Method for detecting analytes

Abstract

The present disclosure provides a method for detecting a target analyte in a sample, the method comprising: a) providing a number of magnetizable particles having a reference magnetic signal known or measured before or after addition of the sample, the particles coated with binding molecules complementary to the target analyte, the present invention relates to a method for detecting a target analyte, comprising the steps of: a) providing a sample comprising the target analyte, b) contacting the sample comprising the target analyte with the magnetizable particles, producing bound and unbound binding agent complexes, c) applying a magnetic field to the sample for a period of time, d) obtaining a magnetic signal of the bound and unbound binding agent complexes in the presence of the magnetic field, e) removing the magnetic field for a period of time, and f) comparing the reference magnetic signal and the magnetic signal, where the difference between the reference magnetic signal and the magnetic signal is associated with the presence and / or quantity of the target analyte in the sample.

Description

Technical Field

[0001] This invention relates to a method for detecting one or more target analytes in a sample, and more specifically, to the use of magnetizable nanoparticles and magnetic sensor systems. The invention also relates to an apparatus for detecting analytes based on the use of magnetizable nanoparticles. Background Technology

[0002] There are many known methods for detecting and quantifying analytes in samples. Such systems require indirect methods to quantify analytes by detecting and measuring the complexes bound to them. Typically, these methods rely on binding or recognition systems, thereby visualizing the adjuvants coated or attached to the bound molecules that bind to the analyte in the sample.

[0003] The binding molecules may include antibodies, enzymes, or pharmacological agents specifically selected based on their affinity for the target analyte. The molecules that directly bind to the analyte may themselves be labeled with enzymes or fluorophores (in the case of fluorescent labeling).

[0004] Alternatively, the molecule directly binding to the analyte may be unlabeled itself, but rather bound to another binder that is itself labeled with an enzyme or fluorophore. This additional labeling procedure amplifies the signal and reduces background staining. One well-known complex is the avidin-biotin complex and the peroxidase-antiperoxidase technique.

[0005] Techniques for detecting and quantifying analytes in samples need to be rapid, sensitive, qualitative, and / or miniaturized to meet the needs of in vitro diagnostics. Miniaturization of devices can lead to slow and inefficient mixing of fluids due to increased viscous forces.

[0006] Point-of-care testing can reduce turnaround time for diagnostic tests, thereby improving workflows and potentially contributing to improved patient care. Such systems must include sensing technologies for detecting biomarkers, such as protein or nucleic acid markers. Magnetizable particles have been used to detect analytes in manual assays ranging from basic research to high-throughput testing.

[0007] Many existing devices for detecting analytes attached to magnetizable particles require complex configurations that are unsuitable or not readily adaptable to miniaturization in point-of-bed detection applications.

[0008] The use of magnetizable particles relies on functionalizing the particles with binding molecules (e.g., antibodies with high affinity for the target analyte) to allow binding to the target analyte, followed by a fluid exchange step for separation and purification. Analyte capture rates have been reported to be proportional to the total surface area of ​​the suspended particles, and therefore proportional to the particle concentration. However, using very high particle concentrations is disadvantageous for downstream processes in integrated, multi-step lab-on-a-chip assays because high particle concentrations typically increase nonspecific particle-particle and particle-surface interactions, enhance field-induced particle aggregation, induce steric hindrance in the particle concentration step, hinder chemical reactions on the particles, and spatially impede reactions between the particles and the biosensing surface.

[0009] The target analyte may be present in low concentrations within the sample, which may also contain high concentrations of background materials, such as blood or saliva. In such complex matrices, the non-specific adhesion of non-target molecules to magnetizable particles can reduce the effectiveness of the assay.

[0010] The magnetic particle-based capture process of a target analyte involves an encounter between two components (the target analyte and the magnetic particle) and can depend on the alignment of their outer surfaces relative to each other in a very specific manner. Therefore, the association rate of the two components can be limited by diffusion and the geometric constraints of the binding sites of the two components, and may also be reduced by the eventual chemical reaction.

[0011] The analyte can be captured in either a flowing or static fluid. In the absence of flow, methods relying on surface-immobilized antibodies are limited by diffusion and may have reduced binding rates.

[0012] After the target analyte is captured by magnetic particles, additional processing is required for detection. If used solely as a carrier, the magnetizable particles typically bind to recognition molecules (such as luminescent or fluorescent labels). For accurate detection, it is important that only the bound analyte is labeled, and only the bound label is detected. This requires several washing or separation steps.

[0013] Magnetizable particles can also be used as markers to indicate the binding of a target analyte at the sensing surface. Agglutination assays utilize a process in which aggregates of particles form when a specific analyte is present in the sample fluid. The degree of aggregation is a measure of the concentration of the analyte in the fluid. Agglutination assays are highly demanding in terms of reagents because the assay is performed in a single step without separation or stringency.

[0014] In magnetic coagulation assays, particle cluster formation is accelerated by agglomerating particles together under the influence of a magnetic field. One problem with this type of method is that when the analyte concentration is much lower than the concentration of magnetizable particles, a small number of particle aggregates form, which are subject to Poisson statistics. The application of the magnetic field can be enhanced by applying it during incubation. However, the magnetic field can also increase nonspecific binding between particles. Nonspecific binding (i.e., binding not mediated by the target analyte) leads to false positive signals. Nonspecific binding can originate from several types of interactions, such as van der Waals interactions, electrostatic interactions, and hydrophobic interactions, resulting in background levels and statistically significant differences in results, thus affecting the limit of quantitation and the accuracy of the method.

[0015] The use of magnetizable particles means that additional forces can be applied to the particles, for example, to separate bonded particles from unbonded particles.

[0016] The evaluation of the analytical performance of a detection method is based on the limit of quantitation (LoQ), which is the lowest concentration of a biomarker that can be quantified with a given required precision.

[0017] Optimizing magnetizable particles and selecting appropriate detection methods for specific applications remains a challenge for the magnetic nanotechnology community due to the increasing demands for detection sensitivity, molecular specificity, and application complexity.

[0018] The use of GMR in immunoassays has been applied to sandwich methods such as ELISA, where molecular targets are immobilized on the sensor surface by adding labeled magnetic probes (see Koh and Josephson, “Magnetic nanoparticle sensors” Sensors 2009: 9; 8130-45 and Yao and Xu, “Detection of magnetic nanomaterials in molecular imaging and diagnosis applications” Nanotechnol. Rev 2014: 3;247-268).

[0019] Some techniques use superconducting quantum interference devices (SQUIDs) to detect and measure Néel relaxation (magnetic dipole misalignment) in magnetically labeled bacteria. In such techniques, a magnetic field is pulsed to induce magnetic dipole alignment, and subsequent dipole misalignment is detected.

[0020] The object of the present invention is to solve one or more of the above-mentioned problems, and / or to provide a method for detecting analytes in samples, and / or at least to provide the public with a useful option. Summary of the Invention

[0021] According to the first aspect, this disclosure can broadly provide a method for detecting a target analyte in a sample, the method comprising: • Provide a quantity of magnetizable particles having a known or measured reference magnetic signal before or after the addition of the sample, the particles being coated with binding molecules complementary to the target analyte. • By contacting a sample containing the target analyte with the magnetizable particles, a bound and unbound binder complex is generated. • Apply a magnetic field to the sample for a period of time. • Obtaining magnetic signals, wherein the magnetic signals are the magnetic signals of the bound and unbound binder complexes in the presence of the magnetic field. • The removal of the magnetic field continues for a period of time, and • Compare the reference magnetic signal and the magnetic signal, wherein the difference between the reference magnetic signal and the magnetic signal is associated with the presence and / or quantity of the target analyte in the sample.

[0022] The reference magnetic signal can be determined by measuring the magnetic signal of the number of magnetizable particles in the absence of the target analyte.

[0023] A reference magnetic signal can be measured at any point between the steps of bringing a sample containing the target analyte into contact with magnetizable particles, applying a magnetic field to the sample for a period of time, obtaining a magnetic signal, and removing the magnetic field for a period of time.

[0024] A magnetic field can be generated using an electromagnet.

[0025] A magnetic field can be generated using permanent magnets.

[0026] Magnetic signals can be obtained after the electromagnetic coil of an electromagnet reaches saturation.

[0027] The target analyte in the sample can be determined by correlating the changes in the measured magnetic signal with respect to a reference / predetermined magnetic signal.

[0028] The method may further include: generating a reference dataset based on known analyte quantity values, and comparing the value obtained from the difference between the reference magnetic signal and the magnetic signal with the reference dataset to determine the quantity of analyte in the sample.

[0029] The magnetic field can be applied for a predetermined time.

[0030] A magnetic field can be applied for approximately 0.1, 0.25, 0.5, 1, 2, 3, 4, or 5 seconds, and any range in between.

[0031] It can remove magnetic fields for approximately 0.1, 0.25, 0.5, 1, 2, 3, 4, or 5 seconds, and any range in between.

[0032] The magnetic field can be removed for a predetermined period of time.

[0033] It can remove the magnetic field for about 3 to 7 seconds.

[0034] The magnetic field can be applied and removed for substantially the same amount of time.

[0035] It can apply and remove magnetic fields for about 1 second.

[0036] The steps of applying a magnetic field to the sample for a period of time, obtaining a magnetic signal, and removing the magnetic field for a period of time can be repeated two or more times.

[0037] Magnetizable particles can be superparamagnetic nanoparticles.

[0038] Magnetizable particles can be ferromagnetic nanoparticles.

[0039] Superparamagnetic nanoparticles can have an average particle size of about 20 nm to about 40 nm.

[0040] Superparamagnetic nanoparticles can have an average particle size of about 30 nm.

[0041] Magnetizable particles can have an average particle size of about 5 to about 5000 nm.

[0042] The method may further include magnetically shielding the sample from the influence of the ambient magnetic field.

[0043] The method may further include detecting an ambient magnetic field and adjusting a reference magnetic signal and a magnetic signal based on the ambient magnetic field.

[0044] It can measure magnetic signals at a sampling rate of at least approximately 10,000 samples per second.

[0045] The sample can be incubated at a temperature of approximately 20°C.

[0046] Magnetic signals can be magnetic field strength.

[0047] The method may further include: • A second magnetic field is applied to the magnetizable particles, which can be positioned above or below the sample. • Measure the magnetic signals of bound and unbound binder complexes in the presence of a magnetic field and a second magnetic field.

[0048] The method can be performed using a sample testing device, which includes: • Sample well or sample reservoir, • One or more magnets, said magnets being located on one of the upper or lower sides of the sample aperture or sample reservoir. • One or more magnets, said one or more magnets being located on another of the upper or lower sides of said sample orifice or sample reservoir, and • A magnetic field sensor, which measures the change of magnetic signal in the sample well or sample reservoir over time.

[0049] Magnetizable particles can be functionalized using molecules that specifically bind to the target analyte.

[0050] Samples and magnetizable particles can be processed using microfluidic devices.

[0051] Microfluidic devices can facilitate the binding of magnetizable particles to analytes.

[0052] One or more electromagnets can generate a magnetic field that changes over time.

[0053] One or more electromagnets can produce continuous amplitude.

[0054] One or more electromagnets can cause the magnetic field to alternate between being switched on and off.

[0055] Magnetic field sensors can measure the change in the strength of a magnetic field generated by magnetizable particles over time.

[0056] The signal output from the magnetic field sensor can be amplified by a signal amplifier.

[0057] The signal output from the magnetic field sensor can be a voltage reading that is proportional to the magnetic signal measured by the magnetic field sensor.

[0058] The amplified signal can be converted from a voltage reading into a digital bit stream, which can then be recorded and / or analyzed by a computer.

[0059] The conversion can be performed using an analog-to-digital converter.

[0060] The method can: • Generate a sufficient magnetic signal within 15 seconds to detect and / or quantify the target analyte in the sample, or • Has a limit of detection (LOD) of at least about 0.05 pg / mL, or • Has a limit of quantitation (LOQ) of at least about 0.1 pg / mL, or • One or more of the above.

[0061] The second magnetic field can be applied from the opposite side of the magnetic field.

[0062] The second magnetic field can be provided using permanent magnets.

[0063] The second magnetic field can be provided using an electromagnet.

[0064] Electromagnets can be positioned closer to the sample than permanent magnets.

[0065] The electromagnet can be calibrated to have a magnetic field strength that is at least twice that of a permanent magnet.

[0066] Magnetic signals can be processed using Fast Fourier Transform (FFT).

[0067] Before processing with FFT, the magnetic signal can be preprocessed by truncating and / or concatenating the magnetic signal in one or more dimensions.

[0068] Windowing can be applied to the magnetic signal before FFT processing.

[0069] According to another aspect, this disclosure can be broadly provided as an apparatus for detecting an analyte in a sample, the apparatus comprising: • Sample orifice, which may be separate from or integrated into the microfluidic device. • A magnet, used to generate a magnetic field. • A magnetic field sensor configured to measure a magnetic signal of magnetizable particles in the sample pore in the presence of the magnetic field, the magnetic signal being detected from an overall magnetic response proportional to the size of the aggregate.

[0070] As used in this specification, the term "comprising" means "consisting of at least in part with". When interpreting a description in this specification that includes this term, the feature preceding the term in each description must be present, but other features may also be present. Related terms such as "comprise" and "comprised" will be interpreted in the same manner.

[0071] The intention is to include references to the range of numbers disclosed herein (e.g., 1 to 10) as well as references to all rational numbers within that range (e.g., 1, 1.1, 2, 3, 3.9, 4, 5, 6, 6.5, 7, 8, 9, and 10) and any range of rational numbers within that range (e.g., 2 to 8, 1.5 to 5.5, and 3.1 to 4.7).

[0072] This invention may also be broadly defined to include parts, elements, and features individually or collectively mentioned or specified in the description of this application, as well as any or all combinations of any two or more of said parts, elements, or features, and where specific integers having known equivalents in the field to which this invention pertains are referred to herein, such known equivalents are considered to be incorporated herein as if listed separately. Many structural variations, as well as a wide range of different embodiments and applications, of the invention will be apparent to those skilled in the art without departing from the scope of the invention as defined in the appended claims. The disclosure and description herein are entirely illustrative and are not intended to be limiting in any way. Attached Figure Description

[0073] The invention will now be described by way of example only and with reference to the accompanying drawings, in which: Figure 1 It is the magnetic field sensor signal output during the sample reading phase in N cycles (times) according to the embodiments of this disclosure.

[0074] Figure 1 a shows the magnet-on phase at the beginning of the reading cycle and represents a curve of the average value obtained from the magnetic field sensor over time according to an embodiment of the present disclosure.

[0075] Figure 1 b shows the magnet disconnection phase, which represents a curve of the average value from the magnetic field sensor over time according to an embodiment of the present disclosure.

[0076] Figure 2 It is a sample introduction device / microfluidic chip according to an embodiment of the present invention. Detailed Implementation

[0077] This disclosure can be broadly described as providing a method for detecting and / or quantifying a target analyte in a sample.

[0078] The described method for detecting a target analyte in a sample may broadly include the following steps: • Provide a quantity of magnetizable particles having a known or measured reference magnetic signal before or after the addition of the sample, the particles being coated with binding molecules complementary to the target analyte. • By contacting a sample containing the target analyte with the magnetizable particles, a bound and unbound binder complex is generated. • Apply a magnetic field to the sample for a period of time. • Obtaining magnetic signals, wherein the magnetic signals are the magnetic signals of the bound and unbound binder complexes in the presence of the magnetic field. • The removal of the magnetic field continues for a period of time, and • Compare the reference magnetic signal and the magnetic signal, wherein the difference between the reference magnetic signal and the magnetic signal is associated with the presence and / or quantity of the target analyte in the sample.

[0079] The described method is based on the concept of measuring changes in the magnetic signal generated by magnetizable particles due to Brownian rotation or diffusion, which allows for the quantification of the amount of magnetizable particle-analyte complex (bound binder complex), and then allows for the determination of the amount of analyte in the sample.

[0080] Brownian motion (i.e., rotation or translation) of particles can be influenced by a number of factors, including but not limited to temperature, viscosity of the suspending medium, particle size, and particle surface characteristics. For example, the surface characteristics of magnetizable particles (such as analytes) can alter the hydrodynamic properties of the resulting chemical conjugates.

[0081] As the concentration of the analyte changes, the conjugation / aggregation of magnetizable particles changes due to the chemical bonding between the analyte and the binder, between binders, and between particles, resulting in particles with a range of hydrodynamic sizes across different analyte concentrations.

