Spectroscopic analysis system and method

Abstract

At least one light source may be configured to illuminate a region of interest of the subject, and at least one optical sensor may be configured to non-invasively measure reflected light within the region of interest. At least one processor may perform processing including receiving at least one measured value of the light from the at least one optical sensor. From the measured value of the 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

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 477,989, filed December 30, 2022, which is incorporated by reference in its entirety. [Background technology]

[0002] background Spectroscopic analysis is based on the use of single or multiple light sources that generate energy at various wavelengths to illuminate biological tissue(s), where one or more sensors are strategically placed to detect and measure the energy emitted from the sample at different locations. The distributed localization of light sources and receivers enables techniques such as triangulation and time resolution to distinguish signals from locations within a multidimensional tissue sample.

[0003] There are many ways in which the light source(s) and sensor(s) can be positioned and operated so that many measurements can be made. Each of the metrics described above corresponds to energy contained within a predefined spectral bandwidth of the emitted energy. The resulting energy spectrum produced can be analyzed in many ways to derive physical or chemical properties of the measured sample. Summary of the Invention

[0004] Disclosure Overview Described herein are devices and methods for the non-invasive in vivo quantitative measurement of biomarkers, chemicals, and chromophores in differentiated biological tissue layers and their associated blood supply. More specifically, this disclosure describes new and more effective ways of deriving desired parameter values ​​of biomarker, chemical, and chromophore concentrations from spectrophotometric measurements.

[0005] The systems and methods described herein may employ optical methodologies for noninvasively measuring biomarker levels, chemical concentrations, and chromophore concentrations in different biological tissue layers. Additionally, the optical methodologies taught herein enable the measurement of biomarkers, chemicals, and chromophores, including, but not limited to, oxygenated hemoglobin, deoxygenated hemoglobin, methemoglobin, carboxyhemoglobin, muscle oxygenation, muscle oxygen consumption, nitric oxide, s-nitrosothiols, water, glycogen, and internal training load.

[0006] Some exemplary embodiments may include a system comprising at least one light source configured to illuminate a region of interest of a subject, at least one light sensor configured to noninvasively 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 processes including receiving at least one measured value of light from the at least one light sensor, processing the at least one measured value and at least a portion of a probabilistic model to thereby determine an irradiance distribution in the region of interest of the subject, calculating at least one characteristic attenuation coefficient for at least one wavelength of light from the irradiance distribution, and determining at least one body characteristic of the region of interest of the subject based on the at least one characteristic attenuation coefficient and the at least one measured value of light.

[0007] Some exemplary embodiments may include a system comprising: a plurality of light sources configured to illuminate a region of interest of a subject, each light source of the plurality of light sources configured to emit light of a different wavelength; at least one optical sensor configured to noninvasively 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 comprising receiving a plurality of measured values ​​of light of the different wavelengths from the at least one optical sensor; and determining at least one thickness or depth of at least one tissue layer within the region of interest of the subject from the plurality of measured values ​​of light.

[0008] Some example embodiments may include a method including: illuminating an area of ​​interest of a subject with at least one light source; detecting reflected light from the area of ​​interest of the subject with at least one light sensor; receiving, by at least one processor, at least one measured value of light from the at least one light sensor; determining an irradiance distribution of the area of ​​interest of the subject using the at least one measured value and a probabilistic model as inputs; calculating, by the at least one processor, at least one characteristic attenuation coefficient of at least one wavelength of light from the irradiance distribution; and determining, by the at least one processor, 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 measured value of the light.

[0009] Some exemplary embodiments may include a method that includes illuminating a region of interest of a subject with a plurality of light sources, each light source of the plurality of light sources emitting light of a different wavelength; detecting the light within the region of interest of the subject with at least one light sensor; receiving, by at least one processor, a plurality of measured values ​​of the light of the different wavelengths from the at least one light sensor; and determining, by the at least one processor, from the plurality of measured values ​​of the light, at least one thickness or depth of at least one tissue layer within the region of interest of the subject.

[0010] In at least some of the example embodiments, the process and / or method may further include determining at least one physical characteristic of the region of interest of the subject based on the at least one measured value of the light and at least one thickness or depth of the at least one tissue layer. In at least some of the example embodiments, the determining may include applying the measured values ​​of the light to at least one scattering phase function associated with at least one layer type in the region of interest of the subject.

[0011] In at least some of the exemplary embodiments, the at least one body characteristic may include one or more of a hydration measurement, an internal training load, an oxygenated hemoglobin measurement, a deoxygenated hemoglobin measurement, a total hemoglobin measurement, a blood volume measurement, muscle oxygenation, muscle oxygen consumption, an active nitric oxide measurement, an active s-nitrosothiol measurement, fat thickness, and melanin content.

[0012] In at least some of the exemplary embodiments, the at least one physical characteristic may include a combination of pulse oximetry and nitric oxide.

[0013] In at least some of the example embodiments, the at least one measured value of the light can include a time series of measurements and the at least one body characteristic can include a time series of characteristics. The process and / or method can further include generating a value representative of the endogenous S-nitrosothiol content of the tissue in the region of interest from the time series of characteristics and storing the value representative of the endogenous S-nitrosothiol content of the tissue in the region of interest in a non-transitory computer-readable medium.

[0014] In at least some exemplary embodiments, the time series of characteristics 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 the linearity of the relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements to provide a set of parameters and generating a value representing the endogenous S-nitrosothiol content of the tissue in the region of interest from the set of parameters. In at least some exemplary embodiments, generating the value may include 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 a value representing the endogenous S-nitrosothiol content of the tissue in the region of interest from the slope of the best-fit line.

[0015] In at least some of the exemplary embodiments, the determination of the at least one physical characteristic may be performed during one of a period of exercise by the subject and a period of time immediately following the period of exercise by the subject.

[0016] In at least some exemplary embodiments, the process and / or method may further include displaying, by at least one display device in communication with the at least one processor, at least one indicator of the at least one physical characteristic. In at least some exemplary embodiments, the at least one indicator may include guidance related to the subject's internal training load, e.g., guidance directed to reducing injury risk. In at least some exemplary embodiments, the at least one indicator may include information related to the subject's muscle oxygen consumption, e.g., information including a VO2 index.

[0017] In at least some of the example embodiments, the probabilistic model may include a three-dimensional model having six degrees of freedom, which may include position coordinates and direction cosines. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a wearable optical device according to some embodiments of the present disclosure. [Figure 2] 1 is a set of illustrations of modeled geometry for light, according to some embodiments of the present disclosure. [Figure 3] 1 is a graph illustrating exemplary ray paths for photons, according to some embodiments of the present disclosure. [Figure 4] 1 is a graph illustrating exemplary wavelengths and their optical distance through a fat layer thickness, according to some embodiments of the present disclosure. [Figure 5] 1 is an exemplary set of LEDs and photodiodes of optical hardware according to some embodiments of the present disclosure. [Figure 6] 1 is an example of optical hardware used to control and capture light and process data according to some embodiments of the present disclosure. [Figure 7]1 is a flowchart illustrating a method used to implement a computational process for generating biomarker levels, chemical concentrations, and chromophore concentrations in different tissue layers, according to some embodiments of the present disclosure. [Figure 8] 1 is a graph illustrating an example of muscle oxygen saturation (SmO2) measurements for a subject during exercise, according to some embodiments of the present disclosure. [Figure 9] 1 is a chart illustrating an example of local muscle oxygen consumption (mVO2) measurement for a subject during exercise, according to some embodiments of the present disclosure. [Figure 10] 1 is a graph illustrating an example of non-invasive measurement of active nitric oxide in blood in an exercising subject, according to some embodiments of the present disclosure. [Figure 11] 1 is a graph illustrating an example of measuring a subject's internal training load (ITL) during exercise and illustrating the relationship between internal training load and external training load (ETL), according to some embodiments of the present disclosure. [Figure 12] 1 illustrates an exemplary measurement device according to some embodiments of the present disclosure. [Figure 13] FIG. 1 is a schematic block diagram illustrating an exemplary system of hardware components in which example systems and methods disclosed herein may be implemented. [Figure 14] 1 illustrates an example of a system for generating a value representative of endogenous S-nitrosothiol content of tissue in a region of interest of a subject, according to some embodiments of the present disclosure. [Figure 15] 1 illustrates another example of a system for generating a value representative of endogenous S-nitrosothiol content of tissue in a region of interest of a subject, according to some embodiments of the present disclosure. [Figure 16] 1 is a chart illustrating PNO levels for a patient during exercise, according to some embodiments of the present disclosure. [Figure 17] 1 is a chart illustrating VO2 levels for a patient during exercise, according to some embodiments of the present disclosure. [Figure 18]1 depicts a chart of a time series of UO2 measurements and a time series of VO2 measurements for an athlete operating a full-body exercise bicycle using one sensor to record blood volume and oxygen saturation, according to some embodiments of the present disclosure. [Figure 19] 1 depicts a chart of a time series of UO2 measurements and a time series of VO2 measurements for an athlete operating a full-body exercise bicycle using one sensor to record blood volume and oxygen saturation, according to some embodiments of the present disclosure. [Figure 20] 1 depicts a chart of an athlete's maximum NO power recorded weekly over a six-month period, according to some embodiments of the present disclosure. [Figure 21] 1 depicts a chart illustrating the relationship between an athlete's maximum NO endurance and the athlete's critical power recorded over a six-week period as a scatter plot, according to some embodiments of the present disclosure. [Figure 22] 1 illustrates an example method for generating a value representative of endogenous S-nitrosothiol content of tissue in a region of interest of a subject, according to some embodiments of the present disclosure. [Figure 23] 1 illustrates another example of a method for generating a value representative of endogenous S-nitrosothiol content of tissue in a region of interest of a subject, according to some embodiments of the present disclosure. [Figure 24] 1 illustrates a further method for generating a value representative of endogenous S-nitrosothiol content of tissue in a region of interest of a subject, according to some embodiments of the present disclosure. [Figure 25] 1 depicts metrics and use cases for a professional sports platform using (NO), according to some embodiments of the present disclosure. [Figure 26] 1 depicts a bar graph of an athlete's recovery metrics as maximum (NO) recovery levels over three sets of repetitions, according to some embodiments of the present disclosure. [Figure 27] 1 illustrates an individual's nitric oxide (PNO) over time and (NO) regeneration over time according to some embodiments of the present disclosure. [Figure 28] 1 depicts graphs illustrating the effects of exercise on muscle oxygenation (SmO2) and s-nitrosothiols (PNO), according to some embodiments of the present disclosure. [Figure 29] 1 depicts graphs illustrating the effect of exercise on muscle oxygenation (SmO2) and s-nitrosothiol (PNO), with (+) correlations nested within (-) correlations and (+) correlations nested within (+) correlations, in accordance with some embodiments of the present disclosure. [Figure 30] Figure 30A depicts representative traces from a control βC93 mouse and a corresponding βC93A mutant animal according to some embodiments of the present disclosure. Figure 30B depicts basal pO2 in the gastrocnemius muscle from a control βC93 mouse and a corresponding βC93A mutant animal according to some embodiments of the present disclosure. Figure 30C depicts the rate of post-occlusion recovery of muscle pO2 from a control βC93 mouse and a corresponding βC93A mutant animal according to some embodiments of the present disclosure. [Figure 31] Figure 31A depicts SNO-Hb isolated from fresh arterial blood for a patient group according to some embodiments of the present disclosure. Figure 31B depicts FeNO levels for a patient group according to some embodiments of the present disclosure. Figure 31C depicts total HbNO for a patient group according to some embodiments of the present disclosure. Figure 31D depicts the ratio of SNO to total HbNO for a patient group according to some embodiments of the present disclosure. [Figure 32]Figure 32A depicts representative near-infrared sensor measurements of Hb oxygenation recovery over time in healthy controls and PAD patients, according to some embodiments of the present disclosure. Figure 32B depicts reperfusion recovery half-time in a patient group using a cuff at the ankle and measuring at the foot, according to some embodiments of the present disclosure. Figure 32C depicts reperfusion recovery half-time in a patient group using a cuff at the thigh and measuring at the foot, according to some embodiments of the present disclosure. Figure 32D depicts reperfusion recovery half-time in a patient group using a cuff at the thigh and measuring at the calf, according to some embodiments of the present disclosure. Figure 32E depicts the correlation between SNO-Hb levels and recovery half-time, according to some embodiments of the present disclosure. [Figure 33] 1 depicts the correlation between NOHb measurements and clinical chemistry results according to some embodiments of the present disclosure. [Figure 34] 1 depicts the correlation between NOHb measurements and NIRS half-time to hyperemia, according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0019] In the following description, for purposes of explanation and instruction, numerous specific details are set forth in order to provide an understanding of one or more exemplary embodiments. However, it may be apparent to one skilled in the art that other embodiments of the disclosed systems and methods may be practiced without some or all of these specific details.

