Spectrometry system and method

Abstract

At least one light source may be configured to illuminate a region of interest of a subject, and at least one light sensor may be configured to non-invasively measure reflected light within the region of interest. The at least one processor may perform processing including receiving at least one measurement of light from the at least one light sensor. From the measurement of light, the processor may determine at least one thickness or depth of at least one tissue layer within the region of interest of the subject, and / or perform further processing to determine at least one physical characteristic of the region of interest of the subject.

Description

[0001] Cross - reference to related applications:

[0002] This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 477,989, filed on December 30, 2022, which is hereby incorporated by reference in its entirety. Background Art:

[0003] Spectrometry is based on irradiating biological tissue using a single or multiple light sources that generate energy at various wavelengths. One or more sensors are then strategically placed to detect and measure the energy leaving the sample at different locations. The scattered positioning of the source and the receiver allows techniques such as triangulation and time resolution to distinguish signals from locations within a multi - dimensional tissue sample.

[0004] There are many ways in which the source and sensors can be arranged and operated such that many measurements can be made. Each of the metrics mentioned above corresponds to the energy contained within a predefined spectral bandwidth of the exiting energy. The resulting energy spectrum that is generated can be analyzed in many ways to obtain the physical or chemical characteristics of the measured sample. Summary of the Invention:

[0005] Devices and methods for non - invasive in - vivo quantitative measurement of biomarkers, chemicals, and chromophores in differentiated biological tissue layers and associated blood supplies are described herein. More specifically, the present disclosure describes new and more effective methods for obtaining desired parameter values of biomarker, chemical, and chromophore concentrations based on spectrophotometric measurements.

[0006] The systems and methods described herein can employ optical methods to non - invasively measure biomarker levels, chemical concentrations, and chromophore concentrations in different biological tissue layers. Additionally, the optical methods taught herein allow for the measurement of biomarkers, chemicals, and chromophores, including but not limited to oxyhemoglobin, deoxyhemoglobin, methemoglobin, carboxyhemoglobin, muscle oxygenation, muscle oxygen consumption, nitric oxide, S - nitrosothiols, water, glycogen, and internal training load.

[0007] Some example embodiments may include a system that includes: at least one light source configured to illuminate a region of interest of a subject; at least one light sensor configured to non-invasively measure reflected light within the region of interest of the subject; at least one processor in communication with the sensor; and at least one non-transitory computer-readable medium storing machine-readable instructions. When executed by the at least one processor, the instructions may cause the at least one processor to perform a process that includes: receiving at least one measurement of light from the at least one light sensor; processing the at least one measurement and processing at least a portion of a stochastic model to determine an irradiance distribution of the region of interest of the subject; calculating at least one characteristic attenuation coefficient of at least one wavelength of light based on the irradiance distribution; and determining at least one physical characteristic of the region of interest of the subject based on the at least one characteristic attenuation coefficient and the at least one measurement of light.

[0008] Some example embodiments may include a system that includes: a plurality of light sources configured to illuminate a region of interest of a subject, with respective ones of the plurality of light sources configured to emit light of respective different wavelengths; at least one light sensor configured to non-invasively measure reflected light within the region of interest of the subject; at least one processor in communication with the sensor; and at least one non-transitory computer-readable medium storing machine-readable instructions. When executed by the at least one processor, the instructions may cause the at least one processor to perform a process that includes: receiving a plurality of measurements of light of respective different wavelengths from the at least one light sensor; and determining at least one thickness or depth of at least one tissue layer within the region of interest of the subject based on the plurality of measurements of light.

[0009] Some example embodiments may include a method that includes: illuminating a region of interest of a subject with at least one light source; detecting reflected light from the region of interest of the subject with at least one light sensor; receiving at least one measurement of light from the at least one light sensor by at least one processor; using the at least one measurement and a stochastic model as inputs to determine an irradiance distribution of the region of interest of the subject; calculating at least one characteristic attenuation coefficient of at least one wavelength of light by at least one processor based on the irradiance distribution; and determining at least one physical characteristic of the region of interest of the subject by at least one processor based on the at least one characteristic attenuation coefficient and the at least one measurement of light.

[0010] Some example embodiments may include a method that includes: irradiating a region of interest of a subject with a plurality of light sources, where respective light sources of the plurality of light sources emit light of respective different wavelengths; detecting light within the region of interest of the subject with at least one light sensor; receiving, by at least one processor, a plurality of measurements of light of respective different wavelengths from the at least one light sensor; and determining, by the at least one processor, at least one thickness or depth of at least one tissue layer within the region of interest of the subject based on the plurality of measurements of light.

[0011] In at least some example embodiments, the process and / or method may further include: determining at least one physical property of the region of interest of the subject based on at least one measurement of light and at least one thickness or depth of at least one tissue layer. In at least some example embodiments, the determining may include applying the plurality of measurements of light to at least one scattering phase function associated with at least one layer type within the region of interest of the subject.

[0012] In at least some example embodiments, the at least one physical property may include one or more of a water measurement, an internal training load, an oxyhemoglobin measurement, a deoxyhemoglobin measurement, a total hemoglobin measurement, a blood volume measurement, muscle oxygenation, muscle oxygen consumption, a reactive nitric oxide measurement, a reactive S-nitrosothiol measurement, a fat thickness, and a melanin content.

[0013] In at least some exemplary embodiments, the at least one physical property may include a combination of pulse oximetry and nitric oxide.

[0014] In at least some example embodiments, the at least one measurement of light may include a time series of measurements, and the at least one physical property includes a time series of properties. The process and / or method may further include: generating a value representing an endogenous S-nitrosothiol content of tissue within the region of interest based on the time series of properties; and storing the value representing the endogenous S-nitrosothiol content of tissue within the region of interest in a non-transitory computer-readable medium.

[0015] In at least some example embodiments, the time series of properties may include a time series of oxygen saturation measurements and a time series of blood volume measurements. In at least some exemplary embodiments, generating the value may include: determining a linearity of a relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements and providing a set of parameters; and generating a value representing an endogenous S-nitrosothiol content of tissue within the region of interest based on the set of parameters. In at least some example embodiments, generating the value may include: using a linear regression model to provide a best fit line defined by a set of parameters, the set of parameters including a slope of the best fit line; and generating a value representing an endogenous S-nitrosothiol content of tissue within the region of interest based on the slope of the best fit line.

[0016] In at least some example embodiments, the determination of at least one physical property may be performed during one of the exercise sessions of the subject and a time period immediately following the exercise session of the subject.

[0017] In at least some example embodiments, the process and / or method may further include displaying, by at least one display device in communication with at least one processor, at least one indication of at least one physical property. In at least some example embodiments, the at least one indication may include guidance related to the subject's internal training load, such as guidance aimed at reducing the risk of injury. In at least some exemplary embodiments, the at least one indication may include information related to the subject's muscle oxygen consumption, such as information including a VO2 indication.

[0018] In at least some example embodiments, the stochastic model may include a three-dimensional model with six degrees of freedom. In at least some example embodiments, the six degrees of freedom may include position coordinates and direction cosines. BRIEF DESCRIPTION OF THE DRAWINGS:

[0019] Figure 1 is a wearable optical device according to some embodiments of the present disclosure.

[0020] Figure 2 is a set of diagrams of the modeling geometry of light according to some embodiments of the present disclosure.

[0021] Figure 3 is a diagram illustrating an example light path of photons according to some embodiments of the present disclosure.

[0022] Figure 4 is a diagram illustrating an example wavelength and its optical distance through the fat layer thickness according to some embodiments of the present disclosure.

[0023] Figure 5 is an example set of optical hardware LEDs and photodiodes according to some embodiments of the present disclosure.

[0024] Figure 6 is an example of optical hardware for controlling and capturing light and processing data according to some embodiments of the present disclosure.

[0025] Figure 7 is a flowchart illustrating a method for implementing a computational process for generating biomarker levels, chemical substance concentrations, and chromophore concentrations in different tissue layers according to some embodiments of the present disclosure.

[0026] Figure 8 is a diagram illustrating an example of the measurement of muscle oxygen saturation (SmO2) of a subject during exercise according to some embodiments of the present disclosure.

[0027] Figure 9 A graph that illustrates examples of measurements of local muscle oxygen consumption (mVO2) in a subject during exercise according to some embodiments of the present disclosure.

[0028] Figure 10 A graph that illustrates examples of non-invasive measurements of nitric oxide activity in a subject's blood during exercise according to some embodiments of the present disclosure.

[0029] Figure 11 A graph that illustrates examples of measurements of a subject's internal training load (ITL) during exercise according to some embodiments of the present disclosure and shows the relationship between the internal training load and the external training load (ETL).

[0030] Figure 12 Illustrates an example measurement device according to some embodiments of the present disclosure.

[0031] Figure 13 A schematic block diagram of an example system of hardware components that illustrates examples capable of implementing the systems and methods disclosed herein.

[0032] Figure 14 Illustrates an example of a system for generating a value representing the endogenous S-nitrosothiol content of tissue within a region of interest of a subject according to some embodiments of the present disclosure.

[0033] Figure 15 Illustrates another example of a system for generating a value representing the endogenous S-nitrosothiol content of tissue within a region of interest of a subject according to some embodiments of the present disclosure.

[0034] Figure 16 A graph that illustrates the PNO level of a patient during exercise according to some embodiments of the present disclosure.

[0035] Figure 17 A graph that illustrates the VO2 level of a patient during exercise according to some embodiments of the present disclosure.

[0036] Figure 18 A graph depicting time series of UO2 measurements and time series of VO2 measurements of an athlete operating a full-body exercise bike using one sensor for recording blood volume and oxygen saturation according to some embodiments of the present disclosure.

[0037] Figure 19 A graph depicting time series of UO2 measurements and time series of VO2 measurements of an athlete operating a full-body exercise bike using one sensor for recording blood volume and oxygen saturation according to some embodiments of the present disclosure.

[0038] Figure 20 A graph depicting the maximum NO strength of an athlete recorded weekly over a six - month period according to some embodiments of the present disclosure.

[0039] Figure 21 A graph depicting the relationship between the maximum NO endurance and the critical strength of an athlete recorded over a six - week period as a scatter plot according to some embodiments of the present disclosure.

[0040] Figure 22 An example of a method for generating a value representing the endogenous S - nitrosothiol content of tissue within a region of interest of a subject according to some embodiments of the present disclosure.

[0041] Figure 23 Another example of a method for generating a value representing the endogenous S - nitrosothiol content of tissue within a region of interest of a subject according to some embodiments of the present disclosure.

[0042] Figure 24 Another method for generating a value representing the endogenous S - nitrosothiol content of tissue within a region of interest of a subject according to some embodiments of the present disclosure.

[0043] Figure 25 A graph depicting metrics and use cases of a professional sports platform using (NO) according to some embodiments of the present disclosure.

[0044] Figure 26 A bar graph depicting athlete recovery measurements as maximum (NO) - recovery levels at three set numbers according to some embodiments of the present disclosure.

[0045] Figure 27 An example of personal nitric oxide (PNO) over time and (NO) - regeneration over time according to some embodiments of the present disclosure.

[0046] Figure 28 A graph depicting the effect of exercise on muscle oxygenation (SmO2) and s - nitrosothiol (PNO) according to some embodiments of the present disclosure.

[0047] Figure 29 A graph depicting the effect of exercise on muscle oxygenation (SmO2) and s - nitrosothiol (PNO) according to some embodiments of the present disclosure, where a (+) correlation is nested within a (-) correlation, and a (+) correlation is nested within a (+) correlation.

[0048] Figure 30ADepicts representative traces from control βC93 mice and corresponding βC93A mutant animals according to some embodiments of the present disclosure.

[0049] Figure 30B Depicts basal pO2 in the gastrocnemius muscle from control βC93 mice and corresponding βC93A mutant animals according to some embodiments of the present disclosure.

[0050] Figure 30C Depicts the rate of recovery of muscle pO2 after occlusion from control βC93 mice and corresponding βC93A mutant animals according to some embodiments of the present disclosure.

[0051] Figure 31A Depicts SNO-Hb isolated from fresh arterial blood of a patient group according to some embodiments of the present disclosure.

[0052] Figure 31B Depicts the FeNO levels of a patient group according to some embodiments of the present disclosure.

[0053] Figure 31C Depicts the total HbNO of a patient group according to some embodiments of the present disclosure.

[0054] Figure 31D Depicts the ratio of SNO to total HbNO of a patient group according to some embodiments of the present disclosure.

[0055] Figure 32A Depicts representative near-infrared sensor measurements of Hb oxygenation recovery over time in a healthy control group and PAD patients according to some embodiments of the present disclosure.

[0056] Figure 32B Depicts the reperfusion recovery half-life in a patient group measured at the foot using a cuff at the ankle according to some embodiments of the present disclosure.

[0057] Figure 32C Depicts the reperfusion recovery half-life in a patient group measured at the foot using a cuff at the thigh according to some embodiments of the present disclosure.

[0058] Figure 32D Depicts the reperfusion recovery half-life in a patient group measured at the calf using a cuff at the thigh according to some embodiments of the present disclosure.

[0059] Figure 32E Depicts the correlation between SNO-Hb levels and the recovery half-life according to some embodiments of the present disclosure.

[0060] Figure 33 Depicts the correlation between NOHb measurements and clinical chemistry results according to some embodiments of the present disclosure.

[0061] Figure 34 Depicts the correlation between NOHb measurements and the NIRS hyperemic half-life according to some embodiments of the present disclosure. Detailed implementation:

[0062] In the following description, for purposes of explanation and teaching, numerous specific details are set forth in order to provide an understanding of one or more exemplary embodiments. However, it may be apparent to those skilled in the art that other embodiments of the disclosed systems and methods may be practiced without some or all of these specific details.

