Patent Application
20230417702
This disclosure relates generally to lab-on-chip diagnostic platforms, and in particular relates to detection of extracellular vesicle biomarkers using lab-on-a-chip diagnostics.
There is a growing demand for diagnostic markers for early disease detection, which can increase the odds of survivability. Early disease detection requires sensitive diagnostic tools to detect low quantities of biomarkers indicative of disease. Few methods exist with the sensitivity and specificity necessary to detect diseases such as cancer and neurodegenerative diseases before considerable progression has taken place. Thus, it may be useful to develop highly sensitive lab-on-chip tests targeting the detection of early disease.
Extracellular vesicles (EVs) are membranous nanoparticles that facilitate intercellular communication system via their biomolecular components (e.g., proteins, lipids, carbohydrates, and nucleic acids). EVs are dense information compartments continuously released from originating cells which contain biomarkers that mimic those of their originating cells. EVs are present in biological fluids (e.g., blood, urine, cerebrospinal fluid, etc.), and EV-associated markers can exhibit longer half-lives and increased stability than free circulating biomarkers. Thus, EVs provide an accessible source of biomarkers that are continuously released from live cells within the body.
Detection methods for cancers and neurodegenerative diseases rely upon costly, time intensive, and often invasive methods (e.g., tissue biopsy, computerized tomography, magnetic resonance imaging, endoscopy, etc.), and many of these diseases have no available tests for early detection. Currently, liquid biopsy tests rely on free circulating markers released during tumor cell death, rather than a continuous and sustained cellular process such as EV secretion. Thus, EVs represent a valuable bio-compartment for the early, minimally-invasive detection of disease-associated biomarkers from their parent cells (e.g., tumor cells, neurons affected by neurodegeneration, inflammatory cells, etc.). Existing EV-based detection methods use either dirty, “brute force” concentration methods like centrifugation and size exclusion or rely upon dilute circulating concentrations. Many existing EV marker test methods use fluorescence-based detection, which is limited in sensitivity and specificity. It may therefore be useful to improve upon the state of the art via efficient biomarker concentration and sensitive, specific biomarker detection using an electronic method.
In particular embodiments, a biological sample may be loaded onto a solid-state detection apparatus, wherein EVs 102 may be concentrated and/or isolated using DEP. The detection apparatus (e.g., lab-on-a-chip device) may utilize integral microfluidics (either conventional/pneumatic and/or digital) to divide the concentrated EV volume and automate a multi-marker antibody-based capture. As an example and not by way of limitation, the respective biomarker concentrations may be directly quantified using a sensor, wherein data from the sensor may be processed through one or more algorithms, resulting in a composite result.
In particular embodiments, a method for isolating and detecting one or more biomarkers of interest on a dielectrophoresis (DEP) device may include receiving a first biological sample containing one or more biomarkers 104 of interest onto a DEP electrode array of the DEP device. In particular embodiments, one or more processors of the DEP device may instruct the DEP device to apply a DEP force through one or more electrodes of the DEP electrode array. As an example and not by way of limitation, one or more processors of the DEP device may instruct the DEP device to apply a DEP force of a particular strength, direction, period of time, or other configurable setting specific to one or more targeted biomarkers of interest of the biological sample (e.g., EVs). In particular embodiments, one or more sensors of the DEP device may determine a quantity of one or more target biomarkers of interest of the biological sample.
In particular embodiments, a method for detecting a biomarker of interest on a DEP device may include receiving a first biological sample containing one or more biomarkers of interest onto one or more arrays of the DEP device. In particular embodiments, a current may be applied through the one or more electrode arrays, dividing one or more biomarkers from a plurality of biomarkers in the biological sample. In particular embodiments, the DEP device may filter, via one or more microfluidics, a particular biomarker of interest from the plurality of biomarkers in the biological sample into one or more chambers of the DEP device. In particular embodiments, one or more particular biomarkers may be tagged with one or more markers, which may then be detected via one or more sensors, wherein the quantity of each biomarker may be determined and displayed.
In particular embodiments, a method for detecting at least one biomarker of interest on a device may include receiving a first biological sample and transitioning the first biological sample into a first chamber via a microfluidics channel. As an example and not by way of limitation, a first process may be performed on the first biological sample in the first chamber. In particular embodiments, the first biological sample may be passed from the first chamber to a second chamber via a microfluidics channel. As an example and not by way of limitation, a second process may be performed on the first biological sample in the second chamber. In particular embodiments, the first biological sample may be passed from the second chamber to a third chamber via a microfluidics channel. As an example and not by way of limitation, a third process may be performed on the first biological sample in the third chamber.
In particular embodiments, a method for isolating at least one biomarker of interest on a solid-state device ay include receiving a first biological sample onto the solid-state device. The solid-state device may then transition the first biological sample to a DEP chamber. As an example and not by way of limitation, one or more processes may be performed on the first biological sample in the DEP chamber. In particular embodiments, the solid-state device may pass the first biological sample through a microfluidics (MF) channel to a tagging chamber, where one or more biomarkers of interest of the first biological sample may be tagged based on one or more characteristics.
In particular embodiments, the solid-state device may pass one or more biomarkers of interest from the tagging chamber to a sensor chamber. In particular embodiments, the solid-state device may digitally determine, within a sensor chamber, a quantity of the one or more biomarkers of interest based on the quantity of tags within the first biological sample.
In particular embodiments, a method 1800 for isolating at least one biomarker of interest on a solid-state device. At step 1810, one or more processors of the solid-state device may provide instructions to receive a first biological sample into the solid state device. As an example and not by way of limitation, the first biological sample may comprise one or more biomarkers of interest. As another example and not by way of limitation, the first biological sample may be input via a microfluidics (MF) channel.
In particular embodiments, one or more processors of the solid-state device may provide instructions to transition, via the MF, the first biological sample to a DEP chamber and apply an AC current to the first biological sample through the one or more electrodes of the DEP chamber.
In particular embodiments, one or more processors of the solid-state device may provide instructions to one or more components of the solid-state device to transition, from the MF, the first biological sample from the DEP chamber to a tagging chamber.