[0020] 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 based at least in part on. Additionally, when this disclosure or claims recite "a," "an," "first," or "another" element or equivalents, it should be interpreted as including one or more such elements, and not requiring or excluding more than one such element.

[0021] As used herein, the "amount" or "level" of a biological material can refer to any of the volume, mass, saturation, or concentration of the biological material.

[0022] As used herein, a "physiological parameter" or "biological parameter" is a continuous, categorical, or ordinal value that characterizes the physical state of a subject.

[0023] As used herein, a "biomarker" or "biometric parameter" is a measured parameter that is indicative of a subject's health, fitness, or physical performance.

[0024] As used herein, a "subject" is a living organism belonging to the mammalian family.

[0025] As used herein, a "predictive model" is a mathematical model that predicts the future state of a parameter or estimates the current state of a parameter that cannot be directly measured.

[0026] As used herein, a "biometric parameter" is a measured parameter that is indicative of the health or fitness of a subject.

[0027] As used herein, a measurement is performed "non-invasively" if it is not necessary to remove blood or tissue from the subject to perform the measurement.

[0028] As used herein, a "time series" is a sequence of measurements on a relative time scale. The measurements need not be consecutive or regularly spaced.

[0029] As used herein, "providing therapy to a subject" can include administering a therapeutic agent, applying mechanical force or electrical energy to a subject, or instructing a subject to perform a particular movement or task that is believed to provide a therapeutic benefit to the subject.

[0030] As used herein, "PNO," or individual nitric oxide, refers to an individual's level of the bioactive form of NO in blood, derived from red blood cells and identified as S-nitrosothiol in hemoglobin. It should be understood that SNO in RBCs is in equilibrium with other SNO, that PNO can be formed from various sources of NO (and associated NOx), and that release of S-nitrosothiol from RBCs can occur in various ways to generate NO and S-nitrosothiol in tissues, and therefore PNO refers to the bioactive form of NO, including any bioactive form of NO derived from RBCs or otherwise formed to oxidize tissue. Of course, PNO is understood to be a relative metric that represents a direct correlation between hemoglobin oxygen saturation and total hemoglobin. For example, individuals with higher levels of SNO can reoxygenate tissues faster than individuals with lower levels of SNO, e.g., muscle recovery during exercise, such as an exercise routine.

[0031] As used herein, "UO2" is the amount of oxygen used by tissue in a region of interest, measured using a nitric oxide-based calculation. The calculation is derived as the product of the PNO metric and the amount of oxygen utilized in the tissue.

[0032] As used herein, "maximal NO power" is a nitric oxide-based metric that reflects an individual's maximum energy utilization rate, directly related to the true measure of energy utilization rate in watts and derived from the maximum value of the PNO metric during exercise and the maximum energy utilization by muscle tissue; "maximal NO endurance" is a nitric oxide-based metric that reflects an individual's maximum rate of energy delivery, directly related to increases in critical power, and is the gold standard measure of endurance performance, derived from the maximum rate of change in the PNO metric generated during the rest period after exercise.

[0033] As used herein, a calculation or determination is made in "real time" that is available to the user within one minute of the corresponding measurement. In one implementation, the real time calculation is performed within 10 seconds of the measurement.

[0034] Spectroscopy has become a well-established and accepted technique for noninvasively measuring many biological parameters in vivo and in real time. In this context, spectroscopic analysis involves the use of one or more light sources that project electromagnetic energy at multiple wavelengths toward the tissue where measurements are desired, and one or more detectors to detect and measure the energy emitted from the tissue. The energy source is generally located outside the body and directed toward the tissue or body part of interest. The energy propagates through the skin and into subcutaneous tissue, including adipose tissue and skeletal muscle tissue. As the energy propagates through the body, it is absorbed and scattered, as would typically be the case in a turbid medium, creating a completely diffuse energy field. Typically, a portion of the scattered energy reaches the boundary between the tissue and the surrounding environment and propagates transcutaneously out of the tissue. This energy can be detected and measured by placing an appropriate sensor at the appropriate tissue boundary. The detected energy forms a spectroscopic signal that carries a signature of the material through which the energy passed. An important part of any spectroscopic system is how to process the spectroscopic signal, how to extract the signature, and how to interpret the signature.

[0035] By creatively selecting wavelengths used, which may be outside or inside the visible light spectrum, the characteristics and placement of the light source(s) and receiver(s), and the data processing techniques applied, a wide range of materials and chemical properties and concentrations can be detected and quantified. Quantifiable biomarker levels, chemical concentrations, chromophore concentrations, and tissue properties include, but are not limited to, oxygenated hemoglobin, deoxygenated hemoglobin, methemoglobin, carboxyhemoglobin, muscle oxygenation, muscle oxygen consumption, nitric oxide, blood nitric oxide activity, s-nitrosothiols, water, glycogen, adipose tissue thickness, melanin, and internal training load.

[0036] The embodiments discussed herein include spectrophotometric methods for in vivo quantification of biomarkers, chemicals, and chromophores in differentiated tissue layers and their associated blood supply. The disclosed embodiments rely on one or more body-worn sensors, such as the NNOXX wearable, to illuminate a desired tissue volume and then detect and measure the energy emitted from the tissue sample at distinct locations. Distributed localization of light sources and receivers enables techniques such as triangulation and time resolution to distinguish signals from specific locations within a multidimensional tissue sample. There are many ways in which the light source(s) and sensor(s) can be positioned and manipulated to enable many measurements. Each of the metrics described above corresponds to energy contained within a predefined spectral bandwidth of the emitted energy. The resulting generated energy spectrum can be analyzed in many ways to derive physical or chemical properties of the measured sample. The measured samples provide quantitative measurements of biomarkers including, but not limited to, oxyhemoglobin, deoxyhemoglobin, methemoglobin, carboxyhemoglobin, muscle oxygenation, muscle oxygen consumption, nitric oxide, blood nitric oxide activity, s-nitrosothiols, water, glycogen, adipose tissue thickness, melanin, and internal training load.

[0037] The integrated hardware and / or software system then receives information from the body-worn sensors and automatically provides biofeedback, such as biomarker, chemical, and chromophore levels in specified tissue layers or vascular networks. The disclosed embodiments process one or more algorithms that take both in vivo sensor measurements and probabilistic model data as input, thereby providing robust and accurate measurements of one or more physical characteristics of a subject. For example, assume an individual is exercising as a means of improving cardiovascular function. Such an individual may use the NNOXX wearable device or another spectrophotometric device to measure specific biomarker measurements, such as active nitric oxide levels in skeletal muscle tissue, to determine the most effective exercise intensity for achieving the individual's goals. Alternatively, a physician may use the spectrophotometric devices discussed herein to noninvasively measure a patient's blood biomarkers, including, but not limited to, nitric oxide, iron, cholesterol, blood oxygenation, and muscle oxygenation.

[0038] 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 Figures 5, 6, 12, and / or 13, each of which is described in detail below.

[0039] Consider a collimated beam of light passing through a bounded volume of a turbid medium. A turbid medium is a transparent or translucent material that absorbs and scatters any light that passes through it. Collimated beams can be used because the superposition of collimated beams of various intensities can mathematically describe the radiant intensity distribution of any light source. As a beam of light passes through a turbid medium, the energy in the beam is reduced by the processes of absorption and scattering according to equation (1): I(z)=I o exp(-(μ a +μ s )z) (1)

[0040] I in equation (1) orepresents the initial intensity of the beam as it enters the turbid medium. z represents the distance the beam travels in the turbid medium. I(z) is the intensity of the beam at distance z. μ a is equal to the absorption coefficient associated with the turbid medium, which describes the geometric rate at which energy is absorbed from the beam. s is equal to the scattering coefficient associated with the turbid medium, which describes the geometric velocity at which energy is scattered from the beam.

[0041] Equation (1) is the well-known Beer's law, which states that the intensity of a beam of light decreases at an exponential geometric rate due to the combined effects of absorption and scattering. The absorption coefficient μ a and the scattering coefficient μ s The sum of these is called the attenuation coefficient c, as shown in equation (2). The absorption and scattering processes are functionally and mathematically independent. μ a +μ s =c (2)

[0042] Next, we consider a collimated light beam as a bundle of parallel rays. Each ray represents a small portion of the beam's energy, and the superposition of many beams of varying intensity represents the light source. We consider absorption as a continuous process along the path of the ray, obeying Beer's law. Furthermore, scattering is a series of discrete events occurring at random locations along the path of the ray. The distance between scattering events is determined randomly according to equation (3). d=-ln(q) / μ s (3)

[0043] The variable d in Equation (3) represents the distance along the ray between successive scattering events. The variable q in Equation (3) is equal to a normally distributed random number between 0 and 1. The variable μ in Equation (3) s is equal to the scattering coefficient, which is a property of the medium in which the scattering event occurs.

[0044] The scattering phase function, also known as the volume scattering function, is the final parameter that needs to be defined. In the scattering phase function p(θ), the variable p is a probability density function used to predict the polar angles 0°<θ<180° through which the direction of the light ray changes as a result of a scattering event. In a three-dimensional model such as the one described herein, azimuthal angles uniformly distributed between 0°<φ<360° also need to be chosen probabilistically.

[0045] Rays through a turbid medium can be traced probabilistically if individual rays are selected probabilistically from individual beams through the turbid medium according to the source's radiance distribution. Because the selection of the rays determines the initial direction of the rays, and all rays start with the same energy, rays through the turbid medium can be traced probabilistically. Furthermore, by doing this for many individual rays, a plausible light field can be generated.

[0046] The model geometry is shown in Figure 2. Model 210 is three-dimensional with six degrees of freedom (x, y, x, a, b, c), where x, y, and z are position coordinates and a, b, and g are direction cosines. The media volume is a rectangular volume defined by upper and lower boundary surfaces, both of which have dimensions defined on the order of 100 mm x 100 mm. Model 210 is layered by defining horizontal interfacial planes that divide the rectangular volume into parallel horizontal plates or layers. While Figure 2 shows the media volume divided into two layers by a single interfacial plane, any number of interfacial planes and layers can be defined. It is common to define three layers: an upper layer representing skin, a middle layer representing fat, and a lower layer representing muscle. The optical properties and physical thickness of each layer can be defined independently. Multiple light sources (typically LEDs) and multiple photodetectors (typically photodiodes) can be positioned anywhere above the upper media boundary layer.

[0047] Figure 2 shows conceptual ray paths. Ray paths of interest 220 are those that terminate within the active area of ​​the detector. All other ray paths are considered lost due to scattering. Typical scattering coefficient values ​​for skin, fat, and muscle are on the order of 10 / mm to 50 / mm. This means that the characteristic distance between scattering events is on the order of 0.1 mm to 0.02 mm. For a detector positioned 25 mm from the source, the typical path distance for a ray reaching the detector is on the order of 100 mm to 150 mm. This means that a typical ray reaching the detector will have experienced on the order of 1000 to 7500 scattering events. This metric is a good indicator of how scattering the optical field is in a turbid medium.

[0048] Figure 3 shows two calculated ray paths 310. Both rays originate at point (0,0,0) and terminate at the upper boundary surface 25 mm away from the origin. These paths 310 show how typical ray paths can be twisted and intertwined.

[0049] The primary product generated by the stochastic model is the upwelling irradiance distribution incident on the upper medium boundary. This irradiance distribution is calculated by tracing rays on the order of 1.0e+09. Each ray is traced until it experiences 20,000 scattering events or until it intersects one of 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 the ray's intersection are calculated, and the boundary element on which the ray "landed" is identified. Various parameters associated with the ray are then stored in a file associated with this boundary element. Three of the many parameters calculated and stored for each ray include 1) the ray's residual post-absorption "energy," 2) the maximum depth to which the ray penetrates the medium, and 3) the geometric distance the ray traveled in its optical path from the light source to the upper boundary. After all rays have been traced, all ray "energy" values ​​for each boundary element can be summed to give a distribution of energy / unit area related to irradiance.

