Intelligent analysis system and method for endocrine abnormality detection

Abstract

The invention relates to the technical field of medical data processing, in particular to an intelligent analysis system and method for endocrine abnormality detection, and the system comprises a hormone dynamics and receptor interaction analysis unit, an intracellular signal transduction analysis unit, a metabolic network and stress response analysis unit and a system evaluation unit. The hormone kinetics and receptor interaction analysis unit is used for obtaining the concentration of a receptor-hormone compound; the intracellular signal transduction analysis unit is used for obtaining ion current concentration; the metabolic network and stress response analysis unit is used for calculating the stress degree of the cells; and the system evaluation unit is used for calculating an endocrine abnormity degree value. According to the method, a high-precision, personalized and dynamically adaptive analysis result is provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical data processing, and in particular relates to an intelligent analysis system and method for endocrine abnormality detection. Background Art

[0002] The endocrine system regulates various physiological processes in the body through hormones, including metabolism, growth and development, stress response and reproductive function. Hormones, as signal molecules, bind to target receptors, trigger a series of complex signal transduction processes, and regulate cell functions and metabolic activities. However, abnormalities in the endocrine system are often closely related to a variety of major diseases, including diabetes, thyroid dysfunction, polycystic ovary syndrome and osteoporosis. Therefore, the detection and evaluation of endocrine abnormalities are of great significance in modern medical diagnosis. With the advancement of artificial intelligence technology, endocrine detection systems that combine biological big data and intelligent analysis are becoming a new research hotspot. However, existing technologies still have many problems in dynamic analysis, multi-parameter fusion and precision of abnormal diagnosis, and new technical means are urgently needed to make breakthroughs.

[0003] At present, the detection of endocrine abnormalities mainly relies on the detection technology of hormone concentration. Commonly used detection methods in laboratories include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA) and chemiluminescent immunoassay (CLIA). These methods can provide certain disease diagnostic information by detecting the concentration of specific hormones in blood, urine or saliva. For example, the determination of serum insulin, cortisol and thyroid hormone levels is a standard indicator for evaluating pancreatic function, adrenal function and thyroid disease. However, the application of these detection technologies is mainly focused on static detection, that is, a single sampling analysis of the instantaneous concentration of hormones, but fails to reflect the dynamic characteristics of hormone secretion. In addition, traditional technologies usually only focus on the single indicator of hormone concentration, while ignoring the complex effects of hormone signals at the molecular, cellular and metabolic network levels. In recent years, dynamic hormone monitoring technology has gradually attracted attention. For example, subcutaneous microdialysis systems based on continuous monitoring and integrated sensor technology can achieve real-time tracking of hormone concentrations. However, these technologies are usually limited by the types and resolution of monitored hormones, and it is difficult to fully cover the various hormones in the endocrine system. In addition, such technologies often only provide raw data output and lack in-depth analysis of hormone signaling, metabolic network effects, and cellular stress responses, which limits their scope of application in disease diagnosis. Summary of the invention

[0004] The main purpose of the present invention is to provide an intelligent analysis system and method for endocrine abnormality detection, which realizes the full-chain, cross-scale, and multi-dimensional analysis from hormone secretion dynamics, signal transduction to system metabolism. The system uses dynamic concentration feature extraction, nonlinear kinetic modeling, and multi-parameter integration to fully capture the complex characteristics of endocrine abnormalities, and generates quantitative indicators of the degree of endocrine abnormalities through time integration, providing high-precision, personalized, and dynamically adaptive analysis results.

[0005] In order to solve the above problems, the technical solution of the present invention is achieved as follows: An intelligent analysis system for endocrine abnormality detection, the system comprising: a hormone dynamics and receptor interaction analysis unit and an intracellular signal transduction analysis unit; the hormone dynamics and receptor interaction analysis unit is used to extract the dynamic concentration characteristics of each hormone in time by constructing a high-dimensional feature space mapping function based on the inherent parameters of each hormone and the real-time concentration of each hormone at each time; the binding and dissociation process between the hormone and its receptor is described by combining the dynamic concentration characteristics and the inherent parameters of each hormone, a nonlinear dynamic model is established, and the receptor-hormone complex concentration is obtained; the intracellular signal transduction analysis unit is used to convert the receptor-hormone complex concentration into an intracellular concentration response through a multi-stage amplification mechanism based on the receptor-hormone complex concentration and the inherent parameters of each hormone; the modified Nernst-Planck equation is used to analyze the influence of hormone signals on ion channels and transporters, simulate the flow of ions on the cell membrane, and obtain the ion flow concentration.

[0006] Furthermore, the system also includes: a metabolic network and stress response analysis unit; the metabolic network and stress response analysis unit is used to construct a metabolic network model based on flux balance based on ion flow concentration, simulate the influence of hormone signals and ion flow on cell metabolic pathways, and obtain metabolite concentration; evaluate the stress level of cells under endocrine abnormalities, quantify the expression level of stress proteins, and calculate the cell stress level.

[0007] Furthermore, the system also includes: a system evaluation unit; the system evaluation unit is used to calculate the endocrine abnormality degree value based on the metabolite concentration, cell stress level, ion flow concentration and receptor-hormone complex concentration, and compare the endocrine abnormality degree value with a preset abnormal threshold to obtain an analysis and comparison result.

[0008] Furthermore, the intrinsic parameters of hormones include: a reference concentration for describing the hormone concentration under normal conditions, the peak time of hormone secretion, the hormone molecular weight, the hormone half-life, the circadian rhythm frequency, the receptor affinity coefficient, the receptor binding rate constant, the receptor dissociation rate constant, the ion valence, the diffusion coefficient, the activation energy and the inorganic phosphorus concentration.

[0009] Furthermore, the dynamic concentration characteristics are expressed using the following formula: ; in, For time Time High-dimensional concentration characteristics of hormones; is an integer subscript index; For time Time Real-time concentration of hormones; For the Reference concentrations of hormones; For the The peak time of hormone secretion of each hormone; For the The hormone half-life of the hormone; For the The molecular weight of the hormone; is the Boltzmann constant; For body temperature; For the Circadian rhythm frequency of hormones; For the The initial phase angle of the hormone is a set value ranging from 30 degrees to 45 degrees; For the The receptor affinity coefficient of the hormone.

[0010] An intelligent analysis method for endocrine abnormality detection, the method comprising: Step 1: Based on the intrinsic parameters of each hormone and the real-time concentration of each hormone at each time, the dynamic concentration characteristics of each hormone over time are extracted by constructing a high-dimensional feature space mapping function; combining the dynamic concentration characteristics and the intrinsic parameters of each hormone, the binding and dissociation process between the hormone and its receptor is described, and a nonlinear kinetic model is established to obtain the concentration of the receptor-hormone complex; Step 2: Based on the concentration of the receptor-hormone complex and the intrinsic parameters of each hormone, the receptor-hormone complex concentration is converted into a concentration response within the cell through a multi-stage amplification mechanism; the modified Nernst-Planck equation is used to analyze the effect of hormone signals on ion channels and transporters, simulate the flow of ions on the cell membrane, and obtain the ion flow concentration; Step 3: Based on the ion flow concentration, a metabolic network model based on flux balance is constructed to simulate the effects of hormone signals and ion flow on cell metabolic pathways to obtain metabolite concentrations; the degree of cell stress under endocrine abnormalities is evaluated, the expression level of stress proteins is quantified, and the degree of cell stress is calculated; Step 4: Calculate the endocrine abnormality value based on the metabolite concentration, cell stress level, ion flow concentration and receptor-hormone complex concentration, and compare the endocrine abnormality value with the preset abnormality threshold to obtain the analysis and comparison result.

