Infrared spectroscopy systems and methods

Abstract

Apparatus and methods are described including obtaining from a subject at least a first and second blood product sample. A first infrared spectrum of the first blood product sample is obtained by performing a first infrared spectroscopy measurement, and a second infrared spectrum of the second blood product sample is obtained by performing a second infrared spectroscopy measurement. A computer processor analyzes the first and second infrared spectra, and at least partially in response thereto, determines a status of the subject with respect to fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis, and / or ankylosing spondylitis. Other applications are also described.

Description

[0001] INFRARED SPECTROSCOPY SYSTEMS AND METHODS

[0002] CROSS-REFERENCES TO RELATED APPLICATIONS

[0003] The present application claims priority from U.S. Provisional Patent Application No. 63 / 697,528 to Dankner et al., filed September 22, 2024, entitled "Infrared Spectroscopy Systems and Methods," which is incorporated herein by reference.

[0004] TECHNICAL FIELD

[0005] Some applications of the presently disclosed subject matter relate generally to analysis of biological samples, and in particular, to analysis by infrared spectroscopy performed upon biological samples.

[0006] BACKGROUND

[0007] Rheumatoid arthritis is chronic autoimmune rheumatic disease that primarily affects the joints, causing inflammation, pain, swelling and stiffness in joints. While the exact cause of rheumatoid arthritis is unknown, it is believed to involve a combination of genetic and environmental factors. Diagnosis of rheumatoid arthritis is typically based on symptoms, physical examination, blood tests, and imaging. While there is no cure, proper treatment can help manage symptoms and improve quality of life. There are several treatment options for the management of RA, including pain reduction and slowing disease progression, however selecting an appropriate treatment protocol and regime remains a challenge. Additionally, there is currently no established method for personalizing optimal drug selection for each individual case of RA.

[0008] Fibromyalgia syndrome (FMS) is a chronic disorder characterized by widespread musculoskeletal pain, fatigue and tenderness throughout the body. The exact cause of fibromyalgia syndrome is unknown, but it is believed to involve abnormal pain processing in the central nervous system, with contributing factors such as genetics and stress. Currently there is no available lab test (e.g., testing for diagnostic markers) to confirm the diagnosis of fibromyalgia syndrome. Since there is no definitive test for fibromyalgia syndrome, diagnosis relies heavily on clinical symptoms and ruling out other conditions, resulting in a diagnosis process that may take as long as several years. Therefore, there is an unmet medical need to better diagnose fibromyalgia syndrome. While there is no cure, treatment typically involves a combination of medications, exercises and lifestyle changes. There is therefore an ongoing need to provide non-invasive, rapid and precise systems and techniques which can assist in diagnosis and precision medicine of rheumatic / autoimmune diseases and other conditions that have similar symptoms, such as fibromyalgia.

[0009] Infrared spectroscopy is an analytical technique that examines interaction of infrared radiation with molecules. For example, spectroscopy is a technique based on the absorption or reflection of infrared radiation by chemical substances; each chemical substance having unique absorption spectra (a specific “spectral fingerprint”). Typically, infrared spectra are composed of absorption bands each corresponding to specific functional groups related to molecular components such as lipids, proteins, carbohydrates and nucleic acids. Cellular processes, e.g., cell cycle or cellular changes triggered by processes such as carcinogenesis, may cause global changes in blood biochemistry resulting in differences in the infrared spectra when analyzed by infrared spectroscopy techniques. As such, by enhancing spectral differences and / or differentiation between the spectral fingerprints infrared spectroscopy can be used to distinguish between normal and abnormal tissue or blood biopsies.

[0010] SUMMARY

[0011] There is provided in accordance with some applications of the present disclosure, infrared spectroscopy systems and methods for rapid diagnosis, prediction of optimal medical treatment, and / or monitoring of rheumatological conditions including rheumatic autoimmune diseases. Additionally, or alternatively, some applications of the present disclosure provide differential diagnosis of various rheumatic diseases that are distinct conditions but share some similar symptoms. Further additionally or alternatively, some applications of the present disclosure provide differential diagnosis of various rheumatic diseases and medical conditions that share some similar symptoms with rheumatic diseases. For example, spectroscopy systems and methods provided herein are used for rapid diagnosis (and differential diagnosis), prediction of optimal treatment, and / or monitoring of rheumatoid arthritis and fibromyalgia syndrome (FMS).

[0012] In accordance with applications of the present disclosure, the diagnosis, prediction of optimal treatment, and / or monitoring of rheumatic autoimmune diseases and / or fibromyalgia syndrome is conducted by analysis of a biological sample obtained from a subject, e.g., a blood sample, using infrared spectroscopy systems and methods provided herein. In particular, for some applications, the analysis of the blood sample is performed on one or more blood products, using one or more spectral techniques and one or more spectral ranges. For example, for some applications, the analysis of the blood sample by IR spectroscopy is performed on at least two blood product samples in a liquid or dried state. The at least two blood product samples are typically selected from whole blood, PBMC, blood plasma and / or extracellular vesicles. For some applications, a liquid blood product sample is analyzed at room temperature. Alternatively, the liquid blood product sample is analyzed at a biological temperature of approximately 37 degrees Celsius. For some applications, a volume of 18 - 20 microliters of the liquid blood sample is subjected to analysis by IR spectroscopy. For some applications, a volume of 1 -5 microliters of the liquid sample are subjected to analysis by IR spectroscopy. For some applications, the blood product samples are analyzed using in one or more spectral ranges in the mid- and far-infrared regions covering the 4000-30 cm-1 range, as will be described in further detail below.

[0013] A computer processor analyzes the infrared spectra of the blood product derived from the blood sample. Information from the computer processor is typically fed into an output unit that generates an output that is indicative of the particular rheumatic autoimmune disease and / or fibromyalgia syndrome, and characteristics thereof (e.g., stage of the disease, severity of disease and prediction of response to treatment). Additionally, for some applications, the computer processor is configured to perform mathematical calculations on the infrared spectra (e.g., calculating the derivatives of an infrared spectrum, and manipulating between the derivatives with respect to trigonometric, hyperbolic and logarithmic functions) of the blood product obtained from the blood sample, and based on the mathematical calculations, to generate an output indicative of the rheumatic autoimmune disease and / or fibromyalgia syndrome (FM / FMS).

[0014] The inventors have identified that an infrared spectrum of blood samples obtained from patients suffering from rheumatoid arthritis and fibromyalgia syndrome differ from each other and from those of control individuals who do not suffer from these conditions. Additionally, the inventors have identified that an infrared spectrum of blood samples obtained from subjects suffering from rheumatoid arthritis and fibromyalgia syndrome can provide information regarding the severity / stage of the disease and information regarding optimal medical treatments.

[0015] For example, for diagnosis of fibromyalgia syndrome, in accordance with some applications of the present disclosure, blood products from a blood sample (e.g., whole blood, blood plasma and / or extracellular vesicles) are obtained from subjects suspected of suffering from fibromyalgia syndrome. Several optical analysis methods are used to perform mid- and / or far- infrared spectral absorption measurements in multiple blood sample products (e.g., dried and liquid biopsies, of whole blood, plasma and extracellular vesicles) to assist, even unskilled rheumatologists, to non-invasively and rapidly diagnose fibromyalgia syndrome and its severity. Sex and age matched healthy controls and rheumatoid arthritis patients are used as control groups. Advanced algorithms are used in accordance with applications of the present disclosure, to detect and enhance unique and subtle spectral fingerprints associated with clinical and biological markers of the different groups, allowing diagnosis of fibromyalgia syndrome. Analyzing samples from a single subject suspected to be suffering from FM by using more than one blood product at varying states of the blood product, more than one spectral accessory, more than one spectral range, and more than one spectral method, provides a non-invasive, quick and precise system and methodology, identifiable to the naked eye and cost-effective to the health authorities

[0016] There is therefore provided, in accordance with some embodiments of the present disclosure, a method including: obtaining from a subject at least a first and second blood product sample, the first and second blood product samples being different respective blood product samples selected from the group consisting of whole blood, PBMC (peripheral blood mononuclear cells), supernatant of PBMC, liquid plasma, dried plasma, treated plasma and extra cellular vesicles; obtaining a first infrared spectrum of the first blood product sample by performing a first infrared spectroscopy measurement; obtaining a second infrared spectrum of the second blood product sample by performing a second infrared spectroscopy measurement; and using a computer processor: analyzing the first and second infrared spectra; and at least partially in response thereto, determining a status of the subj ect with respect to a condition selected from the group consisting of: fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis.

[0017] In some embodiments, analyzing the first and second infrared spectra includes applying one or more mathematical calculations to at least one of the first and second infrared spectra, the one or more mathematical calculations being selected from the group consisting of: a first derivative, a second derivative, a third derivative, a trigonometric function, a hyperbolic function, and a logarithmic function.

[0018] In some embodiments, obtaining the first and second infrared spectra includes obtaining a first and second Fourier Transformed infrared (FTIR) spectra.

[0019] In some embodiments, the method further includes obtaining a third blood product sample and obtaining a third infrared spectrum of the third blood product sample by performing a third infrared spectroscopy measurement. In some embodiments, at least one of the first and second infrared spectra includes an absorption spectrum and obtaining the first and second infrared spectra includes obtaining at least one of the first and second infrared spectra as an absorption spectrum.

[0020] In some embodiments, at least one of the first and second infrared spectra includes an absorption spectrum obtained by reflection measurements and obtaining the first and second infrared spectra includes obtaining at least one of the first and second infrared spectra as an absorption spectrum obtained by reflection measurements.

[0021] In some embodiments, determining the status of the subject with respect to the selected condition includes determining that the subject is currently suffering from the selected condition.

[0022] In some embodiments, determining the status of the subject with respect to the selected condition includes determining that the subject is not currently suffering from the selected condition.

[0023] In some embodiments, determining the status of the subject with respect to the selected condition includes determining that the subject is at risk of the selected condition developing.

[0024] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first spectral range; and performing the second infrared spectroscopy measurement includes performing the measurement at a second spectral range; the first and second spectral range being different respective spectral ranges selected from the group consisting of: mid-infrared and far-infrared.

[0025] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first spectral range within a mid-infrared spectral range; and performing the second infrared spectroscopy measurement includes performing the measurement at a second spectral range within a mid-infrared spectral range; the first and second spectral range being different respective spectral ranges.

[0026] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first spectral range within a mid-infrared spectral range; and performing the second infrared spectroscopy measurement includes performing the measurement at a second spectral range within a mid-infrared spectral range; the first and second spectral range being the same spectral ranges.

[0027] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first spectral range within a far-infrared spectral range; and performing the second infrared spectroscopy measurement includes performing the measurement at a second spectral range within a far-infrared spectral range; the first and second spectral range being different respective spectral ranges.

[0028] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first spectral range within a far-infrared spectral range; and performing the second infrared spectroscopy measurement includes performing the measurement at a second spectral range within a far-infrared spectral range; the first and second spectral range being the same spectral ranges.

[0029] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first temperature of the first blood product sample; and performing the second infrared spectroscopy measurement includes performing the measurement at a second temperature of the second blood product sample, the first and second temperatures being different respective temperatures.

[0030] In some embodiments, the first temperature and the second temperature are selected from the group consisting of room temperature and approximately 37 degrees Celsius.

[0031] In some embodiments: obtaining the first blood product sample includes obtaining a dried plasma sample, the dried plasma sample being at room temperature; obtaining the second blood product sample includes obtaining a liquid plasma sample, the liquid plasma sample being at approximately 37 degrees Celsius; performing the first infrared spectroscopy measurement includes performing the measurement on the dried plasma sample using a microplate- / microtiter- reader accessory; and performing the second infrared spectroscopy measurement includes performing the measurement on the liquid plasma sample using an attenuated total reflectance (ATR)-silicon- crystal-based accessory .

[0032] In some embodiments, further including: obtaining a blood product sample of extra cellular vesicles; obtaining a third infrared spectrum of the of the extra cellular vesicles sample by performing infrared spectroscopy measurement by using the attenuated total reflectance (ATR)-silicon-crystal-based accessory; and using the computer processor, analyzing the third infrared spectra, and determining the status of the subject with respect to the selected condition includes determining the status of the subject with respect to the selected condition, at least partially in response to the first, second, and third infrared spectra.

[0033] In some embodiments, the method further includes: obtaining a blood sample of treated plasma; obtaining one or more infrared spectra of the treated plasma sample by performing one or more infrared spectroscopy measurements selected from the group consisting of: an infrared spectroscopy measurement using the attenuated total reflectance (ATR)-silicon-crystal-based accessory, an infrared spectroscopy measurement by using an attenuated total reflectance (ATR)- diamond-based accessory, and an infrared spectroscopy measurement by using a microplate- / microtiter- reader accessory.

[0034] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement using a first accessory; and performing the second infrared spectroscopy measurement includes performing the measurement using a second accessory.

[0035] In some embodiments, the first and second accessory are different respective accessories selected from the group consisting of: an attenuated total reflectance (ATR)-silicon-crystal-based accessory, a microplate- / microtiter- reader accessory, and an attenuated total reflectance (ATR)- diamond-based accessory.

[0036] There is further provided, in accordance with some embodiments of the present disclosure, a computer software product for use with at least a first and second blood product sample obtained from a subject, the first and second blood product samples being different respective blood product samples selected from the group consisting of: whole blood, PBMC (peripheral blood mononuclear cells), supernatant of PBMC, liquid plasma, dried plasma, treated plasma and extra cellular vesicles, the computer software product including a tangible non-transitory computer- readable medium in which program instructions are stored, which instructions, when read by a processor, cause the processor to: analyze a first infrared spectrum of the first blood product sample that was obtained by performing a first infrared spectroscopy measurement; analyze a second infrared spectrum of the second blood product sample that was obtained by performing a second infrared spectroscopy measurement; and in response thereto, determine a status of the subject with respect to a condition selected from the group consisting of fibromyalgia syndrome, rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis.

