Predictive diagnostic tests for early detection and monitoring of disease

Abstract

A method and system for detecting and diagnosing diseases, including cancer, in which a patient sample is obtained from the patient and analyzed by absorbance spectroscopy in the near-infrared and mid-infrared range to create a spectroscopic signature. This signature is processed by a computational engine using one or more machine learning techniques to determine whether the spectroscopic signature indicates the presence of one or more diseases, including cancer. Embodiments operate outside of traditionally accepted wavelength ranges, facilitating faster, simpler, and more reliable testing.

Description

[Technical Field]

[0001] FIELD OF THE INVENTION This application relates generally to the field of diagnostic testing, and more particularly to spectroscopic diagnostics. [Background technology]

[0002] Cancer remains a significant health problem worldwide and is the second leading cause of death in the United States. Early cancer detection remains the best weapon against this disease, enabling better treatment options and patient outcomes. Yet, current technologies for cancer screening and early detection are risky, expensive, and time-consuming to help win the battle against cancer. Cancer screening is only recommended for high-risk populations based on specific age ranges, smoking history, alcohol use, and potential environmental exposures. Even for populations that do not meet these criteria, such as those with human papillomavirus-associated head and neck cancer, cancer incidence is increasing. Cancer tests and procedures used for cancer screening and diagnosis include radiological imaging, endoscopy, biopsy, and cytology.

[0003] Tests such as computed tomography (CT), X-ray, and positron emission tomography (PET) carry the risk of radiation exposure, which can potentially cause cancer in healthy individuals. Endoscopy and biopsy are invasive and can cause discomfort. Because these methods for detecting cancer at an early stage depend on personal behavior (e.g., scheduling mammograms, colonoscopies, and Pap smears), the discomfort and risks can lead to anxiety and reluctance to undergo prompt testing. In addition to being invasive, histopathological diagnosis carries a high rate of false negatives and false positives due to misdiagnosis. This is due in part to the histological similarity of some tumor types and the poor cellular differentiation that makes it difficult to identify their tissue of origin.

[0004] Various methods of optical spectroscopy have begun to be used for early cancer diagnosis, but few have shown promising results. Fourier transform spectroscopy (or FTIR) detection systems have been investigated. While many cancer biomarkers often have low sensitivity and specificity in the infrared ("IR") or near-infrared range (e.g., 900-3080 nm), systems using higher wavelengths across this significant wavelength range have shown some promise. However, FTIR spectroscopy has shown promise due to the fact that it offers a simple, rapid, relatively accurate, inexpensive, nondestructive, and amenable to automation compared to existing screening, diagnosis, management, and monitoring methods. Infrared spectroscopy is an important tool for the screening and detection of cancer and other diseases because it can help improve patient outcomes by informing decision-making and providing early diagnosis. An example of FTIR spectroscopy is described in Kenneth A. Kristoffersen et al., "Fourier-transform infrared spectroscopy for monitoring proteolytic reactions using dry-films treated with trifluoroacetic acid," 10 SCI REP 7844 (2020) (available at https: / / doi.org / 10.1038 / s41598-020-64583-3).

[0005] Near-infrared (NIR) spectroscopy is not a new approach for cancer screening, but recently published studies have recommended the use of higher wavelengths and combined techniques for measuring multiple types of analytes. The most frequently used range for NIR spectroscopy in cancer detection has been 600–1100 nm. Longer wavelengths tend to be largely absorbed by water, making it much more difficult to see any difference. Water, while optically transparent, is highly absorbing below 200 nm and also absorbs some NIR and more mid- and far-infrared light.

[0006] Additional attempts have been made to utilize liquid biopsy as a means of non-invasive cancer detection. An example of liquid biopsy is given in U.S. Pat. No. 10,288,615, which describes using blood to detect cancer. Additionally, studies and disclosures have been presented that measure the water content in normal cells relative to cancer cells. An example of an NIR spectroscopy technique is given in U.S. Pat. No. 7,706,862, which describes NIR spectral optical imaging using water absorption wavelengths. Spectral optical imaging in the near infrared (NIR) at one or more key water absorption wavelengths is used to identify differences in water content between regions of cancerous or precancerous tissue and regions of normal tissue. Tissue regions in late-stage cancer have more water content than normal tissue.

[0007] The primary water absorption "fingerprint" wavelengths include at least one of 980 nm, 1195 nm, 1456 nm, 1944 nm, 2880 nm to 3360 nm, and 4720 nm. At least one reference wavelength within the 400 nm to 6000 nm range with low or no water absorption, such as 4500 nm, 2230 nm, 1700 nm, 1300 nm, 1000 nm, and 800 nm, is used to create a reference image for comparison with images taken at the primary water absorption wavelength. While NIR can be used to measure water absorption by cells, it is not the most reliable technique and does not always produce clear results. Because water absorbs most wavelengths, imaging can be difficult and accuracy is limited. Molecular fingerprinting involves looking for differences in specific biomarkers and predetermined disease biomarkers that constitute a disease fingerprint. For example, a comprehensive approach to identifying the molecular fingerprint of cancer is needed to determine not just the presence of cancer but also the type of cancer. Fingerprinting is based on spectroscopic signatures using broad-spectrum spectroscopy, a concept well supported by previous research. Using infrared (IR) spectroscopy, differences in serum components can be recorded to generate unique spectroscopic signatures characteristic of different health states. However, any previous studies using similar techniques have focused on longer wavelength IR absorbance to analyze serum biomolecules. Therefore, there is a current need for technologies for non-invasive, low-cost, and low-risk early cancer detection. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] U.S. Patent No. 10,288,615 [Patent Document 2] U.S. Patent No. 7,706,862 [Non-patent literature]

[0009] [Non-Patent Document 1] Kenneth A. Kristoffersen et al., “Fourier-transform infrared spectroscopy for monitoring proteolytic reactions using dry-films treated with trifluoroacetic acid,” 10 SCI REP 7844 (2020) Summary of the Invention [Means for solving the problem]

[0010] The present disclosure provides methods and systems for the diagnosis of disease by performing absorbance spectroscopy in the near-infrared to mid-infrared (NIR-MIR) range of a patient specimen and analyzing the resulting spectroscopic signature to determine the presence of the disease, such as cancer.

[0011] According to one aspect of the present disclosure, a method for diagnosing a disease includes analyzing a sample obtained from a patient by absorbance spectroscopy in the NIR-MIR range to create a spectroscopic signature. The spectroscopic signature is received by a processor. The processor determines whether the spectroscopic signature results in an indication of the presence of the disease and outputs the result. The method optionally includes administering a therapy to the patient to treat the disease when the result indicates the disease is present.