[0011] The intelligent analysis system and method for endocrine abnormality detection of the present invention have the following beneficial effects: Traditional endocrine detection methods are mostly static analysis of single sampling, which is difficult to capture the dynamic characteristics of hormone secretion. The present invention uses a hormone dynamics and receptor interaction analysis unit, uses a high-dimensional feature space mapping function, extracts the dynamic characteristics of hormone concentration in time, and combines a nonlinear dynamic model to accurately describe the binding and dissociation process between hormones and receptors. The design can track the dynamic changes of hormone secretion in real time, including key parameters such as secretion peak time, circadian rhythm frequency and half-life, and provide a high-resolution dynamic description of the hormone secretion pattern under abnormal conditions. This dynamic analysis capability can significantly improve the detection sensitivity of early endocrine abnormalities and avoid the problem of missed diagnosis caused by insufficient data at a single time point. The endocrine system is a highly complex nonlinear dynamic network. The present invention significantly improves the simulation capability of hormone signal transduction, amplification and its system-level effects by introducing a nonlinear dynamic model. For example, the nonlinear model of receptor-hormone complex concentration accurately captures the complex interactive behavior under a multi-hormone environment by combining dynamic concentration characteristics, receptor saturation effects and binding / dissociation rates. At the same time, in the modeling of metabolic networks, the system combines dynamic hormone signals with the kinetic behavior of metabolic pathways for the first time, truly reflecting the multidimensional regulatory effects of hormone signals on metabolic networks. This kinetic modeling capability enables the system to more accurately capture the complex dynamic characteristics of endocrine abnormalities. The intelligent analysis system of the present invention realizes cross-scale integrated analysis from the molecular level to the cellular level and then to the system level. Through dynamic concentration feature extraction and kinetic modeling, the system first describes the kinetic behavior of hormones at the molecular level; through the quantification of signal transduction and ion flow, it further reveals the conduction and amplification process of hormone signals at the cellular level; finally, through metabolic network and stress response analysis, it reveals the cascade effect of hormone signals at the system metabolic level. This cross-scale comprehensive analysis capability enables the system to comprehensively analyze the manifestations of endocrine abnormalities at different biological levels, providing comprehensive data support for the accurate diagnosis and classification of abnormal states. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic diagram of the system structure of an intelligent analysis system for endocrine abnormality detection provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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 creative work should fall within the scope of protection of the present invention.

[0014] Example 1: Reference Figure 1 , an intelligent analysis system for endocrine abnormality detection, the system comprising: a hormone dynamics and receptor interaction analysis unit, an intracellular signal transduction analysis unit, a metabolic network and stress response analysis unit and a system evaluation unit; the hormone dynamics and receptor interaction analysis unit is used to extract the dynamic concentration characteristics of each hormone in time by constructing a high-dimensional feature space mapping function based on the inherent parameters of each hormone and the real-time concentration of each hormone at each time; combining the dynamic concentration characteristics and the inherent parameters of each hormone, describing the binding and dissociation process between the hormone and its receptor, establishing a nonlinear kinetic model, and obtaining the receptor-hormone complex concentration; the intracellular signal transduction analysis unit is used to convert the receptor-hormone complex concentration and the inherent parameters of each hormone into a nonlinear kinetic model through a multi-stage amplification mechanism. The complex concentration is converted into a concentration response in the cell; the modified Nernst-Planck equation is used to analyze the influence of hormone signals on ion channels and transporters, and the flow of ions on the cell membrane is simulated to obtain the ion flow concentration; the metabolic network and stress response analysis unit is used to construct a metabolic network model based on flux balance based on the ion flow concentration, simulate the influence of hormone signals and ion flow on the cell metabolic pathway, and obtain the metabolite concentration; the stress degree of cells under endocrine abnormalities is evaluated, the expression level of stress proteins is quantified, and the cell stress degree is calculated; the system evaluation unit is used to calculate the endocrine abnormality degree value according to the metabolite concentration, the cell stress degree, the ion flow concentration and the receptor-hormone complex concentration, and compare the endocrine abnormality degree value with the preset abnormality threshold to obtain the analysis and comparison result.

[0015] Specifically, the design of the hormone dynamics and receptor interaction analysis unit is based on a deep understanding of the mechanism of action of hormones in vivo. Its core lies in capturing the temporal dynamic changes of hormone concentrations and accurately describing the binding and dissociation process between hormones and target receptors, thereby providing data support for the intelligent detection of endocrine abnormalities. In the endocrine system, hormones, as signal molecules, are secreted by glands into the blood or intercellular fluid. Their concentrations fluctuate over time and bind to specific receptors at the cellular level, triggering signal pathways and a series of physiological reactions. However, this dynamic process is affected by many factors, including the secretion rate, diffusion coefficient, metabolic clearance rate of hormones, and the distribution and activity state of receptors, so it is highly complex. In order to accurately analyze this process, the present invention establishes a complete description method from hormone secretion to receptor binding through high-dimensional feature space mapping functions and nonlinear dynamic modeling. The temporal dynamic characteristics of hormone concentration are the basis of this unit analysis. Hormone concentrations usually show a rapid upward trend in the early stage of secretion, and then gradually decrease to a stable level due to diffusion and metabolism. In this process, the change in hormone concentration is affected by the regulation of the secretion source and external stimuli, and may present periodic fluctuations or short-term pulse signals. The present invention first converts the dynamic changes of hormone concentration into feature vectors by constructing a high-dimensional feature space mapping function to comprehensively characterize its change pattern. For example, the feature vector may include the slope of concentration change, peak time, fluctuation frequency, and diffusion rate. These features not only reflect the concentration distribution of hormones at different time points, but also capture the dynamic behavior of hormone concentration changes, providing multi-dimensional information support for subsequent modeling.