In particular embodiments, one or more processors of the solid-state device may provide instructions to one or more components of the solid-state device to assign a tag to each of the one or more biomarkers of interest of the first biological sample. As an example and not by way of limitation, each tag may define a particular label of a plurality of labels. In particular embodiments, a first biomarker of interest of the first biological sample may be assigned a first tag based on one or more particular characteristics. In particular embodiments, a second biomarker of interest of the first biological sample may be assigned a second tag based on one or more particular characteristics.
In particular embodiments, one or more processors of the solid-state device may provide instructions to one or more components of the solid-state device to transition, via the MF, the first biological sample to one or more detector chambers, wherein each detector chamber is programmed for a particular biomarker of interest.
In particular embodiments, one or more processors of the solid-state device may provide instructions to one or more components of the solid-state device to digitally determine, by one or more detectors within the one or more detector chambers, a quantity of the one or more biomarkers of interest based on the assigned tags.
Certain technical challenges exist for early cancer detection. One technical challenge may include relying on free circulating markers released during tumor cell death The solution presented by the embodiments disclosed herein to address this challenge may be a solid-state device for isolating specific biomarker concentration. Another technical challenge may include detecting biomarker concentration. The solution presented by the embodiments disclosed herein to address this challenge may be a solid-state device for isolating specific biomarkers, but also digitally quantifying the biomarker concentration.
Certain embodiments disclosed herein may provide one or more technical advantages. A technical advantage of the embodiments may include utilizing dielectrophoresis (DEP) on a solid-state device. Another technical advantage of the embodiments may include utilizing a plurality of chambers on the solid-state device, wherein each chamber may perform a specific process on a biological sample. Certain embodiments disclosed herein may provide none, some, or all of the above technical advantages. One or more other technical advantages may be readily apparent to one skilled in the art in view of the figures, descriptions, and claims of the present disclosure.
The embodiments disclosed herein are only examples, and the scope of this disclosure is not limited to them. Particular embodiments may include all, some, or none of the components, elements, features, functions, operations, or steps of the embodiments disclosed herein. Embodiments according to the invention are in particular disclosed in the attached claims directed to a method, an apparatus, or a system, wherein any feature mentioned in one claim category, e.g. method, can be claimed in another claim category, e.g. system, as well. The dependencies or references back in the attached claims are chosen for formal reasons only. However any subject matter resulting from a deliberate reference back to any previous claims (in particular multiple dependencies) can be claimed as well, so that any combination of claims and the features thereof are disclosed and can be claimed regardless of the dependencies chosen in the attached claims. The subject-matter which can be claimed comprises not only the combinations of features as set out in the attached claims but also any other combination of features in the claims, wherein each feature mentioned in the claims can be combined with any other feature or combination of other features in the claims. Furthermore, any of the embodiments and features described or depicted herein can be claimed in a separate claim and/or in any combination with any embodiment or feature described or depicted herein or with any of the features of the attached claims.
In particular embodiments, a biological sample may be loaded onto an electrode array, wherein EVs 102 may be concentrated and/or isolated using dielectrophoresis (DEP). The detection apparatus (e.g., lab-on-a-chip device) may utilize integral microfluidics (either conventional/pneumatic and/or digital) to divide the concentrated EV volume and automate a multi-marker antibody-based capture. As an example and not by way of limitation, the respective biomarker concentrations may be directly quantified using an electro-chemical sensor, wherein data from the sensor may be processed through one or more algorithms, resulting in a composite result.
In particular embodiments, biological fluids may be collected via standard point of care procedures, including but not limited to vacutainer tube-based blood draw (e.g., in the case of blood), spinal tap (e.g., in the case of cerebrospinal fluid), and urine collection devices (e.g., in the case of urine). In particular embodiments, biological fluids may be processed to remove interfering cells. As an example and not by way of limitation, processing of the biological fluids may involve centrifugation, membrane-based filtration, and/or other standard preparation procedures to prepare biological samples for testing. The resulting samples may be plasma, serum, CSF, and/or urine.
In particular embodiments, the prepared biological sample may be flowed over an array of energized electrodes, which impart a dielectrophoretic (DEP) force on particles within the biological sample. The strength and direction of the DEP force may be specific to a plurality of biological particles within the sample, allowing EVs 102 to be isolated from the biological sample and further concentrated. As used herein, “dielectrophoresis (DEP)” may refer to a phenomenon in which a force is exerted on a dielectric particle, molecule, or macromolecular structure in an aqueous or organic solution when it is subjected to a non-uniform electric field. In particular embodiments, the period of time the DEP force is applied may be specific to a particular biomarker of interest within the biological sample.
In particular embodiments, the isolated and concentrated EVs may be mixed with immunochemical reagents and allowed to incubate. After immunochemistry, the respective one or more samples may be loaded onto a sensor array. As an example and not by way of limitation, the original volume of the EVs 102 may or may not be split into multiple droplets at any point during this process, depending on the necessary workflow. In particular embodiments, the sensor array may contain one or more digital sensors, wherein each digital sensor may determine a quantity of one or more biomarkers of interest (e.g., EVs 102).
In particular embodiments, one or more EVs 102 may be floating within a solution, wherein biomarkers 104 may be found on the surface of EV 102. As an example and not by way of limitation, labeling of biomarkers 104 may include but are not limited to binding of an antibody, an antibody coupled to an enzyme, an antibody coupled to a metal nanoparticle, an antibody coupled to a cleavable single-stranded DNA barcode, an antibody coupled to a cleavable single-stranded DNA barcode that is coupled to a metal nanoparticle, an antibody coupled to a cleavable single-stranded DNA barcode that is coupled to an enzyme, an antibody coupled to a cleavable single-stranded DNA barcode that is coupled to an enzyme, and/or an antibody coupled to a cleavable single-stranded DNA barcode that is coupled to an enzyme. The cleavable linkages to the antibody could include but are not limited to proteolytic, chemical, electrochemical and photolytic labile compounds. As an example and not by way of limitation, EV 102 may contain particular biomarkers of interest on the surface of EV 102, wherein the detector surface may be functionalized to capture EV 102 as well as biomarker 104, wherein the detectable event may be used to quantify the biomarker's presence. Table 1, below, outlines a plurality of configurations, EV labels, label release mechanisms, surface capture mechanisms, and detected events.
In particular embodiments, biomarkers 104 may be tagged with one or more labels and/or markers.