[0063] As used herein, the term "includes" means including but not limited to, and the term "including" means including but not limited to. The term "based on" means at least partially based on. Additionally, when the present disclosure or claims refer to "a," "an," "first," or "another" element or its equivalent, it should be construed to include one or more such elements, neither requiring nor precluding two or more such elements.

[0064] As used herein, the "amount" or "level" of a biological substance can represent any of the volume, mass, saturation, or concentration of the biological substance.

[0065] As used herein, a "physiological parameter" or "biological parameter" is a continuous, categorical, or ordinal value that characterizes the physical state of a subject.

[0066] As used herein, a "biomarker" or "biometric parameter" is a measured parameter that indicates the health, physical fitness, or physical performance of a subject.

[0067] As used herein, a "subject" is an organism belonging to the class Mammalia.

[0068] As used herein, a "prediction model" is a mathematical model that either predicts the future state of a parameter or estimates the current state of a parameter that cannot be directly measured.

[0069] As used herein, a "biometric parameter" is a measured parameter that indicates the health or physical fitness of a subject.

[0070] As used herein, a measurement is made "non-invasively" if it does not require the removal of blood or tissue from the subject to perform the measurement.

[0071] As used herein, a "time series" is a series of measurements on a relative time scale. The measurements need not be continuous or made at constant intervals.

[0072] As used herein, "providing treatment to a subject" can include administering a therapeutic agent, applying mechanical or electrical energy to the subject, or instructing the subject to perform specific movements or tasks that are considered to provide a therapeutic benefit to the subject.

[0073] As used herein, "PNO" or personal nitric oxide is the individual level of the bioactive form of NO in the blood, which is derived from red blood cells and labeled with S-nitrosothiols in hemoglobin, and it should be understood that SNO in RBCs is in equilibrium with other SNOs, and PNO can be formed from NO (and related NOx) from different sources, and the release of S-nitrosothiols from RBCs can occur in different ways to generate NO and S-nitrosothiols in tissues, and thus PNO represents NO bioactivity, including any bioactive form of NO that is derived from RBCs or otherwise formed to supply oxygen to tissues. It should be understood that PNO is a relative measure that represents the direct correlation between the oxygen saturation of hemoglobin and total hemoglobin. For example, an individual with a higher SNO level can re-oxygenate tissues faster compared to an individual with a lower SNO level, such as during muscle recovery during a strenuous period like an exercise routine.

[0074] As used herein, "UO2" is the amount of oxygen used by the tissue in the region of interest measured using nitric oxide-based calculations. The calculation is derived as the product of the PNO metric and the amount of oxygen used in the tissue.

[0075] As used herein, "maximum NO power" is a nitric oxide-based measure that reflects the maximum energy utilization of an individual, which is directly related to the true measurement of energy utilization in watts and is derived from the maximum value of the PNO metric and the maximum energy utilization of muscle tissue during exercise, and "maximum NO endurance" is a nitric oxide-based measure that reflects the maximum energy supply rate of an individual, which is directly related to the increase in critical power, the gold standard measurement of endurance performance, and is derived from the maximum rate of change of the PNO metric generated during the rest period after exercise.

[0076] As used herein, "in real time" performs the calculation or determination when it is available to the user within one minute of the corresponding measurement. In one implementation, the real-time calculation is performed within ten seconds of the measurement.

[0077] Spectroscopy has become a well-established and recognized method for non-invasively measuring many biological parameters in vivo and in real time. In this context, spectroscopy involves the use of one or more sources and one or more detectors, where the one or more sources project electromagnetic energy of multiple wavelengths towards the tissue to be measured, and the one or more detectors are used to detect and measure the energy leaving the tissue. The energy source is typically located outside the body and directed towards the tissue or body part of interest. The energy propagates through the skin and into the subcutaneous tissue, including adipose and skeletal muscle tissue. As the energy propagates through the body, it is absorbed and scattered, as it typically is in a turbid medium, and a fully diffused energy field is produced. Typically, a portion of the scattered energy reaches the boundary between the tissue and the surrounding environment and propagates out of the tissue through the skin. This energy can be detected and measured by placing an appropriate sensor at the appropriate tissue boundary. The detected energy forms a spectral signal that carries the imprint of the material through which the energy has passed. An important part of any spectral system is how the spectral signal is processed, how the imprint is extracted, and how the imprint is interpreted.

[0078] By creatively choosing the wavelengths used (which can be outside or within the visible spectrum), the characteristics and placement of the sources and receivers, and the data processing techniques applied, a wide range of materials as well as chemical properties and concentrations can be detected and quantified. Quantifiable biomarker levels, chemical substance concentrations, chromophore concentrations, and tissue properties include but are not limited to oxyhemoglobin, deoxyhemoglobin, methemoglobin, carboxyhemoglobin, muscle oxygenation, muscle oxygen consumption, nitric oxide, nitric oxide activity in the blood, S-nitrosothiols, water, glycogen, adipose tissue thickness, melanin, and internal training load.

[0079] The embodiments discussed herein include methods of spectrophotometry for in vivo quantification of biomarkers, chemicals, and chromophores in differentiated tissue layers and the associated blood supply of the tissue layer. The disclosed embodiments may rely on one or more body-worn sensors, such as the NNOXX wearable device, irradiate a desired tissue volume, and then detect and measure the energy exiting the tissue sample at different locations. The dispersed positioning of the source and receiver allows techniques such as triangulation and time resolution to distinguish signals from specific locations within the multi-dimensional tissue sample. There are many ways in which the source and sensors can be arranged and operated such that many measurements can be made. Each of the metrics mentioned above corresponds to energy contained within a predefined spectral bandwidth of the exiting energy. The resulting energy spectrum can be analyzed in many ways to obtain the physical or chemical characteristics of the measured sample. The measured sample provides a quantitative measurement of biomarkers that include, but are not limited to, oxyhemoglobin, deoxyhemoglobin, methemoglobin, carboxyhemoglobin, muscle oxygenation, muscle oxygen consumption, nitric oxide, nitric oxide activity in the blood, S-nitrosothiols, water, glycogen, adipose tissue thickness, melanin, and internal training load.

[0080] The integrated hardware and / or software system then receives information from the body-worn sensors and automatically provides biofeedback, such as specifying the levels of biomarkers, chemicals, and chromophores in the tissue layer or vascular network. The disclosed embodiments may process one or more algorithms that take both in vivo sensor measurements and stochastic model data as input, thereby providing a robust and accurate measurement of one or more physical characteristics of the subject. For example, assume an individual is exercising as a means of enhancing cardiovascular function. Such an individual may use the NNOXX wearable device or another spectrophotometric device to measure a specified biomarker measurement, such as the level of active nitric oxide in skeletal muscle tissue, to determine the most effective exercise intensity to achieve their goal. Alternatively, a doctor may use the spectrophotometric devices discussed herein to non-invasively measure a patient's blood biomarkers, including but not limited to nitric oxide, iron, cholesterol, blood oxygenation, and muscle oxygenation.

[0081] In the systems and methods described herein, the following processes may be performed by one or more hardware and / or software systems, such as those described herein with respect to Figure 5 , Figure 6 , Figure 12 and / or Figure 13 For example, each hardware and / or software system is described in detail below.

[0082] Consider a collimated beam passing through a bounded volume of a turbid medium. The turbid medium is a transparent or translucent material that absorbs and scatters any light passing through it. A collimated beam can be used because the superposition of collimated beams of different intensities can mathematically describe the radiation intensity distribution of any light source. When the beam passes through the turbid medium, the energy in the beam is reduced by absorption and scattering processes according to Equation (1).

[0083] I(z) = I o exp(-(μ a + μ s )z) (1)

[0084] In Equation (1), I o represents the initial intensity of the beam when it enters the turbid medium. z represents the distance the beam travels through the turbid medium. I(z) is the intensity of the beam at distance z. μ a equals the absorption coefficient associated with the turbid medium, which describes the geometric rate of energy absorption from the beam. Finally, μ s equals the scattering coefficient associated with the turbid medium, which describes the geometric rate of energy scattering from the beam.

[0085] Equation (1) is the well-known Beer's law, which states that the intensity of the beam decreases exponentially at a geometric rate through the combined effects of absorption and scattering. The absorption coefficient μ a and the scattering coefficient μ s sum is called the attenuation coefficient c, as shown in Equation (2). The processes of absorption and scattering are both functionally and mathematically independent.

[0086] μ a + μ s = c (2)

[0087] Now consider the collimated beam as a bundle of parallel rays. Each ray represents a small fraction of the energy of the beam, and the superposition of many beams of different intensities represents the light source. According to Beer's law, absorption is considered a continuous process along the path of the ray. In addition, scattering is a series of discrete events that occur at random positions along the path of the ray. The distance between scattering events is randomly determined according to Equation (3).

[0088] d = -ln(q) / μ s (3)

[0089] The variable d in Equation (3) represents the distance between successive scattering events along the ray. The variable q in Equation (3) equals a random number that follows a normal distribution between 0 and 1. The variable μ s in Equation (3) equals the scattering coefficient, which is a property of the medium in which the scattering events occur.

[0090] The scattering phase function, also known as the volume scattering function, is the final parameter that must be defined. In the scattering phase function p(θ), the variable p is a probability density function used to predict the polar angle 0° < θ < 180°, through which the direction of the light ray will be changed due to scattering events. In a three-dimensional model such as described herein, the azimuth angle uniformly distributed between must also be randomly selected.

[0091] If a single light ray is randomly selected from a single beam passing through a turbid medium according to the radiation distribution of the light source, the light rays passing through the turbid medium can be randomly traced. The light rays passing through the turbid medium can be randomly traced because selecting the light ray determines its initial direction, and all light rays start with the same energy. In addition, by performing this operation on many individual light rays, the possible light field can be developed.

[0092] The geometry of the model is shown in Figure 2 . The model 210 is three-dimensional and has six degrees of freedom (x, y, x, a, b, c), where x, y, and z are position coordinates, and a, b, g are direction cosines. The medium volume is a rectangular volume defined by an upper boundary plane and a lower boundary plane, both of which have defined dimensions of approximately 100 mm × 100 mm. The model 210 is stratified by defining a horizontal middle plane that divides the rectangular volume into parallel horizontal blocks or layers. Figure 2 It is shown that the medium volume is divided into two layers by a single middle plane, but any number of middle planes and layers can be defined. Usually, three layers are defined: the top layer represents the skin, the middle layer represents the fat, and the bottom layer represents the muscle. The optical properties and physical thickness of each layer can be defined independently. Multiple light sources (usually LEDs) and multiple optical detectors (usually photodiodes) can be placed at any position on the upper medium boundary layer.

[0093] The conceptual light ray path is as shown in Figure 2 . The light ray path 220 of interest is the light ray path that ends within the effective area of the detector. All others are considered lost due to scattering. The typical scattering coefficient values for the skin, fat, and muscle are approximately 10 / mm to 50 / mm. This means that the characteristic distance between scattering events is approximately 0.1 mm to 0.02 mm. For a detector located 25 mm from the source, the typical path distance of the light rays reaching the detector will be approximately 100 m to 150 mm. This means that the typical light rays reaching the detector will experience approximately 1000 to 7500 scattering events. This metric is a good indicator of how the light field diffuses within the turbid medium.

[0094] Figure 3Two calculated optical paths 310 are presented. Both rays originate at the point (0, 0, 0) and terminate at the upper boundary plane that is 25 mm from the origin. These paths 310 show how a typical optical path winds and coils.

[0095] The main product generated by the random model is the upwelling irradiance distribution incident on the upper medium boundary. This irradiance distribution is calculated by tracking rays on the order of 1.0e+09. Each ray is tracked until it undergoes 20,000 scattering events or until it intersects one of the six medium boundaries (the upper and lower horizontal boundaries plus the four vertical boundaries). If the ray intersects the upper horizontal boundary, the coordinates of its intersection point are calculated and the boundary element into which it "falls" is identified. Then, various parameters associated with the ray are stored in a file associated with that boundary element. Three of the many parameters calculated and stored for each ray include: 1) the residual, post-absorption "energy" of the ray, 2) the maximum depth the ray penetrates the medium, and 3) the geometric distance the ray travels on its journey from the source to the upper boundary plane. After all rays are tracked, the "energy" values of all rays for each boundary element can be summed, giving the energy / unit area distribution associated with the irradiance.

[0096] Once the irradiance distribution on the upper boundary plane is calculated, the relative signal that would be received by any size detector placed at any location on the upper boundary plane can be calculated. This is called the relative signal because the unit is (residual ray energy) / mm. When each ray is selected at the source, each ray is assigned an energy value of 1.0; then, as the ray passes through the various layers of the turbid medium, this value is decreased by absorption. The irradiance value calculated for each element of the upper boundary plane is the sum of the residual energy values or unabsorbed energy values of the rays that intersect the upper boundary plane within the bounds of that element. This allows the scattering loss to be calculated as follows.

[0097] I 检测 = N * F * I o * exp(-Σμ a,i * L i ) (4)

[0098] The variable I in Equation (4) 检测 equals the "energy" incident on the effective area of one optical detector in the optical detector. The variable N in Equation (4) equals the total number of rays tracked. The variable I in Equation (4) O equals the initial energy (1.0) assigned to each ray. The variable F in Equation (4) equals the loss due to scattering. The variable μ in Equation (4) a,i equals the absorption coefficient of layer L i The variable L in Equation (4) iis equal to the path length or distance that light travels through layer I. Finally, the variable exp(-Σμ a,i *L i ) in Equation (4) is equal to the cumulative absorption loss. By setting all μ a,i to 0, we obtain Equation (5), and since I o = 1, we can derive Equation (6).

