acquiring a sample assay dataset may include, for example, running a sample assay on an assay device 101 associated with the assay system environment 100 by a protocol manager 612 associated with the assay system environment 100 in order to generate a sample assay dataset. Acquiring a sample assay dataset may also include, additionally or alternatively, retrieving a sample assay dataset from any storage location associated with the assay system environment 100 by an analysis manager 620.

[0116] In operation 1608 of process 1600, the computing system may determine the value of the sample assay volume. The value of the sample assay volume is determined by the analysis manager 620 according to, for example, the sample assay dataset and calibration model, i.e., the selected model calibration equation and fitted parameter values ​​from operation 1604. In the embodiment, an inverted form of the selected model calibration equation is used to facilitate the determination of the value of the sample assay volume. In the embodiment, the analysis manager determines the value of the sample assay volume using an equation-solving algorithm.

[0117] The operation of process 1600 can be performed by various managers associated with the computing systems (local and networked) of the assay system environment 100. The various operations of process 1600 can be performed on one or more of the computing systems associated with the assay system environment 100, and there is no requirement that any of the operations be performed on the same computing system.

[0118] Additional embodiments include the following:

[0119] Embodiment 1 is an assay system calibration method comprising: performing multiple calibration assays on a plurality of calibration samples, each having a defined amount of analyte and a different amount of analyte, on an assay system to obtain a plurality of calibration assay signal values; generating a calibration dataset by at least one processing unit, which includes a plurality of quantity values ​​according to a defined amount and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples; and selecting a calibration model equation by at least one processing unit that associates the defined amount with the plurality of calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation having a modified Hill slope that depends on a function of the quantity values, and at least The method includes: identifying the respective values ​​for fitting parameters that fit a calibration model equation to a calibration dataset using one processing unit; generating a calibration model that includes the calibration model equation and the respective values ​​for fitting parameters using at least one processing unit; performing at least one sample assay on at least one test sample on an assay system to obtain at least one sample assay signal value; generating a sample assay dataset that includes at least one sample assay signal value using at least one processing unit; and obtaining a sample volume value determined according to the calibration model and at least one sample assay signal value using at least one processing unit.

[0120] Embodiment 2 includes Embodiment 1, wherein in the calibration model equation, the modified Hills slope depends on a function of the reciprocal of the value of the quantity.

[0121] Embodiment 3 includes Embodiment 2, wherein the calibration model equation is

number

[0122] Embodiment 4 includes Embodiments 1 to 3, wherein in the calibration model equation, the modified Hills slope depends on a function of the natural logarithm of the reciprocal of the quantity value.

[0123] Embodiment 5 includes Embodiments 1 to 4, and the calibration model equation is,

number

[0124] Embodiment 6 includes Embodiments 1-5, and the value of the sample quantity is obtained according to the calibration model, and the equation

number

[0125] Embodiment 7 includes Embodiments 1 to 6, and the calibration model equation is,

number

[0126] Embodiment 8 includes Embodiments 1 to 7, and the calibration model equation is,

number

[0127] Embodiment 9 includes Embodiments 1 to 8 and includes determining the value of each fitting parameter that minimizes the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

[0128] Embodiment 10 includes Embodiments 1 to 10, wherein the mean square error is 1 / y 2 It is calculated using a model.

[0129] Embodiment 11 is an assay system comprising at least one memory unit, at least one processing unit programmed according to instructions on the at least one memory unit, and at least one assay system component configured to be controlled by the at least one processing unit, wherein the at least one processing unit controls the at least one assay system component to perform a plurality of calibration assays on a plurality of calibration samples, each having a defined amount of analyte and a calibration sample having different amounts of analytes, to obtain a plurality of calibration assay signal values, to generate a calibration dataset comprising a plurality of quantity values ​​according to a defined amount and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples, and to select a calibration model equation that associates the defined amount with the plurality of calibration assay signal values. The assay system is configured to perform the following: select a calibration model equation which is a modified four-parameter logistic regression fitted equation and a modified hill slope which depends on the value of the quantity; identify the respective values ​​for the fitted parameters that fit the calibration model equation to a calibration dataset; generate a calibration model which includes the calibration model equation and the respective values ​​for the fitted parameters; control at least one assay system component to perform at least one sample assay on at least one test sample to obtain at least one sample assay signal value; generate a sample assay dataset which includes at least one sample assay signal value; and obtain a value for the sample quantity determined according to the calibration model and at least one sample assay signal value.

