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WO2025212124A1 - Réseau de capteurs pour diagnostic par fluorescence - Google Patents

Réseau de capteurs pour diagnostic par fluorescence

Info

Publication number
WO2025212124A1
WO2025212124A1 PCT/US2024/039849 US2024039849W WO2025212124A1 WO 2025212124 A1 WO2025212124 A1 WO 2025212124A1 US 2024039849 W US2024039849 W US 2024039849W WO 2025212124 A1 WO2025212124 A1 WO 2025212124A1
Authority
WO
WIPO (PCT)
Prior art keywords
fluorophore
sample
structures
sensor array
molecules
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2024/039849
Other languages
English (en)
Inventor
Stanislav PILETSKY
Daniel Heller
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Memorial Sloan Kettering Cancer Center
Original Assignee
Memorial Sloan Kettering Cancer Center
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Memorial Sloan Kettering Cancer Center filed Critical Memorial Sloan Kettering Cancer Center
Publication of WO2025212124A1 publication Critical patent/WO2025212124A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6452—Individual samples arranged in a regular 2D-array, e.g. multiwell plates
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B82—NANOTECHNOLOGY
    • B82Y—SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
    • B82Y20/00—Nanooptics, e.g. quantum optics or photonic crystals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
    • G01N21/643—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" non-biological material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/58—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/84—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving inorganic compounds or pH
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection

Definitions

  • a computing device may use a computer vision algorithm to process data acquired via such sensors.
  • VOCs volatile organic chemicals
  • a sensor platform which can be used for the detection of arbitrary molecules of interest or for clinical diagnosis using blood, saliva, or urine samples.
  • the sensor may include an array of environmentally sensitive fluorescent probes.
  • Each probe is placed on a separate pixel or several pixels of the sensor array, and has a different coating, such that it responds slightly differently to environmental stimuli to every other pixel upon addition of a test solution, such as blood or saliva.
  • a test solution such as blood or saliva.
  • the sensor resembles a two-dimensional quick response (QR) code, with each pixel displaying a different value based on the presence of biomolecules, such as proteins, lipids, and salts or change in the environment such as acidity (e.g., pH), temperature, oxygen concentration, etc.
  • QR quick response
  • the sensor uses a machine learning approach and can be trained on analytes with known classification, in order to identify these analytes in unknown samples. For example, by training the sensor with blood from an ovarian cancer patient versus blood from a healthy donor, the ovarian cancer status of unknown blood samples can be predicted. This can also be used for the detection of other molecules of interest, such as drugs, explosives, and environmental pollutants.
  • This assay may involve a microarray, which can be produced via low-cost inkjet printing or microdroplet dispensing. This format allows orders of magnitude higher levels of multiplexing, facilitating a single sensor to detect multiple diseases simultaneously. Given the small size of a microarray, this format also reduces the volume of patient sample needed per test to potentially just a few microliters.
  • the environmentally sensitive fluorescent probe is single walled carbon nanotubes functionalized with small molecules or coated with polymers.
  • the approach can be generalized for printing other environmentally sensitive fluorophores, such as dye molecules, metal nanoparticles, polymer dots, quantum dots or other articles familiar to specialists in fluorescence and diagnostics.
  • this sensor is compatible with in vitro diagnostic tools such as microneedle arrays (each needle of which can be printed with a separate nanotube).
  • this printed format may be compatible with lateral flow devices, being able to print directly onto the lateral flow surface.
  • Inkjet printing of nanotubes can be used to produce fluorescence-based printed nanotube sensors. Machine learning can be used to interpret test results.
  • the system may include a computing system having one or more processors coupled with memory in communication with the imaging device.
  • the computing system may receive, via the imaging device, the image of the plurality of light signals from the plurality of fluorophore structures.
  • the computing system may generate, using the plurality of light signals of the image, a response code defining a plurality of responses by the corresponding plurality of fluorophore structures to the fluorescent light due to the respective fluorescence response profile in each fluorophore structure of the one or more fluorophore structures.
  • the computing system may determine a classification of the molecules of interest of the sample based on the response code.
  • the computing system may provide an output to identify the classification of the molecules of interest of the sample.
  • At least one of the plurality of fluorophore structures may be configured with an environmental sensitivity with respect to fluorescence in at least one of a spatial or temporal domain.
  • the environmental sensitivity may correspond to a change in at least one of an absorbance intensity or a spectral characteristic.
  • the one or more fluorophore structures may include a carbon nanotube, a polymer dot, a quantum dot, or a fluorescent dye.
  • the respective fluorescence response profile of the one or more fluorophore structures may differ from at least one other fluorophore structure of the one or more fluorophore structures.
  • At least one fluorophore structure of the plurality of fluorophore structures may include a carbon nanotube.
  • the respective fluorescence response profile of the at least one fluorophore structure is based on at least one of a structural defects, chirality, single-walled, multi-walled, polymer coating, surfactant, peptide, protein, saccharide in the carbon nanotube.
  • the plurality of fluorophore structures may be grafted to, confined within, adjacent to, or colocalized with a binding agent.
  • the binding agent may include at least one of antibodies, nanobodies, antigens, enzymes, aptamers, or molecularly imprinted polymers.
  • the plurality of fluorophore structures may be arranged in a one-dimensional line, a two- dimensional area, or three-dimensional volume of the substrate.
  • the substrate may be formed from at least one of a paper material, plastic material, a metal material, a glass material, a fabric material, a leather material, a semiconductor material, a metal oxide material, a polymer material, epidermis, lateral flow strip, aerogel, or a composite.
  • FIG. 3 depicts a block diagram of a process for classifying the molecules of interest in the system for classifying samples, in accordance with one or more implementations
  • FIG. 7 depicts a block diagram of a server system and a client computer system, in accordance with one or more implementations.
  • Section A describes systems and methods for classifying samples using fluorophore sensor arrays.
  • Section B describes a network environment and computing environment which may be useful for practicing various embodiments described herein.
  • each pixel is covered with ink containing different environmentally sensitive fluorescent carbon nanotubes.
  • the fluorescence spectrum of each pixel can be measured using a fluorescence microscope, a charge-coupled device (CCD) camera, or other fluorimeter.
  • CCD charge-coupled device
  • each pixel responds differently to the analyte and media components, pH, polarity change, oxygen level, etc., due to differences in ink composition (nanotube coating and defects, and presence of surfactants and other additives).
  • a neural network is trained to recognize array responses to specific analytes or physico-chemical change. For example, by training the array using serum samples from patients with ovarian cancer versus serum from healthy donors and patients with other conditions, the sensor can be made to diagnose ovarian cancer.
  • pixels can be functionalized with specific recognition agents such as antibodies, aptamers, molecularly imprinted polymers, etc.
  • the agents may be physically wrapped or adsorbed onto the nanotubes, or covalently linked or grafted onto the nanotube surface via, for example, radical chemistry or other coupling chemistry, or via incorporation into gels such as microgel or aerogel.
  • a nanotube array may be used as an anti -counterfeiting label.