[0037] In some embodiments, the first and second infrared spectra include first and second Fourier Transformed infrared (FTIR) spectra.

[0038] In some embodiments, the instructions cause the processor to analyze a third infrared spectrum of a third blood product sample that was obtained by performing a third infrared spectroscopy measurement.

[0039] In some embodiments, at least one of the first and second infrared spectra includes an absorption spectrum.

[0040] In some embodiments, at least one of the first and second infrared spectra includes an absorption spectrum obtained by reflection measurements.

[0041] In some embodiments, the instructions cause the processor to analyze the first and second infrared spectra by applying one or more mathematical calculations to at least one of the first and second infrared spectra, the one or more mathematical calculations being selected from the group consisting of a first derivative, a second derivative, a third derivative, a trigonometric function, a hyperbolic function, and a logarithmic function.

[0042] In some embodiments, the instructions cause the processor to determine the status of the subject with respect to the selected condition by determining that the subject is currently suffering from the selected condition.

[0043] In some embodiments, the instructions cause the processor to determine the status of the subject with respect to the selected condition by determining that the subject is not currently suffering from the selected condition. In some embodiments, the instructions cause the processor to determine the status of the subject with respect to the selected condition by determining that the subject is at risk of the selected condition developing.

[0044] In some embodiments, the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained at a first spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained at a second spectral range, the first and second spectral range being different respective spectral ranges selected from the group consisting of: mid-infrared and far-infrared.

[0045] In some embodiments, the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a mid-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the mid-infrared spectral range; the first and second spectral range being different respective spectral ranges.

[0046] In some embodiments, the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a mid-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the mid-infrared spectral range; the first and second spectral range being the same spectral ranges.

[0047] In some embodiments, the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a far-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the far-infrared spectral range; the first and second spectral range being different respective spectral ranges.

[0048] In some embodiments, the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a far-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the far-infrared spectral range; the first and second spectral range being the same spectral ranges.

[0049] In some embodiments, the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained using a first accessory; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained using a second accessory.

[0050] In some embodiments, the first and second accessories are different respective accessories selected from the group consisting of: an attenuated total reflectance (ATR)-silicon-crystal-based accessory, a microplate- / microtiter- reader accessory, and an attenuated total reflectance (ATR)- diamond-based accessory.

[0051] In some embodiments, the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained at a first temperature of the first blood product sample; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained at a second temperature of the second blood product sample, the first and second temperatures being different respective temperatures.

[0052] In some embodiments, the first temperature and the second temperature are selected from the group consisting of: room temperature and approximately 37 degrees Celsius.

[0053] In some embodiments, the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of a dried plasma sample at room temperature that was obtained using a microplate- / microtiter- reader accessory; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of a liquid plasma sample at approximately 37 degrees Celsius that was obtained using an attenuated total reflectance (ATR)-silicon-crystal-based accessory.

[0054] In some embodiments, the instructions cause the processor to: analyze a third infrared spectrum of a blood product sample of extra cellular vesicles, the third infrared spectrum having been obtained using the attenuated total reflectance (ATR)-silicon- crystal-based accessory; and determine the status of the subject with respect to the selected condition selected from the group consisting of fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis and ankylosing spondylitis, in response to analyzing the first, second, and third infrared spectra.

[0055] In some embodiments, the instructions cause the processor to analyze an infrared spectrum of a blood product sample of treated plasma.

[0056] In some embodiments, the infrared spectrum of the blood product sample of treated plasma was obtained by performing one or more infrared spectroscopy measurements selected from the group consisting of an infrared spectroscopy measurement by using the attenuated total reflectance (ATR)-silicon-crystal-based accessory, an infrared spectroscopy measurement by using an attenuated total reflectance (ATR)-diamond-based accessory, and an infrared spectroscopy measurement by using a microplate- / microtiter- reader accessory.

[0057] There is further provided, in accordance with some embodiments of the present disclosure, apparatus for use with an infrared spectroscopy measurement unit configured to obtain a first infrared spectrum of a first blood product sample obtained from a subject by performing a first infrared spectroscopy measurement, and to obtain a second infrared spectrum of a second blood product sample obtained from the subject by performing a second infrared spectroscopy measurement; the first and second blood product samples being different respective blood product samples selected from the group consisting of whole blood, PBMC (peripheral blood mononuclear cells), supernatant of PBMC, liquid plasma, dried plasma, treated plasma and extra cellular vesicles, the apparatus including: at least one computer processor configured to: analyze the first and second infrared spectra; and in response thereto, determining a status of the subject with respect to a condition selected from the group consisting of: fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis.

[0058] In some embodiments, the first and second infrared spectra include first and second Fourier Transformed infrared (FTIR) spectra.

[0059] In some embodiments, the computer processor is configured to analyze a third infrared spectrum of a third blood product sample that was obtained by performing a third infrared spectroscopy measurement.

[0060] In some embodiments, at least one of the first and second infrared spectra includes an absorption spectrum.

[0061] In some embodiments, at least one of the first and second infrared spectra includes an absorption spectrum obtained by reflection measurements.

[0062] In some embodiments, the computer processor is configured to analyze the first and second infrared spectra by applying one or more mathematical calculations to at least one of the first and second infrared spectra, the one or more mathematical calculations being selected from the group consisting of: a first derivative, a second derivative, a third derivative, a trigonometric function, a hyperbolic function, and a logarithmic function.

[0063] In some embodiments, the computer processor is configured to determine the status of the subject with respect to the selected condition by determining that the subject is currently suffering from the selected condition.

[0064] In some embodiments, the computer processor is configured to determine the status of the subject with respect to the selected condition by determining that the subject is not currently suffering from the selected condition.

[0065] In some embodiments, the computer processor is configured to determine the status of the subject with respect to the selected condition by determining that the subject is at risk of the selected condition developing.

[0066] In some embodiments, the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained at a first spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained at a second spectral range, the first and second spectral range being different respective spectral ranges selected from the group consisting of mid-infrared and far-infrared.

[0067] In some embodiments, the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a mid-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the mid-infrared spectral range; the first and second spectral range being different respective spectral ranges.

[0068] In some embodiments, the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a mid-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the mid-infrared spectral range; the first and second spectral range being the same spectral ranges.

[0069] In some embodiments, the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a far-infrared spectral range; and analyze the second infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the far-infrared spectral range; the first and second spectral range being different respective spectral ranges.

[0070] In some embodiments, the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a far-infrared spectral range; and analyze the second infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the far-infrared spectral range; the first and second spectral range being the same spectral ranges.

[0071] In some embodiments, the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained using a first accessory; and analyze the second infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained using a second accessory.

[0072] In some embodiments, the first and second accessories are different respective accessories selected from the group consisting of: an attenuated total reflectance (ATR)-silicon-crystal-based accessory, a microplate- / microtiter- reader accessory, and an attenuated total reflectance (ATR)- diamond-based accessory.

[0073] In some embodiments, the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained at a first temperature of the first blood product sample; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained at a second temperature of the second blood product sample, the first and second temperatures being different respective temperatures.

[0074] In some embodiments, the first temperature and the second temperature are selected from the group consisting of: room temperature and approximately 37 degrees Celsius.

[0075] In some embodiments, the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of a dried plasma sample at room temperature that was obtained using a microplate- / microtiter- reader accessory; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of a liquid plasma sample at approximately 37 degrees Celsius that was obtained using an attenuated total reflectance (ATR)-silicon-crystal-based accessory.

[0076] In some embodiments, the processor is configured to: analyze a third infrared spectrum of a blood product sample of extra cellular vesicles, the third infrared spectrum having been obtained using the attenuated total reflectance (ATR)-silicon- crystal-based accessory; and determine the status of the subject with respect to the selected condition selected from the group consisting of: fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis and ankylosing spondylitis, in response to analyzing the first, second, and third infrared spectra.

[0077] In some embodiments, the processor is further configured to analyze an infrared spectrum of a blood product sample of treated plasma.

[0078] In some embodiments, the infrared spectrum of the blood product sample of treated plasma was obtained by performing one or more infrared spectroscopy measurements selected from the group consisting of: an infrared spectroscopy measurement by using the attenuated total reflectance (ATR)-silicon-crystal-based accessory, an infrared spectroscopy measurement by using an attenuated total reflectance (ATR)-diamond-based accessory, and an infrared spectroscopy measurement by using a microplate- / microtiter- reader accessory.

[0079] There is further provided, in accordance with some embodiments of the present disclosure, a method including: obtaining from a subject a whole blood sample; obtaining an infrared spectrum of the sample by performing an infrared spectroscopy measurement; and using a computer processor: analyzing the infrared spectra; and in response thereto, determining a status of the subject with respect to a condition selected from the group consisting of: Psoriatic arthritis (PsA), ankylosing spondylitis, and rheumatoid arthritis (RA).

[0080] In some embodiments, performing the infrared spectroscopy measurement includes performing the measurements in the spectral range of 2900-2830 cm-1 using a attenuated total reflectance (ATR)-silicon-crystal-based accessory that is for use with an infrared spectrometer.

[0081] In some embodiments, determining the status of the subject with respect to the selected condition includes determining that the subject is currently suffering from the selected condition.

[0082] In some embodiments, determining the status of the subject with respect to the selected condition includes determining that the subject is not currently suffering from the selected condition. In some embodiments, determining the status of the subject with respect to the selected condition includes determining that the subject is at risk of the selected condition developing.

[0083] There is further provided, in accordance with some embodiments of the present disclosure, a method including: identifying that a subject is suffering from fibromyalgia syndrome; treating a sample of isolated and activated Peripheral Blood Mononuclear Cells (PBMC) obtained from the subject with a fibromyalgia syndrome drug candidate; using an infrared spectroscopy measurement unit, obtaining an infrared spectrum of the sample; and using a computer processor: analyzing the infrared spectrum of the sample; and in response thereto, determining an effectiveness of the drug candidate in treatment of fibromyalgia syndrome.

[0084] In some embodiments, treating the sample with a fibromyalgia syndrome drug candidate includes treating the sample with first and second concentrations of the same drug candidate.

[0085] In some embodiments, treating the sample with a fibromyalgia syndrome drug candidate includes treating the sample with drug candidates selected from the group consisting of: N- Arachidonoyl Ethanol Amine (AEA - N-), Palmitoyl Ethanol Amide (PEA), and Oleyl Ethanol Amid (OEA).

[0086] There is further provided, in accordance with some embodiments of the present disclosure, a method including: identifying that a subject is suffering from rheumatoid arthritis (RA); treating a sample of isolated and activated Peripheral Blood Mononuclear Cells (PBMC) obtained from the subject with a rheumatoid arthritis drug candidate; using an infrared spectroscopy measurement unit, obtaining an infrared spectrum of the sample; and using a computer processor: analyzing the infrared spectrum of the sample; and in response thereto, determining an effectiveness of the drug candidate in treatment of rheumatoid arthritis.

[0087] There is further provided, in accordance with some embodiments of the present disclosure, a method including: obtaining from a subject at least a first and second blood product sample, the first and second blood product samples being different respective blood product samples selected from the group consisting of: whole blood, liquid plasma, dried plasma, treated plasma and extra cellular vesicles; obtaining a first infrared spectrum of the first blood product sample by performing a first infrared spectroscopy measurement using a first infrared (IR) spectroscopy modality; obtaining a second infrared spectrum of the second blood product sample by performing a second infrared spectroscopy measurement using a second IR spectroscopy modality, the first and second IR spectroscopy modality being different respective modalities; and using a computer processor: analyzing the first and second infrared spectra; and in response thereto, determining a status of the subject with respect to a condition selected from the group consisting of: fibromyalgia syndrome, and rheumatoid arthritis.

[0088] In some embodiments, performing the first and second infrared spectroscopy measurement using the first and second infrared (IR) spectroscopy modality includes: performing the first infrared spectroscopy measurement using a first accessory; and performing the second infrared spectroscopy measurement using a second accessory, the first and second accessory are different respective accessories selected from the group consisting of: an attenuated total reflectance (ATR)-silicon-crystal-based accessory, a microplate- / microtiter- reader accessory, and an attenuated total reflectance (ATR)-diamond-based accessory.

[0089] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first spectral range; and performing the second infrared spectroscopy measurement includes performing the measurement at a second spectral range; the first and second spectral range being different respective spectral ranges selected from the group consisting of: mid-infrared and far-infrared.

[0090] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first spectral range within a mid-infrared spectral range; and performing the second infrared spectroscopy measurement includes performing the measurement at a second spectral range within a mid-infrared spectral range; the first and second spectral range being different respective spectral ranges. In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first spectral range within a far-infrared spectral range; and performing the second infrared spectroscopy measurement includes performing the measurement at a second spectral range within a far-infrared spectral range; the first and second spectral range being different respective spectral ranges.

[0091] In some embodiments, performing the first infrared spectroscopy measurement includes performing the measurement at a first temperature of the first blood product sample; and performing the second infrared spectroscopy measurement includes performing the measurement at a second temperature of the second blood product sample; and the first and second temperatures being different respective temperatures selected from the group consisting of room temperature and approximately 37 degrees Celsius.

[0092] There is further provided, in accordance with some embodiments of the present disclosure, a method including: identifying that a subject is suffering from rheumatoid arthritis (RA); using an infrared spectroscopy measurement unit, obtaining an infrared spectrum of the sample; and using a computer processor: analyzing the infrared spectrum of the sample; and in response thereto, determining at least one parameter selected from the group consisting of: a clinical score assessing disease activity in the subject and a biological parameter of the subject.

[0093] In some embodiments, the clinical score includes DAS28 and determining the clinical score includes determining a DAS28 of the subject.

[0094] In some embodiments, the biological parameter includes a level of cytokine IL6 and determining the biological parameter includes determining a level of cytokine IL6.

[0095] In some embodiments, the biological parameter includes a level of TNF-alpha and determining the biological parameter includes determining a level of TNF-alpha.