[0082] For example, when a relatively high concentration of analyte is present in a sample, the binders on magnetizable particles become more saturated with the analyte, thereby reducing the likelihood of interactions between binders and / or between particles (e.g., non-specific binding) and leading to the formation of smaller aggregates.

[0083] Conversely, when there is a relatively low concentration of analyte in the sample, the binder on the magnetizable particles is less saturated by the analyte, thereby increasing the likelihood of interactions between binders and / or between particles, which can lead to the formation of larger aggregates.

[0084] Brownian motion is negatively correlated with the hydrodynamic dimensions of the complex of magnetizable particles and analyte. Therefore, changes in the amount of analyte alter the hydrodynamic dimensions of the magnetizable particles, affecting their effective translational or rotational Brownian motion under an external magnetic field. Changes in Brownian motion can be detected and measured as a global magnetic response proportional to the size of the aggregate.

[0085] The large-scale aggregation of magnetizable particles can produce densely packed particles, thereby increasing dipole-dipole interactions (or dipole coupling). Increased dipole coupling leads to a larger magnetic signal.

[0086] The difference between aggregate formation and the resulting magnetic signal can be used to determine the concentration of the analyte in the sample.

[0087] Unwilling to be bound by theory, in a state of magnetic equilibrium, magnetizable particles attract each other in one dimension but repel each other in another, leading to the formation of microassemblies. Factors such as the size of the magnetizable particles, the amount of target analyte, and the size of the target analyte can affect the distance between microassemblies. For example, a larger amount of analyte can result in a larger, effectively magnetizable particle-analyte complex. Therefore, larger binding complexes can maintain further separation while possessing the same attractive forces that influence the formation of microassemblies, which can be detected as changes in the signal generated by the magnetizable particles.

[0088] The amount of analyte in the sample is determined based on changes in the signal generated by magnetizable particles, as detected by a sensor. The sensor detects changes based on particle aggregation.

[0089] The described method may include multiple stages.

[0090] The first stage can be a pre-sampling stage. The pre-sampling stage may include providing a certain number of magnetizable particles to a sampling device, such as a microfluidic device.

[0091] Magnetizable particles can be functionalized using binders that bind specific target analytes.

[0092] The number of magnetizable particles may have a known or predetermined reference signal. For example, based on a known number of beads present in the device. The reference signal can be any measurable signal generated by the magnetizable particles in the absence of the target analyte. For example, the signal may include a magnetic signal or an electrical signal.

[0093] The signal generated by magnetizable particles can be intrinsic or induced. For example, the generated signal can be inherent to its atomic structure or induced by a magnetic field such as an external magnetic field.

[0094] Alternatively or additionally, the method may include a reference calibration step, which includes measuring the total signal generated by the magnetizable particles in the absence of an analyte. In the reference calibration step, the signal generated by the magnetizable particles is measured using a suitable signal sensor after the stated number of magnetizable particles has been added to the sampling device but before the sample has been added.

[0095] In some implementations, the reference calibration step can be performed simultaneously with the sample readout phase described in the preceding paragraphs. For example, this can be performed using a multiplexing system, where one channel or aperture is used for reference calibration and the other channels are used for sample readout.

[0096] The reference signal provides a baseline comparison for subsequent sample readings. The reference calibration step may take 1, 2, 3, 4, or 5 seconds, and an appropriate range can be selected from any of these values ​​(e.g., about 1 to about 5, about 1 to about 4, about 2 to about 5, about 2 to about 3, or about 3 to about 5 seconds).

[0097] The second stage may include introducing the sample to be analyzed into magnetizable particles.

[0098] In this stage, the sample is brought into contact with magnetizable particles, allowing any analytes present in the sample to be bound by a binder on the magnetizable particles to form a complex. This stage produces both bound and unbound binder complexes.

[0099] Alternatively, the sample to be analyzed can be incubated with functionalized magnetizable nanoparticles before the introduction of the microfluidic device.

[0100] This stage may include sample mixing and analyte binding with a binder (i.e., where functionalized magnetizable particles bind with the analyte). This stage may take about 3, 4, 5, 6, 7, or 8 minutes, and a suitable range can be selected from any of these values ​​(e.g., about 3 to about 8, about 3 to about 7, about 3 to about 5, about 4 to about 8, about 4 to about 6, or about 5 to about 8 minutes).

[0101] The third stage can be the sample reading stage.

[0102] During the sample readout phase, one or more external magnetic fields can be used to induce and alter the equilibrium of magnetizable particles (bound and unbound binder complexes), where transitions between equilibrium states can be measured as changes in the signal over time.

[0103] In some implementations, an external magnetic field can be generated using a magnet. The magnet can be selected from permanent magnets and / or electromagnets.

[0104] In implementations using electromagnets, an external magnetic field can be applied and removed by switching the electromagnet on and off. For example, power can be supplied to or de-energized the electromagnet coil to activate and deactivate the external magnetic field.

[0105] In embodiments using permanent magnets, an external magnetic field can be applied and removed by changing the relative positions of the permanent magnet and the sample. For example, the permanent magnet or sample aperture can be configured to allow the sample to move within or outside the effective range of the permanent magnet according to the phase of the reading cycle.

[0106] In other embodiments, the permanent magnet and the sample remain stationary relative to each other, but a magnetic shielding member can be used to control or redirect the magnetic field generated by the permanent magnet. For example, the movement of a movable magnetic shielding member positioned between the permanent magnet and the sample can be controlled to allow the magnetic field generated by the permanent magnet to reach the sample or prevent it from reaching the sample.

[0107] In some implementations, an external magnetic field can be applied and the sample can be read over multiple cycles. Within each cycle, the external magnetic field can be activated (applied) and deactivated (removed) once or multiple times for a specified duration. For example, the external magnetic field can be applied for 2 seconds and deactivated for 5 seconds.

[0108] Figure 1 This diagram illustrates the magnetic field sensor signal output over N cycles (times) during the sample readout phase, according to an embodiment that uses an electromagnet to generate an external magnetic field. In some embodiments, each cycle can be defined as the time between activations of the external magnetic field. For example, each readout cycle begins when the external magnetic field is activated and ends when it is reactivated after a period of inactivity.

[0109] The activation of the external magnetic field can be referred to as the magnet on-phase, and the deactivation of the external magnetic field can be referred to as the magnet off-phase.

[0110] Figure 1 a is an enlarged inset of the start of the reading cycle and represents a curve showing the average value obtained from the magnetic field sensor over time according to an embodiment of this disclosure. The magnet-on phase can be characterized by multiple events. The described events may overlap or occur simultaneously.

[0111] At event 2a, the electromagnet is activated at the start of the reading cycle and the electromagnetic coil reaches saturation.

[0112] At event 2b, the external magnetic field generated by the electromagnet propagates throughout the sample, and the electromagnetic coil achieves a balance of the input voltage as detected by the magnetic field sensor.

[0113] At event 2c, the propagation of the external magnetic field causes bonded and unbonded magnetizable particles (e.g., SPIONs) to spontaneously adopt magnetic moments. In particular, bonded and unbonded magnetizable particles may adopt polarities opposite to the net vector to achieve coordinated alignment and optimization with nearby magnetizable particles (which have similar magnetism).

[0114] At event 2d, pre-existing aggregates of bound and unbound magnetizable particles and / or aggregates formed through spontaneous magnetic interactions represent rotational and / or translational motions. Magnetic repulsion and magnetic attraction result in a region of magnetic equilibrium.

[0115] At event 2e, the magnetizable particle aggregate aligns with the field lines of the magnetic coil and moves toward the increasing magnetic field gradient (at the external magnetic field source).

[0116] The magnet activation phase (i.e., the external magnetic field) is applied for a period of time. The time period can be a predetermined amount of time.

[0117] The magnet switching phase can be approximately 0.1, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, or 12.0 seconds, and a suitable range can be selected from any of these values ​​(e.g., approximately 0.25 to approximately 12, approximately 0.25 to approximately 5.0, approximately 0.25 to approximately 3.0, approximately 0.25 to approximately 2.0, approximately 0.25 to approximately 1, approximately 0.1 to approximately 12, approximately 0.1 to approximately 5.0, approximately 0.1 to approximately 3.0, approximately 0.1 to approximately 2.0, approximately 0.1 to approximately 1, approximately 0.5 to approximately 12, approximately 0.5 to approximately 5.0, approximately 0.5 to approximately 3). 0.0, about 0.5 to about 2.0, about 0.5 to about 1, 0.75 to about 12, about 0.75 to about 5, about 0.75 to about 3.0, about 0.75 to about 2.0, about 0.75 to about 1.0, about 1.0 to about 3.5, about 1.0 to about 3.25, about 1.0 to about 3.0, about 1.0 to about 2.75, about 1.0 to about 2.5, about 1.0 to about 2.25, about 1.0 to about 2.0, about 1.0 to about 1.75, about 1.0 to about 1.5, about 1.0 to about 1.25, about 1.5 to about 3.0, about 1.5 to about 2.5, about 1.5 to about 2.0, about 2.0 to about 2.25, about 2.5, about 2.0 to about 2.75, about 2.0 to about 3.0, about 2.0 to about 3.25, about 2.0 to about 3.5 seconds).

[0118] Preferably, the magnet switching phase is about 1.0 to about 2.0 seconds.

[0119] To avoid being bound by theory, minimizing the magnet's on-time can reduce heat buildup in the electromagnetic coil, which can affect the accuracy of readings obtained by the sensor.

[0120] In cases where the magnet switching-on phase may be prolonged, a calibration reference can be used to compensate for any inaccuracies in sensor readings that may be introduced due to heat buildup in the electromagnetic coil.

[0121] Figure 1 b is an enlarged inset of the magnet disconnection phase, which represents a curve of the average value from the magnetic field sensor over time according to an embodiment of the present disclosure.

[0122] The magnet disconnection phase is the reverse of the magnet connection phase and can be broadly characterized by the desaturation of the electromagnetic coil (event 3a) and the reversal of events 2b to 2e described in the preceding paragraphs regarding the magnet connection phase.

[0123] The magnet disconnection phase can be approximately 0.1, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 8.0, 9.0, 10.0, 11.0, or 12.0 seconds, and a suitable range can be selected from any of these values ​​(e.g., approximately 0.25 to approximately 12, approximately 0.25 to approximately 5.0, approximately 0.25 to approximately 3.0, approximately 0.25 to approximately 2.0, approximately 0.25 to approximately 1, approximately 0.1 to approximately 12, approximately 0.1 to approximately 5.0, approximately 0.1 to approximately 3.0, approximately 0.1 to approximately 2.0, approximately 0. 1 to 1, about 0.5 to 12, about 0.5 to 5.0, about 0.5 to 3.0, about 0.5 to 2.0, about 0.5 to 1, 0.75 to 12, about 0.75 to 5, about 0.75 to 3.0, about 0.75 to 2.0, about 0.75 to 1.0, about 3.0 to 7.0, about 3.0 to 6.0, about 3.0 to 5.0, about 3.0 to 4.0, about 4.0 to 7.0, about 4.0 to 6.0 or about 4.0 to 5.0 seconds).

[0124] In some implementations, the magnet switching-on phase and the magnet switching-off phase may be substantially equal. For example, the magnet switching-on phase and the magnet switching-off phase may be substantially equal in time, within approximately 1%, approximately 2%, approximately 3%, approximately 4%, approximately 5%, approximately 6%, approximately 7%, approximately 8%, approximately 9%, approximately 10%, approximately 20%, approximately 25%, approximately 30%, approximately 35%, approximately 40%, approximately 45%, or approximately 50% of each other.

[0125] When the magnet-on phase and magnet-off phase are not equal, as discussed above, in some embodiments, the apparatus may include an onboard reference standard. For example, an onboard standard curve generator reads a known amount of analyte to create a standard curve. Therefore, any deviation between the expected result and the measured result can be used to generate a multiplier that can be applied to the sample result to account for any drift in the readings.

[0126] In some implementations, the sample reading phase may include two or more sample reading cycles. For example, the sample reading phase may include two, three, four, five, six, seven, eight, nine, or ten cycles.

[0127] The sample reading phase can be approximately 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, or 70 seconds, and a suitable range can be selected from any of these values ​​(e.g., approximately 5 to approximately 70, approximately 5 to approximately 60, approximately 5 to approximately 50, approximately 5 to approximately 40, approximately 5 to approximately 30, approximately 5 to approximately 20, approximately 5 to approximately 10, approximately 10 to approximately 70, approximately 10 to approximately 60, approximately 10 to approximately 50, approximately 10 to approximately 40, approximately 10 to approximately 30, approximately 10 to approximately 20, approximately 20 to approximately 70, approximately 20 to approximately 50, approximately 20 to approximately 30 seconds).

[0128] In some implementations, the external magnetic field may include a first magnetic field and a second magnetic field.

[0129] The first and second magnetic fields can be generated by magnets located on opposite sides of the sample.

[0130] In some embodiments, a first magnetic field may be generated below the sample, and a second magnetic field may be generated above the sample. In this embodiment, the first magnetic field may attract magnetizable particles downwards, while the second magnetic field may attract magnetizable particles upwards.

[0131] In some implementations, the first magnetic field and the second magnetic field may have the same or different polarities.

[0132] The first magnetic field can be a permanent magnetic field, in which a magnetic field is continuously applied at a constant amplitude during the duration of the sample reading phase.

[0133] The second magnetic field can be a non-permanent magnetic field, wherein the magnetic field is applied only during the read state, such that both the permanent and non-permanent magnetic fields are active during the read state.

[0134] When using more than one external field, as the magnetizable particles transition between equilibrium states while both magnetic fields are active, the signal generated by the magnetizable particles (bound and unbound binder complexes) is measured by a signal sensor in readout mode.

[0135] Signals generated by magnetizable particles can be measured during a portion of the reading state or for a duration thereof.

[0136] Permanent magnetic fields may be weaker than non-permanent magnetic fields.

[0137] A permanent magnetic field can be generated at the far end of the sample, while a non-permanent magnetic field can be generated at the near end of the sample.

[0138] The strength of a non-permanent magnetic field can be modulated.

[0139] Unwilling to be bound by theory, the modulation of this magnetic field (i.e., the bias field) has the primary function of aligning magnetizable particles with the sensor to achieve maximum detection sensitivity during detection. For ferromagnetic particles, since they have their own permanent magnetic field, where the bias field is broken, the magnetic particles become misaligned. For paramagnetic (or superparamagnetic) particles, since their magnetic field must be induced by an external field, the bias field serves the additional function of inducing such a field.

[0140] Modulated non-permanent magnetic fields can be used to support different magnetizable particles, as different particles (whether in terms of chemical composition or physical size) may require different bias field strengths and configurations.

[0141] The magnetic field can be generated by one or more magnetic field generators.

[0142] A permanent magnetic field can be generated using one or more permanent magnets. Alternatively, one or more electromagnets can be configured to apply a continuous and constant magnetic field during the sample readout phase.

[0143] Non-permanent magnetic fields can be generated using one or more electromagnets.

[0144] In some implementations, the electromagnet may be configured to have a magnetic field strength at least twice that of a permanent magnet.

[0145] A non-permanent magnetic field can be applied for a duration of approximately 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, or 3.5 seconds, and a suitable range can be selected from any of these values ​​(e.g., approximately 1.0 to approximately 3.5, approximately 1.0 to approximately 3.25, approximately 1.0 to approximately 3.0, approximately 1.0 to approximately 2.75, approximately 1.0 to approximately 2.5). Approximately 1.0 to approximately 2.25, approximately 1.0 to approximately 2.0, approximately 1.0 to approximately 1.75, approximately 1.0 to approximately 1.5, approximately 1.0 to approximately 1.25, approximately 1.5 to approximately 3.0, approximately 1.5 to approximately 2.5, approximately 1.5 to approximately 2.0, approximately 2.0 to approximately 2.25, approximately 2.5, approximately 2.0 to approximately 2.75, approximately 2.0 to approximately 3.0, approximately 2.0 to approximately 3.25, approximately 2.0 to approximately 3.5 seconds).

[0146] In some implementations, each sample reading cycle may include a first state in which the first magnetic field is active and the second magnetic field is inactive; a second state in which the first magnetic field is active and the second magnetic field is active; and a third state in which the first magnetic field is active and the second magnetic field is inactive.

[0147] The fourth stage can be the data analysis stage.

[0148] The method may include processing raw data output from a magnetic field sensor and analyzing the processed data to quantify the amount of a target analyte in the sample. Raw data processing may be performed using a combination of hardware and software implementations described in detail elsewhere in this specification.

[0149] The signals detected and measured during the readout phase can be recorded as data and analyzed. The magnetic field sensor output can be recorded throughout the duration of the sample readout phase.