  • Each pixel or group of pixels may have a different fluorescence spectrum.
  • the different fluorescence spectra may be due to differences in nanotube ink composition.
  • the different fluorescence spectra may prevent forgery.
  • Custom ink may be synthesized using additive compounds, which can affect the fluorescence spectrum of each ink.
  • Custom ink may be synthesized by filtering a generic ink formulation through a custom separation column (e.g., nanotubes with specific dimensions or surface treatment pass through).
  • the array can be applied for analysis of liquid or gas samples.
  • the array can be applied for single use or for continuous monitoring of analytical samples.
  • the array could also be used for detection of specific molecules of interest, such as drugs, explosives, and environmental pollutants. This would involve training the neural network using samples of known concentrations of the target molecule in order to recognize the spectral fingerprint of that molecule.
  • the array could be used as an anti-counterfeiting label, due to the difficulty of reproducing a specific array without knowing the exact composition of each pixel.
  • the authenticity of the array could be demonstrated by adding solutions or volatiles of known composition in order to reveal their spectral fingerprints.
  • the composition of the array and of the test solutions or mixture would be predefined information for the entity producing the anti -counterfeiting labels.
  • Arrays can be any size — micrometer scale for high throughput imaging using, e.g., fluorescence microscopy, or millimeter or centimeter scale for analysis using point-of-care devices such as cameras or mobile phones. Arrays do not need to be two dimensional — they could be a one-dimensional barcode, or another pattern entirely. Arrays can be produced via inkjet printing, micro-pipetting, aerosol printing, screen printing, or other technologies. Inkjet printing may be the most suitable for production of low-cost arrays.
  • Carbon nanotubes are not necessary for this array; any environmentally sensitive fluorophore would work in the same way (e.g., fluorescent dyes, quantum dots, polymer dots, carbon dots or combinations of the above).
  • any environmentally sensitive fluorophore would work in the same way (e.g., fluorescent dyes, quantum dots, polymer dots, carbon dots or combinations of the above).
  • the advantage of using carbon nanotubes is the opportunity to perform analysis in the near infrared (NIR) or infrared (IR) optical window, with minimal interference from biological and environmental matrixes.
  • carbon nanotubes may be either single-walled carbon nanotubes or multi-walled carbon nanotubes. If nanotubes are used as fluorophores, they may be individually wrapped with DNA, polymers, surfactants (both ionic and non-ionic), peptides, saccharides, or other molecules. If nanotubes are used, different pixels may consist of different chirality nanotubes, with the same or different coating agents in each pixel.
  • Pixels can be individually multiplexed, containing multiple different fluorophores (e.g., different chirality nanotubes, or mixtures of nanotubes and dyes or other fluorophores). These fluorophores could potentially interact with each other differently in the presence of analyte (e.g., increased or decreased non-radiative energy transfer efficiency).
  • the composition of several pixels can be repeated in the same array to increase reproducibility and accuracy of the analysis.
  • the sensor may have each pixel providing a unique or semi-unique response to a particular analyte.
  • each pixel may be created using a unique fhiorophore.
  • the same fluorophore can be deposited onto multiple spots on a substrate that has varying physical properties at different locations. For example, this could take the form of a polymeric sheet with gradients in pore size and hydrophobicity along each axis. This sheet could be the printing substrate itself or could be placed over the fluorophore such that the analyte has to pass through the sheet in order to reach the fluorophore.
  • the array may be produced by printing an array of identical nanotube pixels, and then printing an array of another substrate, such as DNA, peptides, or proteins on top of the nanotubes.
  • Nanotube microarrays can potentially be prepared via laser-assisted alignment of nanotubes, with different pixels showing different alignments. If the array is printed onto a lateral flow device, every pixel could consist of the same compound. Differences in fluorescence response would then be caused by different components of the serum, saliva, or urine advancing along the lateral flow device at different rates, resulting in different pixels being exposed to different fractions of the analyte.
  • pixels can be functionalized with specific recognition agents, such as antibodies, aptamers, and molecularly imprinted polymers, among others.
  • Arrays can be printed on paper, plastic, metal, glass, semiconductors, metal oxide, skin (via temporary or permanent tattoos), and other two- or three-dimensional substrates. Arrays can be produced via inkjet printing, aerosol printing, screen printing, micro-pipetting, stamping, or other technology.
  • pixels can be functionalized with specific recognition agents, such as antibodies, aptamers, molecularly imprinted polymers, etc. These agents may be physically wrapped or adsorbed onto the nanotubes, or covalently linked or grafted onto the nanotube surface via, for example, radical chemistry or other coupling chemistry, or via incorporation into gels such as microgel or aerogel.
  • arrays can be prepared either via directly printing pre-made ink wherein different pixels are composed of different inks or by printing a generic ink mixture on every pixel and then printing additional reagents on top of these pixels to further modify their behavior.
  • multiple inks can be overlaid onto the same pixels, either to increase complexity for anti -counterfeiting, or to alter or tune the fluorescence response upon addition of analyte.
  • the system 100 can include at least one image processing system 105, at least one sensor array 110 (e.g., illustrated in cross-sectional view), at least one light source 115, at least one imaging device 120, and at least one display 125, communicatively coupled with one another via at least one network 130.
  • the image processing system 105 may include at least one image parser 135, at least one code generator 140, at least one sample classifier 145, at least one output handler 150, at least one classification model 155, and at least one database 160, among others.
  • Each of the components in the system 100 as detailed herein may be implemented using hardware (e.g., one or more processors coupled with memory), or a combination of hardware and software as detailed herein in Section B.
  • the image processing system 105 may (sometimes herein generally referred to as a computing system or a server) be any computing device comprising one or more processors coupled with memory and software and capable of performing the various processes and tasks described herein.
  • the image processing system 105 may be in communication with the sensor array 110, the light source 115, the imaging device 120, the display 125, and other devices, via the network 130.
  • the image processing system 105 may be situated, located, or otherwise associated with at least one server group.
  • the server group may correspond to a data center, a branch office, or a site at which one or more servers corresponding to the image processing system 105 are situated.
  • the image processing system 105 may store, using one or more data structures, an association between the sample and the classification of the molecules of interest of the sample.
  • the image parser 135 may retrieve, identify, or receive images of samples via the sensor array 110 from the imaging device 120 to be processed at the image processing system 105.
  • the code generator 140 may generate codes from the images received by the image parser 135.
  • the sample classifier 145 may determine a classification of molecules in the sample using the codes.
  • the output handler 150 may provide information based on the classification of molecules in the sample.
  • the classification model 155 may be any type of machine learning model or artificial intelligence (Al) algorithm to classify molecules or interest in an image.
  • the classification model 155 may have at least one input and at least one output.
  • the output and the input may be related via a set of weights.
  • the input may be at least one image.
  • the output may include the classification of the molecules from the application of the classification model 155 onto the input image in accordance with the set of weights.
  • the set of weights of the classification model 155 may define corresponding parameters to be applied to the input image to generate the output image.
  • the set of weights may be arranged in one or more transform layers. Each layer may specify a combination or a sequence of applications of the parameters to the input and resultant.