[0096] There is further provided, in accordance with some embodiments of the present disclosure, a method including: identifying that a subject is suffering from fibromyalgia (FM); using an infrared spectroscopy measurement unit, obtaining an infrared spectrum of the sample; and using a computer processor: analyzing the infrared spectrum of the sample; and in response thereto, determining at least one parameter selected from the group consisting of: a clinical score assessing disease activity in the subject and a biological parameter of the subject.

[0097] In some embodiments, the clinical score includes a fibromyalgia impact questionnaire (FIQ) and determining the clinical score includes determining a FIQ of the subject.

[0098] In some embodiments, the biological parameter includes a level of beta-NGF and determining the biological parameter includes determining a level of beta-NGF.

[0099] The present disclosure will be more fully understood from the following detailed description of embodiments thereof, taken together with the drawings, in which:

[0100] BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Fig. 1 is a flowchart showing steps of methods that are performed using an infrared spectroscopy system, in accordance with some applications of the present disclosure;

[0102] Fig. 2 is a schematic illustration of components of a sample carrier device used for a far- infrared spectroscopy analysis of a sample, in accordance with some applications of the present disclosure;

[0103] Fig. 3 is a graph representing the second derivative of an absorption spectra based on samples from fibromyalgia syndrome patients and healthy controls, derived in accordance with some applications of the present disclosure;

[0104] Figs. 4A, 4B and 4C are graphs representing the mathematical calculations of an absorption spectra based on samples from fibromyalgia syndrome patients and healthy controls, derived in accordance with some applications of the present disclosure;

[0105] Figs.5 A, 5B, 5C, and 5D are graphs representing spectral analysis of samples from rheumatoid arthritis patients and healthy controls, derived in accordance with some applications of the present disclosure;

[0106] Figs. 6A, 6B, and 6C are graphs representing infrared absorption spectra (Fig. 6A) and a mathematical manipulation (Figs. 6B and 6C) of absorption spectra, based on samples from fibromyalgia syndrome patients, rheumatoid arthritis, and controls, derived in accordance with some applications of the present disclosure;

[0107] Figs. 7A, 7B, 7C, 7D and 7E are graphs representing infrared spectral analysis of patient- derived immune cell supernatants performed for the purpose of prediction of optimal treatment for fibromyalgia syndrome patients, in accordance with some applications of the present disclosure;

[0108] Figs. 8A and 8B are graphs representing mathematical manipulations of a far infrared region of infrared spectra, based on samples from fibromyalgia syndrome patients, rheumatoid arthritis, and controls, derived in accordance with some applications of the present disclosure;

[0109] Fig. 9 is a graph representing mathematical manipulations of infrared spectra of immune cell supernatants and whole blood supernatants, based on samples from RA (Rheumatoid Arthritis) and the PsA (psoriatic Arthritis) patients, derived in accordance with some applications of the present disclosure; and

[0110] Figs. 10A and 10B are graphs showing the correlation coefficient between measured spectral data and biological / clinical parameters, in accordance with some applications of the present disclosure.

[0111] DETAILED DESCRIPTION

[0112] Some applications of the present disclosure provide a multi-modal Fourier-transform infrared (FTIR) spectroscopy system for differential diagnosis of fibromyalgia (FM), rheumatoid arthritis (RA), ankylosing spondylitis (AS), and healthy blood donors (BD). Additionally, for some applications, the infrared spectroscopy systems and methods disclosed herein provide for estimating severity and assessment of effective treatment of inflammatory / autoimmune diseases, such rheumatoid arthritis and conditions with similar symptoms such as fibromyalgia.

[0113] For some applications, the systems and methods provided herein comprise analyzing more than one blood product derived from a blood sample, e.g., whole blood, types of plasma, Peripheral Blood Mononuclear Cells (PBMC), and / or extracellular vesicles. In accordance with some applications of the present disclosure, the blood samples, and products thereof, may be dried samples or liquid samples. For some applications the samples undergo multiple freeze-thaw cycles.

[0114] Additionally, or alternatively, the systems and methods provided herein use more than one spectroscopy technique for obtaining a spectral pattern of the sample (e.g., absorption or reflection measurements). Further additionally or alternatively, the samples are analyzed at more than one spectral range (e.g., mid-infrared and / or far-infrared regions (e.g., 4000-3 Ocm ')) using complementary optical modes. For example, the samples are analyzed using a microplate- / microtiter- reader accessory (e.g., a high-throughput FT-IR microplate / microtiter reader accessory (e.g., an HTS-XT accessory, manufactured by Bruker Corporation (MA, USA))), using an attenuated total reflectance (ATR)-silicon-crystal -based accessory (e.g., a Bio-ATR accessory manufactured by Bruker Corporation (MA, USA)), and / or using an attenuated total reflectance (ATR)-diamond crystal-based accessory (e.g., a Diamond-ATR accessory manufactured by Bruker Corporation (MA, USA)). Additionally, for some applications, the samples are analyzed by the various optical accessories at multiple resolutions, e.g., 4 cm'1, 1 cm'1, 0.5 cm'1, 0.2 cm'1.

[0115] For some applications, the samples are analyzed using a polycrystalline diamond window. For some applications, the samples are analyzed using a circular polycrystalline chemical vapor deposition (CVD) diamond window (manufactured by Diamond Materials GmbH (Freiburg im Breisgau, Germany). For example, the diamond window is 400 micrometer thick and 10 mm in diameter, and is typically unwedged or wedged (for example, at 0.5 degrees to eliminate interference fringes).

[0116] For some such applications, the analysis is performed on both liquid and dried plasma samples. For some applications, the analysis is performed both at room temperature, and at approximately 37 degrees Celsius. In general, the term “room temperature” as used herein should be interpreted as referring to a temperature of between 18 and 26 degrees Celsius, e.g. between 18 and 22 degrees Celsius, e.g., between 24 and 26 degrees Celsius, e.g., between 25 and 25 degrees Celsius. In general, the term “approximately 37 degrees Celsius” as used herein should be interpreted as referring to a temperature of between 35 and 38 degrees Celsius e.g., between 36 and 37. 5 degrees Celsius.

[0117] In accordance with some applications of the present disclosure, further analysis of the infrared spectra is performed by a computer processor, e.g., by running algorithms that perform mathematical calculations on the infrared spectra (such as calculating the second and / or third derivative). Derivative processing is shown to enhance diagnostically relevant features, reduced noise, and improve the signal-to-noise ratio (SNR). Key discriminatory spectral markers were identified by the inventors, including a band at 6 l 2-548cm1and CIL-stretching variations in disordered lipid membranes near 2933cm '. For some applications, clinical and biological data is integrated into the spectral and mathematical analysis in order to create a mutual language between the two (e.g., clinical- biological parameters and infrared spectral fingerprints) and thus, (i) predict the clinical and biological scores and (ii) predict the best medicating treatment. In such a manner, the methods and systems described herein can assist physicians for better diagnosis and / or to predict optimal therapy of rheumatic, inflammatory / autoimmune diseases, such as rheumatoid arthritis and conditions with similar symptoms such as fibromyalgia syndrome.

[0118] For some applications, the spectral data that is acquired using the systems and methods provided herein, is combined with machine-learning classifiers where the augmented spectral dataset is shown to have classification accuracies of up to 99.4 % (Linear Discriminant Analysis). Thus, the multi-modal Fourier-transform infrared (FTIR) spectroscopy system provided herein offers a rapid (e.g., under 6 hour), non-invasive, and reproducible diagnostic workflow. Some examples of the augmented spectral dataset may include the absorption data augmented with one or more of the following: first derivative second derivative ratio between the derivatives (R), a mathematical operator such as: the natural logarithm of the hyperbolic cosine to R (ln(cosh(R))) or the natural logarithm of the absolute value of the derivative ratio (R), with the result being divided by the wavenumber (i.e., w = (x-1)tanh(ln(| / ? |), where x denotes the wavenumber),

[0119] It is noted that although the present disclosure relates mostly to fibromyalgia syndrome and / or rheumatoid arthritis, the scope of the present disclosure includes using any type of bloodproduct and any type of spectral range (in the mid and far IR) and any type of spectral method, to be able to better diagnose and differentiate other rheumatological and / or inflammatory / autoimmune diseases (e.g., ankylosing spondylitis (AS), psoriatic arthritis (PsA)).

[0120] Biological sample

[0121] As described hereinabove, in accordance with applications of the present disclosure, a blood biopsy (i.e., a simple blood test) is processed into blood products that are then analyzed by infrared spectroscopy. The products derived from the blood biopsy include whole blood, plasma, extra cellular vesicles, PBMC (peripheral blood mononuclear cells) and cell supernatants.

[0122] Regarding plasma samples, it is noted that there are several types of plasma samples that can be analyzed in accordance with some applications of the present disclosure. For example:

[0123] (i) Plasma (regular) that is made from blood sample centrifuged with acceleration of 2000g for 15 minutes.

[0124] (ii) Plasma (mixed): a 1 cubic volume of blood sample is mixed with 1 cubic volume of 98% PBS and 2% FBS (the “solvent”). The mixture is loaded over Lymphoprep™ (a density gradient medium), in a test tube, and centrifuged for 30 min with acceleration of 800g. Following this centrifugation step, the liquid plasma or ‘plasma lymphoprep’ which floats over all other ingredients is used in the experiments described herein.

[0125] In accordance with some applications of the present disclosure, the blood product samples can be analyzed in either a dried or liquid state, and at various temperatures, e.g., at room temperature and / or at elevated temperatures such as a biological temperature of approximately 37 degrees Celsius. Typically, the blood product samples are analyzed using several optical methods to perform mid and far infrared spectral absorption measurements in both dried and liquid biopsies (such as plasma and extra cellular vesicles extracted from the plasma), at room and biological temperatures, in order to increase resolution and accuracy.

[0126] For some applications, a volume of 18 - 20 microliters of a liquid blood product sample is analyzed. For other applications, a smaller volume of sample is analyzed. For example, 1 -5 microliters of the liquid sample are subjected to analysis by IR spectroscopy. It was identified by the inventors that using a smaller volume than commonly used (e.g., 1 -5 microliters) typically enhances subtle yet significant bio marker spectral fingerprints (typically due to initial coagulation of the proteins in the sample). It is noted that even when analyzing the same type of blood product sample (e.g. plasma), at the same spectral range, and using the same IR spectroscopy accessory (e.g., an attenuated total reflectance (ATR)-silicon-crystal-based accessory (e.g., a Bio-ATR accessory manufactured by Bruker Corporation (MA, USA)), changes can be detected in the IR spectrum obtained from the smaller volume of 1-5 microliters compared to the larger volume of 18 - 20 microliters. Infrared Spectroscopy

[0127] As described hereinabove, the blood sample products are analyzed by infrared spectroscopy. Spectroscopy is a measuring technique used to discover the infrared spectral absorption pattern (unique fingerprint) of the blood product.

[0128] For some applications, the measurements are performed by one or more spectral techniques using a micropl ate- / microti ter- reader accessory (e.g., a high-throughput FT-IR microplate / microtiter reader accessory (e.g., an HTS-XT accessory, manufactured by Bruker Corporation (MA, USA)), using an attenuated total reflectance (ATR)-silicon-crystal-based accessory (e.g., a Bio-ATR accessory manufactured by Bruker Corporation (MA, USA)), and / or using an attenuated total reflectance (ATR)-diamond crystal-based accessory (e.g., a Diamond-ATR accessory manufactured by Bruker Corporation (MA, USA)).. The spectroscopy method, used in accordance with applications of the present disclosure, can be one or more of the followings measuring techniques: a) Transmittance measurement of a dried blood sample at room temperature in middle infrared (Mid-IR) region of 4000-400 cm'1. For some such applications, a blood droplet is dried on a zinc-selenide transparent microtiter plate. b) Reflectance measurement of a dried blood sample at room temperature in Mid-infrared region of 4000-600 cm'1. For some such applications, a blood droplet is dried on a diffuse aluminum microtiter plate. c) Transmittance measurement of a dried blood product at room temperature, under vacuum conditions, in Far-infrared region of 680-30 cm'1. For some such applications, a blood droplet is dried on a synthetic diamond window whose width is less than half millimeter. d) Transmittance measurement of liquid blood product at room temperature, under vacuum conditions, in Far-infrared region of 680-30 cm'1. For some such applications, a blood droplet is held in between two synthetic diamond windows in a novel transmitting chamber accessory apparatus, described herein below with respect to Fig. 2. e) Evanescent wave spectroscopy (EWS) within attenuated total reflection (ATR) accessory (e.g., a Bio-ATR accessory manufactured by Bruker Corporation (MA, USA)). For some such applications, a liquid blood product is used at a temperature ranging from room temperature to biological temperature of approximately 37 degrees Celsius (or even above the biological temperature) in Mid-infrared region of 3100-900 cm'1. j) Evanescent wave spectroscopy (EWS) within attenuated total reflection (ATR) accessory (e.g., a Diamond-ATR accessory manufactured by Bruker Corporation (MA, USA)). For some such applications, a liquid blood product is used at room temperature in the Midinfrared and the Far-infrared regions of 4000-30 cm'1. g) Attenuated total reflection (ATR) accessory (e.g., a Diamond-ATR accessory manufactured by Bruker Corporation (MA, USA)). For some such applications, a dried blood product is subjected to measurement under vacuum conditions. h) Transmittance measurement and reflectance measurement of a dried blood product sample (e.g., 5 microliters of plasma). The Transmittance measurements are typically performed using a Zinc-Selenide microtiter plate, and the reflectance measurements are performed using Diffuse- Al (aluminum) microtiter plate.

[0129] Mathematical analysis:

[0130] As described hereinabove, the infrared spectra are subjected to analysis by the computer processor by running algorithms that perform mathematical calculations on an infrared spectrum to derive information from the spectrum. The algorithms may also include factoring in additional parameters such as clinical and / or biological data of the patient.