As displayed in Table 1, “ssDNA” may refer to single-stranded DNA and “MNP” may refer to a metal nanoparticle. As demonstrated in
In particular embodiments, a binding event 110 may be detected between biomarker 104 of EV 102 and capture molecule 120 by a sensor. As an example and not by way of limitation, capture molecule 120 may be captured by an antibody. As used herein “binding event” 110 may refer to a detectable event used to quantify the presence of a particular biomarker 104. As an example and not by way of limitation, when binding event 110 occurs, the observed event may either change the capacitance of surface chemistry 122 or increase the number of charge carriers, either of which event may be electronically measured. As particular biomarkers 104 may vary in sensitivity, the detection apparatus may be tuned to a particular range of interest.
As displayed in diagram 100 of
In particular embodiments, a method of capturing EV-derived biomarkers and/or EV-derived biomarker labels may comprise exploiting the chemical reactivity of the surface chemistry 122 to covalently attach antibodies to the surface, wherein the antibody 120 may directly bind its EV-derived biomarker 104.
In particular embodiments, one or more surfaces 124 of field effect transistors may be functionalized using standard surface chemistry processes, such that chemical functional groups may be physically and/or chemically bound through covalent bonds to metals, metal oxides, glassy carbon, graphene, graphene nanoribbons, carbon nanotubes, semiconductors, and/or dielectric surfaces. As used herein “surface chemistry” 122 may refer to the physical and chemical phenomena that occur at the interface between two phases. Surface chemistry 122 may include a molecule with multiple chemical and/or physical sites of reactivity, whereby one site may interact physically and/or chemically with the surface, and the other site is used to directly conjugate nucleic acids, proteins, and/or other molecules of interest. An example of surface chemistry 122 may include but is not limited to molecules, nanoparticles and/or biomolecules containing functional groups such as silanes, thiols, disulfides, phosphonates, phosphonic acids, diazonium, alkenes, carboxylic acids, alkynes, alkanes, amines, ketones, esters, aldehydes, alcohols, amides, imines, hydrazines, ethers, nitriles, aromatics, halides and azides. As an example and not by way of limitation, surface chemistry 122 may be directly conjugated to nucleic acids, proteins, glycans, lipids and/or other molecules used in the capture and/or detection of biomarkers of interest. In particular embodiments, an insulated-gate field-effect transistor (IGFET), extended-gate field-effect transistor (EGFET), and/or ion-sensitive field-effect transistor (ISFET) structure may functionalize the gate oxide rather than a metal gate.
In particular embodiments, a method for manufacturing one or more electrodes to detect one or more EV-derived biomarkers may include functionalizing a metal and/or metal oxide surface (hereinafter “surface”) with one or more chemical groups and herein may be referenced as surface chemistry 122. As an example, and not by way of limitation, the chemical reactivity of the surface may be exploited to covalently attach single-stranded DNA to the surface. As another example and not by way of limitation, the inherent surface reactivity may be exploited to attach functionalized single-stranded DNA directly to the surface.
In particular embodiments, the working electrode surface 124 may function as a detector of one or more biomarkers 104. As used herein, the “working electrode surface” may refer to one or more electrodes in an electrochemical system on which the reaction of interest is occurring. In particular embodiments, one or more EVs 102 may be subsequently brought within close proximity of the metal gates of one or more working electrode surfaces 124 during biomarker 104 capture and/or detection mediated by the biomolecules and/or molecules conjugated to the gate surface (e.g., DNA-based hybridization, antibody binding, and/or enzymatic reactions). As an example and not by way of limitation, the one or more EVs 102 may be concentrated by DEP and brought within close proximity of the metal gates of one or more working electrode surfaces 124 (e.g., capture molecule 120). In particular embodiments, one or more EVs 102 within the biological sample may be labeled via one or more methods, wherein the labeled EV may be attached to the surface of working electrode surface 124. As an example and not by way of limitation, one or more EVs 102 and/or biomarkers 104 may be labeled with one or more tags corresponding to one or more particular characteristics of the EV 102 and/or biomarker 104. As another example and not by way of limitation, one or more EVs 102 and/or biomarkers 104 may be tagged with one or more particular markers, wherein each marker may correspond to a particular characteristic of the EV 102 and/or biomarker 104. It is understood that the terms labelled, tagged, and marked may be used interchangeably to describe the process of identifying one or more particular characteristics of one or more EVs 102 and/or biomarkers 104. Although this disclosure discusses labelling, tagging, and/or marking particular characteristics of one or more EVs 102 and/or biomarkers 104, this disclosure contemplates any suitable method for identifying and characterizing the one or more particular characteristics. In particular embodiments, the label on one or more EVs 102 may be cleaved from the respective biomarker 104, wherein working electrode surface 124 may capture only the label sans EV 102 at the surface via surface chemistry 122. As an example and not by way of limitation, a method of releasing labels that are specific for EV-derived biomarkers may be comprised of a linker that can be cleaved chemically, photolytically, and/or electrochemically. Linkers between biomarker 104 and the detection biomolecule and/or molecule that could be cleaved chemically may include, but are not limited to, esters, carbamates, dialkoxydiphenylsilanes azos, diazos, acylhydrazones, nitrobenzenesulfonamides, acylsulfoamides, and/or disulfides. Linkers between biomarker 104 and the detection biomolecule and/or molecule that could be cleaved photolytically may include, but are not limited to, nitrophenyl ethyl ethers and/or phenacyl esters. Linkers between biomarker 104 and the detection biomolecule and/or molecule that could be cleaved electrochemically may include, but are not limited to, aryl esters and imines.
In particular embodiments, digital sensing may occur when EV biomarkers of interest 104 are detected by changes in ion concentration near the gate surface of working electrode surface 124, mediated by, for example, antibody binding events, nucleic acid hybridization events, and/or enzymatic reactions. As an example and not by way of limitation, the interaction between a biomarker 104 and its capture antibody 120 on the conjugated gate surface modulates the electrical properties of the gate of the field effect transistor, resulting in changes to either the voltage or current across the transistor, depending on the surrounding circuit configuration. This electrical signal may then be amplified and digitized using an analog-to-digital converter.
In particular embodiments, when a force (e.g., DEP force, AC current) is applied to the biological samples within one or more chambers of the solid-state DEP device, the force applied may result in modified ion concentration of the biological sample.