[0012] According to another aspect of the present disclosure, a system for diagnosing the presence of a disease includes a memory and a processor configured to execute instructions stored in the memory to receive a spectroscopic signature created by absorbance spectroscopy in the NIR and / or MIR range of a sample from a patient, determine whether the spectroscopic signature indicates the presence of the disease as a result, and output the result.

[0013] In embodiments, the specimen may be a lysate specimen. The lysate specimen may be obtained by adding a solubilization solution or homogenization solution to a serum specimen. The solubilization solution or homogenization solution may be added to the serum specimen in a 1:1 ratio. In embodiments, at least one of a photobinding solution or a proteolytic reagent is added to the lysate specimen. In embodiments, any combination of a solubilization solution, a homogenization solution, a photobinding solution, and a proteolytic reagent may be added to the lysate specimen.

[0014] Embodiments may further include drying the specimen on an IR reflectance sampling card, which may be coated with aluminum or other non-IR absorbing material.

[0015] In embodiments, the processor can determine whether the spectroscopic signature indicates the presence of a disease by providing the spectroscopic signature to a computational engine that includes a model architecture and one or more model parameters. The computational engine can execute a computational algorithm, such as a machine learning algorithm, configured to provide a result based on the spectroscopic signature, the model architecture, and the one or more model parameters.

[0016] In embodiments, the computation engine may receive feedback indicating the accuracy of the results, and at least one model parameter may be updated based on the feedback.

[0017] According to another aspect of the present disclosure, a method for detecting a pathogen in a specimen is disclosed, the method including receiving a whole blood specimen from a patient into a coagulation cuvette, operating the coagulation cuvette to release a serum specimen into an analysis chamber, inserting the cuvette into a spectrophotometer using near-infrared and / or mid-infrared spectra, determining whether a pathogen is present in the serum specimen, and outputting a result indicating whether the pathogen is detected.

[0018] According to another aspect of the present disclosure, a system for detecting a pathogen in a specimen includes a coagulation cuvette, a spectrophotometer using the near-infrared and / or mid-infrared spectrum, and a processor associated with the spectrophotometer and configured to perform an analysis of the spectrophotometric signature output by the spectrophotometer.

[0019] According to another aspect of the present disclosure, a serum separation cuvette includes a serum analysis chamber including a specimen container and one or more serum channels above the specimen container, and a coagulation chamber including a coagulation agent for coagulating a specimen introduced into the coagulation chamber, a coagulation strainer for removing clotted whole cells from the specimen, and a channel plug, wherein the channel plug and serum channel can form a releasable seal between the coagulation chamber and the analysis chamber so that specimen serum can flow into the analysis chamber.

[0020] Embodiments of the present disclosure provide an artificial intelligence and machine learning-driven optical molecular sensing system and method for disease detection, including early cancer detection. The embodiment includes a portable, self-powered optical sensing device, disposable sampling tube, or microfluidic cassette (microcuvette) with an optical sensing cocktail solution. Data is collected and analyzed by an application with a predictive training database on a smart device or computer (local and cloud-based). The system can be applied to the analysis of cancer-specific biomolecules performed directly in serum. Biomolecular analysis requires a relatively small sample volume (approximately 50-200 μL), and the analysis takes 20 minutes or less without damaging the sample.

[0021] The above summary is not intended to describe each illustrated embodiment or every implementation of the subject matter of this specification. The figures and detailed description that follow more particularly exemplify various embodiments.

[0022] The subject matter herein may be more fully understood from the following detailed description of various embodiments, when considered in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a flow chart illustrating a method for detecting disease using a centrifuge and serum, according to one embodiment. [Figure 2] 1 is a flow chart illustrating a method for detecting disease using a serum cocktail, according to one embodiment. [Figure 3] 1 is a flowchart illustrating a method for detecting a disease using a specialized cuvette, according to one embodiment. [Figure 4] 1 is a flow chart illustrating a method for detecting disease using lysate, according to one embodiment. [Figure 5] FIG. 2 is a schematic diagram illustrating a calculation engine, according to one embodiment. [Figure 6] FIG. 1 is a schematic diagram illustrating a method for training a computation engine, according to one embodiment. [Figure 7] FIG. 10 is a diagram of a normalized spectrum showing the difference in spectroscopy output. [Figure 8] FIG. 10 is a diagram of a normalized spectrum showing the difference in spectroscopy output. [Figure 9] FIG. 10 is a diagram of a normalized spectrum showing the difference in spectroscopy output. [Figure 10] FIG. 10 is a diagram of a normalized spectrum showing the difference in spectroscopy output. [Figure 11A] 10 is a chart illustrating an exemplary output according to one embodiment. [Figure 11B] 10 is a chart illustrating an exemplary output according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0024] While various embodiments are susceptible to various modifications and alternative forms, details thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that the invention is not limited to the particular embodiments described. On the contrary, the invention is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the subject matter defined by the claims.

[0025] Non-invasive diagnosis and early detection of diseases such as cancer remain major issues in today's healthcare. New and emerging non-invasive tests are being developed, including biomarker / genetic tests, liquid biopsies, and breath biopsies. However, high costs, lengthy testing procedures, and low positive test accuracy make the above tests unsuitable for routine testing. The disclosed systems and methods are designed to revolutionize liquid biopsy-based diagnostics, which can be performed directly in serum or plasma, quickly, and at low cost, eliminating the need for multiple, lengthy steps of specimen processing.

[0026] Liquid biopsy-based diagnostics can provide high sensitivity and accuracy for disease detection. Current liquid biopsy techniques for cancer diagnosis include the analysis of circulating tumor cells (CTCs) and cell-free circulating tumor (ct) DNA, mRNA, and microRNA (miRNA) in blood samples. Others involve the isolation and analysis of extracellular vesicle / exosome biomarkers from plasma or serum. These techniques rely on the analysis of a predefined set of biomarkers based on the use of flow cytometry, next-generation sequencing polymerase chain reaction (PCR) and related chemistry-based detection, and protein / glycan detection. However, these technologies are still in their infancy, and their success is limited by multiple factors.