  • the layers may be arranged in accordance with the machine learning algorithm or model for the classification model 155.
  • the classification model 155 such as a clustering algorithm (e.g., A means clustering), a regression algorithm (e.g., linear or logistic regression), a random forest, a decision tree, a support vector machine (SVM), a Naive Bayesian classifier, or an artificial neural network (e.g., convolutional neural network architecture), among others.
  • the classification model 155 may have been initialized, trained, and established to detect the classification using training data (e.g., in accordance with supervised learning techniques).
  • the sensor array 110, the light source 115, and the imaging device 120 may be used to conduct fluorescent imaging, spectroscopy, or microscopy in evaluating a sample.
  • the sensor array 110 may include a set of fluorophore structures (sometimes herein referred to as pixels). Each of the fluorophore structures may have different fluorescence responses to react differently to a sample placed thereon.
  • the light source 115 may radiate, produce, or otherwise emit fluorescent light to illuminate a sample and the fluorophore structures in the sensor array 110.
  • the fluorophore structures on the sensor array 110 may be used to identify molecules of interest in the sample (e.g., identifying biomarkers for cancer or pollutants in a liquid sample), confirming the composition of a sample (e.g., a drug composition), validating the authenticity of the sample (e.g., as an anti-counterfeit measure), establish an identity (e.g., source, origin, or manufacturer) of the sample, track a chain of ownership of the sample (e.g., establish provenance), and enforcing security policies, among others.
  • the sensor array 110 may be situated in a wide variety of applications, such as diagnostic, anti -counterfeiting, validating compositions, biomarker detection, microfluidic applications, or security applications, among others.
  • the imaging device 120 may be any device to acquire images of samples illuminated by the light source 115 through the sensor array 110.
  • the imaging device 120 may be, for example, a fluorescence microscope, a charge-coupled device (CCD), or a fiber optical device, among others.
  • the light source 115 and the imaging device 120 may be in communication with the image processing system 105 via the network 130.
  • the display 125 may be communicatively coupled with the image processing system 105 or any other computing device comprising one or more processors coupled with memory and software and capable of performing the various processes and tasks described herein.
  • the display 125 may display, render, or otherwise present any information provided by the image processing system 105 or the images of samples acquired via the imaging device 120.
  • the process 200 may include or correspond to operations in the system 100 to illuminate a sample and acquire images through the sensor array.
  • the sensor array 110 may have or include at least one substrate 205.
  • the substrate 205 may comprise any material, such as a paper material, plastic material (e.g., polyethylene, polypropylene, polyvinyl chloride, polystyrene, or polyethylene terephthalate), a metallic material (e.g., aluminum, steel, copper, titanium, or nickel), a ceramic material (e.g., alumina, zirconia, silicon carbon, or magnesium oxide), a glass material (e.g., quartz glass, lead crystal, silicate glass, or tempered glass), a semiconductor material (e.g., silicon, germanium, gallium arsenide, indium phosphide, or cadmium telluride), a metal oxide material, a polymer material, epidermal material (e.g., human skin), a gel (e.g., aerogel), or a composite, among others.
  • plastic material e.g., polyethylene, polypropylene, polyvinyl chloride, polystyrene, or polyethylene terephthalate
  • the substrate 205 may be part of another device.
  • the substrate 205 may be part of a lateral flow strip that is part of a diagnostic device, including a sample pad, a conjugate pad, a test line, and a control line.
  • a fluid to be evaluated may be placed on the sample pad and flow from the sample pad through the lateral flow strip upon which the substrate 205 is disposed.
  • the substrate 205 may have at least one first side 210 and at least one second side 215.
  • the first side 210 may correspond to one lateral surface of the substrate 205.
  • the second side 215 may be opposite of the first side 210 and may correspond to another lateral surface of the substrate 205.
  • the first side 210 may correspond to the surface of the substrate 205 facing the light source 115 and on which fluorescent light (shown in dotted line) is to be illuminated.
  • the second side 215 may correspond to the surface of the substrate 205 away from the light source 115.
  • the substrate 205 may be of any shape, such as circular, regular, triangular, pentagonal, hexagonal, polygonal, or irregular, among others.
  • the substrate 205 may have any dimension, for example, ranging between 10 nm and 10 cm, among others.
  • the first side 210 and the second side 215 may have the same dimension or different dimensions. While described herein as having two sides, the substrate 205 may have any number of form factors.
  • the substrate 205 may correspond to a particular surface or area upon a human skin or tips of a set of microneedles, among others.
  • the biological sample may be acquired or obtained from a human subject or an animal subject.
  • the sample 225 may include a non-biological sample (e.g., a drug, a forensic sample, environmental pollutant, explosive, consumer good, product of manufacture, or any other item), among others.
  • the sample 225 may be an item of value, such as an identification document (e.g., passport or driver’s license), currency (e.g., coin or paper notes), a weapon, a bag, a computing device (e.g., tablet, smartphone, laptop, or desktop), or a writing utensil, among others.
  • the sample 225 may include a food substance (e.g., fruit, vegetables, grains, plants, meats, cooking oil or derivatives therefrom), fuel (e.g., motor oil, gasoline, or diesel), a perfume (e.g., fragrant oils, aroma compounds, fixatives, or solvents, or any combination thereof), a beverage (e.g., wine, beer, liquor, soda, or water), a construction material (e.g., wood, concrete, brick, cement, steel, glass, stone, or masonry), or a storage container (e.g., metallic, polymer, or wood material), among others.
  • a food substance e.g., fruit, vegetables, grains, plants, meats, cooking oil or derivatives therefrom
  • fuel e.g., motor oil, gasoline, or diesel
  • a perfume e.g., fragrant oils, aroma compounds, fixatives, or solvents, or any combination thereof
  • a beverage e.g., wine, beer, liquor, soda, or water
  • a construction material e
  • the sample 225 may be attached, affixed, or otherwise joined to at least one of the first side 210 or the second side 215 of the substrate 205 of the sensor array 110.
  • the sample 225 may be affixed to the first side 210 or the second side 215 of the substrate 205 of the sensor array 110 using an adhesive.
  • the sensor array 110 affixed to the sample 225 may be used as a barcode to determine an identity, validate an authenticity, or track a chain of ownership of the sample 225, among others.
  • the sample 225 (e.g., cells or cell growth media) may also be placed in contact with the first side 210 or the second side 215 of the substrate 205.
  • the sample 225 may be obtained from a subject.
  • the subject may be a human or an animal at risk of cancer or suffering from a disease or cancer.
  • the disease may include, for example, a blood disease (e.g., anemia, leukemia, hemophilia, sickle cell disease), diabetes, kidney disease, a liver disease (e.g., hepatitis or cirrhosis), or a thyroid disorder, among others.
  • the sample 225 may be taken, collected, or otherwise obtained from at least one anatomical site associated with the disease or cancer within the subject.
  • the sample 225 can include tissue, bone, cartilage, or any other portion of the organ from the anatomical site.
  • the anatomical site may be a primary site or a secondary (e.g., metastasized) site for the cancer.