[0131] In accordance with some applications, the algorithms that are used to analyze the infrared spectra, use one or more of the following:

[0132] (i) The spectral absorption and / or the first, second and third derivatives to the spectral absorption,

[0133] (ii) The ratio between the first to the second derivatives,

[0134] (iii) A single spectral range and / or several spectral ranges,

[0135] (iv) The spectral fingerprint of a one blood product and or more than one blood products,

[0136] (v) The absorption results from one accessory and / or several accessories (in the context of the present application in the specification and the claims, accessory refers to the device into which a sample is inserted to allow application of infrared spectroscopy).

[0137] (vi) The correlation coefficient between the measured spectra or their derivatives to biological parameters and / or clinical parameters that are obtained with respect to a specific patient. On the one hand, the distance between the two spectral plots was measured, x-axis (one belongs to the patient in question and the other can be the average of healthy control group having the same age and sex characters. The distance between the two spectral plots can be measured using Pearson correlation coefficient or by measuring the area captured between the plots. Additionally, for some applications, the distance between the two spectral plots can be measured at the entire spectral range, or at specific spectral windows, (which are sub-ranges within a larger spectral range) or at a combination of several spectral windows, and, optionally, attributing a different weight to the different spectral windows. When the two plots coincide, the coefficient is equal to 1, or the area equals to 0). On the other hand (from a biological perspective) the biological parameters have grade levels, y- axis. Examples of biological parameters include a level of inflammatory or pain-related mediators such as: 0-NGF, TNF-a, IL-6. Clinical parameters may include the severity of a disease course as evaluated by a physician, such as FIQ (Fibromyalgia Impact Questionnaire), or evaluated by DAS28 (Disease activity score in 28 joints for rheumatoid arthritis, which is a commonly used tool to measure disease activity in patients with rheumatoid arthritis (RA)).

[0138] Applications of the present disclosure provide specific algorithms for analysis of the various blood products obtained from the blood sample. In other words, a blood sample that is obtained from a single subject is processed into blood products, and each blood product undergoes specific analyses as described herein. In such a manner, the subject is diagnosed based on multiple spectral tests that are performed on the blood sample derived from the subject. For example, for the purpose of distinguishing a subject suffering from fibromyalgia from a healthy control, or a subject suffering from a condition that is not fibromyalgia syndrome, a specific algorithm is used to analyze one of the blood products derived from the sample (e.g., plasma), and a different specific algorithm is used for a different blood product derived from the same blood sample (e.g., extra cellular vesicles). It is noted that a specifically tailored algorithm is typically used for every blood-product analyzed.

[0139] Overview of the methods and systems

[0140] Reference is now made to Fig. 1, which is a flowchart showing an overview of steps of methods that are performed using an infrared spectroscopy system, in accordance with some applications of the present disclosure. The flowchart shown in Fig. 1 shows an example of the comprehensive system for diagnosis of conditions such as fibromyalgia (FM) and / or rheumatic inflammatory / autoimmune diseases (such as rheumatoid arthritis), patient monitoring and prediction of optimal treatment options. As shown, a blood sample that is obtained from a blood test performed on a subject is processed into blood products: plasma, extra cellular vesicles, treated plasma (also referred to herein as mixed plasma or plasma-lymphoprep), and Peripheral Blood Mononuclear Cells (PBMC). As shown, measurements are performed on each one of the blood product samples by one or more of the following accessories: a microplate / microtiter reader accessory (e.g., an HTS-XT accessory, manufactured by Bruker Corporation (MA, USA)), using an attenuated total reflectance (ATR)-silicon-crystal -based accessory (e.g., a Bio-ATR accessory manufactured by Bruker Corporation (MA, USA)), and / or using an attenuated total reflectance (ATR)-diamond crystal -based accessory (e.g., a Diamond- ATR accessory manufactured by Bruker Corporation (MA, USA)), using different spectroscopy techniques (e.g., transmittance and reflectance). The flow chart shows the timeline for processing and analyzing each sample, as well as the state of the analyzed sample (liquid or dried), the amount of the sample analyzed, and the temperature at which the sample is analyzed. The flow chart additionally shows the spectral ranges used for spectral analysis of the samples, and the type of vessel holding the sample during analysis (e.g., a Zinc- Selenide microtiter plate or a diffusive aluminum microtiter holder plate (as shown with reference to chamber 20 in Fig. 2).

[0141] It is noted that the amount of sample analyzed is shown by way of illustration and not limitation. For example, when using an attenuated total reflectance (ATR)-silicon-crystal-based accessory a sample of 5-20 microliters can be used. Additionally, or alternatively, when using an attenuated total reflectance (ATR)-diamond crystal-based accessory, and a high-throughput FT- IR microplate / microtiter reader accessory a sample of 1-5 microliters can be used.

[0142] As further shown in the flow chart, the infrared spectra are subjected to mathematical analysis performed by a computer processor. The computer processor is additionally configured to determine a correlation between the biological / clinical data and the spectral data.

[0143] It is noted that a single spectral measurement in one specific spectral range, using one blood-product, is usually not enough to diagnose fibromyalgia syndrome (or conditions such as rheumatic, inflammatory / autoimmune disease), however, the inventors have identified that in accordance with some applications of the present disclosure (and as demonstrated by the flow chart in Fig. 1), several measurements, performed on more than one blood-product from a single patient at more than one spectral range and more than one spectral technique create a crystal-clear view to the naked-eye of the type and severity of the rheumatic illness. Additionally, in accordance with some application of the present disclosure, clinical and biological data are integrated into the spectral and mathematical analysis in order to create discover correlations between the clinical -biological parameters and infrared spectral fingerprints and thus, (i) increase the resolution and (ii) predict the best medicating treatment.

[0144] Sample Carrier Device for Spectral Measurements

[0145] Reference is now made to Fig. 2, which is a schematic illustration of components of a sample carrier device comprising chamber 20 used for some infrared spectroscopy analyses of a sample, in accordance with some applications of the present disclosure. Typically, chamber 20 is configured for far infrared spectral measurements of a blood product droplet 40 (e.g., whole blood, plasma, mixed-plasma, PBMC, and / or extra cellular vesicles).

[0146] It is generally noted that far infrared spectral measurements are usually difficult to establish due to absorption noise carried by water and carbon dioxide, thereby posing a problem when analyzing a blood droplet product to explore the far infrared fingerprint of the droplet blood product. This problem can be overcome by using evacuated measurement compartments and dried blood products. However, dried blood products lose spectral data since proteins tend to coagulate and while other components of the sample may evaporate.

[0147] In accordance with some applications of the present disclosure, chamber 20 is provided to overcome these problems by providing a chamber for holding a blood droplet under vacuum conditions. Chamber 20 is specifically structured to hold 5-18 microliters of the blood product droplet which can sustain the vacuum conditions thereby allowing for obtaining far infrared spectral data. In Fig. 2 blood droplet 40 is shown within chamber 20. For some applications, chamber 20 is used for transmittance measurement of a droplet 40 of liquid blood product at room temperature, under vacuum conditions, in far-infrared region of 680-30 cm'1. For some such applications, blood droplet 40 is held in between two synthetic diamond windows 30 in the transmitting chamber 20. Chamber 20 comprises plastic holders 50 and an aluminum holder 60, all structured to hold droplet 40 under vacuum conditions for the far infrared measurements. Typically, one or more (e.g., two) O-Rings seal the chamber on both sides thereby preventing liquid leakage. For some applications diamond window 30 has a diameter of 10 -15 mm, e.g., 11 mm, and a thickness of 0.3 - 0.5 mm, e.g., 0.4 mm. Chamber 20 typically has a diameter of 30 mm and a thickness of 7 mm - 9 mm, e.g., 8 mm. Examples

[0148] The following examples (i)-(vi), describes objectives and needs that lead to solutions / achievements obtained in accordance with some applications of the present disclosure.

[0149] Example (i)

[0150] Objective: To diagnose fibromyalgia syndrome by infrared spectroscopy using small amounts of a blood sample and in a short period of time, e.g., within hours.

[0151] Need: Currently, the time from appearance of first symptoms to the diagnosis of fibromyalgia syndrome, may take as long as six years, as physicians need to rule out other rheumatic or similar diseases and to complete many laboratory and radiological tests (blood tests, CT / MRI scans) which are usually found to be normal in fibromyalgia syndrome patients. Therefore, there is unmet need for quick and precise methods and systems to assist the physicians in the diagnosis processes of fibromyalgia syndrome.

[0152] Achievement: Some applications of the present disclosure provide systems and methods for diagnosis of fibromyalgia syndrome by identifying the infrared fingerprints of fibromyalgia syndrome with a plurality of blood products (e.g., types of whole blood, plasma, PBMC, and extracellular vesicles).

[0153] Example (ii)

[0154] Objective: To develop a blood product (and a preparation method thereof) for use in diagnosis of fibromyalgia syndrome. The blood product is prepared by changing the physical and biological constituents of a blood product (e.g., plasma) by one or more of the following parameters: (a) appropriate type of the blood product diluent, (Z>) the percentage of the blood’s diluent, (c) the centrifuge acceleration level and (d) time interval of the centrifuge machine (which is used to create the blood product as part of the process).

[0155] Need: There is a need to obtain infrared spectra exhibiting enhanced separation between fibromyalgia syndrome patients and control groups, such that the difference between the spectra is visible to the naked eye even to an unskilled rheumatologist.

[0156] Achievement: Some applications of the present disclosure provide a composition of blood and the diluent in the process used to create a ‘Plasma-Lymphoprep’ product (also described herein as ‘treated plasma’, or ‘mixed plasma’), that enhances spectral separation between the tested groups of fibromyalgia syndrome patients, rheumatoid arthritis patients and healthy control group. Additionally, in accordance with some applications of the present disclosure, processing of the blood sample is performed by tailoring optimal centrifugation times and centrifuge acceleration rates which were found by the inventors to result in further enhanced spectral separation between the tested groups. As an example, the following formula includes a composition of the blood product, acceleration rate and time duration of the centrifugation provided for preparation of the blood product, in accordance with some applications of the present disclosure:

[0157] Composition of the blood product - 1 cubic volume of blood mixed with 1 cubic volume of the following diluent: 93% PBS and 7% FBS.

[0158] Centrifugation acceleration rate - centrifuge-acceleration of 450g.

[0159] Centrifugation time duration - time interval of 15 minutes.

[0160] In experiments conducted by the inventors using ‘Plasma-Lymphoprep’ prepared according to the above-described formula, separation of the following groups: fibromyalgia syndrome, rheumatoid arthritis and healthy control group is visible to the naked-eye by using first and / or second derivatives to the absorbance spectra at unique specified spectral windows (e.g., 3950-3750, 3575-3545, 1954-1944, 1730-1710, 1690-1680, 1510-1500, 1250-1110, 670-650, 612-548, 574-492, 476-432, 420-395, 370-30 cm'1). In such a manner, even unskilled rheumatologists are able to diagnose fibromyalgia syndrome, by simple spectral measurement.

[0161] Therefore in accordance with some applications, Plasma-Lymphoprep is prepared using at least some of the above conditions or variations thereof (e.g., within plus-minus 20% (e.g., plusminus 10%) of the parameters provided above).

[0162] Example (iii)

[0163] Objective: To establish an algorithm to distinguish fibromyalgia syndrome from healthy controls or other conditions that are not fibromyalgia syndrome (e.g., types of arthritis e.g., rheumatoid arthritis (RA) and / or Ankylosing Spondylitis (A) and / or psoriatic arthritis (PsA) patients) for various blood products under various preparation conditions.

[0164] Need: While use of regular plasma is common (due to cost effective preparation, and easy drying on a microtiter transparent plate of multi -wells used for large scale measurements), infrared fingerprints are sometimes better pronounced in ‘extra cellular vesicles’ or ‘Plasma-Lymphoprep’ blood products, described herein.

[0165] Achievement: In accordance with some applications of the present disclosure, spectral fingerprints of several blood products were gathered and used to precisely diagnose fibromyalgia syndrome even using regular plasma blood products. Additionally, in accordance with some applications of the present disclosure, spectral fingerprints of several blood products were gathered and used to differentially diagnose fibromyalgia syndrome rheumatoid arthritis (RA) and / or Ankylosing Spondylitis and / or PsA even when using regular plasma blood products.

[0166] Example (iv)

[0167] Objective: To find a correlation between the biological and / or clinical parameters and the spectral behavior (i.e., infrared spectra derived from the blood product samples) in order to estimate the severity of the illness, thereby assisting in choosing an optimal treatment.

[0168] Need: Biological parameters such as IL6, TNF-a, 0-NGF and / or clinical parameters such as FIQ (Fibromyalgia Impact Questionnaire), are typically not sufficient to diagnose the fibromyalgia syndrome. The above-mentioned biological parameters typically influence the spectral absorbance fingerprint, within one or more spectral regions. Thus, there is an unmet need to improve the diagnosis and the management of fibromyalgia syndrome by means of spectral analysis.

[0169] Achievement: In accordance with some applications of the present disclosure, a correlation between the clinical parameter FIQ and the biological parameter 0-NGF to the spectral behavior of fibromyalgia syndrome patient’s blood-product at several spectral regions, was determined.

[0170] Example (v)

[0171] Objective: To predict the optimal medical treatment for the fibromyalgia syndrome patient.

[0172] Need: The currently available therapy for fibromyalgia syndrome patients is not sufficient or not adequate. There is a need to improve management of fibromyalgia syndrome and to develop a precision medicine strategy.

[0173] Achievement: In accordance with some applications of the present disclosure, potential effects of new drug candidates in activated fibromyalgia syndrome-related PBMCs are examined in-vitro and the changes in the level of inflammatory / pain-related mediators are examined in cell supernatants. In accordance with some applications of the present disclosure, the potential effects of three endocannabinoids (each with two concentrations) were evaluated on PBMCs derived from fibromyalgia syndrome patients and a healthy control group by measuring the spectral absorbance changes of cells supernatant products. These experiments for obtaining and analyzing the infrared spectral changes were carried out in parallel with biological tests to establish a protocol which can help to predict the optimal treatment for each fibromyalgia syndrome patient or other diseases. Example (vi)

[0174] Objective: To construct an adequate compartment for far infrared spectral measurements of any blood product droplet of 1-20 microliters, e.g., 5-18 microliters .