[0150] The amount of analyte in a sample can be determined based on changes in the magnetic response of chemically bonded magnetic nanoparticles and analyte complexes detected by a magnetic field sensor.

[0151] In some implementations, the acquired data is analyzed to identify data as described in paragraphs

[103] through

[110] . Figure 1 The dataset portions corresponding to events 2a-2f and 3a-3b shown in a and 1b.

[0152] In some implementations, detection and quantification are derived using data obtained when an external magnetic field is applied or activated. For example, data for processing and analysis can be derived from events 2b to 2f during the magnet's operation after the electromagnetic coil has reached saturation.

[0153] In an implementation using permanent magnets, the data for processing and analysis can be derived from events 2b to 2f during the magnet switching period immediately after the magnetic field from the permanent magnet is applied.

[0154] The analytical performance of a detection method is typically evaluated by measuring the dose-response curve, from which the limit of detection (LoD) can be derived. LoD is the lowest amount of a substance, such as a biomarker, that can be detected at a selected confidence level. The chosen assay (biomarker, biological material, sample matrix, incubation time, etc.) can significantly affect the LoD. The limit of quantitation (LoQ) is also used; it is the lowest concentration of a biomarker that can be quantified with a given desired precision. If the dose-response curve has good sensitivity, i.e., if the signal changes strongly with changes in target concentration, then the LoQ is close to the LoD.

[0155] The method of the present invention can provide a LoQ of about 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.5 or 2.0 pg / mL, and a suitable range can be selected from any of these values.

[0156] The method of the present invention can provide LoD of about 0.1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 or 2.0 pg / mL, and a suitable range can be selected from any of these values.

[0157] In some implementations, the ambient magnetic field is measured at one or more stages. For example, the ambient magnetic field may be measured during the pre-sampling stage, the sample introduction stage, the sample readout stage, and the data analysis stage. The measured ambient magnetic field can be used to adjust a reference magnetic field signal and the magnetic field signal obtained during the sample readout stage.

[0158] In some implementations, the sample can be magnetically shielded from interference from ambient magnetic fields.

[0159] The Discrete Fourier Transform can be used to analyze the spectrum of signals obtained from sensors, for example, by separating time-domain signals into frequency components.

[0160] In some implementations, the Fast Fourier Transform (FFT) algorithm can be used to process the signal data obtained from the sensor. FFT processing can convert the signal obtained from the sensor into individual spectral components, including but not limited to frequency and amplitude.

[0161] Data from sensors can be preprocessed before FFT processing. Signal data can be truncated (and concatenated) in one or more dimensions to achieve the optimal dataset for FFT processing. For example, sensor signal output data can be preprocessed in a manner corresponding to... Figure 1 The events 2a to 2f and / or event 3a shown in a and 1b are truncated in the time dimension.

[0162] In some implementations, one or more portions of the sensor data may be truncated in one or more dimensions.

[0163] In some implementations, a portion of the signal data between coil saturation and desaturation in each cycle is truncated.

[0164] In some implementations, the truncated signal data can be concatenated. The concatenated dataset is then subjected to FFT processing.

[0165] In some implementations, windowing can be applied to the dataset. For example, one or more data windows can be concatenated and subjected to FFT processing.

[0166] In some implementations, the signal data used for FFT processing can be selected from intermediate cycles during the sample readout phase. For example, signal data from the third cycle can be selected for FFT processing from a sample readout phase that includes five cycles.

[0167] The magnetic properties of nanoscale and microscale magnetic materials differ from those of their corresponding bulk magnetic materials. Generally, magnetizable particles are classified as paramagnetic, ferromagnetic, ferrimagnetic, antiferromagnetic, or superparamagnetic based on their magnetic behavior in the presence and absence of an applied magnetic field.

[0168] According to one implementation, superparamagnetic nanoparticles, such as superparamagnetic iron oxide nanoparticles (SPION), can be used to accurately detect and measure changes in Brownian motion as a global magnetic response proportional to the size of the aggregate.

[0169] Superparamagnetic nanoparticles exhibit a tendency to align their magnetic moments in the presence of an external magnetic source and to demagnetize in the absence of a magnetic field.

[0170] Diamagnetic materials do not exhibit dipole moments in the absence of a magnetic field, and in the presence of a magnetic field, they align in the opposite direction to the magnetic field.

[0171] Paramagnetic materials exhibit random dipole moments in the absence of a magnetic field, and in the presence of a magnetic field, they align with the direction of the magnetic field.

[0172] Ferromagnetic materials exhibit aligned dipole moments.

[0173] Ferromagnetic and antiferromagnetic materials exhibit alternating dipole moments.

[0174] In one implementation, the magnetizable particles are paramagnetic particles. Such particles become magnetic when exposed to a magnetic field. Once the magnetic field is removed, the particles begin to lose their magnetic properties.

[0175] In an alternative implementation, the magnetizable particles are ferromagnetic particles. That is, they always exhibit magnetic properties regardless of whether they are subjected to a magnetic field.

[0176] Commercially available magnetizable particles include Dynaparticles M-270, Dynaparticles M-280, Dynaparticles MyOne T1 and Dynaparticles MyOne C1 from Thermo Fisher Scientific, µMACS MicroParticles from Miltenyi Biotec, and SPHERO™ superparamagnetic particles, SPHERO™ paramagnetic particles and SPHERO™ ferromagnetic particles from Spherotech.

[0177] Magnetizable particles can be ferromagnetic particles coated with streptavidin. For example, commercially available streptavidin-coated superparamagnetic particles include Ocean NanoTech SHS30-01. Streptavidin-coated ferromagnetic particles can be functionalized with biotinylated "detection" antibodies.

[0178] Magnetizable particles can be formed from ferrites, which themselves are formed from iron oxides (such as magnetite and maghemite). Various methods are known for synthesizing iron oxide and metal-substituted ferrite magnetizable particles, such as coprecipitation, thermal decomposition, and hydrothermal methods. Coprecipitation methods use stoichiometric amounts of ferrous and ferric salts in an alkaline solution combined with a water-soluble surface coating material (e.g., polyethylene glycol (PEG)), where the coating provides colloidal stability and biocompatibility. The size and properties of the magnetizable particles can be controlled by adjusting the reducing agent concentration, pH, ionic strength, temperature, iron salt source, or the ratio of Fe2+ to Fe3+.

[0179] The size and shape of the magnetizable particles can be customized by altering reaction conditions such as the type of organic solvent, heating rate, surfactant, and reaction time. This method results in a narrow size distribution of magnetizable particles in the range of 10 to 100 nm. Fe²⁺ can be replaced by other metals to improve saturation magnetization.

[0180] During the synthesis process, the magnetizable particles may be coated with a hydrophobic coating. If so, the method of manufacturing the magnetizable particles may include an additional step of ligand exchange, allowing the magnetizable particles to be dispersed in water for further use.

[0181] Magnetizable particles can be manufactured via hydrothermal reduction of polyols, producing water-dispersible magnetizable particles ranging in size from tens to hundreds of nanometers. The size and surface functionalization of the iron oxide magnetizable particles can be optimized by adjusting the solvent system, reducing agent, and type of surfactant used. This method can also be used to synthesize FePt magnetizable particles.

[0182] Magnetizable particles can be manufactured via reverse oil-in-water micelles. This method forms a microemulsion of aqueous nanodroplets containing iron precursors, which is stabilized by a surfactant in the oil phase and magnetic nanoparticles obtained through precipitation. Iron oxide nanocrystals can be assembled by combining the microemulsion with a silica sol-gel, and can be obtained by co-precipitation into magnetizable particles with a diameter greater than 100 nm.

[0183] Metallic magnetizable particles can be monometallic (e.g., Fe, Co, or Ni) or bimetallic (e.g., FePt and FeCo). Alloy magnetizable particles can be synthesized by physical methods including vacuum deposition and vapor-phase evaporation. These methods can produce FeCo magnetizable particles with high saturation magnetization (approximately 207 emu / g) and can be synthesized by reduction with Fe3+ and Co2+ salts.

[0184] Magnetizable particles may include a single metal or metal oxide core. Magnetizable particles may include multiple cores, multilayer magnetic materials, and non-magnetic materials. Magnetizable particles may include a coating of a silica or polymer core with a magnetic shell. Non-magnetic core particles may include silica or other polymers.

[0185] Magnetizable particles may include a dielectric silica core coated with a magnetic shell. The magnetic shell may be formed of Co, FePt, or Fe3O4. The shell may also contain a stabilizer, such as a silica shell or a polyelectrolyte layer. Magnetizable particles may be mesoporous magnetizable particles.

[0186] Coatings on magnetizable particles can define the interactions between the magnetizable particles and biomolecules (such as analytes) and their biocompatibility. The coating can be used to define the surface charge, which, together with the coating, can alter the hydrodynamic dimensions of the magnetic particles. The hydrodynamic dimensions of the magnetizable particles can then modify the functionality of the magnetic particles.

[0187] Magnetizable particles can be coated with specific coatings that provide electrostatic and steric repulsion. Such coatings can help stabilize the magnetizable particles, preventing them from agglomerating or settling.

[0188] Magnetizable particles may contain a coating formed of inorganic materials. Such magnetizable particles can form a core-shell structure. Examples include magnetizable particles coated with biocompatible silica or gold (e.g., silica-coated alloy magnetic nanoparticles, FeCo, and CoPt). The shell provides a platform for modifying the magnetizable particles with ligands (e.g., thiols). Other inorganic coating materials may include titanates or silver. For example, silver-coated iron oxide magnetizable particles can be synthesized and integrated with carbon paste.

[0189] The shell can be formed from silica. The advantage of silica coating is that silica-coated magnetizable particles can be covalently bonded to multifunctional functional molecules and surface reactive groups. The silica shell can be manufactured, for example, using the Stober method based on the sol-gel principle or the Philipse method, or a combination thereof. The core of the magnetizable particle can be coated with tetraethoxysilane (TEOS), for example, by hydrolyzing TEOS under alkaline conditions, which condenses and polymerizes TEOS into a silica shell on the surface of the magnetic core. Cobalt magnetizable particles can be coated using a modified Stober method combining 3-aminopropyltrimethoxysilane and TEOS.

[0190] The Philipse method forms a silica shell of sodium silicate on a magnetic core. A second silica layer can be deposited using the Stober method. The silica can be coated using a reverse microemulsion method. This method can be used with a surfactant. The surfactant can be selected from Igeoal CO-520 to provide a silica shell thickness of about 5 to about 20 nm. Preferably, the reagent used to manufacture the silica shell is selected from amino-terminated silanes or olefin-terminated silanes. Preferably, the amino-terminated silane is (3-aminopropyl)trimethoxysilane (APTMS). Preferably, the olefin-terminated silane is (3-methacryloyloxypropyl)trimethoxysilane.

[0191] Magnetizable particles can be coated with gold. Gold-coated iron oxide nanoparticles can be synthesized by any of the following methods: chemical methods, reverse microemulsions, and laser-promoted methods. Gold-coated magnetizable particles can also be synthesized by directly coating gold onto the core of the magnetizable particles. Alternatively, gold-coated magnetizable particles can be synthesized by using silica as an intermediate layer for the gold coating. Preferably, a reduction method is used to deposit the gold shell onto the magnetizable particles.

[0192] The magnetic core coated with metal oxide or silica can first be functionalized with 3-aminopropyltrimethoxysilane, then gold nanocrystals (from chloroauric acid) of about 2 to about 3 nm are electrostatically attached to the surface, followed by the addition of a reducing agent to form a gold shell. Preferably, the reducing agent is a mild reducing agent selected from sodium citrate or tetra(hydroxymethyl)phosphonium chloride. In some embodiments, the gold shell is formed by the reduction of gold(III) acetate (Au(OOCCH3)3). In some embodiments, the gold shell is formed on a metallic magnetic core (e.g., nickel and iron) by reverse micelles.

[0193] Magnetizable particles can be functionalized with organic ligands. This can be done in situ (i.e., providing functional ligands to the magnetizable particles during the synthesis step) or post-synthesis. Magnetizable particles can be functionalized with terminal hydroxyl (-OH), amino (-NH2), and carboxyl (-COOH) groups. This can be achieved by changing the surfactants used in the hydrothermal synthesis (e.g., dextran, chitosan, or poly(acrylic acid)).

[0194] Post-synthetic functionalization of magnetizable particles allows for the functionalization of custom ligands on any magnetizable particle surface. Functionalization can be achieved through ligand addition and ligand exchange. Ligand addition involves the adsorption of amphiphilic molecules (containing hydrophobic segments and hydrophilic components) to form a bilayer structure. Ligand exchange replaces the original surfactant (or ligand) with a new functional ligand. Preferably, the new ligand contains functional groups capable of binding to the magnetizable particle surface via strong chemical bonding or electrostatic attraction. In some embodiments, the magnetizable particles also include functional groups for stability in water and / or biofunctionalization.

[0195] Magnetizable particles can be coated with ligands that enhance ionic stability. Functional groups can be selected from carboxylates, phosphates, and catechols (e.g., dopamine). Ligands can be siloxane groups used for coating hydroxyl-rich surfaces (e.g., metal oxide magnetic particles or silica-coated magnetic particles). Ligands can be small silane ligands linking the magnetizable particles to various functional ligands (e.g., amines, carboxylates, thiols, and epoxides). Silane ligands can be selected from N-(trimethoxysilylpropyl)ethylenediaminetriacetic acid and (triethoxysilylpropyl)succinic anhydride to provide carboxylate-terminated magnetic particles. Functional groups can be selected from phosphonic acids and catechols (to provide hydrophilic tails). Functional groups can be selected from amino-terminated phosphonic acids. Functional groups can be selected from 3-(trihydroxysilyl)propyl methylphosphonate for dispersion in aqueous solutions. For magnetizable particles dispersed in water, ligands can be selected from dihydroxyhydrocinnamic acid, citric acid, or thiomalic acid.

[0196] In some embodiments, the magnetizable particles are functionalized with polymer ligands. The polymers may be selected from natural polymers (e.g., starch, dextran, or chitosan), PEG, polyacrylic acid (PAA), polymethacrylic acid (PMAA), poly(N,N-methylenebisacrylamide) (PMBBAm), and poly(N,N / -methylenebisacrylamide-co-glycidyl methacrylate) (PMG).

[0197] Functional groups on the surface of magnetizable particles serve as connectors for binding with complementary biomolecules. These biomolecules can be small, such as vitamins, peptides, and aptamers. They can also be larger, such as DNA, RNA, and proteins.

[0198] Regarding nucleic acid attachment, nucleic acids can be conjugated using non-chemical methods (e.g., electrostatic interactions) or chemical methods (e.g., covalent bonding). Nucleic acid chains can be modified with functional groups. These functional groups can be selected from thiols or amines, or any combination thereof.

[0199] The conjugation of larger biomolecules may depend on their specific binding interactions with a wide variety of substrates and synthetic analogs, such as specific receptor-substrate recognition (i.e., antigen-antibody and biotin-antibiotin interactions).

[0200] A specific pair of proteins can be used to immobilize substances onto magnetic particles. Physical interactions include electrostatic interactions, hydrophilic-hydrophobic interactions, and affinity interactions.

[0201] In some implementations, the biomolecules have an opposite charge to the magnetic polymer coating (e.g., polyethyleneimine or polyethyleneimine). For example, positively charged magnetizable particles bind to negatively charged DNA.

[0202] Magnetizable particles can utilize biotin-avidin interactions. Biotin molecules and tetramer streptavidin have site-specific attraction and low non-specific binding, which can be used to control the orientation of interacting biomolecules, such as the exposure of the Fab region of an antibody to its antigen.

[0203] Magnetizable particles can be covalently coupled to biomolecules. Covalent coupling can be selected from homobifunctional / heterobifunctional crosslinking agents (amino), carbodiimide coupling (carboxyl), maleimide coupling (amino), direct reaction (epoxy group), maleimide coupling (thiol group), Schiff base condensation (aldehyde group), and click reaction (alkyne / azide group).

[0204] The magnetizable particles may have an average particle size of about 5, 10, 15, 20, 25, 30, 35, 40, 50, 100, 150, 200, 250, 300, 350, 400, 450 or 500 nm, and a suitable range may be selected from any of these values ​​(e.g., about 5 to about 500, about 5 to about 400, about 5 to about 250, about 5 to about 100, about 5 to about 50, about 10 to about 500, about 10 to about 450, about 10 to about 300, about 10 to about 150, about 10 to about 50, about 50 to about 500, about 50 to about 350, about 50 to about 250, about 50 to about 150, about 100 to about 500, about 100 to about 300, about 150 to about 500, about 150 to about 450 or about 200 to about 500 nm).

