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WO2016154613A1 - Procédés de séparation et d'identification d'analytes biologiques - Google Patents

Procédés de séparation et d'identification d'analytes biologiques Download PDF

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Publication number
WO2016154613A1
WO2016154613A1 PCT/US2016/024446 US2016024446W WO2016154613A1 WO 2016154613 A1 WO2016154613 A1 WO 2016154613A1 US 2016024446 W US2016024446 W US 2016024446W WO 2016154613 A1 WO2016154613 A1 WO 2016154613A1
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Prior art keywords
phase
poly
biological analyte
cells
red blood
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English (en)
Inventor
Jonathan W. HENNEK
Ashok A. Kumar
George M. Whitesides
Ryan P. ADAMS
Alexander B. WILTSCHKO
Carlo Brugnara
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Boston Childrens Hospital
Harvard University
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Boston Childrens Hospital
Harvard University
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    • 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
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • 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
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • 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
    • G16B99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • Aqueous mixtures of two polymers such as poly(ethylene glycol) (PEG) and dextran can separate spontaneously into two aqueous phases, called aqueous two-phase systems.
  • Phase separation in aqueous solutions of polymers is an extraordinary and underexplored phenomenon.
  • the resulting system is often not homogeneous; rather, two discrete phases, or layers, form. These layers are ordered according to density and arise from the limited interaction of the polymers for one another.
  • each phase predominantly consists of water (upwards of 70 - 90% (w/v)), while the polymer component is present in concentrations ranging from micromolar to millimolar.
  • a low interfacial tension and rapid mass transfer of water-soluble molecules across the boundary characterize the interface between layers.
  • Iron deficiency anemia is anemia due to insufficient amount of iron.
  • IDA iron deficiency anemia
  • IDA during pregnancy has been shown to increase the risk of preterm birth and low birth weight; infants with untreated IDA can have permanent cognitive impairments and delayed physical development.
  • Iron supplements provide a simple intervention to treat IDA, but the use of iron supplements when IDA is not present can result in iron overload. The proper diagnosis of IDA is important to connect patients to effective care.
  • Simple interventions such as oral iron supplements, exist for treating IDA. Supplements, however, should be used only when a diagnosis is available in order to avoid possible side effects. These side effects include iron overload, impaired growth in children, and increased risk of severe illness and death in malaria endemic areas.
  • IDA is easily diagnosed in a central laboratory by a complete blood count and measurement of serum ferritin concentration.
  • LMICs a lack of instrumentation, trained personnel, and consistent electricity prohibits effective diagnosis.
  • a rapid, low-cost, and simple to use platform to diagnose IDA is needed. While current clinical capabilities can effectively diagnose IDA in the developed world, many countries lack the expensive instrumentation necessary to detect IDA, especially at the point-of-care.
  • Red blood indices measurements of the properties and numbers of red blood cells— are commonly used for the diagnosis of IDA, because they (in contrast to serum iron or ferritin) respond quickly to changes in the iron level in the body, and require a less painful and less invasive procedure for the patient than the gold standard measurement (iron in bone marrow).
  • Red blood cell indices measured by a complete blood count require a hematology analyzer (a flow cytometer, typically with impedance, photometry, and chemical staining capabilities).
  • a hematology analyzer is expensive ($20,000-$50,000) and requires highly trained personnel and significant technical maintenance. An inexpensive, rapid, and simple method that approaches the specificity and sensitivity provided by a hematology analyzer could find widespread clinical use.
  • Anemia is defined as a condition in which the patient has a low hemoglobin concentration (HGB) in the blood.
  • HGB hemoglobin concentration
  • Various methods have been developed to diagnose anemia in low-resource settings, either by measuring the number of red blood cells (RBCs) per unit volume through spun hematocrit (HCT), or by measuring HGB directly.
  • Anemia both chronic and acute, can, however, have many causes, and a diagnosis limited to "anemia” with no further detailed cellular and/or molecular description does not necessarily provide enough information for the effective treatment of a patient.
  • Anemia associated with microcytic (i.e., smaller cells than normal) and hypochromic (i.e., lower concentration of hemoglobin per cell than normal) cells is mostly a result of IDA or thalassemia trait (a or ⁇ -thalassemias).
  • IDA affects > 10 times more people globally than does ⁇ -thalassemia trait. Due to the dominance of IDA among other conditions causing microcytic, hypochromic (micro/hypo) red blood cells, several studies have shown good diagnostic accuracy for IDA by measuring the number of hypochromic red blood cells. Micro/hypo anemias are also associated with a reduction in the mass density of red blood cells.
  • a tool to distinguish micro/hypo anemia, and thus IDA, quickly from normal blood and other forms of anemia would improve the effectiveness of healthcare, and promote a better use of resources at the level of primary healthcare, in resource-limited countries.
  • Described herein are computer/machine-aided systems and methods for determining and/or predicting the characteristic of a biological analyte of interest.
  • the biological analyte of interest having a recognizable color is separated or distributed in a multi-phase system described herein. Based on its properties (e.g., density, size, shape, and/or mass), the biological analyte spreads across the vertical length of the multi-phase system and a color distribution profile of the biological analyte along the vertical length of the multi-phase system is generated.
  • An Algorithm using a computer/machine is then used to predict one or more characteristics of the biological analyte of interest based on the color distribution profile of the biological analyte of interest.
  • Machine learning attempts to build learning algorithms that learn the associations between properties of data that are taken to always be available (inputs) and properties of data that are taken to not always be available (outputs).
  • regression refers to the issue of predicting continuously-varying outcomes from data by using a computer.
  • prediction is the act of producing, guessing, imputing, or computing an ordinarily unavailable property of data by the machine, given available properties of data.
  • classification refers to discrete outcomes that don't necessarily have a prescribed ordering.
  • MPS refers to a multi-phase system.
  • each of the phases contains a solvent and a phase component which is selected from the group consisting of polymers and surfactants.
  • a phase component which is selected from the group consisting of polymers and surfactants.
  • the resulting system is not homogeneous; rather, two or more discrete phases, or layers, form. These layers are ordered according to density and arise from the exhibit limited interaction of the phase components with one another.
  • the two or more phases or solutions exhibit limited interaction and form distinct, stable phase boundaries between adjacent phases.
  • Each phase can be aqueous or non-aqueous.
  • the non-aqueous phase comprises an organic liquid or an organic solvent.
  • MPS as described herein are used to separate/distribute analytes when the analytes migrate to phases characteristic of their properties, e.g., densities, shape, size, mass, or a combination thereof.
  • the analyte contacts each phase of the multi-phase system sequentially.
  • “sequential contact” means that the analyte contacts and interacts with only one phase (and its phase component) at a time except at the interface where the analyte contacts and interacts with two adjacent phases simultaneously. That is, the interaction of the analyte with the MPS occurs when the MPS has already phase separated and not during the process of phase separation.
  • biological analytes of interest are deposited into a formed MPS and the sedimentation profile of the analyte can be studied.
  • the sedimentation rate of the biological analyte can be affected by its density, size, shape, and mass.
  • biological analytes of interest are mixed with the components of the MPS and the formation of the MPS and the separation of the analyte are accomplished in one step.
  • phase “combination” refers to the combination of a polymer and a surfactant, a combination of two or more polymers, a combination of two or more surfactants, or a combination of any number of polymers and any number of surfactants.
  • polymer includes, but is not limited to, the homopolymer, copolymer, terpolymer, random copolymer, and block copolymer.
  • Block copolymers include, but are not limited to, block, graft, dendrimer, and star polymers.
  • copolymer refers to a polymer derived from two monomeric species; similarly, a terpolymer refers to a polymer derived from three monomeric species.
  • the polymer also includes various morphologies, including, but not limited to, linear polymer, branched polymer, random polymer, crosslinked polymer, and dendrimer systems.
  • polyacrylamide polymer refers to any polymer including polyacrylamide, e.g., a homopolymer, copolymer, terpolymer, random copolymer, block copolymer or terpolymer of polyacrylamide.
  • Polyacrylamide can be a linear polymer, branched polymer, random polymer, crosslinked polymer, or a dendrimer of polyacrylamide.
  • MPS refers to any one of the multi-phase systems described herein.
  • AMPS refers to any one of the aqueous multi-phase systems described herein (i.e., the solvent used in the MPS is water).
  • ATPS refers to an aqueous two-phase polymer system.
  • the MPSs described herein may be used for analysis of
  • the mammal is human.
  • the phase component is a polymer or a combination of two or more polymers.
  • the aqueous multi-phase polymer system can be combined with one or more immiscible organic phases to form a multi-phase system.
  • mixture refers to the combination of two components, which may be mixed or layered one on top of another.
  • the phrase "at the interface" of the adjacent phases of the MPS includes the situation where the biological analytes of interest is between the two adjacent phases or close to the border of one of the two adjacent phases.
  • a system for determining a characteristic of a biological analyte of interest including: a reader for generating a color distribution profile of a biological analyte of interest in a phase-separated multi-phase system under a first assay condition; a memory for storing one or more algorithms and one or more assay conditions, wherein each algorithm is associated with an assay condition and configured to predict a characteristic of the biological analyte of interest based on its color distribution profile in a phase-separated multi-phase system under the assay condition; and wherein at least one of the assay conditions is the first assay condition; a computer processor coupled to the reader and the memory, the computer processor is configured to: receive an input of the first assay condition and the reader-generated color distribution profile of the biological analyte of interest; based on the reader-generated color distribution profile of the biological analyte, predict a characteristic of the biological analyte of interest using the algorithm associated with the first condition;
  • the algorithm is built by machine-learning.
  • the machine-learning comprises a process comprising creating, training, validating and/or testing the algorithm using a plurality of biological analytes with known characteristics.