[0016] After extracting the dynamic characteristics of the hormone, the next step is to describe the interaction with the receptor in combination with its inherent parameters. The binding and dissociation process of hormones and receptors is essentially a chemical kinetic reaction, and its rate is determined by the concentration of the hormone and the availability of the receptor. When the hormone molecule approaches the receptor, it binds with a certain probability to form a receptor-hormone complex, and when the external conditions change or the receptor state is unstable, the complex may dissociate, releasing the free state of the hormone and receptor. This process can be described by a nonlinear kinetic model, and its reaction rate depends not only on the instantaneous concentration of the hormone, but also on the binding rate constant and the dissociation rate constant. Specifically, when the hormone concentration is high, the receptor binding rate increases and the generation rate of the receptor-hormone complex reaches a peak; conversely, when the hormone concentration decreases or the receptor is occupied, the rate of complex dissociation will dominate the entire process. This unit simulates this dynamic behavior in real time and accurately calculates the concentration of the receptor-hormone complex as a direct representation of the signal transmission intensity. In addition to describing the binding and dissociation process, the present invention also considers the effect of hormone diffusion on the binding efficiency. The diffusion of hormone molecules in the intercellular fluid is a physical process, and its rate is affected by factors such as molecular size, medium viscosity and temperature. Diffusion determines the available concentration of hormones near the target receptor, which in turn affects the efficiency of the binding reaction. This unit simulates the diffusion behavior by modeling and dynamically adjusting the input parameter of hormone concentration to more accurately predict the rate of formation of receptor-hormone complexes. This approach is particularly suitable for analyzing special cases where hormone concentration is low or diffusion is limited, such as local signaling in microenvironments.

[0017] The intracellular signal transduction analysis unit is an important component of the present invention for analyzing how hormone signals are transmitted from receptors on the cell membrane to the inside of the cell and trigger a series of physiological responses. Its principle is based on accurate modeling of signal amplification mechanisms and ion dynamic changes. After the hormone binds to the target receptor, its effect is not limited to the surface of the cell membrane, but is amplified, dispersed and integrated in the cell through a complex signal transduction network, thereby affecting the functional state inside the cell. In this process, the intensity and speed of signal transmission determine the sensitivity and response ability of the cell to external stimuli. The intracellular signal transduction analysis unit of the present invention systematically reveals how signals propagate in cells and ultimately affect metabolism or stress responses by quantitatively inputting the receptor-hormone complex, combining the modeling of the intracellular multi-stage amplification mechanism and ion flow. When the hormone binds to the receptor to form a receptor-hormone complex, this complex acts as the starting point of signal transduction and activates downstream intracellular molecules, such as enzymes, ion channels or transporters. This activation usually occurs through two main pathways: one is direct signal amplification that relies on intramembrane receptor complexes, such as cascade reactions triggered by activation of G protein-coupled receptors (GPCRs) or tyrosine kinase receptors (RTKs); the other is further initiating ion flow or the generation of secondary signal molecules by changing the electrochemical environment of the cell membrane. The analysis unit of the present invention is capable of receiving concentration changes of receptor-hormone complexes as input parameters, and based on a multi-stage amplification model of intracellular signals, it simulates the amplification process of signal intensity step by step. The core mechanism of signal amplification includes multiple nonlinear processes, such as acceleration of enzymatic reaction rates, increase in channel opening probability, and geometric multiple generation of secondary signal molecules. These nonlinear processes are integrated into a dynamic model, enabling the system to accurately predict the diffusion and amplification effects of signals in different intracellular components.

[0018] Another key link in intracellular signal transduction is the dynamic changes in ion flow. Hormone signals can directly or indirectly regulate the activity of ion channels and transporters, thereby changing the pattern of ion flow across the membrane. Ions, such as calcium ions ( )、Potassium ion( )、Sodium ion( ) and chloride ions ( ), play a vital role in cell signal transduction, because they are not only the carriers of signals, but also can further affect the activation state of other signal pathways through changes in membrane potential. The present invention accurately simulates the transmembrane ion flow caused by hormone signals by introducing a modified Nernst-Planck equation. Specifically, the model not only takes into account the diffusion driving force of ions, but also combines the influence of membrane potential gradient on ion flow, and further adds the influence of dynamic regulatory factors of hormone signals, so as to fully reflect the changes in electrochemical properties of cell membranes under endocrine abnormalities. This simulation not only provides a basis for the quantification of ion flow concentration, but also provides an accurate basis for the input parameters of subsequent metabolic networks.

[0019] The analytical capabilities of this unit are not limited to the quantification of ion concentrations, but can also dynamically reflect the regulatory characteristics of ion channel opening or closing. The receptor-hormone complex further controls the switch state of ion channels by activating secondary signaling molecules, such as cyclic adenosine monophosphate (cAMP) or phosphatidylinositol (IP3), thereby regulating ion flow. This process has extremely high time resolution and usually occurs on a time scale of milliseconds to seconds. Therefore, this unit can capture the rapidly changing ion flow characteristics during signal transmission through a high-time resolution modeling method, providing real-time data support for endocrine abnormality detection. There is also a localization phenomenon of intracellular signals during signal transduction. Hormone signals usually form a specific spatial distribution in cells through the diffusion of secondary signaling molecules, and this distribution may show significant differences due to the shape, structure or spatial localization of the cell receptor. The present invention accurately describes the local distribution of signals by modeling the diffusion behavior of secondary signaling molecules and combining intracellular structural parameters. This spatial distribution analysis is particularly important for evaluating the impact of endocrine abnormalities on specific functional areas (such as the nucleus, mitochondria or other subcellular organelles). In the intracellular signal transduction analysis unit, hormone signals are amplified, diffused and integrated to form a cell response pattern to external stimuli. This pattern is not only related to the initial concentration of the hormone and the receptor interaction characteristics, but also subject to the complex regulation of the intracellular signal transduction network. By simulating this process, the present invention can fully reveal the dynamic propagation law of hormone signals in cells, and provide strong theoretical support and data support for the intelligent detection of endocrine abnormalities. The entire signal transduction analysis process realizes cross-scale modeling from receptors to ion flows, laying a solid foundation for metabolic network and stress response analysis, while significantly improving the detection accuracy and reliability of the system under different pathological conditions.