[0099] I 检测 = N * F * I o (5)

[0100] F = I 检测 / N (6)

[0101] A unique value of F can be calculated for each data collection element in the upper boundary plane. The value of F depends on the value chosen for the scattering coefficient s i , the scattering phase function chosen for each layer, and the thickness of each layer. All of these inputs are chosen from the open literature. The key point is that the value of F is independent of absorption. Once the value of F has been calculated, the model can be run with non-zero values of μ a,i . A useful procedure is to set all μ a,i to a single value μ a . After this is done, Equation (7) below can be written. Additionally, Equation (7) can be rewritten as Equation (8).

[0102] I 检测 = N * F * I o * exp(-μ a * L) (7)

[0103] L = (-ln(I 检测 / (N * F * I o ))) / μ a (8)

[0104] In this case, L describes the characteristic distance that light travels from the light source to the upper boundary plane. A value of L can be calculated for each element of the plane. By running the model multiple times, each time specifying a different unique value, a set of values of the pair (μ a , L) can be calculated. Then a mathematical relationship between a and L can be derived, and L can be described as a function of μ a , or L = f(μ a ). Once the values of the characteristic attenuation coefficients have been calculated for several wavelengths, they can be put into practical use. Consider the equation:

[0105] μ a (λ) = 2.3Σc i ε i (λ)+ΣV n μn (λ) (12)

[0106] This formula relates the wavelength-dependent characteristic absorption coefficient μ a (λ), extinction coefficient ε i (λ) to several biological parameters, which in this case are volume fraction and concentration c i . The applications described in this article mainly focus on the time-varying optical and biological characteristics of blood supply. Therefore, very small or time-invariant parameters can be set to zero. In this way, formula (12) can be rewritten as

[0107] μ a (λ) = B(Sμ a,HbO (λ) + (1 – S)μ a,Hb (λ)) + Wμ a,水 (λ) (13)

[0108] The variable m a (λ) in formula (13) is the characteristic absorption coefficient measured by the spectrometer. The variable μ a,HbO (λ) in formula (13) represents the absorption coefficient of oxyhemoglobin. The variable μ a,Hb (λ) in formula (13) is equal to the absorption coefficient of deoxyhemoglobin. The variables B, S, and W in formula (13) are equal to the blood volume fraction, oxygen saturation percentage, and water volume fraction, respectively. Finally, the variable μ a,水 (λ) in formula (13) is equal to the absorption coefficient of water. Formula (13) contains three variables B, S, and W whose values are unknown, and all of these variables are independent of wavelength.

[0109] In a completely equivalent way, formula (13) can be rewritten as

[0110] μ a (λ) = 2.3 * c HbO * ε HbO (λ) + 2.3 * c Hb * ε Hb (λ) + Wμ a,水 (λ) (14)

[0111] The variables c HbO and ε HbO in formula (14) are equal to the molar concentration and molar extinction coefficient of oxyhemoglobin, respectively. The variables c Hb and ε Hb in formula (14) are equal to the molar concentration and molar extinction coefficient of deoxyhemoglobin, respectively. The variables W and μ a,水 (λ) in formula (14) are equal to the water volume fraction and absorption coefficient of water, respectively. Formula (14) contains three variables (ε HbO(λ), ε Hb (λ) and μ a,水 (λ)), and three variables (c HbO 、c Hb and W) that are unknown but independent of wavelength.

[0112] Figure 1 is an image of an embodiment of the wearable optical device 100, which can be used to capture, process, and return biomarker levels, chemical concentrations, and chromophore concentrations of non-invasive measurements in different tissue layers. In some embodiments, the device 100 may include a spectrometer using six separate light sources, each with a different spectral output. In Figure 2 the model image, an example embodiment of the optical hardware light-emitting diodes (LEDs) and photodiodes for creating an array of sources and receivers is illustrated relative to the model geometry. As an example of how the hardware of the device 100 can be configured, Figure 5 shows an example embodiment of the optical hardware LEDs 550, 560, 570, 580 and photodiodes 510, 520, 530, 540 that can be used to create an array of sources and receivers. The LEDs in the array can be specified by wavelength and radiant intensity for a specified result. The array of photodiodes can be physically placed at a certain distance from the source for a specified result. Note that although four LEDs and four photodiodes are illustrated in this example, it can be understood that any number of LEDs and / or photodiodes can be included (e.g., six LEDs, each with its own different spectral output). In addition, Figure 6 shows an example embodiment of the optical hardware for controlling and capturing light, processing, and storing in memory before wirelessly transmitting data via Bluetooth. Such hardware may include, but is not limited to, a processor 610, a memory 620, a Bluetooth transceiver 630, and / or an analog front end (AFE) 640. The optical information can be stored in memory before, during, or after the application calculation process. Once the desired result is achieved, the data can be stored on the device, displayed through a connected screen, and transmitted in real time or later via USB, Bluetooth, wireless, or any radio signal.

[0113] The LEDs in the array are specified by wavelength and radiant intensity and are physically placed at a specific distance from the source to achieve a specific result. By implementing six LEDs, each outputting a different wavelength or wavelength band, six independent equations similar to Equation (13) or Equation (14) can be constructed. Then these six equations can be used to calculate the values of the independent variables B, S, and W, or equivalently, the values of c HbO 、c Hb and W can be calculated over time using traditional least squares techniques.

[0114] In a particular exemplary embodiment, the wavelengths of the sources for the device and the positions of the detectors are selected to optimize the spectrometer's sensitivity to changes in the concentrations of oxyhemoglobin and deoxyhemoglobin. In this example, the LED sources are closely grouped together in a manner that minimizes the spacing between them. The peak output wavelengths of the selected LEDs are 535 nm, 655 nm, 760 nm, 800 nm, 855 nm, and 940 nm. The 800 nm wavelength is selected because it is close to the isosbestic wavelength of hemoglobin. At this wavelength, the received signal will exhibit minimal changes due to variations in the concentrations of oxyhemoglobin and deoxyhemoglobin. Additionally, at this wavelength, the received signal will provide a relatively direct indication of the total hemoglobin concentration. The 655 nm and 760 nm wavelengths are selected to provide two sources whose peak output wavelengths are less than the isosbestic wavelength. Similarly, the 855 nm and 940 nm wavelengths are selected to provide two sources whose peak output wavelengths are greater than the isosbestic wavelength. The 535 nm wavelength is selected to provide a received signal that is extremely sensitive to changes in the ratio of the concentration of oxyhemoglobin to the concentration of deoxyhemoglobin. The 535 nm signal received by the photodiode placed closest to the source is dominated by the oxy / deoxy ratio of hemoglobin located near the surface of the irradiated tissue and can thus be used for pulse oximetry. The light reflected or backscattered by the irradiated tissue is received by four photosensitive photodiodes. These photodiodes are placed in a linear array where each photodiode is located at distances of 7 mm, 13 mm, 20 mm, and 28 mm from the geometric center of the LED group. The signal received by the 7 mm diode is dominated primarily by light reflected from tissue located near the irradiated surface. The signals received by the photodiodes placed at greater distances from the light source are less dominated by surface tissue and exhibit increasing characteristics of deeper tissue (tissue located at greater distances from the surface) as the source-to-detector spacing increases. By analyzing and comparing the individual signals from the four separate photodiodes, the optical characteristics associated with various tissue layers can be separately identified.

[0115] In a similar manner, by appropriately selecting the optical wavelengths of the sources, specifying the positions of the detectors, interpreting the received signals using the calculation results of a stochastic model, and establishing an equation similar to Equation (13) or Equation (14), the spectrometers described herein can be adapted to measure other biomarkers and tissue properties, including but not limited to muscle oxygen consumption, muscle oxygen saturation, nitric oxide / s-nitrosothiol, oxyhemoglobin, deoxyhemoglobin, oxymyoglobin, deoxymyoglobin, total hemoglobin / blood volume, carboxyhemoglobin, methemoglobin, glycogen concentration, water, potassium, iron, bile, and melanin. For example, the embodiments described herein can be used to perform the measurements described in detail below.

[0116] The hierarchical structure of the stochastic model enables several unique capabilities, one of which is the ability to empirically estimate the geometric thickness of individual tissue layers. The model supports the definition of any number of individual layers, with a typical number for useful analysis being four. The optical properties and geometric thickness of each layer can be defined separately to represent, for example, skin, adipose tissue, and muscle. Running the model several times, each time specifying a different value for, for example, the thickness of the adipose tissue layer, enables the adipose tissue layer thickness to be defined as a function of measurable optical parameters. This link between the adipose tissue layer thickness and the measurable optical parameters makes it possible to empirically estimate the tissue layer thickness using spectroscopic techniques.

[0117] As an example, Figure 4 shows that the thickness of the adipose tissue layer is a monotonic function of the ratio of two optical parameters, d13 and d20. These optical parameters can be quantified via a combination of stochastic calculations and spectrophotometric measurements. Once the optical parameters are quantified, they can be used to empirically estimate the adipose tissue layer thickness via Figure 4 the graph 410 shown.

[0118] In summary, the advanced data collection device described herein can include six light sources and four independent light sensors. The stochastic light propagation model is fully three-dimensional, has six degrees of freedom, and accommodates three-dimensional tissue models with spatially variable optical properties. Data processing algorithms used by the device have been developed to take full advantage of the spectral diversity of the light sources and the spatial diversity of the light detectors. Overall, this comprehensive set of capabilities provides the ability to measure the optical properties of individual tissue layers and thereby map the volumetric distribution and variability of various biological parameters throughout a three-dimensional tissue sample.

[0119] Figure 7 is a flowchart illustrating a typical method 700 for implementing a computational process for calculating values of various biomarkers, biological properties, chemical substance concentrations, and / or chromophore concentrations in different tissue layers and their associated blood supplies according to some embodiments of the present invention. For example, process 700 can be executed by one or more hardware and / or software systems, such as those described herein with respect to Figure 5 , Figure 6 , Figure 12 and / or Figure 13 (e.g., its processor and memory system). Process 700 can effectively utilize data from any number of wavelengths m from any number of sensors, such as n photodiodes. By executing a process such as process 700, the device can analyze and / or determine various physical, chemical, and / or biological characteristics of a subject.

[0120] At 702, a processor of an apparatus performing process 700 may define locations for storing digital input and output data, such as paths and file names, the digital input and output data including measurement data detected by one or more photodetectors and calculation data determined by a stochastic model. At 704, a processor of an apparatus performing a process such as process 700 may define a configuration of photodiodes. Such a configuration may include the number of photodiodes used, the physical arrangement and geometric location of the photodiodes, and / or the internal configuration of the photodiodes. At 706, a processor of an apparatus performing process 700 may read data collected by the photodiodes, data calculated by the stochastic model, and / or related data from other sources. At 708, a processor of an apparatus performing process 700 may begin processing in a manner consistent with the data read at 706. For example, the processor may evaluate the data according to the formulas described above. Specifically, in some embodiments where there is data for multiple wavelengths, a processor of an apparatus performing process 700 may enter a loop where data is continuously selected and processed for each wavelength. At 710, a processor of an apparatus performing process 700 may select data related to a particular wavelength being processed. For embodiments where there is data for multiple sensors (such as multiple photodiodes), a processor of an apparatus performing process 700 may enter a second loop at 712 where data is continuously selected and processed for each sensor. At 714, a processor of an apparatus performing process 700 may select data related to a particular sensor being processed. At 716, a processor of an apparatus performing process 700 may perform adjustments on the data, such as averaging, offset correction, and / or orthogonal correction. At 718, a processor of an apparatus performing process 700 may calculate optical parameters, such as absorption coefficient, optical path length, and / or effective geometric path length. At 720, a processor of an apparatus performing process 700 may calculate biological parameters, such as blood volume fraction, water volume fraction, and / or percentage of blood oxygenation (for examples of such calculations, see Formulas 12-14 above).

[0121] Biomaterial, chemical substance concentrations, and chromophore levels are not uniformly distributed throughout a human or mammalian body. Accordingly, the ability to measure biomarker, chemical substance, and chromophore levels in different tissue layers provides advantages over traditional measurement techniques. Thus, the spectrophotometric measurement techniques described herein allow for new and more effective methods of quantifying biometric parameters. One such biometric parameter is muscle oxygen saturation (SmO2). Muscle oxygen saturation (SmO2) can only be measured by isolating the aforementioned spectrophotometric measurement to the microvascular capillaries within muscle tissue. The reason for this is that oxygen saturation varies significantly between different regions of the mammalian circulatory system. For example, arteries, arterioles, capillaries, venules, and veins all have different oxygen saturations under normal physiological conditions.Figure 8 Illustrated are SmO2 measurements of an individual during exercise using the measurement methods mentioned above. Muscle oxygenation (SmO2) reflects the dynamic balance of oxygen supply and oxygen utilization in the exercising muscle. Thus, during periods of muscle contraction, SmO2 decreases as oxygen demand exceeds supply. Between muscle contractions, SmO2 increases as oxygen supply exceeds demand. By measuring the muscle oxygenation of a subject, insights can be gained into the main determinants of exercise performance: oxygen supply and oxygen utilization. Then, SmO2 data can be used to design personalized exercise programs to enhance the health and physical fitness of the subject.

[0122] Figure 8 FIG. 800 is an exemplary implementation showing the measurement of muscle oxygen saturation (SmO2) of a subject during exercise. The vertical axis represents the muscle oxygenation level 802 on a scale from zero to one hundred percent, and the horizontal axis represents the duration 804 of the exercise in seconds. The muscle oxygenation level at each time point is indicated on the graph as a solid black line.

[0123] Other biometric parameters that can be non-invasively measured using the spectrophotometric measurement techniques described herein include, but are not limited to, muscle oxygen consumption, nitric oxide, oxyhemoglobin, deoxyhemoglobin, oxymyoglobin, deoxymyoglobin, carboxyhemoglobin, methemoglobin, glycogen concentration, water, potassium, iron, bile, melanin, and adipose tissue thickness.