  • At least one of the algorithms is configured to make continuously-varying prediction. In any one of the embodiments described herein, at least one of the algorithms is configured to make discrete prediction.
  • At least one of the algorithms is configured to predict the characteristic of the biological analyte based on comparing and/or matching one or more color distribution profiles of known biological analytes associated with the first assay condition with the reader-generated color distribution profile of the biological analyte.
  • the biological analyte has a
  • the characteristic of the biological analyte is a disease state or a biological index of the biological analyte.
  • the biological analyte is selected from the group consisting of multicellular organisms, cells, organelles, cell fragments, cell membranes, cell membrane fragments, viruses, virus-like particles, bacteriophage, cytosolic proteins, secreted proteins, signaling molecules, embedded proteins, nucleic acid/protein complexes, organelles, minicells, nucleic acid precipitants, chromosomes, nuclei, mitochondria, chloroplasts, flagella, biominerals, protein complexes, protein aggregates, and combinations thereof.
  • the biological analyte is red blood cell or a population of red blood cell.
  • the characteristic of the red blood cell or the population of red blood cell is one or more indexes selected from the group consisting of the average size of a red blood cell (MCV), the average amount of hemoglobin per red blood cell (MCH), the average amount of hemoglobin per red blood cell (MCHC), the red blood cell distribution width (RDW), percentage of hypochromic red blood cells (%Hypo), hemoglobin concentration (HGB), corpuscular hemoglobin concentration (CH), per unit volume through spun hematocrit (HCT), hemoglobin distribution width (HDW), the number of red blood cells (RBCs), the percentage of red blood cells that are microcytic
  • %Micro %Micro/%Hypo
  • %Hyper the percentage of cells that are hyperchromic red blood cells
  • %Macro the percentage of cells that are microcytic red blood cells
  • the output is a print out or a file or image displayed on a smartphone, a PC, or a monitor.
  • the computer processor is configured to predict the characteristic of the biological analyte based on comparing and/or matching one or more color distribution profiles of known biological analytes associated with the first assay condition with the reader-generated color distribution profile of the biological analyte.
  • the computer processor is configured to identify one or more stored color distribution profiles of known biological analytes associated with the first assay condition which is similar to the reader-generated color distribution profile of the biological analyte.
  • the system further comprises a separation unit comprising the multi-phase system.
  • the multi-phase system comprises at least adjacent first and second phase-separated phases, wherein the first phase comprises a first phase component predominantly dissolved in the solvent of the first phase; and the second phase comprises a second phase component predominantly dissolved in the solvent of the second phase; wherein the solvents of the first and second phases are the same; the first phase component is different from the second phase component; each of the first and second components is selected from the group consisting of a polymer, a surfactant and combinations thereof; and at least one of the first and second phase components comprises a polymer; each of the first and second phases has a different density and the first and second phases, taken together, represent a density gradient; and the first and second phases have a stable interface in-between.
  • the first and second phase components are each selected from the group consisting of Caboxy-polyacrylamide, Dextran, Ficoll, N,N- dimethyldodecylamine N-oxide, poly(2-ethyl-2-oxazoline), poly(acrylic acid), poly(ethylene glycol), poly(methacrylic acid), poly(vinyl alcohol), polyacrylamide, polyethyleneimine, hydroxy ethyl cellulose, poly(2-acrylamido-2-methyl-l-propanesulfonic acid),
  • polyvinylpyrrolidone Nonyl, polyallylamine, (hydroxypropyl)methyl cellulose,
  • diethylaminoethyl-dextran nonylphenol polyoxyethylene 20, copolymer, terpolymer, block copolymer, random polymer, linear polymer, branched polymer, crosslinked polymer, and dendrimer system thereof.
  • the solvent is water.
  • the assay condition is one or more conditions selected from the group consisting of the composition of the multi-phase system and the distribution condition of the biological analyte in the multi-phase system.
  • the distribution condition of the biological analyte in the multi-phase system comprises the separation time of the biological analyte in the multi-phase system and/or the centrifuge force used for the separation of the biological analyte in the multi-phase system.
  • the reader is a scanner, a camera, or smartphone camera.
  • the memory is selected from the group consisting of a hard drive, a thumb drive, a magnetic disk, an optical disk, and magnetic tape.
  • the color distribution profile comprises a distribution of the biological analyte's color luminosity along the vertical length of the multi -phase system.
  • a method for determining a characteristic of a biological analyte of interest including: generating a color distribution profile of a biological analyte of interest in a phase-separated multi-phase system under a first assay condition; generating a database and storing the database in a memory, the database comprising one or more algorithms and one or more assay conditions, wherein each algorithm is associated with an assay condition and configured to predict a characteristic of the biological analyte of interest based on its color distribution profile in a phase- separated multi-phase system under the assay condition; and wherein at least one of the one or more assay conditions is the first assay condition; and based on the reader-generated color distribution profile of the biological analyte, using a computer to predict a characteristic of the biological analyte of interest using the algorithms associated with the first condition.
  • the algorithm is built by machine-learning.
  • the machine-learning comprises a process comprising creating, training, validating and/or testing the algorithm using a plurality of biological analytes with known characteristics.
  • at least one of the algorithms is configured to make continuously-varying prediction. In any one of the embodiments described herein, at least one of the algorithms is configured to make discrete prediction.
  • At least one of the algorithms is configured to predict the characteristic of the biological analyte based on comparing and/or matching one or more color distribution profiles of known biological analytes associated with the first assay condition with the reader-generated color distribution profile of the biological analyte.
  • the biological analyte has a
  • the characteristic of the biological analyte is a disease state or a biological index of the biological analyte.
  • the biological analyte is selected from the group consisting of multicellular organisms, cells, organelles, cell fragments, cell membranes, cell membrane fragments, viruses, virus-like particles, bacteriophage, cytosolic proteins, secreted proteins, signaling molecules, embedded proteins, nucleic acid/protein complexes, organelles, minicells, nucleic acid precipitants, chromosomes, nuclei,
  • mitochondria chloroplasts, flagella, biominerals, protein complexes, protein aggregates, and combinations thereof.
  • the biological analyte is a red blood cell or a population of red blood cell.
  • the characteristic of the red blood cell or the population of red blood cell is one or more indexes selected from the group consisting of the average size of a red blood cell (MCV), the average amount of hemoglobin per red blood cell (MCH), the average amount of hemoglobin per red blood cell (MCHC), the red blood cell distribution width (RDW), percentage of hypochromic red blood cells (%Hypo), hemoglobin concentration (HGB), corpuscular hemoglobin concentration (CH), per unit volume through spun hematocrit (HCT), hemoglobin distribution width (HDW), the number of red blood cells (RBCs), the percentage of red blood cells that are microcytic (%Micro), %Micro/%Hypo, the percentage of cells that are hyperchromic red blood cells (%Hyper), and the percentage of cells that are microcytic red blood cells (%Macro).
  • MCV red blood cell
  • MCH average amount of hemoglobin per red blood cell
  • MCHC average amount of hemoglobin per red blood cell
  • one or more stored color distribution profiles of known biological analytes associated with the first assay condition is compared and/or matched with the generated color distribution profile of the biological analyte to predict the characteristic of the biological analyte.
  • the method further comprises separating the biological analyte of interest in the multi-phase system.
  • multi-phase system comprises at least adjacent first and second phase-separated phases, wherein the first phase comprises a first phase component predominantly dissolved in the solvent of the first phase; and the second phase comprises a second phase component predominantly dissolved in the solvent of the second phase; wherein the solvents of the first and second phases are the same; the first phase component is different from the second phase component; each of the first and second components is selected from the group consisting of a polymer, a surfactant and combinations thereof; and at least one of the first and second phase components comprises a polymer; each of the first and second phases has a different density and the first and second phases, taken together, represent a density gradient; and the first and second phases have a stable interface in-between.
  • the first and second phase components are each selected from the group consisting of Caboxy-polyacrylamide, Dextran, Ficoll, N,N- dimethyldodecylamine N-oxide, poly(2-ethyl-2-oxazoline), poly(acrylic acid), poly(ethylene glycol), poly(methacrylic acid), poly(vinyl alcohol), polyacrylamide, polyethyleneimine, hydroxy ethyl cellulose, poly(2-acrylamido-2-methyl-l-propanesulfonic acid),
  • polyvinylpyrrolidone Nonyl, polyallylamine, (hydroxypropyl)methyl cellulose, diethylaminoethyl-dextran, nonylphenol polyoxyethylene 20, copolymer, terpolymer, block copolymer, random polymer, linear polymer, branched polymer, crosslinked polymer, and dendrimer system thereof.
  • the assay condition is one or more conditions selected from the group consisting of the composition of the multi-phase system and the distribution condition of the biological analyte in the multi-phase system.
  • the distribution condition of the biological analyte in the multi-phase system comprises the separation time of the biological analyte in the multi-phase system and/or the centrifuge force used for the separation of the biological analyte in the multi-phase system.
  • the color distribution profile comprises a distribution of the biological analyte' s color luminosity along the vertical length of the multi -phase system.
  • the "luminosity" of a color and the “intensity” color may be used interchangeably.
  • Fig. 1 illustrates a flow diagram of the method and/or system described herein for determining/predicting the characteristics of a biological analyte of interest, according to one or more embodiments.
  • Fig. 2A illustrates a design of ID A- AMPS rapid test loaded with blood before and after centrifugation for a representative IDA and Normal sample, according to one or more embodiments.
  • Fig. 2B illustrates a schematic of the analysis of the quantity and location of red blood cells in an AMPS test using a digital scanner and a custom computer program, according to one or more embodiments.
  • Fig. 3A shows an example of ID A- AMPS tests after 2 minutes of centrifugation for a representative normal sample, where an image of the tube and its corresponding image with pixels converted to S/V, 1-D red intensity trace, and the first derivative of the trace, according to one or more embodiments.