[0130] Embodiment 12 includes Embodiment 11, wherein in the calibration model equation, the modified Hill slope depends on a function of the reciprocal of the value of the quantity.

[0131] Embodiment 13 includes Embodiments 11-12, and the calibration model equation is,

number

[0132] Embodiment 14 includes Embodiments 11-13, wherein in the calibration model equation, the modified Hills slope depends on a function of the natural logarithm of the reciprocal of the quantity value.

[0133] Embodiment 15 includes embodiments 11 to 14, and the calibration model equation is,

number

[0134] Embodiment 16 includes embodiments 11-15, and the value of the sample quantity is obtained according to the calibration model, by formula

number

[0135] Embodiment 17 includes embodiments 11 to 16, and the calibration model equation is,

number

[0136] Embodiment 18 includes embodiments 11 to 17, and the calibration model equation is,

number

[0137] Embodiment 19 includes Embodiments 11 to 18 and is further configured to identify each value for a fitting parameter by determining the respective value for the fitting parameter that minimizes the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

[0138] Embodiment 20 includes embodiments 11 to 19, wherein the mean square error is 1 / y 2 It is calculated using a model.

[0139] Embodiment 20 is a non-temporary computer-readable medium in which instructions are stored, wherein when an instruction is executed by at least one processing unit, at least one processing unit, via control of an assay system, performs a plurality of calibration assays on a plurality of calibration samples, each having a defined amount of analyte and a different amount of analyte, to obtain a plurality of calibration assay signal values; generates a calibration dataset including a plurality of quantity values ​​according to a defined amount and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples; and selects a calibration model equation that associates the defined amount with the plurality of calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation. The system includes one or more non-temporary computer-readable media that perform the following actions: selecting a modified hill slope that depends on the value of the quantity; identifying the respective values ​​for fitting parameters to fit a calibration model equation to a calibration dataset; generating a calibration model that includes the calibration model equation and the respective values ​​for fitting parameters; performing at least one sample assay on at least one test sample via control of the assay system to obtain at least one sample assay signal value; generating a sample assay dataset that includes at least one sample assay signal value; and obtaining a value for the sample quantity determined according to the calibration model and at least one sample assay signal value.

[0140] Embodiment 22 includes Embodiment 21, wherein in the calibration model equation, the modified Hill slope depends on a function of the reciprocal of the value of the quantity.

[0141] Embodiment 23 includes Embodiments 21-22, and the calibration model equation is,

number

[0142] Embodiment 24 includes embodiments 21-23, wherein in the calibration model equation, the modified Hills slope depends on a function of the natural logarithm of the reciprocal of the quantity value.

[0143] Embodiment 25 includes embodiments 21 to 24, wherein the calibration model equation is

number

[0144] Embodiment 26 includes Embodiments 21-25, and the value of the sample quantity is obtained according to the calibration model, and the equation

[0145]

number

[0146] Embodiment 27 includes embodiments 21 to 26, wherein the calibration model equation is

number

[0147] Embodiment 28 includes embodiments 21 to 27, wherein the calibration model equation is

number

[0148] Embodiment 29 includes Embodiments 21 to 28, and is configured such that at least one processing unit identifies each value for a fitting parameter by determining the value of each fitting parameter that minimizes the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

[0149] Embodiment 30 includes embodiments 21 to 29, wherein the mean square error is 1 / y 2 It is calculated using a model.

[0150] Embodiment 31 is an assay system calibration method comprising: performing multiple calibration assays on a plurality of calibration samples, each having a defined amount of analyte and having different amounts of analytes, on an assay system to obtain a plurality of calibration assay signal values; generating a calibration dataset by at least one processing unit, which includes a plurality of quantity values ​​according to a defined amount and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples; selecting a calibration model equation by at least one processing unit that associates a defined amount with a plurality of calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation, and the modified Hills slope depends on a function of the quantity values; identifying the respective values ​​for fitted parameters that fit the calibration model equation to the calibration dataset by at least one processing unit; generating a calibration model by at least one processing unit, which includes the calibration model equation and fitted parameters; and storing the calibration model by at least one processing unit.