[0175] Need: Far infrared spectral measurements are difficult to establish due to absorption noise carried by water and carbon dioxide. This can be overcome with the use of evacuated measurement compartment and dried blood product. However, dried blood product may lose spectral data, as proteins tend to coagulate and other components may evaporate. Therefore, there is a problem when wishing to explore the far infrared fingerprint of a droplet blood product.

[0176] Achievement: Chamber 20 shown in Fig. 2 is provided in accordance with some applications of the present disclosure. Chamber 20 is shaped and sized to hold 5-18 microliter of a blood product droplet while being subjected to vacuum conditions and allow for analysis by far infrared spectroscopy for obtaining far infrared spectral data.

[0177] Experimental Data

[0178] A series of experiments were conducted by the inventors in accordance with applications of the present disclosure and using the techniques and systems described herein. In accordance with some applications of the present disclosure, in the series of experiments, at least a first and second blood product sample selected from whole blood, PBMC (peripheral blood mononuclear cells), supernatant of PBMC, liquid plasma, dried plasma, treated plasma and / or extra cellular vesicles, were subjected to infrared spectroscopy measurements. The first and second infrared spectra of the first and second samples were analyzed to determine a status of the subject with respect to a medical condition, in particular fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis and ankylosing spondylitis. Typically, analysis of the first and second infrared spectra involves applying one or more mathematical calculations to the spectra, e.g., obtaining a first derivative and / or a second derivative of the spectra. In accordance with some applications of the present disclosure, the at least first and second blood product samples are analyzed at either a dry or liquid state, at first and second spectral ranges, respectively (e.g., mid and far-IR), at first and second temperatures, respectively (e.g., room temperature and biological temperature), using first and second infrared (IR) spectroscopy modality using first and second infrared (IR) spectroscopy accessories. For some applications, the first and second blood product sample are the sample blood product sample (e.g., both plasma), for other applications the first and second blood product sample are different blood product samples (e.g., whole blood and PBMC). Additionally, in a series of experiments, an effectiveness of a drug candidate in treatment of fibromyalgia syndrome was determined by treating a sample of isolated and activated Peripheral Blood Mononuclear Cells (PBMC) obtained from the subject with a fibromyalgia syndrome drug candidate and analyzing an infrared spectrum of the activated Peripheral Blood Mononuclear Cells (PBMC). Further additionally, experiments were conducted showing a correlation between the IR spectrum obtained from a subject suffering from rheumatoid arthritis and clinical / biological parameters of the subject.

[0179] A series of protocols are described hereinbelow which may be used separately or in combination, as appropriate, in accordance with applications of the present disclosure. It is to be appreciated that numerical values are provided by way of illustration and not limitation. Typically, but not necessarily, each value shown is an example selected from a range of values that is within 20% of the value shown. Similarly, although certain steps are described with a high level of specificity, a person of ordinary skill in the art will appreciate that other steps may be performed, mutatis mutandis.

[0180] The experiments described hereinbelow with reference to Example A-H were performed by the inventors in accordance with applications of the present disclosure and using the techniques and systems described hereinabove.

[0181] Examples A-D presented hereinbelow describe experiments demonstrating that, in accordance with some applications of the present disclosure, analysis of various blood product samples by various techniques of infrared spectroscopy can be used for diagnosis of fibromyalgia syndrome (examples A-B) and rheumatoid arthritis (example C) as well as differential diagnosis of fibromyalgia syndrome and rheumatoid arthritis (example D), based on the spectral pattern at selected infrared ranges.

[0182] Example E presented hereinbelow describe experiments demonstrating that in accordance with some applications of the present disclosure, analysis of various blood product samples by various techniques of infrared spectroscopy can be used for prediction of optimal treatment for fibromyalgia syndrome patients.

[0183] Example F presented hereinbelow provides optional mathematical calculations for analysis of infrared spectra in the far infrared region of the spectra, performed in accordance with some applications of the present disclosure.

[0184] Example G presented hereinbelow describe experiments demonstrating the use of whole blood samples to differentiate between types of arthritis. Example H presented hereinbelow shows the correlation coefficient between measured spectral data and biological / clinical parameters.

[0185] Example A

[0186] In this set of experiments blood product samples were analyzed to diagnose fibromyalgia syndrome.

[0187] Reference is made to Fig. 3, which is a graph of the second derivative of absorbance spectra showing the separation between spectral pattern of a total of 18 dried plasma samples from 9 fibromyalgia syndrome patients and 9 healthy controls. The graph in Fig. 3 shows results using the following parameters: Type of blood-product: 5 microliters of dried plasma placed on a Zinc- Selenide plate; Measurement technique: transmittance; Measuring accessory: HTS-XT, Vertex 80v (Bruker Corporation (MA, USA)); Mathematical analysis: second derivative to the absorbance spectrum in the spectral range of 612-548 cm'1. Results: As shown, the healthy control group (indicated by reference numeral 1) has a significantly lower oscillating amplitude with opposite phase with comparison to the fibromyalgia syndrome group (indicated by reference numeral 2). The plasma sample from fibromyalgia syndrome group (2) displays higher-amplitude oscillations with phase-inversion compared to the healthy control group (1) in the low-frequency window, a signature attributed to collective skeletal or lattice vibrations of biomolecules and reflective of altered molecular conformations. Therefore, separation between the fibromyalgia syndrome to the healthy control group is visible to the naked-eye.

[0188] Example B

[0189] In this set of experiments blood product samples were analyzed to diagnosis fibromyalgia syndrome using a different approach with respect to Example A.

[0190] Reference is made to Figs. 4A-4C, which are graphs showing mathematical calculations performed on infrared spectrum of dried plasma samples from 3 fibromyalgia syndrome patients and 3 healthy blood donors in the control group. In the graphs in Figs. 4A-4C the results of the 3 healthy blood donors are indicated by reference number 1 and the results of the 3 fibromyalgia syndrome patients are indicated by reference number 2. In Figs. 4A-C, as in many of the other graphs, “A.U ” is used to denote arbitrary units.

[0191] The graphs in Figs. 4A-4C show results using the following parameters: Type of bloodproduct: dried plasma; Measurement technique: (i) reflection from diffusive aluminum plate and (ii) transmittance through a Zinc-Selenide transmitting plate; Measuring accessory: HTS-XT, Vertex 80v (Bruker Corporation (MA, USA)); Mathematical analysis: both measuring techniques were used to develop a better method to separate between the fibromyalgia syndrome (indicated by reference numeral 2) to the healthy control group (indicated by reference numeral 1) which is visible to the naked-eye. The mathematical analysis includes:

[0192] Step 1: using the computer processor, calculating the first derivative to the absorption spectra measured by reflection, in the spectral range of 3500-2700 cm'1. The HTS-XT reflectance measurement was performed with diffuse-aluminum microtiter plate. (These results are shown in the graph of Fig. 4A)

[0193] Step 2: using the computer processor, calculating the first derivative to the absorption spectra measured from transmittance, in the same spectral range of 3500-2700 cm'1. The HTS-XT transmittance measurement was performed with Zinc-Selenide microtiter plate. (These results are shown in the graph of Fig. 4B)

[0194] Step 3: using the computer processor, multiplying the absorption spectral results from the two measuring techniques in Steps 1 and Step 2 (shown in Figs. 4A-4B) and calculating the 1stderivative to the product. In this calculation, the subtle spectral differences between the groups are enhanced and the signal -to-noise ratio (SNR) is increased by more than an order of magnitude. As shown in this manner, a better separation between the groups over larger spectral range is visible. (These results are shown in the graph of Fig. 4C)

[0195] Example C

[0196] In this set of experiments blood product samples were analyzed to diagnose rheumatoid arthritis.

[0197] Reference is made to Figs.5A, 5B, 5C, and 5D, which are graphs representing spectral analysis of samples from four rheumatoid arthritis patients and four healthy controls (BD (blood donors)), derived in accordance with some applications of the present disclosure. The graphs in Figs. 5A-5D show infrared spectral analysis using a Measuring accessory: Bio-ATR (Bruker Corporation (MA, USA)); and measurement technique of evanescent wave spectroscopy at approximately 37 degrees Celsius.

[0198] Fig. 5A is a graph representing spectral measurements of liquid plasma sample of four rheumatoid arthritis patients (RA34, RA21, RA46, RA32) and four control healthy blood donors (BD58, BD59, BD44, BD31) in the range of 3100-900 cm-1. 15 ml of liquid plasma at approximately 37 degrees Celsius were tested by Bio-ATR. As shown, the raw spectral data is generally mixed between the two groups and not separated (Fig. 5 A shows a blow up of the region indicated by arrow Al around wavenumber 1555 cm-1). Fig. 5B is a graph representing spectral measurements of liquid extracellular vesicles extracted from plasma (“ECV”) sample of four rheumatoid arthritis patients (RA34, RA21, RA46, RA32) and four healthy (BD58, BD59, BD44, BD31) control donors (of the same above- mentioned donors) in the range of 3100-900 cm-1. 15 ml of liquid ECV at approximately 37 degrees Celsius were tested by Bio-ATR. As shown, the raw spectral data is mixed and not separated (Fig. 5B shows a blow up of the region around the main peak).

[0199] Fig. 5C is a graph representing the absorbance spectrum of a liquid plasma sample (indicated by RA21P) and an ECV sample (indicated by RA21ECV) from a single rheumatoid arthritis patient, labeled as RA21. 15 microliters of each one of the samples were analyzed at approximately 37 degrees Celsius by Bio-ATR. As shown, both spectra are very close to each other (although separation between the two, at some spectral region is visible this is not the case on a large scale). Mathematical analysis of the spectra of both samples (RA21MA) is performed by calculating a division of the absorbance spectrum of the plasma sample and the ECV sample. In other words, in a first step the absorbance spectrum of plasma and ECV are measured. In a second step a mathematical calculation is performed by dividing the spectra with respect to each other to obtain a fingerprint of RA21. As shown in Fig. 5C, the mathematical analysis provides an infrared fingerprint of patient RA21 as shown for RA21MA (indicating the mathematical analysis performed on the spectra).

[0200] Fig. 5D is a graph representing the ratio between plasma samples and ECV samples obtained from the four rheumatoid arthritis patients (RA-indicated by reference numeral 5, 6, 7, 8) and the four healthy blood donors (BD-indicated by reference numerals 1,2, 3, 4) by performing the same measuring and analysis process as describe with reference to Fig. 5C. As shown eight distinct spectral fingerprints were obtained for the eight participants, showing the spectral separation between rheumatoid arthritis patients and the healthy donors over a large spectral region.

[0201] Example D

[0202] In this set of experiments, blood product samples were analyzed to distinguish between fibromyalgia syndrome patients, rheumatoid arthritis, and healthy controls. Reference is made to Figs. 6A-D, which show separation between fibromyalgia syndrome to rheumatoid arthritis and the healthy control group. Type of blood-product: plasma-lymphoprep; Measurement technique: evanescent wave spectroscopy at approximately 37 degrees Celsius, Measuring accessory: Bio- ATR (Bruker Corporation (MA, USA)). Fig. 6A shows spectral absorbance, in the 3100-900 cm'1range, of liquid plasmalymphoprep with respect to the following participants: 24 fibromyalgia syndrome (FM), 34 rheumatoid arthritis (RA) and 16 healthy blood donor control group (Healthy BD). Measurement was carried out by the Bio-ATR accessory at approximately 37 degrees Celsius. As shown in Fig. 6A, the spectral absorbance shows poor separation as the fibromyalgia syndrome group (FM) is hidden within the rheumatoid arthritis (RA) and the healthy control group (Healthy BD).

[0203] The top graph in Fig. 6B shows the spectral absorbance of Fig. 6A after several mathematical calculations. The bottom graph in Fig. 6B shows the spectral absorbance of Fig. 6A in the 3000-2830 cm'1range after several mathematical calculations.

[0204] As shown in Fig 6B the groups (fibromyalgia syndrome (FM), rheumatoid arthritis (RA) and healthy blood donor control group (Healthy BD)), are visible to the naked eye. More specifically, first derivative (top graph Fig. 6B) and second derivative (bottom graph Fig. 6B) in the 2985-2830cm'1range highlight group differentiation. FM (first derivative in Fig. 6B) shows distinct concave-down peaks (2932.9cm'1) and concave-up peaks (2847cm'1), both shifted relative to RA and Healthy BD. FM exhibits the lowest full width at half maximum (FWHM) in the 2953- 2912cm'1interval (first derivative in Fig. 6B) and moderate peak-to-peak distance in the 2922- 2910cm'1interval (second derivative in Fig. 6B). RA (second derivative in Fig. 6B) displays a doublet in the 2880-2870cm'1range, whereas FM and Healthy BD are characterized by ‘monkey saddle’ and saddle shapes, respectively.

[0205] Additional possible mathematical calculations and / or analysis by various infrared ranges on the absorbance spectra (e.g., the absorbance spectra of Fig. 6A), may be performed:

[0206] • Calculating the first derivative to the spectral absorbance of Fig. 6A plus one mathematical operation, in the 3000-2830 cm'1range, of plasma-lymphoprep of the above-mentioned groups. This mathematical analysis provides a separation between the groups that is visible to the naked eye.

[0207] • Calculating the second derivative to the spectral absorbance of Fig. 6A in the 1260-1195 cm'1range, of plasma-lymphoprep of the above-mentioned groups (i.e., FM, RA, Healthy BD). This mathematical analysis provides separation between the groups that is visible to the naked eye.

[0208] • Measuring spectral absorbance, in the 4000-30 cm'1range (Mid plus Far IR), of plasma- lymphoprep with respect to the following above-mentioned groups (i.e., FM, RA, Healthy BD). Measurement was carried by the ATR-Diamond accessory (Bruker Corporation (MA, USA)) at room temperature. The two groups show separation in the range between 4000-2400 cm'1.