[0020] The metabolic network is a complex system composed of a series of metabolic pathways, which convert substrates into products through enzymatic reactions to maintain the normal physiological functions of cells. In endocrine regulation, hormone signals not only directly act on key enzymes in metabolic pathways, but also indirectly affect the rate of metabolic reactions by changing the ionic environment in cells. For example, hormones may change the mitochondrial membrane potential through the regulation of ion channels, thereby affecting the efficiency of energy metabolism. The present invention systematically simulates the dynamic regulation of the metabolic network by hormone signals by constructing a metabolic network model based on flux balance. The core of flux balance analysis is to dynamically solve the rate of each reaction in the network using the mass conservation principle of metabolites. This method can comprehensively capture the generation, consumption and transportation process of metabolites in cells, thereby accurately reflecting the dynamic changes of metabolic networks. In the modeling process of metabolic networks, ion flow concentration is integrated into the model as an external input parameter, which directly affects the activity and reaction rate of key enzymes in the network. For example, in the energy metabolism pathway, changes in calcium ion concentration may regulate the activity of enzymes in the tricarboxylic acid cycle, thereby changing the generation rate of ATP. By modeling the relationship between this ion concentration and metabolic reaction, the present invention can quantify the effect of hormone signals on metabolic networks. In addition, the unit also dynamically tracks changes in the concentration of metabolites, including the dynamic distribution of key molecules such as glucose, lactate, and ATP. The concentrations of these molecules reflect the metabolic state of cells under different conditions. Through the analysis of these data, the specific effects of endocrine abnormalities on cellular metabolic functions can be revealed, providing important clues for pathological diagnosis. Stress response is an adaptive response of the metabolic network under external pressure or stimulation. In the context of endocrine abnormalities, this stress response often manifests as a remodeling of metabolic pathways and significant changes in the expression levels of stress proteins. Stress proteins (such as heat shock proteins, antioxidant enzymes, etc.) play a key role in the adaptation of cells to changes in internal and external environments, and their expression levels directly reflect the stress state of cells. The present invention quantifies the dynamic expression levels of stress proteins under different hormone signals by constructing a stress response model. For example, when the intracellular calcium ion concentration increases abnormally, stress proteins may be upregulated through the calcium signaling pathway, and this change will be captured by the system and converted into a quantitative indicator of the degree of stress. In addition, the stress response model can also combine the output of the metabolic network to evaluate the adaptive changes of metabolic pathways under stress conditions, such as whether there is a metabolic bottleneck or insufficient energy supply. This integrated analysis can fully reflect the adaptability of cells under endocrine abnormalities and provide richer information for clinical diagnosis. An important feature of the metabolic network and stress response analysis unit is its cross-scale modeling capability. The metabolic network analyzes the kinetics of chemical reactions at the molecular level, while the stress response analysis captures the overall performance of cells under dynamic pressure at the system level. The combination of the two enables the system to focus on local metabolic changes and global physiological states at the same time, thereby providing a comprehensive understanding of endocrine abnormalities.For example, when a certain hormone signal triggers the accumulation of metabolites in a local metabolic pathway, the system can further analyze whether this local accumulation has a cascade effect on the global metabolic network, thereby evaluating its overall impact on cellular function.

[0021] In the complex regulatory network of the endocrine system, the abnormality of a single parameter is often difficult to fully reflect the functional state of the system as a whole. For example, changes in hormone concentration may only be the primary manifestation of endocrine abnormalities, and its cascade effect on metabolic networks, ion dynamics and stress responses is the key to functional disorders. Therefore, the system evaluation unit of the present invention first converts the output data from multiple analysis units into a comparable numerical format in a standardized manner. For example, the concentration of the receptor-hormone complex reflects the initial intensity of signal transduction, the ion flow concentration describes the transmission of the signal at the cell membrane level, the metabolite concentration reflects the final effect of the signal in the intracellular metabolic network, and the degree of cell stress reveals the overall response ability of the cell. These parameters reflect the state changes of the endocrine system from different dimensions, and after standardization, they can be effectively integrated into a unified evaluation framework. The construction of the evaluation model is based on the principle of multi-parameter weight distribution, aiming to highlight the importance of each parameter under different pathological conditions. The setting of weights is supported by a large amount of experimental data and medical knowledge. For example, in some endocrine diseases, the concentration of metabolites may be the most important indicator, while in other diseases, the change of the concentration of receptor-hormone complexes may be more critical. The system evaluation unit dynamically adjusts the weight coefficient to adapt to the needs of different detection scenarios. This dynamic weight allocation mechanism can not only improve the accuracy of the evaluation results, but also reveal the importance of certain parameters under specific abnormal conditions, providing guidance for subsequent diagnosis and treatment. After obtaining the comprehensive evaluation value, the unit further compares it with the preset abnormal threshold. The threshold is set based on statistical data of healthy people and patient groups, taking into account multiple influencing factors such as age, gender, and living habits. Through this personalized comparison method, the system can distinguish the degree of abnormality of different patients and provide a quantitative abnormality level as the output result. This output not only intuitively reflects the health status of the endocrine system, but also provides an important reference for clinicians' diagnosis.

[0022] Example 2: The intrinsic parameters of hormones include: a reference concentration for describing the hormone concentration under normal conditions, the peak time of hormone secretion, the hormone molecular weight, the hormone half-life, the circadian rhythm frequency, the receptor affinity coefficient, the receptor binding rate constant, the receptor dissociation rate constant, the ion valence, the diffusion coefficient, the activation energy and the inorganic phosphorus concentration.

[0023] Specifically, the reference concentration of hormones is used to describe the average concentration level of hormones under normal physiological conditions and is the benchmark value for detecting abnormalities. The reference concentration provides a comparison standard for the system. When the real-time concentration deviates significantly from this value, it indicates that there may be abnormal secretion or other pathological factors. The peak time of hormone secretion is an important parameter reflecting the dynamic behavior of hormones in time, especially for circadian hormones (such as cortisol or melatonin), the peak time of their secretion is directly related to the normal operation of biological rhythms. By recording and analyzing this parameter, this system can capture endocrine abnormalities caused by circadian rhythm disorders. The molecular weight of hormones is an important determinant of their diffusion rate and transport capacity. Hormones with larger molecular weights usually diffuse more slowly and their range of action is also subject to certain restrictions. The present invention dynamically simulates the distribution of hormones in intercellular fluid or blood by combining molecular weight and diffusion coefficient, thereby accurately describing the concentration changes of hormones near target receptors. This is crucial for accurately modeling the hormone-receptor interaction process. The half-life of hormones is an important parameter for measuring the stability of hormones in the body, which directly affects the duration of their biological activity. Hormones with shorter half-lives (such as insulin) need to be secreted frequently to maintain stable physiological concentrations, while hormones with longer half-lives (such as thyroid hormones) accumulate slowly in the body. The present invention uses half-life parameters, combined with the real-time concentration dynamic curve of hormones, to simulate the metabolic clearance process of hormones, providing a basis for the kinetic modeling of endocrine abnormalities.

[0024] Circadian rhythm frequency reflects the time cycle characteristics of hormone secretion, and it is a characteristic indicator of circadian hormones. By monitoring and analyzing this parameter, the system can determine whether the individual's circadian rhythm is normal, and evaluate the possible impact of biological clock disorders on the endocrine system. Abnormal circadian rhythm frequency is usually closely related to sleep disorders, chronic stress and other related diseases. Receptor affinity coefficient, receptor binding rate constant and receptor dissociation rate constant are key parameters for describing the binding dynamics of hormones and receptors. The affinity coefficient determines the ability of hormone molecules to bind to receptors, while the binding rate and dissociation rate describe the speed of the binding process and the stability of the complex respectively. The present invention accurately simulates the nonlinear process of hormone-receptor interaction through dynamic modeling of these parameters, and calculates the concentration change of receptor-hormone complexes, providing accurate input data for signal transduction analysis. Ion valence and diffusion coefficient are mainly used to analyze the impact of hormone signals on the electrochemical environment of cell membranes. Ion valence determines the mobility of ions in an electric field, while the diffusion coefficient reflects the propagation rate of ions in intercellular fluid or membrane. In combination with these parameters, the present invention simulates the ion flow induced by hormone signals through the modified Nernst-Planck equation and quantifies the changes in transmembrane ion concentration. Activation energy is a parameter that measures the ease of starting a hormone-mediated chemical reaction or biological process. In a metabolic network, the rates of many enzymatic reactions depend on changes in activation energy regulated by hormone signals. The present invention accurately simulates the response characteristics of metabolic pathways under the regulation of hormone signals by dynamically adjusting the activation energy parameters, providing important support for metabolic network modeling. Finally, inorganic phosphorus concentration is an important biochemical indicator for measuring the state of energy metabolism. As a substrate for ATP metabolism, the concentration of inorganic phosphorus directly reflects the energy supply level of the cell. Abnormalities in inorganic phosphorus concentration may indicate metabolic disorders, such as hypophosphatemia, which may lead to impaired energy metabolism. The present invention combines inorganic phosphorus concentration parameters to evaluate the regulatory effect of hormone signals on energy metabolic pathways, providing key input for the overall modeling of metabolic networks.