[0151] Embodiment 32 includes Embodiment 31, wherein in the calibration model equation, the modified Hill slope depends on a function of the reciprocal of the value of the quantity.

[0152] Embodiment 33 includes embodiments 31-32, and the calibration model equation is,

number

[0153] Embodiment 34 includes Embodiments 31-34, wherein in the calibration model equation, the modified Hill slope depends on a function of the natural logarithm of the reciprocal of the quantity value.

[0154] Embodiment 35 includes embodiments 31 to 34, wherein the calibration model equation is

number

[0155] Embodiment 36 includes Embodiments 31-35, and the value of the sample quantity is obtained according to the calibration model, and the equation

number

[0156] Embodiment 37 includes embodiments 31 to 36, wherein the calibration model equation is

number

[0157] Embodiment 38 includes embodiments 31 to 37, wherein the calibration model equation is

number

[0158] Embodiment 39 includes Embodiments 31 to 38 and includes determining the value of each fitting parameter that minimizes the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

[0159] Embodiment 40 includes embodiments 31-39, wherein the mean square error is 1 / y 2 It is calculated using a model.

[0160] Embodiment 41 is an assay system calibration method comprising: obtaining a calibration model by at least one processing unit, which includes a calibration model equation and respective values ​​for fitting parameters, wherein the calibration model equation is a modified four-parameter logistic regression fitting equation, the modified Hillslope depends on a function of the quantity values, and each value for fitting parameters fits the calibration model equation to a calibration dataset, which includes a plurality of quantity values ​​according to a defined quantity and a plurality of calibration assay signal values ​​corresponding to a plurality of calibration samples; performing at least one sample assay on at least one test sample on the assay system to obtain a sample assay signal value; generating a sample assay dataset including the sample assay signal value by at least one processing unit; and determining a sample quantity value determined according to the calibration model and at least one sample assay signal value by at least one processing unit.

[0161] Embodiment 42 includes Embodiment 41, wherein in the calibration model equation, the modified Hills slope depends on a function of the reciprocal of the value of the quantity.

[0162] Embodiment 43 includes Embodiments 41-42, and the calibration model equation is,

number

[0163] Embodiment 44 includes embodiments 41-43, wherein in the calibration model equation, the modified Hills slope depends on a function of the natural logarithm of the reciprocal of the quantity value.

[0164] Embodiment 45 includes Embodiments 41-44, and the calibration model equation is,

number

[0165] Embodiment 46 includes embodiments 41 to 45, and the acquisition of the sample quantity value according to the calibration model is performed using the following formula.

number

[0166] Embodiment 47 includes Embodiments 41-46, and the calibration model equation is,

number

[0167] Embodiment 48 includes embodiments 41 to 47, and the calibration model equation is,

number

[0168] Embodiment 49 includes Embodiments 41 to 48 and includes determining the value of each fitting parameter that minimizes the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

[0169] Embodiment 50 includes Embodiments 41-49, wherein the mean square error is 1 / y 2 It is calculated using a model.

[0170] Embodiment 51 is a non-temporary computer-readable medium on which instructions are stored, wherein when an instruction is executed by at least one processing unit, the processing unit obtains a calibration dataset, which includes the results of assay measurements on a plurality of calibration samples, each having a defined amount of analyte and a different amount of analyte, wherein the calibration dataset includes a plurality of quantity values ​​according to a defined amount and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples, and selects a calibration model equation that relates the defined amount to the plurality of calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation. The system includes one or more non-temporary computer-readable media to perform the following: selecting a modified hill slope that depends on the value of the quantity; identifying the respective values ​​of fitting parameters that fit the calibration model equation to the calibration dataset; generating a calibration model that includes the calibration model equation and the respective values ​​of the fitting parameters; obtaining a sample assay dataset that includes the results of assay measurements for at least one test sample, wherein the test dataset includes at least one sample assay signal value corresponding to at least one test sample; and determining the value of the sample quantity according to the calibration model and the at least one sample assay signal value.

[0171] Embodiment 52 includes Embodiment 51, wherein in the calibration model equation, the modified Hill slope depends on a function of the reciprocal of the value of the quantity.