[0209] • Calculating the second derivative to the spectral absorbance of the Mid plus Far IR, in the 670-570 cm'1range, of plasma-lymphoprep, measured by the ATR-Diamond accessory, at room temperature of the above-mentioned two groups. The separation between the groups is visible to the naked eye.

[0210] For some applications, an algorithm for processing an infrared spectrum, such as in Fig. 6 A, including some of the following mathematical analysis, was found to produce a clear spectral separation between the tested groups (e.g., FM, RA, Healthy BD):

[0211] Algorithm example 1 :

[0212] 1. Use the ratio between the first to the second derivatives (‘ratio’), on a number (e.g., 80) of unknown participants, in the spectral range of 1700-1380 cm'1, to create ‘groups of similar behavior’. For example, in the conducted experiment 15 such groups were created. (Who may be in such a group of similar behavior? E.g., same patient before and after treatment, different patients with the same disease that happen to be similar at this spectral range, different patients with different diseases that happen to be similar at this spectral range, a patient and a healthy blood donor that happen to be similar at this spectral range.)

[0213] 2. Go to the first derivative of the unknown with comparison to the first derivative of the known plasma-lymphoprep to search for high correlation between the two (results of plasma to that of plasma-lymphoprep). It was found that one of the unknown participants suffers from a variant of rheumatoid arthritis and another one of the unknown participants is a fibromyalgia syndrome patient. There are few spectral windows to search correlation.

[0214] 3. The results from step 2 are to be examined with respect to the ‘groups of similar behavior’ found in step 1. The results must be unified to the same group.

[0215] 4. Go to the second derivative of the unknown with comparison to the second derivative of the known plasma-lymphoprep, at the spectral range of 1154-1180 cm'1. The plasma results show a small peak separation, the fibromyalgia syndrome peak is located around 1204 cm' the RA-variant peak is located around 1205.5 cm'1and the healthy blood donor control group peak is located around 1207 cm'1.

[0216] 5. Go to step 1 and create new groups of similar behavior in the 3000-2800 cm'1spectral range. 6. Find the new cropped groups from steps 1 and 5.

[0217] 7. Go to step 4 in the spectral region of step 5 to re-color the unknowns.

[0218] 8. As long as there is an unknown of doubts (to which group it belongs) repeat steps 5-7 in a new spectral window or divide the 3000-2800 cm'1spectral window to small windows.

[0219] Algorithm example 2:

[0220] Calculating the first derivative to the spectral absorbance (the absorbance spectrum shown in Fig. 6A) plus one mathematical operation, in the 3000-2830 cm'1range, of two blood-products: (i) plasma-lymphoprep with respect to the following participants: 24 fibromyalgia syndrome (FM), 34 rheumatoid arthritis (RA) and 16 Healthy control group (Healthy BD) (shown in Fig. 6B); (ii) regular plasma of 80 unknown participants including subjects of the following groups: fibromyalgia syndrome, variant of rheumatoid arthritis (Ankylosing Spondylitis) and healthy control group (not shown). Measurements were carried out by the Bio-ATR accessory at approximately 37 degrees Celsius.

[0221] Algorithm example 3:

[0222] Calculating the second derivative to the spectral absorbance, in the 1254-1180 cm'1range, of two blood-products: (i) plasma-lymphoprep with respect to the following participants: 24 fibromyalgia syndrome (FM), 34 rheumatoid arthritis (RA) and 16 Healthy control group (Healthy BD); (ii) regular plasma of 80 unknown participants before separating the unknown participants into the following groups: fibromyalgia syndrome, variant of rheumatoid arthritis (Ankylosing Spondylitis) and healthy control group.

[0223] Al orithm example 4:

[0224] Calculating the second derivative to the spectral absorbance, in the 1254-1180 cm'1range, of two blood-products: (i) plasma-lymphoprep with respect to the following participants: 24 fibromyalgia syndrome (lymphoprep-FM), 34 rheumatoid arthritis (lymphoprep-RA) and 16 Healthy control group (lymphoprep-healthy BD); and (ii) regular plasma of 80 unknown participants after separating the unknown participants into the following groups: fibromyalgia syndrome (plasma-FM), variant of rheumatoid arthritis (plasma-Ankylosing Spondylitis) and healthy control group (plasma-healthy BD). These results are shown in Fig. 6C.

[0225] Example E

[0226] In this set of experiments treatment agents were tested on samples in vitro to examine an effect of the treatment agents on spectral behavior of the samples, indicating suitable and effective treatment options for a fibromyalgia syndrome patient. Reference is made to Figs. Figs. 7A, 7B, 7C, 7D, and 7E, which are graphs representing infrared spectral analysis of patient-derived immune cells (PBMC) supernatants obtained following in vitro activation of the PBMC and with or without application of endocannabinoids, performed for the purpose of prediction of optimal treatment for fibromyalgia syndrome patients.

[0227] In accordance with applications of the present disclosure, two different measurements were carried out at two different temperatures at specified spectral range in order to create (i) a best fit between the patient to the medication type and concentration, (ii) to monitor between the patient with respect to himself with different medications and concentrations and (iii) to predict the best medicating treatment.

[0228] Biological sample preparation procedure

[0229] PBMC were activated with the following molecules: PHA (Phytohemagglutinin 10 mg / ml) + LPS (Lipopolysaccharide 100 ng / ml). Three endocannabinoids were tested in-vitro on the activated PBMC cells. The obtained supernatant was used for spectral measurements. The three endocannabinoids are: AEA - N-ArachidonoylEthanolAmine , PEA - Palmitoyl Ethanol Amide, OEA - Oleyl Ethanol Amid. Each endocannabinoid was tested at two different concentrations: the higher concentration being 10-8M and the lower concentration being 10-10M.

[0230] Mathematical analysis

[0231] Pearson correlation coefficient was used to examine the proximity between the infrared measurements of (a) healthy control group and (b) the patient after treatment with respect to the type and concentrations of treatment agents. Graph plots obtained: spectra, first derivate of the spectra, second derivative of the spectra and third derivative of the spectra.

[0232] Type of blood-product: cell supernatants of PBMCs which were used as liquid or dried samples for the infrared measurement. Measurement technique: liquid supernatant - evanescent wave spectroscopy at approximately 37 degrees Celsius. For dried supernatant - transmittance at room temperature. Measuring accessory: Bio-ATR and HTS-XT (manufactured by Bruker Corporation (MA, USA)).

[0233] Fig. 7A is a graph showing the impact of medication on the spectral absorbance behavior in the large mid-infrared range of 3750-500 cm-1. The average spectrum of fibromyalgia syndrome patient-derived cell supernatants is shown. The measurements are of dried supernatant of PBMC with no activation is marked in the graph as ‘PBMC’. Their average after activation is shown in the graph and marked as ‘PBMC-ACT’. The supernatant of activated PBMC with the addition of medication (AEA at 10-8M concentration) is marked in the graph as ‘PBMC- ACT+AEA’. The ellipses in graph 7A emphasize a region with spectral differences.

[0234] Figs. 7B and 7C are a graph showing the infrared spectrum of cell supernatants derived from PBMC of a fibromyalgia syndrome patient labeled FM12 as follows: PBMC were activated with medication AEA, PEA, and OEA, each with two concentrations. The graph in Fig. 7B shows results for the medications AEA, PEA, and OEA at a concentration of 10'8M. The graph in Fig. 7C shows results for the medications AEA, PEA, and OEA at a concentration of 10'10M. Results are also shown with respect to a healthy blood donor control group (healthy BD). Table 1: shows the correlation coefficient with respect to the third, second and first derivatives to the absorbance spectra and the spectra itself with two possibilities (for baseline corrections) between three healthy donors of the control group after activation to the three fibromyalgia syndrome patients. (For example: AEA with concentration of IO'10is the best treatment for FM10 to get closest to two of the healthy donors (named BD103, BD104 and BD105).

[0235] Table 2: the correlation coefficient between the patient (after activation) with respect to himself / herself, when treated with medications.

[0236] For example: the most efficient medication for FM10 is OEA with concentration of IO'10because the correlation coefficient is lowest.

[0237] Fig. 7D shows infrared spectroscopy measurements carried out with another accessory, (Bio-ATR accessory manufactured by Bruker Corporation (MA, USA)), at a temperature of approximately 37 degrees Celsius, on the same supernatant from the same patients and same healthy control group, as shown in Figs. 7A-C. It was found by the inventors that the spectral region of 1280-1090 cm-1 is one of several optimal spectral windows to analyze the effect of medication. Using this spectral range allows upgrading the correlation model to best fit a treatment to a patient. Fig. 7D shows the spectra in the 1250-1090 cm-1 region. Spectra of supernatants derived from activated PBMC of healthy control group are marked as Healthy BD-Act. Spectra of the supernatants derived from activated PBMC with the addition of medications of three fibromyalgia syndrome patients are marked based on the medication and concentration used: FM- Act+PEA 1O'8 / 1O'10, FM-Act+AEA 10'10, FM-Act+AEA 10'8. Spectra of Supernatants derived from activated PBMC of fibromyalgia syndrome patients with activation only (without adding medications), is marked FM-Act.

[0238] Reference is made to Fig. 7E, which is a graph representing infrared spectral analysis of patient-derived immune cells (PBMC) supernatants obtained following in vitro activation of the PBMC, and with or without application of endocannabinoids (OEA), performed for the purpose of prediction of optimal treatment for fibromyalgia syndrome patients.

[0239] Activated PBMC (PBMC- ACT) were activated with the following molecules: PHA (Phytohemagglutinin 10 mg / ml) + LPS (Lipopolysaccharide 100 ng / ml). OEA - Oleyl Ethanol Amid at a concentration 10-10M was tested in-vitro on the activated PBMC cells. The obtained supernatant was used in a dry state at room temperature for spectral measurements by HTS-XT.

[0240] Fig. 7E shows the impact of medication (in this case OEA at a concentration 10-10M) on the spectral absorbance behavior of samples obtained from a fibromyalgia syndrome patient.

[0241] Spectra marked ‘PBMC’ refer to the supernatants derived from non-activated PBMC, i.e., in the basal state thereof, of the fibromyalgia patient. Spectra marked ‘PBMC-ACT’ refer to the supernatants derived from activated PBMC of the fibromyalgia patient. Spectra marked PBMC- ACT+OEA’ refers to the supernatants derived from activated PBMC of the fibromyalgia patient that were additionally treated with OEA medication at the concentration of 10-10M. As shown in Fig. 7E, treatment of the activated PBMC with the medication resulted in a spectra that was closer to the spectra of the ‘PBMC’ (basal state) compared to the activated PBMC (‘PBMC-ACT’), thereby showing a positive effect of the medication.

[0242] Example F

[0243] As described hereinabove, the infrared spectra are subjected to analysis by the computer processor by running algorithms that perform mathematical calculations on an infrared spectrum to derive information from the spectrum. In this set of experiments, far infrared measurements were conducted and subjected to mathematical manipulations. For example, mathematical tools such as sinusoidal, hyperbolic and logarithmic functions, were applied. The spectral measurements were performed using dried plasma-lymphoprep on a synthetic diamond plate. The following mathematical tools were used (R being the ratio between the first and second derivatives):

[0244] R = lstDerivative / 2ndderivative Logarithm in e base to the cosine hyperbolic of R,

[0245] W = In(coshR)

[0246] Cosine hyperbolic to the logarithm of square root of R squared,

[0247] Q = Cosh(ln R2)

[0248] Cosine hyperbolic to the ratio between R and the wavenumbers (X which is the frequency in cm4),

[0249] M = Cosh(R / X)

[0250] Trigonometric sinus of the multiplication between 1stto the 2ndderivatives,

[0251] P = Sin(lstder. 2ndder)

[0252] The 4th derivative is used:

[0253] 4thderivative

[0254] Additional possible mathematical analysis: the hyperbolic tangent was applied to the natural logarithm of the absolute value of the derivative ratio (R), and the result was divided by the wavenumber (i.e., w = (x-1)tanh(ln(| / ?|), where x denotes the wavenumber).

[0255] Reference is now made to Fig. 8A which is a graph showing the ratio between the first and second derivatives(R) in the narrow Far InfraRed band of 121.60-121.85 cm-1. The measurements in the graph were performed on dried plasma-lymphoprep blood product samples from fibromyalgia syndrome patients (FM), rheumatoid arthritis patients (RA) and healthy blood donor control group (Healthy BD), in accordance with some applications of the present disclosure.

[0256] Reference is now made to Fig. 8B which is a graph showing cosine hyperbolic of the ratio between the first and second derivatives(R), in the narrow Far InfraRed band of 85.15-85.35 cm- 1. The measurements in the graph were performed on dried plasma-lymphoprep blood product samples from fibromyalgia syndrome patients (FM), rheumatoid arthritis patients (RA) and healthy blood donor control group (Healthy BD), in accordance with some applications of the present disclosure.

[0257] Example G

[0258] In this set of experiments whole blood obtained from RA (Rheumatoid Arthritis) and the PsA (psoriatic Arthritis) patients were analyzed and compared to isolated PBMC cells from these same patients.

[0259] Typically, whole blood is easily obtained as a fresh sample from a subject and can be analyzed immediately without undergoing processing that is typically involved when preparing blood products such as plasma or extracellular vesicles.

[0260] Fig. 9 is a graph representing mathematical manipulations of infrared spectra in the range of 2900-2830 cm-1 of supernatant from isolated PBMC and supernatant from whole blood, based on samples from RA (Rheumatoid Arthritis) and the PsA (psoriatic Arthritis) patients, derived in accordance with some applications of the present disclosure. The samples were analyzed at approximately 37 degrees Celsius using Bio-ATR. As shown, the spectral differences in the spectra of the whole blood allows distinguishing between RA (Rheumatoid Arthritis) and the PsA (psoriatic Arthritis) in the specified spectral range shown in the graph in Fig. 9. While the graph in Fig. 9 shows differentiation between RA (Rheumatoid Arthritis) and the PsA (psoriatic Arthritis) using whole blood at the particular spectral range of the graph in Fig 9, it is noted that differentiation between RA (Rheumatoid Arthritis) and the PsA (psoriatic Arthritis) is possible using the supernatant of PBMC samples that are analyzed at a spectral range that is not shown in the graph of Fig. 9.