In particular embodiments, digital output for one or more biomarkers 104 may be analyzed by one or more machine-learning algorithms. As an example and not by way of limitation, the one or more machine-learning algorithms may include supervised, unsupervised, semi-supervised, deep, and/or reinforcement learning algorithms. In particular embodiments, the deep learning algorithms may include any artificial neural networks (ANNs) that may be utilized to learn deep levels of representations and abstractions from large amounts of data. For example, the deep learning algorithms may include ANNs, such as a multilayer perceptron (MLP), an autoencoder (AE), a convolution neural network (CNN), a recurrent neural network (RNN), long short term memory (LSTM), a grated recurrent unit (GRU), a restricted Boltzmann Machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a generative adversarial network (GAN), and deep Q-networks, a neural autoregressive distribution estimation (NADE), an adversarial network (AN), attentional models (AM), deep reinforcement learning, and so forth.
In particular embodiments, digital output for one or more biomarkers 104 may be analyzed by one or more classification machine-learning algorithms or functions which may include any algorithms that may utilize a supervised learning model (e.g., logistic regression, naïve Bayes, stochastic gradient descent (SGD), k-nearest neighbors, decision trees, random forests, support vector machine (SVM), and so forth) to learn from the data input to the supervised learning model and to make new observations or classifications based thereon.
Although this disclosure references the forementioned machine-learning algorithms, this disclosure contemplates any suitable machine-learning algorithm. In particular embodiments, the one or more machine-learning algorithms may analyze a wide variety of data, allowing for detection of early cancer (e.g., Stage I, Stage II) and/or other diseases of interest.
In particular embodiments, as demonstrated in
In particular embodiments, the event 110 may be detected by working electrode surface 124 and surface chemistry 122.
In particular embodiments, the event 160 may be detected by working electrode surface 124 and surface chemistry 122.
In particular embodiments, as represented in
In particular embodiments, as represented in
In particular embodiments, diagram 170 may represent configuration “G-1” of Table 1, wherein label 140 may be an antibody-ssDNA barcode-MNP with a chemical label release mechanism. In the example of configuration “G-1”, the surface capture mechanism may be a DNA barcode hybridizing to complementary ssDNA, wherein the detected binding event 110 may be MNP proximity.
In particular embodiments, diagram 170 may represent configuration “G-2” of Table 1, wherein label 140 may be an antibody-ssDNA barcode-MNP with a photo label release mechanism. In the example of configuration “G-2”, the surface capture mechanism may be a DNA barcode hybridizing to complementary ssDNA, wherein the detected binding event 110 may be MNP proximity.
In particular embodiments, diagram 170 may represent configuration “G-3” of Table 1, wherein label 140 may be an antibody-ssDNA barcode-MNP with an electrochemical label release mechanism. In the example of configuration “G-3”, the surface capture mechanism may be a DNA barcode hybridizing to complementary ssDNA, wherein the detected binding event 110 may be MNP proximity.
In particular embodiments, the event 110 may be detected by working electrode surface 124 and surface chemistry 122.
In particular embodiments, diagram 190 may represent configuration “F-2” of Table 1, wherein label 140 may be an antibody-ssDNA barcode with a photo label release mechanism. In the example of configuration “F-2”, the surface capture mechanism may be a DNA barcode hybridizing to complementary ssDNA, wherein the detected event 160 may be polymerase activity using a DNA polymerase.
In particular embodiments, diagram 190 may represent configuration “F-3” of Table 1, wherein label 140 may be an antibody-ssDNA barcode with an electrochemical label release. In the example of configuration “F-3”, the surface capture mechanism may be a DNA barcode hybridizing to complementary ssDNA, wherein the detected event 160 may be polymerase activity using a DNA polymerase.
In particular embodiments, diagram 190 may represent configuration “H-1” of Table 1, wherein label 140 may be an antibody-ssDNA barcode-enzyme with a chemical label release mechanism. In the example configuration “H-1”, the surface capture mechanism may be DNA barcode hybridizing to complementary ssDNA, wherein the detected event 160 may be polymerase activity using a DNA polymerase.
In particular embodiments, diagram 190 may represent configuration “H-2” of Table 1, wherein label 140 may be an antibody-ssDNA barcode-enzyme with a photo label release mechanism. In the example configuration “H-2”, the surface capture mechanism may be DNA barcode hybridizing to complementary ssDNA, wherein the detected event 160 may be polymerase activity using a DNA polymerase.
In particular embodiments, diagram 190 may represent configuration “H-3” of Table 1, wherein label 140 may be an antibody-ssDNA barcode-enzyme with an electrical label release mechanism. In the example configuration “H-3”, the surface capture mechanism may be DNA barcode hybridizing to complementary ssDNA, wherein the detected event 160 may be polymerase activity using a DNA polymerase.
In particular embodiments, the event 160 may be detected by working electrode surface 124 and surface chemistry 122.
In particular embodiments, flowchart 200 may continue by passing the biological sample to the DEP chamber 208, wherein DEP chamber waste 206 may be output from sample prep chamber 204 and DEP reagent input 209 may be input to sample preparation chamber 204.
In particular embodiments, the biological sample may be passed from sample preparation chamber 204 to the isolation/concentration chamber 210. In particular embodiments, multiple fluid reagents may be supplied to and from one or more fluid chambers using bulk and/or consumable reagent containers. In particular embodiments, the multiple fluid reagents may be supplied to and from one or more fluid chambers using a fluid directing manifold and a motive force that is either hydraulic, pneumatic, or electrostatic in nature. In the example where electrostatic forces are used, a method of electrowetting the fluids that use electrodes may be used, wherein the electrodes may be located on either the top or bottom of the fluid channels and chambers and wherein the electrodes may be coated with thin films selected to achieve a hydrophobicity or hydrophilicity to support electrowetting.
In particular embodiments, DEP chamber waste 206 may be output from the isolation/concentration chamber 210 and DEP reagent input 209 may be input to the isolation/concentration chamber 210. In particular embodiments, the biological sample may be transitioned from isolation/concentration chamber 210 to a labeling chamber 214. As an example and not by way of limitation, one or more processes (e.g., labeling biomarkers) may occur within labeling chamber 214. In particular embodiments, DEP reagent input 209 may consist of one or more bulk reagents, such as bulk reagent Z′ 218, bulk reagent B′ 220, and/or bulk reagent A′222.