[0172] Embodiment 53 includes Embodiments 51-52, and the calibration model equation is,

number

[0173] Embodiment 54 includes Embodiments 51-53, wherein in the calibration model equation, the modified Hills slope depends on a function of the natural logarithm of the reciprocal of the quantity value.

[0174] Embodiment 55 includes Embodiments 51 to 54, wherein the calibration model equation is

number

[0175] Embodiment 56 includes embodiments 51 to 55, and the acquisition of sample quantity values ​​according to the calibration model is performed using the following equation.

number

[0176] Embodiment 57 includes embodiments 51 to 56, wherein the calibration model equation is

number

[0177] Embodiment 58 includes embodiments 51 to 57, wherein the calibration model equation is

number

[0178] Embodiment 59 includes Embodiments 51 to 58, and is configured such that at least one processing unit identifies each fitting parameter by determining the value of each fitting parameter that minimizes the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

[0179] Embodiment 60 includes embodiments 51-59, wherein the mean square error is 1 / y 2 It is calculated using a model.

[0180] Embodiment 61 is a computer implementation method performed by a system comprising at least one memory unit and at least one processing unit programmed according to instructions on the at least one memory unit, the method comprising: obtaining a calibration model comprising a calibration model equation and respective values ​​for fitting parameters, wherein the calibration model equation is a modified four-parameter logistic regression fitting equation relating a defined quantity to a plurality of calibration assay signal values, the modified Hillslope depends on a function of the quantity values, and each value for fitting parameters fits the calibration model equation to a calibration dataset comprising a plurality of quantity values ​​according to a defined quantity and a plurality of calibration assay signal values ​​corresponding to a plurality of calibration samples; obtaining a sample assay dataset comprising the results of a sample assay measurement for at least one test sample, wherein the sample assay dataset comprises at least one sample assay signal value corresponding to at least one test sample; and determining a value for a sample quantity according to the calibration model and at least one sample assay signal value, by at least one processing unit.

[0181] Embodiment 62 includes Embodiment 61, wherein in the calibration model equation, the modified Hills slope depends on a function of the reciprocal of the value of the quantity.

[0182] Embodiment 63 includes embodiments 61-62, wherein the calibration model equation is

number

[0183] Embodiment 64 includes embodiments 61-63, wherein in the calibration model equation, the modified Hills slope depends on a function of the natural logarithm of the reciprocal of the quantity value.

[0184] Embodiment 65 includes embodiments 61 to 64, wherein the calibration model equation is

number

[0185] Embodiment 66 includes embodiments 61-65, and the value of the sample quantity is obtained according to the calibration model, and the equation

number

[0186] Embodiment 67 includes embodiments 61 to 66, wherein the calibration model equation is

number

[0187] Embodiment 68 includes embodiments 61 to 67, wherein the calibration model equation is

number

[0188] Embodiment 69 includes Embodiments 61 - 68 and involves determining each value of the fitting parameters by identifying each value of the fitting parameters that minimizes the mean squared error between a calibration dataset and one or more estimates or predictions obtained from a calibration model.

[0189] Embodiment 70 includes Embodiments 61 - 69, where the mean squared error is calculated using the 1 / y 2 model.

[0190] It will be readily apparent to those skilled in the art that other suitable modifications and adaptations to the methods and applications described herein can be made without departing from the scope of any of the embodiments.

[0191] Although specific embodiments have been shown and described herein, it should be understood that the claims should not be limited to the specific forms or arrangements of the components described and shown. Exemplary embodiments are disclosed herein and specific terms are used, but they are used only in a general and descriptive sense and not for purposes of limitation. Modifications and variations of the embodiments are possible in light of the above teachings. Therefore, it should be understood that the embodiments can be practiced other than as specifically described.

[0192] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference as if each individual publication, patent, or patent application were specifically and individually indicated to be incorporated by reference.

Claims

1. A calibration method for an assay system, On an assay system, multiple calibration assays are performed on multiple calibration samples, each containing a defined amount of analyte and a calibration sample containing different amounts of the analyte, to obtain multiple calibration assay signal values. A calibration dataset is generated by at least one processing unit, which includes a plurality of quantitative values ​​according to the defined quantities and the plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples. The process involves selecting a calibration model equation that relates the defined quantity to the plurality of calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation having a modified hill slope that depends on a function of the quantity values. The at least one processing unit identifies the respective values ​​for the fitting parameters that fit the calibration model equation to the calibration dataset, The at least one processing unit generates a calibration model that includes the calibration model equations and the respective values ​​for the fitting parameters, On the assay system, at least one sample assay is performed on at least one test sample to obtain at least one sample assay signal value. The at least one processing unit generates a sample assay dataset containing the at least one sample assay signal value, A method comprising obtaining a value of the sample volume determined according to the calibration model and the at least one sample assay signal value by the at least one processing unit.