[0124] Figure 9 Graph 900 illustrates the measurement of muscle oxygen consumption (mVO2) of an individual during exercise. The vertical axis represents the muscle oxygen consumption level 902, and the horizontal axis represents the duration 904 of the exercise in seconds. The muscle oxygen consumption level at each time point is represented by a solid black line on the graph. Before exercise, muscle oxygen consumption (mVO2) is low and increases during exercise, then returns to the baseline level after exercise. Muscle oxygen consumption (mVO2) is closely related to VO2, which is the gold standard physical fitness measurement recorded using a laboratory-grade metabolic analyzer. Thus, muscle oxygen consumption can be used to quantify the current physical fitness level of an individual.

[0125] Figure 10Graph 1000 shows the levels of reactive nitric oxide (also known as S-nitrosothiol) in an individual during exercise. The vertical axis represents the level of reactive nitric oxide, and the horizontal axis represents the duration of exercise in seconds. The level of reactive nitric oxide at any given point in time is represented by the solid black line on the graph. Reactive nitric oxide is a measure of nitric oxide released from circulating red blood cells during exercise. Reactive nitric oxide causes blood vessels to dilate, resulting in increased blood flow and oxygen delivery to the brain, heart, and exercising muscles. Therefore, higher levels of reactive nitrogen are associated with improved physical fitness, better cognitive health, and a lower risk of Alzheimer's disease and cardiovascular disease. Additionally, by measuring the level of reactive nitric oxide in a subject during exercise, the optimal type, amount, intensity, duration, and frequency of exercise can be determined to improve the biomarker parameters. Thus, the health, physical fitness, and physical performance of the subject are enhanced.

[0126] Figure 11 Graph 1110 shows the internal training load of an individual during exercise and the relationship between the internal training load and the external training load (ETL) in a scatter plot 1120. The vertical axis represents the internal training load, and the horizontal axis represents the duration of exercise in seconds. The internal training is represented by the solid black line on the graph. The internal training load (ITL) can only be quantified by separating spectrophotometric measurements into the muscle tissue layer and its associated blood supply. The reason is that the internal training load quantifies the total metabolic work performed by active skeletal muscles during exercise. The internal training load is closely related to the external training load, which is defined as the amount of physical work performed by the subject during exercise, as Figure 8 shown. However, the internal training load has many advantages over the external training load. For example, the internal training load (ITL) measurement can be used to quantify the energy consumption and total workload of an individual during exercise. Additionally, quantifying the internal training load allows an individual to reduce their risk of injury and personalize their exercise training program in an unprecedented way, resulting in greater improvement in physical fitness.

[0127] Using the embodiments described herein provides the ability to non-invasively measure multiple biomarker measurements in differentiated tissue layers and their associated blood supplies for a subject. Specifically, measuring the biological and physiological parameters at discrete locations within the body can provide unprecedented insights into the health, physical fitness, and physical performance of the subject. Additionally, the aforementioned biomarker measurements can be used to effect positive changes in the physical state of the subject. For example, a subject can use the previously mentioned muscle oxygen consumption and reactive nitric oxide measurements to determine the optimal type, amount, intensity, duration, and frequency of exercise to elicit a desired physiological response, such as increased endurance, reduced risk of injury, or enhanced cognition.

[0128] Figure 12An exemplary measurement device 1200 is illustrated. As described herein, the measurement device 1200 can be worn on a patient. For example, the measurement device 1200 can be an embodiment of the device 100 described above and can include Figure 5 and Figure 6 the physical structures depicted in, as described above. The measurement device 1200 can include one or more light sources 1201 (e.g., light-emitting diodes, organic light-emitting diodes, lasers, or a single light source that generates multiple wavelengths). For example, the light source 1201 can include Figure 5 the LED 550 - 580 of the embodiment of. One or more light sources 1201 can form an array. A control circuit and / or a processor can control one or more light sources 1201. For example, one or more of the systems 100 and / or 200 can be embedded in, coupled to, or communicate with the measurement device 1200. Thus, the processor 112 / 214 can control one or more light sources 1201. One or more light sources 1201 can generate light of multiple wavelengths.

[0129] The measurement device 1200 can include one or more optical receivers 1202 (e.g., photodiodes or optical sensors). For example, the optical receiver 1202 can include Figure 5 the photodiodes 510 - 540 of the embodiment of. One or more optical receivers 1202 can form an array. One or more optical receivers 1202 can be placed at a predetermined distance from one or more light sources 1201. One or more optical receivers 1202 can be used as one or more of the sensors 102 / 202 of the system 100 and / or 200. Information received from one or more optical receivers 1202 can be converted in an analog-to-digital converter. The measurement device 1200 can include one or more processors (e.g., processor 112 / 214) for processing data from one or more optical receivers 1202. The measurement device 1200 can include a communication interface (e.g., WiFi, 5G, etc.) for transmitting data to an external system and / or processor. The measurement device 1200 can include one or more memory devices (e.g., computer-readable media 110 / 212) for storing data.

[0130] The measurement device 1200 can include a user interface that includes a display, tactile, and / or audio output 1203 for presenting data on the device. Alternatively, the data can be presented on an external system. For example, the measurement device 1200 can communicate with a personal computer or a mobile device via the communication interface and output to the user through the personal computer or the mobile device. In this example, the personal computer or the mobile device can be used as the system 100 and / or 200. The external device can be configured to print at least a portion of the data.

[0131] The measurement device 1200 may include one or more additional sensing elements, including but not limited to: a thermometer and a bioimpedance sensor, which may be included in the sensors 102 / 202. Data collected by the additional sensing elements may provide enhanced measurements and post-processing analysis.

[0132] Operation of the measurement device 1200 may include initiating a data capture sequence. The data capture sequence may include activating one or more light sources 1201 in an on-and-off timing sequence. The data capture sequence may include activating one or more optical receivers 1202 at a predetermined distance in conjunction with the activation of the one or more light sources 1201. Different wavelengths of the generated light may be configured for different substances within the body. Different predetermined distances between the optical receivers 1202 and the light sources 1201 may be for different depths within the body. As an example, the light sources 1201 may be operated to emit light as described in detail above. The measurement device 1200 may capture signals from the one or more optical receivers 1202. The captured signals may be pre-processed (i.e., run through an analog-to-digital converter and / or filtered). The captured signals may be converted into biomarkers. The biomarkers may be further processed, stored, and / or transmitted (e.g., on the device and / or to an external system). For example, the captured signals may be processed as described in detail above.

[0133] Figure 13 FIG. is a schematic block diagram of an example system 1300 that is an example of a hardware component capable of implementing the systems and methods disclosed herein. The system 1300 may include various systems and subsystems. The system 1300 may include one or more of a personal computer, a laptop computer, a mobile computing device, a workstation, a computer system, a device, an application specific integrated circuit (ASIC), a server, a server BladeCenter, a server farm, etc. In some embodiments, the system 1300 may be part of or communicable with the device 100 described above, and may include Figure 5 and Figure 6 the physical structure depicted in or communicable with the physical structure as described above.

[0134] System 1300 may include a system bus 1302, a processing unit 1304, a system memory 1306, memory devices 1308 and 1310, a communication interface 1312 (e.g., a network interface), a communication link 2214, a display 1316 (e.g., a video screen), and an input device 1318 (e.g., a keyboard, a touch screen, and / or a mouse). The system bus 1302 may communicate with the processing unit 1304 and the system memory 1306. Additional memory devices 1308 and 1310 (such as a hard disk drive, a server, a stand-alone database, or other non-volatile memory) may also communicate with the system bus 1302. The system bus 1302 interconnects the processing unit 1304, the memory devices 1306 and 1310, the communication interface 1312, the display 1316, and the input device 1318. In some examples, the system bus 1302 also interconnects additional ports (not shown), such as Universal Serial Bus (USB) ports.

[0135] The processing unit 1304 may be a computing device and may include an application specific integrated circuit (ASIC). The processing unit 1304 executes an instruction set to implement the operations of the examples disclosed herein. The processing unit may include a processing core.

[0136] The additional memory devices 1306, 1308, and 1310 may store data, programs, instructions, text, or compiled forms of database queries, and any other information that may be required to operate a computer. The memories 1306, 1308, and 1310 may be implemented as computer-readable media (integrated or removable), such as a memory card, a disk drive, a compact disc (CD), or a server accessible via a network. In certain examples, the memories 1306, 1308, and 1310 may include text, images, video, and / or audio, some of which may be provided in a format that is understandable by humans.

[0137] Additionally or alternatively, the system 1300 may access an external data source or query source via the communication interface 1312, which may communicate with the system bus 1302 and the communication link 1314.

[0138] In operation, the system 1300 may be used to implement one or more portions of the system according to the present implementation, such as the system described in detail above. According to certain examples, computer-executable logic for implementing a diagnostic system resides on the system memory 1306 and one or more of the memory devices 1308 and 1310. The processing unit 1304 executes one or more computer-executable instructions originating from the system memory 1306 and the memory devices 1308 and 1310. As used herein, the term "computer-readable medium" refers to a medium that participates in providing instructions to the processing unit 1304 for execution. The medium may be distributed across multiple discrete components, all of which may be operably connected to a common processor or a collection of related processors.

[0139] In some embodiments, the systems and methods described above can be used in and / or with diagnostic systems, and more specifically, those diagnostic systems related to the non-invasive measurement of endogenous S-nitrosothiols. One of ordinary skill in the art will understand that the systems and methods described above can be used to obtain and / or process measurements such as those indicated below.

[0140] Nitric oxide (NO) is associated with many physiological effects, including smooth muscle relaxation, vasodilation, inflammatory responses, and inhibition of platelet adhesion and aggregation. Searching for natural reservoirs of NO and for methods to regulate the levels of bioavailable NO and its alternative bioactive forms can provide means to control these physiological effects. Nitric oxide (NO) and S-nitrosothiols (SNO) are carried by hemoglobin along with oxygen. SNO is the bioactive form of NO and is the only endogenous active form of NO that can survive in the blood because NO itself cannot escape from red blood cells. SNO is released from hemoglobin in tissues, for example, during hypoxia or during exercise, to dilate blood vessels and thereby supply oxygen to the tissues. Thus, SNO released from RBCs controls microvascular blood flow in tissues, and without this SNO the tissues cannot be oxygenated (Zhang PNAS 2015; Premont Circ Res 2019). Therefore, SNO levels are a key component of VO2 (the volume of oxygen consumed by the tissues). While non-invasive means are available to detect oxyhemoglobin, there are no such means available to detect the endogenous levels of NO or SNO.

[0141] The amount of oxygen consumed by an individual (VO2) is currently the gold standard measurement of physical fitness used by physicians and physiologists worldwide. VO2 represents the combined ability of the lungs, cardiovascular, and muscle systems to uptake, transport, and consume oxygen. Traditional systems and methods for measuring VO2 are invasive and / or require tightly controlled conditions. For example, traditional VO2 measurements require athletes to wear masks in a laboratory, and the cost of the measuring equipment can be as high as $35,000.

[0142] The embodiments described herein utilize the fact that nitric oxide release during exercise determines how much oxygen the muscles can use and, thus, monitor an individual's nitric oxide level to determine physical fitness and / or other physiological characteristics. One value that can be measured (referred to as personalized nitric oxide (PNO)) is a measure of how much reactive nitric oxide is released from circulating red blood cells during exercise. Reactive nitric oxide, represented by S-nitrosothiols in the blood, dilates the blood vessels that deliver oxygen to tissues, including the heart and brain, and thus the patient's nitric oxide level is closely associated with their health. By monitoring this metric, as well as other metrics described below, the system can determine how much nitric oxide the exerciser releases in response to exercise, and the intensity at which the exerciser needs to exercise, how long they should exercise, and what type of exercise is most suitable for them, thereby providing improvements in physical fitness, performance, and health. The oxygen saturation measured through the small blood vessels in the muscle is related to the individual and the environment, and thus the PNO metric derived from this measurement is related to different patients and environments. However, it should be understood that while the measurement may be relevant, it is only relevant to a certain extent, and an individual's PNO measurement can be used as a reliable indicator of the individual's health and physical fitness.

[0143] The blood volume and oxygen saturation sensors used to calculate UO2 are portable, lightweight, and will be five percent smaller than many of the devices currently on the market for VO2 measurement. Additionally, standard VO2 testing and measurement tools rely on exhaled gas concentrations to measure whole-body oxygen consumption. However, because these central measurements are taken, they miss important information, such as what metabolic processes are occurring in the muscles. Therefore, they cannot reveal why an individual's VO2 maximum is not higher. Because UO2 measurements are taken at the muscle level (and are affected by nitric oxide concentration), it not only measures oxygen consumption but also allows determination of the rate-limiting factors that increase oxygen consumption (e.g., lack of blood flow or poor muscle use of oxygen). This not only provides a diagnostic measurement tool for physical fitness but also indicates what exercise prescription is needed to improve health and physical fitness. For example, the origin of the UO2 change can be determined based on the measured time series and identified as a limitation in oxygen supply (represented as restricted blood flow) or a limitation in oxygen utilization (represented as muscle function defects).

[0144] Figure 14An example of a system 1400 for generating values representative of the endogenous S-nitrosothiol content of tissue within a region of interest of a subject is illustrated. It should be understood that the system 1400 can determine values representative of the endogenous S-nitrosothiol content of tissue both non-invasively and in real time. This can be used to match the S-nitrosothiol content of the tissue and metrics derived from that content to the actions or biometric parameters of the subject during or after exercise or another physiological or external occlusion of blood flow from muscle tissue. The system 1400 can include a set of at least one sensor 1402 that non-invasively measures biometric parameters within the region of interest to provide at least one time series of measurements of the biometric parameters. In one example, where the biometric parameters include blood volume and oxygen saturation, the set of sensors 1402 can include a single sensor that measures both blood volume and oxygen saturation or multiple sensors that collectively provide these measurements. In one implementation, a single optical sensor uses near-infrared spectroscopy to measure both oxygen saturation and blood volume, and thus determine blood flow based on changes in total hemoglobin concentration and oxygen saturation. It should be understood that the set of sensors 1402 can include additional sensors that typically record multiple biometric parameters within the region of interest of the subject.