In particular embodiments, when an alternating current (AC) waveform is run through positive electrodes 304 and negative electrodes 306, the one or more EVs 102 may experience an attractive force that is a function of the RF frequency, voltage, plasma conductivity, EV particle size, and EV particle charge. It is understood that for any given combination of voltages, a subrange of particle sizes may experience a net attraction to the one or more electrodes.
In this example, the positive electrodes 304 and/or negative electrodes 306 may be constructed by metal or any other suitable material. In particular embodiments, the positive electrodes 304 and/or negative electrodes 306 may be separated by dielectric material 302. In particular embodiments, EVs 102 in the band of attraction may be attracted to one or more of the positive electrodes 304 and/or negative electrodes 306 on the surface of the dielectric material 302.
In particular embodiments, a polymer film is coated on the surface of the one or more positive 304 and/or one or more negative electrodes 306 and dielectric 302. This polymer film is a multicomponent mixture that is spin-cast onto the surface of the semiconductor wafer/chips. This material does not interfere with the electrical properties of the one or more electrodes, has porosity tuned for the band of attraction, and is tuned not to be adsorptive. After casting, the film may be cured using UV light or thermal energy.
In particular embodiments, one or more syringes 510 of the detection apparatus may be actuated by one or more motors 520, wherein motor 520 may contain one or more gears. In particular embodiments, the detection apparatus may move fluid (e.g., a biological sample) through manifold 530 to a connected plurality of solenoid actuated values 540. It is understood that the solenoid actuated valves may be arranged in parallel, series, or any other suitable configuration.
In particular embodiments, the detection apparatus may move fluid through one or more reservoirs 550, wherein the one or more reservoirs 550 may be constructed by centrifuge tubes or any other suitable material. In particular embodiments, one or more syringes 510 of the detection apparatus may be actuated by one or more motors 520, wherein motor 520 may contain one or more gears. In particular embodiments, the detection apparatus may move fluid (e.g., a biological sample) through manifold 530 to a connected plurality of solenoid actuated values 540. It is understood that the solenoid actuated valves may be arranged in parallel, series, or any other suitable configuration. In particular embodiments, the detection apparatus may move fluid through one or more reservoirs 550, wherein the one or more reservoirs 550 may be constructed by centrifuge tubes or any other suitable material.
In particular embodiments, the detection apparatus may move fluid through one or more isolation and/or tagging chambers 560, wherein tagging of the fluid and/or isolation of the fluid may occur. As an example and not by way of limitation, the one or more isolation and/or tagging chambers 560 may tag particular chemical groups.
In particular embodiments, the detection apparatus may move fluid through and one or more sensor chambers 570. As an example and not by way of limitation, the detection apparatus may receive one or more biological samples via one or more reservoirs 550, wherein the biological sample may be passed to one or more tagging and/or isolation chambers 560 and subsequently passed to one or more sensor chambers 570. Although this disclosure discusses a particular order of processing biological samples within the detection apparatus, this disclosure contemplates any suitable order of processing biological samples within the detection apparatus.
In particular embodiments, particular bulk reagents may be dedicated to one or more specific chambers 560. As an example and not by way of limitation, chambers 560 may include detector chambers, wherein the detector chambers may receive one or more “detector” reagents. As another example and not by way of limitation, particular detector reagents may be input to one or more particular chambers 560, or a particular detector reagent may be common to all of chambers 560. As an example and not by way of limitation, each chamber of the one or more chambers 560 may label a particular biomarker. For example, one chamber 560 may label one particular biomarker, such as “biomarker 1,” wherein another chamber 560 may label “biomarker 2.” In this example, one or more chambers 560 may cleave specific labels, wherein the labels may be chemically bonded to one or more sensors and ultimately digitally quantified. In particular embodiments, the one or more chambers 560 may receive fluid (e.g., biological sample) input and output waste from each particular chamber.
In particular embodiments, DEP chamber 620 may process the one or more biological samples, wherein biological sample may be passed to digital microfluidics (DMF) channel 630 until biological sample reaches one or more of a tagging chamber. It is understood that DMF channel 630 may also be a microfluidics channel as opposed to a digital microfluidics (DMF) channel. As displayed by diagram 600 of
In particular embodiments, DEP chamber 620 may process the one or more biological samples, wherein a biological sample may be passed to digital microfluidics (DMF) channel 630 until the biological sample reaches one or more of a tagging chamber. As displayed by diagram 700 of
In particular embodiments, DEP chamber 620 may process the one or more biological samples, wherein the biological sample(s) may be passed to digital microfluidics (DMF) channel 630 until the biological sample(s) reach one or more of a tagging chamber. As an example and not by way of limitation, DMF channel 630 may be a microfluidics channel. As displayed by diagram 800 of
In particular embodiments, each of reference electrode 920, counter electrode 930, and/or working electrode 940 may be fabricated from distinct materials. In particular embodiments, any one of reference electrode 920, counter electrode 930, and/or working electrode 940 may be functionalized, while the remaining electrodes are not functionalized. As an example and not by way of limitation, working electrode 940 may be functionalized while reference electrode 920 and counter electrode 930 may not be functionalized. In particular embodiments, the potential, impedance, and/or current at each of reference electrode 920, counter electrode 930, and/or working electrode 940 may be monitored with one or more ADCs (e.g., ADCs 960, 962), wherein the current may be stored for determination of biomarker quantification.
In particular embodiments, the detection apparatus (e.g., device) for quantifying EV-derived biomarkers in a biological sample may include a device platform capable of holding a cartridge. In particular embodiments, the detection apparatus (e.g., device) for quantifying EV-derived biomarkers in one or more biological samples (e.g., blood, plasma, serum, cerebrospinal fluid, lymphatic fluid, saliva, urine, fecal matter, cell lysate, cell culture fluid) may include a device platform capable of holding a cartridge and delivering samples, reagents, light, and/or electrical pulses to multiple fluid and electrical channels on a cartridge. As an example and not by way of limitation, the device platform may include an electronics board with logic and/or power circuitry for driving and sensing electronic devices, sensors, and/or electrodes. As another example and not by way of limitation, the device platform may include a fluidics control system consisting of fluid reservoirs, tubing, and either pneumatic, hydraulic, or electrical fluidical controls, wherein the fluidic controls may be manually and/or electronically actuated.