  • Fig. 3B shows an example of ID A- AMPS tests after 2 minutes of centrifugation for a representative IDA sample, where an image of the tube and its corresponding image with pixels converted to S/V, 1-D red intensity trace, and the first derivative of the trace, according to one or more embodiments.
  • Fig. 4A shows example of micro/hypo sample (laid on its side) after 2 minutes of centrifugation, according to one or more embodiments; and Fig. 4B shows red intensity versus distance plots averaged for 152 samples showing discrimination between normal (solid blue) and micro/hypo anemic (dashed red) samples at 2, 6, and 10 minutes
  • centrifugation according to one or more embodiments.
  • Fig. 5A illustrates receiver operating characteristic (ROC) curves for hypochromia having different threshold values for the percentage of hypochromic red blood cells (%Hypo) as determined by visual evaluation of the IDA-AMPS test, according to one or more embodiments.
  • Fig. 5B illustrates receiver operating characteristic (ROC) curves for diagnosis of hypochromia (%Hypo > 3.9%), micro/hypo anemia, and IDA as determined by visual evaluation of the IDA-AMPS test, according to one or more embodiments.
  • ROC receiver operating characteristic
  • AUC area under the curve
  • ROC receiver operating characteristic
  • Fig. 7A illustrates Machine learning prediction results for %Hypo
  • %Hypo Predicted %Hypo compared to a hematology analyzer (True %Hypo), according to one or more embodiments.
  • Fig. 8 illustrates a reader training guide used to assign redness score to IDA- AMPS tests, according to one or more embodiments.
  • Fig. 9 is a flow chart illustrating the classification of the diagnosis of hypochromia, micro/hypo anemia, iron deficiency anemia, and ⁇ -thalassemia trait used in this study based on hematological indices measured by a hematology analyzer (Advia 2120, Siemens), according to one or more embodiments.
  • Multi-phase systems have been described previously and used for a variety of applications, e.g., separation of biological analytes. See, e.g., PCT/US14/35697, filed on April 28, 2014, WO2012/024688, filed on August 22, 2011, WO2012/024693, filed on August 22, 2011, WO2012/024690, filed on August 22, 2011, and WO2012/024691, filed on August 22, 2011, all of which are hereby incorporated by reference herein in their entirety.
  • existing multi-phase separation systems often require human users' visual observation to detect whether a biological analyte is present or not at certain locations of the multiphase system, e.g., at the boundary of two adjacent phases.
  • the dynamic/thermodynamic distribution profile of a biological analyte in a multi-phase system is often affected by more than one characteristics of the analyte (e.g., density, mass, shape, size, volume, chemical compositions), thus such distribution profile can provide information-rich data which cannot be interpreted by human observation.
  • computer/machine-aided systems for determining/predicting the characteristic of a biological analyte of interest through algorithm, e.g., machine-assisted data regression/classification are described.
  • the biological analyte of interest has a recognizable color and is separated or distributed in a multi-phase system described herein. Based on its properties (e.g., density, size, shape, and/or mass), the biological analyte spread across the vertical length (height) of the multi-phase system and a color distribution profile of the biological analyte along the vertical length of the multi-phase system is generated.
  • Computer-aided algorithm e.g., data regression or classification, is then used to predict a characteristic of the biological analyte of interest.
  • the characteristic of the biological analyte is a disease state or a biological index of the biological analyte.
  • a system for determining a characteristic of a biological analyte of interest comprising: a reader for generating a color distribution profile of a biological analyte of interest in a phase-separated multi-phase system under a first assay condition; a memory for storing one or more algorithms and one or more assay conditions, wherein each algorithm is associated with an assay condition and configured to predict a characteristic of the biological analyte of interest based on its color distribution profile in a phase-separated multi-phase system under the assay condition; and wherein at least one of the assay conditions is the first assay condition; a computer processor coupled to the reader and the memory, the computer processor is configured to: receive an input of the first assay condition and the reader-generated color distribution profile of the biological analyte of interest; based on the reader-generated color distribution profile of the biological analyte, predict a characteristic of the biological analyte of interest using the algorithm associated with the first condition
  • the biological analyte of interest has a color (e.g., red blood cells) recognizable to a machine (e.g., a scanner).
  • the biological analyte of interest is dyed with a recognizable color.
  • stains or other reactants can be included in the MPS prior to mixing with the analyte.
  • the analyte can be pre-stained.
  • the dyed/stained biological analyte maintains the same characteristics of the undyed biological analyte.
  • the white blood cells (leukocytes) are stained/dyed with Acridine orange to differentiate subpopulations.
  • E.coli is stained with
  • Fluorescein isothiocyanate (FITC) labeled antibodies In some specific embodiments, mitochondria in living cells is stained using rhodamine 123. In some specific embodiments, circulating tumor cells are stained by Hematoxylin and Eosin (H&E). In some specific embodiments, platelets can be stained to have a visible color.
  • H&E Hematoxylin and Eosin
  • multi-phase systems include two or more phase-separated phases each containing a phase component and these phases are arranged by density and form a density gradient.
  • the biological analyte of interest is separated by a MPS according to its density or according to its settlement rate in the MPS.
  • the settlement rate of the analyte can be affected by many factors such as its mass, volume, chemical composition, and shape.
  • a biological analyte of interest is separated in the multiphase system and a reader is used to record a color distribution profile of the biological analyte spreading through the vertical length of the multi-phase system. As shown in Step 1 of Fig. 1, such a color picture showing an analyte of interest distributed in a MPS may be inputted into the system.
  • Non-limiting examples of the reader include a scanner, a smart phone having a scanner or camera, and a camera. In some embodiments, more than one reader can be used.
  • the color distribution profile is optionally further simplified or processed to reduce the data complexity (Step 2 of Fig. 1).
  • a color picture of the biological analyte separated in the multiphase system is taken by a scanner or a camera. The background of the picture is then removed.
  • the color intensity values for each row of pixels in the picture are summed and a one-dimensional plot of "color luminosity" versus distance of the multiphase system is compiled.
  • These process steps can further simplify the computational load by reducing the data' s dimensions, and/or reducing the redundancy in the dataset by grouping together adjacent pixels that are co-varying.
  • a color distribution profile representing a plot of color luminosity vs. the vertical length of the MPS is then generated (Step 3 of Fig. 1).
  • the computed/machine-aided system comprises a memory storing a database.
  • the memory also referred to as "memory device”
  • the memory may be removable.
  • Some exemplary memory devices include hard drive, thumb drive, magnetic disk, an optical disk, and magnetic tape.
  • the memory device can further store reference data that can be used as a baseline for comparison.
  • the memory device may also be used for storing software, computer algorithms, and temporary files created by the computer processor during analysis of the data from the reader.
  • the data can be stored in the form of images, time-evolution spectra, static or time-dependent photodetector signals, or color intensities along the length of the multiphase system.
  • the database includes one or more algorithms and one or more assay conditions, where each algorithm is associated with an assay condition and configured to predict a characteristic of the biological analyte of interest based on its color distribution profile in a phase-separated multi-phase system under the assay condition; and where at least one of the assay conditions is the first assay condition.
  • the characteristic of the biological analyte may be a disease state or a biological index of the biological analyte.
  • properties of the analyte include healthy or diseased state of cell, color, porosity, tendency to swell, size, shape, chemical composition, or distribution of cell.
  • each of the data set corresponds to the color distribution profile of a known biological analyte with a known characteristic under an assay condition.
  • the assay condition includes the composition of the multiphase system and the settlement conditions of the biological analyte.
  • the machine/computer aided system as described herein use algorithms, built by using a plurality of color distribution profiles of known analytes, to predict the characteristic of an unknown biological analyte of interest.
  • the computer processor of the system is capable of receiving an input of the first assay condition and the reader-generated color distribution profile of the biological analyte of interest (Step 4 of Fig. 1).
  • the computer processor may be coupled to the reader and the memory by wire or wirelessly.
  • the computer processor is configured to, based on the reader-generated color distribution profile of the biological analyte, predict a
  • the computer processor is configured to identify one or more stored color distribution profiles of known biological analytes associated with the first assay condition which is similar to the reader-generated color distribution profile of the biological analyte.
  • the computer processor is configured to reduce the computational load by reducing the data complexity. In some embodiments, any combination or manipulation of the color/color space could be used.
  • the reader is configured to detect absorbance or fluorescents.
  • the "color luminosity" may be generated by summing the RGB values.
  • the colorspace is converted to HSV (hue, saturation and value-a cylindrical-coordinate representations of points in an RGB color model) and S/V is used as the "color luminosity” value.
  • a color picture of the biological analyte separated in the multiphase system is taken by a scanner or a camera and the color intensity values for each row of pixels in the picture are summed and a one-dimensional plot of "color luminosity" versus distance of the multiphase system is compiled.
  • process steps can further simplify the computational load by reducing the data's dimensions, and/or reducing the redundancy in the dataset by grouping together adjacent pixels that are co-varying.
  • a particular manipulation is chosen which matches human's visual intuition of "how red the tube should be” at any given point along its length. It is contemplated that any manipulation of the raw pixel values can be part of a machine learning algorithm.
  • the computer processor uses one or more algorithms to predict the characteristic of the biological analyte.
  • Machine learning refers to the process of creating, training, validating and testing algorithms that learn from and adapt to data.
  • machine learning is used to build algorithms that can predict properties of new analytes by being trained on a set of labeled analytes. These labels may either be continuously varying, in the case of predicting blood parameters such as percentage of hypochromic red blood cells (%Hypo), or may be discrete classes, in the case of predicting whether a patient has anemia.
  • %Hypo percentage of hypochromic red blood cells
  • 'regression' The case of making continuously-varying predictions is called 'regression', and the case of making discrete predictions is called 'classification'.