[0050] Once the irradiance distribution on the upper boundary surface has been calculated, it is possible to calculate the relative signal that would be received by a detector of any size placed anywhere on the upper boundary surface. This is called the relative signal because its units are (residual ray energy) / mm. When rays are selected at the light source, each ray is assigned an energy value of 1.0, and then as the ray passes through various layers of the turbid medium, this value decreases due to absorption. The irradiance value calculated for each element on the upper boundary surface is the sum of the residual or unabsorbed energy values ​​of the rays that intersect the upper boundary surface within the limits of that element. This allows the scattering loss to be calculated as follows: I 検出 =N*F*I o *exp(-Σμ a,i *L i ) (4) The variable I in equation (4) 検出 is equal to the "energy" incident on the active area of ​​one of the photodetectors. The variable N in equation (4) is equal to the total number of rays being traced. The variable I in equation (4) O is equal to the initial energy (1.0) assigned to each ray. The variable F in equation (4) is equal to the loss due to scattering. The variable μ in equation (4) a,i is the layer L i The variable L in equation (4) is equal to the absorption coefficient for i is equal to the path length or distance traveled by the ray through layer I. Finally, the variable exp(-Σμ a,i *L i ) is equal to the cumulative absorption loss. a,i By setting to 0, we obtain equation (5) and I o = 1, we can derive equation (6). I 検出 =N * F * I o (5) F=I 検出 / N (6)

[0051] A unique value of F can be calculated for each data collection element within the upper boundary surface. The value of F is calculated based on the scattering coefficient s i It depends on the values ​​chosen for μ, the scattering phase function chosen for each layer, and the thickness of each layer. All these inputs are chosen from the open literature. The important point is that the value of F is independent of absorption. Once the value of F is calculated, the model can be used to calculate μ a,i A useful process can be performed for all μ a,i a single value μ a Once this is done, we can write the following equation (7): Additionally, equation (7) can be rewritten as equation (8). I 検出 =N * F * I o * exp(-μ a *L) (7) L=(-ln(I 検出 / (N * F * I o ))) / μ a (8)

[0052] L in this case describes the characteristic distance traveled by a ray from the light source to the upper boundary surface. The value of L can be calculated for each element of the plane. By running the model multiple times and specifying a different unique value each time, (μ a , L) pairs of values ​​can then be calculated. Then, the mathematical relationship between a and L can be derived, and L can be calculated as μ a as a function of, or L=f(μ a ) Once the values ​​of the characteristic attenuation coefficient have been calculated for several wavelengths, the characteristic attenuation coefficient can be put into practice. Consider the following equation: μ a (λ)=2.3Σc i ε i (λ)+ΣV n μ n (λ) (12) This formula expresses the wavelength-dependent characteristic absorption coefficient μ a (λ), extinction coefficient ε i(λ) is expressed as a function of some biological parameters, in this case, the volume fraction and the concentration c i The application described herein focuses primarily on the time-varying optical and biological properties of the blood supply. Therefore, parameters that are very small or time-invariant can be set to zero. By doing so, equation (12) can be rewritten as follows: μ a (λ)=B(S μ a,HbO (λ)+(1-S)μ a,Hb (λ))+W μ a,水分 (λ) (13)

[0053] The variable m in equation (13) a (λ) is the characteristic absorption coefficient measured by the spectrometer. The variable μ in Eq. a,HbO (λ) represents the absorption coefficient for oxygenated hemoglobin. The variable μ in Equation (13) a,Hb (λ) is equal to the absorption coefficient for deoxygenated hemoglobin. The variables B, S, and W in equation (13) are equal to the blood volume fraction, oxygen saturation percentage, and water volume fraction, respectively. Finally, the variable μ in equation (13) a,水分 (λ) is equal to the absorption coefficient for water. Equation (13) contains three variables with unknown values: B, S, and W, all of which are wavelength independent. In a completely equivalent manner, equation (13) can be rewritten as follows: μ a (λ)=2.3 * c HbO * ε HbO (λ)+2.3 * c Hb * ε Hb (λ)+W μ a,水分 (λ) (14)

[0054] The variable c in equation (14) HbO and ε HbO are equal to the molar concentration and molar extinction coefficient of oxygenated hemoglobin, respectively. The variable c in Eq. Hb and εHb are equal to the molar concentration and molar extinction coefficient of deoxyhemoglobin, respectively. The variables W and μ in equation (14) a,水分 (λ) are equal to the water volume fraction and the absorption coefficient for water, respectively. Equation (14) is written with three variables (ε HbO (λ), ε Hb (λ), and μ a,水分 (λ) and three unknown but wavelength-independent variables (c HbO , c Hb , and W).

[0055] FIG. 1 is an image of an embodiment of a wearable optical device 100 that can be used to capture, process, and return noninvasively measured biomarker levels, chemical concentrations, and chromophore concentrations in different tissue layers. In some embodiments, device 100 can include a spectrometer that uses six individual light sources, each with a different spectral output. In the model image of FIG. 2, an exemplary embodiment of optical hardware light-emitting diodes (LEDs) and photodiodes used to create the array of light sources and receivers is illustrated relative to the model geometry. As an example of how the hardware of device 100 can be configured, FIG. 5 shows an exemplary embodiment of optical hardware LEDs 550, 560, 570, 580 and photodiodes 510, 520, 530, 540 that can be used to create the array of light sources and receivers. The LEDs in the array can be specified by wavelength and radiance for a specified result. The array of photodiodes can be physically positioned a certain distance from the light source for a specified result. It should be noted that while four LEDs and four photodiodes are illustrated in this example, it can be understood that any number of LEDs and / or photodiodes may be included (e.g., six LEDs, each with its own distinct spectral output). Additionally, FIG. 6 illustrates an exemplary embodiment of optical hardware used to control and capture light, process it, and store it in memory before wirelessly transmitting the data via Bluetooth. Such hardware may include, but is not limited to, a processor 610, memory 620, a Bluetooth transceiver 630, and / or an analog front end (AFE) 640. The light information may be stored in memory before, during, or after a computational process is applied. Once the desired result is achieved, the data may be stored in the device, shown via a connected screen, and transmitted in real time or at a later time via USB, Bluetooth, wireless, or any radio signal.

[0056] The LEDs in the array are specified by wavelength and radiance and are physically positioned at specific distances from the light source to achieve a specific result. By implementing six LEDs, each outputting a different wavelength or waveband, six independent equations similar to equation (13) or equation (14) can be constructed. These six equations can then be used to calculate the independent variables B, S, and W, or equivalently, c HbO , c Hb , and W values ​​can be calculated and then calculated over time using conventional least squares techniques.

[0057] In one specific exemplary embodiment, the wavelengths of the light sources and the positions of the detectors relative to the device are selected to optimize the sensitivity of the spectrometer to variations in oxygenated and deoxygenated hemoglobin concentrations. In this example, the LED light sources are closely spaced to minimize separation 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 was selected because it is close to the isosbestic wavelength of hemoglobin. At this wavelength, the received signal exhibits minimal fluctuations due to changes in oxygenated and deoxygenated hemoglobin concentrations. Furthermore, at this wavelength, the received signal provides a relatively direct indication of total hemoglobin concentration. The 655 nm and 760 nm wavelengths were selected to provide two light sources with peak output wavelengths less than the isosbestic wavelength. Similarly, wavelengths of 855 nm and 940 nm were selected to provide two light sources with peak output wavelengths greater than the isosbestic wavelength. The 535 nm wavelength was selected to provide a received signal extremely sensitive to variations in the ratio of oxygenated to deoxygenated hemoglobin concentrations. The 535 nm signal received by the photodiode located closest to the light source is dominated by the oxygenated / deoxygenated hemoglobin ratio, which is located near the surface of the illuminated tissue and can therefore be used for pulse oximetry. Light reflected or backscattered by the illuminated tissue is received by four light-sensitive photodiodes. These photodiodes are arranged in a linear array with individual photodiodes located at distances of 7 mm, 13 mm, 20 mm, and 28 mm from the geometric center of the LED cluster. The signal received by the 7 mm diode is strongly dominated by light reflected by tissue located near the illuminated surface. The signal received by a photodiode positioned at a greater distance from the light source(s) will be less strongly dominated by surface tissue and will exhibit characteristics that become more characteristic of deeper tissue (tissue located further from the surface) as the source-detector separation increases.By analyzing and comparing the individual signals from the four separate photodiodes, it is possible to isolate and identify the optical properties associated with the various tissue layers.

[0058] In a similar manner, by appropriately selecting the light wavelength of the light source, specifying the detector position, interpreting the received signal using the calculation results of the probabilistic model, and formulating 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, oxygenated hemoglobin, deoxygenated hemoglobin, oxygenated myoglobin, deoxygenated myoglobin, 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.

[0059] The hierarchical structure of the probabilistic 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. By running the model several times, for example, specifying a different value for adipose tissue layer thickness each time, it becomes possible to define the thickness of the adipose tissue layer as a function of optical parameters that can be measured. This link between adipose tissue layer thickness and measurable optical parameters makes it possible to empirically estimate tissue layer thickness using spectroscopic techniques.

[0060] 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 through a combination of probabilistic calculations and spectrophotometric measurements. Once the optical parameters are quantified, they can be used to empirically estimate the adipose tissue layer thickness via the graph 410 shown in Figure 4.

[0061] In summary, the advanced data collection device described herein can include six light sources and four independent optical sensors. The stochastic light propagation model is fully three-dimensional with six degrees of freedom, corresponding to a three-dimensional tissue model with spatially variable optical properties. The 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 photodetectors. Taken as a whole, this integrated set of capabilities provides the ability to measure the optical properties of individual layers of tissue, thereby mapping the volumetric distribution and variability of various biological parameters throughout a three-dimensional tissue sample.

[0062] 7 is a flowchart illustrating an exemplary method 700 used to implement a computational process for calculating values ​​of various biomarkers, biological properties, chemical concentrations, and / or chromophore concentrations in distinct tissue layers and their associated blood supplies, according to some embodiments of the present invention. Process 700 may be performed by one or more hardware and / or software systems, such as those described herein with respect to FIGS. 5, 6, 12, and / or 13 (e.g., processors and memory systems thereof). Process 700 may effectively utilize data for any number of wavelengths m from any number of sensors, such as n photodiodes. By performing a process such as process 700, a device may analyze and / or determine various physical, chemical, and / or biological properties of a subject.

[0063] At 702, a processor of a device performing process 700 can define locations, such as paths and file names, where digital input and output data, including measurement data as detected by one or more photodetectors and calculated data determined by a probabilistic model, is stored. At 704, a processor of a device performing a process such as process 700 can define a photodiode configuration. Such a configuration may include the number of photodiodes used, the physical layout and geometric location of the photodiodes, and / or the internal configuration of the photodiodes. At 706, a processor of a device performing process 700 can read data collected by the photodiodes, data calculated by the probabilistic model, and / or related data from other light sources. At 708, a processor of a device performing process 700 can begin processing in a manner consistent with the data read in 706. For example, the processor may evaluate the data according to the equations described above. In particular, in some embodiments where data for multiple wavelengths exists, a processor of a device performing process 700 can enter a loop in which data is selected and processed for each wavelength in succession. At 710, the processor of the device performing process 700 may select data associated with the particular wavelength being processed. For embodiments in which data exists for multiple sensors, such as multiple photodiodes, the processor of the device performing process 700 may enter a second loop at 712 in which data is selected and processed for each sensor in succession. At 714, the processor of the device performing process 700 may select data associated with the particular sensor being processed. At 716, the processor of the device performing process 700 may perform adjustments to the data, such as averaging, offset correction, and / or orthogonality correction. At 718, the processor of the device 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 the device performing process 700 may calculate biological parameters such as blood volume fraction, water volume fraction, and / or blood oxygenation percentage (see Equations 12-14 above for examples of such calculations).

[0064] Biological substances, chemical concentrations, and chromophore levels are not uniformly distributed throughout the human or mammalian body. Therefore, the ability to measure biomarker, chemical, and chromophore levels in different tissue layers provides advantages over older measurement techniques. The spectrophotometric measurement techniques described herein therefore enable new and more effective ways 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 measurements to the microcapillaries within muscle tissue. The reason for this is that oxygen saturation varies significantly between different regions of the mammalian circulatory system. For example, arteries, small arteries, capillaries, small veins, and veins all have different oxygen saturations under normal physiological conditions. Figure 8 illustrates SmO2 measurements on an exercising individual using the aforementioned measurement methodology. Muscle oxygenation (SmO2) reflects the dynamic balance between muscle oxygen supply and oxygen utilization during exercise. Therefore, during muscle contraction, when oxygen demand replaces supply, SmO2 decreases. During muscle contraction, when oxygen supply replaces demand, SmO2 increases. By measuring a subject's muscle oxygenation, insights can be gained into the key determinants of exercise performance, namely, oxygen supply and oxygen utilization. SmO2 data can then be used to design personalized exercise protocols to improve the subject's health and fitness.