[0145] Each of the sensor interface 1404, the prediction model 1406, and the user interface 1408 can be implemented as machine-readable instructions stored on a non-transitory computer-readable medium 1410 and executed by an associated processor 1412. The sensor interface 1404 can receive the time series of measurements of the biometric parameters from the set of sensors 1402 and condition the data for use at the prediction model 1404. The prediction model 1404 can also utilize data about the subject stored at the computer-readable medium 1410, including, for example, age, gender, genomic data, nutritional information, drug intake, and relevant medical history, as well as any other measured physiological parameters.

[0146] The prediction model 1410 can utilize one or more pattern recognition algorithms, each of which can analyze the data provided via the sensor interface 1404 and any additional data to assign continuous or categorical parameters to regions of interest, thereby representing the amount of endogenous S-nitrosothiols present in the regions of interest. In the case of using multiple classification or regression models, an arbitration element can be utilized to provide consistent results from multiple models. The training process of a given classifier will vary depending on its implementation, but training generally involves statistically aggregating the training data into one or more parameters associated with the output classes. For rule-based models, such as decision trees, domain knowledge provided by one or more human experts, for example, can substitute or supplement the training data when selecting the rules used to classify users using the extracted features. Any of a variety of techniques can be used for classification algorithms, including support vector machines (SVMs), regression models, self-organizing maps, fuzzy logic systems, data fusion processes, boosting and bagging methods, rule-based systems, or artificial neural networks (ANNs).

[0147] For example, an SVM classifier can utilize multiple functions called hyperplanes to conceptually divide the boundaries in an N-dimensional feature space, where each dimension in the N-dimensions represents an associated feature of the feature vector. The boundaries can define the ranges of feature values associated with each class. Thus, the continuous or categorical output value of a given input feature vector can be determined based on its position in the feature space relative to the boundaries. In one implementation, the SVM can be implemented via a kernel method using a linear or non-linear kernel. The trained SVM classifier can converge to a solution where the optimal hyperplane has a maximized margin to the associated features.

[0148] An ANN classifier can include multiple nodes with multiple interconnections. Values from the feature vector can be provided to multiple input nodes. Each input node can provide these input values to a layer of one or more intermediate nodes. A given intermediate node can receive one or more output values from the previous nodes. The received values can be weighted according to a series of weights established during the training of the classifier. The intermediate node can transform the values it receives into a single output according to a transfer function at the node. For example, the intermediate node can sum the received values and apply a rectification function to the sum. The output of the ANN can be a continuous or categorical output value. In one example, the last layer of nodes provides confidence values for the output classes of the ANN, where each node has an associated value representing the confidence of one of the associated output classes of the classifier. The confidence values can be based on a loss function such as the cross-entropy loss function. The loss function can be used to optimize the ANN. In one example, the ANN can be optimized to minimize the loss function.

[0149] Many ANN classifiers are fully connected and feedforward. However, convolutional neural networks include convolutional layers, where nodes from the previous layer are only connected to a subset of the nodes in the convolutional layer. Recurrent neural networks are a class of neural networks where the connections between nodes form a directed graph along a time series. Different from feedforward networks, recurrent neural networks can incorporate feedback from states caused by earlier inputs, such that the output of a recurrent neural network for a given input can be a function not only of the input, but also of one or more previous inputs. As an example, long short-term memory (LSTM) networks are a modified version of recurrent neural networks, which makes it easier to remember past data in memory.

[0150] Rule-based classifiers can apply a set of logical rules to the extracted features to select an output class. The rules can be applied sequentially, where the logical result at each step affects the analysis at subsequent steps. The specific rules and their sequence can be determined based on any one or all of training data, analogical reasoning from previous cases, or existing domain knowledge. An example of a rule-based classifier is the decision tree algorithm, where the values of the features in a feature set are compared with corresponding thresholds in a hierarchical tree structure to select the class of a feature vector. Random forest classifiers are a modification of the decision tree algorithm using the bagging or "bootstrap aggregating" method. In this method, multiple decision trees can be trained on random samples of the training set, and the average (e.g., mean, median, or mode) result across multiple decision trees is returned. For a classification task, the result from each tree will be categorical, and thus the modal result can be used.

[0151] The output of the prediction model 1406 can be a continuous parameter representing the amount of endogenous S-nitrosothiol present in the region of interest, or a categorical parameter representing, for example, an increase or decrease in the amount of endogenous S-nitrosothiol present in the region of interest, or a class representing a range of that amount. The output of the prediction model 1406 can be stored, for example, in an electronic health record database and / or provided to a user at an associated display via the user interface 1408.

[0152] Figure 15Another example of system 1500 for non-invasively and in real-time generating a value representing the endogenous S-nitrosothiol content of tissue within a region of interest of a subject is illustrated. In one example, the tissue is muscle tissue, the endogenous S-nitrosothiol is the endogenous S-nitrosothiol generated by hemoglobin, and the amount of the endogenous S-nitrosothiol is determined during muscle exercise or after physiological or external occlusion of blood flow from the muscle tissue. System 1500 may include a near-infrared spectroscopy (NIRS) sensor 1502 that non-invasively measures each of blood volume and oxygen saturation within the region of interest to provide a time series of blood volume measurements and a time series of oxygen saturation measurements. In one implementation, the time series of blood volume measurements may be represented as a time series of total hemoglobin metrics. The spectroscopic sensor may include a strap such that the sensor is strapped against the skin. The strap may be flexible and / or elastic. As an example, the sensor may be incorporated into a wristband. One of ordinary skill in the art will recognize that the strap or any other garment element may be configured to interface the measuring device as described herein to any relevant region of interest.

[0153] The nitric oxide (NO) calculation component 1510 may be implemented as machine-readable instructions stored on a non-transitory computer-readable medium 1512 and executed by an associated processor 1514. The NO calculation component 1510 may include a sensor interface 1522, a regression model 1524, and a user interface 1526. The sensor interface 1522 may receive the time series of blood volume measurements and the time series of oxygen saturation measurements from the NIRS sensor and condition the data for use at the regression model 1524.

[0154] The regression model 1524 may determine the relationship between the time series of blood volume values and the time series of oxygen saturation values and provide at least one parameter representing the determined relationship. In one example, the relationship is linear and the ordered pairs provided by the two time series may be fit to a best-fit line. In this example, the parameter provided is the slope of the best-fit line, where the amount of endogenous S-nitrosothiol in the tissue is derived from the slope. In another example, the parameter is derived from the correlation coefficient between oxygen saturation and blood volume. The value may then be provided to the user via the user interface 1526. In one example, instead of or in addition to directly displaying the value, the value may be used to calculate in real-time other metrics representing the health and fitness of the subject.

[0155] Figure 16Graph 1600 illustrates the PNO levels of a patient during exercise. The vertical axis 1602 represents the PNO level, and the horizontal axis 1604 represents the duration of the exercise in seconds, where the PNO level at each time is indicated as the shaded area 1606 on the graph. The patient is given a rest period 1608 of approximately one hundred seconds, and it can be seen that the PNO level 1606 remains level during this period and for a short time thereafter.

[0156] As noted above, VO2 is the gold standard measurement of physical fitness used by doctors. Traditionally, VO2 measurement requires invasive testing and expensive laboratory equipment, but system 1500 allows this measurement to be performed non-invasively within local tissue, making it available during activities of daily living (defined as UO2). Figure 17 Graph 1700 illustrates the UO2 levels of a patient during exercise. The vertical axis 1702 represents the UO2 level, and the horizontal axis 1704 represents the duration of the exercise in seconds, where the UO2 level at each time is indicated as the shaded area 1706 on the graph. The patient is given a rest period 1708 of approximately one hundred seconds, and it can be seen that the UO2 level 1706 drops sharply during the rest period.

[0157] The UO2 measurement is a nitric oxide-related measurement of local muscle oxygen consumption, which is created to behave in the same manner as a true VO2 measurement. Specifically, UO2 can be generated based on local nitric oxide measurements and blood flow to represent a measure of the available oxygen for muscle tissue in the region of interest. As Figure 18 and Figure 19 shown, the UO2 measurement is an excellent surrogate for VO2, and UO2 can be measured using a sensor that costs approximately one percent of the cost of even a low-end VO2 measurement device.

[0158] Figure 18 Graph 1800 depicts the time series of UO2 measurements 1802 and the time series of VO2 measurements 1804 of an athlete operating a full-body exercise bike using one sensor for recording blood volume and oxygen saturation. The left vertical axis 1806 represents VO2 in mL / kg / min, the right vertical axis 1808 represents UO2 in arbitrary units, and the horizontal axis 1806 represents the elapsed time. It can be seen that there is a very strong correlation (r = 0.95) between the measured UO2 1802 and the measured VO2 1804. It should thus be clear that an estimated VO2 max can be derived from the UO2 measurement. The estimated VO2 max in this graph is 68 mL / kg / min as derived from UO2.

[0159] Similarly, Figure 19Graph 1900 depicts the time series of UO2 measurement 1902 and the time series of VO2 measurement 1904 of an athlete operating a full-body exercise bike using two sensors for recording blood volume and oxygen saturation on different limbs. The left vertical axis 1906 represents VO2 in mL / kg / min, the right vertical axis 1908 represents UO2 in arbitrary units, and the horizontal axis 1910 represents elapsed time. It can be seen that there is a very strong correlation (r = 0.95) between the measured UO2 1902 and the measured VO2 1904.

[0160] The level of reactive nitric oxide reflects the supply of oxygen: the better the supply and utilization of oxygen, the better the performance. Maximum (NO) power and maximum NO endurance are nitric oxide-related measurements that can be generated by the system and are closely related to an individual's actual power output and maximum endurance level.

[0161] Figure 20 Graph 2000 depicts the maximum NO power of an athlete recorded weekly over a six-month period. The left vertical axis 2002 represents the maximum NO power in arbitrary units, the right vertical axis 2004 represents the athlete's maximum power output in watts, the horizontal axis 2006 represents elapsed time in weeks. As the athlete's physical fitness increases (measured by the increase in maximum power output 2008 in watts), their maximum NO power 2010 also increases. The very strong correlation (R2 = 0.95) between the measured maximum NO power 2010 and the maximum power output 2008 (watts) establishes maximum NO power as an excellent biomarker of performance.

[0162] Figure 21 Graph 2100 is depicted, which illustrates the relationship between the improvement in maximum NO endurance and their critical power (the gold standard for endurance, measured in watts) of a group of twenty-one athletes recorded over a six-week period as a scatter plot. The vertical axis 2102 represents the percentage improvement in maximum NO endurance of the twenty-one athletes, and the horizontal axis 2104 represents the improvement in the athletes' critical power in watts. It can be seen from the graph that there is a significant correlation between the improvement in maximum NO endurance and the improvement in critical power, thus establishing maximum NO endurance as a non-invasive measurement of endurance.

[0163] It should be understood that specific exercises and other treatments can be prescribed to a patient based on the patient's values for these metrics. For example, an individual with a higher maximum NO strength compared to their maximum NO endurance can extract oxygen from the blood and utilize it in skeletal muscle at a greater rate than the rate at which it can be delivered. By performing lower-intensity, longer-duration exercises in a continuous manner, these individuals will see the best gains in their exercise programs. For example, Mr. Jones, twenty-eight years old, with a maximum NO strength of 6 and a maximum NO endurance of 3, can be prescribed to run for twenty minutes three days a week at 50 - 55% of his maximum UO2. This is expected to improve his maximum NO endurance over the course of several weeks.

[0164] Alternatively, an individual with a higher maximum NO endurance compared to their maximum NO strength can supply oxygen to the working muscles at a much faster rate than they can extract oxygen from the blood and use it for energy production. These individuals will see the best gains in their exercise programs by performing high-intensity, short-duration work rounds interspersed with rest periods. In these cases, there will be a very large and sharp increase in UO2, with relatively low points between exercise bouts. For example, Ms. Benneton, a forty-eight-year-old cyclist, with a maximum NO endurance of 7 and a maximum NO strength of 2.5, can be prescribed to perform near-maximum-intensity sprints two days a week until her UO2 stops rising, then rest for three minutes and repeat six sets. This is expected to improve both her maximum NO strength and maximum UO2 over the course of several weeks.

[0165] In another example, an individual with a higher upper-body PNO level compared to their lower-body PNO level will be instructed to reallocate their force output such that they reduce the amount of work their upper body is doing and increase the amount of work their lower body is doing. In this way, they will increase their lower-body PNO level, resulting in a greater whole-body PNO value, which can be maintained for a longer duration. This will increase the delivery of oxygen to the brain, heart, and muscles. In another example, exercises can be assigned to individuals with early-onset Alzheimer's disease to improve their PNO levels, and this improves blood flow to the brain. For example, Ms. Levy, seventy years old, with early-onset Alzheimer's disease, can be prescribed to walk for thirty minutes daily with the goal of increasing her PNO by 5. Ms. Levy can be prescribed to perform a set bike routine for thirty minutes each day such that her PNO increases to 15 during the fitness process. At six months, improvement in both her memory and her baseline PNO is expected.

[0166] As discussed, VO2 is currently the gold standard measurement of physical fitness used by doctors and physiologists worldwide and represents the combined ability of the lungs, cardiovascular, and muscular systems to take up, transport, and consume oxygen. UO2 measurement is a nitric oxide-related measurement of local muscle oxygen consumption that is created to behave in the same manner as the true VO2 measurement. Specifically, UO2 can be generated based on local nitric oxide measurements and blood flow to represent a measure of the available oxygen for muscle tissue in the region of interest. As Figure 5 and Figure 6 shown, the UO2 measurement is an excellent surrogate marker for VO2.