  • a set of known, labeled analytes are first divided into “train”, “validation” and “test” sets.
  • the computer algorithm is configured to accurately predict the labels of the "train” set. This configuration is then tested on the "validation” set.
  • the process of reconfiguring the algorithm, training, and validating is repeated until satisfactory performance on the validation set has been achieved.
  • the performance of the computer algorithm is evaluated on the final data set, the "test" set, which contains analytes that the computer algorithm has not processed before.
  • the computer algorithms that can be used for producing continuously-varying predictions include, but are not limited to linear regression, support vector regression, random forest regression, neural networks and Gaussian process regression.
  • the computer algorithms which can be used for producing predictions of the class of analyte include, but are not limited to, logistic regression, support vector classification, neural networks, random forest classifier, Gaussian process classifier, boosting and naive Bayes.
  • logistic regression is used in comparing the stored color distribution profile of known analyte with that of the biological analyte of interest.
  • support vector regression (SVR) with a radial basis function kernel is used in comparing the stored color distribution profile of known analyte with that of the biological analyte of interest (see, e.g. , Step 5 of Fig. 1).
  • the reader and/or the computer processor can further carry out additional data manipulation before and/or after receiving the data from reader.
  • the reader and/or the computer processor can carry out image cropping, edge detection, thresholding, area detection, and/or the like.
  • the computer processor can determine tiebreakers for color and/or edge determinations.
  • the processing unit further includes software such as, image analysis, statistical analysis, comparison with stored calibration curves etc., can be used to analyze the results and rapidly print out a meaningful response to the user of the system.
  • the diseased biological analyte differs from the healthy analyte in shape, size, mass, density, or a combination thereof.
  • the separation of the diseased analyte in multi-phase systems, under similar condition results in a color
  • the system described herein stores one or more algorithms built by machine-leaning using one or more known color distribution profiles of analytes with known characteristics. By using computer algorithm built from analyzing known biological analytes of interest, the system will predict the characteristic of the unknown biological analyte (Step 5 of Fig. 1). In certain
  • a property of a new analyte is predicted.
  • the algorithm has accumulated knowledge of the relationships of all of the input values of the color profile distribution of the analyte.
  • the computer algorithm exploits knowledge of these relationships in order to make predictions of the properties of the analyte. For example, the computer algorithm may learn that a more intense color in a particular physical location of the analyte may indicate with high probability that the analyte has a certain class label, or is drawn from a patient with a particular disease.
  • the different types of computer algorithms outlined above all learn to relate varying
  • the processor will then generate an output of the prediction of the characteristic of the biological analyte of interest (Step 6 of Fig. 1).
  • the output may be in the form of a print out, a file or image displayed on a smartphone, a PC, or generally a monitor.
  • the biological analyte is deposited into a pre-formed multi-phase system, which, under certain assay conditions, generates a color distribution profile of the biological analyte through the vertical length of the multiphase system.
  • the biological analytes are mixed with the components (e.g., solvent, phase components) for the multi-phase system to form the multi-phase system with the biological analyte distributed therein in one step.
  • the multi-phase system is unit independent from the computer/machine-aided systems disclosed herein. In other embodiments, the
  • computer/machine-aided system further comprises a separation unit comprising the multiphase system.
  • Types of biological analytes that can be analyzed include, without limitation, cells, cancer cells, stem cells, cell extracts, tissue extracts, cell organelles, cell fragments, cell membranes, cell membrane fragments, viruses, virus-like particles, bacteriophage, cytosolic proteins, secreted proteins, signaling molecules, embedded proteins, nucleic acid/protein complexes, nucleic acid precipitants, chromosomes, nuclei, mitochondria, chloroplasts, flagella, biominerals, protein complexes, phage, minicells, and protein aggregates, tissues, organisms, small molecules, large-sized molecules, e.g., biomolecules including proteins, and particles.
  • the types of cells used in the disclosed methods include mammalian cells selected from the group consisting of gland cells (e.g., exocrine secretory epithelial cells, salivary gland mucous cells, salivary gland serous cells, Von Ebner's gland cells, mammary gland cells, lacrimal gland cells, ceruminous gland cells, eccrine sweat gland dark cells, eccrine sweat gland clear cells, apocrine sweat gland cells, gland of Moll cells, aebaceous gland cells, Bowman's gland cells, Brunner's gland cells, seminal vesicle cells, prostate gland cells, bulbourethral gland cells, bartholin's gland cells, gland of littre cells, uterine endometrial cells, isolated goblet cells, stomach lining mucous cells, gastric gland zymogenic cells, gastric gland oxyntic cells, pancreatic acinar cells, paneth cells, type II pneumocyte cells, and Clara cells), hormone secreting
  • gland cells
  • juxtaglomerular cells racula densa cells, peripolar cells, and mesangial cells
  • epithelial cells lining closed internal body cavities e.g., blood vessel and lymphatic vascular endothelial fenestrated cells, blood vessel and lymphatic vascular endothelial continuous cells, blood vessel and lymphatic vascular endothelial splenic cells, synovial cells, serosal cells, squamous cells, columnar cells, dark cells, vestibular membrane cells, stria vascularis basal cells, stria vascularis marginal cells, Claudius cells, Boettcher cells, choroid plexus cells, pia- arachnoid squamous cells, pigmented and non-pigmented ciliary epithelial cells, corneal endothelial cells, and peg cells), ciliated cells of the respiratory tract cells, oviduct cells, uterine endometrium cells, rete testis cells, and ductulus efferens cells
  • osteoblast/osteocytes osteoprogenitor cells
  • hyalocytes of vitreous body of eye stellate cells of perilymphatic space of ear, hepatic stellate cells, pancreatic stele cells, contractile cells, skeletal muscle cells, heart muscle cells, smooth muscle cells, blood and immune cells (e.g., erythrocyte, megakaryocyte, monocyte, connective tissue macrophage, epidermal langerhans, osteoclast, dendritic cell, microglial cell, neutrophil granulocyte, eosinophil granulocyte, basophil granulocyte, mast cell, T cell, suppressor T cell, cytotoxic T cell, natural killer T cell, B cell, and reticulocyte), Stem cells and committed progenitors for the blood and immune system (e.g., pigment cells, melanocytes, and retinal pigmented epithelial cells), germ cells (e.g., oocyte, spermatid, spermatocyte, spermat
  • the biological analyte is selected from the group consisting of cells, organelles, cell fragments, cell membranes, cell membrane fragments, viruses, virus-like particles, bacteriophage, cytosolic proteins, secreted proteins, signaling molecules, embedded proteins, nucleic acid/protein complexes, organelles, minicells, nucleic acid precipitants, chromosomes, nuclei, mitochondria, chloroplasts, flagella, biominerals, protein complexes, protein aggregates, urine, saliva, feces, bacteria or algae in water (ground water, streams, oceans, etc.), bacteria (e.g., ecoli) in food, and combinations thereof.
  • the biological analyte is selected from the group consisting of bacteria, circulation tumor cells, and parasites, and combinations thereof.
  • the analytes of interest are healthy red blood cell, diseased red blood cell, or sickle cell.
  • the biological analyte is selected from the group consisting of normal erythrocyte with hemoglobin Hb AA, Hb CC, and Hb AS, sickle cell erythrocyte with hemoglobin Hb SS, Hb SC, HbSbeta + , HbSD, HbSE and HbSO, reticulocyte, predominantly hypochromic red blood cells (e.g., iron deficiency anemia (IDA)), predominantly microcytic red blood cells (e.g., ⁇ -thalessemia trait ( ⁇ - ⁇ )), ⁇ - thalassemia minor, hemoglobin H disease, Bart's hydrops fetalis, other a-thalassemias, normal red blood cells, and white blood cells.
  • hypochromic red blood cells e.g., iron deficiency anemia (IDA)
  • microcytic red blood cells e.g., ⁇
  • the characteristic of the biological analyte is a disease state or a biological index of the biological analyte.
  • the index of the biological analytes include shape, size, density, mass, width, volume of the biological analyte.
  • the characteristic of the red blood cell is one or more indices selected from the group consisting of the average size of a red blood cell (mean corpuscular volume, MCV), the average amount of hemoglobin per red blood cell (mean corpuscular hemoglobin, MCH), the average amount of hemoglobin per volume of red blood cells (mean corpuscular hemoglobin concentration, MCHC), and the red blood cell distribution width (RDW).
  • the disease state of the biological analyte is anemia or the sickle state of the cells.
  • anemia include microcytic anemia, hypochromic anemia, iron deficiency anemia, and ⁇ -thalassemia trait. See, Blood 2014, 123, 615-624, for additional examples of anemia.
  • the multi-phase system comprises at least adjacent first and second phase-separated phases, wherein the first phase comprises a first phase component predominantly dissolved in the solvent of the first phase; and the second phase comprises a second phase component predominantly dissolved in the solvent of the second phase; wherein the solvents of the first and second phases are the same; the first phase component is different from the second phase component; each of the first and second components is selected from the group consisting of a polymer, a surfactant and combinations thereof; and at least one of the first and second phase components comprises a polymer; each of the first and second phases has a different density and the first and second phases, taken together, represent a density gradient; and the first and second phases have a stable interface in-between.
  • the first and second phase components are each selected from the group consisting of Caboxy-polyacrylamide, Dextran, Ficoll, N,N- dimethyldodecylamine N-oxide, poly(2-ethyl-2-oxazoline), poly(acrylic acid), poly(ethylene glycol), poly(methacrylic acid), poly(vinyl alcohol), polyacrylamide, polyethyleneimine, hydroxy ethyl cellulose, poly(2-acrylamido-2-methyl-l-propanesulfonic acid),
  • polyvinylpyrrolidone Nonyl, polyallylamine, (hydroxypropyl)methyl cellulose,
  • diethylaminoethyl-dextran nonylphenol polyoxyethylene 20, copolymer, terpolymer, block copolymer, random polymer, linear polymer, branched polymer, crosslinked polymer, and dendrimer system thereof.
