Metabolite detection method for diagnosing lithangiuria

Abstract

The invention discloses a metabolite detection method for diagnosing lithangiuria, and relates to the technical field of metabolite detection. The method comprises the following steps: collecting and preprocessing a urine sample, and generating a composite fluorescence signal based on the preprocessed urine sample so as to obtain a composite function; constructing an orbital function system, and performing projection processing on a composite function to obtain initial orbital energy; based on the initial orbital energy, a mutual exclusion function between metabolites is introduced, an effective energy contribution value is calculated, and a decoupled energy response amount is obtained; converting the decoupled energy response quantity into a concentration estimation value, and generating a rotation response value based on the concentration estimation value; generating a coupling threshold based on the concentration estimation value and the rotation response value; and carrying out risk scoring based on the coupling threshold value to obtain an individual risk score value of the urinary calculi. The invention solves the problems of inaccurate concentration estimation of the metabolite and low metabolite detection precision caused by inaccurate analysis of the metabolite under complex interference conditions in the existing metabolite detection method.

Description

Technical Field

[0001] The present invention relates to the technical field of metabolite detection, and in particular to a metabolite detection method for diagnosing urinary calculi. Background Art

[0002] The formation of urinary stones is closely related to multiple metabolic factors. The abnormal accumulation of common metabolites in urine, such as oxalic acid, uric acid, citric acid, and calcium ions, is the primary mechanism that triggers stone formation. Currently, common clinical detection methods include chemical analysis, ion-selective electrode methods, and high-performance liquid chromatography. However, most of these methods rely on the analysis of single metabolites in a laboratory setting and are highly dependent on the testing environment, sample processing, and operational procedures. This makes it difficult to achieve rapid, simultaneous multi-component detection and lacks the ability to provide immediate on-site assessment.

[0003] Existing metabolite detection methods face problems such as overlapping fluorescence signals, strong interference, and low resolution when faced with the simultaneous presence of multiple metabolites. This is especially true when using multiple fluorescent probes for detection, as the signals excited by multiple metabolites have similar wavelengths and similar time response characteristics, making signal spectrum interpretation difficult and seriously affecting the accuracy of concentration quantification. In addition, most traditional concentration estimation models are based on the ideal linear response assumption, ignoring the response perturbation caused by factors such as probe aging, decreased excitation stability, and changes in sample viscosity during the actual detection process, resulting in concentration offsets or valuation distortions, reducing the reliability of the diagnosis. Therefore, there is an urgent need to provide a metabolite detection method for diagnosing urinary stones to solve the above problems. Summary of the Invention

[0004] The present invention provides a metabolite detection method for diagnosing urinary stones to solve the problem that existing metabolite detection methods have inaccurate analysis of metabolites under complex interference conditions, thereby leading to inaccurate metabolite concentration estimation and low metabolite detection accuracy.

[0005] A metabolite detection method for diagnosing urinary tract stones of the present invention comprises the following steps: S1. Collect and pre-process a urine sample, generate a composite fluorescence signal based on the pre-processed urine sample, obtain a composite function based on the composite fluorescence signal, construct an orbital function system, project the composite function, and obtain preliminary orbital energy; S2. Based on the preliminary orbital energy, the mutual repulsion function between metabolites is introduced to calculate the effective energy contribution value, thereby obtaining the decoupled energy response; the decoupled energy response is converted into a concentration estimate through nonlinear transformation, and a rotation response value is generated based on the concentration estimate; based on the concentration estimate and the rotation response value, a coupling gate value is generated; risk scoring is performed based on the coupling gate value to obtain an individual risk score for urinary stones.

[0006] Preferably, the S1 specifically includes: The pretreated urine sample is introduced into the microfluidic chip. Different channels in the microfluidic chip correspond to different detection reaction areas. Fluorescent probes are pre-embedded in the detection reaction areas. The pretreated urine sample undergoes a specific binding reaction with the fluorescent probe to obtain a composite fluorescent signal.

[0007] Preferably, the S1 specifically includes: Perform photoelectric conversion on the composite fluorescence signal to obtain a composite function.