[0065] 8 is a graph 800 illustrating an exemplary embodiment providing muscle oxygen saturation (SmO2) measurements for a subject during exercise. The vertical axis represents muscle oxygenation level 802 on a scale of zero to 100 percent, and the horizontal axis represents the duration of exercise 804 in seconds. The muscle oxygenation level at each time point is indicated by a solid black line on the graph.

[0066] Other biometric parameters that can be measured noninvasively using the spectrophotometric techniques described herein include, but are not limited to, muscle oxygen consumption, nitric oxide, oxyhemoglobin, deoxyhemoglobin, oxymyoglobin, deoxygenated myoglobin, carboxyhemoglobin, methemoglobin, glycogen concentration, water, potassium, iron, bile, melanin, and adipose tissue thickness.

[0067] FIG. 9 is a chart 900 illustrating muscle oxygen consumption (mVO2) measurements for an individual during exercise. The vertical axis represents muscle oxygen consumption level 902, and the horizontal axis represents the duration of exercise 904 in seconds. The muscle oxygen consumption level at each time point is represented by a solid black line on the graph. Muscle oxygen consumption (mVO2) is low before exercise, increases during exercise, and returns to baseline levels after exercise. Muscle oxygen consumption (mVO2) is strongly correlated with VO2, the gold-standard fitness measure recorded by laboratory-grade metabolic analyzers. Therefore, muscle oxygen consumption can be used to quantify an individual's current level of fitness.

[0068] FIG. 10 is a chart 1000 illustrating an individual's levels of reactive nitric oxide (reactive nitric oxide), also known as s-nitrosothiol, during exercise. The vertical axis represents reactive nitric oxide levels, and the horizontal axis represents the duration of exercise in seconds. The levels of reactive nitric oxide at any given time point are represented on the graph by a solid black line. Reactive nitric oxide is a measure of nitric oxide released from circulating red blood cells during exercise. Reactive nitric oxide dilates blood vessels, resulting in increased blood flow and oxygen delivery to the brain, heart, and exercising muscles. Therefore, higher levels of reactive nitric oxide are associated with improved physical fitness, better cognitive health, and a reduced risk of Alzheimer's disease and cardiovascular disease. Additionally, measuring a subject's levels of reactive nitric oxide during exercise can determine the optimal type, amount, intensity, duration, and frequency of exercise to improve the biomarker parameters, thereby enhancing the subject's health, fitness, and physical performance.

[0069] FIG. 11 is a chart 1110 illustrating an individual's internal training load during exercise and illustrating the relationship between internal training load and external training load (ETL) in a scatter plot 1120. The vertical axis represents internal training load, and the horizontal axis represents the duration of exercise in seconds. Internal training is represented by the solid black line on the graph. Internal training load (ITL) can only be quantified by isolating spectrophotometric measurements of the muscle tissue layer and its associated blood supply. The reason for this is that internal training load quantifies the total metabolic work performed by active skeletal muscle during exercise. Internal training load is strongly related to external training load, which is defined as the physical amount performed by a subject during exercise, as illustrated in FIG. 8. However, internal training load has many advantages over external training load. For example, internal training load (ITL) measurements can be used to quantify an individual's energy expenditure and total workload during exercise. Additionally, quantifying internal training load allows individuals to reduce injury risk and personalize their exercise training regimens in unprecedented ways, resulting in greater improvements in fitness.

[0070] Utilizing the embodiments described herein, a subject is provided with the ability to noninvasively measure several biomarker measurements in differentiated tissue layers and the blood supply associated with those differentiated tissue layers. Specifically, measuring biometric and physiological parameters at discrete locations within the body can provide unprecedented insight into a subject's health, strength, and physical performance. Furthermore, such biomarker measurements can be used to effect beneficial changes in a subject's physical state. For example, a subject may use such muscle oxygen consumption and active nitric oxide measurements to determine the optimal type, amount, intensity, duration, and frequency of exercise to elicit a desired physiological response, such as improved endurance, reduced risk of injury, or enhanced cognition.

[0071] FIG. 12 illustrates an exemplary measuring device 1200. The measuring device 1200 described herein may be wearable by a patient. For example, the measuring device 1200 may be an embodiment of the device 100 described above and may include the physical structure depicted in FIGS. 5 and 6, as described above. The measuring device 1200 may include one or more light sources 1201 (e.g., light-emitting diodes, organic light-emitting diodes, lasers, or a single light source producing multiple wavelengths). For example, the light source 1201 may include the LEDs 550-580 of the embodiment of FIG. 5. The one or more light sources 1201 may form an array. Control circuitry and / or a processor may control the one or more light sources 1201. For example, one or more of the systems 100 and / or 200 may be embedded within, coupled to, or in communication with the measuring device 1200. Thus, the processor 112 / 214 may control the one or more light sources 1201. One or more light sources 1201 may generate light at multiple wavelengths.

[0072] The measuring device 1200 may include one or more optical receivers 1202 (e.g., photodiodes or optical sensors). For example, the optical receivers 1202 may include photodiodes 510-540 in the embodiment of FIG. 5. The one or more optical receivers 1202 may form an array. The one or more optical receivers 1202 may be positioned at a predetermined distance from the one or more light sources 1201. The one or more optical receivers 1202 may function as one or more of the sensor(s) 102 / 202 of the system 100 and / or 200. Information received from the one or more optical receivers 1202 may be converted in an analog-to-digital converter. The measuring device 1200 may include one or more processors for processing data from the one or more optical receivers 1202 (e.g., processor 112 / 214). The measuring device 1200 may include a communication interface (e.g., Bluetooth, WiFi, 5G, etc.) for communicating data to an external system and / or processor. The measurement device 1200 can include one or more memory devices (eg, computer-readable media 110 / 212) for storing data.

[0073] The measurement device 1200 may include a user interface including a display, tactile, and / or audio output 1203 for presenting data on the device. Alternatively, the data may be presented on an external system. For example, the measurement device 1200 may communicate with a personal computer or mobile device via a communications interface and output to a user via the personal computer or mobile device. In this example, the personal computer or mobile device may function as systems 100 and / or 200. The external device may be configured to print at least a portion of the data.

[0074] 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 sensor(s) 102 / 202. The data collected by the additional sensing elements may provide enhanced measurement and post-processing analysis.

[0075] Operation of the measuring device 1200 may include initiating a data capture sequence. The data capture sequence may include activating one or more light sources 1201 in a timed on-off sequence. The data capture sequence may include activating one or more light receivers 1202 at predetermined distances in conjunction with the activation of the one or more light sources 1201. Different wavelengths of generated light may be configured to target different substances within the body. Different predetermined distances between the light receiver 1202 and the light source 1201 may target different depths within the body. As an example, the light source 1201 may be operable to emit light as described in detail above. The measuring device 1200 may capture signals from the one or more light receivers 1202. The captured signals may be preprocessed (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.

[0076] 13 is a schematic block diagram illustrating an exemplary system 1300 of hardware components capable of implementing example systems and methods disclosed herein. System 1300 may include various systems and subsystems. System 1300 may include one or more of a personal computer, a laptop computer, a mobile computing device, a workstation, a computer system, an appliance, an application-specific integrated circuit (ASIC), a server, a server BladeCenter, a server farm, etc. In some embodiments, system 1300 may be part of or in communication with device 100 described above, or system 1300 may include or in communication with the physical structures depicted in FIGS. 5 and 6, as described above.

[0077] The 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 input devices 1318 (e.g., a keyboard, touch screen, and / or a mouse). The system bus 1302 may be in communication 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 standalone database, or other non-volatile memory, may also be in communication 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 a universal serial bus (USB) port.

[0078] The processing unit 1304 may be a computing device and may include an application specific integrated circuit (ASIC). The processing unit 1304 executes a set of instructions to implement the example operations disclosed herein. The processing unit may include a processing core.

[0079] Additional memory devices 1306, 1308, and 1310 can store data, programs, instructions, database queries, in text or compiled form, and any other information that may be necessary for the computer to operate. Memories 1306, 1308, and 1310 can be implemented as computer-readable media (integrated or removable), such as memory cards, disk drives, compact discs (CDs), or servers accessible over a network. In particular examples, memories 1306, 1308, and 1310 can comprise text, images, video, and / or audio, portions of which may be available in a human-understandable format.

[0080] Additionally or alternatively, the system 1300 can access external data or query sources via a communication interface 1312 that can communicate with the system bus 1302 and a communication link 1314 .

[0081] In operation, system 1300 can be used to implement one or more portions of a system according to the present embodiments, such as the systems described in detail above. Computer-executable logic for implementing the diagnostic system resides in system memory 1306 and one or more of memory devices 1308 and 1310, according to a particular example. Processing unit 1304 executes one or more computer-executable instructions obtained from system memory 1306 and memory devices 1308 and 1310. As used herein, the term "computer-readable medium" refers to any medium that participates in providing instructions to processing unit 1304 for execution. This medium may be distributed among several separate assemblies all operatively connected to a common processor or set of related processors.

[0082] In some embodiments, the above-described systems and methods can be used in and / or with diagnostic systems, more particularly diagnostic systems related to the noninvasive measurement of endogenous S-nitrosothiols. Those skilled in the art will recognize that the above-described systems and methods can be used, for example, to obtain and / or process measurements such as those described below.

[0083] Nitric oxide (NO) has been associated with many physiological effects, including smooth muscle relaxation, vasodilation, inflammatory responses, and inhibition of platelet adhesion and aggregation. Identifying natural reservoirs of NO and methods to regulate the levels of bioavailable NO and alternative bioactive forms of this NO may provide a means to control these physiological effects. Nitric oxide (NO) and S-nitrosothiols (SNOs) are transported by hemoglobin along with oxygen. SNOs are the bioactive form of NO and are the only endogenously active form of NO that can survive in blood because NO itself cannot escape from red blood cells. SNOs are released from hemoglobin in tissues, for example, under hypoxia or during exercise, to dilate blood vessels and thereby oxygenate the tissue. Thus, SNOs released from RBCs control microvascular blood flow in tissues; without SNOs, tissues cannot be oxygenated (Zhang PNAS 2015; Premont Circ Res 2019). Therefore, SNO levels are an important component of VO2 (the amount of oxygen consumed by tissues). Although noninvasive means are available to detect oxygenated hemoglobin, no such means are available to detect endogenous levels of NO or SNO.

[0084] The amount of oxygen an individual consumes (VO2) is currently the gold standard measurement of physical fitness used by physicians and physiologists worldwide. VO2 represents the combined capacity of the pulmonary, cardiovascular, and muscular systems to take in, transport, and consume oxygen. Traditional systems and methods for measuring VO2 are invasive and / or require carefully controlled conditions. For example, traditional VO2 measurements require athletes to wear a mask in a lab, and measurement tools can cost over $35,000.

[0085] The embodiments described herein take advantage of the fact that nitric oxide release during exercise determines how much oxygen is available for muscle use, and therefore monitor an individual's nitric oxide levels to determine physical fitness and / or other physiological characteristics. One value that can be measured, called personalized nitric oxide (PNO), is a measurement of how much active nitric oxide is released from circulating red blood cells during exercise. Because active nitric oxide, represented by S-nitrosothiols in the blood, opens blood vessels that deliver oxygen to tissues, including the heart and brain, a patient's nitric oxide levels are strongly related to the patient's health. By monitoring this metric, as well as other metrics described below, the system can determine how much nitric oxide the locomotor system released in response to exercise, as well as how hard the locomotor system needs to exercise, how long the patient should exercise, and what style of exercise is best suited for the patient to provide improvements in strength, performance, and health. The measured oxygen saturation in small blood vessels in muscle is relative to the individual and the context, and therefore the PNO metric derived from this measurement is relative across patients and contexts, although it will be appreciated that while measurements can be relative, they are only relative to a certain extent, and that an individual's PNO measurement can be used as a reliable indicator of the individual's health and fitness.

[0086] The blood volume and oxygen saturation sensors used to calculate UO2 are portable, lightweight, and less than 5 percent of the devices currently on the market for VO2 measurement. Furthermore, standard VO2 testing and measurement tools rely on exhaled gas concentrations to measure whole-body oxygen consumption. However, by making these primary measurements, they miss important information, such as what metabolic processes are occurring in the muscles. As a result, they cannot reveal why an individual's VO2 max is not elevated. Because UO2 measurements are performed at the muscle level (affected by nitric oxide concentrations), they not only measure oxygen consumption but also enable the determination of rate-limiting factors for increasing oxygen consumption (such as insufficient blood flow or insufficient muscle oxygen use). This not only provides a diagnostic measurement tool for physical fitness, but also indicates what exercise prescriptions are needed to improve health and fitness. For example, the origin of changes in UO2 can be determined from a time series of measurements and identified as either limited oxygen supply, manifested as limited blood flow, or limited oxygen utilization, manifested as a deficit in muscle function.