[0027] First, due to the heterogeneity of cancer, no single liquid biopsy platform can consistently and reliably measure a given set of biomarkers for early cancer detection. Second, sample analysis, which requires multiple steps of preparation, manipulation, lengthy chemical reactions, detection, and data analysis, is prone to technical and human error, not to mention the high costs for technology development and utility in clinical settings. Finally, current liquid biopsy technologies require large sample volumes for analysis, and samples cannot be reused for multiple assays. The solution to these problems is an innovative, nondestructive, broad-spectrum molecular fingerprint-based liquid biopsy technology that goes straight from serum to detection and delivers results within 10–30 minutes without lengthy sample processing. Furthermore, samples can be reused for multiple assays.

[0028] The disclosed optical sensing is based on the spectrophotometric analysis of cancer-specific molecular "fingerprints" produced by complex mixtures of biomolecules present in serum. This technique works on the principle that blood samples from patients with advanced cancer contain biomolecules with structural compositions that differ from samples from healthy individuals. These include differences in biochemical components, proteins, circulating cell-free DNA / RNA / miRNA, and glycans (carbohydrates) encapsulated in lipid-coated extracellular vesicles (EVs), such as exosomes, which are present in serum as well as soluble in serum.

[0029] Exosomes are small membrane-bound EVs secreted by immune cells for intercellular communication, including those responding to the presence of cancer. Although they are smaller than other EVs, such as apoptotic bodies (released from dying cells) and microvesicles found in serum, they contain biological components crucial to cancer cells. More importantly, cancer cells generally secrete more exosomes and process more biomolecules than healthy cells. The overall cancer-associated exosome content is distinguishable from healthy exosomes because they are used to mediate communication between other cancer cells within the tumor microenvironment (or niche) to regulate and suppress immune responses and establish a niche that supports cancer growth and spread. Additionally, serum biomolecules, particularly the densely packed cargo within exosomes, form distinct molecular interactions and chemical bonds that result in unique molecular fingerprints for different physiological and pathological conditions.

[0030] Unlike other techniques that focus specifically on DNA, mRNA, miRNA, or exosomes, the present disclosure detects a complex mixture of all components present in serum and larger EVs and exosomes. This is beneficial because, at a minimum, cancer-associated components are also found in larger microvesicles and apoptotic bodies of dying cancer cells. By measuring the biochemical and physical properties of serum and EV components, differences in molecular fingerprints can be correlated with the presence of different types of cancer. The fingerprints are based on spectroscopic signatures using broad-spectrum spectroscopy. This concept is well supported by multiple studies reported in the literature and our own preliminary data. Using infrared (IR) spectroscopy, differences in serum molecular composition can be recorded to generate unique spectroscopic signatures, or fingerprints, characteristic of different health states.

[0031] The present disclosure provides rapid, low-cost, and low-risk screening and monitoring for early signs of diseases such as cancer using a small blood sample. Because the present disclosure can use optical sensing technology, the sample is not destroyed and can be used for other tests without requiring additional blood draws. The cancer sensing strategy disclosed herein explores differences in broad, biomarker-independent cancer fingerprints and combines them with signals specific to a given cancer biomarker to generate over 1,000 data points in a single reading (approximately 2 seconds per scan). This technique enables early detection of different types of cancer from just one test using a single sample.

[0032] The disclosed technology measures serum for molecular fingerprinting using a base component of absorption spectra ranging from visible and near-infrared (VIS / NIR) to mid-infrared (MIR) light, combining modulated NIR / MIR spectra with a specific biomarker-specific fluorescence spectrum. This allows sensing devices embodying the present disclosure to be compact, portable, and battery-powered. In addition to the base spectroscopic signature and biomarker-specific signal that define the molecular fingerprint, we also measure modulated absorption spectra using specific compounds, peptides, and antibodies designed to bind to specific sets of cancer-specific proteins, nucleic acids, and carbohydrates to create a broad molecular fingerprint for a multi-cancer detection platform and increase the accuracy of early cancer detection.

[0033] Throughout this specification, wavelength or wavenumber ranges are referred to as "mid-IR" or "near-IR." Depending on the context, these terms can have different meanings in various technical disciplines. However, for purposes of this document, near-IR or NIR should be understood to refer to light having wavelengths between about 600 nm and about 2500 nm, and for most of the applications described herein, between 600 nm and 1100 nm. Mid-IR or MIR refers to light having a wavelength range between about 1250 nm and about 25,000 nm, and for most of the applications described herein, between 2500 nm and 25,000 nm. An "infrared light source" refers to one or more light sources that generate or emit radiation in the infrared wavelength range; for example, an "infrared light source" can include wavelengths in the mid-IR (2-2.5 microns). The infrared light source may generate radiation over most of these wavelength subranges, or may have a tuning range that is a subset of one of the wavelength ranges, or may provide radiation over multiple discrete wavelength ranges, e.g., 2.5 to 4 microns or 5 to 13 microns. The radiation source may be one of a number of sources, including thermal or global sources, supercontinuum laser sources, frequency combs, difference frequency generators, sum frequency generators, harmonic generators, optical parametric oscillators (OPOs), optical parametric generators (OPGs), quantum cascade lasers (QCLs), nanosecond, picosecond, femtosecond, and attosecond laser systems, CO2 lasers, heated cantilever probes or other tiny heaters, and / or any other source that creates a beam of radiation. The source may be narrowband, e.g., having a spectral width less than 10 cm or less than 1 cm, or broadband, e.g., having a spectral width greater than 10 cm, greater than 100 cm, or greater than 500 cm.

[0034] "Infrared absorption spectrum" refers to a spectrum proportional to the wavelength dependence of the infrared absorption coefficient, absorbance, or similar indication of an analyte's IR absorption properties. An example of an infrared absorption spectrum is the absorption measurement produced by a Fourier transform infrared spectrometer (FTIR), i.e., an FTIR absorption spectrum. Generally, infrared light is either absorbed (i.e., part of an infrared absorption spectrum), transmitted (i.e., part of an infrared transmission spectrum), or reflected. The reflection or transmission spectrum of collected light can have different intensities at each wavelength compared to the intensity at that wavelength in the probe light source.

[0035] Terms such as "about" or "approximately" are synonymous and are used to indicate that the value modified by such terms has an understood range associated with it, which may be ±20%, ±15%, ±10%, ±5%, or ±1%.

[0036] Embodiments of the present disclosure are directed to disease detection devices and procedures generally shown in flow path 100 of Figure 1. While the present disclosure focuses on cancer as the diagnosis of interest, by way of example, the principles, devices, and methods of the present disclosure may be applicable to the diagnosis of other diseases, such as heart disease, diabetes, COVID-19, etc.