[0167] Individuals known to have Alzheimer's disease have low VO2. VO2 depends largely on microvascular blood flow, and nitric oxide from red blood cells controls blood flow. Thus, it stands to reason that the nitric oxide-based measurement described herein as UO2 tracks VO2 and, therefore, PNO can be used to predict the VO2 maximum in an individual. Accordingly, in another embodiment, the present invention provides a method of determining the risk of Alzheimer's disease or early-onset disease by using UO2 as a biomarker. If their UO2 (and PNO) is improved, then Alzheimer's disease will also be improved and prevented. In one aspect, the present invention provides a method of determining the risk of a disease associated with reduced blood flow, such as dementia or other cognitive decline associated with blood flow or cardiovascular / cardiometabolic disease. For example, an exercise regimen is prescribed for an individual with Alzheimer's disease and their UO2 measurement is taken over time to determine improvement of the disease. For example, UO2 is measured at a start time point before the start of the exercise regimen and at a second time point (and optionally additional time points) to determine if the UO2 value has increased, thereby reflecting, for example, an improvement in cognitive function or dementia. Other complementary tests can be used, including cognitive tests known to those skilled in the art, to further evaluate the improvement of the individual's disease state.

[0168] In another example, Mr. Jack is a sixty-year-old businessman suffering from heart disease. He can be prescribed an exercise program of thirty minutes per day to increase his PNO level by 13. In this example, his exercise program can be increased to forty minutes over time, with the PNO doubling, indicating an increased ability to supply oxygenated blood to the myocardium. In another example, Ms. Stevenson is a sedentary mother of three young children, and she has difficulty keeping up with them in her daily life, with a maximum UO2 of 43. She is prescribed an exercise program consisting of two days per week. The first day includes twenty to thirty minutes of moderate-intensity exercise at 50-60% of her maximum UO2, and the second day includes three five-minute exercise sessions at 75-85% of her maximum UO2. This is expected to increase her physical fitness and energy, as well as her maximum UO2. Similarly, in another example, Mr. James is a sixty-year-old businessman suffering from diabetes. His baseline blood sugar is 200. He can be prescribed an exercise program of 20 minutes per day to increase his PNO level by 12. In this example, his exercise program can be increased to forty minutes over time, with the PNO doubling, indicating an increased ability to supply oxygenated blood to the muscles and reduce his resting blood sugar.

[0169] In view of the foregoing structural and functional features described above, with reference to Figures 22 to 24 the example methods will be better understood. Although, for purposes of simplifying the description, Figures 22 to 24 the example methods are shown and described as being executed sequentially, it should be understood and appreciated that the present examples are not limited to the order illustrated, as in other examples, some actions may occur in a different order, multiple times, and / or simultaneously than the order shown and described herein. Additionally, not all of the described actions need to be performed to implement the methods. For example, each of these methods can be performed by Figure 14 system 1400 and / or Figure 15 system 1500.

[0170] Figure 22An example of method 2200 for generating a value representing the endogenous S-nitrosothiol content of tissue within a region of interest of a subject is illustrated. At 2202, biometric parameters may be non-invasively measured by sensor 1402 / 1502 within the region of interest of the subject to provide a time series of measurements of the biometric parameters. In one example, these measurements are made while the subject is engaged in exercise. In another example, these measurements are made during a rest period after the subject has engaged in exercise. In another example, the measurements may be made immediately after a physiological or external occlusion of blood flow to the region of interest or after a physiological consumption of oxygen. In one implementation of this example, an overshoot response in which one of blood flow and oxygen saturation is above baseline after inducing hypoxia is measured to provide one of a time series of oxygen saturation measurements and a time series of blood volume measurements, and a prediction model uses the overshoot or rate value to generate a value representing the endogenous S-nitrosothiol content of tissue within the region of interest.

[0171] At 2204, a value representing the endogenous S-nitrosothiol content of tissue within the region of interest may be generated by processor 1412 / 1514 via prediction model 1406 / 224 from the time series. In one implementation, a linear relationship between a first time series representing blood volume and a second time series representing oxygen saturation is determined, and the value is determined based on the linear relationship. For example, the two time series may be provided to a linear regression model to provide a best fit line of oxygen saturation versus blood volume over time, where the value representing the endogenous S-nitrosothiol content of tissue within the region of interest is derived from the slope of the best fit line. At 2206, the value representing the endogenous S-nitrosothiol content of tissue within the region of interest may be stored by processor 1412 / 1514 in a memory implemented as a non-transitory computer-readable medium 1410 / 1512. In one example, method 2200 may be performed before and after a treatment provided to the subject to determine the effect of the treatment on the endogenous S-nitrosothiol content within the region of interest by comparing the value generated after the treatment with the stored value generated before the treatment. The stored value may also be used to generate one or more of a maximum nitric oxide tolerance measure, a maximum nitric oxide strength measure, an available oxygen consumption measure, and a personalized nitric oxide measure for the subject.

[0172] Figure 23Another example of a method for generating a value representing the endogenous S-nitrosothiol content of tissue within a region of interest of a subject is illustrated. At 2302, the blood volume and oxygen saturation within the region of interest of the subject can be non-invasively measured by sensors 1402 / 1502 to provide a first time series of oxygen saturation measurements and a second time series of blood volume measurements. For example, both the blood volume and oxygen saturation in the tissue can be determined via near-infrared spectroscopy. In one example, these measurements are made while the subject is engaged in exercise. At 2304, the linear relationship between the first time series and the second time series can be determined by processors 1412 / 1514 via prediction models 1406 / 224. For example, the two time series can be provided to a linear regression model to provide the best fit line of the change over time between oxygen saturation and blood volume.

[0173] At 2306, processors 1412 / 1514 can generate a value representing the endogenous S-nitrosothiol content of tissue within the region of interest based on the linear relationship between the first time series and the second time series. In one implementation, where the linear relationship is represented as the best fit line between the first time series and the second time series, a value representing the endogenous S-nitrosothiol content of tissue within the region of interest can be derived from the slope of the best fit line. At 2308, the value representing the endogenous S-nitrosothiol content of tissue within the region of interest can be stored by processors 1412 / 1514 in a memory implemented as a non-transitory computer-readable medium 1410 / 1512. The stored value can also be used to generate one or more of a maximum nitric oxide tolerance measure, a maximum nitric oxide strength measure, an available oxygen consumption measure, and a personalized nitric oxide measure for the subject.

[0174] Figure 24 Another method 2400 for generating a value representing the endogenous S-nitrosothiol content of tissue within a region of interest of a subject is illustrated. At 2402, the subject is instructed to engage in aerobic exercise. In one example, the subject can be instructed to ride an exercise bike. At 2404, the blood volume and oxygen saturation within the region of interest of the subject's muscle affected by the exercise can be non-invasively measured by sensors 1402 / 1502 to provide a first time series of oxygen saturation measurements and a second time series of blood volume measurements.

[0175] At 2406, at least one of the time series of blood volume and the time series of oxygen saturation within the region of interest can be adjusted by the processor 1412 / 1514 to eliminate external influences. Blood flow in tissues is mediated by many different factors, including prostaglandins, catecholamines, nitric oxide, temperature, kinins, adenosine triphosphate (ATP), hypoxia, and similar factors. For example, kinins regulate increased flow during inflammation, and NO mediates shear and Ach-induced vasodilation. To facilitate measurement of the effect of NO released from hemoglobin on blood flow, the collected blood flow data can be adjusted to eliminate the effects of these factors. For example, most aerobic exercises involve repetitive contractions of muscles over a somewhat predictable period of time. This reduction in blood volume can be quantified as a periodic signal, represented as another time series, which can be removed from the time series of blood volume measurements. Other physiological effects on one or both of blood volume and oxygen saturation will vary with location and specific exercise, and these non-linear effects on the relationship between blood volume and oxygen saturation can be identified and removed from the time series by adding signals representing these effects.

[0176] At 2408, a statistical process can be performed by the processor 1412 / 1514 to identify the linear relationship between blood flow derived from the time series of blood volume measurements and oxygen saturation, and generate a value representing the endogenous S-nitrosothiol content of the tissue within the region of interest based on this linear relationship. For example, a linear regression analysis can be performed, and the slope of the best fit line generated in the regression analysis can be used to quantify the linear relationship between blood flow and oxygen saturation. In cases where the muscle is particularly hypoxic and the slope cannot be easily established, a correlation coefficient between the two parameters can also be generated and used to evaluate the linear relationship. At 2410, the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest can be stored by the processor 1412 / 1514 in the memory implemented as the non-transitory computer-readable medium 1410 / 1512.

[0177] Figure 25 Metrics and use cases of professional athletes are illustrated. Specific metrics can be associated with different positive exercise outcomes, such as increases in maximum speed, strength, or endurance. Additionally, metrics can help monitor, predict, and plan an athlete's recovery and regeneration. For example, measurement of nitric oxide can be used to help develop an exercise activity that causes the maximum endogenous increase in nitric oxide in a person's blood supply. In another example, nitric oxide recovery can be monitored to determine how much rest an athlete needs during a competition or between sets. In a third example, nitric oxide regeneration can be monitored and / or improved through targeted exercise to reduce the risk of injury and accelerate the return to competition from injury.

[0178] Figure 26Depicts an example measurement of maximum NO recovery relative to the number of sets performed during an exercise routine. Maximum (NO)-recovery is derived from the rate of re-oxygenation in muscle tissue after an exercise bout and is affected by various factors such as (NO) concentration, breathing pattern, and aerobic fitness level. Maximum (NO)-recovery can tell athletes how well they are recovering in real-time and when they are fully recovered after an exercise bout. Additionally, an individual can train to increase their maximum (NO) recovery score, which will allow them to recover faster between or after exercise bouts. As indicated by the graph, an athlete's maximum NO recovery peaks at two sets. A threshold can be set for the decline from the peak maximum NO recovery to determine the maximum number of sets an athlete should perform. Through adjustment, an athlete can peak at a greater number of sets and / or the rate of decline after the peak can be reduced.

[0179] Figure 27 Depicts example measurements of PNO and maximum NO regeneration over time. PNO measurements are graphed for an athlete during an exercise session with alternating work and rest periods. When an athlete exercises, PNO increases. During the recovery period, PNO falls back. The more PNO an athlete generates, the better their health and performance. PNO can also be used to reduce the risk of injury and lower the risk of an individual re-injuring themselves during rehabilitation. PNO levels can be compared between a healthy leg and an injured leg, which informs the athlete of their ability to handle loading.

[0180] By comparing the nitric oxide levels of an injured person taken at different times, the level of tissue damage recovery for injury repair relative to full recovery can be determined. Measurements of maximum NO regeneration can be calculated at rest. To record maximum NO regeneration, a cuff can be placed on an individual's upper arm or upper thigh, and then a biosensor can be placed on a large muscle distal to the cuff. The cuff can be inflated until the pressure occludes blood flow to the limb. Once blood flow is occluded, the individual can remain still for a period of time with the cuff inflated, after which the cuff can deflate automatically, allowing blood to flow back into the limb. Biomarker measurements can be recorded during the post-ischemic reperfusion period and can be used to calculate the NO level.

[0181] In an example measurement, the maximum NO regeneration scores of an athlete's left and right legs are depicted within 12 weeks after undergoing right ACL surgery. The difference between the healthy left leg and the injured right leg can be easily tracked throughout the recovery process. This metric can be used in combination with existing metrics such as intensity. This metric can be used to optimize a player's training program to enable them to return to the field as quickly as possible without an excessive risk of injury.

[0182] As Figure 28As shown, when measuring PNO or muscle oxygenation (SmO2), there are simultaneous and separate trends. The macroscopic trend represents the autoregulation of blood flow. When oxygen is utilized in skeletal muscle, the muscle blood flow increases compensatorily. When this occurs, an inverse linear correlation can be observed between muscle oxygenation and total hemoglobin (THb), which is a measure of muscle blood volume (not depicted). Thus, an overall increase in nitric oxide and S-nitrosothiols (PNO) can be observed. This response can last from several seconds to several minutes.

[0183] The microscopic trend represents active hyperemia. When the muscle contracts, blood flow is restricted and thus the oxygen level drops. Then, during the muscle relaxation phase before the next contraction, blood flow increases and oxygen saturation rises. During active hyperemia, SmO2 and THb are linearly correlated. The active hyperemia response is depicted as a rapid increase and decrease in PNO in the microscopic trend in Figure 28 The microscopic trend is depicted as a rapid increase and decrease in PNO.

[0184] Those of ordinary skill in the art will note that the macroscopic trend is the overall increase in PNO during exercise, but within the macroscopic trend there are microscopic trends consisting of smaller increases / decreases in PNO. These two trends work together to regulate muscle blood flow.

[0185] As Figure 29 shown, there are also cases where the macroscopic trend and the microscopic trend show a positive linear correlation, such as when muscle contraction has a stronger-than-usual effect on blood flow. Thus, the autoregulation response cannot be clearly observed. However, because linear correlations, whether positive or negative, are being examined, PNO still increases.

[0186] In Figure 29 there are (+) correlations nested within (-) correlations and (+) correlations nested within (+) correlations. Although it is not possible to visually distinguish between the two, this possibility is worth mentioning (the second group has a higher PNO for reasons other than the fact that it is a + / + correlation).

[0187] After tissue injury, the injured person may think that the tissue injury has fully recovered and may place full weight on the injured tissue, and / or the injured body part has fully regained function, when in fact the tissue injury has not fully recovered. The actual complete tissue injury recovery can be determined by comparing the nitric oxide levels from the injured limb and the uninjured limb of the same type on the opposite side (i.e., arm or leg). Based on the metrics described herein, the injured person can continue to engage in sufficient activities, reducing the likelihood of re-injury.