[0087] FIG. 14 illustrates an example of a system 1400 for generating a value representative of endogenous S-nitrosothiol content of tissue within a region of interest in a subject. It will be appreciated that system 1400 can noninvasively and simultaneously determine a value representative of endogenous S-nitrosothiol content of tissue in real time. This can be used to match the S-nitrosothiol content of tissue and metrics derived therefrom to subject effects or biometric parameters during or after exercise, or to other physiological or external obstructions to blood flow from muscle tissue. System 1400 can include a set 1402 of at least one sensor that noninvasively measures a biometric parameter within the region of interest to provide a time series of measurements of at least one of the biometric parameters. In one example, where the biometric parameter includes blood volume and oxygen saturation, sensor set 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 measures both oxygen saturation and blood volume using near-infrared spectroscopy to determine blood flow according to changes in total hemoglobin concentration and oxygen saturation. It will be appreciated that the set of sensors 1402 can include additional sensors that record biometric parameters generally within the region of interest of the subject.

[0088] Each of the sensor interface 1404, the predictive model 1406, and the user interface 1408 may 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 may receive a time series of measurements of biometric parameters from the set of sensors 1402 and prepare the data for use in the predictive model 1404. The predictive model 1404 may also utilize data about the subject stored on the computer-readable medium 1410, including, for example, age, gender, genomic data, nutritional information, medication intake, and relevant medical history, as well as any other measured physiological parameters.

[0089] The predictive 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 a continuous or classification parameter to the region of interest that represents the amount of endogenous S-nitrosothiols present in the region of interest. When multiple classification or regression models are used, an arbitration element can be utilized to provide consistent results from the multiple models. The training process for a given classifier varies depending on the implementation of the training process, but training generally involves statistical aggregation of training data into one or more parameters associated with output classes. For rule-based models such as decision trees, domain knowledge, such as that provided by one or more human experts, can be used in place of or to supplement the training data when selecting rules for classifying users using extracted features. Any of a variety of techniques can be utilized 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).

[0090] For example, an SVM classifier can conceptually divide boundaries in an N-dimensional feature space using multiple functions called hyperplanes, with each of the N dimensions representing one associated feature of the feature vector. The boundaries can define a range of feature values ​​associated with each class. Thus, for a given input feature vector, a continuous or categorical output value can be determined according to the input feature vector's location in the feature space relative to the boundaries. In one implementation, an SVM can be implemented via a kernel method using a linear or nonlinear kernel. A trained SVM classifier can converge to a solution where the optimal hyperplane has a maximized margin for the associated feature.

[0091] An ANN classifier may include multiple nodes with multiple interconnections. Values ​​from a feature vector may be provided to multiple input nodes. Each input node may provide these input values ​​to one or more layers of hidden nodes. A given hidden node may receive one or more output values ​​from a previous node. The received values ​​may be weighted according to a set of weights established during training of the classifier. The hidden node may convert the received values ​​into a single output according to a transfer function at the node. For example, the hidden node may sum the received values ​​and subject the sum to a normalized linear function. The output of the ANN may be a continuous output value or a classification output value. In one example, the final layer of nodes provides confidence values ​​for the output classes of the ANN, with each node having an associated value representing the confidence in one of the classifier's associated output classes. The confidence values ​​may be based on a loss function, such as a cross-entropy loss function. The loss function may be used to optimize the ANN. In one example, the ANN may be optimized to minimize the loss function.

[0092] Many ANN classifiers are fully connected and feedforward. However, convolutional neural networks contain convolutional layers in which nodes from previous layers are connected to only a subset of nodes in the convolutional layer. Recurrent neural networks are a class of neural networks in which the connections between nodes form a directed graph along a time sequence. Unlike feedforward networks, recurrent neural networks can incorporate feedback from states caused by previous inputs, such that the output of a recurrent neural network for a given input can be a function of not only the input but also one or more previous inputs. As an example, long short-term memory (LSTM) networks are a modified version of recurrent neural networks that facilitates remembering past data.

[0093] A rule-based classifier may apply a set of logical rules to extracted features to select an output class. The rules are applied in order, and the logical outcome at each step may influence the analysis at later steps. The specific rules and sequence of rules may be determined from any or all of the training data, analogical reasoning from previous cases, or existing domain knowledge. One example of a rule-based classifier is a decision tree algorithm, which selects a class for a feature vector by comparing the values ​​of features in a feature set with corresponding thresholds in a hierarchical tree structure. A random forest classifier is a modification of the decision tree algorithm that uses a bootstrap aggregation, or "bagging," technique. In this technique, multiple decision trees may be trained on random samples of the training set, and an average (e.g., mean, median, or mode) result across the multiple decision trees is returned. For classification tasks, the results from each tree are categorical, and therefore the mode result can be used.

[0094] The output of the predictive 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 such amounts. The output of the predictive model 1406 can be stored, for example, in an electronic health record database and / or provided to a user on an associated display via the user interface 1408.

[0095] FIG. 15 illustrates another example of a system 1500 for noninvasively generating values ​​representative of endogenous S-nitrosothiol content in tissue within a region of interest of a subject in real time. In one example, the tissue is muscle tissue, the endogenous S-nitrosothiol is endogenous S-nitrosothiol generated from hemoglobin, and the amount of endogenous S-nitrosothiol is determined during muscle exercise or after physiological or external obstruction of blood flow from the muscle tissue. The system 1500 may include a near-infrared spectroscopy (NIRS) sensor 1502 that noninvasively measures 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 spectroscopy sensor may include a strap such that the sensor is strapped to the skin. The strap may be flexible and / or elastic. As an example, the sensor may be incorporated into a wristband. Those skilled in the art will recognize that a strap or any other garment element may be configured to link a measurement device to any relevant area of ​​interest as described herein.

[0096] A nitric oxide (NO) calculation assembly 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 assembly 1510 may include a sensor interface 1522, a regression model 1524, and a user interface 1526. The sensor interface 1522 may receive a time series of blood volume measurements and a time series of oxygen saturation measurements from a NIRS sensor and prepare the data for use in the regression model 1524.

[0097] The regression model 1524 may determine a 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 fitted with a best-fit line. In this example, the parameter provided is the slope of the best-fit line, and the amount of endogenous S-nitrosothiol in the tissue is derived from this slope. In another example, the parameter is derived from the correlation coefficient between oxygen saturation and blood volume. This 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 in real-time calculations of other metrics representative of the subject's health and fitness.

[0098] 16 is a chart 1600 illustrating PNO levels for a patient during exercise. The vertical axis 1602 represents PNO levels, and the horizontal axis 1604 represents the duration of exercise in seconds, with the PNO levels at each time shown as shaded areas 1606 on the graph. The patient was given a rest period 1608 of approximately 100 seconds. It can be seen that the PNO levels 1606 remained level during this period and for a short period thereafter.

[0099] As mentioned above, VO2 is the gold standard metric of physical fitness used by physicians. Traditionally, VO2 measurement requires invasive testing and expensive lab equipment, but system 1500 allows this measurement to be performed noninvasively within local tissue, making system 1500 usable during activities of daily living (defined as UO2). FIG. 17 is a chart 1700 illustrating UO2 levels for a patient during exercise. Vertical axis 1702 represents UO2 level, and horizontal axis 1704 represents the duration of exercise in seconds, with the UO2 level at each time shown as a shaded area 1706 on the graph. The patient was given a rest period 1708 of approximately 100 seconds. It can be seen that the UO2 level 1706 dropped sharply during the rest period.

[0100] The UO2 measurement is a nitric oxide-related metric of local muscle oxygen consumption that is designed to behave similarly to a true VO2 measurement. Specifically, UO2 can be generated as a function of local nitric oxide measurements and blood flow to represent a metric of usable oxygen available to muscle tissue in a region of interest. As shown in Figures 18 and 19, the UO2 measurement is an excellent surrogate for VO2, and UO2 can be measured using sensors that are approximately 100 times cheaper than even low-cost VO2 measurement devices.

[0101] FIG. 18 depicts a chart 1800 of a time series of UO2 measurements 1802 and a time series of VO2 measurements 1804 for an athlete operating a full-body exercise bicycle using a single sensor to record 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 elapsed time. A very strong correlation (r=0.95) can be seen between the measured UO2 1802 and the measured VO2 1804. From this, it should be clear that an estimated VO2 max can be estimated from the UO2 measurements. The estimated VO2 max in this figure is 68 ml / kg / min, derived from the UO2.

[0102] 19 depicts a chart 1900 of a time series of UO2 measurements 1902 and a time series of VO2 measurements 1904 for an athlete operating a full-body exercise bicycle using two sensors on different limbs to record blood volume and oxygen saturation. 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. An extremely strong correlation (r=0.95) can be seen between measured UO2 1902 and measured VO2 1904.

[0103] Active nitric oxide levels reflect oxygen delivery, and the better the oxygen delivery and utilization, the better the performance. Maximum NO power and maximum NO endurance are nitric oxide-related measurements that can be produced by the system that correlate strongly with an individual's actual power output and maximum endurance levels.

[0104] 20 depicts a chart 2000 of an athlete's maximum NO power recorded weekly over a six-month period. The left vertical axis 2002 represents maximum NO power in arbitrary units, the right vertical axis 2004 represents the athlete's maximum power output in watts, and the horizontal axis 2006 represents time elapsed in weeks. As the athlete increases their fitness, as measured by an increase in maximum power output in watts 2008, the athlete's maximum NO power 2010 also increases. The very strong correlation (R2=0.95) between the measured maximum NO power 2010 and maximum power output 2008 establishes maximum NO power as a good biomarker of performance.

[0105] 21 depicts a chart 2100 illustrating the relationship between improvement in maximum NO endurance for a group of 21 athletes and the athletes' critical power (the gold standard for endurance measured in watts) recorded over a six-week period as a scatter plot. Vertical axis 2102 represents the percentage improvement in maximum NO endurance for the 21 athletes, and horizontal axis 2104 represents the improvement in the athletes' critical power in watts. As can be seen from the chart, there is a significant correlation between improvement in maximum NO endurance and improvement in critical power, establishing maximum NO endurance as a non-invasive measure of endurance.

[0106] It will be appreciated that specific exercise and other therapies can be prescribed to a patient based on their values ​​for these metrics. For example, individuals with high maximum NO power relative to maximum NO endurance are able to extract oxygen from blood and utilize this blood at a greater rate than can deliver it to skeletal muscles. These individuals will see the best benefits from their exercise regimen by performing lower intensity, longer duration exercise in a continuous manner. For example, a 28-year-old Mr. Jones with a maximum NO power of 6 and a maximum NO endurance of 3 could be prescribed 20 minutes of running at 50-55% of his maximum UO2 three days a week. This would be expected to improve Mr. Jones's maximum NO endurance over the course of several weeks.

[0107] Alternatively, individuals with high maximal NO endurance relative to maximal NO power are able to supply oxygen to working muscles at a rate much faster than oxygen can be extracted from the blood and utilized for energy production. These individuals will see the best benefits of an exercise regimen by performing high-intensity, short-duration work periods interspersed with rest periods. In these cases, there will be very large, steep increases in UO2 with relatively low points between exercise periods. For example, Mrs. Benneton, a 48-year-old cyclist with a maximal NO endurance of 7 and a maximal NO power of 2.5, could be prescribed sprints at near-maximal intensity two days a week until UO2 stops rising, followed by six sets of three minutes of rest. This would be expected to improve both Mrs. Benneton's maximal NO power and maximal UO2 over the course of several weeks.

[0108] In another example, an individual with a high PNO level in their upper body compared to their lower body is instructed to redistribute their power output to reduce the amount of work their upper body is doing and increase the amount of work their lower body is doing. By doing so, the individual increases PNO levels in their lower body, leading to greater whole-body PNO values ​​that can be sustained for longer durations. This increases oxygen delivery to the brain, heart, and muscles. In another example, an individual suffering from early-onset Alzheimer's disease can be assigned exercise to improve their PNO level, which improves blood flow to the brain. For example, Mrs. Levy, a 70-year-old with early-onset Alzheimer's disease, could be prescribed a daily 30-minute walking exercise with the goal of increasing Mrs. Levy's PNO by 5. Mrs. Levy could be prescribed a daily 30-minute cycling routine that is titrated over the course of practice to increase Mrs. Levy's PNO to 15. At six months, improvement in both Mrs. Levy's memory and Mrs. Levy's baseline PNO would be expected.