[0037] A whole blood sample 102 is drawn from a patient and centrifuged 104 to extract serum 106. The serum is then optically analyzed 108 to create a spectral signature 110, or "molecular fingerprint," of the EVs and exosomes present in the patient's blood. This spectral signature contains unique peaks for each type of molecule present in the serum, including lipids, proteins, carbohydrates, and nucleic acids.

[0038] Although a whole blood sample 102 is described as the target sample in this and other examples herein, in embodiments, other samples may be used, including, but not limited to, cerebrospinal fluid or other liquid biopsies, saliva, urine, etc. Analysis of solid or liquefied tissue, or vapors, e.g., exhaled breath, is also contemplated. Whole blood may generally be desirable due to the ease and non-invasive nature of drawing a whole blood sample, as well as the overall representation of blood relative to cells of various systems of the body.

[0039] Centrifuging 104 the specimen removes whole blood cells from the serum 106 and prevents larger whole cells from obscuring the EVs and exosomes. In embodiments, centrifuging the specimen may be omitted, allowing the whole blood specimen to be analyzed by spectrophotometer 108.

[0040] In embodiments, a sensor cocktail solution can be added to serum 106 prior to analysis by optical sensor 108. The sensor cocktail solution is a mixture of antibodies, peptides, and / or molecular binding reagents against cancer-specific proteins and biomolecules, including EGF, INHBA, CD44, and other extracellularly secreted / released molecules present in serum associated with different types of cancer. The antibodies and peptides are either unbound for measurements of modulated absorption spectra or labeled with fluorescence resonance energy transfer (FRET) fluorophores for analysis of fluorescence spectra. The addition of the sensor cocktail modifies the output signature according to whether the target molecule is present, providing presence / absence detection of whether the cancer-specific molecule is present in the sample. Target antibodies can be labeled by any means available in the art, but such modification is not required for target identification under the principles and methods of the present disclosure.

[0041] Various possible cocktail combinations are envisioned, consisting of various antibodies, peptides, and molecular binding reagents known in the art. For example, a general cancer detection solution may identify a broad array of EVs and exosomes associated with various cancer lines to provide a screen measure for whether more targeted testing is appropriate. Other cocktail solutions may be tailored specifically to cellular products associated with specific cancer lines, such as lung cancer, breast cancer, etc., or to generate outputs unique to specific cancer lines.

[0042] The analyte may be analyzed by a spectrophotometer 108 to produce a spectroscopic signature 110. While any broad-spectrum spectroscopy frequency may be used, in some desirable embodiments, the infrared (IR), near-infrared (NIR), or visible (VIS) light spectrum may be used. Shorter wavelengths in these ranges may be desirable to allow smaller, more compact spectrophotometers to be used, which may additionally increase the portability of the required equipment, improving the mobility and availability of testing. Shorter wavelength spectrophotometers may also be desirable due to lower power requirements and easier sample preparation since aqueous analytes can be analyzed.

[0043] Despite these advantages offered by the NIR or MIR range, the spectroscopic signature 110 created is often complex due to the broad overtone and combined bands created by the NIR or MIR absorbance. As described in more detail below with respect to FIG. 4, the computational engine 600 may be configured to receive the spectroscopic signature 110 and automatically receive one or more interpretations of the spectroscopic signature 110.

[0044] FIG. 2 is a schematic diagram illustrating possible modes of specimen collection and preparation, according to an embodiment.

[0045] First system 200 can include rapid blood clotting microcentrifuge tubes for blood collection and a high-speed microcentrifuge for serum separation, optically clear microcuvettes for serum and sensor cocktail mix, Bluetooth / Wi-Fi connected optical sensors, and a computer or smart device with a software application that includes or can be communicatively connected to calculation engine 600. Thus, system 200 can provide a laboratory-based analytical system.

[0046] The second system 300 can include a serum separator microcuvette designed to collect a drop of blood from a finger prick, which can be performed at home for personal use or in other non-laboratory settings. The collected serum can be mixed with a pre-formed sensor cocktail and analyzed in a portable optical sensor, or sent to a laboratory for more comprehensive analysis. Thus, the system 300 can provide a more portable analytical system, such as a home or mobile system.

[0047] FIG. 3 is a schematic diagram illustrating a serum separation microcuvette provided by an embodiment. A patient or other user may have a small, low-power spectrophotometer, such as a NIR or VIS spectrophotometer, either personally owned or available in a public setting such as a pharmacy or clinic. Such a machine, personal or public, may be designed to directly receive and analyze the serum separation microcuvette. A user may self-screen for cancer or other diseases of interest by performing a finger prick 302 and depositing a few drops of whole blood into the serum separation microcuvette 304. The serum separation microcuvette may be placed in an optical sensor 306 and analyzed by an associated processor 308. Output results are generated 310 and may be presented to the user, or the system may be configured to allow the user to send the results directly to a healthcare provider, such as by appropriately contacting a healthcare provider or by integrating with an electronic medical record system.

[0048] Serum separation microcuvette 400 includes a clotting chamber 410 and a serum analysis chamber 420. In embodiments, microcuvette 400 may be pre-organized to aid in its ease of use. The clotting chamber 410 is placed above the serum analysis chamber 420. A user may place a few drops of blood into the upper clotting chamber 410, for example, by fingerstick. Clotting blood cells are trapped in clotting chamber 410, and serum is allowed to flow down or by gravity into the lower serum analysis chamber 420.

[0049] The clotting chamber 410 includes a self-locking cap 412, a coagulation strainer 414, and a serum channel plug 416. The clotting chamber 410 is coated with a clotting agent, which causes blood or other biological specimens to begin clotting upon application to the chamber. The self-locking cap 412 may be configured, for example, with an interference fit, to permanently seal the open top of the clotting chamber 410. The bottom of the clotting chamber may be releasably sealed, such as by a channel plug 416. The coagulation strainer 414 may be integrated with the cap 412, or another element placed on top of the channel plug 416, to serve to capture coagulated cells so that the serum can be released into the lower analysis chamber 420. The channel plug 416 may retain the serum and cells of the specimen within the clotting chamber 410 until enough cells are removed by the strainer 414 to produce a satisfactory serum specimen. The plug 416 may be released, such as by lifting or twisting the clotting chamber 410, to allow serum to flow into the lower analysis chamber 420.