[0025] Example 3: The dynamic concentration characteristics are expressed using the following formula: ; in, For time Time High-dimensional concentration characteristics of hormones; is an integer subscript index; For time Time Real-time concentration of hormones; For the Reference concentrations of hormones; For the The peak time of hormone secretion of each hormone; For the The hormone half-life of the hormone; For the The molecular weight of the hormone; is the Boltzmann constant; For body temperature; For the Circadian rhythm frequency of hormones; For the The initial phase angle of the hormone is a set value ranging from 30 degrees to 45 degrees; For the The receptor affinity coefficient of the hormone.

[0026] Specifically, the concentration normalization part of the formula is calculated by converting the real-time concentration Divide by the reference concentration , realizing the dimensionless processing of hormone concentration. The purpose of this operation is to eliminate the differences in concentration magnitude between different hormones, so that the concentration changes of different hormones can be compared and analyzed on the same scale. The standardized concentration value can intuitively reflect the degree of deviation of the hormone concentration from its normal reference value, which is of great significance for detecting endocrine abnormalities. When the real-time concentration of a hormone is significantly higher or lower than the reference concentration, the system can quickly identify potential abnormal conditions. The next time dynamic weight part is calculated through a Gaussian function The concentration of hormones is time-weighted. This part reflects the peak time of hormone secretion and half-life Impact on concentration characteristics. The secretion of hormones in the body usually has a specific time pattern. For example, some hormones reach a peak secretion at a specific time of the day, and their concentrations then gradually decrease due to the influence of half-life. The introduction of the Gaussian function enables the formula to highlight the concentration variation characteristics of hormones near the peak secretion time, while weakening the concentration influence away from the peak time point, thereby more accurately capturing the dynamic behavior of hormone secretion.

[0027] The physical properties of the molecule are modified in part by The molecular weight of the hormone With temperature Combined with the concentration characteristics, this term reflects the diffusion capacity of hormone molecules and their movement characteristics in the body environment. The larger the molecular weight of the hormone, the lower its diffusion rate is usually, and thus its distribution range in the body is also limited. The Boltzmann constant and body temperature It reflects the kinetic energy and diffusion behavior of molecules in a thermodynamic environment. Through this correction, the formula not only takes into account the absolute value of hormone concentration, but also incorporates its physical motion characteristics into the calculation of dynamic characteristics, making the concentration characteristics more biologically meaningful. The circadian rhythm characteristics of hormones are introduced into the dynamic concentration characteristics. The secretion of many hormones has a clear circadian rhythm. For example, the secretion of cortisol and melatonin shows periodic changes at different times of the day. and the initial phase angle The specific manifestation of this periodic characteristic is described. By introducing the modulation of the cosine function, the formula can capture the periodic changes in hormone secretion, so that the dynamic concentration characteristics not only reflect the immediate concentration of the hormone, but also include its long-term secretion law. This is particularly important for detecting endocrine abnormalities caused by biological clock disorders, because many endocrine diseases are closely related to the circadian rhythm disorders of hormones. Finally, the receptor affinity weighted part The dynamic concentration characteristics are further adjusted to reflect the binding characteristics between hormone and receptor. Describes the strength of the hormone binding to its receptor. The higher the affinity, the tighter the hormone binds to the receptor and the more efficient the signal transduction. The formula can strengthen the influence of high-affinity hormones in dynamic features, making the role of these hormones in the signal transduction process more prominent. This treatment ensures that the dynamic concentration features not only reflect the concentration changes of hormones, but also take into account their actual effects in signal transduction.

[0028] Example 4: A nonlinear kinetic model was established using the following formula to obtain the receptor-hormone complex concentration: ; in, Indicates time Time The concentration of receptor-hormone complex corresponding to each hormone; is the cell membrane dielectric constant; For the The receptor binding rate constant corresponding to each hormone; is the free receptor concentration; is the total receptor concentration; Indicates time Time The concentration of receptor-hormone complex corresponding to each hormone; is an integer subscript index, and ; For the The receptor dissociation rate constant corresponding to the hormone; is the activation energy, which represents the The energy barrier that needs to be overcome during the binding or dissociation of a hormone to its receptor; is the gas constant; Type of hormone.

[0029] Specifically, the left side of the formula represents the concentration of the receptor-hormone complex The rate of change over time consists of two main components. First, the positive response term includes the hormone concentration , free receptor concentration , receptor binding rate constant , and high-dimensional concentration features . High-dimensional concentration features Calculated by the aforementioned high-dimensional feature extraction formula, it reflects the dynamic behavior of hormone concentration at a specific time point. The dielectric constant of the cell membrane This affects the binding efficiency of hormones and receptors, reflecting the regulatory effect of the cell membrane physical environment on receptor-hormone interaction. The negative reaction term is determined by the receptor dissociation rate constant and receptor-hormone complex concentrations Determines the dissociation process of the complex. In the forward reaction term, the hormone concentration Free receptor concentration The product of indicates that the binding rate of hormone to receptor depends not only on the concentration level of hormone, but also on the number of free receptors. Represents the saturation effect of the receptor, that is, when the total concentration of the receptor-hormone complex approaches the total receptor concentration When , further complex reactions are inhibited. This term reflects the limited nature of receptor resources and ensures that the model remains physiologically reasonable at high hormone concentrations. High-dimensional concentration features By taking the square root of the ratio of the dielectric constant to the receptor binding rate, the receptor binding rate is adjusted to make it more suitable for the electrochemical environment of the cell membrane. The dissociation reaction term is relatively simple and is directly related to the concentration of the complex. Proportional to the dissociation rate constant This term reflects the natural dissociation of the complex in the absence of new hormone molecules. Dissociation rate constant It describes the speed at which the complex returns from a stable state to a free state and is an important parameter that determines the lifetime of the complex.