[0008] Preferably, the S1 specifically includes: During the projection process of the composite function, the preliminary orbital energy is calculated based on the instantaneous energy density and the normalized modulation function.

[0009] Preferably, the S2 specifically includes: The average splitting correction is performed based on the effective energy contribution value to obtain the decoupled energy response.

[0010] Preferably, the S2 specifically includes: Set the rotation factor and project the concentration estimate into the rotation domain space to obtain the rotated response value.

[0011] Preferably, the S2 specifically includes: In the process of risk scoring, the individual risk score value of urinary stones is generated based on the coupling gate value, concentration estimation value and auxiliary variables combined with the activation function.

[0012] Preferably, the S2 specifically includes: The individual risk score of urinary stones is compared with the preset threshold to divide the risk level.

[0013] The beneficial effects of the technical solution of the present invention are: 1. By introducing an orbital function system, a multi-track time-domain mapping model was established for the first time in metabolite detection scenarios. This model enables accurate spectral decomposition of multiple metabolite signals under the conditions of complex fluorescence signal superposition. Compared to existing linear separation methods using Fourier transforms or wavelength discrimination, this method overcomes the time-frequency overlap problem and can simultaneously process complex interference signals from multiple metabolites, effectively improving the detection throughput and concentration resolution capabilities per unit sample, significantly enhancing detection accuracy and specificity.

[0014] 2. A mutual repulsion function Ψ between metabolites based on energy difference-phase difference is adopted. When there is orbital overlap between different metabolites, the initial orbital energy estimate can be dynamically corrected, effectively solving the problem of "energy misjudgment" under high-crossover signals. It has highly nonlinear response capabilities and can adaptively adjust the separation process according to the fluorescence perturbation characteristics of different metabolites. It is a major breakthrough in traditional linear weighted estimation.

[0015] 3. By projecting the concentration estimate into the periodic rotation domain space and then forming a logic gate function with other metabolites, the synergistic or complementary relationship between metabolites can be effectively characterized, and the "interactive risk pattern" can be identified. At the same time, behavioral variables such as water intake, protein intake, and sodium intake are introduced into risk judgment. It can not only analyze from a metabolic perspective, but also guide user behavior optimization to reduce risks and achieve proactive health management feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a metabolite detection method for diagnosing urinary stones according to the present invention. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following describes in detail a specific scheme of a metabolite detection method for diagnosing urinary stones provided by the present invention in conjunction with the accompanying drawings.

[0020] Refer to the attached Figure 1 , which shows a flow chart of a metabolite detection method for diagnosing urinary stones provided by one embodiment of the present invention, the method comprising the following steps: S1. Collect and pre-process a urine sample, generate a composite fluorescence signal based on the pre-processed urine sample, obtain a composite function based on the composite fluorescence signal, construct an orbital function system, project the composite function, and obtain preliminary orbital energy.

[0021] Collect a urine sample, and perform pretreatment on the urine sample such as filtration, centrifugation, precipitation, etc. to obtain a pretreated urine sample; the pretreatment adopts technical means well known to those skilled in the art, which will not be described in detail here. The pretreated urine sample is introduced into a microfluidic chip. The microfluidic chip is existing and has a multi-channel shunt unit structure. Different channels correspond to different detection reaction areas. A highly selective fluorescent probe is embedded in the detection reaction area. The fluorescent probes correspond to metabolites such as oxalic acid, citric acid, uric acid, and calcium ions. After the pretreated urine sample is introduced, a specific binding reaction occurs with the fluorescent probe to obtain a composite fluorescent signal. Specific binding reaction refers to the selective recognition and binding of the fluorescent probe at the molecular level with the target metabolite, such as oxalic acid, uric acid, citric acid, calcium ions, etc., through the recognition group embedded in its structure, thereby causing a change in the specific fluorescent signal.