[0050] Analysis chamber 420 includes a reservoir 422 and a drain channel 424. Drain channel 424 at the top of analysis chamber 420 can direct serum to reservoir 422 below, providing some additional analyte filtration. Channel 424 may also participate in a releasable seal between clotting chamber 410 and analysis chamber 420. For example, channel 424 may interface with plug 416 of the clotting chamber to prevent analyte from entering analysis chamber 420 until plug 416 is released, e.g., by lifting clotting chamber 410 and removing plug 416 from between channel 424. Once collected in reservoir 422, serum may be analyzed, such as by spectroscopy. In embodiments, the analysis chamber may also serve to enable cocktail mixing by adding target antibodies or peptides onto channel 424 or directly into reservoir 422, such that serum mixes with the target substance as it collects in reservoir 422. At least the container 422, and in embodiments all or any components of the cuvette 400, are formed from an optically transparent material.

[0051] In embodiments, the disclosed method can be used with a lysate, generally shown in flow path 500 in FIG. 4 . A serum sample 502 is prepared by drawing a whole blood sample from a patient and centrifuging it to remove red blood cells. An EV solubilization and homogenization solution is then added to the serum 502 in a 1:1 or optimal ratio to dissolve the EVs in the serum 502 and create a lysate 504. A base sample 506 consisting of the lysate alone can be used for optical fingerprinting, or the lysate 504 can be modified prior to optical fingerprinting. A first modified sample 508 can include an optical binding solution added to the lysate for optical fingerprinting. The optical binding solution can include antibodies, peptides, and other components for cancer-type-specific proteins and biomolecules. A second modified sample 510 can include a proteolytic reagent added to the lysate 504. The proteolytic reagent can break down peptides in the lysate into smaller peptides for optical fingerprinting. In some embodiments, a combination of two or more of lysate alone, lysate with a photobinding solution, and lysate with a proteolytic reagent may be used as the optical fingerprinting specimen.

[0052] The optical fingerprinting specimen may then be applied and dried onto an attenuated total reflectance ("ATR") crystal 512 or an infrared ("IR") reflectance sampling card 514. Once the optical fingerprinting specimen is dried, Fourier transform infrared spectroscopy (FTIR) is performed 516 using systems and methods known in the art. When FTIR is performed on an ATR crystal, an IR beam travels into the ATR crystal, which has a defined refractive index, and passes through the specimen on the dried film. The IR beam is refracted back into the crystal, and a detector detects the resulting wavelengths to create a spectroscopic signature.

[0053] In some embodiments, the optical fingerprinting specimen is dried onto an IR reflectance sampling card 514, such as an aluminum-coated card. Aluminum is highly reflective and reflects IR beams, and other coatings are contemplated, such as coatings containing one or more materials, such as gold, indium, tin oxide, zinc oxide, etc. The coated IR reflectance sampling card is then subjected to FTIR 516, where an IR beam passes through the card and dried specimen, reflecting the IR beam that is detected by a detector, and the reflected beam creates a spectroscopic signature.

[0054] Serum analysis using the disclosed systems and methods can produce results within 30 minutes, 20 minutes, or 10 minutes, depending on the system configuration. The present disclosure can be advantageously used in low-resource environments using an integrated system consisting of an optical sensor device, either a microcentrifuge or a serum separation microcuvette, and a smartphone or laptop computer. After separation from the thrombus, the serum sample placed inside the microcuvette is analyzed using the optical sensor device to perform base and modulated absorbance spectroscopy. Using the methods and systems disclosed herein, small samples can yield conclusive results; for example, samples as small as 50 μl, 60 μl, 70 μl, 80 μl, or even less can be effectively used.

[0055] 5 is a schematic diagram illustrating components of a computational engine 600, according to one embodiment. In an embodiment, computational engine 600 may use one or more computational techniques, including artificial intelligence techniques such as machine learning, to enable efficient interpretation of spectroscopic signatures.

[0056] Many different machine learning algorithms exist, and in general, machine learning algorithms seek to approximate an ideal target function f that best maps the input variables x (domain) to the output variables y (range), and thus y=f(x).

[0057] Therefore, machine learning algorithms as approximations of f are suitable for providing predictions of y. Supervised machine learning algorithms generate models for approximating f based on training data sets, each of which is associated with an output y. Supervised algorithms generate models that approximate f through a training process in which predictions can be formulated based on the outputs y associated with the training data sets. The training process can be repeated until the model achieves a desired level of accuracy on the training data.

[0058] Other machine learning algorithms do not require training. Unsupervised machine learning algorithms generate models that approximate f by inferring structure, relationships, themes, and / or similarities present in the input data. For example, rules may be extracted from the data, mathematical processes may be applied to systematically reduce redundancy, or data may be structured based on similarities. Semi-supervised algorithms, such as hybrids of supervised and unsupervised approaches, may also be employed.

[0059] In particular, the range y of f may be, among other things, a set of collections of classification schemes, whether formally enumerated, extensible, or imprecise, such that the domain x is classified, e.g., for labeling, categorization, etc.; a set of clusters of data, where the clusters may be determined based on features of the domain x and / or intermediate range y′; or a continuous variable, such as a single value, a series of values, etc.

[0060] Regression algorithms for machine learning can model f over a continuous range y. Examples of such algorithms include ordinary least squares regression (OLSR), linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines (MARS), and local estimation scatterplot smoothing (LOESS).

[0061] Clustering algorithms can be used, for example, to infer f and describe hidden structure from data, including unlabeled data. Such algorithms include k-means, mixture models, neural networks, and hierarchical clustering, among others. Anomaly detection algorithms can also be employed.

[0062] Classification algorithms address the difficult task of identifying to which of a set of collections of categories (ranges y) one or more observations (domains x) belong. Such algorithms are typically supervised or semi-supervised, based on a training set of data. Algorithms can include linear classifiers such as Fisher's Linear Discriminant, logistic regression, and naive Bayes classifiers; support vector machines (SVMs) such as least-squares support vector machines; quadratic classifiers; kernel estimation; decision trees; neural networks; and learning vector quantization, among others.

[0063] Computational engine 600 may be a discrete software module in that it may be single, separate and / or unique and may be portable in the sense that computational engine 600 may be stored or transmitted for execution in potentially multiple execution environments, such as physical or virtual computer systems or software platforms executing in computer systems, such as runtime environments, operating systems, platform software, etc.

[0064] The computation engine 600 may include an executable computation algorithm 602, such as any of the machine learning algorithms previously described herein or other suitable machine learning algorithms apparent to one of ordinary skill in the art. A suitable machine learning algorithm may be configured to run within the computation engine 600 based on input parameters, including, for example, domain data and / or configuration parameters, as input to the algorithm to generate machine learning results, such as range data and / or other output data. For example, the computation algorithm 602 may be provided as a software object or method subroutine, as a procedure or function in a software library. Thus, the machine learning algorithm is executable to perform machine learning functions, including any or all of a training phase of operation for the algorithm to train the computation engine 600 in a supervised or semi-supervised manner, and / or a processing phase of operation for the algorithm to provide one or more machine learning results.