[0205] The magnetizable particles may have an average particle size of about 500, 550, 600, 650, 700, 750, 800, 850, 900, 950 or 1000 nm, and a suitable range may be selected from any of these values ​​(e.g., about 500 to about 1000, about 500 to about 850, about 500 to about 700, about 550 to about 1000, about 550 to about 800, about 600 to about 1000, about 600 to about 900, about 650 to about 1000, about 650 to about 950, about 650 to about 800 or about 700 to about 1000 nm).

[0206] The magnetizable particles may have an average particle size of about 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500 or 5000 nm, and a suitable range may be selected from any of these values ​​(e.g., about 1000 to about 5000, about 1000 to about 4000, about 1500 to about 5000, about 1500 to about 4500, about 1500 to about 3500, about 2000 to about 5000, about 2000 to about 4000, about 2500 to about 5000, about 2500 to about 3500, about 3000 to about 5000 nm).

[0207] The particle size variation of the magnetizable particles can be less than 25%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2% or 1%, and a suitable range can be selected from any of these values.

[0208] In some implementations, the magnetizable particles may comprise 30 nm superparamagnetic particles.

[0209] In some implementations, the magnetizable particles may comprise 50 nm superparamagnetic particles.

[0210] In some implementations, the binder and magnetizable particles (beads) can be provided in a specified ratio.

[0211] The binder and magnetizable particles can have a ratio of approximately 10:1, 9:1, 8:1, 7:1, or 6:1. The binder to magnetizable particle ratios are 5:1, 4:1, 3:1, 2:1, 1.5:1, 1:1, 0.75:1, 0.5:1, 0.25:1, 1:0.25, 1:0.5, 1:0.75, 1:1.5, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, or 1:10, and a suitable range can be selected from any of these values ​​(e.g., about 10:1 to about 1:10, about 9:1 to about 1:9, about 8:1 to about 1:8, about 7:1 to about 1:7, about 6:1 to about 1:6, about 5:1 to about 1:5, about 4:1 to about 1:4, about 3:1 to about 1:3, about 2:1 to about 1:2, about 0.75:1 to about 1:0.75).

[0212] The binder-to-magnetizable particle ratio can be the ratio of the number of magnetizable particles to the amount of binder. For example, a binder-to-bead ratio of 0.75:1 means that 0.75 ng of binder corresponds to 1 μg of beads.

[0213] Continuing with the example above, 30 nm beads (Ocean Nanotech) can have a molar concentration of 34 fmole / μl or 0.034 μM, and the number of beads per μl (where the bead concentration is 1 μg / μl) can be calculated to be approximately 2.047 x 10⁻⁶. 10 Beads (34x10) -15 Multiply by Avogadro's constant = 34 x 10 -15 x 6.0 23 x1023 equals 2.047x10 10 (each bead)

[0214] The molecular weight of the binder can be approximately 150977.24 g / mol. Therefore, the total amount of 0.75 ng of binder is 2.99 x 10⁻⁶. 9 One binder (the number of moles of binder is 0.75 ng / 150977.24 g / mol = 4.97 x 10⁻⁶) -15 (moles). To obtain the amount of binder per 0.75 ng, multiply the number of moles by Avogadro's constant = 4.97 x 10⁻⁶. -15 Moles multiplied by 6.023 x 10 23 Equals 2.99 x 10 9 (a binder).

[0215] The molar ratio was calculated to be 2.047 x 10⁻⁶. 10 Each bead / 2.99 x 10 9 One binder = 6.846. That is, approximately one binder for every seven beads. The surface area of ​​a 30 nm diameter bead is approximately 2827.43 nm. 2 Therefore, one binder would correspond to 7 x 2827.43 nm. 2 = 19792.01 nm 2 .

[0216] In some implementations, the binder and the magnetizable particle surface can be provided with a specified binder-to-surface-area ratio.

[0217] The binder-to-surface-area ratio can be from about 1 to about 2,000, about 2,500, about 3,000, about 3,500, about 4,000, about 4,500, about 5,000, about 5,500, about 6,000, about 6,500, about 7,000, about 7,500, about 8,000, about 8,500, about 9,000, about 9,500, about 10,000, about 10,500, about 11,000, about 11,500, about 12,000, about 12,500, about 13,000, about 13, 500, approximately 14,000, approximately 14,500, approximately 15,000, approximately 15,500, approximately 16,000, approximately 16,500, approximately 17,000, approximately 17,500, approximately 18,000, approximately 18,500, approximately 19,000, approximately 19,500, approximately 20,000, approximately 20,500, approximately 21,000, approximately 21,500, approximately 22,000, approximately 22,500, approximately 23,000, approximately 23,500, approximately 24,000, approximately 24,500, or approximately 25,000 nm 2 And you can choose a suitable range from any of these values.

[0218] Particles can be tethered or untethered. Tethered particles are tethered to larger secondary particles (macromolecules). Untethered particles diffuse freely throughout the sample, while tethered particles have limited diffusivity and diffuse freely within the sample along their chain. As described above, the amount of analyte in the sample is determined based on changes in the signal detected by the sensing module. The sensing module detects changes based on the net movement of the particles.

[0219] The particles can adhere to other objects, such as larger secondary particles or molecules. Magnetizable particles can also adhere to surfaces. Attachment to other objects or surfaces allows the magnetizable beads to be positioned in a specific location while retaining the ability to undergo Brownian diffusion (within the constraints of the attachment or tether), which can be detected and measured by a device.

[0220] The tethering advantageously allows the ability to retain particles at specific locations within a larger shared volume while simultaneously allowing them to undergo Brownian diffusion. Consequently, multiple types of magnetizable particles (depending on the type of analyte identification or other characteristics) can all be in their discrete locations (e.g., aligned with a specific magnetic sensor) within the shared volume, and the distance between multiple target analytes (e.g., aligned with a specific magnetic sensor) within the shared volume can be constant. This allows for multiple detection of different target analytes within a single volume.

[0221] The non-magnetic beads or surfaces tethered to the microchannel allow for this multiple detection because the non-magnetic beads can act as “anchors” to hold the tethered particles in place through a combination of size, surface chemistry, and interaction with their local environment.

[0222] For example, magnetizable particles can be molecularly tethered to larger non-magnetizable particles (such as latex beads), such that the magnetizable particles are positioned in a specific region due to the larger non-magnetizable beads, but can still diffuse freely within the constraints of the tether. In another example, magnetizable particles can be molecularly tethered to a surface, such as the surface of a microfluidic device corresponding to the sensing area of ​​a sensing module.

[0223] Non-magnetizable particles may include any suitable non-magnetizable particles, including but not limited to latex beads, polystyrene beads or other types of polymer beads.

[0224] In some embodiments, non-magnetizable particles, such as latex beads having surface chemicals (such as amines and carboxyl groups), may have molecular chains (e.g., polyethylene glycol-PEG) attached thereto, such that one end of the molecular chain is connected to the latex bead (having chemicals compatible with the surface of the latex bead) and the other end is connected to a magnetizable bead (having chemicals compatible with the surface of the magnetic bead, such as biotin on a chain of streptavidin attached to the surface of the magnetic bead), thereby forming a tethered connection between the two beads.

[0225] The length of the molecular chain can be approximately 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, or 80 nm.

[0226] The method can be performed using a microfluidic device or system.

[0227] Microfluidics may require a degree of sample preparation. Sample preparation may include cell lysis, washing, centrifugation, separation, filtration, and elution. In some embodiments, sample preparation is performed off-chip. Alternatively, sample preparation is performed on-chip.

[0228] In some configurations, the sample to be analyzed can be added directly to the sample well or microfluidic device without additional processing. The microfluidic system may include a fluid. The fluid may be selected from phosphate-buffered saline (PBS). The phosphate-buffered saline may contain dipotassium hydrogen phosphate (K₂HPO₄), sodium chloride (NaCl), and disodium phosphate (Na₂HPO₄). PBS provides a continuous phase for the particle suspension.

[0229] Microfluidic systems enable faster analysis and shorter response times. They also offer the ability to automate sample preparation, reducing the risk of contamination and human error. Furthermore, microfluidic systems require low sample volumes. Diffusion distances can be reduced by increasing the surface area to volume ratio, reducing reagent consumption through microfabrication and nanofabrication of channels and chambers, and / or automating all steps of the process.

[0230] In some configurations, the microfluidic system comprises rigid or flexible materials and may include electronics that can be integrated into the device. The electronics may include wireless communication electronics.

[0231] Microfluidic systems can be either flow-through or stationary systems. For example, a microfluidic system may include a magnetic field sensor that is stationary relative to the microfluidic system.

[0232] Microfluidic systems can operate passively. For example, a microfluidic system can operate under passive diffusion. That is, a microfluidic system does not require actively generated flow to operate effectively.

[0233] A microfluidic system may include a network of reservoirs, which may be connected via microfluidic channels. These microfluidic channels may be configured for active or passive metering. This allows sample fluid to be drawn into the microfluidic channels and into the sample chamber.

[0234] Microfluidic systems allow for miniaturization, which enables lab-on-a-chip applications. Microfluidic systems can be used as part of biosensors, for example, including channels for acquiring biological samples (e.g., saliva and / or gingival crevicular fluid and / or tears and / or sweat, etc.), processing fluids (e.g., in combination with one or more reagents and / or detecting interactions with biomolecules, etc.).

[0235] Microfluidic systems can be implemented in the form of microfluidic chips. A microfluidic chip comprises a set of channels at the micrometer or millimeter scale, provided, for example, by molding or etching, onto a material (such as glass, silicon, or other types of polymers) or a combination of materials. The microfluidic channels can be interconnected to form a channel network. The length of the channels can range from a few millimeters to several centimeters.

[0236] A microfluidic chip may include one or more ports for receiving samples and / or reagents. For example, a microfluidic chip may include a sample inlet port and a reagent port.

[0237] A microfluidic chip may include multiple detection regions. Each detection region defines a channel portion for the detection and quantification of analytes or biomarkers in a sample. The detection regions of the microfluidic chip correspond to the positions of the device's magnetic sensors, such that when the microfluidic chip is placed on the detection surface of the device, each detection region is perpendicularly aligned with its corresponding magnetic / other sensor.

[0238] The detection area can be located anywhere along the channel. In some implementations, the detection area is located at the channel junction. That is, the detection area is located at the intersection of two or more channels.

[0239] The channel junction may include a reaction / detection orifice. The reaction / detection orifice may be larger than the channel size.

[0240] Microfluidics may require a degree of sample preparation. Sample preparation may include cell lysis, washing, centrifugation, separation, filtration, and elution. In some embodiments, sample preparation is performed off-chip. Alternatively, sample preparation is performed on-chip.

[0241] Microfluidic chips can be supplied in a 'ready-to-use' form. For example, a microfluidic chip may be pre-loaded with all the necessary elements and cell separation (such as binding complexes and reagents) for performing analyte detection and quantification. That is, the 'ready-to-use' form only requires adding the sample to the microfluidic device.

[0242] The reaction / detection wells may be pre-loaded with a binding agent complex for binding one or more target analytes. The binding agent complex may be provided within a gel matrix in the reaction / detection well. For example, each reaction / detection well may include a binding agent complex containing a hydrogel, agarose gel, or agar. The binding agent complex is described in detail later in this specification.

[0243] The binder complex and / or reagents can be added to the reaction / detection wells before use.

[0244] Microfluidic systems may include rigid or flexible materials and may include electronics that can be integrated into a microfluidic chip. The electronics may include wireless communication electronics.

[0245] Microfluidic systems can be either flow-through or stationary systems. For example, a microfluidic system may include a magnetic field or other sensors that are stationary relative to the microfluidic system.

[0246] Microfluidic systems can operate passively. For example, a microfluidic system can operate under passive diffusion. That is, a microfluidic system does not require actively generated flow to operate effectively.

[0247] A microfluidic system may include a network of reservoirs, which may be connected via microfluidic channels. These microfluidic channels may be configured for active or passive metering. This allows sample fluid to be drawn into the microfluidic channels and into the sample chamber.

[0248] The passageway can be arranged in a cross-shadow configuration.

[0249] Microfluidic systems may include microfluidic channels configured to allow different samples and / or detection regions on the device to enter at different times. For example, a microfluidic device integrated into or on an aligner may be configured to provide timing through time sampling of the fluid. For example, a microfluidic system may be designed to sample in a temporal sequence and at controlled timing. In some variations, the timing of the fluid within the microchannel may be actively timed, for example, by opening the channel via the release of a valve (e.g., an electromechanical valve, a solenoid valve, a pressure valve). Examples of valves controlling the fluid in a microfluidic network include piezoelectric, electrokinetic, and chemical methods.

[0250] The channels of a microfluidic chip may include wicking structures. Wicking structures can increase the rate at which fluid is delivered via capillary action. Wicking structures may include porous media, such as paper-based materials.

[0251] Microfluidic chips may include multiple microfluidic channels arranged sequentially. Fluid is drawn into the microfluidic channel at a metered rate. The timing of sample entry into the channel can be staggered.

[0252] Microfluidics enable signal multiplexing. They can be used to sample and / or measure multiple biomarkers within controlled intervals. For example, microfluidics can provide access to one or more sample chambers. Microfluidics may include one or more valves controlled by control circuitry within the device. These valves may be interconnected. Therefore, microfluidics are suitable for performing the simultaneous detection of multiple analytes in a common sample volume. Additionally or alternatively, microfluidics can be configured to perform simultaneous multiplex detection of multiple samples of the same target.

[0253] The microfluidic channel may have a cross-sectional area in the range of 0.001 to 0.01 mm2, 0.01 to 0.1 mm2, 0.1 to 0.25 mm2, 0.25 to 0.5 mm2, 0.1 to 1 mm2, 0.5 to 1 mm2, 1 to 2 mm2 or 2 to 10 mm2, and the available range may be selected between any of these values.

[0254] In some implementations, the microfluidic receives a predetermined sample volume in the range of about 0.1 to 1 μL, 1 to 5 μL, 5 to 10 μL, 10 to 20 μL, or 20 to 50 μL or more, and the available range can be selected between any of these values.

[0255] Figure 2 An example of a sample introduction device / microfluidic chip is shown. The microfluidic chip may include multiple channels arranged to guide a sample from a sample insertion region to a detection region; and functionalized particles for analyte detection.

[0256] The channel may have the cross-sectional dimensions described above, and more preferably 0.01 mm² (0.1 mm x 0.1 mm). The channel may have a variable length. For example, the channel can be 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 250, or 300 mm long, and an available range can be selected between any of these values ​​(e.g., approximately 1 to 10, 1 to 20, 1 to 50, 1 to 100, 1 to 200, 1 to 300, 10 to 20, 10 to 40, 10 to 60, 10 to 80, 10 to 100, 50 to 100, 50 to 150, 50 to 200, 50 to 250, 50 to 300, 100 to 200, or 100 to 300 mm long).

[0257] The aforementioned channel dimensions facilitate passive capillary flow.

[0258] In use, the sample is introduced into the microfluidic device through the sample insertion region. The sample insertion region may include an inlet port.

[0259] A filter membrane may be present in the insertion region to separate desired components of the sample while allowing those components to pass through. For example, plasma may be allowed into the microfluidic chip, but cells may not. The presence of a filter membrane depends on the nature of the sample and whether it contains components that are not expected to enter the microfluidic chip.

[0260] Plasma-cell separation can be caused by device configuration or device configuration.

[0261] Once introduced into the insertion region, the sample will then come into contact with the microfluidic channel and flow through the rest of the channel loop.

[0262] Microfluidic systems can be implemented as lab-on-a-chip systems. A lab-on-a-chip system may include one or more magnetic sensors adjacent to a channel. For example, microfluidic device 1 may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, or 30 magnetic sensors arranged around the microfluidic device.

[0263] A lab-on-a-chip may include two or more magnets (such as permanent magnets or electromagnets) arranged adjacent to channels, which can be activated to attract magnetizable particles through a liquid in the channels to enhance mixing. Mixing may be performed, for example, for 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 minutes, and a suitable range can be selected from any of these values. The timing of mixing may depend on the assay requirements, such as sample volume, viscosity, composition, and the detection range of the target analyte.

[0264] To achieve mixing, magnets (e.g., electromagnets) can be positioned at substantially opposite ends of a channel or microfluidic device. For example, the magnets can be controlled or switched such that they push / pull the magnetizable particles toward one end of the orifice / channel or microfluidic device, and then the effect reverses to pull the magnetizable particles toward the other end of the orifice / channel or microfluidic device. This cycle can be repeated multiple times until the desired level of mixing is achieved.