[0030] The second formula Activation energy involved , which is a parameter that describes the energy barrier that needs to be overcome during the binding or dissociation of hormones from receptors. and temperature The activation energy is standardized so that it can be compared and applied under different temperature conditions. The introduction of activation energy allows the model to take into account the effect of temperature on the hormone-receptor interaction process, thereby improving the adaptability and accuracy of the model under different physiological conditions. The intelligent analysis system of the present invention can calculate and track the concentration changes of the receptor-hormone complex in real time through the nonlinear kinetic model in Example 4. This model not only takes into account the instantaneous concentration of the hormone and the availability of the receptor, but also integrates the effects of the secretion pattern, molecular properties and cell membrane environment of the hormone through parameters such as dynamic concentration characteristics and activation energy. This enables the system to provide more detailed and dynamic analysis results when faced with complex endocrine abnormalities. For example, in some endocrine diseases, the total concentration of the receptor There may be changes in the timing of peak hormone secretion and half-life may deviate from normal values, and these changes can be accurately captured and quantified through nonlinear dynamic models. In addition, the multi-hormone interactions in the model are summed up by This design ensures that the model can handle the complex situation where multiple hormones coexist and influence each other, further improving the reliability and practicality of the system in practical applications. With this model, the intelligent analysis system can comprehensively consider the dynamic behavior and receptor interaction of multiple hormones and generate accurate receptor-hormone complex concentration data, which will serve as the key input for subsequent signal transduction and metabolic network analysis.

[0031] Example 5: The receptor-hormone complex concentration is converted into a concentration response within the cell by the following formula: ; in, Indicates time Time The concentration response of the receptor-hormone complex corresponding to the hormone in the cell; is the number of layers of the cascade reaction; is the dissociation constant; is the ATP concentration; For the The Michaelis constant of the stage amplification; is the change in free energy of phosphorylation; is an integer index; An integer index.

[0032] Specifically, the summation symbol in the formula represents the cumulative effect of signals on multiple reaction levels, where is the number of layers in the cascade reaction. Each layer of the reaction is multiplied by the product term It is expressed as, reflecting the amplification effect of the signal at each level. This multi-level amplification mechanism simulates the typical process of intracellular signal transduction, such as enzymatic reaction and protein kinase cascade reaction, so that the weak initial signal can be effectively amplified and produce significant cellular response. In the formula, The concentration of the receptor-hormone complex is partially normalized to a dimensionless quantity, is the dissociation constant, which reflects the affinity of hormone binding to receptor. A value of 0 indicates high affinity, meaning that even at lower hormone concentrations, the receptor can effectively bind to the hormone and promote signal transduction. Through this standardized treatment, the formula can effectively compare and analyze different hormones and receptors, ensuring the universality and applicability of the model. The next product term The regulation of ATP concentration on reaction rate in each amplification process is described. As the main energy molecule in cells, ATP provides necessary energy support during signal transduction and drives the activity of various enzymatic reactions and protein kinases. For the The Michaelis constant of the stage amplification reflects the half-saturation concentration of ATP in each stage of the reaction. Through this term, the formula can dynamically adjust the efficiency of signal transduction and simulate the effect of changes in ATP concentration on signal amplification, thereby more realistically reflecting the regulation of intracellular energy status on signal transduction.

[0033] Square the entire expression The signal amplification effect at each level is further enhanced, simulating the nonlinear amplification characteristics commonly seen in biological systems. This square relationship means that the signal strength increases exponentially at each level, ensuring that even a weak initial signal can produce a significant cellular response after multiple levels of amplification. This is particularly important for endocrine abnormality detection, because the early signals of many endocrine diseases may be relatively weak. Through this efficient amplification mechanism, the system can capture these subtle abnormal changes. The exponential decay term in the formula describes the attenuation of the signal over time, where For the The half-life of a hormone. This term reflects the natural attenuation trend during signal transduction and simulates the self-regulatory mechanism of intracellular signals. Under physiological conditions, signal transduction is not unlimited, but is regulated by time and feedback mechanisms, avoiding excessive signal amplification and imbalance of cell function. By introducing this attenuation term, the formula can more accurately simulate the dynamic changes of signals in cells and ensure the biological rationality of the model. Finally, Indicates the change in phosphorylation free energy, which is an important parameter for energy conversion in the signal transduction process. Phosphorylation reaction is a key step in intracellular signal transduction. It further transmits and amplifies the signal through protein kinase catalyzing the phosphorylation of substrate proteins. It reflects the energy demand and release of the phosphorylation reaction, and has an important impact on the efficiency and stability of signal transduction. Incorporating this energy parameter into the formula allows the model to not only take into account the concentration changes of the signal, but also integrate the dynamic process of energy conversion, thus improving the model's ability to simulate complex signal transduction mechanisms within cells.

[0034] Example 6: The flow of ions on the cell membrane is simulated by the following formula to obtain the ion flow concentration: ; in, Indicates time At the time The ion flow concentration corresponding to the ion regulated by the hormone. Hormones indirectly affect the transmembrane flow of ions by regulating ion channels or transport proteins. For the Diffusion coefficient of the hormone; is the gradient operator; For the first The ion valence corresponding to the ion regulated by the hormone; is the Faraday constant; is the membrane potential; is the film viscosity.

[0035] Specifically, the first term of the formula Describes the diffusion process driven by ion concentration gradient. Diffusion is the natural migration of ions on both sides of the cell membrane due to concentration differences. The diffusion coefficient Characterizes the diffusion capacity of ions in a medium. Ion concentration gradient The larger the value, the stronger the diffusion driving force and the faster the migration rate of ions. Through this term, the formula can reflect the passive migration behavior of ions on the cell membrane, which is the basic mechanism for cells to maintain the balance of ions in the internal and external environments. The effect of the electric field on the ion flow is taken into account. This term is derived from the modified Nernst-Planck equation, which describes the membrane potential gradient The driving force on charged ions. is the valence of the ion, reflecting the amount of charge carried by the ion; is the Faraday constant, which connects the conversion relationship between charge and matter; is the gas constant, is the absolute temperature. The larger the value, the stronger the driving effect of the electric field on the ions, especially for charged ions, the influence of the electric field is significant. Through this term, the formula can dynamically reflect the regulatory effect of changes in the electrochemical environment of the cell membrane on the flow of ions, which is of great significance for understanding how hormone signals affect ion distribution by changing membrane potential. The signal source term is introduced to describe the regulatory effect of intracellular signal transduction on ion flow. is the intracellular concentration response corresponding to the concentration of the receptor-hormone complex, is the membrane viscosity, which reflects the fluidity and resistance of the membrane. This term indicates that hormones regulate the signal transduction process through receptor-hormone complexes, further affecting the transmembrane flow of ions. For example, hormone signals may activate specific ion channels or transporters, thereby changing the influx or outflow rate of ions. Through this term, the formula can closely integrate the intracellular signal transduction process with the ion flow, and realize the full-chain simulation from external hormone signals to intracellular ion dynamics.

[0036] Example 7: The metabolite concentration was calculated using the following formula: ; in, For time Time The concentration of metabolites corresponding to the hormone; is the proton electrochemical potential difference, which is the set value; is the membrane potential difference, is the set value; is the substrate concentration, which is equal to .