[0065] The computation engine 600 may further include a data storage device 604 for storing data from the algorithm. The data storage device may be a volatile or non-volatile storage device, such as a memory. The data storage device 604 may be used to store data required by the algorithm, such as machine learning parameters 606, and machine learning data structures, including, among other things, representations of the machine learning model 608, such as a tree data structure, a representation of a regression analysis data structure, a representation of a neural network data structure, variables, and any other data that may be stored by a machine learning algorithm that will be apparent to one skilled in the art. Thus, in this manner, the computation engine 600 provides a discrete encapsulation of one or more machine learning algorithms and the data required for or by such algorithms.

[0066] Computation engine 600 may further include an interface 610 for communicating outside of computation engine 600 to receive inputs and deliver outputs. For example, machine learning parameters, including configuration information, training data, and machine learning input (domain) information, may be communicated via the input as at least part of input data 612. Results 614, such as predictions generated by the model, may be communicated as outputs via the interface. Feedback 616 may include additional inputs that may be used during individual operations of training computation engine 600 or provided as inputs during normal operation of computation engine 600 to provide iterative learning.

[0067] Interface 610 can receive user input and provide output to a user regarding the configuration of computation engine 600. Interface 610 can include a mobile application, a web-based application, or any other executable application framework. The interface can reside on, be presented on, or be accessed by any computing device capable of communicating with various components of computation engine 600, receiving user input, and presenting output to a user. In embodiments, the interface can reside on or be presented on a smartphone, a tablet computer, a laptop computer, or a desktop computer.

[0068] Computational engine 600 may implement any suitable machine learning algorithm or architecture having any number of layers, such as convolutional layers, activation layers, pooling layers, etc. Furthermore, computational engine 600 may be trained by any suitable training method. In one embodiment, computational engine 600 may include a single-layer neural network and may be trained using supervised training techniques.

[0069] Training can be by targeting known molecules and by providing a spectrum of known diagnostics and controls. In embodiments, results can be normalized to total absorbance before analysis to increase overall clarity and consistency of results.

[0070] In one embodiment, the training data may include spectroscopic signatures 110, which may be labeled with known diagnoses, such as healthy (cancer-free), breast cancer, lung cancer, other cancer, or a combination thereof. Thus, the computational engine 600 may classify a given spectroscopic signature input to indicate a diagnosis (along with its likelihood).

[0071] The training data may also include spectroscopic signatures 110 that are labeled based on the presence (or lack thereof) of known molecules of interest (lipids, proteins, etc.). Thus, the computational engine can classify a given spectroscopic signature input as indicating the presence (along with their likelihood) of one or more molecules of interest.

[0072] In addition to the spectroscopic signature 110, in embodiments, the input to the calculation 112 (whether in the training data or in the data to be evaluated) may include additional information about the patient, including demographic data (age, ethnicity, etc.), health characteristics (height, weight, medical history, medications, etc.), etc. Thus, the calculation engine 600 can use the additional patient data to determine the output.

[0073] 6 is a flowchart illustrating a method 1000 for training a computational engine 600. At 1002, a labeled dataset spectroscopic signature 110 may be received or provided; as described above, the dataset may further include additional patient data. At 1004, the labeled dataset may be divided into training data and test data; for example, the training dataset may include a certain percentage, such as 60%, of the entries in the labeled dataset. At 1006, the training dataset, including associated known diagnoses, molecular presence, or other factors, may be provided to the model. At 1008, the model may generate a first set of parameters by any appropriate method.

[0074] At 1010, some or all of the test dataset may be provided to the model without known entries. At 1012, predictions generated by the model may be compared to the known entries for each input in the test dataset. If the recommendation at 1012 indicates an acceptable amount of error, additional testing may occur by iterating at 1016. If the recommendation at 1012 indicates an unacceptable amount of error, the classification parameters may be modified before proceeding to the iteration at 1016. This testing phase may be repeated until the test dataset is exhausted, the amount of error steadily reaches a minimum threshold for a set period of time, or some other criterion is met.

[0075] 7-10 are charts showing examples recorded by embodiments of the present disclosure described in detail below. [Example]

[0076] Proof of concept A 200 μl serum sample per patient was collected by centrifugation of clotted whole blood. Spectroscopy was performed to measure base and modulated absorption spectra in the range of 403 nm to 724 nm with a resolution (interval) of approximately 3–4 nm. All absorption spectra were run consecutively for approximately 5–10 seconds or until the absorption signal stabilized (usually within 20 seconds). The final absorption spectra for each sample were averaged over the total measurement time. Averaged base absorption spectra of serum samples from four healthy (non-cancer) individuals, three lung cancer patients, and two breast cancer patients were normalized to the total absorbance value for each sample individually and plotted for visualization (Figure 7). While distinctive spectra were observed between healthy individuals and lung cancer patients, differences between healthy individuals and breast cancer patients were less obvious but still discernible. [Example]

[0077] specificity To determine whether serum spectroscopy from cancer patients was a measure of cancer-related molecular fingerprints, lung and breast cancer serum samples were mixed with six different healthy serum conditions. If the observed absorption spectrum depended on the total serum content, regardless of cancer, cancer-specific spectroscopy would be masked by non-cancer healthy serum components, resulting in indistinguishable spectra between healthy and cancer samples. To increase serum complexity, two healthy serum samples were mixed 1:1 to generate six different serum combinations. Each cancer serum sample was mixed 1:1 with each healthy serum mixture, creating 18 lung cancer samples and 12 breast cancer samples at a 50% dilution for each sample. The cancer samples diluted 50% into the different healthy serum combinations produced more distinctive absorption spectra compared to the six different healthy serum combinations (Figure 8). These results demonstrate that molecular fingerprint spectroscopy was specific to the lung and breast cancer samples tested. [Example]

[0078] modulation We examined whether the absorption spectrum could be modulated using anti-human epidermal growth factor (EGF) antibodies. EGF is an important growth factor produced by cancer cells for tumor formation and is present at higher levels in serum from cancer patients. The hypothesis was that anti-EGF antibodies would bind to the EGF protein present in the serum of cancer patients, altering its overall molecular binding and resulting in a change in the protein's optical absorption spectrum. To prove this hypothesis, healthy serum and lung cancer serum were mixed with 1 μg of antibody (at a total of 1% by volume) and incubated for 0, 10, and 20 minutes at ambient temperature.