[0265] According to one embodiment, the described method can be performed using a device for detecting analytes in a sample, said device essentially comprising the following: • Sample orifice, which may be separate from or integrated into the microfluidic device. • A magnet used to generate an external magnetic field. • A magnetic signal sensor for measuring a magnetic signal within the sample well, wherein the magnetic signal sensor is adapted to measure the magnetic signal when the external magnetic field is active.

[0266] According to another embodiment, the described method can be performed using a device for detecting analytes in a sample, the device essentially consisting of the following: • Sample orifice, which may be separate from or integrated into the microfluidic device. • A first magnet positioned below the sample aperture, the first magnet being adapted to continuously apply a magnetic field. • A second magnet positioned above the sample well, the second magnet being adapted to apply a discontinuous magnetic field. • A magnetic signal sensor for measuring a magnetic signal within the sample well, wherein the magnetic signal sensor is adapted to measure the magnetic signal when both the continuous magnetic field and the discontinuous magnetic field are active.

[0267] A magnetic field generator may include a magnet.

[0268] A magnetic field generator can produce a magnetic field in a direction perpendicular to the sensor. For example, the magnetic field generator can produce a magnetic field from above and / or below the magnetic field sensor, such that the magnetic field is perpendicular to the body of the magnetic field sensor.

[0269] A magnetic field generator can produce a magnetic field in a direction parallel to the sensor. For example, a magnetic field generator can produce a magnetic field from the side of the magnetic field sensor, making the magnetic field parallel to the main body of the magnetic field sensor.

[0270] The device may include a combination of magnetic field generators that generate magnetic fields in both the vertical and parallel directions relative to the sensor.

[0271] The magnetic field generator can be configured to generate a magnetic field from below and / or above the sample.

[0272] The magnet can be an electromagnet. An electromagnet can apply a field strength of about 0.5, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45 or 50 Gauss, and a suitable range can be selected from any of these values.

[0273] In some implementations, the magnet may include a combination of a permanent magnet and an electromagnet. For example, the first magnet may be a permanent magnet that applies a continuous magnetic field, and the second magnet may be an electromagnet that applies a discontinuous magnetic field.

[0274] The permanent magnet may include any suitable permanent magnet. In some embodiments, the permanent magnet may be selected from one or more of ceramics, samarium cobalt (SmCo), alnico (AlNiCo), and neodymium iron boron magnets.

[0275] The magnet can be applied with a field strength of approximately 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 5, 10, 50, or 100 Gauss, and a suitable range can be selected from any of these values. Sample data are then acquired as described.

[0276] Magnetizable particles are sensed by a magnetic sensor.

[0277] The magnetic sensor can be selected from spintronic sensors, atomic magnetometers (AM), nuclear magnetic resonance (NMR) systems, fluxgate sensors, Faraday induction coil sensors, diamond magnetometers, and magnetic domain wall-based sensors.

[0278] Volume-based sensors, such as planar Hall effect (PHE) sensors, offer simple and rapid sample preparation and detection. Surface-based sensors, such as giant magnetoresistance (GMR), offer a lower detection limit (single particle) due to the short distance between the magnetizable particle and the sensor. However, these techniques typically require laborious sample and / or substrate preparation. Optimizing magnetizable particles and selecting appropriate detection methods for specific applications remains challenging for the magnetic nanotechnology community due to the increasing demands for detection sensitivity, molecular specificity, and application complexity. Spintronic sensors are available from giant magnetoresistance (GMR), tunneling magnetoresistance (TMR), anisotropic magnetoresistance (AMR), and planar Hall effect (PHE) sensors.

[0279] The GMR effect was discovered in the 1980s and has traditionally been used for data logging. Spin valves offer higher sensitivity and a micrometer-scale design. Spin valve GMR sensors consist of an artificial magnetic structure with alternating ferromagnetic and nonmagnetic layers. The magnetoresistive effect is caused by spin-orbit coupling between conduction electrons passing through the different layers. Changes in magnetoresistive resistance provide quantitative analysis through this spin-correlated sensor. GMR sensors can be used to detect DNA-DNA or protein (antibody)-DNA interactions. The size of the sensor array can be adjusted to detect individual magnetizable particles. GMR sensors can be used in combination with antiferromagnetic particles.

[0280] The planar Hall effect is based on the anisotropic magnetoresistance effect of ferromagnetic materials and is used in exchange-biased permalloy planar sensors. PHE sensors can be spin-valve PHE or PHE bridge sensors. PHE sensors may be capable of single-particle sensing.

[0281] As those skilled in the art will understand, Brownian motion or Brownian diffusion can mean that particles can move in any direction, including toward a magnetic field sensor or an electric field sensor. The magnetic signal detected by a magnetic field sensor is based on the net movement of bound and unbound magnetizable particles. The electrical signal detected by an electric field sensor is based on the change in impedance as the particles move through a continuous phase (e.g., PBS).

[0282] When bound and unbound particles are positioned near a magnetic or electric field sensor, they may be located on or near the surface of the sample well or sample reservoir wall until they are released. Once released from their proximity to the magnetic or electric field sensor, the particles may translate or rotate. Given that they are in close proximity to the surface of the sample well or sample reservoir before being released from the biasing system, bound and unbound magnetizable particles may initially tend to move with approximately 180° of freedom of movement relative to the surface of the sample well or sample reservoir.

[0283] A magnetic field can be generated and positioned in a manner that maximizes its effect on magnetizable particles but minimizes its effect on the magnetic field sensor. The magnetic field generator can be generated and / or positioned adjacent to the magnetic field sensor. In some embodiments, the magnetic field generator is positioned above, below, or beside the magnetic field sensor. In some embodiments, the magnetic field generator can be positioned on the same vertical or horizontal plane as the magnetic field sensor.

[0284] The magnetic field can be gradually reduced.

[0285] The magnetic field can be removed immediately.

[0286] The shape of a magnetic field can be variable.

[0287] When the magnetic field applied to the sample decreases and / or is removed, the bound and unbound binder complexes are released from the magnetic field and can diffuse freely from their proximity to the magnetic field sensor (translational motion). The binder complexes can also rotate relative to the magnetic field sensor when the magnetic field applied to the sample decreases and / or is removed (rotational motion).

[0288] The magnetic field sensor can be an on-chip magnetometer. The magnetic field sensor may have a sensitivity of at least 1 mV / V / Gauss. In some embodiments, the magnetic field sensor can detect and / or measure magnetic fields of at least about 10 mGauss, 1 mGauss, 100 μGauss, or 10 μGauss.

[0289] Magnetic field sensors may include multiple axes, such as single-axis, two-axis, or three-axis.

[0290] The magnetic field sensor may be a Honeywell HMC 1021S magnetometer. In another embodiment, the magnetic field sensor may be a Honeywell HMC1041Z magnetometer. In other embodiments, the magnetic field sensor may be selected from the group consisting of Honeywell HMC 1001, HMC 1002, HMC 1022, HMC 1051, HMC 1052, HMC 1053, or HMC 2003 magnetometers.

[0291] Magnetic field sensors may include custom magnetic field sensors with custom components.

[0292] To achieve high levels of accuracy and sensitivity, the magnetic sensor of the device may include a high sampling rate. The magnetic sensor can sample at sampling rates of approximately 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, or 250 kHz, and a suitable range can be selected from any of these values ​​(e.g., approximately 10 to approximately 250, approximately 10 to approximately 200, approximately 10 to approximately 150, approximately 10 to approximately 100, approximately 100 to approximately 250, approximately 100 to approximately 200, approximately 100 to approximately 150 kHz). The ADC sampling rate of the magnetic sensor can be from about 100 kHz to about 200 kHz.

[0293] The magnetic sensor can have a sampling rate of approximately 150 kHz per channel.

[0294] Multiple magnetic sensors can be placed on the detection surface to simultaneously measure changes in the magnetic field. For example, the detection surface may include two, three, four, five, six, seven, eight, nine, ten, twelve, fourteen, sixteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, eighteen, or fifty magnetic sensors.

[0295] The magnetic field sensors can be placed in a relatively small area within the device. For example, 24 magnetic field sensors can be arranged in an area of ​​approximately 13 mm x 19 mm. Due to the short microfluidic channels used with this magnetic field sensor configuration, this configuration enables faster data sampling time. This configuration further enables a smaller and more portable device.

[0296] The device may include about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 magnetic field sensors per cm2 printed circuit board, and an available range may be selected between any of these values ​​(e.g. about 5 to about 15, about 5 to about 13, about 5 to about 10, about 6 to about 15, about 6 to about 12, about 6 to about 9, about 7 to about 15, about 7 to about 14, about 7 to about 13, about 7 to about 10, about 8 to about 15, about 8 to about 14, about 8 to about 11, about 9 to about 15, about 9 to about 13 or about 10 to about 15 sensors per cm2 printed circuit board).

[0297] In some implementations, multiple magnetic field sensors can be used simultaneously to measure changes in the magnetic field. For example, for small portable applications and field laboratory or clinical applications, 50, 60, 70, 80, 90, 100, 110, or 120 magnetic field sensors can be used, and an available range can be selected between any of these values ​​(e.g., about 50 to about 120, about 50 to about 100, about 50 to about 90, about 50 to about 80, about 60 to about 120, about 60 to about 110, about 60 to about 90, about 70 to about 110, about 70 to about 90, about 80 to about 120, or about 80 to about 110 magnetic field sensors).

[0298] In some implementations, multiple magnetic field sensors can be used simultaneously to measure changes in the magnetic field. For example, 1000, 1250, 1500, 1750, 2000, 2250, 2500, 2750, or 3000 magnetic field sensors may be used for laboratory or clinical, research, or industrial applications.

[0299] Data acquisition from the sensor can be synchronized with the microfluidic device. This allows data from the detection sensor to be characterized as either sample data or environmental or ambient data. For example, detecting a signal by a magnetic sensor that no sample is injected into the microfluidic device would characterize that data as environmental or ambient data. Characterizing data as environmental or ambient data helps establish context and can also aid in preparing calibration data.

[0300] When a magnetic sensor detects a signal after a sample is injected into a microfluidic device, the signal is aligned with the magnetizable particles positioned in close proximity to the magnetic sensor. This data can be characterized as sample data.

[0301] This sensing system utilizes an implementation scheme that allows for microfluidic quality control measurements, thereby confirming sample displacement and sensing time.

[0302] Data acquisition from the sensor can be continuous. That is, the magnetic sensor continuously transmits signals, and the data is characterized as sample data or background data based on the synchronization of data collection and sample injection into the microfluidic device.

[0303] Sensor data can be acquired over a period of time to measure changes in the magnetic signal from magnetizable particles. Actions or events can be inferred from the sensed changes in the magnetic signal. These actions or events may include movement of the magnetizable particles due to fluid flow, external magnetic forces, or diffusion.

[0304] The target analyte can be any substance or molecule that is complementary to the binding molecule provided to the magnetizable particle and can be bound by said binding molecule. For example, the target analyte can be selected from the group consisting of: proteins, peptides, nucleic acids, lipids or carbohydrates, biochemical substances, biological agents, viruses, bacteria, etc.

[0305] The target analyte can be a protein or fragment thereof selected from the group consisting of antibodies, enzymes, signal transduction molecules or hormones.

[0306] The target analyte can be a nucleic acid selected from the group consisting of DNA, RNA, cDNA, mRNA, or rRNA.

[0307] The method can detect more than one target analyte in a single sample. For example, the method can detect two or more, three or more, four or more, five or more, ten or more, fifteen or more, twenty or more, forty or fifty or more target analytes in a single sample.

[0308] The sample to be analyzed can be any sample containing one or more target analytes. For example, the sample can be a clinical, veterinary, environmental, food, forensic, or other suitable biological sample.

[0309] Clinical samples can be autologous fluids. For example, bodily fluids can be selected from blood, sweat, saliva, urine, sputum, semen, mucus, tears, cerebrospinal fluid, amniotic fluid, gastric juice, gingival crevicular fluid, or interstitial fluid.

[0310] Environmental samples can be selected from the following groups: water, soil, or aerosols.

[0311] The advantage of this invention lies in the fact that sample preparation is neither laborious nor difficult. Sample preparation utilizes established biochemistry for molecular functionalization and attachment, whether on microfluidic surfaces or magnetizable particle surfaces.

[0312] The sample to be analyzed can be added directly into the sample well or microfluidic device without any additional processing.

[0313] The sample may undergo one or more sample processing steps. It should be understood that the appropriate sample processing steps may depend on the type and / or nature of the sample to be analyzed. In some embodiments, the sample processing steps may be selected from the group consisting of dilution, filtration, or extraction (e.g., liquid-liquid, solid phase). This can also be achieved through microfluidic features and design or the use of centripetal forces. For example, whole blood samples may be filtered using cellulose-based filters or other filters to separate the plasma to be analyzed.

[0314] The method may include combining a sample to be analyzed with a formulation containing freely diffusible magnetizable particles coated with binding molecules (binding agent complexes) complementary to a target analyte in a sample well or sample reservoir. Where appropriate, the term 'binding agent complex' may be used interchangeably to refer to magnetizable particles coated with binding molecules.

[0315] In some implementations, the magnetizable particles may have limited diffusivity. This can occur when the magnetizable particles are cross-linked or derivatized with a macromolecule. The macromolecule can be a hydrogel or a PEG linker. This can happen when using a device to perform multiple assays to detect multiple targets or samples within a single sample.

[0316] The method of this invention can enhance the rate at which binding molecules bind to a target analyte by providing a mobile and freely diffusing binding agent complex in solution. When the sample and the binding agent complex formulation are combined, the binding agent complex is freely diffusing, and the binding molecules can interact with the target analyte throughout the entire sample volume. Because both the binding agent complex and the target analyte are freely diffusing and suspended in the sample volume, the average physical distance between the target analyte and the binding agent complex can be relatively small. Therefore, the binding rate can be increased, and binding equilibrium can be achieved significantly faster.

[0317] In assays such as ELISA, binding molecules, such as antibodies, are immobilized on the surface of a macroscopic object, such as a test well. In this approach, the physical distance between the target analyte and the antibody can vary significantly depending on the analyte's position within the sample volume. For example, a target analyte near the top of the sample volume may be quite far from the immobilized antibody and is unlikely to be captured and bound. Therefore, the binding rate may be limited by the rate at which the target analyte diffuses toward the immobilized antibody within the sample volume.

[0318] The sample and binder complex can be combined for an appropriate amount of time to allow the binding molecules to reach binding equilibrium. In some embodiments, the appropriate amount of time to allow binding to reach equilibrium can be about 1, 2, 3, 4, 5, 10, 20, 30, 45, 60, 90, 120, 180, 240, 300, or 360 seconds, and an available range can be selected between any of these values ​​(e.g., about 1 to 30, 1 to 60, 1 to 120, 10 to 30, 10 to 60, 10 to 90, 30 to 60, 30 to 90, 30 to 120, 60 to 90, 60 to 120, 60 to 180, 90 to 120, 90 to 180, 90 to 240, 180 to 240, 180 to 300, 180 to 360 seconds).

[0319] A magnetic field generator can be used to induce magnetohydrodynamic mixing of a sample to increase the rate at which binding equilibrium is reached. In such an embodiment, the magnetic field generator is used to induce movement of the binder complex within the sample volume.

[0320] It should be understood that the method of the present invention can be widely used in any application that requires the detection and / or quantification of a target analyte. In particular, the method can be used where the presence of the target analyte in the sample is required. • Rapid measurement, or • Sensitive measurement, or • Quantitative determination, or • In any combination of (i) to (iii) in the application.

[0321] For example, suitable applications may include clinical, veterinary, environmental, food safety, or forensic applications.

[0322] In some implementations, clinical applications may include diagnostic detection of biomarkers in samples that can indicate clinical disease. In one example, the method can be used for rapid, sensitive, and quantitative diagnostic detection of specific antibodies in blood samples that can indicate potential infection by a pathogen. In another example, the method can be used for diagnostic detection of specific protein biomarkers overexpressed in cancer. Diagnostic detection can be performed on samples from different species.

[0323] Clinical ailments can be caused by infections such as those from bacteria, fungi, viruses (e.g., hepatitis, SARS-CoV-19, and HIV) (e.g., biomarkers such as antibodies against hepatitis, SARS-CoV-19, and HIV), and parasites (e.g., microbial parasites [e.g., malaria], nematodes, and insect parasites).