  • the biological analyte is distributed in the multi-phase system to generate a color distribution profile under certain assay conditions.
  • the assay condition is one or more conditions selected from the group consisting of the composition of the multiphase system (e.g., phase component and solvent, and amounts thereof) and the distribution condition of the biological analyte in the multi-phase system.
  • the assay condition is one or more conditions selected from the group consisting of the force used for the settlement of the biological analyte in the multi-phase system, and the time allowed for the settlement of the biological analyte in the multi-phase system.
  • the phase components are any of the phase components described herein.
  • the solvent used in the multi-phase is water.
  • the solvent used in the multi-phase is an organic solvent.
  • the amount of the solvent used is 1 ⁇ , 2 ⁇ , 5 ⁇ , 10 ⁇ , 20 ⁇ , 50 ⁇ , 100 ⁇ , or 1 ml. Any other suitable amount for the solvent is contemplated.
  • the multiphase system is placed on top of the biological analyte.
  • the biological analyte is deposited at the top of the multiphase system.
  • the biological analyte is allowed to settle in the multi-phase system under gravity.
  • the biological analyte is allowed to settle in the multi-phase system under centrifugation.
  • the centrifugation time is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20 minutes. Any known centrifugation force may be used. Non-limiting examples of the centrifugation force include 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 13500, 14000, 15000, 20000, 50000, 100000 g, or the centrifugation force is in any ranges limited by any two of the values disclosed herein.
  • the assay conditions also include thermodynamic conditions or dynamic conditions.
  • the biological analyte is separated by the multi-phase systems under thermodynamic conditions.
  • thermodynamic condition refers to the scenario that the biological analyte reaches its location in the multi-phase system characteristic of its density.
  • thermodynamic location of the analyte is determined by the density gradient of the multiphase system and the density of the analyte. Once reaching its thermodynamic location in the multi-phase system, the biological analyte will not further change its location therein even upon further settlement under centrifugation.
  • the biological analyte is separated by the multi-phase systems under dynamic conditions.
  • dynamic condition refers to the scenario that the biological analyte has not reached its thermodynamic location in the multiphase system.
  • Various other properties of the analyte e.g., mass, volume, width, chemical composition, shape, may affect the settlement rate of the analyte. If allowed to settle for sufficient time, the biological analyte will eventually reach its thermodynamic location in the multiphase system.
  • the color distribution profile under dynamic conditions provides an information-rich data set for the analyte, because it provides information not only related to the density of the analyte, but also its mass, volume, width, chemical composition, shape, or a combination of any two or more of these factors.
  • the distribution profile is a recorded video over time of the biological analyte settling and moving through the MPS.
  • the video can be recorded by the use of a high speed camera, the use of a strobe light, the use of scanning optics (e.g., CD/DVD drive), or the use of a linear CCD array.
  • a method for determining a characteristic of a biological analyte of interest comprising: generating a color distribution profile of a biological analyte of interest in a phase-separated multi-phase system under a first assay condition; generating a database and storing the database in a memory, the database comprising one or more algorithms and one or more assay conditions, wherein each algorithm is associated with an assay condition and configured to predict a characteristic of the biological analyte of interest based on its color distribution profile in a phase- separated multi-phase system under the assay condition; and wherein at least one of the one or more assay conditions is the first assay condition; and based on the reader-generated color distribution profile of the biological analyte, using a computer to predict a characteristic of the biological analyte of interest using the algorithms associated with the first condition.
  • the algorithm is built by machine-learning.
  • the machine-learning comprises a process comprising creating, training, validating and/or testing the algorithm using a plurality of biological analytes with known characteristics.
  • the method further comprises separating the biological analyte of interest in the multi-phase system.
  • Described herein are multi-phase systems including two or more phase-separated phases each containing a phase component.
  • the MPS can be a two- or three-phase system as disclosed herein. However, MPSs containing more than three phases are also contemplated.
  • the MPSs described herein have important biological applications, including, but not limited to, enrichment of reticulocytes, diagnosis of iron deficiency anemia and ⁇ -thalessemia trait, and diagnosis of sickle cell disease and its subtypes.
  • MPSs as described herein are used to separate analytes ⁇ e.g., red blood cells, reticulocytes, erythrocytes, etc.) from each other or from impurities and other objects in the sample.
  • the analytes migrate to phases characteristic of their densities, and in so doing, contact each phase of the multi-phase system sequentially.
  • “sequential contact” means that the analyte contacts and interacts with only one phase (and its phase component) at a time except at the interface between two phases. That is, the interaction of the analyte with the MPS occurs when the MPS has already phase separated and not during the process of phase separation.
  • the pH, osmolality, and the polymer used in the preparation of the phase separated components are selected to be compatible with the cells to be analyzed or separated.
  • the concept of the multi-phase system is further explained herein.
  • the resulting system is not homogeneous; rather, two or more discrete phases, or layers, form. These layers are ordered according to density and arise from the exhibited limited interaction of the phase components with one another.
  • the two or more phases or solutions thus exhibit limited interaction and form distinct phase boundaries between adjacent phases.
  • the two adjacent phases have rapid material exchange and reach a thermodynamic, stable equilibrium. Thus, the phase separation and phase boundary are stable and not easily disturbed.
  • Each phase can be aqueous or non-aqueous.
  • the non-aqueous phase comprises an organic liquid or an organic solvent.
  • the MPS is also called an aqueous multi-phase system (AMPS).
  • the multi-phase systems disclosed herein comprise two or more zones or regions that are phase-separated from each other, wherein each of the two or more phases comprises a phase component.
  • the phase component is a polymer or a combination of two or more polymers.
  • Non-limiting examples of polymer used in the formation of a phase include dextran, polysucrose (herein referred to by the trade name "Ficoll"), poly(vinyl alcohol), poly(2-ethyl-2-oxazoline), poly(methacrylic acid), poly(ethylene glycol), polyacrylamide, polyethyleneimine, hydroxyethyl cellulose, polyvinylpyrrolidone, carboxy-polyacrylamide, poly(acrylic acid), poly(2-acrylamido-2-methyl-l-propanesulfonic acid), dextran sulfate, diethylaminoethyl-dextran, chondroitin sulfate A, poly(2-vinylpyridine-N-oxide), poly(diallyldimethyl ammonium chloride), poly(styrene sulfonic acid), polyallylamine, alginic acid, nonylphenol polyoxyethylene, poly(bisphenol A carbonate),
  • polydimethylsiloxane polystyrene, poly(4-vinylpyridine), polycaprolactone, polysulfone, poly(methyl methacrylate-co-methacrylic acid), poly(methyl methacrylate),
  • a polymer includes its homopolymer, copolymer, terpolymer, block copolymer, random polymer, linear polymer, branched polymer, crosslinked polymer, and/or dendrimer system.
  • phase components are selected so that the resulting phases are phase- separated from each other.
  • phase-separation refers to the phenomena where two or more solutions, each comprising a phase component, when mixed together, form the same number of distinct phases where each phase has clear boundaries and is separated from other phases.
  • Each phase component used in the solution is selected to be soluble in the solvent of the phase, so that each resulting phase is a distinct solution of the phase component and each phase is phase-separated from other adjacent phase(s).
  • each phase component is selected to predominantly reside in one particular phase of the multi-phase system.
  • every phase can contain varying amounts of other phase components from other phases in the MPS, in addition to the selected desired phase component in that phase.
  • the phase component composition in each phase of the multiphase system recited herein generally refers to the starting phase component composition of each phase, or to the predominant phase component composition of each phase.
  • the boundary between every two adjacent phases is also called the interface between the two phases.
  • the MPS is placed in a container and there is also an interface formed between the bottom phase and the container.
  • the MPS described herein to analyze the biological analyte is a two-phase aqueous system.
  • the two phase systems include aqueous two-phase systems where the phase component combination of the two phases is selected from the group consisting of:
  • the MPS described herein to analyze the biological analyte is a three-phase aqueous system.
  • the three phase systems include aqueous three-phase systems where wherein the phase component combination of the three phases is selected from the group consisting of:
  • poly(methacrylic acid] polyacrylamide N,N-dimethyldodecylamine N-oxide poly(methacrylic acid] polyacrylamide CHAPS
  • poly(methacrylic acid] polyethyleneimine carboxy -polyacrylamide poly(methacrylic acid] polyethyleneimine 1-O-Octyl-B-D-glucopyranoside poly(methacrylic acid] polyethyleneimine Pluronic
  • polyethyleneimine - PEI polyethyleneimine - PEI
  • polyvinylpyrrolidone - PVP polyvinylpyrrolidone
  • the MPS used herein are further described below.
  • the concentration of the phase component in each phase is selected so that the resulting density of each phase will fall in the range of density as described herein.
  • the concentration of the first phase component in the first phase or the concentration of the second phase component in the second phase is between about 1-40 % (w/v).
  • the concentration of the first phase component in the first phase and the concentration of the second phase component in the second phase are each independently about 9.0%, 9.3%, 9.5%, 10.0%, 10.1%, 10.3%, 10.5%, 10.6%, 10.8%, 1 1.0%, 1 1.1%, 1 1.4%), 1 1.6%, or 12.0% (w/v). Ranges bounded by any of the specific values noted above are also contemplated.
  • the concentration of the first phase component in the first phase or the concentration of the second phase component in the second phase is about 9.0%-12.0% (w/v).