  • thyroid cancer e.g., papillary, follicular, medullary, or anaplastic
  • the clinician examining the subject may collect the samples 225 via biopsy from the thyroid of the subject.
  • the sensor array 110 may include a set of fluorophore structures 220 A-N (hereinafter generally referred to as fluorophore structures 220) disposed, situated, or arranged along the substrate 205.
  • fluorophore structures 220 (sometimes herein referred to as pixels) may span or extend at least partially (e.g., as depicted) or fully between the first side 210 and the second side 215 of the substrate 205.
  • the set of fluorophore structures 220 may be disposed, arrayed, or otherwise arranged in a onedimensional line (e.g., a line or a curve along the substrate 205), a two-dimensional (e.g., across a region of a surface of the substrate 205), or a three-dimensional volume (e.g., with some fluorophore structures 220 deposited on top of one another within the substrate 205).
  • the set of fluorophore structures 220 may be disposed, arrayed, or otherwise arranged along the substrate 205 in a pattern.
  • the pattern may be used to establish an identity of the sample 225 (e.g., to which the sensor array 110 is affixed), validate an authenticity of the sample 225, or track a chain of ownership of the sample 225, among others.
  • the fluorophore structures 220 in the sensor array 110 may be used as a barcode affixed to a product or item corresponding to the sample 225 to establish the identity, validate the authenticate, or track the chain of ownership of the product or item.
  • Each of the fluorophore structures 220 may include, for example, a carbon nanotube, a polymer dot, a quantum dot, a fluorescent dye, or any fluorophores, among others.
  • the carbon nanotube may include rolled-up sheets of graphene, single-walled nanotubes (SWNTs), or multi -walled nanotubes (MWNTs).
  • SWNTs single-walled nanotubes
  • MWNTs multi -walled nanotubes
  • the size of the quantum dots or polymer dots can be adjusted to change the color of the light emitted or to emit light at different wavelengths.
  • the fluorescent dye may re-emit light upon light excitation.
  • the fluorophore structures 220 may be grafted to, confined within, adjacent to, or otherwise colocalized with one or more binding agents.
  • the binding agents may include, for example, antibodies, proteins, peptides, nanobodies, antigens, enzymes, aptamers, or molecularly imprinted polymers
  • the set of fluorophore structures 220 may be arranged in any pattern, such as a linear formation, grid formation, staggered formation, a cross formation, or irregular pattern.
  • Each fluorophore structure 220 may correspond to a respective portion along the first side 210 (or the second sides 215) off the substrate 205.
  • the portion may be of any shape, such as circular, regular, triangular, pentagonal, hexagonal, polygonal, or irregular, among others.
  • the diameter of the fluorophore structures 220 may be of any dimension ranging between nanometers and millimeters.
  • the diameter of the fluorophore structures 220 may range between 0.5-3 nm for SWNTs, between 3-100 nm for MWNTs, between 1-10 nm for quantum dots, between 10-100 nm for polymer dots, and between 1- 10 pm, among others.
  • Each fluorophore structure 220 may be separated from at least one other fluorophore structure 220 at a distance.
  • one fluorophore structure 220 may have a distance of between 1 nm and 1 cm, relative to the adjacent fluorophore structure 220.
  • Each of the fluorophore structures 220 may have or be characterized by a unique fluorescence response profile to the fluorescent light.
  • at least one fluorophore structure 220 may have a fluorescence response profile different from another fluorophore structure 220.
  • at least one of the fluorophore structures 220 may be configured with an environmental sensitivity with respect to fluorescence in at least one of a spatial or temporal domain. The environmental sensitivity may correspond to, may be correlated with, or otherwise may include a change in at least one of an absorbance intensity or a spectral characteristic of the fluorophore structure 220.
  • each of the fluorophore structures 220 may have a different fluorescence response profile. Due to the different response profile or environmental sensitivity (or both), each fluorophore structure 220 may generate fhiorophores with unique fluorescence spectra when affixed to the sample 225.
  • the fluorophore structures 220 can undergo a reaction to the sample 225, changing the fluorescent properties of the fluorophore structures 220.
  • the fluorescence response profile may identify, define, or characterize a change in fluorescence properties of the fluorophore structure 220 in response to undergoing a reaction (e.g., a physicochemical reaction) with the sample 225.
  • a reaction e.g., a physicochemical reaction
  • Each fluorophore structure 220 may absorb the fluorescent light emitted upon the first side 210 at a particular wavelength and emit another light signal in accordance with the fluorescence properties of the fluorophore structure 220.
  • the reaction may be a change in the intensity, color, or pattern of the transmitted light.
  • the sample 225 itself may be used to treat the set of fluorophore structures 220 to alter, set, or otherwise change the fluorescent properties of the fluorophore structures 220.
  • the reaction of the individual fluorophore structures 220 with the sample 225 may include, for example, an electrostatic interaction, a hydrophobic interaction, a specific interaction, an oxidation reaction, a reduction reaction, or an ionic reaction, among others.
  • Electrostatic interactions may correspond to the attraction or repulsion between charged particles between the fluorophore structure 220 and the sample 225.
  • Hydrophobic interactions may correspond to interactions between nonpolar molecules of the fluorophore structure 220 in the presence of water in the sample 225.
  • Specific interactions refer to certain types of interactions between specific molecules or functional groups in the sample 225 with the fluorophore structure 220 (e.g., binding agents in the nanotube to specific antibodies in a biological sample).
  • Oxidation may correspond to a reaction in which fluorophore structure 220 loses electrons or changes its oxidation state. Reduction may correspond to the gain of electrons by the fluorophore structure 220. An ionic interaction may correspond to the electrostatic interactions to form positive or negatively charged ions between the fluorophore structure 220 and the sample 225.
  • the fluorescence response profile may, for example, include an excitation wavelength, an emission wavelength, a quantum yield, a Stokes’ shift, or dark fraction, among others.
  • the excitation wavelength may define a range of wavelengths of the incoming fluorescent light the materials in the fluorophore structure 220 can absorb.
  • the fluorescence response profile may be dependent on specific properties of the fluorophore structure 220 and the molecular interactions occurring within or around the fluorophore structure 220.
  • the differences in the fluorescence response profile may be due to differences in: structural defects, chirality, single-walled, multi-walled nanotubes, polymer coating, surfactant, peptide, protein, saccharide, or other modification to carbon nanotubes, among others.
  • the fluorophore structures 220 can be structured to be sensitive to a particular molecule.
  • the fluorescent light can create a specific pattern or color change that is difficult to replicate.
  • the sensor array 110 may be exposed to additional solutions or gases to instigate, initiate, or otherwise initiate a change in the fluorescent profiles of the fluorophore structure 220. With the change to pre-identified fluorescent profiles, the fluorophore structures 220 on the sensor array 110 may be used for various purposes, including validation.
  • the fluorescence response profile of each of the fluorophore structures 220 may be dependent on the structure of the fluorophore structure 220 itself, such as the presence of a coating agent, a defect, a chirality, single-walled, multi-walled, polymer surfactant, peptide, or saccharide.