[0324] Clinical diagnoses can be selected from diseases such as: heart disease (biomarkers such as BNP), cancer (e.g., solid organ cancer, blood cancer, other cancers) (e.g., biomarkers such as Ca-125 and other tumor markers), neurological diseases (e.g., multiple sclerosis, Alzheimer's disease, Parkinson's disease, Huntington's disease) (e.g., biomarkers such as CNS immunoglobulins), respiratory diseases (e.g., biomarkers such as serum ACE), liver diseases (e.g., biomarkers such as liver function tests and albumin), and kidney diseases (e.g., biomarkers such as creatinine and protein).

[0325] Clinical ailments can be caused by organ damage or failure such as: brain injury (e.g., biomarkers such as glial fibrillary acidic protein or GFAP), kidney injury (e.g., biomarkers such as serum creatine), heart injury (e.g., biomarkers such as creatine kinase-muscle), lung injury (e.g., biomarkers such as intercellular adhesion molecule-1 or ICAM1), or liver injury (e.g., biomarkers such as alkaline phosphatase).

[0326] Clinical disorders can be selected from endocrine disorders such as diabetes (e.g., biomarkers such as insulin, elevated HbA1C, thyroid dysfunction, thyroid hormones), pituitary disorders (e.g., biomarkers such as ACTH, prolactin, gonadotropins, thyroid-stimulating hormone, growth hormone, antidiuretic hormone), parathyroid disorders (e.g., biomarkers such as parathyroid hormone), adrenal disorders (e.g., biomarkers such as cortisol, aldosterone, adrenaline, DHEAS), sex hormone imbalances (e.g., biomarkers such as androgens and estrogens), carcinoid tumors (e.g., biomarkers such as 5-HIAA, VIPoma, serum VIP), and elevated bone turnover (e.g., biomarkers such as P1NP).

[0327] Clinical disorders can be selected from lipid dysregulation (e.g., biomarkers such as cholesterol and triglycerides). Clinical disorders can be caused by nutritional imbalances (e.g., vitamin deficiency, malabsorption syndrome, malnutrition, vitamin metabolism disorders) (e.g., biomarkers such as vitamin levels, iron levels, and mineral levels).

[0328] Clinical diagnoses can be derived from inflammation or inflammatory conditions (e.g., biomarkers such as ESR, Crp, and other acute-phase proteins).

[0329] Clinical diagnoses can be selected from autoimmune diseases (e.g., biomarkers such as specific antibody markers).

[0330] Clinical diagnoses can be selected from allergic diseases (e.g., biomarkers such as trypsin).

[0331] Clinical diagnoses can be derived from physical trauma such as electric shock (e.g., biomarkers such as creatine kinase).

[0332] Clinical conditions can be selected from immunodeficiency disorders (such as common variant immunodeficiency) (such as biomarkers such as complement, leukocytes and immunoglobulins).

[0333] Clinical disorders can be selected from coagulation disorders (e.g., thrombotic tendency) (e.g., biomarkers such as coagulation factors and other markers).

[0334] Clinical disorders can be selected from hereditary or acquired enzyme disorders, deficiencies or excesses, and other congenital or acquired metabolic defects (e.g., Bart syndrome, congenital adrenal hyperplasia), (e.g., biomarkers such as electrolytes, enzyme levels, and enzyme metabolites).

[0335] Clinical disorders can be selected from electrolyte disturbances such as hyperkalemia and hypernatremia (e.g., biomarkers such as electrolytes).

[0336] Clinical conditions can be selected from adverse drug reactions or poisoning (e.g., biomarkers such as drug levels and drug metabolite levels).

[0337] Clinical illnesses can be caused by adverse reactions or poisoning resulting from exposure to chemical or biological weapons or other environmental chemical and biological agents.

[0338] In veterinary medicine, clinical diagnoses can be selected from any biomarker of kidney failure, FIV / AIDS (cats), cancer, and organ function / failure.

[0339] In some implementation schemes, the clinical disease can be a disease of a veterinary subject such as a cat, dog, cow, sheep, horse, pig, or rat.

[0340] In some implementations, environmental applications may include detecting contaminants in environmental samples. Environmental contaminants may be selected from, for example, lead, particulate matter, microplastics, and hormones.

[0341] For example, the method can be used to monitor and quantify heavy metals in water samples.

[0342] In some implementations, food safety applications may include the detection of pathogens in food samples. For example, methods may be used to rapidly and sensitively detect post-pasteurization contamination of milk with bacterial pathogens.

[0343] Example 1. Detection sensitivity The purpose of this study is to test the sensitivity of the detection.

[0344] A specific amount of magnetizable particles is added to the microfluidic system for detection. The system setup is summarized below.

[0345] • Magnetic field generator ○ Electromagnet above the microfluidic system ○ Permanent magnet beneath the microfluidic system • Signal sensor ○ Honeywell HMC 2003 Triaxial Magnetic Sensor ○ An analog-to-digital converter (ADC) connected to the oscilloscope records data at 10,000 samples per second. • Magnetizable particles ○ Functionalized 30 nm superparamagnetic beads - coated with streptavidin and linked via biotin and anti-human serum albumin binding agent • Target analyte ○ Human serum albumin • Number of particles: ○ Sample 1: (21.6℃): Control – 0 pg / mL particles ○ Sample 2: 0.1 pg / mL particles ○ Sample 3: 1 pg / mL particles ○ Sample 4: 10 pg / mL particles ○ Sample 5: 100 pg / mL particles Sample 6: 1,000 pg / mL particles ○ Sample 7: 10,000 pg / mL particles • Number of sample read cycles ○ Five cycles • Electromagnetic (EM) activation on / off time per cycle ○ EM - Connect for two seconds ○ EM - Disconnect for two seconds After being introduced into the microfluidic system, the particles are brought to a first equilibrium (equilibrium A) under the influence of a permanent magnetic field. An electromagnet is then activated for two seconds to bring the particles to a second equilibrium (equilibrium B). The electromagnet is then deactivated to allow the particles to return to a third equilibrium (equilibrium C) – see Table 1. A magnetic field sensor measures the changes in the magnetic signal generated by the particles transitioning between different equilibrium states.

[0346] Table 1 shows the results of five sample reading cycles for seven samples.

[0347] Table 1 shows the magnetic field sensor outputs (in voltmeter) from 0 pg / ml to 10,000 pg / ml.

[0348] Table 1.

[0349] 2. Magnetic Balance Detection (MED) The purpose of this embodiment is to demonstrate the quantitative detection of a biomarker analyte using the claimed method. This method uses 30 nm superparamagnetic particles. In this embodiment, a binder-to-particle ratio of 0.75:1 was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0350] Table 2.

[0351] The results in Table 2 have an R² value of 0.95.

[0352] The analyte sample with a concentration of 1,000 pg / ml was excluded due to sample processing error.

[0353] 2.1 Experiment Description The experimental design parameters are shown below.

[0354] • Magnetizable particles ○ Nanocs MP25-AV-0.5 (30 nm) streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor ○ Honeywell HMC 2003 magnetometer • Amplifier Honeywell HMC2003 Built-in Amplifier • Data collection ○ Siglent SDS1204X-E Oscilloscope ○ Sampling rate of 10,000 samples / second The total running time on the instrument is approximately 35 seconds. • Magnet ○ Top magnet – Copper coil electromagnet with a coil gauge of 0.2 mm. Connect to a 3.23V DC power supply.

[0355] ○ Five top magnet actuation cycles, each cycle consisting of: a two-second magnet on-time followed by a five-second magnet off-time. • Device settings ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Top Magnet □ Intermediate Components – Sample □ Bottom Component – ​​Sensor ○ All components are vertically aligned through the center of each component.

[0356] • For each biomarker analyte concentration tested ○ 1 μL Magnetizable Particles – Nanocs Superparamagnetic Beads (2 mg / mL) ○ 0.75 ng anti-albumin antibody – biotinylated “detection” antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin protein (from DY1455 ELISA kit) were obtained through serial dilution.

[0357] ○ All components were mixed and sensed in a 10 μL test volume. 2.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0358] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all five cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0359] 3. Quantitative detection of biomarker analytes The purpose of this experiment is to demonstrate the quantitative detection of biomarker analytes. In this example, 30 nm superparamagnetic particles were used.

[0360] Table 3.

[0361] The results in Table 3 have an R-value of 0.94. 2 value.

[0362] 3.1 Experiment Description The experimental design parameters are shown below.

[0363] • Magnetizable particles ○ Nanocs MP25-AV-0.5 (30 nm) streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor ○ Honeywell HMC 2003 magnetometer • Amplifier Honeywell HMC2003 Built-in Amplifier • Data collection ○ Siglent SDS1204X-E Oscilloscope ○ Sampling rate of 10,000 samples / second The total running time on the instrument is approximately 35 seconds. • Magnet ○ Top magnet – Copper coil electromagnet with a coil gauge of 0.2 mm. Connect to a 3.33V DC power supply.

[0364] ○ Bottom magnet – A 25 mm diameter DC cylindrical electromagnet. Disconnect from power supply. ○ Five top magnet actuation cycles, each cycle consisting of: a two-second magnet on-time followed by a five-second magnet off-time. ○ The bottom magnet is not actuated or energized.

[0365] • Device settings ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Top Magnet □ Intermediate Components – Sample □ Intermediate component – ​​Sensor (located below the sample) □ Bottom Component – ​​Bottom Magnet • All components are vertically aligned through the center of each component.

[0366] • For each biomarker analyte concentration tested ○ 1 μL Magnetizable Particles – Nanocs Superparamagnetic Beads (2 mg / mL) ○ 1 nanogram anti-albumin antibody – a biotinylated “detection” antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin protein (from DY1455 ELISA kit) were obtained through serial dilution.

[0367] ○ All components were mixed and sensed in a 10 μL test volume. 3.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0368] Therefore, the sensor output corresponding to the magnet-on phase of each cycle is averaged, and then averaged for all five cycles. The result is then fitted to the best-fit line and the R² value is derived.

[0369] The purpose of this experiment is to demonstrate the quantitative detection of 30 nm superparamagnetic particles using different biomarker analytes using the MED detection method.

[0370] Table 4.

[0371] The results in Table 4 have an R-value of 0.94. 2 value.

[0372] 3.3 Experimental Description The experimental design parameters are shown below.

[0373] • Magnetizable particles ○ Nanocs MP25-AV-0.5 (30 nm) streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-feline TNF-α) from the DY2586 ELISA kit • Magnetic sensor ○ Honeywell HMC 2003 magnetometer • Amplifier Honeywell HMC2003 Built-in Amplifier • Data collection ○ Siglent SDS1204X-E Oscilloscope ○ Sampling rate of 10,000 samples / second The total running time on the instrument is approximately 35 seconds. • Magnet ○ Top magnet – A copper coil electromagnet with a coil gauge of 0.2 mm. Connect to a 0.5V DC power supply.

[0374] ○ Bottom magnet – DC cylindrical electromagnet with a diameter of 25 mm.

[0375] Disconnect from power supply ○ Five top magnet actuation cycles, each cycle consisting of: a two-second magnet on-time followed by a five-second magnet off-time. ○ The bottom magnet is not actuated or energized.

[0376] • Device settings ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Top Magnet □ Intermediate Components – Sample □ Intermediate component – ​​Sensor (located below the sample) □ Bottom Component – ​​Bottom Magnet □ All components are vertically aligned through the center of each component.

[0377] • For each biomarker analyte concentration tested ○ 1 μL Magnetizable Particles – Nanocs Superparamagnetic Beads (2 mg / mL) ○ 1 ng anti-TNF-α antibody – Biotinylated “detection” antibody from the DY2586 ELISA kit ○ Different concentrations of recombinant feline TNF-α protein (from DY2584 ELISA kit) were obtained through serial dilution.

[0378] ○ All components were mixed and sensed in a 10 μL test volume. 3.4 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0379] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, then averaged for all five cycles, and the best-fit line is fitted to derive R. 2 value.

[0380] 4. Experiment 1 The purpose of this experiment is to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles with a lower binder-to-bead ratio (0.5:1).

[0381] Table 5.

[0382] The results in Table 2 have an R-value of 0.91. 2 value.

[0383] 4.1 Experiment Description The experimental design parameters are shown below.

[0384] • Magnetizable particles ○ Nanocs MP25-AV-0.5 (30 nm) streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor ○ Honeywell HMC 2003 magnetometer • Amplifier Honeywell HMC2003 Built-in Amplifier • Data collection ○ Siglent SDS1204X-E Oscilloscope ○ Sampling rate of 10,000 samples / second The total running time on the instrument is approximately 35 seconds. • Magnet ○ Top magnet – Copper coil electromagnet with a coil gauge of 0.2 mm. Connect to a 3.23V DC power supply.

[0385] ○ Five top magnet actuation cycles, each cycle consisting of: a two-second magnet on-time followed by a five-second magnet off-time. • Device settings ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Top Magnet □ Intermediate Components – Sample □ Bottom Component – ​​Sensor □ All components are vertically aligned through the center of each component.

[0386] • For each biomarker analyte concentration tested ○ 1 μL Magnetizable Particles – Nanocs Superparamagnetic Beads (2 mg / mL) ○ 0.5 ng anti-albumin antibody – biotinylated “detection” antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin protein (from DY1455 ELISA kit) were obtained through serial dilution.

[0387] ○ All components were mixed and sensed in a 10 μL test volume. 4.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0388] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, then averaged for all five cycles, and the best-fit line is fitted to derive R. 2 value.

[0389] 5. Experiment 2 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio affected interparticle interactions in the absence of the analyte.

[0390] Table 6.

[0391] The results in Table 6 have an R-value of 0.99. 2 value.

[0392] 5.1 Experiment Description The experimental design parameters are shown below.

[0393] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Top Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connect to a 0.63V DC power supply that is programmable via RIGOL DP832.

[0394] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time. • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Top Magnet □ Intermediate Components – Sample □ Bottom Component – ​​Sensor ○ All components are vertically aligned through the center of each component.

[0395] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (2-fold dilution, from 100 pg / ml to 3.125 pg / ml).

[0396] ○ All components were mixed and sensed in a 5 μL test volume (0.5 μg beads per sample). 5.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0397] The sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all five cycles. This is then fitted to the best-fit line, and R0 is derived. 2 value.

[0398] 6. Experiment 3 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0399] Table 7.

[0400] The results in Table 7 have an R-value of 0.98. 2 value.

[0401] 6.1 Experiment Description The experimental design parameters are shown below.

[0402] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Top Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connect to a 0.63V DC power supply that is programmable via RIGOL DP832.

[0403] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0404] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Top Magnet □ Intermediate Components – Sample □ Bottom Component – ​​Sensor ○ All components are vertically aligned through the center of each component.

[0405] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (4-fold dilution, from 100 pg / ml to 0.098 pg / ml).

[0406] All components were mixed and sensed in a 5 μL test volume (0.5 μg beads per sample). 6.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0407] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0408] 7. Experiment 4 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0409] Table 8.

[0410] The results in Table 8 have an R-value of 0.99. 2 value.

[0411] 7.1 Experiment Description The experimental design parameters are shown below.

[0412] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Top magnet - Rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness).

[0413] ○ Bottom magnet - Rectangular copper coil electromagnet with a coil specification of 0.1 mm (dimensions 22x25 mm and thickness 3.5 mm).

[0414] ○ The two magnets are connected together in a Helmholtz arrangement under a 0.63V DC power supply that is programmable via the RIGOL DP832.

[0415] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0416] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Top Magnet □ Intermediate Components – Sample □ Subsequent Components – Sensors □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0417] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0418] ○ All components were mixed and sensed in a 5 μL test volume (0.5 μg beads per sample). 7.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0419] The sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all five cycles. This is then fitted to the best-fit line, and R0 is derived. 2 value.

[0420] 8. Experiment 5 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0421] Table 9.

[0422] The results in Table 9 have an R-value of 0.91. 2 value.

[0423] 8.1 Experiment Description The experimental design parameters are shown below.

[0424] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.63V DC power supply that is programmable via RIGOL DP832.

[0425] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0426] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0427] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0428] ○ All components were mixed and sensed in a 5 μL test volume (0.5 μg beads per sample). 8.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0429] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0430] 9. Experiment 6 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0431] Table 10.

[0432] The results in Table 10 have an R-value of 0.90. 2 value.

[0433] 9.1 Experiment Description The experimental design parameters are shown below.

[0434] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.63V DC power supply that is programmable via RIGOL DP832.

[0435] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time. • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0436] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0437] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 9.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0438] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0439] 10. Experiment 7 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0440] Table 11.

[0441] The results in Table 11 have an R-value of 0.96. 2 value.