  • the first and second phase components for the aqueous two-phase system for enrichment of reticulocytes are each selected from the group consisting of Caboxy-polyacrylamide, Dextran, Ficoll, ⁇ , ⁇ -dimethyldodecylamine N-oxide, poly(2- ethyl-2-oxazoline), poly(acrylic acid), poly(ethylene glycol), poly(methacrylic acid), poly(vinyl alcohol), polyacrylamide, polyethyleneimine, hydroxyethyl cellulose, poly(2- acrylamido-2-methyl-l-propanesulfonic acid), polyvinylpyrrolidone, Nonyl, polyallylamine, (hydroxypropyl)methyl cellulose, diethylaminoethyl-dextran, and nonylphenol
  • the aqueous two-phase system for enrichment of reticulocytes has dextran and Ficoll as its the first and second phase components, respectively.
  • Ficoll having a molecular weight of 70 K Da or 400 Da is used.
  • dextran having a molecular weight of 500 K Da is used.
  • Ficoll or dextran with other molecular weight known in the art can be used.
  • the differences in the densities of the phases of MPSs provide a means to perform density-based separations.
  • the interfaces between phases mark discontinuities (on the molecular scale) is between continuous fluid phases of different density.
  • the densities (PA and P B ) of the phases above and below the interface establish the range of densities for components (pc) that will localize at the interface (PA > pc > ⁇ ) ⁇
  • the interfacial surface energy between the phases of a MPS is astonishingly low (from nJ m "2 to mJ m "2 ); a low interfacial surface energy reduces the mechanical stress on cells as they pass through the interface.
  • the MPSs described herein offer several advantages: i) they are thermodynamically stable, ii) they self-assemble rapidly (t ⁇ 15 minutes, 2000 g) on centrifugation or slowly (t ⁇ 24 hours) on settling in a gravitational field, iii) they can differentiate remarkably small differences in density ( ⁇ ⁇ 0.001 g cm "3 ), and iv) they provide well-defined interfaces that facilitate both the identification and extraction of sub-populations of cells by concentrating them to quasi- two-dimensional surfaces.
  • the various components of the blood sample naturally settle in the MPS to their thermodynamically stable states.
  • the reticulocytes contact one or more of the two phases sequentially.
  • the enriched reticulocytes will settle to a location in the MPS characteristic of its density, e.g. , at the interface between phases of lower and higher density than the reticulocytes.
  • the multi-phase system containing the blood sample can be centrifuged. The use of centrifuge facilitates the settlement process, by speeding up the migration of the biological analyte, e.g., reticulocyte, to a location in the MPS characteristic of its density.
  • the multi-phase system and the human blood sample (placed on top of the MPS) is centrifuged for about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or 30 minutes.
  • the centrifuging process is conducted only for a short period of time, e.g. , about 1, 2, 3, 4, or 5 minutes, the analyte in the blood sample may not have reached its thermodynamic state. Ranges bounded by any of the specific values noted above are also contemplated.
  • the centrifuging process is stopped while the reticulocyte is still migrating through the phases in the MPS.
  • Such shorter centrifuge can reveal the size or shape profile of the reticulocytes, as reticulocytes of the same density but with different sizes or shapes can have different settlement rates in the MPS (given sufficient time, it is expected that all reticulocytes with the same density will occupy the same location - regardless of differences in sedimentation rates).
  • the volume ratio of the human blood sample to the multiphase system is about 4: 1, 2: 1, 1 : 1, 1 :2, 1 :3, 1 :4, 1 :5, or 1 :6, or 0.15-4: 1, or 0.2-2: 1, or 0.3-1 : 1. Ranges bounded by any of the specific values noted above are also contemplated. In some embodiments, the volume ratio of the human blood sample to the multiphase system is about 1 : 1.
  • the tonicity of a MPS system is a colligative property that depends primarily on the number of dissolved particles in solution.
  • the tonicity of the MPS can be adjusted by using a tonicity adjusting agent.
  • tonicity adjusting agent include dextrose, glycerin, mannitol, KCl, and NaCl.
  • the biological analyte e.g., blood cells
  • the biological analyte can change its size or shape in response to the change of the tonicity or pH of the MPS.
  • changing tonicity or pH will affect the biological analyte' s size and thereby its density and/or migration speed in the MPS.
  • changing tonicity or pH will affect the biological analyte' s shape and thereby its migration speed through the MPS phases.
  • the tonicity or pH can affect different cells differently. For instance, tonicity or pH may affect reticulocytes and erythrocytes to different extents. Therefore, changing tonicity or pH may provide another parameter to improve the separation and enrichment of the biological analyte, e.g., reticulocytes.
  • tonicity or pH may affect reticulocytes and erythrocytes to different extents. Therefore, changing tonicity or pH may provide another parameter to improve the separation and enrichment of the biological analyte, e.
  • Applicants have surprisingly found that hypertonic MPS provides superior enrichment results for reticulocytes.
  • the addition of certain tonicity-adjusting agents may also change the density of all the phases in the MPS. For instance, the addition of NaCl or KCl will increase the density and tonicity of each of the MPS phases. This provides another way of fine-tuning the density ranges of the MPS phases.
  • Nycodenz can be added to the phases in the MPSs to adjust the density alone without affecting the tonicity of the phases.
  • the enriched reticulocytes are collected with a yield of more than 0.5%, 1%, 1.5%, 2%, 3%, 4%, 5%, 10%, or 20% using the method described herein.
  • the method described herein can be adopted to various scales, e.g., the microliter scale, the milliliter scale, or the multi-liter scale.
  • the MPS is a two-phase system and the density ranges of the two phases are described herein and selected to allow the iron deficiency microcytic and/or hypochromic red blood cells characteristic of iron deficiency anemia or the ⁇ / ⁇ - thasassemias to be easily identified.
  • the densities of the two phases may be selected so that iron deficiency anemia red blood cells or ⁇ -thalessemia trait red blood cells will settle and reside in the interface of the first and second phases to allow easy
  • the MPS is an aqueous two- phase system having the first and second densities at about 1.0784 g/cm 3 and 1.0810 g/cm 3 , respectively.
  • the first and second phase components are dextran and Ficoll, respectively.
  • the aqueous multi-phase system further comprises: a third aqueous phase comprising a third phase component and having a third density between about 1.073 g/cm 3 and about 1.093 g/cm 3 ; wherein the third density is higher than the first density but lower than the second density; and the third phase component comprises at least one polymer.
  • the third density is less than about 0.002 g/cm 3 , 0.0019 g/cm 3 , 0.0018 g/cm 3 , 0.0017 g/cm 3 , 0.0016 g/cm 3 , 0.0015 g/cm 3 , 0.0014 g/cm 3 , 0.0013 g/cm 3 , 0.0012 g/cm 3 , 0.0011 g/cm 3 , 0.0010 g/cm 3 , 0.0009 g/cm 3 , 0.0008 g/cm 3 , 0.0007 g/cm 3 , 0.0006 g/cm 3 , 0.0005 g/cm 3 , 0.0004 g/cm 3 , 0.0003 g/cm 3 , 0.0002 g/cm 3 , or 0.0001 g/cm 3 lower than the second density. Ranges bounded by any of the specific values noted above are
  • the first, third, and second densities are about 1.040-1.055 g/cm 3 , 1.075-1.085 g/cm 3 , and 1.080-1.085 g/cm 3 , respectively. In one specific embodiment, the first, third, and second densities are about 1.0505 g/cm 3 , 1.0810 g/cm 3 , and 1.0817 g/cm 3 , respectively. In one specific embodiment, the first, third, and second phase components are PVA, dextran and Ficoll, respectively.
  • the first and second phase components are selected so that the resulting two phases phase separate to form the two-phase system.
  • the first, third, and second phase components are selected so that the resulting three phases phase separate to form the three-phase system.
  • the first, second, and third phase components are each selected from the group consisting of Caboxy- polyacrylamide, Dextran, Ficoll, ⁇ , ⁇ -dimethyldodecylamine N-oxide, poly(2-ethyl-2- oxazoline), poly(acrylic acid), poly(ethylene glycol), poly(methacrylic acid), poly(vinyl alcohol), polyacrylamide, polyethyleneimine, hydroxyethyl cellulose, poly(2-acrylamido-2- methyl- 1-propanesulfonic acid), polyvinylpyrrolidone, Nonyl, polyallylamine, (hydroxypropyl)methyl cellulose, diethylaminoethyl-dextran, and nonylphenol
  • any two- or three- phase system described herein can be used, provided that the density of each phase falls in the ranges of the phase densities described herein.
  • a single phase is used and a color distribution profile of the analyte in the single phase is generated.
  • the single phase is viscous.
  • the concentration of the phase component in each phase also can be fine-tuned to adjust the density of each phase so that that the density of each phase falls in the ranges of the phase densities described herein.
  • the density ranges of the phases can also be achieved by adding additives such as Nycodenz.
  • the concentration of the first phase component in the first phase or the concentration of the second phase component in the second phase is between about 1-40 % (w/v). In some specific embodiments, the concentration of the first phase component in the first phase or the concentration of the second phase component in the second phase is about 5%, 10%, 15%, 20%), or 25%) (w/v).
  • the concentration of the third phase component in the third phase is between about 1-40 %> (w/v) or about 5%, 10%>, 15%, 20%, or 25% (w/v). In some specific embodiments, the concentration of the first phase component in the first phase, the concentration of the third phase component in the third phase, or the concentration of the second phase component in the second phase is about 5%>-25%>, 10%- 20%, or 15%-20% (w/v).
  • the tonicity of a MPS system can be adjusted using a tonicity adjusting agent including, but are not limited to, dextrose, glycerin, mannitol, NaH 2 P0 4 (or its hydrate form), KH 2 P0 4 , KC1, and NaCl.
  • a tonicity adjusting agent including, but are not limited to, dextrose, glycerin, mannitol, NaH 2 P0 4 (or its hydrate form), KH 2 P0 4 , KC1, and NaCl.