[0109] As discussed, VO2 is currently the gold standard measurement of physical fitness used by physicians and physiologists worldwide and represents the integrated ability of the pulmonary, cardiovascular, and muscular systems to take in, transport, and consume oxygen. UO2 measurements are nitric oxide-related metrics of local muscle oxygen consumption designed to behave similarly to true VO2 measurements. Specifically, UO2 can be generated as a function of local nitric oxide measurements and blood flow to represent a metric of usable oxygen available to muscle tissue in a region of interest. As shown in Figures 5 and 6, UO2 measurements are excellent surrogates for VO2.

[0110] Individuals with Alzheimer's disease are known to have low VO2. VO2 is highly dependent on microvascular blood flow, and nitric oxide from red blood cells controls blood flow. Therefore, it stands to reason that nitric oxide-based measurements, described herein as UO2, track VO2 and, therefore, PNO can be used to predict VO2 max in individuals. Accordingly, in another embodiment, the present invention provides a method for determining risk of Alzheimer's disease or early-onset disease by using UO2 as a biomarker. If their UO2 (and PNO) is improved, improvement or protection against Alzheimer's disease will occur. In one aspect, the present invention provides a method for determining risk of diseases associated with reduced blood flow, such as dementia or other declines in cognitive function associated with blood flow, or cardiovascular / cardiometabolic disease. For example, individuals with Alzheimer's disease are prescribed an exercise regimen, and their UO2 measurements are taken over time to determine improvement in the disease. For example, UO2 is measured at a starting time point before the exercise regimen is initiated, and at a second time point (and optionally further time points) to determine whether there is an increase in UO2 values, e.g., reflecting an improvement in cognitive function or dementia. Other complementary tests, including cognitive tests known to those skilled in the art, can be used to further assess improvement in an individual's disease state.

[0111] In another example, Mr. Jack is a 60-year-old businessman with heart disease. He could be prescribed a 30-minute daily exercise regimen to improve his PNO level of 13. In this example, Jack's exercise regimen could be increased to 40 minutes, accompanied by a doubling of his PNO over time, representing an increased ability to provide oxygenated blood to the heart muscle. In another example, Mrs. Stevenson is a sedentary mother of three young children who struggles to perform daily activities with a maximum UO2 of 43. Mrs. Stevenson is prescribed an exercise regimen consisting of 20-30 minutes of moderate-intensity exercise at 50-60% of Mrs. Stevenson's maximum UO2 on day 1, and three 5-minute exercise periods at 75-85% of Mrs. Stevenson's maximum UO2 on day 2. This would be expected to increase Mrs. Stevenson's strength and energy, as well as her maximum UO2. Similarly, in another example, Mr. James is a 60-year-old businessman with diabetes. James's baseline blood glucose level was 200. James could be prescribed a 20-minute daily exercise regimen to improve James's PNO level of 12. In this example, James' exercise regimen could be increased to 40 minutes, with a doubling of PNO over time representing an increased ability to provide oxygenated blood to the muscles and lower James' resting blood glucose level.

[0112] Given the above structural and functional features, exemplary methods may be better understood with reference to Figures 22-24. For ease of explanation, the exemplary methods of Figures 22-24 are shown and described as being performed sequentially; however, it should be understood and appreciated that the examples are not limited by the illustrated order, as some operations may be performed multiple times and / or simultaneously in other examples in a different order than shown and described herein. Furthermore, not all operations described need be performed to implement the method. Each of these methods may be performed, for example, by system 1400 of Figure 14 and / or system 1500 of Figure 15.

[0113] FIG. 22 illustrates an example of a method 2200 for generating a value representative of endogenous S-nitrosothiol content of tissue within a region of interest of a subject. At 2202, biometric parameters may be noninvasively measured by sensor(s) 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 taken while the subject is engaged in exercise. In another example, these measurements are taken during a rest period after the subject has engaged in exercise. In a further example, measurements may be taken immediately after a physiological or external obstruction of blood flow to the region of interest or a physiological deprivation of oxygen. In one implementation of this example, an overshoot response in one of blood flow and oxygen saturation above baseline after induced hypoxia is measured to provide one of a time series of oxygen saturation measurements and a time series of blood volume measurements, and a predictive model uses the overshoot value or the rate value to generate a value representative of endogenous S-nitrosothiol content of tissue within the region of interest.

[0114] At 2204, values ​​representing the endogenous S-nitrosothiol content of tissue within the region of interest can be generated by the processor 1412 / 1514 from the time series via the predictive model 1406 / 224. 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 values ​​are determined according to this linear relationship. For example, the two time series can be submitted to a linear regression model to provide a best-fit line between oxygen saturation and blood volume over time, and 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 2206, the values ​​representing the endogenous S-nitrosothiol content of tissue within the region of interest can be stored by the processor 1412 / 1514 in memory implemented as a non-transitory computer-readable medium 1410 / 1512. In one example, method 2200 can be performed before and after a therapy is provided to a subject, and the effect of the therapy on endogenous S-nitrosothiol content in a region of interest can be determined by comparing values ​​generated after the therapy with stored values ​​generated before the therapy. The stored values ​​can also be used to generate one or more of a maximum nitric oxide endurance metric, a maximum nitric oxide power metric, a usable oxygen consumption metric, and a personalized nitric oxide metric for the subject.

[0115] FIG. 23 illustrates another example of a method for generating values ​​representative of endogenous S-nitrosothiol content of tissue within a region of interest of a subject. At 2302, blood volume and oxygen saturation within the region of interest of the subject may be noninvasively measured by sensor(s) 1402 / 1502 to provide a first time series of oxygen saturation measurements and a second time series of blood volume measurements. For example, both blood volume and oxygen saturation in the tissue may be determined by near-infrared spectroscopy. In one example, these measurements are taken while the subject is engaged in exercise. At 2304, a linear relationship between the first and second time series may be determined by the processor 1412 / 1514 via a predictive model 1406 / 224. For example, the two time series may be provided to a linear regression model to provide a best-fit line between oxygen saturation and blood volume over time.

[0116] At 2306, a value representing the endogenous S-nitrosothiol content of the tissue in the region of interest may be generated by the processor 1412 / 1514 from the linear relationship between the first and second time series. In one implementation in which the linear relationship is represented as a best-fit line between the first and second time series, the value representing the endogenous S-nitrosothiol content of the tissue in the region of interest may be derived from the slope of the best-fit line. At 2308, the value representing the endogenous S-nitrosothiol content of the tissue in the region of interest may be stored by the processor 1412 / 1514 in memory implemented as a non-transitory computer-readable medium 1410 / 1512. The stored values ​​may also be used to generate one or more of a maximum nitric oxide endurance metric, a maximum nitric oxide power metric, a usable oxygen consumption metric, and a personalized nitric oxide metric for the subject.

[0117] 24 illustrates a further method 2400 for generating values ​​representative of endogenous S-nitrosothiol content of tissue in a region of interest of a subject. At 2402, the subject is instructed to engage in aerobic exercise. In one example, the subject could be instructed to cycle on an exercise bicycle. At 2404, blood volume and oxygen saturation in the region of interest of the subject's muscle affected by the exercise can be noninvasively measured by sensor(s) 1402 / 1502 to provide a first time series of oxygen saturation measurements and a second time series of blood volume measurements.

[0118] At 2406, at least one of the time series of blood volume and the time series of oxygen saturation within the region of interest may be refined by the processor 1412 / 1514 to remove external influences. Blood flow in tissue 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 may be refined to remove the effects of these factors. For example, most aerobic exercise involves repeated muscle contractions at somewhat predictable intervals. This decrease in blood volume may be quantified as a periodic signal, represented as a separate time series, which may be removed from the time series of blood volume measurements. Other physiological effects on either or both of blood volume and oxygen saturation vary with position and specific motion, and these nonlinear 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.

[0119] At 2408, a statistical process may be performed by the processor 1412 / 1514 to identify a linear relationship between blood flow derived from the time series of blood volume measurements and oxygen saturation, and a value representing the endogenous S-nitrosothiol content of the tissue within the region of interest is generated from the linear relationship. For example, a linear regression analysis may be performed, and the slope of the best-fit line generated by the regression analysis may be used to quantify the linear relationship between blood flow and oxygen saturation. A correlation coefficient between the two parameters may also be generated and used to evaluate a linear relationship where muscle is particularly oxygen-deprived and the slope cannot be easily determined. At 2410, the value representing the endogenous S-nitrosothiol content of the tissue within the region of interest may be stored by the processor 1412 / 1514 in memory embodied as a non-transitory computer-readable medium 1410 / 1512.

[0120] FIG. 25 illustrates metrics and use cases for professional athletes. Specific metrics may correlate to various beneficial athletic outcomes, such as improvements in maximal speed, power, or endurance. Additionally, metrics may assist in monitoring, predicting, and planning an athlete's recovery and regeneration. For example, nitric oxide measurements may be used to help develop athletic activities that cause the greatest endogenous increase in nitric oxide in a person's blood supply. In another example, nitric oxide recovery may be monitored to determine how much rest an athlete needs within a game or between sets. In a third example, nitric oxide regeneration may be monitored and / or improved through targeted exercises to reduce injury risk and facilitate return to play after injury.

[0121] FIG. 26 illustrates an exemplary measurement of maximum NO recovery versus the number of sets performed in an exercise routine. Maximum NO recovery is derived from the rate of reoxygenation in muscle tissue after a period of exercise, and maximum NO recovery is affected by various factors, such as NO concentration, breathing pattern, and aerobic fitness level. Maximum NO recovery can inform athletes about how they recovered during live exercise and whether they fully recovered after a period of exercise. Additionally, individuals can train to increase their maximum NO recovery score, allowing them to recover more quickly after or during periods of exercise. As can be seen in the chart, an athlete's maximum NO recovery peaks at two sets. A threshold for decline from peak maximum NO recovery can be set to determine the maximum amount of sets an athlete should perform. Through training, athletes may achieve their peak with a greater number of sets and / or the rate of decline after the peak value may decrease.

[0122] FIG. 27 depicts exemplary measurements of PNO and maximum NO regeneration over time. PNO measurements chart an athlete during an exercise session through alternating periods of work and rest. While the athlete is exercising, PNO increases. During recovery periods, PNO decreases back down. The more PNO an athlete produces, the better their health and performance. PNO can also be used to reduce injury risk and reduce re-injury risk in individuals undergoing rehabilitation. PNO levels can be compared between healthy and injured legs to inform the athlete about their ability to handle loads.

[0123] By comparing the injured individual's nitric oxide levels taken at different times, the level of tissue injury recovery achieved 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 the individual's upper arm or upper thigh, and a biosensor can then be placed on a large muscle distal to the cuff. The cuff can be inflated until pressure occludes blood flow to the limb. Once blood flow is occluded, the individual can inflate the cuff and hold it still for a period of time, after which the cuff can automatically deflate, allowing blood to re-flow to the limb. Biomarker measurements can be recorded during the post-ischemia-reperfusion period, and NO levels can be calculated using the biomarker measurements.

[0124] In an exemplary measurement, an athlete's maximum NO regeneration score is plotted on the athlete's left and right legs over a 12-week period following 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 conjunction with existing metrics, such as strength. This metric can be used to optimize a player's training regimen in order to return the player to the field as quickly as possible without the risk of undue injury.

[0125] As can be seen in Figure 28, distinct trends occur simultaneously when measuring PNO or muscle oxygenation (SmO2). The macroscopic trend represents autoregulation of blood flow. As oxygen becomes available in skeletal muscle, muscle blood flow increases compensatory. 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 shown). As a result, an overall increase in nitric oxide and s-nitrosothiols (PNO) can be observed. This response can last from a few seconds to a few minutes.

[0126] The micro trend represents active hyperemia. When a muscle contracts, blood flow is restricted, resulting in a decrease in oxygen levels. 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 in the micro trend of Figure 28 as a rapid increase and decrease in PNO.

[0127] Those skilled in the art will note that while the macrotrend is the overall increase in PNO during exercise, within the macrotrend there is a microtrend consisting of smaller increases / decreases in PNO. These two trends combine to regulate muscle blood flow.