[0022] The composite fluorescence signal is photoelectrically converted to obtain a composite function; specifically, the composite fluorescence signal, i.e., a multi-wavelength light signal, is converted into a continuous-time composite function through an existing photoelectric detection system. , indicating that in the detection cycle The original spectrum superposition signal after internal photoelectric conversion, It represents the maximum value of the detection cycle, in which the wavelengths emitted by the fluorescent probes bound to different metabolites overlap and present complex interference characteristics in the time domain. In order to separate the preliminary orbital energy of each metabolite, a set of orbital function systems with time perturbation and frequency envelope change capabilities are established to define each metabolite. The orbital basis functions are: ; in, is the total number of metabolites; Indicates the The ideal time orbital response function of the metabolite is used to simulate the theoretical change profile of the fluorescence intensity of the composite function on the time axis, and serves as the orbital basis function of the orbital function system; It is a time variable, which represents the time process from the start of detection to the current point, that is, the detection period; For the The theoretical central time point of the metabolite excitation response determines the time symmetry center of the orbital basis function and is a reference value obtained through spectral response delay experimental calibration; is the track width parameter, which indicates the temporal extension of the track basis function, equivalent to the “response lifetime” or “probe response window” of the track in the time domain, and is set by the reaction time window experiment; It is the basic response frequency of the fluorescent probe, which describes the resonance characteristics between the fluorescent probe excitation light and the metabolite structure and is determined according to the specific application scenario; It is The phase perturbation amplitude of the metabolite fluorescence signal is used to simulate the transient response perturbation of the fluorescent probe under unstable excitation and is determined according to the specific application scenario requirements; is the orbital perturbation frequency, which is used to control the periodic distribution of the orbital phase oscillation amplitude and is obtained according to the expert experience method.

[0023] After obtaining the orbital function system, the composite function Projected onto each orbit for energy matching, that is, for the composite function Perform projection processing to obtain preliminary orbital energy: ; in, It is The initial orbital energy of a metabolite is the relative response intensity of the metabolite in the chip detection reaction area, but there is overlap and interference between the orbitals, which requires a splitting and decoupling process; in the above formula, the instantaneous energy density is extracted by the square operation and the normalized modulation function is superimposed To ensure the amplification of the high value range within the time period.

[0024] S2. Based on the preliminary orbital energy, the mutual repulsion function between metabolites is introduced to calculate the effective energy contribution value, thereby obtaining the decoupled energy response; the decoupled energy response is converted into a concentration estimate through nonlinear transformation, and a rotation response value is generated based on the concentration estimate; based on the concentration estimate and the rotation response value, a coupling gate value is generated; risk scoring is performed based on the coupling gate value to obtain an individual risk score for urinary stones.

[0025] To achieve energy splitting decoupling, a mutual repulsion function between metabolites was introduced based on the preliminary orbital energy. , whose structure is completed through the logical reinforcement mechanism of nonlinear transformation: ; in, It is The initial orbital energy of the metabolite is The effective energy contribution value after dynamic repulsion correction when the initial orbital energy of the metabolites undergoes orbital overlap; It is The initial orbital energy of the metabolite; It is The phase perturbation amplitude of the fluorescence signal of the metabolite; It is the suppression intensity factor, which is used to control the steepness of the exponential function. The larger the value, the steeper it is, which reflects the tolerance to track crossing. It is determined according to the expert experience method, and the reference value range is ; Is the energy regularization constant, used to prevent the denominator from being zero, and is a very small positive number with a value range of ; Describes the orbital phase difference.

[0026] Furthermore, the effective energy contribution value of each metabolite was average split and corrected to obtain the decoupled energy response value ; The energy response after decoupling is obtained Finally, a nonlinear transformation process is introduced to convert it into a concentration estimate: ; in, It is Estimated concentrations of metabolites; is the fitting coefficient, which is used to reflect the overall sensitivity of orbital energy to concentration mapping. It is obtained based on regression fitting experiments and the reference value range is ; Indicates the The effective energy density per unit time of each metabolite in its trajectory is processed by square root and used as the main estimate of concentration; By introducing the proportional relationship between phase perturbation and orbital center time, the purpose is to correct the response deviation of different probes under different metabolic reaction delays. If the orbital center time offset of a metabolite is large or the phase perturbation is small, the impact on the concentration estimation is small. It represents the ratio of the orbital perturbation frequency to the fundamental response frequency of the fluorescent probe.