  • the aqueous multi-phase system for the diagnosis of iron deficiency anemia and/or ⁇ -thalessemia trait is isotonic.
  • cells are tagged with NPs specific to certain markers (e.g., CD4).
  • additives are used to specifically destroy certain cells (RBC lysis with saponin) or to create aggregation (adding prothrombin to aggregate platelets).
  • the machine/computer-aided system and/or method described herein are used for the detection of iron deficiency anemia (IDA) and/or the prediction of red blood cell index.
  • IDA iron deficiency anemia
  • Aqueous multiphase systems are aqueous solutions of polymers and surfactants that spontaneously phase segregate and form discrete, immiscible layers.
  • each phase is an interface with a molecularly sharp step in density; these steps in density can be used to separate subpopulations of cells by density.
  • the phases of an AMPS can be tuned to have very small steps in density ( ⁇ ⁇ 0.001 g/cm 3 ), can be made
  • AMPS as a tool to enrich reticulocytes from whole blood, and to detect sickle cell disease.
  • AMPS AMPS to diagnose IDA, by exploiting the fact that RBCs in patients with micro/hypo anemia have lower density than those of healthy patients.
  • using only a drop of blood a volume easily obtainable from a finger prick
  • we can detect, by eye, low density RBCs and diagnose IDA in under three minutes; this method had a true positive rate (sensitivity) of 84%, with a 95%o confidence interval (CI) of 72-93%>, and a true negative rate (specificity) of 78% (CI 68-86%).
  • the diagnostic accuracy of the system disclosed herein is improved by imaging each AMPS test with a digital scanner and analyzing the distribution of red color— corresponding to the RBCs— found in the tube.
  • Red blood cell indices are used to diagnose many diseases and, therefore, predicting their values quickly and simply may be clinically useful.
  • the sedimentation rate of red blood cells is related to important red-cell indices
  • the sedimentation rate of red blood cells through a fluid is a function of several physical characteristics of the cells: mass, volume, size, shape, deformability, and density (mass per unit volume). These characteristics are related, directly or indirectly, to a number of red blood cell indices, including, mean corpuscular volume (MCV, fL) or the average size of a red blood cell, mean corpuscular hemoglobin (MCH, pg/cell) or the average amount of hemoglobin per cell, mean corpuscular hemoglobin concentration (MCHC, g/dL) or the average amount of hemoglobin per volume of blood, red blood cell distribution width (RDW, %) or the distribution in volume of the RBCs.
  • MCV mean corpuscular volume
  • MH mean corpuscular hemoglobin
  • MCHC mean corpuscular hemoglobin concentration
  • RW red blood cell distribution width
  • HCT hematocrit
  • #RBCs total number of RBCs
  • HGB total hemoglobin concentration in the blood
  • %Micro The percentage of red blood cells that are microcytic
  • %Hypo The percentage of red blood cells that are hypochromic
  • %Micro as the percentage of RBCs of MCV ⁇ 60 fL and %Hypo as the percentage of RBCs with MCHC ⁇ 28 g/dL.
  • IDA corresponds to a decrease in MCV, MCH, MCHC, and HGB, and an increase in RDW, %Hypo, and %Micro.
  • Several other hemoglobinopathies have been shown to affect the density of RBCs and could affect the performance of a density- based test.
  • Sickle cell disease, and spherocytosis are known to increase the density of some or all of the population of RBCs, while ⁇ -thai as semi a, a-thalassemia, and malaria decrease RBC density.
  • Possible markers used to make a diagnosis of IDA include transferrin saturation, and ferritin. These methods, however, are time consuming and impractical in many settings; extensive research has focused on using red blood cell indices to diagnose IDA.
  • hypochromia the condition of having hypochromic RBCs— as %hypo > 3.9%
  • micro/hypo anemia the condition of having hypochromic RBCs and low HGB— as %hypo > 3.9% and when HGB ⁇ 12.0 g/dL for females over 15 yrs, ⁇ 13.0 g/dL for males over 15 yrs, ⁇ 11.0 g/dL for children under 5 yrs, and ⁇ 11.5 g/dL for children 5 to 15 yrs, 8 3) IDA as micro/hypo anemia when %micro/%hypo ⁇ 1.5, and 4) ⁇ -thalassemia.
  • Fig. 9 is a flow chart illustrating the classification, i.e., the diagnosis of hypochromia, micro/hypo anemia, iron deficiency anemia, and ⁇ -thalassemia trait used in this study based on hematological indices measured by a hematology analyzer (Advia 2120, Siemens).
  • An AMPS with n total phases will contain n+1 interfaces ⁇ e.g., there are 3 interfaces in a two phase system: air/phase-1, phase- l/phase-2, and phase-2/container).
  • a properly designed AMPS may: i) have a top layer with density greater than that of plasma and its components (>1.025 g cm "3 ) in order to minimize dilution of the AMPS, ii) have a bottom layer less dense than the average red blood cell density (which are represented by a Gaussian distribution where mature erythrocytes have a density of 1.095 g cm "3 and immature erythrocytes ⁇ i.e., reticulocytes) of 1.086 g cm "3 ) such that normal blood will pack at the bottom of the tube, iii) maintain biocompatibility by tuning the pH (7.4) and osmolality (290 mOsm/kg) to match blood, and iv) undergo phase separation in a short amount of time ( ⁇ 5 minutes) under centrifugation ⁇ e.g., 13,700 g, the speed of the StatSpin CritS
  • AMPS A simple two-phase AMPS (IDA-AMPS-2) to diagnose microcytic and hypochromic anemia and IDA by the presence of a band or streak of redness above the packed hematocrit; 2) A three-phase AMPS (IDA-AMPS-3) to capture microcytic and hypochromic RBCs at two liquid/liquid interfaces and to provide additional information about the density distribution of the RBCs of a patient.
  • IDA-AMPS-2 simple two-phase AMPS
  • IDA-AMPS-3 to capture microcytic and hypochromic RBCs at two liquid/liquid interfaces and to provide additional information about the density distribution of the RBCs of a patient.
  • IDA-AMPS-3 contained 10.2% (w/v) partially hydrolyzed poly(vinyl alcohol) (containing 78% hydroxyl and 22% acetate groups) (MW -6 kD), 5.6% (w/v) dextran (MW -500 kD), and 7.4% (w/v) Ficoll (MW -400 kD).
  • An AMPS diagnostic system easy to use, rapid, and fieldable
  • a drop (5 ⁇ ) of blood is loaded at the top of the tube through capillary action enabled by a small hole in the side of the tube; the hole allows the blood to enter the tube up to and not beyond the hole (by capillary wicking).
  • CV coefficient of variance
  • a elastomeric silicone sleeve is then slid over the hole to prevent the blood leaking during centrifugation.
  • Up to 12 tubes can then be loaded into the hematocrit centrifuge and spun for the desired time.
  • a centrifuge that cost -$1,600 (CritSpin, Iris Sample Processing).
  • HWLab a more portable centrifuge manufactured by HWLab was used that provides similar performance and costs $150 ($60 each for orders > 400 units).
  • the total time needed to perform this assay is less than ten minutes (it can be done in as little as three minutes), and all of the components, including a battery to power the centrifuge, can fit into a backpack.
  • a lead-acid 12V car battery is chosen because it is widely available, has a long life cycle, is relatively low cost, and can be charged by nearly every car and truck in the world as well as by solar panels).
  • 4 lithium ion cells e.g., 18650 cells
  • 9 primary alkaline batteries are used.
  • IDA-AMPS provides three bins of density in which red blood cells can collect: 1) the top/middle (T/M) interface (RBCs ⁇ 1.081 g cm "3 ), 2) the middle/bottom (M/B) interface (RBCs > 1.081 g cm “3 and ⁇ 1.0817 g cm “3 ), and 3) the bottom/seal (B/S) interface (RBCs > 1.0817 g cm “3 ) (Fig. 2A).
  • T/M top/middle
  • M/B middle/bottom
  • B/S bottom/seal interface
  • Blood is loaded into the top of the tube, from a finger prick, using capillary action provided by a hole in the side of the tube.
  • a silicone sleeve is used to cover the hole to prevent leakage during centrifugation.
  • Normal and IDA blood can be differentiated, by eye, after only 2 minutes of centrifugation.
  • White blood cells (leukocytes) collect at the Top/Middle interface. In some cases white blood cells can agglomerate with RBCs, resulting in a slight red color at the Top/Middle interface, even in a normal sample.
  • red cells were more prevalent at the interfaces, while in others, the red color was highly visible in the phases of the AMPS.
  • the guide was available to readers during each reading for reference. An average score was determined based on concordance between at least two of the readers.
  • AMPS aqueous multiphase systems
  • the AMPS test can be evaluated, by eye, and used to diagnose IDA with an AUC of 0.88.
  • IDA is a nutritional disorder, molecular diagnostics are not useful for diagnosis, except for a rare hereditary form of IDA called "iron refractory IDA".
  • the IDA-AMPS test described herein is able to detect microcytic and hypochromic RBCs and diagnose IDA with an AUC comparable to other metrics that have found clinical use, suggesting that it could find widespread use as a screening tool for IDA.
  • this method may find use in rural clinics where large fractions of the population at risk for IDA, such as children and pregnant women, seek care in LMICs.
  • this test may also find use in veterinary medicine.
  • IDA in livestock, especially pigs is increasingly common due to modern rearing facilities that eliminate the animals' exposure to iron-containing soil; IDA in pigs can cause weight loss, retarded growth, and an increased susceptibility to infection.
  • the IDA-AMPS test described herein is a new approach to diagnosing IDA and, using machine learning algorithms, to predict red blood cell indices. Instead of directly measuring a biological marker such as concentration of hemoglobin or serum ferritin, our method relies on observing the way in which red blood cells move through a viscous media (a function of their density as well as size and shape) to make a diagnosis. This approach may be applied to other diseases or biological applications.