  • the single-walled or multi-walled may correspond to a structure of walls of molecules in the fluorophore structure 220, such as single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT).
  • SWCNT single-walled carbon nanotubes
  • MWCNT multi-walled carbon nanotubes
  • the presence of polymers may correspond to inclusion of polymer molecules within the molecules of the fluorophore structure 220.
  • surfactant also referred to herein as surface-active agent
  • the presence of peptide may be to introduce chain of amino acids into the fluorophore molecules in each fluorophore structure 220.
  • the arrangement of the fluorophore structure 220 along the first side 210 of the substrate 205 and the fluorescence response profile of each fluorophore structure 220 can be set or configured based on an application.
  • the substrate 205 may correspond to a surface of an item (e.g., as a label), and the set of fluorophore structures 220 may be arranged in a matrix barcode pattern (e.g., a bar code, a quick-response (QR) code, or a universal product code (UPC)) along the surface.
  • the distinct fluorescence patterns can form a QR code or fingerprint.
  • the QR code or fingerprint can be used to identify molecules, specific analytes, patterns, or medical conditions, among others.
  • Each fluorophore structure 220 may have a different fluorescence response profile, with various additives and structural elements to set the fluorescence properties of the fluorophore structure 220 in reacting with the molecules of the underlying items.
  • the arrangement and setting of the response profiles may make it difficult for another party to replicate the substrate 205 forming the label, or the item itself.
  • the arrangement of the plurality of fluorophore structures 220 in accordance with the pattern may be used to produce a unique fluorescence spectra in response to light.
  • the ink may be particular to each respective fluorophore structure 220, synthesized using additive compounds that affect the fluorescence spectrum of each ink (e.g., allowing for a tailored response profile for each ink).
  • the set of fluorophore structures 220 may be produced, created, or otherwise formed using one or more a fluorophore mixture (e.g., in the form of a solution), particularly an entity.
  • the fluorophore mixture may be modified using at least one an inclusion of additives, a purification of the solution, a size separation, chromatography, or a light treatment, among others.
  • the purification of the solution further comprises separation of carbon nanotubes with different chirality, surface treatment, and length or chemical modification.
  • the fluorophore mixture used to form the fluorophore structures 220 may result in each fluorophore structure 220 having a unique fluorescence response profile.
  • the set of fluorophore structures 220 of the sensor array 110 may form a unique bar code to generate fluorophores with unique fluorescence spectra when affixed to the sample 225.
  • the fluorophore structures 220 may be unique to the particular sensor array 110, making it difficult to reproduce the barcode (e.g., as represented by the set of fluorophore structures 220).
  • the use of different form factors (e.g., two- dimensional or three-dimensional) for the fluorophore structures 220 may also increase the difficulty of reproducing the barcode corresponding to the set of fluorophore structures 220.
  • the test solution 230 includes a ligand to bind, conjugate, or otherwise react with the fluorophore structures 220.
  • the ligand may include, for example, a receptor ligand (an agonist or antagonist ligand), an enzyme ligand, a transport protein, an allosteric ligand, or an ionic ligand, among others.
  • the ligand may bind, conjugate, or otherwise react with the recognition agents in the fluorophore structures 220, such as antibodies, aptamers, and molecularly imprinted polymers, among others.
  • Administering the sensor array 110 with the test solution 230 containing complementary DNA can reveal the pattern.
  • the specific pattern e.g., brand or company logo
  • fluorophore structures 220 conjugated to DNA sequences can be revealed when the test solution 230 containing the complementary DNA is applied.
  • the resulting fluorescence pattern can highlight the pattern against the background, providing a clear and unique validation that is difficult to replicate without knowledge of the DNA sequence.
  • the complexity added by the non-specific DNA-conjugated fluorophore structures 220 may obscure the exact composition and arrangement of the specific DNA sequences.
  • the incorporation of non-specific DNA-conjugated fluorophore structures 220 as an additional layer of obfuscation further protects the integrity of the sensor array 110, ensuring that those with the correct test solution 230 can validate the authenticity of the pattern.
  • the sensor array 110 may be arranged, disposed of, or otherwise situated within a vessel containing the sample 225.
  • the vessel may be an assay, such as a microtiter plate, and the sensor array 110 may be printed within an inside of a microtiter plate well.
  • Each microtiter plate well can contain a unique sensor array 110 pattern with fluorophore structures 220 conjugated to specific recognition agents, allowing parallel processing of different test solutions 230 and increasing diagnostic efficiency.
  • the fluorescence patterns in each microtiter plate well may be used to identify and validate multiple targets, such as biomolecules, pathogens, or chemical compounds, among others, within the sample 225.
  • the light emitted by the light source 115 may be dependent on the fluorophores to be targeted.
  • the wavelength may be between 515-525 nm; for green fluorophores, between 500-515 nm; or for red fluorophores, between 550-690 nm, among others.
  • the imaging device 120 may obtain, generate, or otherwise acquire at least one image 240 of the set of light signals 235 from the sensor array 110.
  • the set of light signals 235 may correspond to the set of fluorophore structures 220.
  • each light signal 235 may be dependent on the fluorescence response profile of the fluorophore structure 220 from which the light signal 235 was emitted, and may contain different colors, intensities, or patterns, among others.
  • Each light signal 235 may be, for example, fluorescent light having a wavelength between 250-750 nm.
  • the imaging device 120 may acquire the image 240 in accordance with fluorescence imaging, microscopy, or spectroscopy, among others.
  • the image registration may be in accordance with any number of techniques, such as intensity-based registration (e.g., mutual information or normalized cross-correlation), feature-based registration (e.g., using object detection, scale-invariant feature transform, or speeded up robust features), or deformable image registration (e.g., using B-spline registration), among others.
  • intensity-based registration e.g., mutual information or normalized cross-correlation
  • feature-based registration e.g., using object detection, scale-invariant feature transform, or speeded up robust features
  • deformable image registration e.g., using B-spline registration
  • the code generator 140 may output, produce, or otherwise generate at least one response code 325.
  • the response code 325 may identify or define a set of responses by the corresponding set of fluorophore structures 220 of the sensor array 110. The set of responses may be due to the respective fluorescence response profile in each fluorophore structure 220.
  • the response code 325 may include one or more values for each light signal 235, and may characterize one or more corresponding parameters (e.g., intensity, a color, or pattern) in the respective light signal 235.
  • the code generator 140 may traverse through the set of light signals 235 identified from the image 240.
  • the code generator 140 may determine the parameters of the light signal 235. Based on the parameters, the code generator 140 may determine the corresponding code for the response code 325. The determination may be in accordance with a function mapping the parameters to values for creating the response code 325.
  • the classification 330 may identify various characteristics about the material of the sample 225, such as a presence or concentration of toxic compounds, drugs, explosives, or environmental pollutants, among others. In some embodiments, the classification 330 may identify various biological or chemical properties about the material of the sample 225, such as a pH level, a salt level, a salt type, a redox species, a concentration of a gas (e.g., oxygen), a temperature, a presence of contaminants, or type of cells present, among others. For instance, when a cell or cell growth media is placed in contact with the sensor array 110 as the sample 225, the classification 330 may identify nutrients, metabolites, cell confluency, pH, presence or concentration of toxins, and other biologically relevant parameters, among others.