2. Assay system, At least one memory unit, At least one processing unit programmed according to instructions on the at least one memory unit, The assay system includes at least one assay system component configured to be controlled by the at least one processing unit, wherein the at least one processing unit is Controlling at least one assay system component to perform multiple calibration assays on multiple calibration samples, each having a defined amount of analyte and a calibration sample having different amounts of the analyte, thereby obtaining multiple calibration assay signal values. To generate a calibration dataset including a plurality of quantity values ​​according to the defined quantity, and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples, The selection of a calibration model equation that relates the defined quantity to the plurality of calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation, and the modified Hill slope depends on a function of the quantity values. Identifying the respective values ​​for the fitting parameters that fit the calibration model equation to the calibration dataset, To generate a calibration model that includes the calibration model equations and the respective values ​​for the fitting parameters, Controlling the at least one assay system component to perform at least one sample assay on at least one test sample and obtain at least one sample assay signal value, An assay system configured to generate a sample assay dataset containing the at least one sample assay signal value, and to obtain a sample volume value determined according to the calibration model and the at least one sample assay signal value.

3. The assay system according to claim 2, wherein the calibration model equation depends on a function of the reciprocal of the value of the quantity.

4. The aforementioned calibration model equation is, [Math 6] The assay system according to claim 3, defined as such, where x represents a quantity value, y represents an assay signal value, and A, B, C, D, and E are fitting parameters.

5. The assay system according to claim 2, wherein the calibration model equation depends on a function of the natural logarithm of the reciprocal of the value of the quantity.

6. The aforementioned calibration model equation is, [Number 7] The assay system according to claim 5, defined as, where x represents the value of the amount, y represents the assay signal value, and A, B, C, D, and E are fitting parameters.

7. The assay system according to claim 6, wherein obtaining the value of the sample volume according to the calibration model is performed using an equation. [Number 8]

8. The aforementioned calibration model equation is, [Number 9] The assay system according to claim 2, defined as such, where x represents the value of the amount, y represents the assay signal value, C represents a selected constant, and A, B, D, and E are fitting parameters.

9. The aforementioned calibration model equation is, [Number 10] The assay system according to claim 2, defined as such, where x represents the value of the amount, y represents the assay signal value, C represents a selected constant, and A, B, D, and E are fitting parameters.

10. The assay system according to claim 2, wherein the at least one processing unit is further configured to identify the respective values ​​for the fitting parameters by determining the respective values ​​for the fitting parameters that minimize the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

11. The aforementioned mean squared error is 1 / y ２ The assay system according to claim 10, which is calculated using a model.

12. One or more non-temporary computer-readable media in which instructions are stored, wherein when the instructions are executed by at least one processing unit, the at least one processing unit is provided with The assay system controls the execution of multiple calibration assays on multiple calibration samples, each containing a defined amount of analyte and a calibration sample having different amounts of the analyte, thereby obtaining multiple calibration assay signal values. To generate a calibration dataset including a plurality of quantity values ​​according to the defined quantity, and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples, The selection of a calibration model equation that relates the defined quantity to the plurality of calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation, and the modified Hill slope depends on a function of the quantity values. Identifying the respective values ​​for the fitting parameters that fit the calibration model equation to the calibration dataset, To generate a calibration model that includes the calibration model equations and the respective values ​​for the fitting parameters, The assay system control is used to perform at least one sample assay on at least one test sample and to obtain at least one sample assay signal value. One or more non-temporary computer-readable media that perform the following: generate a sample assay dataset containing the at least one sample assay signal value; and obtain a sample volume value determined according to the calibration model and the at least one sample assay signal value.