[0261] Example H

[0262] Figs 10A -10B are plots showing the correlation coefficient between measured spectral data and biological / clinical parameters, in accordance with some applications of the present disclosure.

[0263] The plots shown in Figs. 10A and 10B show a plot with respect to the biological score of interleukin-6 cytokine (IL6) (shown in Fig. 10A) and a plot with respect to the clinical score of Disease Activity Score (DAS28) within 14 pairs of joints (Fig. 10B) as a function of the spectral correlation coefficient between a specific spectral plot of an RA (Rheumatoid Arthritis) patient to the average plot of healthy women or to a plot of a RA patient (RA patient number RAI 05 whose Disease Activity Score of 0.7 was close to the average of the healthy women).

[0264] Referring now to Fig. 10 A.

[0265] Spectral measurements: conducted using Bio-ATR accessory (Bruker Corporation (MA, USA)) at the 3100-900 cm-1 region. Plasma samples were obtained from women only in the ages 40-70. Samples of 5 microliters and 20 microliters at approximately 37 degrees Celsius were subjected to the spectral analysis.

[0266] Biological parameters: Average IL6 levels measured for the RA patients was 10.03, while the average IL6 levels measured for healthy women was 1.54.

[0267] The plot in Fig. 10A shows the RA IL6 vs. correlation-coefficient (%) to the spectral absorption of the average of healthy women (n=19).

[0268] Referring now to Fig. 10B.

[0269] Spectral measurements: conducted using HTS-XT accessory (Bruker Corporation (MA, USA))at the 771-609 cm-1 region. Plasma samples were obtained from women only in the ages 46-68. Dried plasma samples were subjected to the spectral analysis on a zinc-selenide microtiter plate at room temperature.

[0270] Biological parameters: Disease Activity Score (DAS28) within 14 pairs of joints.

[0271] The plot in Fig. 10B shows the RA DAS28 vs. correlation-coefficient (%) to the spectral absorption of RA patient number RA105 whose Disease Activity Score of 0.7 was close to the average of the healthy women).

[0272] Thus, in accordance with some applications of the present disclosure, analysis of blood biopsies by IR spectroscopy in accordance with the apparatus and techniques described herein, allows accurate prediction and determination of clinical and biological parameters of the subject from whom the blood biopsy was drawn. In particular as it relates to level of inflammatory or pain-related mediators such as: 0-NGF, TNF-a, IL-6, and Disease Activity Score DAS28 (Disease activity score in 28 joints for rheumatoid arthritis, which is a commonly used tool to measure disease activity in patients with rheumatoid arthritis (RA)).

[0273] It is further noted that for Fibromyalgia patients analysis of blood biopsies by IR spectroscopy in accordance with the apparatus and techniques described herein, allows accurate prediction and determination of clinical parameters that include the severity of a disease course as evaluated by a physician, such as FIQ (Fibromyalgia Impact Questionnaire), and of biological parameters such as levels of pain cytokine 0-NGF .

[0274] Reference is again made to Examples A-H, described above. It is noted that the experiments described herein above are non-limiting examples. It is noted that in accordance with some applications of the present disclosure, when obtaining a first infrared (IR) spectrum by performing a first IR spectroscopy measurement on a first blood product sample obtained from a subject, and a second infrared (IR) spectrum by performing a second IR spectroscopy measurement on a second blood product sample from the same subject, any one or more parameters can be varied. In other words, in accordance with some applications of the present disclosure, the first and second IR spectrums are obtained with at least one variable parameter with respect to each other. Examples of these parameters refer to, but are not limited to: a type of blood product derived from the blood sample of the subject, an amount of the blood product sample subjected to the infrared spectroscopy, a state of the blood product sample (e.g., dry or liquid), a temperature of the blood product sample, an IR spectroscopy modality used to obtain the IR spectrum, an IR spectroscopy accessory used to obtain the IR spectrum, and a spectral range at which the IR spectrum is obtained.

[0275] The inventors have identified that obtaining at least two infrared (IR) spectrum while varying one or more of the above listed parameters in the process of obtaining the IR spectrum, typically enhances subtle spectral differences and / or differentiation between the spectral fingerprints and can be used to distinguish between fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis and ankylosing spondylitis.

[0276] Applications of the disclosure described herein can take the form of a computer program product accessible from a computer-usable or computer-readable medium (e.g., a non-transitory computer-readable medium) providing program code for use by or in connection with a computer or any instruction execution system, such as the computer processor referred to herein. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Typically, the computer-usable or computer readable medium is a non-transitory computer-usable or computer readable medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random-access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read / write (CD-R / W) and DVD.

[0277] A data processing system suitable for storing and / or executing program code will include at least one processor (e.g., the computer processor referred to herein) coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments of the disclosure.

[0278] Network adapters may be coupled to the processor to enable the processor to become coupled to other processors or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.

[0279] Computer program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the C programming language or similar programming languages.

[0280] It will be understood that algorithms described herein can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer (e.g., the computer processor referred to herein) or other programmable data processing apparatus, create means for implementing the functions / acts specified in the algorithms described in the present application. These computer program instructions may also be stored in a computer- readable medium (e.g., a non-transitory computer-readable medium) that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function / act specified in the flowchart blocks and algorithms. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the algorithms described in the present application.

[0281] The computer processor referred to herein is typically a hardware device programmed with computer program instructions to produce a special purpose computer. For example, when programmed to perform the algorithms described herein, the computer processor typically acts as a special purpose sample-analysis computer processor. Typically, the operations described herein that are performed by the computer processor transform the physical state of memory, which is a real physical article, to have a different magnetic polarity, electrical charge, or the like depending on the technology of the memory that is used.

[0282] It will be appreciated by persons skilled in the art that the present disclosure is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present disclosure includes both combinations and sub-combinations of the various features described hereinabove, as well as variations and modifications thereof that are not in the prior art, which would occur to persons skilled in the art upon reading the foregoing description.

Claims

CLAIMS1. A method comprising: obtaining from a subject at least a first and second blood product sample, the first and second blood product samples being different respective blood product samples selected from the group consisting of: whole blood, PBMC (peripheral blood mononuclear cells), supernatant of PBMC, liquid plasma, dried plasma, treated plasma and extra cellular vesicles; obtaining a first infrared spectrum of the first blood product sample by performing a first infrared spectroscopy measurement; obtaining a second infrared spectrum of the second blood product sample by performing a second infrared spectroscopy measurement; and using a computer processor: analyzing the first and second infrared spectra; and at least partially in response thereto, determining a status of the subj ect with respect to a condition selected from the group consisting of: fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis.

2. The method according to claim 1, wherein analyzing the first and second infrared spectra comprises applying one or more mathematical calculations to at least one of the first and second infrared spectra, the one or more mathematical calculations being selected from the group consisting of: a first derivative, a second derivative, a third derivative, a trigonometric function, a hyperbolic function, and a logarithmic function.

3. The method according to claim 1, wherein obtaining the first and second infrared spectra comprises obtaining a first and second Fourier Transformed infrared (FTIR) spectra.

4. The method according to claim 1, further comprising obtaining a third blood product sample and obtaining a third infrared spectrum of the third blood product sample by performing a third infrared spectroscopy measurement.

5. The method according to claim 1, wherein at least one of the first and second infrared spectra comprises an absorption spectrum and wherein obtaining the first and second infrared spectra comprises obtaining at least one of the first and second infrared spectra as an absorption spectrum.

6. The method according to claim 1, wherein at least one of the first and second infrared spectra comprises an absorption spectrum obtained by reflection measurements and whereinobtaining the first and second infrared spectra comprises obtaining at least one of the first and second infrared spectra as the absorption spectrum obtained by reflection measurements.

7. The method according to any one of claims 1-6, wherein determining the status of the subject with respect to the selected condition comprises determining that the subject is currently suffering from the selected condition.

8. The method according to any one of claims 1-6, wherein determining the status of the subject with respect to the selected condition comprises determining that the subject is not currently suffering from the selected condition.

9. The method according to any one of claims 1-6, wherein determining the status of the subject with respect to the selected condition comprises determining that the subject is at risk of the selected condition developing.

10. The method according to any one of claims 1-9, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first spectral range; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second spectral range; the first and second spectral range being different respective spectral ranges selected from the group consisting of mid-infrared and far-infrared.

11. The method according to any one of claims 1 -9, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first spectral range within a mid-infrared spectral range; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second spectral range within a mid-infrared spectral range; the first and second spectral range being different respective spectral ranges.

12. The method according to any one of claims 1-9, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first spectral range within a mid-infrared spectral range; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second spectral range within a mid-infrared spectral range; the first and second spectral range being the same spectral ranges.

13. The method according to any one of claims 1-9,wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first spectral range within a far-infrared spectral range; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second spectral range within a far-infrared spectral range; the first and second spectral range being different respective spectral ranges.

14. The method according to any one of claims 1-9, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first spectral range within a far-infrared spectral range; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second spectral range within a far-infrared spectral range; the first and second spectral range being the same spectral ranges.

15. The method according to any one of claims 1-13, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first temperature of the first blood product sample; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second temperature of the second blood product sample, the first and second temperatures being different respective temperatures.

16. The method according to claim 15, wherein the first temperature and the second temperature are selected from the group consisting of: room temperature and approximately 37 degrees Celsius.

17. The method according to any one of claims 1-13, wherein: obtaining the first blood product sample comprises obtaining a dried plasma sample, the dried plasma sample being at room temperature; obtaining the second blood product sample comprises obtaining a liquid plasma sample, the liquid plasma sample being at approximately 37 degrees Celsius; performing the first infrared spectroscopy measurement comprises performing the measurement on the dried plasma sample using a microplate- / microtiter- reader accessory; and performing the second infrared spectroscopy measurement comprises performing the measurement on the liquid plasma sample using an attenuated total reflectance (ATR)-silicon- crystal-based accessory .

18. The method according to claim 17, further comprising:obtaining a blood product sample of extra cellular vesicles; obtaining a third infrared spectrum of the of the extra cellular vesicles sample by performing infrared spectroscopy measurement by using the attenuated total reflectance (ATR)-silicon-crystal-based accessory; and using the computer processor, analyzing the third infrared spectra, wherein determining the status of the subject with respect to the selected condition comprises determining the status of the subject with respect to the selected condition, at least partially in response to the first, second, and third infrared spectra.

19. The method according to claim 17 or claim 18, further comprising: obtaining a blood sample of treated plasma; obtaining one or more infrared spectra of the treated plasma sample by performing one or more infrared spectroscopy measurements selected from the group consisting of: an infrared spectroscopy measurement using the attenuated total reflectance (ATR)-silicon-crystal-based accessory, an infrared spectroscopy measurement by using an attenuated total reflectance (ATR)- diamond-based accessory, and an infrared spectroscopy measurement by using a microplate- / microtiter- reader accessory.

20. The method according to any one of claims 1-13, wherein performing the first infrared spectroscopy measurement comprises performing the measurement using a first accessory; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement using a second accessory.

21. The method according to claim 20, wherein the first and second accessory are different respective accessories selected from the group consisting of: an attenuated total reflectance (ATR)- silicon-crystal-based accessory, a microplate- / microtiter- reader accessory, and an attenuated total reflectance (ATR)-diamond-based accessory.

22. A computer software product for use with at least a first and second blood product sample obtained from a subject, the first and second blood product samples being different respective blood product samples selected from the group consisting of: whole blood, PBMC (peripheral blood mononuclear cells), supernatant of PBMC, liquid plasma, dried plasma, treated plasma and extra cellular vesicles, the computer software product comprising a tangible non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a processor, cause the processor to:analyze a first infrared spectrum of the first blood product sample that was obtained by performing a first infrared spectroscopy measurement; analyze a second infrared spectrum of the second blood product sample that was obtained by performing a second infrared spectroscopy measurement; and in response thereto, determine a status of the subject with respect to a condition selected from the group consisting of fibromyalgia syndrome, rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis.

23. The computer software product according to claim 22, wherein the first and second infrared spectra comprise first and second Fourier Transformed infrared (FTIR) spectra.

24. The computer software product according to claim 22, wherein the instructions cause the processor to analyze a third infrared spectrum of a third blood product sample that was obtained by performing a third infrared spectroscopy measurement.

25. The computer software product according to claim 22, wherein at least one of the first and second infrared spectra comprises an absorption spectrum.

26. The computer software product according to claim 22, wherein at least one of the first and second infrared spectra comprises an absorption spectrum obtained by reflection measurements.

27. The computer software product according to claim 22, wherein the instructions cause the processor to analyze the first and second infrared spectra by applying one or more mathematical calculations to at least one of the first and second infrared spectra, the one or more mathematical calculations being selected from the group consisting of a first derivative, a second derivative, a third derivative, a trigonometric function, a hyperbolic function, and a logarithmic function.

28. The computer software product according to any one of claims 22-27, wherein the instructions cause the processor to determine the status of the subject with respect to the selected condition by determining that the subject is currently suffering from the selected condition.

29. The computer software product according to any one of claims 22-27, wherein the instructions cause the processor to determine the status of the subject with respect to the selected condition by determining that the subject is not currently suffering from the selected condition.

30. The computer software product according to any one of claims 22-27, wherein the instructions cause the processor to determine the status of the subject with respect to the selected condition by determining that the subject is at risk of the selected condition developing.

31. The computer software product according to any one of claims 22-30, wherein the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained at a first spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained at a second spectral range, the first and second spectral range being different respective spectral ranges selected from the group consisting of: mid-infrared and far-infrared.

32. The computer software product according to any one of claims 22-30, wherein the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a mid-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the mid-infrared spectral range; the first and second spectral range being different respective spectral ranges.