  • Centrifugation of blood through IDA-AMPS provides a clear diagnostic for micro/hypo anemia
  • IDA-AMPS-2 provides two bins of density in which blood can collect: 1) blood of low density ( ⁇ 1.081 g cm “3 ) at the interface between the top and bottom phases (T/B), and 2) normal blood (> 1.085 g cm “3 ) at the bottom of the tube above the white sealing clay.
  • IDA- AMPS-3 provides three bins of density: the T/M interface ( ⁇ 1.081 g cm “3 ), the M/B interface (> 1.081 g cm “3 and ⁇ 1.0817 g cm “3 ), and normal blood at the bottom of the tube.
  • Fig. 3A and 3B show examples of IDA-AMPS tests after 2 minutes of
  • FIGs. 3A-B show, for a representative normal (3 A) and IDA (3B) sample, i) a scanned test image, ii) its corresponding red intensity image where each pixel was converted to S/V, iii) 1-dimentional red intensity trace, and iv) the first derivative of the 1-dimentional red intensity trace.
  • Digital analysis of images of the IDA-AMPS tests enables the direct comparison of a large number of samples.
  • Figs. 4A-B the average red intensity for all normal and micro/hypo samples is plotted as a function of distance from the sealed (bottom) end of the tube for different centrifugation times; the shaded region represents the 99% confidence intervals.
  • the red intensity difference between the normal and micro/hypo anemic samples in the majority of the tube is high; most of the red color is spread throughout the phases.
  • the centrifugation time increases, the signal decreases in the phases and increases at the interfaces as red blood cells reach their equilibrium position based on their density.
  • Receiver operating characteristic (ROC) curves were generated for visual analysis of IDA-AMPS-3 (Figs. 5A-5B) using the 1-5 redness threshold for hypochromia, micro/hypo anemia, and IDA.
  • the AUC, sensitivity, and specificity of ID A- AMPS is also comparable to that of a test for IDA using the reticulocyte hemoglobin concentration (CHr)— a red blood cell parameter measured by a hematology analyzer (AUC of 0.91, sensitivity of 93.2% and a specificity of 83.2%). Although not perfect, this performance for CHr has been high enough to gain popularity in clinical use.
  • CHr reticulocyte hemoglobin concentration
  • Intra-reader Reader 1 0.995 (0.994 0.997)
  • Intra-reader Reader 2 0.985 (0.979 0.989)
  • Machine learning providing a method to predict blood parameters and diagnose IDA as an alternative to blinded readers
  • Machine learning is a powerful approach for finding an efficient way to make predictions or decisions from data.
  • the general problem of predicting continuously-varying outcomes from data is called “regression”, and predicting classes, or labels, from data is called “classification”.
  • regression The general problem of predicting continuously-varying outcomes from data
  • classification predicting classes, or labels, from data
  • regression we apply standard machine learning techniques to 1) the classification problem of distinguishing micro/hypo anemic samples from normal samples and 2) the regression problem of predicting continuously-varying red blood cell indices from images of the ID A- AMPS test.
  • PCA principle component analysis
  • the algorithm analyzes the test data set only one time. For this reason, the results for the AUC calculation are presented without error bars.
  • the test provides excellent discrimination for micro/hypo anemia; the AUC for IDA-AMPS diminishes after 6 minutes of centrifugation.
  • Beta-thai as semi a minor (i.e., ⁇ -thalassemia trait, ⁇ - ⁇ ) and a-thalassemia trait are benign genetic disorders that can present a
  • red blood cell indices should have an impact on the distribution and movement of cells in a gradient.
  • the way in which an object moves through an AMPS is related to the density, shape, and size of that object. Many of the parameters measured by a hematology analyzer— so called red blood cell indices— should be related to the distribution and movement of red blood cells in an AMPS.
  • Fig. 7A illustrates Machine learning prediction results for %Hypo
  • %Hypo (Predicted %Hypo) compared to a hematology analyzer (True %Hypo).
  • a Pearson' s r of 1.00 would represent perfect correlation between the machine learning predictions and the values measured by the hematology analyzer.
  • the ability of a machine learning algorithm to predict any variable in a regression problem is related to the total size of the data set. While the number of patients tested here are substantial for a prototype POC device, the predictive ability of the algorithm could likely be improved by increasing the of the data set.
  • Table 4 illustrates hemoglobin concentration thresholds used to define anemia in the study.
  • Table 5 illustrates populations of interest for the patients involved in the assessment of the IDA-AMPS test.
  • Table 5 Populations of interest for the patients involved in the assessment of the IDA- AMPS test.
  • Receiver operating characteristic (ROC) curves and their corresponding area under the curve (AUC) and 95% confidence intervals were calculated in MatLab. Lin's concordance correlation coefficient was calculated using an open-license tool from the National Institute of Water and Atmospheric research of New Zealand
  • the IDA-AMPS test for this Patient A might have a strong band of red only in the bottom phase of the AMPS with some RBCs settled at the M/B interface.
  • Patient B may have 15%) hypochromic RBCs, but those RBCs might have a larger distribution in MCHC (some very low, some only slightly below the threshold).
  • the IDA-AMPS test for this patient might appear to have a strong red streak in both the bottom and middle phases and a small number of RBCs settled at the T/M and M/B interfaces. In both cases, the patients have red cells above the bottom packed cells that are visible by eye and would be classified as IDA, even though the distribution of the red cells is different.
  • EDTA ethylenediaminetetra-acetic acid disodium salt
  • Mallinckrodt sodium phosphate dibasic
  • EMD potassium phosphate monobasic
  • EMD sodium chloride
  • ID A- AMPS was prepared mixing, in a volumetric flask 10.2% (w/v) partially hydrolyzed poly(vinyl alcohol) (containing 78% hydroxyl and 22% acetate groups) (MW -6 kD), 5.6% (w/v) dextran (MW -500 kD), 7.4% (w/v) Ficoll (MW -400 kD), 5 mM EDTA (to prevent coagulation), 9.4 mM sodium phosphate dibasic, and 3.0 mM potassium phosphate monobasic. The solution was brought to volume and the pH was brought to 7.40 ⁇ 0.01 (Orion 2 Star, Thermo Scientific) using sodium hydroxide and hydrochloric acid.
  • the osmolality was measured to 290 ⁇ 15 using a vapor pressure osmometer (Vapro 5500, Wescor). We measured density with an oscillating U-tube densitometer (DMA35 Anton Paar). Rapid tests were prepared as described previously.
  • machine/computer-aided system and method as described herein can be used to analyze sedimentation data and complete blood count.
  • Supervised learning approach may be used to map characteristics of sedimentation data to common hematological parameters.
  • Machine learning provides a method to interpret medical data that is inherently complex and contains multiple dimensions. When applied to medical images, techniques from machine learning are used to perform computer assisted diagnosis in mammograms. When used on complex temporal data, such as electrocardiograms, these methods also aid in the identification of pathologies.
  • the sedimentation of blood in AMPS provides an opportunity to apply machine learning to images of capillaries that evolve over time. Scans of the sedimentation of blood in AMPS over time provide an information-rich set of data with signals that are related to the dynamics of the cells moving through the fluid phases.
  • Fig. 9 shows typical results for line scans of the red luminosity of capillary tubes for blood with different levels of hypochromic red blood cells.
  • initial data will come from a digital scanner with a transmission mode. After spinning blood for short intervals of time in a standard
  • microhematocrit centrifuge scans are collected to create a record of the sedimentation through the tubes over time. Although this information will have lower time resolution than a fully functional analytical micro-centrifuge, this alternative provides a contingency plan to begin analyzing the dynamics of sedimentation to identify parameters that correlate with the measurements of a CBC.
  • AMPS will be developed in SA 2, but a number of AMPS from previous work and preliminary work can be used while other systems are in
  • Validation A common pitfall in applying learning algorithms to rich datasets is over-fitting— fitting a function so tightly to the learning data that natural variations result in poor performance when testing the function on actual test data. Due to the risk of over-fitting in machine learning generally, there are very standard techniques and approaches to avoid it.
  • a key first step is to divide available labeled data into three broad categories: 1) training data, 2) validation data, and 3) test data. The algorithm will be fit on training data, and evaluated on validation data in order to improve the specific parameter choices (every algorithm has "knobs" that need to be set correctly in order to achieve good predictive performance, such as the number of dimensions to reduce image data via PCA).

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Abstract

L'invention concerne un système comprenant: un lecteur servant à générer un profil de répartition de couleur d'un analyte biologique d'intérêt dans un système multiphasique (MPS) séparé en phases sous une première condition de dosage; une mémoire servant à conserver un ou plusieurs algorithmes et une ou plusieurs conditions de dosage, chaque algorithme étant associé à une condition de dosage et configuré pour prédire une caractéristique de l'analyte biologique d'intérêt d'après son profil de répartition de couleur sous la condition de dosage; et au moins une des conditions de dosage étant la première condition de dosage; un processeur d'ordinateur couplé au lecteur et à la mémoire, le processeur d'ordinateur étant configuré pour: recevoir une entrée de la première condition de dosage et le profil de répartition de couleur de l'analyte biologique d'intérêt généré par le lecteur; prédire une caractéristique de l'analyte biologique d'intérêt en utilisant l'algorithme associé à la première condition; et fournir une sortie l'identifiant.
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CN111198262A (zh) * 2018-11-19 2020-05-26 苏州迈瑞科技有限公司 一种用于尿液有形成分分析仪的检测装置及方法
WO2025075824A1 (fr) * 2023-10-02 2025-04-10 Siemens Healthcare Diagnostics Inc. Interface de sédiments urinaires avec compartimentage de classification probabiliste et ses procédés de production et d'utilisation

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