[0442] 10.1 Experiment Description The experimental design parameters are shown below.

[0443] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.9 V DC power supply that is programmable via RIGOL DP832.

[0444] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0445] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0446] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0447] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 10.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0448] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0449] 11. Experiment 8 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0450] Table 12.

[0451] The results in Table 12 have an R-value of 1.00. 2 value.

[0452] 11.1 Experiment Description The experimental design parameters are shown below.

[0453] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.9 V DC power supply that is programmable via RIGOL DP832.

[0454] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0455] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component - Sample □ Intermediate Components - Sensors □ Bottom Components - Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0456] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0457] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 11.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0458] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0459] 12. Experiment 9 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0460] Table 13.

[0461] The results in Table 13 have an R-value of 0.98. 2 value.

[0462] 12.1 Experiment Description The experimental design parameters are shown below.

[0463] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.6V DC power supply that is programmable via RIGOL DP832.

[0464] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0465] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0466] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0467] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 12.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0468] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0469] 13. Experiment 10 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0470] Table 14.

[0471] The results in Table 14 have an R-value of 0.99. 2 value.

[0472] 13.1 Experiment Description The experimental design parameters are shown below.

[0473] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.5V DC power supply that is programmable via RIGOL DP832.

[0474] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0475] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0476] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0477] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 13.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0478] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0479] 14. Experiment 11 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0480] Table 15.

[0481] The results in Table 15 have an R-value of 0.96. 2 value.

[0482] 14.1 Experiment Description The experimental design parameters are shown below.

[0483] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.5V DC power supply that is programmable via RIGOL DP832.

[0484] ○ Five top magnet actuation cycles, each cycle consisting of: a 3-second magnet on-time followed by a 5-second magnet off-time.

[0485] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0486] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (2-fold dilution, from 10,000 pg / ml to 312.5 pg / ml).

[0487] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 14.2 Data Processing The sensor output during the 3-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0488] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0489] 15. Experiment 12 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0490] Table 16.

[0491] The results in Table 16 have an R-value of 0.99. 2 value.

[0492] 15.1 Experiment Description The experimental design parameters are shown below.

[0493] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.5V DC power supply that is programmable via RIGOL DP832.

[0494] ○ Five top magnet actuation cycles, each cycle consisting of: a 3-second magnet on-time followed by a 5-second magnet off-time. • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0495] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (2-fold dilution, from 10,000 pg / ml to 312.5 pg / ml).

[0496] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 15.2 Data Processing The sensor output during the 3-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0497] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0498] 16. Experiment 13 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0499] Table 17.

[0500] The results in Table 17 have an R-value of 0.91. 2 value.

[0501] 16.1 Experiment Description The experimental design parameters are shown below.

[0502] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet – A rectangular copper coil electromagnet with a coil gauge of 0.2 mm. Bottom Magnet – A rectangular copper coil electromagnet with a coil gauge of 0.2 mm (36 x 33.5 mm and 5.3 mm thickness). Connected to a 0.3V DC power supply programmable via RIGOL DP832.

[0503] ○ Five top magnet actuation cycles, each cycle consisting of: a 3-second magnet on-time followed by a 5-second magnet off-time. • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0504] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (2-fold dilution, from 10,000 pg / ml to 312.5 pg / ml).

[0505] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 16.2 Data Processing The sensor output during the 3-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0506] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0507] 17. Experiment 14 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0508] Table 18.

[0509] The results in Table 18 have an R-value of 0.96. 2 value.

[0510] 17.1 Experiment Description The experimental design parameters are shown below.

[0511] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.2 mm (36 x 33.5 mm and 5.3 mm thickness). Connected to a 0.3 V DC power supply that is programmable via RIGOL DP832.

[0512] ○ Five top magnet actuation cycles, each cycle consisting of: a 3-second magnet on-time followed by a 5-second magnet off-time. • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0513] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0514] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 17.2 Data Processing The sensor output during the 3-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0515] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0516] 18. Experiment 15 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0517] Table 19.

[0518] The results in Table 19 have an R-value of 0.92. 2 value.

[0519] 18.1 Experiment Description The experimental design parameters are shown below.

[0520] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.5V DC power supply that is programmable via RIGOL DP832.

[0521] ○ Five top magnet actuation cycles, each cycle consisting of: a 3-second magnet on-time followed by a 5-second magnet off-time. • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0522] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0523] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 18.2 Data Processing The sensor output during the 3-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0524] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and R is derived. 2 value.

[0525] 19. Experiment 16 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 50 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0526] Table 20.

[0527] The results in Table 20 have an R-value of 1.00. 2 value.

[0528] In this and subsequent embodiments, the harmonics correspond to the magnet actuation cycle. For example, the 5th harmonic and the 1st harmonic correspond to the fifth and first magnet actuation cycles, as described in the experimental description below. In this experiment, the 5th harmonic is divided by the 1st harmonic.

[0529] 19.1 Experiment Description The experimental design parameters are shown below.

[0530] • Magnetizable particles: ○ SuperMag streptavidin beads, 50 nm (Product ID: SV0050) ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.2 mm (36 x 33.5 mm and 5.3 mm thickness). Connects to a DC power supply that is programmable via RIGOL DP832.

[0531] ○ Five bottom magnet actuation cycles, each cycle consisting of: a 1-second magnet on-time, a step voltage of 0.2V, and a step and drop every 1 second.

[0532] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0533] • For the concentration of each biomarker analyte tested: ○ 1µL Magnetizable Particles – Ocean NanoTech SuperMag Streptomycin Beads ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0534] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 19.2 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0535] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0536] 20. Experiment 17 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 50 nm superparamagnetic particles. The binder-to-particle ratio (0.75:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0537] Table 20.

[0538] The results in Table 20 have an R-value of 0.99. 2 value.

[0539] 20.1 Experiment Description The experimental design parameters are shown below.

[0540] • Magnetizable particles: ○ Ocean NanoTech SuperMag Streptomycin Beads, 50 nm (Product ID: SV0050) ○ Functionalized with biotinylated "detection" antibody (anti-feline TNFα) from the DY2586 ELISA kit • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.2 mm (36 x 33.5 mm and 5.3 mm thickness). Connects to a DC power supply that is programmable via RIGOL DP832.

[0541] ○ Five bottom magnet actuation cycles, each cycle consisting of: a 1-second magnet on-time, a step voltage of 0.2V, and a step and drop every 1 second.

[0542] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0543] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech SuperMag Streptokinin Beads (1 mg / ml) ○ 0.75 ng anti-TNFα antibody - biotinylated "detection" antibody from the DY2586 ELISA kit ○ Different concentrations of recombinant feline TNFα protein (from DY2586 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0544] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 20.2 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0545] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0546] 21. Experiment 18 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 50 nm superparamagnetic particles. The binder-to-particle ratio (1.5:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0547] Table 21.

[0548] The results in Table 21 have an R-value of 0.94. 2 value.

[0549] 21.1 Experiment Description The experimental design parameters are shown below.

[0550] • Magnetizable particles: ○ Ocean NanoTech SuperMag Streptomycin Beads, 50 nm (Product ID: SV0050) ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.2 mm (36 x 33.5 mm and 5.3 mm thickness). Connect to a 0.4V DC power supply that is programmable via RIGOL DP832.

[0551] ○ 30 bottom magnet actuation cycles, each cycle including: 1 second magnet on time and 1 second magnet off time.

[0552] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0553] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech SuperMag Streptokinin Beads (1 mg / ml) ○ 1.5 nm anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0554] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 21.2 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0555] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0556] 22. Experiment 19 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 50 nm superparamagnetic particles. The binder-to-particle ratio (1.5:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0557] Table 22.

[0558] The results in Table 22 have an R-value of 0.99. 2 value.

[0559] 22.1 Experiment Description The experimental design parameters are shown below.

[0560] • Magnetizable particles: ○ Ocean NanoTech SuperMag Streptomycin Beads, 50 nm (Product ID: SV0050) ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ ST sensor (LIS2MDL) • Amplifier: ○ ST sensor built-in amplifier • Magnet: ○ Bottom Magnet - An oval copper coil electromagnet with a coil specification of 0.2 mm (43.8 x 33.5 mm and 5.3 mm thickness). Connect to a 0.4V DC power supply that is programmable via RIGOL DP832.

[0561] ○ Five bottom magnet actuation cycles, each cycle consisting of: 2 seconds of magnet on-time and 1 second of magnet off-time.

[0562] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0563] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech SuperMag Streptokinin Beads (1 mg / ml) ○ 1.5 nm anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 100,000 pg / ml to 0.1 pg / ml).

[0564] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 22.2 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0565] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0566] 23. Experiment 20 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 50 nm superparamagnetic particles. The binder-to-particle ratio (1.5:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0567] Table 23.

[0568] The results in Table 23 have an R-value of 0.91. 2 value.

[0569] 23.1 Experiment Description The experimental design parameters are shown below.

[0570] • Magnetizable particles: ○ Ocean NanoTech SuperMag Streptomycin Beads, 50 nm (Product ID: SV0050) ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ ST sensor (LIS2MDL) • Amplifier: ○ ST sensor built-in amplifier • Magnet: ○ Bottom Magnet - An oval copper coil electromagnet with a coil specification of 0.2 mm (43.8 x 33.5 mm and 5.3 mm thickness). Connect to a 1V DC power supply that is programmable via RIGOL DP832.

[0571] ○ 25 bottom magnet actuation cycles, each cycle including: 1 second magnet on time and 1 second magnet off time.

[0572] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0573] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech SuperMag Streptokinin Beads (1 mg / ml) ○ 1.5 nm anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 100,000 pg / ml to 0.1 pg / ml).

[0574] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 23.2 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0575] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0576] 24. Experiment 21 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 50 nm superparamagnetic particles. The binder-to-particle ratio (1.5:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0577] Table 24.

[0578] The results in Table 24 have an R-value of 0.92. 2 value.

[0579] 24.1 Experiment Description The experimental design parameters are shown below.

[0580] • Magnetizable particles: ○ Ocean NanoTech SuperMag Streptomycin Beads, 50 nm (Product ID: SV0050) ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ ST sensor (LIS2MDL) • Amplifier: ○ ST sensor built-in amplifier • Magnet: ○ Top magnet - Circular flat-bottomed EM-coil (flat coil) design (20.6 mm radius, 1.8 mm thickness). Coil gauge 0.9 mm. Connects to a 0.3V DC power supply that is programmable via RIGOL DP832.

[0581] ○ Five bottom magnet actuation cycles, each cycle consisting of: 2 seconds of magnet on-time and 1 second of magnet off-time.

[0582] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top component – ​​Magnet □ Intermediate Components – Sample □ Bottom Component – ​​Sensor ○ All components are vertically aligned through the center of each component.

[0583] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech SuperMag Streptokinin Beads (1 mg / ml) ○ 1.5 nm anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 100,000 pg / ml to 0.1 pg / ml).

[0584] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 24.2 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0585] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0586] 25. Experiment 22 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 50 nm superparamagnetic particles. The binder-to-particle ratio (1.5:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0587] Table 25.

[0588] The results in Table 25 have an R-value of 0.95. 2 value.

[0589] 25.1 Experiment Description The experimental design parameters are shown below.

[0590] • Magnetizable particles: ○ Ocean NanoTech SuperMag Streptomycin Beads, 50 nm (Product ID: SV0050) ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ ST sensor (LIS2MDL) • Amplifier: ○ ST sensor built-in amplifier • Magnet: ○ Bottom Magnet - An oval copper coil electromagnet with a coil gauge of 0.2 mm (43.8 x 33.5 mm and 5.3 mm thickness). Connected to a 1V DC power supply programmable via RIGOL DP832. Time-controlled operation using an 80% duty cycle SSR switch.

[0591] ○ 10 bottom magnet actuation cycles, each cycle including: 1 second magnet on time and 1 second magnet off time.

[0592] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0593] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech SuperMag Streptokinin Beads (1 mg / ml) ○ 1.5 nm anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 100,000 pg / ml to 0.1 pg / ml).

[0594] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 25.2 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0595] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0596] 26. Experiment 23 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 50 nm superparamagnetic particles. The binder-to-particle ratio (1.5:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0597] Table 26.

[0598] The results in Table 26 have an R-value of 0.93. 2 value.

[0599] 26.1 Experiment Description The experimental design parameters are shown below.

[0600] • Magnetizable particles: ○ Ocean NanoTech SuperMag Streptomycin Beads, 50 nm (Product ID: SV0050) ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ ST sensor (LIS2MDL) • Amplifier: ○ ST sensor built-in amplifier • Magnet: ○ Bottom Magnet - An oval copper coil electromagnet with a coil gauge of 0.2 mm (43.8 x 33.5 mm and 5.3 mm thickness). Connected to a 1V DC power supply programmable via RIGOL DP832. Time-controlled operation using an 80% duty cycle SSR switch.

[0601] ○ Five bottom magnet actuation cycles, each cycle consisting of 1 second of magnet on-time and 1 second of magnet off-time.

[0602] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0603] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech SuperMag Streptokinin Beads (1 mg / ml) ○ 1.5 nm anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 100,000 pg / ml to 0.1 pg / ml).

[0604] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 26.2 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0605] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0606] 27. Experiment 24 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 2.1 μm ferromagnetic particles. The binder-to-particle ratio (10:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0607] Table 27.

[0608] The results in Table 27 have an R-value of 0.95. 2 value.

[0609] 27.1 Experiment Description The experimental design parameters are shown below.

[0610] • Magnetizable particles: ○ SpheroTech 2.1 um ferromagnetic beads (SVFM 20-5) coated with streptavidin.

[0611] ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ ST sensor (LIS2MDL) • Amplifier: ○ ST sensor built-in amplifier • Magnet: ○ Bottom Magnet - An oval copper coil electromagnet with a coil gauge of 0.2 mm (43.8 x 33.5 mm and 5.3 mm thickness). Connected to a 1V DC power supply programmable via RIGOL DP832. Time-controlled operation using an 80% duty cycle SSR switch.

[0612] ○ Five bottom magnet actuation cycles, each cycle consisting of 1 second of magnet on-time and 1 second of magnet off-time.

[0613] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0614] • For the concentration of each biomarker analyte tested: ○ 1µL Magnetizable Particles – SpheroTech 2.1µm Ferromagnetic Beads (1% w / v) ○ 10 nanograms anti-albumin antibody - a biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 100,000 pg / ml to 0.1 pg / ml).

[0615] ○ All components were mixed and sensed in a 5 μL test volume (0.5 μg beads per sample). 27.2 Data Processing Sensor data related to the initial EM-coil saturation (as demonstrated by the increase in the sensed magnetic field) up to the maximum coil saturation (for a given / set power level) were selected, with a total measurement period of 0.05 seconds for EM-on to establish equilibrium. While the entire duration of EM-on equilibrium is approximately 1.0 second per cycle, the focus here is on mirroring the aforementioned time window and data location. That is, 0.05 seconds are now captured before EM-coil desaturation begins and until the EM-coil desaturation level is reached. In summary, aggregated, concatenated datasets of these two data windows (as described above) are collected, reflecting a focus on a total time period of approximately 0.10 seconds per EM-coil power modulation cycle.

[0616] The connected data is then processed using an automated FFT (Fast Fourier Transform) calculation tool, and the output of each processed data point is manually inspected.

[0617] Therefore, the sensor output corresponding to the magnet-on period (from EM-coil saturation to equilibrium, and from the last moment of coil equilibrium to the start of EM-coil desaturation to the complete desaturation of the EM-coil) represents the entire dataset in this data processing design. Fast Fourier Transform analysis is used to obtain the detected fundamental frequency amplitude and the associated harmonic amplitude for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficient (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0618] 28. Experiment 25 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 2.1 μm ferromagnetic particles. The binder-to-particle ratio (10:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0619] Table 28.

[0620] The results in Table 28 have an R-value of 0.97. 2 value.

[0621] 28.1 Experiment Description The experimental design parameters are shown below.

[0622] • Magnetizable particles: ○ SpheroTech 2.1 um ferromagnetic beads (SVFM 20-5) coated with streptavidin.

[0623] ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ ST sensor (LIS2MDL) • Amplifier: ○ ST sensor built-in amplifier • Magnet: ○ Bottom Magnet - An oval copper coil electromagnet with a coil gauge of 0.2 mm (43.8 x 33.5 mm and 5.3 mm thickness). Connected to a 1V DC power supply programmable via RIGOL DP832. Time-controlled operation using an 80% duty cycle SSR switch.