[0128] As can be seen in Figure 29, the macro- and micro-trends may exhibit a positive linear correlation, as would be the case if muscle contraction had a stronger than normal effect on blood flow. As a result, the autoregulatory response cannot be clearly observed. However, whether positive or negative, PNO still increases because a linear correlation is being considered.

[0129] In Figure 29, there are (+) correlations nested within (-) correlations, and (+) correlations nested within (+) correlations. It is not possible to visually distinguish between the two, but the possibility is worth mentioning (the second set has a higher PNO for reasons other than being + / + correlations).

[0130] Following tissue injury, an injured person may believe that the tissue injury has fully healed and be able to bear full weight on the injured tissue and / or fully function the injured body part, when in fact the tissue injury has not yet fully healed. A determination of actual tissue injury fullness can be made by comparing nitric oxide levels from the injured limb with the opposite, uninjured limb of the same type (i.e., arm or leg). Based on the metrics described herein, an injured person may be able to proceed with full activity with a reduced likelihood of re-injury. [Example]

[0131] Example: Research To establish the significance of SNOs, specifically SNO-Hb-βCys93, in clinically relevant measurements of hypoxic vasodilation, we utilized mice expressing human Hb. Mice lacking SNO-Hb exhibit several cardiovascular defects resulting from impaired hypoxic vasodilation. This study examined the functional consequences of Cys93 SNOs, emulating a standard clinical protocol for reactive hyperemia: reoxygenation (measured by pO2 using a needle electrode) in the gastrocnemius muscle 5 minutes after femoral artery occlusion.

[0132] Figure 30A depicts representative traces from control βC93 mice and corresponding βC93A mutant animals. In βC93 mice, which have normal hypoxic vasodilator activity, the tissue pO2 response following release of arterial occlusion (i.e., restoration of femoral artery flow) was a rapid recovery that overshot baseline. In contrast, βC93A animals exhibited a delayed response, with muscle oxygenation not returning to baseline during the 5-minute recording interval following release. Group data comparison, as shown in Figure 30B, indicated that basal pO2 in the gastrocnemius muscle was significantly lower in βC93A mice (p=0.032), consistent with our previous study. Group data comparison also indicated that muscle pO2 recovery after 5 minutes in βC93A mice was blunted compared to βC93 controls (46±17 vs. 28±15 mmHg, p=0.004). Group data comparison also showed that the rate of post-occlusion recovery in muscle pO2 was significantly reduced to approximately half the normal rate (0.23 ± 0.15 vs. 0.13 ± 0.11 mmHg / sec, respectively) in γβC93 mice compared with that in γβC93 mice, p = 0.036, as shown in Figure 30C. Thus, SNO-Hb deficiency reduces the rate and overall efficiency of tissue oxygenation resulting from brief interruptions in blood flow.

[0133] RBC SNO levels were measured in 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) defects in oxygenation: diabetic peripheral arterial disease (PAD), heart failure with reduced ejection fraction (HF), COPD, and sickle cell disease (SCD) as well as age-matched healthy controls. Specific inclusion criteria and disease status for each cohort are provided in the expanded method. Fifty-three subjects were enrolled, with 49 individuals completing the study (13 healthy, 13 PAD, 6 HF, 9 COPD, and 8 SCD).

[0134] RBCs were processed on-site and quantified for SNO-Hb and iron-nitrosyl Hb by Hg-coupled photolysis-chemiluminescence within approximately 1 hour of procurement from the radial artery. Values ​​from nine subjects were discarded due to instrument malfunction (i.e., the diagnosis and decision to discard those data was made by technical staff who were unaware of the patient's physiological status). Results from the remaining samples are presented in Figures 31A-31D.

[0135] In normal volunteers (n = 10), as shown in Figure 31A, intra-arterial RBC SNO-Hb levels were 2.6 ± 1.3 per 1000 Hb, concentrations similar to those recorded in other groups of healthy subjects. SNO-Hb levels in the HF cohort (n = 5, 2.5 ± 0.6 per 1000 Hb) and COPD cohort (n = 5, 2.0 ± 1.3) were not different from controls. 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 in normal controls (p < 0.05). SNO-Hb levels may be reduced due to a global decrease in NO production or a processing defect within the Hb molecule that prevents the intramolecular transfer of NO from heme to thiols, which is reflected in an increased amount of inactive FeNO, as previously reported for SCD, PH, and healthy subjects under hypoxia. Notably, the total amount of NO bound to Hb (HbNO) was not different from normal in any patient group, as shown in Figure 31C. However, as shown in Figure 31B, HbFeNO concentrations were significantly higher than normal in PAD, COPD, and SCD patient groups, 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. Exploratory analyses of correlations between SNO-Hb and various clinical chemistry parameters were also performed. Figure 31C depicts total HbNO, and Figure 31D depicts the ratio of SNO to total HbNO for the identified patient groups. As shown in Figure 33 (n = 33), analysis of the dataset identified an inverse correlation between plasma nitrite levels versus SNO-Hb and the ratio of nitrite to SNO-Hb / total HbNO. 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 an NO processing defect in RBCs from PAD, COPD, and SCD patients.

[0136] This study sought 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 after a brief period of limb blood flow occlusion. Patients with SCD were excluded from this group of the study due to the possibility that lower limb ischemia could induce vaso-occlusive crisis. Subjects in another cohort were placed in a semi-supine position with inflatable cuffs wrapped around the upper thigh and lower calf near the ankle. Each cuff was rapidly inflated over 1 second to stop arterial blood flow (target pressure = systolic blood pressure + approximately 130 mmHg, maximum 300 mmHg). The occlusion period was held for 5 minutes, after which the cuff pressure was released and tissue oxygenation was measured for 5 minutes. There was a 5-minute recovery interval between the two cuff inflation / recording sessions; the ankle cuff was used first for recordings on the foot, and the thigh cuff was used second for separate recordings on both the foot and upper calf. Occlusion tests were performed on 45 subjects, and NIRS tracings were analyzed offline by individuals blinded to the subjects' disease status or RBC SNO levels. Thirteen of the resulting NIRS recordings were deemed uninterpretable by an independent analysis group due to leg movement and / or insufficient signal resolution and were therefore excluded, leaving data from 11 healthy subjects, 8 PAD, 6 HF, and 7 COPD patients for comparison purposes.

[0137] The experimental endpoint was the half-time (t) to tissue oxygenation recovery measured in seconds (i.e., 50% return to baseline), and findings are presented in Figures 32A-32E. Representative foot tissue oxygenation recovery traces from one healthy subject and one PAD patient are shown in Figure 32A after release of the thigh cuff. The healthy subject had a robust and rapid reoxygenation response with a t of 10 seconds, while the PAD patient exhibited a delayed tissue oxygenation response with a t of 22 seconds. This closely resembles the rapid recovery of tissue pO in βC93 control mice after release of femoral artery occlusion, compared to the slower recovery in βC93A mice, as shown in Figure 30A. Quantified group data (mean ± SD) showing t for foot reoxygenation after ankle and thigh cuff occlusion, and for calf reoxygenation after thigh occlusion, are presented in Figures 32B, 32C, and 32D, respectively. For all three metrics, healthy subjects recorded mean t values ​​of approximately 10 seconds, consistent with previous studies. Importantly, as a group, there was a direct correlation between SNO-Hb levels and reperfusion rate, as shown in Figure 32E. Although numerically higher mean t values ​​were observed in all three patient groups, reperfusion half-times of the ankle or foot after cuff inflation were significantly increased only in the PAD subject group. Furthermore, as shown in Figure 34, there was a significant inverse correlation between paw reperfusion t½ vs. SNO-Hb levels and paw reperfusion t½ vs. the ratio of SNO-Hb to total HbNO, but not paw reperfusion t½ vs. FeNO levels, linking RBC SNO to the reoxygenation response.

[0138] Microvascular blood flow is impaired in many clinical conditions, leading to tissue ischemia. However, drugs that increase blood flow do not improve tissue oxygenation. Clinical measurements of blood flow have focused on endothelial components, particularly NO, which play no role in tissue oxygenation. On the other hand, there is strong evidence that blood flow, which governs tissue oxygenation, is regulated by S-nitrosohemoglobin. In other words, blood flow, which lowers blood pressure, is regulated by endothelial NO, whereas blood flow, which regulates tissue oxygenation, is controlled by RBC-SNO. Reactive hyperemia is an increase in blood flow following transient ischemia that occurs to restore tissue oxygenation. Reactive hyperemia, on the other hand, has been attributed to endothelial NO. RBC-SNO plays a major role in both vasodilatory and blood flow responses in mice. This study expands that work to include direct measurements of tissue oxygenation in both mice and humans. Importantly, this study demonstrates that SNO-Hb is required for oxygenating hypoxic tissues and that a deficiency of SNO-Hb leads to impaired oxygenation. Furthermore, SNO-Hb levels in patients predict tissue oxygenation after a short period of localized hypoxia, representing the primary biomarker of microcirculatory blood flow.

[0139] This study presents a three-gas model for the respiratory cycle, in which O2 / NO are simultaneously loaded onto Hb, and SNO-Hb releases vasodilatory SNO to regulate blood flow with tissue oxygen delivery. Thus, in mutant mice unable to transport or distribute SNO from βCys93, tissue oxygenation is extensively impaired. In addition to tissue hypoxia under basal conditions, mutant mice exhibit defects in tissue oxygenation under systemic hypoxia and focal ischemia, as shown in Figure 30B. Conversely, hypoxic conditions that impair oxygen loading or otherwise impair the allosteric transfer of Hb also impair S-nitrosylation. This manifests itself either in lower SNO-Hb levels or a lower ratio of SNO-Hb to total HbNO, since NO is still bound to the heme iron and simply cannot transfer to Cys93. Correspondingly, and confirming previous reports, patients with disease states characterized by oxygen pathology (COPD, PAD, and SCD) exhibited lower levels of SNO-Hb and lower SNO / HbNO, as shown in Figures 31A-31D. Predictably, the ratio of SNO-Hb to total Hb-associated NO (SNO-Hb + Hb FeNO, total HbNO), indicating the accumulation of inactive FeNO, was a more sensitive measure of loss of biological activity than SNO-Hb alone. Thus, NO / SNO processing defects are observed in multiple diseases characterized by defects in tissue oxygenation and may be causally linked by a shared inability of Hb to convert FeNO to SNO-βCys93-Hb. Similarly, as shown in Figure 33, we found that the ratios of SNO-Hb to nitrite and SNO-Hb / HbNO to blood nitrite were in fact inversely correlated, consistent with previous findings that higher nitrite blocks the formation of SNO-Hb, thus indicating that nitrite levels are independent of blood flow or tissue oxygenation.

[0140] Patients with PAD have well-characterized microvascular dysfunction. When examining recovery from transient lower limb ischemia, as shown in Figures 32A-32E, reperfusion in this cohort was significantly delayed, and in all cases, the t1 / 2 to reoxygenation was longer compared to healthy controls. Importantly, a significant correlation was found between SNO-Hb levels and oxygenation rate across all patient cohorts, although no statistical difference in t1 / 2 to reoxygenation was observed in other cohorts. Taken together, these results, along with genetic validation in mice, suggest that SNO-Hb is a key driver of blood flow autoregulation, whereby tissue blood flow controls tissue oxygenation. These findings have multiple clinical implications. Reactive hyperemia, previously considered a metric of endothelial function, has a significant RBC SNO component. More generally, endothelial NO and RBC SNO have distinct 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 because SNO-Hb was directly correlated with reperfusion rate. Furthermore, this study demonstrates that reactive hyperemia testing is a useful measure of SNO-Hb function in patient populations. The ability to enhance RBC SNO may improve tissue oxygenation and could have broad clinical utility. In certain embodiments, exercise is used to benefit oxygenation in subjects with disease states. For example, subjects may have heart disease, vascular disease, diabetes, cancer, frailty, and / or muscle disorders (e.g., muscle metabolic dysregulation), in which PNO could be diagnostic, prognostic, and / or potentially therapeutic.

[0141] Specific details are set forth 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 in order to avoid obscuring the embodiments in 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.

[0142] The techniques, blocks, steps, and means described above can be implemented in various ways. For example, these techniques, blocks, steps, and means can be implemented in hardware, software, or a combination thereof. In the case of a hardware implementation, the processing unit can be implemented in 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 any combination thereof.

[0143] Also, it should be noted that the embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. While a flowchart may describe operations as a sequential process, many of the operations may be performed in parallel or simultaneously. Additionally, the order of operations may be rearranged. A process terminates when its operations are completed, but could have additional steps not included in the diagram. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.