13. A calibration method for an assay system, On an assay system, multiple calibration assays are performed on multiple calibration samples, each having a defined amount of analyte and a different amount of said analyte, to obtain multiple calibration assay signal values; and at least one processing unit generates a calibration dataset including multiple quantity values ​​according to the defined amount and the multiple calibration assay signal values ​​corresponding to the multiple calibration samples. The process involves selecting a calibration model equation that relates the defined quantity to the plurality of calibration assay signal values ​​using at least one processing unit, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation, and the modified Hills slope depends on a function of the quantity values. The at least one processing unit identifies the respective values ​​for the fitting parameters that fit the calibration model equation to the calibration dataset, The at least one processing unit generates a calibration model including the calibration model equations and the fitting parameters, A method comprising storing the calibration model by at least one processing unit.

14. A calibration method for an assay system, Obtaining a calibration model, including the calibration model equations and their respective values ​​for the fitting parameters, by at least one processing unit, The calibration model equation is a modified four-parameter logistic regression fitted equation that associates a defined quantity with multiple calibration assay signal values, and the modified Hills slope depends on a function of the quantity values. The acquisition of the respective values ​​for the fitting parameters, which fit the calibration model equation to a calibration dataset comprising a plurality of values ​​of quantities according to the defined quantities and a plurality of calibration assay signal values ​​corresponding to a plurality of calibration samples, On the assay system, perform at least one sample assay on at least one test sample and obtain at least one sample assay signal value. The at least one processing unit generates a sample assay dataset containing the at least one sample assay signal value, A method comprising determining, by at least one processing unit, a sample volume value determined according to the calibration model and the at least one sample assay signal value.

15. The method according to any one of claims 1, 13, or 14, wherein in the calibration model equation, the modified hill slope depends on a function of the reciprocal of the value of the quantity.

16. The aforementioned calibration model equation is, [Math 21] The method according to claim 15, defined as, in the formula, x represents a quantity value, y represents an assay signal value, and A, B, C, D, and E are fitting parameters.

17. The method according to any one of claims 1, 13, or 14, wherein in the calibration model equation, the modified hill slope depends on a function of the natural logarithm of the reciprocal of the value of the quantity.

18. The aforementioned calibration model equation is, [Number 22] The method according to claim 17, defined as, in the formula, x represents the value of the amount, y represents the assay signal value, and A, B, C, D, and E are fitting parameters.

19. The method according to claim 18, wherein obtaining the value of the sample quantity according to the calibration model is performed using an equation. [Number 23]

20. The aforementioned calibration model equation is, [Number 24] The method according to any one of claims 1, 13, or 14, defined as such, where x represents the value of the amount, y represents the assay signal value, C represents a selected constant, and A, B, D, and E are fitting parameters.

21. The aforementioned calibration model equation is, [Number 25] The method according to any one of claims 1, 13, or 14, defined as such, where x represents the value of the amount, y represents the assay signal value, C represents a selected constant, and A, B, D, and E are fitting parameters.

22. The method according to any one of claims 1, 13, or 14, wherein identifying the respective values ​​for the fitting parameters includes determining the respective values ​​of the fitting parameters that minimize the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

23. The aforementioned mean squared error is 1 / y ２ The method according to claim 22, calculated using a model.

24. One or more non-temporary computer-readable media in which instructions are stored, wherein when the instructions are executed by at least one processing unit, the processing unit is provided with Obtaining a calibration dataset, which includes the results of assay measurements on a plurality of calibration samples, each having a defined amount of analyte and a calibration sample having different amounts of said analyte, wherein the calibration dataset includes a plurality of quantity values ​​according to the defined amount and a plurality of calibration assay signal values ​​corresponding to the plurality of calibration samples. The selection of a calibration model equation that relates the defined quantity to the plurality of calibration assay signal values, wherein the calibration model equation is a modified four-parameter logistic regression fitted equation, and the modified Hill slope depends on a function of the quantity values. Identifying the respective values ​​of the fitting parameters that fit the calibration model equation to the calibration dataset, To generate a calibration model that includes the calibration model equation and the respective values ​​of the fitting parameters, Obtaining a sample assay dataset containing the results of assay measurements for at least one test sample, wherein the test dataset contains at least one sample assay signal value corresponding to the at least one test sample. One or more non-temporary computer-readable media that perform the following: determining the value of the sample volume according to the calibration model and the at least one sample assay signal value.