In particular embodiments, a fluidics cartridge may be constructed by a molded top material and molded bottom material, wherein a space between the top and bottom molded material may house one or more printed circuit boards and/or microelectronics chip(s). As an example and not by way of limitation, the top molded material and bottom molded material may be constructed of plastic or any other suitable material. In particular embodiments, one or more adhesive layer(s) may create a fluidic seal between chambers and/or channels of the fluidics cartridge. In particular embodiments, a “spacer” layer in conjunction with the one or more printed circuit board(s) may define the boundaries of the fluid chambers and/or channels. As an example and not by way of limitation, the spacer may be constructed by plastic or any other suitable material. In particular embodiments, the fluidics cartridge may include ports dedicated to the ingress and egress of fluids for each channel. In particular embodiments, components comprising the walls of one or more fluid chambers and/or channels may be coated with a non-biofouling film. In particular embodiments, one or more valves may be included to direct a multiplicity of samples and/or reagents to and from the various inlet and outlet ports and/or reservoirs.
In particular embodiments, the detection apparatus for quantifying EV-derived biomarkers in a biological sample may include a fluidics cartridge with multiple chambers and/or channels. In particular embodiments, a detection apparatus for quantifying EV-derived biomarkers in a biological sample may include printed circuits and/or a microelectronics silicon-based integrated chip(s) (e.g., complementary metal-oxide semiconductor (CMOS) or silicon-based integrated chip), wherein the chip(s) may be constructed to create a DEP cavity (e.g., DEP chamber 620). As an example and not by way of limitation, the DEP cavity may isolate and capture particles of a tunable size and/or charge range from one or more biological samples (e.g., plasma sample).
In particular embodiments, the device for quantifying EV-derived biomarkers 104 in a plasma sample may include a printed circuit board and/or a silicon-based integrated chip with electrodes, wherein the printed circuit board and/or silicon based integrated chip may electrostatically control one or more fluidic motion systems. In particular embodiments, the device for quantifying EV-derived biomarkers in a plasma sample may include a fluid supply (e.g., sample input 610) and/or waste management system. In particular embodiments, the device for quantifying EV-derived biomarkers in a biological sample may include one or more reaction chambers. As an example and not by way of limitation, the one or more reaction chambers may include a DEP structure, wherein the DEP structure may support the labeling and/or cleaving of EV-derived biomarker labels.
In particular embodiments, the device for quantifying EV-derived biomarkers in a biological sample may include one or more printed circuits and/or microelectronic chips containing electrodes for electrical capturing and detecting of labeled EVs and/or cleaved EV-biomarker labels. In particular embodiments, the device for quantifying EV-derived biomarkers 104 in a biological sample may include a plurality of electronics and/or sensors, wherein the electronics and/or sensors may report digital sensor responses quantifying the presence of biomarkers 104 and/or biomarker labels.
In particular embodiments, the EV-biosensor device may perform DEP particle filtering to separate EVs 102 from other biological components, wherein the EV-biosensor device may be constructed by one or more silicon chips. As an example and not by way of limitation, the top of the one or more silicon chips may be constructed by a metal layer, wherein the metal layer may contain an interdigitated alternating polarity electrode arrangement. In particular embodiments, the EV-biosensor device may contain an RF AC waveform generator off-chip, wherein in RF AC waveform generator may apply one or more of a particular waveform across one or more electrodes (e.g., reference electrode 920, counter electrode 930, working electrode 940). In particular embodiments, the EV-biosensor device may include an anti-bio-fouling surface coating or other coating on electrode and/or non-electrode surfaces within DEP chamber 620.
In particular embodiments, each of reference electrode 920, counter electrode 940, and/or working electrode 940 may be fabricated from distinct materials. In particular embodiments, any one of reference electrode 920, counter electrode 930, and/or working electrode 940 may be functionalized, while the remaining electrodes are not functionalized, or functionalized differently. In particular embodiments, the potential, impedance, and/or current at each of reference electrode 920, counter electrode 930, and/or working electrode 940 may be monitored with one or more ADCs (e.g., ADCs 960, 962, 1020) wherein the current may be stored for determination of biomarker quantification.
In particular embodiments, top surface 1382 may consist of electrical pass-through 1320, one or more reference electrodes 920, and one or more counter electrodes 930. As an example and not by way of limitation, length 1312 of top surface 1382 may measure approximately twenty (20) millimeters and width 1310 of top surface 1382 may measure approximately ten (10) millimeters. Although this disclosure discusses an approximate length 1312 and width 1310 of top surface 1382 and bottom surface 1380, this disclosure contemplates any suitable length 1312 and of 43 width 1310. Although this disclosure discusses a particular electrode configuration (e.g., positioning of counter electrode 930, reference electrode 920, working electrode 940), this disclosure contemplates any suitable configuration of counter electrode 930, reference electrode 920, and/or working electrode 940.
In particular embodiments, the DEP electrode array may be arranged in an interdigitated configuration. In particular embodiments, the DEP electrode array may include a working electrode 940, reference electrode 920, and/or counter electrode 930. As an example and not by way of limitation, the DEP electrode array may include one or more positive electrodes and one or more negative electrodes.
In particular embodiments, at step 1430, one or more sensors of the DEP device may determine a quantity of one or more target biomarkers 104 of interest of the biological sample. As an example and not by way of limitation, the sensors may quantify the total number of tags, labels, and/or markers corresponding to particular biomarkers 104. In particular embodiments, the quantity of one or more particular biomarkers 104 may be output and presented via a GUI of one or more computing devices.
Particular embodiments may repeat one or more steps of the method of
In particular embodiments, the DEP electrode array may be arranged in an interdigitated configuration. In particular embodiments, the DEP electrode array may include a working electrode 940, reference electrode 920, and/or counter electrode 930. As an example and not by way of limitation, the DEP electrode array may include one or more positive electrodes and one or more negative electrodes.
In particular embodiments, in response to current of a particular configuration being applied to one or more electrode arrays, one or more biomarkers 104 may be separated from the volume of the biological sample.
At step 1530, one or more processors of the DEP device may instruct the DEP device to filter, via one or more microfluidics, one or more biomarkers 104 from the plurality of biomarkers 104 of the biological sample. As an example and not by way of limitation, the biomarkers 104 may be filtered into one or more chambers of the DEP device.