[0027] Based on the concentration estimation value, the risk assessment reasoning and discrimination processing is carried out. First, in order to introduce the periodic change characteristics and eliminate the linear correlation between metabolites, the first The rotation coefficient of the metabolite, The rotation factor of the metabolite Estimated concentrations of metabolites Projecting to the rotation domain space, that is, mapping the concentration estimate to the circular orbit, we get Response values ​​of metabolites in the rotation domain space , that is, the rotation response value; then based on the concentration estimate and the response value in the rotation domain space, two rotation response values ​​are simulated through the logic gate function and The interactive risk contribution , indicating the metabolites and The logical coupling activation strength of the two metabolites is used to indicate whether the metabolite combination may produce a synergistic pathogenic contribution in the current urine sample, that is, the coupling gate value of the two metabolites.

[0028] Finally, all coupling gate values ​​are processed through the weighted risk score calculation formula to obtain the individual risk score value of urinary stones : ; in, is the activation function, which can be a Sigmoid function; It is The values ​​of auxiliary variables, such as water intake, protein intake, salt, etc., are processed by standardization such as Z-score and Min-Max normalization; It is The risk sensitivity coefficient of the auxiliary variable is determined according to the expert experience method, and the reference value range is ; is the total number of auxiliary variables; is the risk offset, which is determined according to the specific application scenario, and the reference value range is ; represents a nonlinear combination of all auxiliary variables; the individual risk score for urinary stones is and the threshold value preset based on expert experience For comparison, represents the lower limit of the threshold, Indicates the upper limit of the threshold. When , it is low risk; When the risk is medium, , it is high risk.

[0029] In summary, a metabolite detection method for diagnosing urinary stones was completed.

[0030] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0031] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0032] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A metabolite detection method for diagnosing urinary tract stones, characterized in that: The following steps are involved: S1. collecting and pre-processing a urine sample, and generating a composite fluorescent signal based on the pre-processed urine sample; Based on the composite fluorescence signal, a composite function is obtained; an orbital function system is constructed, and the composite function is projected to obtain preliminary orbital energy; S2. Based on the preliminary orbital energy, the mutual repulsion function between metabolites is introduced to calculate the effective energy contribution value, thereby obtaining the decoupled energy response; the decoupled energy response is converted into a concentration estimate through nonlinear transformation, and the rotation response value is generated based on the concentration estimate; Based on the concentration estimate and the rotation response value, a coupling gate value is generated; based on the coupling gate value, a risk score is performed to obtain an individual risk score for urinary stones.

2. The metabolite detection method for diagnosing urinary tract stones according to claim 1, characterized in that: Said S1 specifically includes: The pretreated urine sample is introduced into the microfluidic chip. Different channels in the microfluidic chip correspond to different detection reaction areas. Fluorescent probes are pre-embedded in the detection reaction areas. The pretreated urine sample undergoes a specific binding reaction with the fluorescent probe to obtain a composite fluorescent signal.

3. The metabolite detection method for diagnosing urinary tract stones according to claim 2, characterized in that: Said S1 specifically includes: Perform photoelectric conversion on the composite fluorescence signal to obtain a composite function.

4. The metabolite detection method for diagnosing urinary tract stones according to claim 3, characterized in that: Said S1 specifically includes: During the projection process of the composite function, the preliminary orbital energy is calculated based on the instantaneous energy density and the normalized modulation function.

5. The metabolite detection method for diagnosing urinary tract stones according to claim 1, characterized in that: Said S2 specifically includes: The average splitting correction is performed based on the effective energy contribution value to obtain the decoupled energy response.

6. The metabolite detection method for diagnosing urinary tract stones according to claim 1, characterized in that: Said S2 specifically includes: Set the rotation factor and project the concentration estimate into the rotation domain space to obtain the rotated response value.

7. The metabolite detection method for diagnosing urinary tract stones according to claim 1, characterized in that: Said S2 specifically includes: In the process of risk scoring, the individual risk score value of urinary stones is generated based on the coupling gate value, concentration estimation value and auxiliary variables combined with the activation function.

8. The metabolite detection method for diagnosing urinary tract stones according to claim 7, characterized in that: Said S2 specifically includes: The individual risk score of urinary stones is compared with the preset threshold to divide the risk level.

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