33. The computer software product according to any one of claims 22-30, wherein the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a mid-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the mid-infrared spectral range; the first and second spectral range being the same spectral ranges.

34. The computer software product according to any one of claims 22-30, wherein the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a far-infrared spectral range; andanalyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the far-infrared spectral range; the first and second spectral range being different respective spectral ranges.

35. The computer software product according to any one of claims 22-30, wherein the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a far-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the far-infrared spectral range; the first and second spectral range being the same spectral ranges.

36. The computer software product according to any one of claims 22-34, wherein the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained using a first accessory; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained using a second accessory.

37. The computer software product according to claim 36, wherein the first and second accessories are different respective accessories selected from the group consisting of: an attenuated total reflectance (ATR)-silicon-crystal-based accessory, a microplate- / microtiter-reader accessory, and an attenuated total reflectance (ATR)-diamond-based accessory.

38. The computer software product according to any one of claims 22-34, wherein the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained at a first temperature of the first blood product sample; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained at a second temperature of the second blood product sample, the first and second temperatures being different respective temperatures.

39. The computer software product according to claim 38, wherein the first temperature and the second temperature are selected from the group consisting of: room temperature and approximately 37 degrees Celsius.

40. The computer software product according to any one of claims 22-34, wherein the instructions cause the processor to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of a dried plasma sample at room temperature that was obtained using a microplate- / microtiter- reader accessory; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of a liquid plasma sample at approximately 37 degrees Celsius that was obtained using an attenuated total reflectance (ATR)-silicon-crystal-based accessory.

41. The computer software product according to claim 40, wherein the instructions cause the processor to: analyze a third infrared spectrum of a blood product sample of extra cellular vesicles, the third infrared spectrum having been obtained using the attenuated total reflectance (ATR)-silicon- crystal-based accessory; and determine the status of the subject with respect to the selected condition selected from the group consisting of: fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis and ankylosing spondylitis, in response to analyzing the first, second, and third infrared spectra.

42. The computer software product according to claim 40 or claim 41 , wherein the instructions cause the processor to analyze an infrared spectrum of a blood product sample of treated plasma.

43. The computer software product according to claim 42, wherein the infrared spectrum of the blood product sample of treated plasma was obtained by performing one or more infrared spectroscopy measurements selected from the group consisting of: an infrared spectroscopy measurement by using the attenuated total reflectance (ATR)-silicon-crystal-based accessory, an infrared spectroscopy measurement by using an attenuated total reflectance (ATR)-diamond-based accessory, and an infrared spectroscopy measurement by using a microplate- / microtiter- reader accessory.

44. Apparatus for use with an infrared spectroscopy measurement unit configured to obtain a first infrared spectrum of a first blood product sample obtained from a subject by performing a first infrared spectroscopy measurement, and to obtain a second infrared spectrum of a second blood product sample obtained from the subject by performing a second infrared spectroscopymeasurement; the first and second blood product samples being different respective blood product samples selected from the group consisting of whole blood, PBMC (peripheral blood mononuclear cells), supernatant of PBMC, liquid plasma, dried plasma, treated plasma and extra cellular vesicles, the apparatus comprising: at least one computer processor configured to: analyze the first and second infrared spectra; and in response thereto, determining a status of the subject with respect to a condition selected from the group consisting of: fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis.

45. The apparatus according to claim 44, wherein the first and second infrared spectra comprise first and second Fourier Transformed infrared (FTIR) spectra.

46. The apparatus according to claim 44, wherein the computer processor is configured to analyze a third infrared spectrum of a third blood product sample that was obtained by performing a third infrared spectroscopy measurement.

47. The apparatus according to claim 44, wherein at least one of the first and second infrared spectra comprises an absorption spectrum.

48. The apparatus according to claim 44, wherein at least one of the first and second infrared spectra comprises an absorption spectrum obtained by reflection measurements.

49. The apparatus according to claim 44, wherein the computer processor is configured to analyze the first and second infrared spectra by applying one or more mathematical calculations to at least one of the first and second infrared spectra, the one or more mathematical calculations being selected from the group consisting of: a first derivative, a second derivative, a third derivative, a trigonometric function, a hyperbolic function, and a logarithmic function.

50. The apparatus according to any one of claims 44-49, wherein the computer processor is configured to determine the status of the subject with respect to the selected condition by determining that the subject is currently suffering from the selected condition.

51. The apparatus according to any one of claims 44-49, wherein the computer processor is configured to determine the status of the subject with respect to the selected condition by determining that the subject is not currently suffering from the selected condition.

52. The apparatus according to any one of claims 44-49, wherein the computer processor is configured to determine the status of the subject with respect to the selected condition by determining that the subject is at risk of the selected condition developing.

53. The apparatus according to any one of claims 44-52, wherein the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained at a first spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained at a second spectral range, the first and second spectral range being different respective spectral ranges selected from the group consisting of: mid-infrared and far-infrared.

54. The apparatus according to any one of claims 44-49, wherein the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a mid-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the mid-infrared spectral range; the first and second spectral range being different respective spectral ranges.

55. The apparatus according to any one of claims 44-49, wherein the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a mid-infrared spectral range; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the mid-infrared spectral range; the first and second spectral range being the same spectral ranges.

56. The apparatus according to any one of claims 44-49, wherein the computer processor is configured to:analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a far-infrared spectral range; and analyze the second infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the far-infrared spectral range; the first and second spectral range being different respective spectral ranges.

57. The apparatus according to any one of claims 44-49, wherein the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained within a far-infrared spectral range; and analyze the second infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained within the far-infrared spectral range; the first and second spectral range being the same spectral ranges.

58. The apparatus according to any one of claims 44-56, wherein the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained using a first accessory; and analyze the second infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained using a second accessory.

59. The apparatus according to claim 58, wherein the first and second accessories are different respective accessories selected from the group consisting of: an attenuated total reflectance (ATR)- silicon-crystal-based accessory, a microplate- / microtiter- reader accessory, and an attenuated total reflectance (ATR)-diamond-based accessory.

60. The apparatus according to any one of claims 44-56, wherein the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of the first blood product sample that was obtained at a first temperature of the first blood product sample; andanalyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of the second blood product sample that was obtained at a second temperature of the second blood product sample, the first and second temperatures being different respective temperatures.

61. The apparatus according to claim 60, wherein the first temperature and the second temperature are selected from the group consisting of: room temperature and approximately 37 degrees Celsius.

62. The apparatus according to any one of claims 44-56, wherein the computer processor is configured to: analyze the first infrared spectrum of the first blood product sample by analyzing an infrared spectrum of a dried plasma sample at room temperature that was obtained using a microplate- / microtiter- reader accessory; and analyze the second infrared spectrum of the second blood product sample by analyzing an infrared spectrum of a liquid plasma sample at approximately 37 degrees Celsius that was obtained using an attenuated total reflectance (ATR)-silicon-crystal-based accessory.

63. The apparatus according to claim 62, wherein the processor is configured to: analyze a third infrared spectrum of a blood product sample of extra cellular vesicles, the third infrared spectrum having been obtained using the attenuated total reflectance (ATR)-silicon- crystal-based accessory; and determine the status of the subject with respect to the selected condition selected from the group consisting of: fibromyalgia syndrome, rheumatoid arthritis, psoriatic arthritis and ankylosing spondylitis, in response to analyzing the first, second, and third infrared spectra.

64. The apparatus according to claim 62 or claim 63, wherein the processor is further configured to analyze an infrared spectrum of a blood product sample of treated plasma.

65. The apparatus according to claim 64, wherein the infrared spectrum of the blood product sample of treated plasma was obtained by performing one or more infrared spectroscopy measurements selected from the group consisting of: an infrared spectroscopy measurement by using the attenuated total reflectance (ATR)-silicon-crystal-based accessory, an infrared spectroscopy measurement by using an attenuated total reflectance (ATR)-diamond-based accessory, and an infrared spectroscopy measurement by using a microplate- / microtiter- reader accessory.

66. A method comprising:obtaining from a subject a whole blood sample; obtaining an infrared spectrum of the sample by performing an infrared spectroscopy measurement; and using a computer processor: analyzing the infrared spectra; and in response thereto, determining a status of the subject with respect to a condition selected from the group consisting of: Psoriatic arthritis (PsA), ankylosing spondylitis, and rheumatoid arthritis (RA).

67. The method according to claim 66, wherein performing the infrared spectroscopy measurement comprises performing the measurements in the spectral range of 2900-2830 cm-1 using a attenuated total reflectance (ATR)-silicon-crystal-based accessory that is for use with an infrared spectrometer.

68. The method according to claim 66 or claim 67, wherein determining the status of the subject with respect to the selected condition comprises determining that the subject is currently suffering from the selected condition.

69. The method according to claim 66 or claim 67, wherein determining the status of the subject with respect to the selected condition comprises determining that the subject is not currently suffering from the selected condition.

70. The method according to claim 66 or claim 67, wherein determining the status of the subject with respect to the selected condition comprises determining that the subject is at risk of the selected condition developing.

71. A method comprising: identifying that a subject is suffering from fibromyalgia syndrome; treating a sample of isolated and activated Peripheral Blood Mononuclear Cells (PBMC) obtained from the subject with a fibromyalgia syndrome drug candidate; using an infrared spectroscopy measurement unit, obtaining an infrared spectrum of the sample; and using a computer processor: analyzing the infrared spectrum of the sample; and in response thereto, determining an effectiveness of the drug candidate in treatment of fibromyalgia syndrome.

72. The method according to claim 71, wherein treating the sample with a fibromyalgia syndrome drug candidate comprises treating the sample with first and second concentrations of the same drug candidate.

73. The method according to claim 71, wherein treating the sample with a fibromyalgia syndrome drug candidate comprises treating the sample with drug candidates selected from the group consisting of N-Arachidonoyl Ethanol Amine (AEA), Palmitoyl Ethanol Amide (PEA), and Oleyl Ethanol Amid (OEA).

74. A method comprising: identifying that a subject is suffering from rheumatoid arthritis (RA); treating a sample of isolated and activated Peripheral Blood Mononuclear Cells (PBMC) obtained from the subject with a rheumatoid arthritis drug candidate; using an infrared spectroscopy measurement unit, obtaining an infrared spectrum of the sample; and using a computer processor: analyzing the infrared spectrum of the sample; and in response thereto, determining an effectiveness of the drug candidate in treatment of rheumatoid arthritis.

75. A method comprising: obtaining from a subject at least a first and second blood product sample, the first and second blood product samples being different respective blood product samples selected from the group consisting of: whole blood, liquid plasma, dried plasma, treated plasma and extra cellular vesicles; obtaining a first infrared spectrum of the first blood product sample by performing a first infrared spectroscopy measurement using a first infrared (IR) spectroscopy modality; obtaining a second infrared spectrum of the second blood product sample by performing a second infrared spectroscopy measurement using a second IR spectroscopy modality, the first and second IR spectroscopy modality being different respective modalities; and using a computer processor: analyzing the first and second infrared spectra; and in response thereto, determining a status of the subject with respect to a condition selected from the group consisting of: fibromyalgia syndrome, and rheumatoid arthritis.

76. The method according to claim 75, wherein performing the first and second infrared spectroscopy measurement using the first and second infrared (IR) spectroscopy modality comprises: performing the first infrared spectroscopy measurement using a first accessory; and performing the second infrared spectroscopy measurement using a second accessory, wherein the first and second accessory are different respective accessories selected from the group consisting of: an attenuated total reflectance (ATR)-silicon-crystal-based accessory, a microplate- / microtiter- reader accessory, and an attenuated total reflectance (ATR)-diamond- based accessory.

77. The method according to claim 75, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first spectral range; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second spectral range; the first and second spectral range being different respective spectral ranges selected from the group consisting of: mid-infrared and far-infrared.

78. The method according to claim 75, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first spectral range within a mid-infrared spectral range; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second spectral range within a mid-infrared spectral range; the first and second spectral range being different respective spectral ranges.

79. The method according to claim 75, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first spectral range within a far-infrared spectral range; and wherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second spectral range within a far-infrared spectral range; the first and second spectral range being different respective spectral ranges.

80. The method according to claim 75, wherein performing the first infrared spectroscopy measurement comprises performing the measurement at a first temperature of the first blood product sample; andwherein performing the second infrared spectroscopy measurement comprises performing the measurement at a second temperature of the second blood product sample; and the first and second temperatures being different respective temperatures selected from the group consisting of: room temperature and approximately 37 degrees Celsius.

81. A method comprising: identifying that a subject is suffering from rheumatoid arthritis (RA); using an infrared spectroscopy measurement unit, obtaining an infrared spectrum of the sample; and using a computer processor: analyzing the infrared spectrum of the sample; and in response thereto, determining at least one parameter selected from the group consisting of: a clinical score assessing disease activity in the subject and a biological parameter of the subject.

82. The method according to claim 81, wherein the clinical score includes DAS28 and wherein determining the clinical score comprises determining a DAS28 of the subject.

83. The methos according to claim 81, wherein the biological parameter includes a level of cytokine IL6 and wherein determining the biological parameter comprises determining a level of cytokine IL6.

84. The methos according to claim 81, wherein the biological parameter includes a level of TNF-alpha and wherein determining the biological parameter comprises determining a level of TNF-alpha.

85. A method comprising: identifying that a subject is suffering from fibromyalgia (FM); using an infrared spectroscopy measurement unit, obtaining an infrared spectrum of the sample; and using a computer processor: analyzing the infrared spectrum of the sample; and in response thereto, determining at least one parameter selected from the group consisting of: a clinical score assessing disease activity in the subject and a biological parameter of the subject.

86. The method according to claim 85, wherein the clinical score includes a fibromyalgia impact questionnaire (FIQ) and wherein determining the clinical score comprises determining a FIQ of the subject.

87. The methos according to claim 81, wherein the biological parameter includes a level of beta-NGF and wherein determining the biological parameter comprises determining a level of beta-NGF.

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