25. One or more non-temporary computer-readable media according to claim 12 or 24, wherein in the calibration model equation, the modified hill slope depends on a function of the reciprocal of the value of the quantity.

26. The aforementioned calibration model equation is, [Number 26] One or more non-transient computer-readable media according to claim 25, defined as such, where x represents a quantity value, y represents an assay signal value, and A, B, C, D, and E are fitting parameters.

27. One or more non-temporary computer-readable media according to claim 12 or 24, wherein in the calibration model equation, the modified hill slope depends on a function of the natural logarithm of the reciprocal of the value of the quantity.

28. The aforementioned calibration model equation is, [Number 27] One or more non-transient computer-readable media according to claim 27, defined as such, where x represents a quantity value, y represents an assay signal value, and A, B, C, D, and E are fitting parameters.

29. One or more non-temporary computer-readable media according to claim 28, wherein obtaining the value of the sample quantity according to the calibration model is performed using an equation. [Number 28]

30. The aforementioned calibration model equation is, [Number 29] One or more non-temporary computer-readable media according to claim 12 or 24, defined as such, where x represents a quantity value, y represents an assay signal value, C represents a selected constant, and A, B, D, and E are fitting parameters.

31. The aforementioned calibration model equation is, [Number 30] One or more non-temporary computer-readable media according to claim 12 or 24, defined as such, where x represents a quantity value, y represents an assay signal value, C represents a selected constant, and A, B, D, and E are fitting parameters.

32. One or more non-temporary computer-readable media according to claim 12 or 24, wherein the at least one processing unit is configured to identify the respective values ​​of the fitting parameters by determining the respective values ​​of the fitting parameters that minimize the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

33. The aforementioned mean squared error is 1 / y ２ One or more non-temporary computer-readable media according to claim 32, calculated using a model.

34. A computer implementation method, which is executed by a system comprising at least one memory unit and at least one processing unit programmed according to instructions on the at least one memory unit, Obtaining a calibration model, including the calibration model equations and corresponding values ​​for the fitting parameters, by at least one processing unit, The calibration model equation is a modified four-parameter logistic regression fitted equation that associates a defined quantity with multiple calibration assay signal values, wherein the modified Hills slope depends on a function of the quantity values. The respective values ​​for the fitting parameters are obtained to fit the calibration model equation to a calibration dataset which includes a plurality of values ​​of quantities according to the defined quantities and a plurality of calibration assay signal values ​​corresponding to a plurality of calibration samples. A method comprising: obtaining a sample assay dataset containing the results of a sample assay measurement for at least one test sample using the at least one processing unit, wherein the sample assay dataset contains at least one sample assay signal value corresponding to the at least one test sample; and determining a sample volume value according to the calibration model and the at least one sample assay signal value using the at least one processing unit.

35. The method according to claim 34, wherein in the calibration model equation, the modified hill slope depends on a function of the reciprocal of the value of the quantity.

36. The aforementioned calibration model equation is, [Number 31] The method according to claim 35, defined as, in the formula, x represents a quantity value, y represents an assay signal value, and A, B, C, D, and E are fitting parameters.

37. The method according to claim 34, wherein in the calibration model equation, the modified hill slope depends on a function of the natural logarithm of the reciprocal of the value of the quantity.

38. The aforementioned calibration model equation is, [Number 32] One or more non-transient computer-readable media according to claim 27, defined as such, where x represents a quantity value, y represents an assay signal value, and A, B, C, D, and E are fitting parameters.

39. The method according to claim 38, wherein obtaining the value of the sample quantity according to the calibration model is performed using an equation. [Number 33]

40. The aforementioned calibration model equation is, [Number 34] The method according to claim 34, defined as, in the formula, x represents the value of the amount, y represents the assay signal value, C represents a selected constant, and A, B, D, and E are fitting parameters.

41. The aforementioned calibration model equation is, [Number 35] The method according to claim 34, defined as, in the formula, x represents the value of the amount, y represents the assay signal value, C represents a selected constant, and A, B, D, and E are fitting parameters.

42. The method according to claim 34, wherein identifying the respective values ​​of the fitting parameters includes determining the respective values ​​of the fitting parameters that minimize the mean squared error between the calibration dataset and one or more estimates or predictions obtained from the calibration model.

43. The aforementioned mean squared error is 1 / y ２ The method according to claim 42, calculated using a model.

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