In particular embodiments, at step 1540, one or more processors of the DEP device may instruct the DEP device to tag one or more biomarkers 104 of the biological sample with a marker. As an example and not by way of limitation, a first biomarker may be marked with a first marker, and a second biomarker may be marked with a second marker. As another example and not by way of limitation, a marker may be a plurality of labels, tags, or other form of identifier. In particular embodiments, a first marker may include a particular label of a plurality of labels. As an example and not by way of limitation, the plurality of labels may include an antibody label, metal nanoparticle (MNP) label, single-stranded DNA (ssDNA) label, or any other suitable label.
In particular embodiments, at step 1550, one or more sensors of one or more chambers of the DEP device may detect a quantity of particular biomarkers 104. At step 1560, the DEP device may output and present a composite result of the quantity of particular biomarkers via a GUI of a computing device. In particular embodiments, one or more additional biological samples may be run on the DEP device simultaneously or in parallel.
Particular embodiments may repeat one or more steps of the method of
In particular embodiments, at step 1630 one or more processors of the DEP device may instruct the DEP device to transition the first biological sample from the first chamber to a second chamber via one or more microfluidics channels. As an example and not by way of limitation, a particular process may be performed on the first biological sample in the second chamber. The particular process performed on the first biological sample in the second chamber may be the same or different process from the process performed on the first biological sample in the first chamber. As an example and not by way of limitation. The second chamber may comprise a concentration chamber, where current applied to the first biological sample in the concentration chamber results in modified ion concentration.
In particular embodiments, at step 1640 one or more processors of the DEP device may instruct the DEP device to transition the first biological sample from the second chamber to a third chamber via the microfluidics channel. As an example and not by way of limitation, a third process may be performed on the first biological sample in the third chamber. In particular embodiments, the third chamber may comprise an isolation chamber, wherein current applied to the first biological sample in the isolation chamber may result in a separation of particular biomarkers of interest of the first biological sample.
In particular embodiments, one or more reagents may be input into any one of the first, second, or third chambers. In particular embodiments, any one of the first, second, or third chambers may output a waste product comprising a portion of the first biological sample and a portion of the one or more reagents resulting from the first process. It is understood that the DEP device may repeat this process with a plurality of biological samples (e.g., a second biological sample, third biological sample, etc). In particular embodiments, one or more biological samples may be processed simultaneously or in parallel.
Particular embodiments may repeat one or more steps of the method of
At step 1720, the one or more processors of the solid-state device may instruct one or more components of the solid-state device to transition the first biological sample to a DEP chamber. As an example and not by way of limitation, one or more processes may be performed on the first biological sample in the DEP chamber. In particular embodiments, the DEP chamber may contain one or more electrode arrays. As an example and not by way of limitation, each of the one or more electrode arrays may be arranged in an interdigitated configuration. As another example and not by way of limitation, each of the one or more electrode arrays may include a working electrode 940, reference electrode 920, and counter electrode 930.
In particular embodiments, at step 1730, one or more processors of the solid-state device may instruct one or more components of the solid-state device to pass the first biological sample through a microfluidics (MF) channel to a tagging chamber.
In particular embodiments, at step 1740, one or more processors of the solid-state device may instruct one or more components of the solid-state device to process the first biological sample in the tagging chamber. As an example and not by way of limitation, one or more biomarkers of interest of the first biological sample may be tagged based on one or more characteristics. In particular embodiments, the one or more biomarkers of interest of the first biological sample may be tagged with a particular label of a plurality of labels. As an example and not by way of limitation, the plurality of labels may include at least an antibody label, metal nanoparticle (MNP) label, and/or single-stranded DNA (ssDNA) label.
In particular embodiments, at step 1750, one or more processors of the solid-state device may instruct one or more components of the solid-state device to pass the one or more biomarkers of interest to a sensor chamber.
In particular embodiments, at step 1760, one or more processors of the solid-state device may instruct one or more components of the solid-state device to digitally determine, within the sensor chamber, a quantity of the one or more biomarkers of interest based on the quantity of tags within the first biological sample.
It is understood that the solid-state device may repeat this process with a plurality of biological samples (e.g., a second biological sample, third biological sample, etc). In particular embodiments, one or more biological samples may be processed simultaneously or in parallel.
Particular embodiments may repeat one or more steps of the method of
At step 1820, one or more processors of the solid-state device may provide instructions to transition, via the MF, the first biological sample to a DEP chamber and apply an AC current to the first biological sample through the one or more electrodes of the DEP chamber. In particular embodiments, one or more electrodes of the DEP chamber may include at least a working electrode 940, reference electrode 920, and counter electrode 930.
At step 1830, one or more processors of the solid-state device may provide instructions to one or more components of the solid-state device to transition, from the MF, the first biological sample from the DEP chamber to a tagging chamber.
At step 1840, one or more processors of the solid-state device may provide instructions to one or more components of the solid-state device to assign a tag to each of the one or more biomarkers of interest of the first biological sample. As an example and not by way of limitation, each tag may define a particular label of a plurality of labels. As another example and not by way of limitation, the plurality of labels may include, but are not limited to, an antibody label, metal nanoparticle (MNP) label, or single-stranded DNA (ssDNA) label. In particular embodiments, a first biomarker of interest of the first biological sample may be assigned a first tag based on one or more particular characteristics. In particular embodiments, a second biomarker of interest of the first biological sample may be assigned a second tag based on one or more particular characteristics.
At step 1850, one or more processors of the solid-state device may provide instructions to one or more components of the solid-state device to transition, via the MF, the first biological sample to one or more detector chambers, wherein each detector chamber is programmed for a particular biomarker of interest.
At step 1860, one or more processors of the solid-state device may provide instructions to one or more components of the solid-state device to digitally determine, by one or more detectors within the one or more detector chambers, a quantity of the one or more biomarkers of interest based on the assigned tags.
It is understood that the solid-state device may repeat this process with a plurality of biological samples (e.g., a second biological sample, third biological sample, etc). In particular embodiments, one or more biological samples may be processed simultaneously or in parallel.
Particular embodiments may repeat one or more steps of the method of
This disclosure contemplates any suitable number of computer systems 1900. This disclosure contemplates computer system 1900 taking any suitable physical form. As example and not by way of limitation, computer system 1900 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (e.g., a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, an augmented/virtual reality device, or a combination of two or more of these. Where appropriate, computer system 1900 may include one or more computer systems 1900; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks.