[0037] Specifically, the core part of the formula describes the metabolite concentration The rate of change of ion flow is determined by the generation process driven by ion flow and the consumption process limited by substrate kinetics. In the generation process, the driving force of ion flow comes from the transmembrane electrochemical gradient. This gradient is formed by the difference in ion concentration and membrane potential, and is the key energy source for cells to maintain metabolic function. The formula is expressed by a term including ion concentration , membrane potential gradient and the diffusion coefficient The expression of quantifies the contribution of ion flow to metabolite production. The higher the ion concentration and the larger the membrane potential gradient, the faster the metabolite production rate, which reflects that the cell can efficiently operate the metabolic pathway under energy-sufficient conditions. However, the production of metabolites is not unlimited, and its rate is strictly regulated by substrate concentration and enzyme kinetics. The consumption term in the formula describes the metabolite production under substrate concentration through a kinetic model. This part of the model adopts the typical Michaelis-Menten kinetic form, in which the ratio of substrate concentration to its Michaelis constant determines the reaction rate. Higher substrate concentrations can promote metabolite production, but when the substrate concentration is close to or below the Michaelis constant, the production rate drops significantly, which simulates the inhibitory effect of substrate deficiency on the metabolic network. Through this dynamic restriction mechanism, the formula can not only capture the balance between metabolite production and consumption, but also reflect the potential impact of substrate concentration fluctuations on metabolic pathways.

[0038] The formula further considers the energy regulation of transmembrane electrochemical potential difference and membrane potential difference on metabolite production. and membrane potential difference Together they determine the free energy reserve of the cell, which is used to drive a series of key reactions in the metabolic pathway. The formula incorporates energy parameters into the calculation through an exponential term, which not only enables the model to reflect the efficient operation of the metabolic network under normal physiological conditions, but also simulates changes in the rate of metabolite production under energy-limited conditions. For example, in certain endocrine abnormalities, hormone signals may cause abnormal fluctuations in membrane potential, which in turn affects the transmembrane proton gradient, ultimately having a profound impact on the overall function of the metabolic network. Through the dynamic simulation of the formula, these changes in energy states can be accurately quantified, providing an important basis for abnormality detection. It is worth noting that the formula will also affect the ion flow concentration of ion flow. Tightly coupled with metabolic reactions. Ion flow not only drives metabolite generation, but also has an important influence on the consumption process by regulating electrochemical potential and membrane potential. This bidirectional mechanism is widely present in many biological systems. For example, in mitochondria, the proton gradient not only drives the synthesis of ATP, but also further affects the metabolic balance by regulating the transmembrane transport of substrates and products. The formula captures the dynamic characteristics of multi-factor interactions in metabolic networks by integrating these complex mechanisms into a unified mathematical framework, providing deep analytical capabilities for intelligent analysis systems. The application of this formula in intelligent analysis systems is particularly suitable for detecting the early effects of endocrine abnormalities on metabolic networks. For example, in the case of disordered hormone signals, the system can capture subtle changes in ion flow and metabolic reactions through formulas and generate accurate dynamic curves of metabolite concentrations. These dynamic features can not only be used to determine whether metabolic functions are normal, but also provide specific information on abnormal patterns, such as excessive accumulation or rapid consumption of metabolites. By combining the metabolite concentrations corresponding to different hormones, the system can construct a comprehensive metabolic network state diagram, thereby achieving accurate detection of endocrine abnormalities.

[0039] Example 8: The degree of cell stress is calculated by the following formula: ; in, For the The inorganic phosphorus concentration of the hormone; is the intracellular calcium ion concentration; is the protein folding energy; For the The concentration of heat shock proteins corresponding to the hormone; For time Time The degree of cellular stress corresponding to each hormone.

[0040] Specifically, the concentration of heat shock proteins It is an important indicator of stress response. Heat shock proteins (HSPs) are a class of protective proteins widely expressed by cells when responding to high temperatures, oxidative stress or other stresses. Their main function is to help damaged proteins fold back into the correct three-dimensional structure or prevent further aggregation. Normalization to a fixed ratio (such as 1.2) reflects the change in heat shock protein concentration relative to the baseline value. This normalization process can compare the expression changes of heat shock proteins under different hormone conditions, thereby revealing the changing pattern of stress intensity. Secondly, the exponential term in the formula Protein folding energy Protein folding energy reflects the energy barrier that heat shock proteins need to overcome to repair damaged proteins. is the Boltzmann constant, is the absolute temperature. A higher folding energy means that more heat shock protein activity is required to maintain protein stability, and therefore the stress level is higher. By introducing this term, the formula can dynamically capture the changes in heat shock protein activity of cells under different temperature and pressure conditions, thereby more accurately reflecting the stress state.

[0041] The second major factor in the formula is the calcium ion concentration Calcium ions are a key regulator in cell signaling. Under stress conditions, intracellular calcium ion concentrations usually increase significantly. The change in calcium ion concentration is introduced into the calculation of stress degree. When the calcium ion concentration increases, the value of this item also increases, reflecting the state of cells activating more stress pathways due to the enhancement of calcium signals. For example, calcium ions can activate a variety of signaling proteins, such as calmodulin kinase (CaMK), thereby further amplifying stress signals. Finally, the formula It reflects the influence of cell energy status on stress response. ATP is the main energy molecule in cells, and the concentration of inorganic phosphorus It reflects the metabolic state after ATP decomposition. Under stress conditions, the cell's demand for ATP increases, so the ratio of ATP to inorganic phosphorus becomes an important indicator of energy supply capacity. The formula introduces this ratio into the calculation of stress level in the form of square root, so that the stress capacity of cells under energy-deficient conditions can be quantified.

[0042] Example 9: Calculate the endocrine abnormality value by the following formula: ; in, For time The degree of endocrine abnormality at that time; is the number of hormone types; is the normalized mean of the set cell stress level; is the normalized mean value of the set ion current concentration; is the set mean metabolite concentration; The concentration of the receptor-hormone complex is normalized to the mean.