  • a gas e.g., oxygen
  • the classification 330 may identify nutrients, metabolites, cell confluency, pH, presence or concentration of toxins, and other biologically relevant parameters, among others.
  • the sample classifier 145 may find or identify the expected response code for the sample 225 to check against the generated response code 325. When the response codes match, the sample classifier 145 may determine or identify the sample 225 as authenticated. Conversely, when the response codes do not match, the sample classifier 145 may determine or identify the sample 225 as unauthenticated.
  • the sample classifier 145 may determine the classification in accordance with a function of the response code 325.
  • the function may include a mapping between values of the response code 325 with a set of candidate classifications.
  • the sample classifier 145 may find the candidate classification 330 matching the response code 325 to identify the molecules of interest in the sample 225.
  • the sample classifier 145 may store, using one or more data structures, an association between the sample 225 and the classification 330 of the molecules of interest of the sample 225.
  • the data structures may include, for example, an array, a matrix, a linked list, a stack, a queue, a tree, a graph, or a hash table, among others.
  • the sample classifier 145 may apply the classification model 155 to the response code 325 to determine the classification 330.
  • the classification model 155 may have been initialized, trained, and established using a training dataset (e.g., in accordance with supervised learning).
  • the training dataset may identify or include a set of examples. Each example may include a sample response code and a corresponding classification 330 of the molecules of interest.
  • the sample response of the training dataset may be acquired from an instance of the sensor array 110 with the same arrangement of the set of fluorophore structures 220 with the same fluorescence response profile when reacting to the sample.
  • the sample response in each example may have been applied to the classification model 155 to generate a predicted classification.
  • a loss metric is determined by comparing the predicted classification and the expected classification in the training dataset. Using the loss metric, the classification model 155 may be updated. This process may be repeated until convergence of the classification model 155.
  • the sample classifier 145 may input or feed the response code 325 into the classification model 155. In feeding, the sample classifier 145 may process the response code 325 in accordance with the set of weights of the classification model 155. From processing, the sample classifier 145 may produce, output, or otherwise generate the classification identifying the molecules of interest in the sample 225. With the determination, the sample classifier 145 may store, using one or more data structures, an association between the sample 225 and the classification 330 of the molecules of interest of the sample 225.
  • the output handler 150 executing on the image processing system 105 may generate or provide an output 340 to identify the classification 330 of the molecules of interest of the sample.
  • the output handler 150 may format the classification results for presentation or further analysis. The formatting may involve converting the data into a visually accessible format (e.g., charts, graphs, or comprehensive reports).
  • the output 340 can be displayed on a display 125.
  • the display 125 (or a computing device connected thereto) may display, render, or otherwise present the output 340 from the image processing system 105.
  • the output 340 may also include the image 240.
  • the output handler 150 may provide the output 340 to identify the validation of the sample 225 as one of authenticated or authenticated.
  • the fluorophore structures 220 of the sensor array 110 may be disposed, positioned, affixed, or otherwise situated on the sample 225 for any number of applications.
  • the fluorophore structure 220 can be printed on or administered to a container to hold the sample 225.
  • the container can include, for example, a cell culture flask, a petri dish, or a roux bottle, among others.
  • the container can be used in laboratory or research setting for the growth of cell cultures, organoids, spheroids, bacteria, or other biological specimens and organisms.
  • the fluorophore structures 220 can first be synthesized and functionalized as described earlier.
  • the fluorophore structures 220 can then be printed or coated onto the inner surfaces of the flasks using techniques such as inkjet printing, micro-pipetting, etc.
  • the fluorophore structures 220 can be utilized in bioreactors to monitor the production of biopharmaceuticals, enabling real-time observation of pH levels, oxygen concentration, and nutrient availability, ensuring optimal growth conditions for the production of vaccines, antibodies, and other therapeutic agents.
  • the health, growth, and environmental conditions of the specimens can be monitored (e.g., using the light signals 235). This application can aid in experimental protocols and can ensure precise control and timely interventions to maintain optimal growth conditions for the biological samples.
  • the sensor array 110 can be affixed, placed, or otherwise attached to produce (e.g., fruits, vegetables, or other plants).
  • the fluorophore structures 220 can be prepared and then applied to the surface of the produce or to containers holding the produce using adhesives or surface treatments that ensure stability and functionality.
  • the fluorophore structure 220 can be affixed, placed, or otherwise attached to a container, including the produce, to assess ripeness or detect contamination.
  • the quality and safety of the produce can be monitored using the sensor array 110, as the produce is transported through the supply chain. By scanning the light signals 235 from the fluorophore structure 220, users can obtain real-time data on the produce ripeness levels and any signs of contamination.
  • the sensor array 110 can be integrated into irrigation systems to monitor soil moisture levels and nutrient content, ensuring optimal growing conditions and preventing over- or under-watering of crops.
  • the fluorophore structures 220 can be embedded or coated onto the construction materials during manufacturing or applied to existing structures (e.g., using adhesives or coatings).
  • the sensor array 110 can be used in smart homes to monitor indoor air quality, detecting pollutants like carbon monoxide, volatile organic compounds (VOCs), and mold spores.
  • the fluorophore structure 220 can be attached to biological implants (e.g., as hip replacements, dental implants, cardiac stents, orthopedic screws, artificial heart valves, pacemakers, etc.), to measure (e.g., using the light signals 235) biological factors in vivo, allowing for continuous monitoring of the implant’s environment and detecting potential issues such as infection, inflammation, or implant degradation.
  • the fluorophore structures 220 can be incorporated into the implant materials during manufacturing or coated onto the implants using biocompatible adhesives.
  • the sensor array 110 can be affixed to the inside of sample tubes used to collect patient blood, urine, or saliva samples, allowing for in-line automated measurement and disease diagnosis at sample collection or handling facilities.
  • the fluorophore structure 220 can monitor the stability and integrity of the samples during transport and storage, ensuring that they remain viable for accurate testing. Dip-coating or spraying techniques can be used to coat the inner surfaces of the sample tubes with the fluorophore structures 220.
  • the sensor array 110 can detect (e.g., using the light signals 235) changes in temperature, pH, and other environmental conditions that might compromise the sample, providing alerts if the sample is at risk of degradation. This can streamline the diagnostic process, providing immediate and accurate data on the sample conditions.
  • the sensor array 110 can be affixed to the inside of fish tanks, animal housing, or plant housing to monitor environmental conditions and can allow for continuous observation of factors such as water quality, temperature, and pH levels.
  • the fluorophore structures 220 can be integrated into the materials of the fish tanks, animal housing, or plant housing during production or by applying them with adhesives or coatings to existing structures.
  • the fluorophore structure 220 of the sensor array 110 can be used to detect (e.g., using the light signals 235) the presence of harmful pathogens or contaminants, providing early warnings to prevent the spread of disease among fish, animals, or plants.