[0188] Example: Study

[0189] To establish the importance of SNO, particularly SNO-Hb-βCys93, in clinically relevant measurements of hypoxic vasodilation, mice expressing human Hb were utilized. The mice lacked SNO-Hb and exhibited numerous cardiovascular defects caused by impaired hypoxic vasodilation. In this study, the functional consequences of Cys93 SNO were simulated using a standard clinical protocol for reactive hyperemia: reperfusion of the gastrocnemius muscle after five minutes of femoral artery occlusion was examined (measured by pO2 using a needle electrode).

[0190] Figure 30A Representative traces from control βC93 mice and corresponding βC93A mutant animals are depicted. In βC93 mice with normal hypoxic vasodilatory activity, the tissue pO2 response rapidly recovered after arterial occlusion release (i.e., restoration of femoral artery blood flow), even exceeding baseline. In contrast, βC93A animals exhibited a delayed response, and muscle oxygenation did not recover to baseline during the recording interval after five minutes of release. Group data comparisons showed that basal pO2 in the gastrocnemius muscle was significantly lower in βC93A mice, as Figure 30B shown; p = 0.032, consistent with our previous studies. Group data comparisons also showed that the recovery of muscle pO2 five minutes after occlusion was attenuated in βC93A mice compared to the βC93 control group (46 ± 17 mm Hg vs. 28 ± 15 mm Hg; p = 0.004). Group data comparisons further showed that the rate of post-occlusion recovery of muscle pO2 was significantly reduced in βC93A mice compared to γBC93 mice, to approximately half of the normal rate (0.23 ± 0.15 mm Hg / sec vs. 0.13 ± 0.11 mm Hg / sec), as Figure 30C shown; p = 0.036. Thus, SNO-Hb deficiency reduces the rate and overall efficiency of tissue oxygenation caused by a brief interruption of blood flow.

[0191] RBC SNO levels were measured in age-matched healthy control subjects and patients diagnosed with diseases characterized by systemic (i.e., heart failure and chronic obstructive pulmonary disease (COPD)) or peripheral (i.e., peripheral vascular disease and sickle cell disease) oxygenation defects: diabetic peripheral artery disease (PAD); heart failure with reduced ejection fraction (HF); COPD; and sickle cell disease (SCD). Specific inclusion criteria and disease states for each cohort are provided in the Extended Methods. Fifty-three subjects were recruited, and 49 individuals completed the study (13 healthy, 13 PAD, 6 HF, 9 COPD, and 8 SCD).

[0192] Process RBCs ex vivo and quantify SNO-Hb and nitrosylferro Hb by Hg-coupled photolysis-chemiluminescence within approximately 1 h after collection from the radial artery. Values from nine subjects were discarded due to instrument malfunction (i.e., the decision to diagnose and discard those data was made by technicians unaware of the patient's physiological status). Results from the remaining samples are presented in Figures 31A to 31D in.

[0193] In normal volunteers (n = 10), the arterial RBC SNO-Hb level was 2.6 ± 1.3 / 1000 Hb, as Figure 31A shown, concentrations similar to those recorded in groups of other healthy subjects. SNO-Hb levels in the HF group (n = 5; 2.5 ± 0.6 / 1000 Hb) and the COPD group (n = 5; 2.0 ± 1.3) were not different from the control group. However, the amount of SNO-Hb in blood from PAD (n = 11; 1.5 ± 1.2) and SCD (n = 5; 0.9 ± 0.6) patients was significantly lower than that in the normal control group (p < 0.05). The level of SNO-Hb may be decreased due to an overall reduction in NO production or due to a processing defect within the Hb molecule preventing the intramolecular transfer of NO from heme to thiol, as previously reported for SCD, PH, and healthy subjects under hypoxia, which is reflected in an increase in the amount of inactive FeNO. Notably, in any patient group, the total amount of NO bound to Hb (HbNO) was not different from the normal value, as Figure 31C shown. However, as Figure 31B shown, the HbFeNO concentration was significantly higher than the normal value in the PAD, COPD, and SCD patient groups, thus reflecting a significant decrease in the ratio of SNO to total HbNO in all groups except HF: from 0.69 ± 0.13 and 0.67 ± 0.16 in normal volunteers and HF patients, respectively, to 0.36 ± 0.30 in PAD, 0.36 ± 0.22 in COPD, and 0.24 ± 0.20 in SCD. An exploratory analysis of the correlation between SNO-Hb and various clinical chemistry parameters was also performed. Figure 31C Depicts total HbNO, and Figure 31D depicts the ratio of SNO to total HbNO in the identified patient groups. As Figure 33 (n = 33) shown, analysis of the data set identified an inverse correlation between plasma nitrite levels and SNO-Hb and between nitrite and the SNO-Hb / total HbNO ratio. The differences in SNO-Hb and FeNO levels, the ratio of SNO to total HbNO, and the negative correlation between plasma nitrite and NO bioactivity all suggest a defect in NO processing in RBCs from PAD, COPD, and SCD patients.

[0194] This study aimed to confirm the role of SNO-Hb in reactive hyperemia as demonstrated in βC93A mice. After blood collection, calf and foot tissue oxygenation was measured using a near-infrared spectroscopy (NIRS) device following a brief period of limb blood flow occlusion. Patients with SCD were excluded from this study due to the possibility of inducing vaso-occlusive crisis from leg ischemia. Subjects in the other groups were placed semi-supine, with inflatable cuffs wrapped around the upper thigh and lower calf near the ankle. Each cuff was rapidly inflated within one second to stop arterial blood flow (target pressure = systolic blood pressure + approximately 130 mmHg; maximum 300 mmHg), and the occlusion period was maintained for five minutes, then the cuff pressure was released and tissue oxygenation was measured for five minutes. There was a five-minute recovery interval between the two cuff inflation / recording sessions, with recording first performed at the foot using the ankle cuff and second at both the foot and upper calf using the thigh cuff. Occlusion testing was performed on 45 subjects, with the NIRS traces analyzed offline by individuals unaware of the subjects' disease status or RBC SNO levels. Thirteen of the resulting NIRS recordings were deemed uninterpretable by the independent analysis group due to leg movement and / or poor signal resolution and were thus excluded, leaving data from 11 healthy, 8 PAD, 6 HF, and 7 COPD patients for comparison purposes.

[0195] The experimental endpoint was the half-life (t1 / 2) measured in seconds to the recovery of tissue oxygenation (i.e., 50% return to baseline), and the results are presented in Figures 32A to 32E and. Figure 32A Representative foot tissue oxygenation recovery traces from one healthy subject and one PAD patient after release of the thigh cuff are shown in. The healthy subject had a robust and rapid reoxygenation response, with a t1 / 2 of 10 seconds, while the PAD patient exhibited a delayed tissue oxygenation response, with a t1 / 2 of 22 seconds. This is very similar to the rapid recovery of tissue pO2 and the slower recovery in βC93A mice compared to βC93 control mice after femoral artery occlusion release, as shown in Figure 30A Quantified group data (mean ± SD) are presented in Figure 32B , Figure 32C and Figure 32D showing the t1 / 2 of foot reoxygenation after ankle and thigh cuff occlusion and calf reoxygenation after thigh occlusion. For all three measurements, the mean t1 / 2 value recorded for healthy subjects was approximately 10 seconds, consistent with previous studies. Importantly, there was a direct correlation between SNO-Hb levels and reperfusion rate as a group, as shown in Figure 32EAs shown. Numerically higher mean t1 / 2 values were observed for all three patient groups, but a significant increase in the foot reperfusion half-life after ankle or cuff inflation was only seen in the group of PAD subjects. Additionally, there was a significant inverse correlation between the foot reperfusion t1 / 2 and the SNO-Hb level and between the SNO-Hb and total HbNO ratio, but not with the FeNO level, as Figure 34 shown, thus linking RBC SNO to the reoxygenation response.

[0196] Microvascular blood flow is impaired in many clinical conditions, leading to tissue ischemia. However, drugs that increase blood flow do not improve tissue oxygenation. Additionally, clinical measurements of blood flow focus on the endothelial component, particularly NO, which plays no role in tissue oxygenation. On the other hand, there is strong evidence that blood flow governing tissue oxygenation is regulated by S-nitrosohemoglobin. In other words, blood flow maintaining blood pressure is regulated by endothelial NO, while blood flow regulating tissue oxygenation is controlled by RBC-SNO. Reactive hyperemia is an increase in blood flow after transient ischemia that occurs to restore tissue oxygenation. And reactive hyperemia has been attributed to endothelial NO. RBC-SNO plays a major role in both vasodilation and blood flow responses in mice. The current study extends this work to include direct measurements of tissue oxygenation in mice and humans. Importantly, the study shows that SNO-Hb is required to supply oxygen to hypoxic tissues and that defects in SNO-Hb result in impaired oxygenation. Additionally, the level of SNO-Hb in patients predicts tissue oxygenation after transient local hypoxia, thus indicating the first biomarker of microcirculatory blood flow.

[0197] The study presents a model of a 3-gas model of the respiratory cycle, in which O2 / NO are simultaneously loaded onto Hb and then SNO-Hb releases vasodilatory SNO to regulate blood flow through tissue oxygen delivery. In mutant mice unable to carry or distribute SNO from βCys93, tissue oxygenation is thus severely impaired. In addition to tissue hypoxia under basal conditions, mutant mice exhibit defects in tissue oxygenation under global hypoxia and local ischemia, as Figure 30B shown. Conversely, hypoxic conditions that impair oxygen loading or otherwise impair allosteric transitions in Hb also impair S-nitrosylation. This is manifested as lower levels of SNO-Hb or a lower ratio of SNO-Hb to total HbNO, because NO remains bound to heme iron and only it cannot be transferred to Cys93. Correspondingly, patients with disease states characterized by oxygen pathologies (COPD, PAD, and SCD) exhibit lower levels of SNO-Hb and lower SNO / HbNO, as Figures 31A to 31DAs shown, this confirmed previous reports. Predictably, the ratio of SNO-Hb to total Hb-related NO (SNO-Hb plus Hb FeNO; total HbNO) indicates the accumulation of inactive FeNO and, compared to SNO-Hb measurements alone, it is a more sensitive measure of loss of biological activity. Thus, NO / SNO handling defects are observed in multiple diseases characterized by tissue hypoxia, and this may be causally linked to the inability of Hb to convert FeNO to SNO-βCys93-Hb. For the same reason, we found that the ratio of SNO-Hb to nitrite and the ratio of SNO-Hb / HbNO to blood nitrite were actually inversely correlated, consistent with previous results that higher nitrite blocks SNO-Hb formation, as Figure 33 shown. It can be seen that nitrite levels are not related to blood flow or tissue oxygenation.

[0198] Patients with PAD are characterized by significant microvascular dysfunction. When tested for recovery from brief limb ischemia, reperfusion in this cohort was significantly delayed, as Figures 32A to 32E shown, and in all cases, the t1 / 2 to reoxygenation was longer compared to healthy controls. Importantly, while no statistical differences in t1 / 2 to reoxygenation were observed in other cohorts, a significant correlation was found between SNO-Hb levels and oxygenation rate in all patient cohorts. Collectively, combined with genetic validation in mice, these results suggest that SNO-Hb is a key driver of blood flow autoregulation, whereby tissue blood flow controls tissue oxygenation. These results have multiple clinical implications. Reactive hyperemia, previously regarded as a measure of endothelial function, has a significant RBC SNO component. More generally, endothelial NO and RBC SNO have different roles, the former in vascular health and the latter in tissue health. This result adds to the body of research pointing to RBC SNO-Hb as a biomarker of tissue oxygenation status, particularly since SNO-Hb is directly related to the reperfusion rate. In addition, this study suggests that the reactive hyperemia test is a useful measure of SNO-Hb function in patient populations. The ability to enhance RBC SNO can improve tissue oxygenation and may have broad clinical utility. In certain embodiments, exercise is used to provide oxygenation benefits to a subject with a disease state. For example, the subject may have heart disease, vascular disease, diabetes, cancer, frailty, and / or muscle disease (e.g., muscle metabolic disorders), and PNO would be diagnostic, prognostic, and / or potentially therapeutic.

[0199] Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it should be understood that the embodiments may be practiced without these specific details. For example, physical components may be shown in block diagrams so as not to obscure the embodiments with unnecessary details. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary details to avoid obscuring the embodiments.

[0200] The implementation manners of the technologies, blocks, steps, and means described above can be accomplished in various ways. For example, these technologies, blocks, steps, and means can be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing unit can be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions described above, and / or combinations thereof.

[0201] In addition, it should be noted that the embodiments can be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many operations can be performed in parallel or simultaneously. In addition, the order of the operations can be rearranged. The process terminates when its operations are completed, but it may have additional steps not included in the figure. The process can correspond to a method, a function, a program, a subroutine, a subprogram, etc. When the process corresponds to a function, its termination corresponds to the function returning to the calling function or the main function.

[0202] Furthermore, the embodiments can be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and / or any combination thereof. When implemented in software, firmware, middleware, scripting languages, and / or microcode, the program code or code segments for performing the necessary tasks can be stored in a machine-readable medium such as a storage medium. The code segments or machine-executable instructions can represent a process, a function, a subroutine, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures, and / or program statements. The code segments can be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, and / or memory contents. The information, arguments, parameters, data, etc. can be passed, forwarded, or transmitted in any suitable manner (including memory sharing, message passing, ticket passing, network transmission, etc.).

[0203] For firmware and / or software implementations, methods may be implemented using modules (e.g., programs, functions, etc.) that perform the functions described herein. Any machine-readable medium tangibly embodying instructions may be used to implement the methods described herein. For example, software code may be stored in a memory. The memory may be implemented within or external to a processor. As used herein, the term "memory" refers to any type of long-term, short-term, volatile, non-volatile, or other storage medium and is not limited to any particular type of memory or any particular number of memories, or the type of medium on which the memories are stored.