[0079] At 10 minutes (data not shown) and 20 minutes of incubation, a slight increase in the absorption spectrum of healthy serum was observed between 403 nm and 425 nm, whereas the absorbance of lung cancer serum was specifically modulated at different wavelengths (Figures 9 and 10, Chart A). The absorbance of lung cancer serum shifted upward in the shorter wavelength region after 10 minutes of incubation and downward after 20 minutes of incubation (Figure 10, Chart B). In contrast to the 0 and 10 minutes of incubation, the serum shifted upward in the longer wavelength region after 20 minutes in the presence of EGF antibody (Figure 10, Chart C). These results indicate that the increased absorbance at shorter wavelengths was due to the presence of unbound antibody. Upon binding to the EGF protein in cancer patient serum, the antibody increased the absorbance at longer wavelengths, creating a modulated absorption spectrum. The increase in absorbance appeared to correlate with the increase in wavelength. As part of our technology development, spectroscopy will be performed beyond 724 nm to wavelengths up to 950 nm or longer to determine if further increases in absorbance are observed. [Example]

[0080] identification To demonstrate whether the disclosed system can distinguish lung cancer from breast cancer serum samples and healthy serum samples, a neural network machine learning algorithm was used to train and build a predictive model using normalized data from serum spectroscopy. All tested samples, including undiluted and diluted (up to 64-fold) samples, were included in a leave-one-out cross-validation experiment, in which one sample was withheld and the remaining samples were used to train the model. The cross-validation process was repeated until all samples were tested. The results of the machine learning cross-validation are visualized in Figure 11A. The predictive model was able to accurately classify healthy samples with a specificity of 97.9%, lung cancer samples with a specificity of 100%, and breast cancer samples with a specificity of 92.9%, regardless of sample dilution (Figure 11B). More breast cancer samples can be added to ensure sufficient training and improve prediction accuracy.

[0081] In one embodiment, system 100 and / or its components or subsystems may include computing devices, microprocessors, modules, and other computers or computing devices, which may be any programmable device that receives digital data as input, is configured to process the input according to instructions or algorithms, and provides results as output. In one embodiment, the computing devices and other such devices described herein may be, include, contain, or be coupled to a central processing unit (CPU) configured to execute computer program instructions. Thus, the computing devices and other such devices described herein are configured to perform basic arithmetic, logical, and input / output operations.

[0082] Computing devices and other devices described herein may include memory. Memory may include volatile or nonvolatile memory as required by an associated computing device or processor to provide space for executing instructions or algorithms, as well as to provide space for storing the instructions themselves. In one embodiment, volatile memory may include, for example, random access memory (RAM), dynamic random access memory (DRAM), or static random access memory (SRAM). In one embodiment, nonvolatile memory may include, for example, read-only memory, flash memory, ferroelectric RAM, hard disk, floppy disk, magnetic tape, or optical disk storage. The foregoing list in no way limits the types of memory that may be used, as these embodiments are given by way of example only and are not intended to limit the scope of the present disclosure.

[0083] In one embodiment, a system or its components may comprise or include various modules or engines, each of which is constructed, programmed, configured, or otherwise adapted to autonomously perform a function or set of functions. As used herein, the term “engine” is defined as either an actual device, component, or arrangement of components implemented using hardware, such as with an application-specific integrated circuit (ASIC) or field-programmable gate array (FPGA), or as a combination of hardware and software, such as with a microprocessor system and a set of program instructions, which (when executed) adapt the engine to perform a particular function that transforms the microprocessor system into a dedicated device. An engine may also be implemented as a combination of two functions, with some functions facilitated solely by hardware and other functions facilitated by a combination of hardware and software. In some implementations, at least a portion, and possibly all, of the engine may run on processors of one or more computing platforms consisting of hardware that executes an operating system, system programs, and application programs (e.g., one or more processors, data storage devices such as memory or drive storage, input / output facilities such as network interface devices, video devices, keyboards, mice, or touchscreen devices, etc.), while also implementing the engines using multitasking, multithreading, distributed processing (e.g., cluster, peer-to-peer, cloud, etc.), or other such techniques, as appropriate. Accordingly, each engine may be implemented in a variety of physically feasible configurations and generally should not be limited to any particular implementation illustrated herein unless such limitations are expressly stated. Additionally, an engine may itself be composed of two or more sub-engines, each of which may be considered an engine in itself.Moreover, while in the embodiments described herein, each of the various engines corresponds to a defined autonomous function, it should be understood that in other contemplated embodiments, each function may be distributed among two or more engines. Similarly, in other contemplated embodiments, multiple defined functions may be implemented by a single engine that performs those functions, possibly in parallel with other functions, or may be distributed among a different set of engines than those specifically illustrated in the examples herein.

[0084] Various embodiments of systems, devices, and methods have been described herein. These embodiments are provided by way of example only and are not intended to limit the scope of the claimed invention. Moreover, it should be appreciated that various features of the described embodiments can be combined in various ways to create numerous additional embodiments. Moreover, while various materials, dimensions, shapes, configurations, locations, and the like have been described for use with the disclosed embodiments, others in addition to those disclosed may be utilized without departing from the scope of the claimed invention.

[0085] Those skilled in the art will recognize that the subject matter herein may include fewer features than shown in any individual embodiment described above. The embodiments described herein are not intended to be an exhaustive representation of ways in which various features of the subject matter herein can be combined. Accordingly, the embodiments are not mutually exclusive combinations of features; rather, various embodiments may include combinations of different individual features selected from different individual embodiments, as will be understood by those skilled in the art. Moreover, elements described with respect to one embodiment may be implemented in other embodiments even when not described in such embodiment, unless otherwise noted.

[0086] Although a dependent claim may refer to a specific combination with one or more other claims in the claims, other embodiments may also include combinations of a dependent claim with the subject matter of each other dependent claim, or combinations of one or more features with other dependent or independent claims. Such combinations are suggested herein unless it is stated that a particular combination is not intended.

[0087] Any incorporation by reference of the above documents is limited so that no subject matter contrary to the express disclosure herein is incorporated. Any incorporation by reference of the above documents is further limited so that no claims not contained in that document are incorporated by reference herein. Any incorporation by reference of the above documents is also further limited so that any definitions provided within that document are not incorporated by reference herein, unless expressly included herein.