[0144] Furthermore, embodiments may 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 to perform the necessary tasks may be stored in a machine-readable medium such as a storage medium. A code segment or machine-executable instructions may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, and / or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, ticket passing, network transmission, etc.

[0145] For a firmware and / or software implementation, the methodologies may be implemented with modules (e.g., procedures, functions, etc.) that perform the functions described herein. Any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein. For example, software code may be stored in a memory. The memory may be implemented within the processor or external to the 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 or number of memories or the type of medium on which the memory is stored.

[0146] Additionally, as disclosed herein, the term "storage medium" can refer to one or more memories for storing data, including read-only memory (ROM), random-access memory (RAM), magnetic RAM, core memory, magnetic 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 non-removable storage devices, optical storage devices, wireless channels, and / or various other storage media that can contain or carry instruction(s) and / or data.

[0147] In the foregoing description, specific details have been set forth to provide a thorough understanding of exemplary implementations of the systems and methods described in this disclosure. However, it will be apparent that implementations may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form so as not to obscure exemplary implementations in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail so as to avoid obscuring the examples. While the description of the exemplary implementations provides those skilled in the art with an effective description for implementing an exemplary embodiment, it should be understood that various changes can be made in the function and arrangement of elements without departing from the spirit and scope of the present disclosure. Accordingly, the present invention is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of any appended claims.

Claims

1. 1. A system comprising: at least one light source configured to illuminate a region of interest of the object; at least one optical sensor configured to non-invasively measure reflected light within the region of interest of the object; at least one processor in communication with the sensor; at least one non-transitory computer-readable medium storing machine-readable instructions; Equipped with The machine-readable instructions, when executed by the at least one processor, cause the at least one processor to: receiving at least one measured value of light from the at least one light sensor; determining an irradiance distribution for the region of interest of the object using the at least one measured value and at least a portion of a probabilistic model as inputs; calculating at least one characteristic attenuation coefficient for at least one wavelength of the light from the irradiance distribution; 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 measured value of the light; A system that performs processing including the steps of:

2. 2. The system of claim 1, wherein the at least one physical characteristic comprises one or more of a hydration measurement, an internal training load, an oxygenated hemoglobin measurement, a deoxygenated hemoglobin measurement, a total hemoglobin measurement, a blood volume measurement, muscle oxygenation, muscle oxygen consumption, an active nitric oxide measurement, an active s-nitrosothiol measurement, fat thickness, and melanin content.

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

4. the at least one measured value of the light comprises a time series of measurements, the at least one body characteristic comprises a time series of characteristics, and the processing comprises: generating a value from the time series of characteristics representative of endogenous S-nitrosothiol content of tissue within the region of interest; storing the values ​​representing the endogenous S-nitrosothiol content of the tissue within the region of interest in the non-transitory computer-readable medium; The system of claim 1 further comprising:

5. The system of 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. said generating said value comprising: determining a linearity of a relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements to provide a set of parameters; generating from the set of parameters a value representative of the endogenous S-nitrosothiol content of the tissue within the region of interest; The system of claim 5 , comprising:

7. said generating said value comprising: 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; generating the value representing the endogenous S-nitrosothiol content of the tissue in the region of interest from the slope of the best-fit line; The system of claim 6 , comprising:

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

9. The system of 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 indicator of the at least one physical characteristic.

10. The system of claim 9 , wherein the at least one indicator comprises guidance related to the subject's internal training load.

11. The system of claim 10 , wherein the guidance is directed to reducing a risk of injury.

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

13. The system of claim 12 , wherein the information includes a VO2 index.

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

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

16. 1. A system comprising: a plurality of light sources configured to illuminate a region of interest of the subject, each light source of the plurality of light sources configured to emit light of a different wavelength; at least one optical sensor configured to non-invasively measure reflected light within the region of interest of the object; at least one processor in communication with the sensor; at least one non-transitory computer-readable medium storing machine-readable instructions; Equipped with The machine-readable instructions, when executed by the at least one processor, cause the at least one processor to: receiving a plurality of measured values ​​of the different wavelengths of light from the at least one light sensor; determining at least one thickness or depth of at least one tissue layer within the region of interest of the subject from the plurality of measured values ​​of the light; performing a process including system.

17. The process comprises: determining at least one physical characteristic of the region of interest of the subject based on the at least one measured value of the light and the at least one thickness or depth of the at least one tissue layer. The system of claim 16 further comprising:

18. 18. The system of claim 17, wherein the at least one physical characteristic comprises one or more of a hydration measurement, an internal training load, an oxygenated hemoglobin measurement, a deoxygenated hemoglobin measurement, a total hemoglobin measurement, a blood volume measurement, muscle oxygenation, muscle oxygen consumption, an active nitric oxide measurement, an active s-nitrosothiol measurement, fat thickness, and melanin content.

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

20. the at least one measured value of the light comprises a time series of measurements, the at least one body characteristic comprises a time series of characteristics, and the processing comprises: generating a value from the time series of characteristics representative of endogenous S-nitrosothiol content of tissue within the region of interest; storing the values ​​representing the endogenous S-nitrosothiol content of the tissue within the region of interest in the non-transitory computer-readable medium; Further comprising:

20. The system of claim 17.

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

22. said generating said value comprising: determining a linearity of a relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements to provide a set of parameters; generating from the set of parameters a value representative of the endogenous S-nitrosothiol content of the tissue within the region of interest; 22. The system of claim 21, comprising:

23. said generating said value comprising: 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; generating the value representing the endogenous S-nitrosothiol content of the tissue in the region of interest from the slope of the best-fit line; 23. The system of claim 22, comprising:

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

25. 20. The system of claim 17, wherein the processing further comprises displaying, by at least one display device in communication with the at least one processor, at least one indicator of the at least one physical characteristic.

26. 26. The system of claim 25, wherein the at least one indicator comprises guidance related to the subject's internal training load.

27. 27. The system of claim 26, wherein the guidance is directed to reducing a risk of injury.

28. 26. The system of claim 25, wherein the at least one indicator comprises information related to muscle oxygen consumption of the subject.

29. 30. The system of claim 28, wherein the information includes a VO2 index.

30. 17. The system of claim 16, wherein the determining comprises applying a plurality of measured values ​​of the light to at least one scattering phase function associated with at least one layer type within the region of interest of the object.

31. The system of claim 16 , wherein the determining comprises applying the plurality of measured values ​​and a probabilistic model as inputs.

32. 32. The system of claim 31, wherein the probabilistic model comprises a three-dimensional model having six degrees of freedom.

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

34. 1. A method comprising: illuminating a region of interest of the object with at least one light source; detecting, with at least one optical sensor, reflected light from the region of interest of the object; receiving, by at least one processor, at least one measured value of light from the at least one light sensor; determining, by the at least one processor, an irradiance distribution for the region of interest of the object using the at least one measured value and at least a portion of a probabilistic model as inputs; calculating, by the at least one processor, at least one characteristic attenuation coefficient for the at least one wavelength of the light from the irradiance distribution; determining, by the at least one processor, 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 measured value of the light; A method comprising:

35. 35. The method of claim 34, wherein the at least one physical characteristic comprises one or more of a hydration measure, an internal training load, an oxygenated hemoglobin measure, a deoxygenated hemoglobin measure, a total hemoglobin measure, a blood volume measure, muscle oxygenation, muscle oxygen consumption, an active nitric oxide measure, an active s-nitrosothiol measure, fat thickness, and melanin content.

36. 35. The method of claim 34, wherein the at least one physical characteristic comprises a combination of pulse oximetry and nitric oxide.

37. The at least one measured value of the light comprises a time series of measurements, and the at least one body characteristic comprises a time series of characteristics, and the method further comprises: generating, by the at least one processor, a value representative of endogenous S-nitrosothiol content of tissue within the region of interest from the characteristics of the time series; storing, by the at least one processor, in a non-transitory computer-readable medium, the values ​​representing the endogenous S-nitrosothiol content of the tissue within the region of interest; The method of claim 4 further comprising:

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

39. said generating said value comprising: determining a linearity of a relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements to provide a set of parameters; generating from the set of parameters a value representative of the endogenous S-nitrosothiol content of the tissue within the region of interest; 39. The method of claim 38, comprising:

40. said generating said value comprising: 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; generating the value representing the endogenous S-nitrosothiol content of the tissue in the region of interest from the slope of the best-fit line; 40. The method of claim 39, comprising:

41. 35. The method of claim 34, wherein the determining of the at least one physical characteristic is performed during one of a period of exercise by the subject and a period of time immediately following the period of exercise by the subject.

42. 35. The method of 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. 43. The method of claim 42, wherein the at least one indicator comprises guidance related to the subject's internal training load.

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

45. 43. The method of claim 42, wherein the at least one indicator comprises information related to muscle oxygen consumption of the subject.

46. 46. ​​The method of claim 45, wherein the information includes a VO2 index.

47. 35. The method of claim 34, wherein the probabilistic model comprises a three-dimensional model having six degrees of freedom.

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

49. 1. A method comprising: illuminating a region of interest of the object with a plurality of light sources, each of the light sources emitting a different wavelength of light; detecting reflected light within the region of interest of the object with at least one optical sensor; receiving, by at least one processor, a plurality of measured values ​​of the different wavelengths of light from the at least one light sensor; 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 from the plurality of measured values ​​of the light; A method comprising:

50. 50. The method of claim 49, further comprising determining, by the at least one processor, at least one physical characteristic of the region of interest of the subject based on the at least one measured value of the light and the at least one thickness or depth of the at least one tissue layer.

51. 51. The method of claim 50, wherein the at least one physical characteristic comprises one or more of a hydration measure, an internal training load, an oxygenated hemoglobin measure, a deoxygenated hemoglobin measure, a total hemoglobin measure, a blood volume measure, muscle oxygenation, muscle oxygen consumption, an active nitric oxide measure, an active s-nitrosothiol measure, fat thickness, and melanin content.

52. 51. The method of claim 50, wherein the at least one physical characteristic comprises a combination of pulse oximetry and nitric oxide.

53. The at least one measured value of the light comprises a time series of measurements, and the at least one body characteristic comprises a time series of characteristics, and the method further comprises: generating, by the at least one processor, a value representative of endogenous S-nitrosothiol content of tissue within the region of interest from the characteristics of the time series; storing, by the at least one processor, in a non-transitory computer-readable medium, the values ​​representing the endogenous S-nitrosothiol content of the tissue within the region of interest; 51. The method of claim 50, further comprising:

54. 54. The method of claim 53, wherein the time series of characteristics comprises a time series of oxygen saturation measurements and a time series of blood volume measurements.

55. said generating said value comprising: determining a linearity of a relationship between the time series of blood volume measurements and the time series of oxygen saturation measurements to provide a set of parameters; generating from the set of parameters a value representative of the endogenous S-nitrosothiol content of the tissue within the region of interest; 55. The method of claim 54, comprising:

56. said generating said value comprising: 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; generating the value representing the endogenous S-nitrosothiol content of the tissue in the region of interest from the slope of the best-fit line; 56. The method of claim 55, comprising:

57. 51. The method of claim 50, wherein the determining of the at least one physical characteristic is performed during one of a period of exercise by the subject and a period of time immediately following the period of exercise by the subject.

58. 51. The method of 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 characteristic.

59. 59. The method of claim 58, wherein the at least one indicator comprises guidance related to the subject's internal training load.

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

61. 59. The method of claim 58, wherein the at least one indicator comprises information related to muscle oxygen consumption of the subject.

62. 62. The method of claim 61, wherein the information includes a VO2 index.

63. 50. The method of claim 49, wherein the determining comprises applying a plurality of measured values ​​of the light to at least one scattering phase function associated with at least one layer type within the region of interest of the object.

64. 50. The method of claim 49, wherein said determining comprises applying said plurality of measured values ​​and a probabilistic model as inputs.

65. 65. The method of claim 64, wherein the probabilistic model comprises a three-dimensional model having six degrees of freedom.

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

67. The system of claim 1 , wherein the at least one physical characteristic comprises at least one of a diagnosis of injury, a metric of recovery from injury, and a prognosis of re-injury.

68. 20. The system of claim 17, wherein the at least one physical characteristic comprises at least one of a diagnosis of injury, a metric of recovery from injury, and a prognosis of re-injury.

69. 35. The method of claim 34, wherein the at least one physical characteristic comprises at least one of a diagnosis of injury, a metric of recovery from injury, and a prognosis of re-injury.

70. 51. The method of claim 50, wherein the at least one physical characteristic comprises at least one of a diagnosis of injury, a metric of recovery from injury, and a prognosis of re-injury.

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