[0204] In addition, as disclosed herein, the term "storage medium" may represent one or more memories for storing data, including read-only memory (ROM), random access memory (RAM), magnetic RAM, magnetic core memory, disk storage media, optical storage media, flash memory devices, and / or other machine-readable media for storing information. The term "machine-readable medium" includes, but is not limited to, portable or fixed storage devices, optical storage devices, wireless channels, and / or various other storage media capable of storing instructions and / or data.

[0205] In the foregoing description, specific details have been set forth in order to provide a thorough understanding of example implementations of the systems and methods described in this disclosure. However, it will be apparent that various implementations may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown in block diagram form as components so as not to obscure example implementations with unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. The description of example implementations will provide those skilled in the art with an enabling description for implementing example embodiments, but it should be understood that various changes may be made to the functions and arrangements of the elements without departing from the spirit and scope of this disclosure. Accordingly, this disclosure is intended to cover all such changes, modifications, and variations that fall within the scope of any of the appended claims.

Claims

1. A system comprising: At least one light source configured to irradiate an area of interest of a subject; At least one light sensor configured to non-invasively measure reflected light within the area of interest of the subject; At least one processor in communication with the sensor; And At least one non-transitory computer-readable medium storing machine-readable instructions that, when executed by the at least one processor, cause the at least one processor to perform processing, the processing including: Receiving at least one measurement of light from the at least one light sensor; Using the at least one measurement and using at least a portion of a stochastic model as an input to determine an irradiance distribution of the area of interest of the subject; Calculating at least one characteristic attenuation coefficient of at least one wavelength of the light based on the irradiance distribution; and Determining at least one physical characteristic of the area of interest of the subject based on the at least one characteristic attenuation coefficient and the at least one measurement of light.

2. The system according to claim 1, wherein the at least one physical characteristic includes one or more of water measurement, internal training load, oxyhemoglobin measurement, deoxyhemoglobin measurement, total hemoglobin measurement, blood volume measurement, muscle oxygenation, muscle oxygen consumption, reactive nitric oxide measurement, reactive S-nitrosothiol measurement, fat thickness, and melanin content.

3. The system according to claim 1, wherein the at least one physical characteristic includes a combination of pulse oximetry and nitric oxide.

4. The system according to claim 1, wherein the at least one measurement of light includes a measured time series, and the at least one physical characteristic includes a time series of characteristics, and the processing further includes: Generating a value representing an endogenous S-nitrosothiol content of tissue within the area of interest based on the time series of characteristics; And Storing the value representing the endogenous S-nitrosothiol content of the tissue within the area of interest in the non-transitory computer-readable medium.

5. The system according to claim 4, wherein the time series of characteristics includes a time series of oxygen saturation measurements and a time series of blood volume measurements.

6. The system according to claim 5, wherein generating the value includes: Determining a linearity of a relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements and providing a set of parameters; And Generating the value representing the endogenous S-nitrosothiol content of the tissue within the area of interest based on the set of parameters.

7. The system according to claim 6, wherein generating the value includes: Using a linear regression model to provide a best fit line defined by the set of parameters, the set of parameters including a slope of the best fit line; And Generating the value representing the endogenous S-nitrosothiol content of the tissue within the area of interest based on the slope of the best fit line.

8. The system according to claim 1, wherein the at least one processor is configured to perform the determination of the at least one physical characteristic during one of an exercise period of the subject and a time period immediately following the exercise period of the subject.

9. The system according to claim 1, wherein the processing further comprises displaying, by at least one display device in communication with the at least one processor, at least one indication of the at least one physical characteristic.

10. The system according to claim 9, wherein the at least one indication comprises guidance related to an internal training load of the subject.

11. The system according to claim 10, wherein the guidance is intended to reduce the risk of injury.

12. The system according to claim 9, wherein the at least one indication comprises information related to muscle oxygen consumption of the subject.

13. The system according to claim 12, wherein the information comprises a VO2 indication.

14. The system according to claim 1, wherein the stochastic model comprises a three-dimensional model having six degrees of freedom.

15. The system according to claim 14, wherein the six degrees of freedom comprise position coordinates and direction cosines.

16. A system, comprising: a plurality of light sources configured to illuminate a region of interest of a subject, each respective light source of the plurality of light sources being configured to emit light of a respective different wavelength; at least one light sensor configured to non-invasively measure reflected light within the region of interest of the subject; at least one processor in communication with the sensor; and at least one non-transitory computer-readable medium storing machine-readable instructions that, when executed by the at least one processor, cause the at least one processor to perform processing, the processing comprising: receiving, from the at least one light sensor, a plurality of measurements of the respective different wavelengths of light; and determining, based on the plurality of measurements of light, at least one thickness or depth of at least one tissue layer within the region of interest of the subject.

17. The system according to claim 16, wherein the processing further comprises: Determining at least one physical characteristic of the region of interest of the subject based on the at least one measurement of light and the at least one thickness or depth of the at least one tissue layer.

18. The system according to claim 17, wherein the at least one physical characteristic comprises one or more of water measurement, internal training load, oxyhemoglobin measurement, deoxyhemoglobin measurement, total hemoglobin measurement, blood volume measurement, muscle oxygenation, muscle oxygen consumption, reactive nitric oxide measurement, reactive S-nitrosothiol measurement, fat thickness, and melanin content.

19. The system according to claim 17, wherein the at least one physical characteristic comprises a combination of pulse oximetry and nitric oxide.

20. The system according to claim 17, wherein the at least one measurement of light comprises a measured time series, and the at least one physical characteristic comprises a time series of characteristics, and the processing further comprises: Generating a value representing the endogenous S-nitrosothiol content of the tissue within the region of interest according to the time series of the characteristics; And Storing the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest in the non-transitory computer-readable medium.

21. The system according to claim 20, wherein the time series of the characteristics includes a time series of oxygen saturation measurements and a time series of blood volume measurements.

22. The system according to claim 21, wherein generating the value includes: Determining the linearity of the relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements, and providing a set of parameters; And Generating the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest according to the set of parameters.

23. The system according to claim 22, wherein generating the value includes: Using a linear regression model to provide a best-fit line defined by the set of parameters, the set of parameters including the slope of the best-fit line; And Generating the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest according to the slope of the best-fit line.

24. The system according to claim 17, wherein the at least one processor is configured to perform the determination of the at least one physical characteristic during one of an exercise period of the subject and a period immediately following the exercise period of the subject.

25. The system according to claim 17, wherein the processing further includes displaying, by at least one display device in communication with the at least one processor, at least one indication of the at least one physical characteristic.

26. The system according to claim 25, wherein the at least one indication includes guidance related to the internal training load of the subject.

27. The system according to claim 26, wherein the guidance is aimed at reducing the risk of injury.

28. The system according to claim 25, wherein the at least one indication includes information related to the muscle oxygen consumption of the subject.

29. The system according to claim 28, wherein the information includes a VO2 indication.

30. The system according to claim 16, wherein the determination includes applying the plurality of measurement values of light to at least one scattering phase function associated with at least one layer type within the region of interest of the subject.

31. The system according to claim 16, wherein the determination includes applying the plurality of measurement values and a random model as inputs.

32. The system according to claim 31, wherein the random model includes a three-dimensional model with six degrees of freedom.

33. The system according to claim 32, wherein the six degrees of freedom include position coordinates and direction cosines.

34. A method, comprising: Irradiating a region of interest of a subject by at least one light source; Detecting reflected light from the region of interest of the subject by at least one light sensor; Receiving, by at least one processor, at least one measurement value of light from the at least one light sensor; The irradiance distribution of the region of interest of the subject is determined by the at least one processor using the at least one measurement value and using at least a portion of a stochastic model as an input; At least one characteristic attenuation coefficient of the light is calculated by the at least one processor based on the irradiance distribution; And Based on the at least one characteristic attenuation coefficient and the at least one measurement value of the light, at least one physical characteristic of the region of interest of the subject is determined by the at least one processor.

35. The method according to claim 34, wherein the at least one physical characteristic includes one or more of water measurement, internal training load, oxyhemoglobin measurement, deoxyhemoglobin measurement, total hemoglobin measurement, blood volume measurement, muscle oxygenation, muscle oxygen consumption, reactive nitric oxide measurement, reactive S-nitrosothiol measurement, fat thickness, and melanin content.

36. The method according to claim 34, wherein the at least one physical characteristic includes a combination of pulse oxygen saturation and nitric oxide.

37. The method according to claim 4, wherein the at least one measurement value of the light includes a measured time series, and the at least one physical characteristic includes a time series of characteristics, the method further comprising: Generating, by the at least one processor, a value representing the endogenous S-nitrosothiol content of the tissue within the region of interest based on the time series of characteristics; And Storing, by the at least one processor, the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest in a non-transitory computer-readable medium.

38. The method according to claim 37, wherein the time series of characteristics includes a time series of oxygen saturation measurements and a time series of blood volume measurements.

39. The method according to claim 38, wherein generating the value comprises: Determining the linearity of the relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements and providing a set of parameters; And Generating, based on the set of parameters, the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest.

40. The method according to claim 39, wherein generating the value comprises: Using a linear regression model to provide a best fit line defined by the set of parameters, the set of parameters including the slope of the best fit line; And Generating, based on the slope of the best fit line, the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest.

41. The method according to claim 34, wherein the determination of the at least one physical characteristic is performed during one of an exercise period of the subject and a time period immediately following the exercise period of the subject.

42. The method according to claim 34, further comprising displaying, by at least one display device in communication with the at least one processor, at least one indication of the at least one physical characteristic.

43. The method according to claim 42, wherein the at least one indication includes guidance related to the internal training load of the subject.

44. The method according to claim 43, wherein the guidance is aimed at reducing the risk of injury.

45. The method according to claim 42, wherein the at least one indication includes information related to the muscle oxygen consumption of the subject.

46. The method according to claim 45, wherein the information includes a VO2 indication.

47. The method according to claim 34, wherein the stochastic model includes a three-dimensional model with six degrees of freedom.

48. The method according to claim 47, wherein the six degrees of freedom include position coordinates and direction cosines.

49. A method, comprising: irradiating an area of interest of a subject with a plurality of light sources, each respective light source of the plurality of light sources emitting light of a respective different wavelength; detecting, by at least one light sensor, reflected light within the area of interest of the subject; receiving, by at least one processor, a plurality of measurements of the light of the respective different wavelengths from the at least one light sensor; and determining, by the at least one processor, at least one thickness or depth of at least one tissue layer within the area of interest of the subject based on the plurality of measurements of the light.

50. The method according to claim 49, further comprising determining, by the at least one processor, at least one physical property of the area of interest of the subject based on the at least one measurement of the light and the at least one thickness or depth of the at least one tissue layer.

51. The method according to claim 50, wherein the at least one physical property includes one or more of water measurement, internal training load, oxyhemoglobin measurement, deoxyhemoglobin measurement, total hemoglobin measurement, blood volume measurement, muscle oxygenation, muscle oxygen consumption, active nitric oxide measurement, active s-nitrosothiol measurement, fat thickness, and melanin content.

52. The method according to claim 50, wherein the at least one physical property includes a combination of pulse oximetry and nitric oxide.

53. The method according to claim 50, wherein the at least one measurement of the light includes a measured time series, and the at least one physical property includes a time series of properties, the method further comprising: generating, by the at least one processor, a value representing the endogenous S-nitrosothiol content of the tissue within the area of interest based on the time series of the properties; and storing, by the at least one processor, the value representing the endogenous S-nitrosothiol content of the tissue within the area of interest in a non-transitory computer-readable medium.

54. The method according to claim 53, wherein the time series of the properties includes a time series of oxygen saturation measurements and a time series of blood volume measurements.

55. The method according to claim 54, wherein generating the value includes: determining the linearity of the relationship between the time series of the blood volume measurements and the time series of the oxygen saturation measurements, and providing a set of parameters; and Generating the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest based on the set of parameters.

56. The method according to claim 55, wherein generating the value comprises: Using a linear regression model to provide a best-fit line defined by the set of parameters, the set of parameters including the slope of the best-fit line; And Generating the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest based on the slope of the best-fit line.

57. The method according to claim 50, wherein the determination of the at least one physical property is performed during one of the exercise session of the subject and the period immediately following the exercise session of the subject.

58. The method according to claim 50, further comprising displaying, by at least one display device in communication with the at least one processor, at least one indication of the at least one physical property.

59. The method according to claim 58, wherein the at least one indication includes guidance related to the internal training load of the subject.

60. The method according to claim 59, wherein the guidance is aimed at reducing the risk of injury.

61. The method according to claim 58, wherein the at least one indication includes information related to the muscle oxygen consumption of the subject.

62. The method according to claim 61, wherein the information includes a VO2 indication.

63. The method according to claim 49, wherein the determination includes applying the plurality of measurements of light to at least one scattering phase function associated with at least one layer type within the region of interest of the subject.

64. The method according to claim 49, wherein the determination includes applying the plurality of measurements and a stochastic model as inputs.

65. The method according to claim 64, wherein the stochastic model includes a three-dimensional model with six degrees of freedom.

66. The method according to claim 65, wherein the six degrees of freedom include position coordinates and direction cosines.

67. The system according to claim 1, wherein the at least one physical property includes at least one of a diagnosis of injury, a measure of injury recovery, and a prognosis of re-injury.

68. The system according to claim 17, wherein the at least one physical property includes at least one of a diagnosis of injury, a measure of injury recovery, and a prognosis of re-injury.

69. The method according to claim 34, wherein the at least one physical property includes at least one of a diagnosis of injury, a measure of injury recovery, and a prognosis of re-injury.

70. The method according to claim 50, wherein the at least one physical property includes at least one of a diagnosis of injury, a measure of injury recovery, and a prognosis of re-injury.

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