[0624] ○ Five bottom magnet actuation cycles, each cycle consisting of 1 second of magnet on-time and 1 second of magnet off-time.

[0625] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0626] • For the concentration of each biomarker analyte tested: ○ 1µL Magnetizable Particles – SpheroTech 2.1µm Ferromagnetic Beads (1% w / v) ○ 10 nanograms anti-albumin antibody - a biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 100,000 pg / ml to 0.1 pg / ml).

[0627] ○ All components were mixed and sensed in a 5 μL test volume (0.5 μg beads per sample). 28.2 Data Processing Sensor data was selected relating to the initial EM-coil saturation (as demonstrated by the increase in the sensed magnetic field) up to the maximum coil saturation (for a given / set power level), with a total measurement period of 0.05 seconds for EM-on to establish equilibrium. While the entire duration of EM-on equilibrium is approximately 1.0 second in total duration per cycle, the focus here is on mirroring the aforementioned time window and data location. That is, 0.05 seconds are now captured before EM-coil desaturation begins and until the EM-coil desaturation level is reached. A concatenated dataset of two data windows (as described above) is used, reflecting the focus on a total time period of approximately 0.10 seconds of EM-coil power modulation per cycle, occurring only in the middle third of the cycle.

[0628] The connected data is then processed using an automated FFT (Fast Fourier Transform) calculation tool, and the output of each processed data point is manually inspected.

[0629] Therefore, the sensor output corresponding to one-third of the magnet-on period (from EM-coil saturation to equilibrium, and from the last moment of coil equilibrium to the start of EM-coil desaturation to complete EM-coil desaturation, a series cycle) represents the entire dataset in this data processing design. Fast Fourier Transform analysis is used to obtain the detected fundamental frequency amplitude and the associated harmonic amplitude for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficient (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0630] 29. Experiment 26 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 2.1 μm ferromagnetic particles. The binder-to-particle ratio (10:1) was tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0631] Table 29.

[0632] The results in Table 29 have an R-value of 0.97. 2 value.

[0633] 29.1 Experiment Description The experimental design parameters are shown below.

[0634] • Magnetizable particles: ○ SpheroTech 2.1 um ferromagnetic beads (SVFM 20-5) coated with streptavidin.

[0635] ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ ST sensor (LIS2MDL) • Amplifier: ○ ST sensor built-in amplifier • Magnet: ○ Bottom Magnet - An oval copper coil electromagnet with a coil gauge of 0.2 mm (43.8 x 33.5 mm and 5.3 mm thickness). Connected to a 1V DC power supply programmable via RIGOL DP832. Time-controlled operation using an 80% duty cycle SSR switch.

[0636] ○ Five bottom magnet actuation cycles, each cycle consisting of 1 second of magnet on-time and 1 second of magnet off-time.

[0637] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Magnet ○ All components are vertically aligned through the center of each component.

[0638] • For the concentration of each biomarker analyte tested: ○ 1µL Magnetizable Particles – SpheroTech 2.1µm Ferromagnetic Beads (1% w / v) ○ 10 nanograms anti-albumin antibody - a biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human serum albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 100,000 pg / ml to 0.1 pg / ml).

[0639] ○ All components were mixed and sensed in a 5 μL test volume (0.5 μg beads per sample). 29.2 Data Processing Sensor data was selected relating to the initial EM-coil saturation (as demonstrated by the increase in the sensed magnetic field) up to the maximum coil saturation (for a given / set power level), with a total measurement period of 0.05 seconds for EM-on to establish equilibrium. While the entire duration of EM-on equilibrium is approximately 1.0 second in total duration per cycle, the focus here is on mirroring the aforementioned time window and data location. That is, 0.05 seconds are now captured before EM-coil desaturation begins and until the EM-coil desaturation level is reached. A concatenated dataset of two data windows (as described above) is used, reflecting the focus on a total time period of approximately 0.10 seconds of EM-coil power modulation per cycle, occurring only in the middle third of the cycle.

[0640] The connected data is then processed using an automated FFT (Fast Fourier Transform) calculation tool, and the output of each processed data point is manually inspected.

[0641] Therefore, the sensor output corresponding to one-third of the magnet-on period (from EM-coil saturation to equilibrium, and from the last moment of coil equilibrium to the start of EM-coil desaturation to complete EM-coil desaturation, a series cycle) represents the entire dataset in this data processing design. Fast Fourier Transform analysis is used to obtain the detected fundamental frequency amplitude and the associated harmonic amplitude for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficient (R²) between the indicated concentrations. 2 (Values) to reflect the correlation between concentration and harmonics, amplitude and ratio.

[0642] 30. Experiment 27 The purpose of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. Different binder-to-particle ratios (0.75:1) were tested to determine whether this ratio could affect interparticle interactions in the absence of the analyte.

[0643] Table 30.

[0644] The results in Table 30 have an R-value of 0.88. 2 value.

[0645] 30.1 Experiment Description The experimental design parameters are shown below.

[0646] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Top Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connect to a 0.63V DC power supply that is programmable via RIGOL DP832.

[0647] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0648] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Top Magnet □ Intermediate Components – Sample □ Bottom Component – ​​Sensor • All components are vertically aligned through the center of each component.

[0649] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (4-fold dilution, from 100 pg / ml to 0.098 pg / ml).

[0650] ○ All components were mixed and sensed in a 5 μL test volume (0.5 μg beads per sample). 30.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0651] The sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged for all five cycles. This is then fitted to the best-fit line, and R0 is derived. 2 value.

[0652] 31. Experiment 27a The purpose of this experiment is to observe the effect of R when the sensor values ​​obtained in Experiment 27 are processed using FFT. 2 Differences in values.

[0653] Table 31.

[0654] The results in Table 31 have an R-value of 0.97. 2 value.

[0655] When FFT was used to process the sensor value output obtained in Experiment 27, R was observed. 2 The value increases.

[0656] 31.1 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0657] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet switching on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R² values) between the indicated concentrations, reflecting the correlation between concentration and harmonics, and amplitude and ratio.

[0658] 32. Experiment 28 The aim of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. We tested different binder-to-particle ratios (0.75:1) to observe whether this ratio could affect particle-particle interactions in the absence of the analyte.

[0659] Table 32.

[0660] The results in Table 32 have an R-value of 0.89. 2 value.

[0661] 32.1 Experiment Description The experimental design parameters are shown below.

[0662] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 1V DC power supply that is programmable via RIGOL DP832.

[0663] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time.

[0664] • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0665] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (10-fold dilution, from 10,000 pg / ml to 0.1 pg / ml).

[0666] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 32.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0667] The sensor output corresponding to the magnet-on phase is averaged for each cycle, and then averaged for all 5 cycles. This is then fitted to the best-fit line, and the R² value is derived.

[0668] 33. Experiment 28a The purpose of this experiment is to observe the effect of R when the sensor value output obtained in Experiment 28 is processed using FFT. 2 Differences in values.

[0669] Table 33.

[0670] The results in Table 33 have an R-value of 0.95. 2 value.

[0671] When FFT was used to process the sensor value output obtained in Experiment 28, R was observed. 2 The value increases.

[0672] 33.1 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0673] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet switching on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R² values) between the indicated concentrations, reflecting the correlation between concentration and harmonics, and amplitude and ratio.

[0674] 34. Experiment 29 The aim of this experiment was to demonstrate the quantitative detection of biomarker analytes using the MED detection method and 30 nm superparamagnetic particles. We tested different binder-to-particle ratios (0.75:1) to observe whether this ratio could affect particle-particle interactions in the absence of the analyte.

[0675] Table 34.

[0676] The results in Table 34 have an R-value of 0.84. 2 value.

[0677] 34.1 Experiment Description The experimental design parameters are shown below.

[0678] • Magnetizable particles: ○ Ocean NanoTech SHS30-01 (30 nm) Streptavidin-coated superparamagnetic particles ○ Functionalized with biotinylated "detection" antibody (anti-human serum albumin) from the DY1455 ELISA kit. • Magnetic sensor: ○ Honeywell HMC 1041z Magnetometer • Amplifier: ○ Texas Instruments INA819 amplifier • Magnet: ○ Bottom Magnet - A rectangular copper coil electromagnet with a coil specification of 0.1 mm (22x25 mm in size and 3.5 mm in thickness). Connected to a 0.5V DC power supply that is programmable via RIGOL DP832.

[0679] ○ Five top magnet actuation cycles, each cycle consisting of: a 2-second magnet on-time followed by a 5-second magnet off-time. • Device settings: ○ The sample is positioned between the sensor and the magnet, such that: □ Top Component – ​​Sample □ Intermediate Component – ​​Sensor □ Bottom Component – ​​Bottom Magnet ○ All components are vertically aligned through the center of each component.

[0680] • For the concentration of each biomarker analyte tested: ○ 1 μL Magnetizable Particles – Ocean NanoTech Superparamagnetic Beads (1 mg / mL) ○ 0.75 nanograms anti-albumin antibody - biotinylated "detection" antibody from the DY1455 ELISA kit ○ Different concentrations of recombinant human albumin (from DY1455 ELISA kit) were obtained by serial dilution (2-fold dilution, from 10,000 pg / ml to 312.5 pg / ml).

[0681] ○ All components were mixed and sensed in a 5 μL test volume (2 μg beads per sample). 34.2 Data Processing The sensor output during the 2-second magnet-on phase of each cycle is processed to exclude data points corresponding to magnet actuation (on or off), thus disregarding the effects of magnet energization or de-energization. An automated tool was created for this data processing, and each processed output is manually inspected.

[0682] Therefore, the sensor output corresponding to the magnet-on phase in each cycle is averaged, and then averaged over all 5 cycles. This is then fitted to the best-fit line, and the R² value is derived.

[0683] 35. Experiment 29 The purpose of this experiment is to observe the effect of R when the sensor value output obtained in Experiment 29 is processed using FFT. 2 Differences in values.

[0684] Table 35.

[0685] The results in Table 35 have an R-value of 0.94. 2 value.

[0686] When FFT was used to process the sensor value output obtained in Experiment 29, R was observed. 2 The value increases.

[0687] 35.1 Data Processing The sensor outputs, including the magnet's on and off states and the magnet's energization or de-energization, are considered as a complete dataset. The data is then processed using an automated FFT (Fast Fourier Transform) tool, and each processed output is manually inspected.

[0688] Therefore, a Fast Fourier Transform (FFT) is used to process the sensor output corresponding to the period of magnet switching on and off. FFT analysis is employed to obtain the detected fundamental frequency amplitude and the associated harmonic amplitudes for each concentration. These amplitudes and ratios are then applied to quantitatively determine the correlation coefficients (R² values) between the indicated concentrations, reflecting the correlation between concentration and harmonics, and amplitude and ratio.

Claims

1. A method for detecting a target analyte in a sample, the method comprising: a) providing a number of magnetizable particles having a known or measured reference magnetic signal before or after addition of the sample, the particles being coated with a binding molecule complementary to the target analyte, b) contacting the sample comprising the target analyte with the magnetizable particles, resulting in bound and unbound binding agent complexes, c) applying a magnetic field to the sample for a period of time, d) obtaining a magnetic signal, the magnetic signal being the magnetic signal of the bound and unbound binding agent complexes in the presence of the magnetic field, e) removing the magnetic field for a period of time, and f) comparing the reference magnetic signal and the magnetic signal, wherein the difference between the reference magnetic signal and the magnetic signal correlates to the presence and / or amount of the target analyte in the sample.

2. The method of claim 1, wherein the reference magnetic signal is determined by measuring the magnetic signal of the number of magnetizable particles in the absence of the target analyte.

3. The method of claim 2, wherein the reference magnetic signal is measured at any point between steps b) to e).

4. The method of any one of claims 1 to 3, wherein the magnetic field is generated using an electromagnet.

5. The method of any one of claims 1 to 4, wherein the magnetic signal is obtained after the electromagnetic coil of the electromagnet reaches saturation.

6. The method of any one of claims 1 to 3, wherein the magnetic field is generated using a permanent magnet.

7. The method of any one of claims 1 to 6, wherein the quantification of the target analyte in the sample is determined by correlating the measured magnetic signal to the change in the reference / predetermined magnetic signal.

8. The method of any one of claims 1 to 7, further comprising: generating a reference data set based on known analyte amount values, comparing the value obtained from the difference between the reference magnetic signal and the magnetic signal to the reference data set to determine the amount of analyte in the sample.

9. The method of any one of claims 1 to 8, wherein the magnetic field is applied for a predetermined time.

10. The method of claim 9, wherein the magnetic field is applied for about 0.1 to about 5 seconds.

11. The method of any one of claims 1 to 10, wherein the magnetic field is removed for a predetermined time.

12. The method of claim 11, wherein the magnetic field is removed for about 0.1 to about 5 seconds.

13. The method of any one of claims 1 to 12, wherein the magnetic field is applied and removed for substantially equal amounts of time.

14. The method of claim 13, wherein the magnetic field is applied and removed for about 1 second.

15. The method of any one of claims 1 to 14, wherein steps c) to e) are repeated two or more times.

16. The method of any one of claims 1 to 15, wherein the magnetizable particles are superparamagnetic nanoparticles or ferromagnetic nanoparticles.

17. The method of any one of claims 1 to 16, wherein the magnetizable particles have an average particle size of about 20 nm to about 60 nm.

18. The method of any one of claims 1 to 17, wherein the magnetizable particles have an average particle size of about 30 nm.

19. The method of any one of claims 1 to 18, further comprising shielding the sample from an ambient magnetic field.

20. The method of any one of claims 1 to 19, further comprising measuring the ambient magnetic field, and adjusting the reference magnetic signal and the magnetic signal based on the ambient magnetic field.

21. The method of any one of claims 1 to 20, wherein the magnetic signal is measured at a sampling rate of at least about 10,000 samples per second.

22. The method of any one of claims 1 to 21, wherein the sample is incubated to a temperature of about 20 °C.

23. The method of any one of claims 1 to 22, wherein the magnetic signal is a magnetic field strength.

24. The method of any one of claims 1 to 23, performed using a sample testing device comprising: • a sample well or sample reservoir, • a magnet positioned on one of an upper side or a lower side of the sample well or sample reservoir, and • a magnetic field sensor for measuring changes in the magnetic signal in the sample well or sample reservoir over time.

25. The method of any one of claims 1 to 24, wherein the magnetizable particles are functionalized with molecules that specifically bind to the target analyte.

26. The method of any one of claims 1 to 25, wherein the sample and the magnetizable particles are processed by a microfluidic device.

27. The method of any one of claims 1 to 26, wherein the microfluidic device facilitates binding between the magnetizable particles and analytes.

28. The method of any one of claims 1 to 27, wherein one or more magnets produce a magnetic field that varies over time.

29. The method of any one of claims 1 to 28, wherein the one or more magnets are capable of producing a continuous magnitude.

30. The method of any one of claims 1 to 29, wherein the one or more magnets are capable of alternating the magnetic field between on and off.

31. The method of claim 24, wherein the magnetic field sensor measures changes in magnetic field strength produced by the magnetizable particles over time.

32. The method of any one of claims 24 to 31, wherein a signal output from the magnetic field sensor is enhanced by a signal amplifier.

33. The method of any one of claims 24 to 32, wherein the signal output from the magnetic field sensor is a voltage reading proportional to the magnetic signal measured by the magnetic field sensor.

34. The method of claim 32, wherein the amplified signal is converted from a voltage reading to a digital bit stream and recorded and / or analyzed by a computer.

35. The method of claim 34, wherein the conversion is performed by an analog-to-digital converter.

36. The method of any one of claims 1 to 35, wherein the method: a) generates sufficient magnetic signal within 15 seconds to detect and / or quantify the target analyte in the sample, or b) has a limit of detection (LOD) of at least about 0.05 pg / mL, or c) has a limit of quantification (LOQ) of at least about 0.1 pg / mL, or d) one or more of (a) to (c).

37. The method of any one of claims 1 to 36, wherein the magnetic signal is processed using a fast Fourier transform (FFT).

38. The method of claim 37, wherein the magnetic signal is pre-processed by truncating and / or concatenating the magnetic signal in one or more dimensions prior to processing using the FFT.

39. The method of claim 37 or claim 38, wherein the magnetic signal is windowed prior to FFT processing.

40. A device for detecting an analyte in a sample, the device comprising: • a sample well, the sample well being separate from or integrated into a microfluidic device, • a magnet for generating a magnetic field, • a magnetic field sensor configured to measure a magnetic signal of a magnetizable particle in the sample well in the presence of the magnetic field, the magnetic signal being detected from an overall magnetic response that is proportional to the size of an aggregate.

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