[0043] Specifically, in the summation part of the formula, the number of hormone types Determines the range of hormones analyzed by the system. Each hormone is analyzed by its corresponding specific biological parameter (stress level). , ion current concentration , metabolite concentration , receptor-hormone complex concentration ) weights and normalizes the contribution of each hormone to the abnormality. This structure not only ensures that the independent effects of different hormones on the abnormality are included in the evaluation, but also reflects the importance of each parameter through weight distribution. The weight coefficients 0.3, 0.3, 0.2 and 0.2 in the formula are empirically set based on biological significance and experimental data. and ion current concentration Each of them has a weight of 30%, indicating that in the evaluation of endocrine abnormalities, the influence of these two on the functional status of the system is more significant. It reflects the adaptability of cells to endocrine abnormalities and is usually a sensitive indicator of early disease. It reflects the regulation of the electrochemical environment of the cell membrane by endocrine signals and is an important part of hormone signal transduction. and receptor-hormone complex concentrations Each accounts for 20% of the weight, indicating that although the impact of metabolic processes and hormone-receptor interactions on endocrine status is slightly lower than the first two, they are still key factors that cannot be ignored. The introduction of is to eliminate the differences in dimensions and scales of different parameters so that they can be weighted compared within the same evaluation framework. The setting of the normalized mean is usually based on the experimental data of healthy individuals. Through this processing, the formula can effectively reflect the degree of deviation of different parameters from the normal state. For example, if the degree of cell stress caused by a certain hormone is significantly higher than the normalized mean , then its corresponding weighted value will increase, thus affecting the degree of endocrine abnormality. The integral form of the formula provides a dynamic cumulative evaluation method for endocrine abnormality detection. As time goes by, the system will continue to accumulate the changing trends of multiple parameters and generate a gradually increasing abnormality value. This design can capture the cumulative effect of abnormal states in the endocrine system and is particularly suitable for dynamic monitoring and evaluation of disease progression. For example, in some chronic endocrine disorders (such as diabetes or thyroid disease), the abnormal state may gradually worsen over time, and the value calculated by this formula The value can intuitively reflect the development trend of the disease and provide an important basis for clinical decision-making.

[0044] Embodiment 10: An intelligent analysis method for endocrine abnormality detection, the method comprising: Step 1: Based on the intrinsic parameters of each hormone and the real-time concentration of each hormone at each time, the dynamic concentration characteristics of each hormone over time are extracted by constructing a high-dimensional feature space mapping function; combining the dynamic concentration characteristics and the intrinsic parameters of each hormone, the binding and dissociation process between the hormone and its receptor is described, and a nonlinear kinetic model is established to obtain the concentration of the receptor-hormone complex; Step 2: Based on the concentration of the receptor-hormone complex and the intrinsic parameters of each hormone, the receptor-hormone complex concentration is converted into a concentration response within the cell through a multi-stage amplification mechanism; the modified Nernst-Planck equation is used to analyze the effect of hormone signals on ion channels and transporters, simulate the flow of ions on the cell membrane, and obtain the ion flow concentration; Step 3: Based on the ion flow concentration, a metabolic network model based on flux balance is constructed to simulate the effects of hormone signals and ion flow on cell metabolic pathways to obtain metabolite concentrations; the degree of cell stress under endocrine abnormalities is evaluated, the expression level of stress proteins is quantified, and the degree of cell stress is calculated; Step 4: Calculate the endocrine abnormality value based on the metabolite concentration, cell stress level, ion flow concentration and receptor-hormone complex concentration, and compare the endocrine abnormality value with the preset abnormality threshold to obtain the analysis and comparison result.

[0045] As described above, 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 aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, 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.

Claims

1. An intelligent analysis system for endocrine abnormality detection, characterized in that: The system includes: a hormone dynamics and receptor interaction analysis unit and an intracellular signal transduction analysis unit; the hormone dynamics and receptor interaction analysis unit is used to extract the dynamic concentration characteristics of each hormone in time by constructing a high-dimensional feature space mapping function based on the inherent parameters of each hormone and the real-time concentration of each hormone at each time; the binding and dissociation process between the hormone and its receptor is described by combining the dynamic concentration characteristics and the inherent parameters of each hormone, and a nonlinear dynamic model is established to obtain the receptor-hormone complex concentration; the intracellular signal transduction analysis unit is used to convert the receptor-hormone complex concentration into an intracellular concentration response through a multi-stage amplification mechanism based on the receptor-hormone complex concentration and the inherent parameters of each hormone; and the modified Nernst-Planck equation is used to analyze the influence of hormone signals on ion channels and transport proteins, simulate the flow of ions on the cell membrane, and obtain the ion flow concentration.

2. The intelligent analysis system for endocrine abnormality detection according to claim 1, characterized in that: The system also includes: a metabolic network and stress response analysis unit; the metabolic network and stress response analysis unit is used to construct a metabolic network model based on flux balance based on ion flow concentration, simulate the influence of hormone signals and ion flow on cell metabolic pathways, and obtain metabolite concentrations; evaluate the stress level of cells under endocrine abnormalities, quantify the expression level of stress proteins, and calculate the cell stress level.

3. The intelligent analysis system for endocrine abnormality detection according to claim 2, characterized in that: The system also includes: a system evaluation unit; the system evaluation unit is used to calculate the endocrine abnormality value according to the metabolite concentration, the cell stress level, the ion flow concentration and the receptor-hormone complex concentration, and compare the endocrine abnormality value with the preset abnormality threshold to obtain an analysis and comparison result.

4. The intelligent analysis system for endocrine abnormality detection according to claim 3, characterized in that: The intrinsic parameters of hormones include: reference concentration used to describe the hormone concentration under normal conditions, peak time of hormone secretion, hormone molecular weight, hormone half-life, circadian rhythm frequency, receptor affinity coefficient, receptor binding rate constant, receptor dissociation rate constant, ion valence, diffusion coefficient, activation energy and inorganic phosphorus concentration.

5. The intelligent analysis system for endocrine abnormality detection according to claim 4, characterized in that: The dynamic concentration characteristics are expressed using the following formula: ； in, For time Time High-dimensional concentration characteristics of hormones; is an integer subscript index; For time Time Real-time concentration of hormones; For the Reference concentrations of hormones; For the The peak time of hormone secretion of each hormone; For the The hormone half-life of the hormone; For the The molecular weight of the hormone; is the Boltzmann constant; For body temperature; For the Circadian rhythm frequency of hormones; For the The initial phase angle of the hormone is a set value ranging from 30 degrees to 45 degrees; For the The receptor affinity coefficient of the hormone.

6. An intelligent analysis method for endocrine abnormality detection, characterized in that: The method comprises: Step 1: Based on the intrinsic parameters of each hormone and the real-time concentration of each hormone at each time, the dynamic concentration characteristics of each hormone over time are extracted by constructing a high-dimensional feature space mapping function; combining the dynamic concentration characteristics and the intrinsic parameters of each hormone, the binding and dissociation process between the hormone and its receptor is described, and a nonlinear kinetic model is established to obtain the concentration of the receptor-hormone complex; Step 2: Based on the concentration of the receptor-hormone complex and the intrinsic parameters of each hormone, the receptor-hormone complex concentration is converted into a concentration response within the cell through a multi-stage amplification mechanism; the modified Nernst-Planck equation is used to analyze the effect of hormone signals on ion channels and transporters, simulate the flow of ions on the cell membrane, and obtain the ion flow concentration; Step 3: Based on the ion flow concentration, a metabolic network model based on flux balance is constructed to simulate the effects of hormone signals and ion flow on cell metabolic pathways to obtain metabolite concentrations; the degree of cell stress under endocrine abnormalities is evaluated, the expression level of stress proteins is quantified, and the degree of cell stress is calculated; Step 4: Calculate the endocrine abnormality value based on the metabolite concentration, cell stress level, ion flow concentration and receptor-hormone complex concentration, and compare the endocrine abnormality value with the preset abnormality threshold to obtain the analysis and comparison result.

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