[0088] For purposes of interpreting the claims, it is expressly intended that the provisions of 35 U.S.C. §112(f) not be invoked unless the specific words "means for" or "step for" appear in the claims. [Explanation of symbols]

[0089] 100 Flowpath, System 102 whole blood specimens 104 Centrifugation 106 Serum 108 Spectrophotometers, optical sensors 110 Spectral Signature 200 First System 300 Second System 400 microcuvettes 410 Coagulation chamber 412 Self-locking cap 414 Coagulation strainer 416 Serum Channel Plug 420 Serum Analysis Chamber 422 Container 424 drainage channel 500 Flowpath 502 serum samples 504 Melt 506 base specimens 508 First Modified Specimen 510 Second Modified Specimen 512 Attenuated Total Reflection ("ATR") Crystal 514 Infrared ("IR") Reflectance Sampling Card 516 Fourier Transform Infrared Spectroscopy (FTIR) 600 Calculation Engines 602 Computational Algorithms 604 Data storage device 606 Machine Learning Parameters 608 Machine Learning Models 610 Interface 612 input data 614 results 616 Feedback

Claims

1. A method for diagnosing a disease, comprising: Obtaining a cell-free sample having extracellular vesicles; lysing the acellular sample to produce a lysate sample; adding at least one of an optical molecule binding solution or a proteolytic reagent to the lysate sample to produce a processed lysate sample; drying the processed lysate sample onto an IR-reflective or non-IR-absorbing sampling card; analyzing the processed sample by absorbance spectroscopy in the near infrared and / or mid-infrared range to generate a spectroscopic signature, the step of analyzing the processed lysate sample comprising: receiving, by a processor, the spectroscopic signature corresponding to the processed lysate sample; determining, by the processor, whether the spectroscopic signature corresponding to the processed lysate sample results in an indication of the presence of the disease; outputting the results by the processor; A method comprising:

2. A method for diagnosing a disease, comprising: Obtaining an acellular sample having extracellular vesicles from a serum sample; lysing the cell-free sample to generate a lysate sample by adding a solubilizing or homogenizing solution to the cell-free sample; adding at least one of an optical molecule binding solution or a proteolytic reagent to the lysate sample to produce a processed lysate sample; drying the processed lysate sample onto an IR-reflective or non-IR-absorbing sampling card; analyzing the processed sample by absorbance spectroscopy in the near infrared and / or mid-infrared range to generate a spectroscopic signature, the step of analyzing the processed lysate sample comprising: receiving, by a processor, the spectroscopic signature corresponding to the processed lysate sample; determining, by the processor, whether the spectroscopic signature corresponding to the processed lysate sample results in an indication of the presence of the disease; outputting the results by the processor; A method comprising:

3. 3. The method of claim 2, wherein the solubilization solution or the homogenization solution is added to the serum sample in a 1:1 ratio.

4. The method of claim 1 , wherein the IR reflectance sampling card is coated with aluminum.

5. determining, by the processor, whether the processed lysate spectroscopic signature results in an indication of the presence of the disease; providing the spectroscopic signature to a computational engine that includes a model architecture and one or more model parameters; executing, by the computational engine, a computational algorithm configured to provide the result based on the spectroscopic signature, the model architecture, and the one or more model parameters; 2. The method of claim 1, comprising:

6. providing feedback to the calculation engine indicating the accuracy of the results; updating the one or more model parameters based on the results, the spectroscopic signature, and the feedback; 6. The method of claim 5, further comprising:

7. 1. A system for diagnosing the presence of a disease, comprising: a sample tube configured to hold a sample having extracellular vesicles (EVs); an EV solubilization and homogenization solution configured to lyse the EVs to form a lysate sample; at least one of an optical molecule binding solution or a proteolytic reagent configured to modify the lysate sample; Memory and 1. A processor, comprising: analyzing the lysate sample dried onto an IR reflectance sampling card or a non-IR absorbing sampling card by absorbance spectroscopy in the near infrared and / or mid-infrared range to generate a spectroscopic signature, wherein analyzing the lysate sample includes: receiving, by the processor, a spectroscopic signature corresponding to the lysate sample; determining by the processor whether the spectroscopic signature corresponding to the lysate sample results in an indication of the presence of the disease; outputting the results by the processor; a processor configured to execute instructions stored in the memory to perform A system comprising:

8. The system of claim 7 , wherein the lysate sample is derived from a serum sample.

9. The system of claim 7 , wherein the lysate sample is derived from a whole blood sample.

10. 10. The system of claim 9, wherein the lysate sample is obtained by adding a solubilization or homogenization solution to a serum sample.

11. The system of claim 10, wherein the solubilization solution or the homogenization solution is added to the serum sample in a 1:1 ratio.

12. The processor determines whether the processed lysate spectroscopic signature indicates the presence of the disease. providing the processed melt spectroscopic signature to a computational engine including a model architecture and one or more model parameters; executing, by the computational engine, a computational algorithm configured to provide the result based on the processed melt spectroscopic signature, the model architecture, and the one or more model parameters; The system of claim 7, configured to determine by:

13. The processor: providing feedback to the calculation engine indicating the accuracy of the results; and updating the one or more model parameters based on the results, the processed melt spectroscopic signature, and the feedback; and 13. The system of claim 12, further configured to execute instructions in the memory to:

14. 1. A method for detecting a pathogen in a sample, comprising: receiving a patient's whole blood sample into a clotting cuvette; operating the clotting cuvette to release a serum sample into an analysis chamber fluidly coupled to the clotting cuvette; mixing the serum sample with at least one target substance in the analysis chamber to produce a processed serum sample; inserting the coagulation cuvette into a spectrophotometer configured for near-infrared and / or mid-infrared spectral analysis; determining whether one or more pathogens are present in the processed serum sample; outputting a result indicating whether the one or more pathogens were detected; A method comprising:

15. A system for detecting a pathogen in a sample, comprising: a coagulation cuvette; a spectrophotometer for near-infrared and / or mid-infrared spectral analysis; associated with said spectrophotometer and analyzing a spectrophotometric signature output by said spectrophotometer; receiving a patient's whole blood sample into a clotting cuvette; operating the clotting cuvette to release a serum sample into an analysis chamber fluidly coupled to the clotting cuvette; mixing the serum sample with at least one target substance in the analysis chamber to produce a processed serum sample; inserting the coagulation cuvette into a spectrophotometer configured for near-infrared and / or mid-infrared spectral analysis; determining whether one or more pathogens are present in the processed serum sample; outputting a result indicating whether the one or more pathogens were detected; a processor configured to execute the A system comprising:

Images