  • the fluorophore structure 220 can monitor humidity levels and light exposure, ensuring that optimal conditions are maintained for the health and growth of the organisms.
  • the fluorophore structure 220 can be used to detect (e.g., using the light signals 235) contamination in a liquid.
  • the image processing system 105 can process the light signals 235 reflected from the sensor array 110 placed in a swimming pool to detect contamination in the water.
  • pool managers can receive real-time data on water quality, including levels of contaminants and chemical balance.
  • the fluorophore structure 220 can be used for monitoring (e.g., using the light signals 235) wastewater purity at chemical manufacturing sites.
  • the fluorophore structure 220 By attaching the fluorophore structure 220 to wastewater outlets, facilities can continuously monitor the water quality, detecting contamination and ensuring compliance with environmental regulations.
  • the fluorophore structures 220 can be coated onto the outlet surfaces using techniques that ensure durability and responsiveness to contaminants (e.g., electroplating, sol-gel coating, thermal spraying, and chemical vapor deposition).
  • the sensor array 110 can be integrated into wearable health monitoring devices, such as smartwatches, fitness trackers, and skin patches, to continuously monitor physiological parameters.
  • the fluorophore structure 220 can detect early signs of health issues.
  • the wearable health monitoring devices can track parameters such as glucose levels, dehydration, electrolyte balance, and other vital signs.
  • users can receive real-time feedback on their health status, and can enable proactive health management and timely interventions.
  • athletes can use the wearable health monitoring devices to monitor hydration levels during intense physical activities. Patients with chronic conditions such as diabetes can benefit from continuous glucose monitoring, reducing the risk of complications and improving overall disease management.
  • the sensor arrays 110 can be integrated into personal protective equipment (PPE) for workers in hazardous environments to continuously monitor exposure to toxic chemicals and provide real-time alerts, enhancing safety protocols.
  • PPE personal protective equipment
  • the fluorophore structure 220 can be used in environmental monitoring systems to detect pollutants and hazardous substances in air, soil, and water. By deploying sensor arrays 110 in various locations, environmental agencies can monitor the presence of heavy metals, pesticides, industrial chemicals, or other contaminants, ensuring compliance with environmental regulations and protecting public health.
  • the fluorophore structures 220 can be embedded in buoys for real-time water quality monitoring in lakes and rivers, detecting pollutants (e.g., lead, mercury, nitrates, etc.).
  • the sensor arrays 110 can be installed in storm drains and sewage systems to monitor and control the discharge of harmful substances into natural water bodies.
  • FIG. 4A depicted is a diagram 400 of an example diagnostic response code of a sample.
  • the inclusion of analytes in the sample can cause different light signals to show through the sensors.
  • the sensor arrays 110 can detect and analyze various analytes, and can include analytes in complex mediums like serum, saliva, or urine.
  • Each pixel of the sensor array 110 may respond differently to the presence of an analyte due to variations in ink composition. Pixels may contain multiple fluorophores for added complexity and can be applied for anti-counterfeiting or for tuning the fluorescence response to different analytes.
  • the response of the array can be analyzed through various methods, including fluorescent microscopy, CCD cameras, fiber optical devices, or even a mobile phone camera.
  • the nano-sensor in FIG. 4B can detect, using the different collective responses of the entire array, ovarian cancer, breast cancer, bowel cancer, thyroid cancer, or lung cancer.
  • FIG. 5 depicted is an illustrative flow diagram of a method 500 for detecting molecules of interest.
  • the method 500 can be executed, performed, or otherwise carried out by the system 100 or 700.
  • a light source may emit light toward a first side of a substrate to illuminate a sample having molecules of interest (505).
  • An imaging device may acquire an image of the light signals (510).
  • a computing system e.g., the image processing system 105
  • the computing system may receive, via the imaging device, the image of the light signals (515).
  • the computing system may generate a response code (520).
  • the computing system may determine a classification of the molecules of interest of the sample based on the response code (525).
  • the computing system may provide an output to identify the classification of the molecules of interest of the sample (530).
  • FIG. 7 shows a simplified block diagram of a representative server system 700, client computing system 714, and network 726 usable to implement certain embodiments of the present disclosure.
  • server system 700 or similar systems can implement services or servers described herein or portions thereof.
  • Client computing system 714 or similar systems can implement clients, described herein.
  • the systems 100 described herein can be similar to the server system 700.
  • Server system 700 can have a modular design that incorporates a number of modules 702 (e.g., blades in a blade server embodiment); while two modules 702 are shown, any number can be provided.
  • Each module 702 can include processing unit(s) 704 and local storage 706.
  • Local storage 706 can include volatile storage media (e.g., DRAM, SRAM, SDRAM, or the like) or non-volatile storage media (e.g., magnetic or optical disk, flash memory, or the like). Storage media incorporated in local storage 706 can be fixed, removable, or upgradeable as desired. Local storage 706 can be physically or logically divided into various subunits, such as a system memory, a read-only memory (ROM), and a permanent storage device.
  • the system memory can be a read-and-write memory device or a volatile read-and-write memory, such as dynamic random-access memory.
  • the system memory can store some or all of the instructions and data that processing unit(s) 704 need at runtime.
  • the ROM can store static data and instructions that are needed by processing unit(s) 704.
  • the permanent storage device can be a non-volatile read-and-write memory device that can store instructions and data even when module 702 is powered down.
  • storage medium includes any medium in which data can be stored indefinitely (subject to overwriting, electrical disturbance, power loss, or the like) and does not include carrier waves and transitory electronic signals propagating wirelessly or over wired connections.
  • local storage 706 can store one or more software programs to be executed by processing unit(s) 704, such as an operating system or programs implementing various server functions such as functions of the system 100 or any other system described herein, or any other server(s) associated with system 100 or any other system described herein.
  • processing unit(s) 704 such as an operating system or programs implementing various server functions such as functions of the system 100 or any other system described herein, or any other server(s) associated with system 100 or any other system described herein.
  • Software refers generally to sequences of instructions that, when executed by processing unit(s) 704, cause server system 700 (or portions thereof) to perform various operations, thus defining one or more specific machine embodiments that execute and perform the operations of the software programs.
  • the instructions can be stored as firmware residing in read-only memory or program code stored in non-volatile storage media that can be read into volatile working memory for execution by processing unit(s) 704.
  • Software can be implemented as a single program or a collection of separate programs or program modules that interact as desired. From local storage 706 (or non-local storage described below), processing unit(s) 704 can retrieve program instructions to execute and data to process in order to execute various operations described above.
  • a wide area network (WAN) interface 710 can provide data communication capability between the local area network (interconnect 708) and the network 726, such as the Internet. Technologies can be used, including wired (e.g., Ethernet, IEEE 802.3 standards) or wireless technologies (e.g., Wi-Fi, IEEE 802.11 standards).
  • wired e.g., Ethernet, IEEE 802.3 standards
  • wireless technologies e.g., Wi-Fi, IEEE 802.11 standards.