Where appropriate, one or more computer systems 1900 may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example, and not by way of limitation, one or more computer systems 1900 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems 1900 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.
In particular embodiments, computer system 1900 includes a processor 1902, memory 1904, storage 1906, an input/output (I/O) interface 1908, a communication interface 1910, and a bus 1912. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement. In particular embodiments, processor 1902 includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processor 1902 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 1904, or storage 1906; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 1904, or storage 1906. In particular embodiments, processor 1902 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 1902 including any suitable number of any suitable internal caches, where appropriate. As an example, and not by way of limitation, processor 1902 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memory 1904 or storage 1906, and the instruction caches may speed up retrieval of those instructions by processor 1902.
Data in the data caches may be copies of data in memory 1904 or storage 1906 for instructions executing at processor 1902 to operate on; the results of previous instructions executed at processor 1902 for access by subsequent instructions executing at processor 1902 or for writing to memory 1904 or storage 1906; or other suitable data. The data caches may speed up read or write operations by processor 1902. The TLB s may speed up virtual-address translation for processor 1902. In particular embodiments, processor 1902 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 1902 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 1902 may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors 1902. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.
In particular embodiments, memory 1904 includes main memory for storing instructions for processor 1902 to execute or data for processor 1902 to operate on. As an example, and not by way of limitation, computer system 1900 may load instructions from storage 1906 or another source (such as, for example, another computer system 1900) to memory 1904. Processor 1902 may then load the instructions from memory 1904 to an internal register or internal cache. To execute the instructions, processor 1902 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 1902 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 1902 may then write one or more of those results to memory 1904. In particular embodiments, processor 1902 executes only instructions in one or more internal registers or internal caches or in memory 1904 (as opposed to storage 1906 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 140 (as opposed to storage 1906 or elsewhere).
One or more memory buses (which may each include an address bus and a data bus) may couple processor 1902 to memory 1904. Bus 1912 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 1902 and memory 1904 and facilitate accesses to memory 1904 requested by processor 1902. In particular embodiments, memory 1904 includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory 1904 may include one or more memory devices 104, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.
In particular embodiments, storage 1906 includes mass storage for data or instructions. As an example, and not by way of limitation, storage 1906 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage 1906 may include removable or non-removable (or fixed) media, where appropriate. Storage 1906 may be internal or external to computer system 1900, where appropriate. In particular embodiments, storage 1906 is non-volatile, solid-state memory. In particular embodiments, storage 1906 includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates mass storage 1906 taking any suitable physical form. Storage 1906 may include one or more storage control units facilitating communication between processor 1902 and storage 1906, where appropriate. Where appropriate, storage 1906 may include one or more storages 1906. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.
In particular embodiments, I/O interface 1908 includes hardware, software, or both, providing one or more interfaces for communication between computer system 1900 and one or more I/O devices. Computer system 1900 may include one or more of these I/O devices, where appropriate. One or more of these I/O devices may enable communication between a person and computer system 1900. As an example, and not by way of limitation, an I/O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I/O device or a combination of two or more of these. An I/O device may include one or more sensors. This disclosure contemplates any suitable I/O devices and any suitable I/O interfaces 1906 for them. Where appropriate, I/O interface 1908 may include one or more device or software drivers enabling processor 1902 to drive one or more of these I/O devices. I/O interface 1908 may include one or more I/O interfaces 1906, where appropriate. Although this disclosure describes and illustrates a particular I/O interface, this disclosure contemplates any suitable I/O interface.
In particular embodiments, communication interface 1910 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 1900 and one or more other computer systems 1900 or one or more networks. As an example, and not by way of limitation, communication interface 1910 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface 1910 for it.
As an example, and not by way of limitation, computer system 1900 may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, computer system 1900 may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these. Computer system 1900 may include any suitable communication interface 1910 for any of these networks, where appropriate. Communication interface 1910 may include one or more communication interfaces 1910, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.
In particular embodiments, bus 1912 includes hardware, software, or both coupling components of computer system 1900 to each other. As an example, and not by way of limitation, bus 1912 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Bus 1912 may include one or more buses 1912, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.
Herein, “or” is inclusive and not exclusive, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A or B” means “A, B, or both,” unless expressly indicated otherwise or indicated otherwise by context. Moreover, “and” is both joint and several, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A and B” means “A and B, jointly or severally,” unless expressly indicated otherwise or indicated otherwise by context.
Herein, “automatically” and its derivatives means “without human intervention,” unless expressly indicated otherwise or indicated otherwise by context.
The embodiments disclosed herein are only examples, and the scope of this disclosure is not limited to them. Embodiments according to the invention are in particular disclosed in the attached claims directed to a method, a storage medium, a system and a computer program product, wherein any feature mentioned in one claim category, e.g. method, can be claimed in another claim category, e.g. system, as well. The dependencies or references back in the attached claims are chosen for formal reasons only. However, any subject matter resulting from a deliberate reference back to any previous claims (in particular multiple dependencies) can be claimed as well, so that any combination of claims and the features thereof are disclosed and can be claimed regardless of the dependencies chosen in the attached claims. The subject-matter which can be claimed comprises not only the combinations of features as set out in the attached claims but also any other combination of features in the claims, wherein each feature mentioned in the claims can be combined with any other feature or combination of other features in the claims. Furthermore, any of the embodiments and features described or depicted herein can be claimed in a separate claim and/or in any combination with any embodiment or feature described or depicted herein or with any of the features of the attached claims.
The scope of this disclosure encompasses all changes, substitutions, variations, alterations, and modifications to the example embodiments described or illustrated herein that a person having ordinary skill in the art would comprehend. The scope of this disclosure is not limited to the example embodiments described or illustrated herein. Moreover, although this disclosure describes and illustrates respective embodiments herein as including particular components, elements, feature, functions, operations, or steps, any of these embodiments may include any combination or permutation of any of the components, elements, features, functions, operations, or steps described or illustrated anywhere herein that a person having ordinary skill in the art would comprehend. Furthermore, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative. Additionally, although this disclosure describes or illustrates particular embodiments as providing particular advantages, particular embodiments may provide none, some, or all of these advantages.
This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application No. 63/354,903, filed 23 Jun. 2023, which is incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63354903 | Jun 2022 | US |