  • local storage 706 is intended to provide working memory for processing unit(s) 704, providing fast access to programs or data to be processed while reducing traffic on interconnect 708.
  • Storage for larger quantities of data can be provided on the local area network by one or more mass storage subsystems 712 that can be connected to interconnect 708.
  • Mass storage subsystem 712 can be based on magnetic, optical, semiconductor, or other data storage media. Direct attached storage, storage area networks, network-attached storage, and the like can be used. Any data stores or other collections of data described herein as being produced, consumed, or maintained by a service or server can be stored in mass storage subsystem 712.
  • additional data storage resources may be accessible via WAN interface 710 (potentially with increased latency).
  • Server system 700 can operate in response to requests received via WAN interface 710.
  • one of the modules 702 can implement a supervisory function and assign discrete tasks to other modules 702 in response to received requests.
  • Work allocation techniques can be used. As requests are processed, results can be returned to the requester via WAN interface 710. Such operation can generally be automated.
  • WAN interface 710 can connect multiple server systems 700 to each other, providing scalable systems capable of managing high volumes of activity. Other techniques for managing server systems and server farms (collections of server systems that cooperate) can be used, including dynamic resource allocation and reallocation.
  • Processing unit(s) 716 and storage device 718 can be similar to processing unit(s) 704 and local storage 706 described above. Suitable devices can be selected based on the demands to be placed on client computing system 714; for example, client computing system 714 can be implemented as a “thin” client with limited processing capability or as a high-powered computing device. Client computing system 714 can be provisioned with program code executable by processing unit(s) 716 to enable various interactions with server system 700.
  • Network interface 720 can provide a connection to the network 726, such as a wide area network (e.g., the Internet) to which WAN interface 710 of server system 700 is also connected.
  • network interface 720 can include a wired interface (e.g., Ethernet) or a wireless interface implementing various RF data communication standards, such as Wi-Fi, Bluetooth, or cellular data network standards (e g., 3G, 4G, LTE, etc ).
  • User input device 722 can include any device (or devices) via which a user can provide signals to client computing system 714; client computing system 714 can interpret the signals as indicative of particular user requests or information.
  • user input device 722 can include any or all of a keyboard, touch pad, touch screen, mouse or other pointing device, scroll wheel, click wheel, dial, button, switch, keypad, microphone, and so on.
  • User output device 737 can include any device via which client computing system 714 can provide information to a user.
  • user output device 737 can include display-to-display images generated by or delivered to client computing system 714.
  • the display can incorporate various image generation technologies, e.g., a liquid crystal display (LCD), light-emitting diode (LED), including organic light-emitting diodes (OLED), projection system, cathode ray tube (CRT), or the like, together with supporting electronics (e.g., digital -to-analog or analog-to-digital converters, signal processors, or the like).
  • Some embodiments can include a device such as a touchscreen that functions as both input and output device.
  • other user output devices 737 can be provided in addition to or instead of a display. Examples include indicator lights, speakers, tactile “display” devices, printers, and so on.
  • Some embodiments include electronic components, such as microprocessors, storage, and memory that store computer program instructions in a computer readable storage medium. Many of the features described in this specification can be implemented as processes that are specified as a set of program instructions encoded on a computer readable storage medium. When these program instructions are executed by one or more processing units, they cause the processing unit(s) to perform various operations indicated in the program instructions. Examples of program instructions or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter.
  • Blocks need not correspond to physically distinct components. Blocks can be configured to perform various operations, e.g., by programming a processor or providing appropriate control circuitry, and various blocks might or might not be reconfigurable depending on how the initial configuration is obtained. Embodiments of the present disclosure can be realized in a variety of apparatus including electronic devices implemented using any combination of circuitry and software.
  • Embodiments of the disclosure can be realized using a variety of computer systems and communication technologies, including, but not limited to, specific examples described herein.
  • Embodiments of the present disclosure can be realized using any combination of dedicated components, programmable processors, or other programmable devices.
  • the various processes described herein can be implemented on the same processor or different processors in any combination. Where components are described as being configured to perform certain operations, such configuration can be accomplished, e.g., by designing electronic circuits to perform the operation, by programming programmable electronic circuits (such as microprocessors) to perform the operation, or any combination thereof.
  • Computer programs incorporating various features of the present disclosure may be encoded and stored on various computer readable storage media; suitable media includes magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, and other non-transitory media.
  • Computer readable media encoded with the program code may be packaged with a compatible electronic device, or the program code may be provided separately from electronic devices (e.g., via Internet download or as a separately packaged computer-readable storage medium).

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  • Bioinformatics & Cheminformatics (AREA)
  • Nanotechnology (AREA)
  • Biophysics (AREA)
  • Optics & Photonics (AREA)
  • Inorganic Chemistry (AREA)
  • Evolutionary Biology (AREA)
  • Genetics & Genomics (AREA)
  • Theoretical Computer Science (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Hospice & Palliative Care (AREA)
  • Oncology (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Medical Informatics (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)

Abstract

L'invention concerne une plateforme de capteurs qui peut être utilisée pour la détection de molécules d'intérêt arbitraires ou pour un diagnostic clinique à l'aide de solutions test. Le capteur peut comprendre un réseau de sondes rapporteurs fluorescentes sensibles à l'environnement. Chaque sonde est placée sur un pixel séparé ou plusieurs pixels du réseau de capteur et a un revêtement différent, de telle sorte qu'elle répond légèrement différemment à des stimuli environnementaux par rapport à chaque autre pixel lors de l'ajout de la solution test.
PCT/US2024/039849 2023-07-27 2024-07-26 Réseau de capteurs pour diagnostic par fluorescence Pending WO2025212124A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US202363516047P 2023-07-27 2023-07-27
US63/516,047 2023-07-27
US202463553033P 2024-02-13 2024-02-13
US63/553,033 2024-02-13

Publications (1)

Publication Number Publication Date
WO2025212124A1 true WO2025212124A1 (fr) 2025-10-09

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WO (1) WO2025212124A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180144092A1 (en) * 2016-11-21 2018-05-24 Johnson & Johnson Vision Care, Inc. Biomedical sensing methods and apparatus for the detection and prevention of lung cancer states
US20180356414A1 (en) * 2015-11-23 2018-12-13 Massachusetts Institute Of Technology Protein corona phase molecular recognition
WO2022159625A1 (fr) * 2021-01-21 2022-07-28 Memorial Sloan Kettering Cancer Center Réseaux de nanocapteurs de perception machine et modèles de calcul pour l'identification de signatures de réponse spectrale

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180356414A1 (en) * 2015-11-23 2018-12-13 Massachusetts Institute Of Technology Protein corona phase molecular recognition
US20180144092A1 (en) * 2016-11-21 2018-05-24 Johnson & Johnson Vision Care, Inc. Biomedical sensing methods and apparatus for the detection and prevention of lung cancer states
WO2022159625A1 (fr) * 2021-01-21 2022-07-28 Memorial Sloan Kettering Cancer Center Réseaux de nanocapteurs de perception machine et modèles de calcul pour l'identification de signatures de réponse spectrale

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