Method for dynamically monitoring enamel demineralization risk of orthodontic patient based on salivary enzyme activity spectrum

Abstract

The invention relates to the technical field of oral medicine, in particular to a method for dynamically monitoring the enamel demineralization risk of an orthodontic patient based on a salivary enzyme activity spectrum, and the method comprises the following steps: S1, obtaining a saliva sample from the orthodontic patient at least twice in an orthodontic treatment process; s2, treating the saliva sample to obtain a standardized saliva sample; s3, analyzing the standardized saliva sample to obtain at least two different enzymatic characteristic indexes of at least one enzyme or enzyme system in the saliva, and reflecting multiple molecular characteristics of the enzyme or enzyme system or response of the enzyme or enzyme system to environmental change through the enzymatic characteristic indexes; and S4, dynamically evaluating the enamel demineralization risk of the orthodontic patient based on the change of the enzymological characteristic indexes obtained at different time points. By analyzing multi-dimensional molecular behaviors and dynamic changes of enzymes or enzyme systems in saliva, the method aims at overcoming the defects of the prior art in the aspects of early warning ability, dynamic nature and individualized evaluation, and early, dynamic and individualized monitoring of the enamel demineralization risk of an orthodontic patient is achieved.

Description

Technical Field

[0001] This invention relates to the field of oral medicine technology, and in particular to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity spectra. Background Technology

[0002] Orthodontic treatment, due to the increased plaque retention caused by its appliances (such as brackets and archwires), alters the oral microenvironment and significantly increases the risk of enamel demineralization. Enamel demineralization is the process of mineral loss from tooth enamel, initially manifesting as visible chalky white patches (white spot lesions, WSLs). WSLs not only affect aesthetics but can also develop into dental caries. Therefore, effective monitoring and early intervention of enamel demineralization risk during orthodontic treatment are crucial.

[0003] Current methods for monitoring enamel demineralization primarily rely on clinical examinations, such as visual observation and palpation of enamel sclerosing vessels (WSLs) by dentists. While these methods are simple to perform, they can only detect demineralization lesions that have already undergone structural damage and reached a certain degree of severity, typically in the mid-to-late stages of the disease. They lack sensitivity to early, minute mineral losses and are therefore unable to provide timely warnings. More advanced auxiliary examination techniques, such as quantitative light-induced fluorescence (QLF) or optical coherence tomography (OCT), can detect subclinical demineralization, but they still mainly assess structural or optical changes in enamel and require specific equipment, making them relatively complex to operate. A common limitation of these methods is that they are essentially diagnosing or assessing existing lesions, rather than providing early prediction and dynamic tracking of risk states.

[0004] Saliva-based analysis methods have been explored for assessing oral health and caries risk due to their non-invasive nature. Traditional saliva testing focuses on macroscopic indicators such as saliva flow rate, pH, buffering capacity, bacterial count, or the activity of certain single enzymes. While these indicators reflect the overall oral environment, they often lack sufficient sensitivity and specificity to predict early, subtle changes in enamel demineralization risk in individual patients within an orthodontic context. The activity of a single salivary enzyme fails to comprehensively reflect the functional state of the enzyme within the complex oral microenvironment and its sensitive response to minor perturbations, limiting its potential as an early, dynamic, and personalized risk indicator.

[0005] In summary, existing technologies have significant shortcomings in monitoring the risk of enamel demineralization in orthodontic patients: they lack early warning capabilities before the onset of clinical lesions; they are mostly focused on the diagnosis of existing lesions; and traditional non-invasive saliva analysis methods are not sensitive enough to early, subtle risk changes, making it difficult to achieve dynamic and accurate assessment of individual risk status. Therefore, there is an urgent need for a new monitoring method that can provide early warning, high sensitivity, multi-dimensionality, dynamic and individualized results. Summary of the Invention

[0006] To address the technical problems of the prior art, this invention provides a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity spectra. By analyzing the multidimensional molecular behavior and dynamic changes of enzymes or enzyme systems in saliva, this method aims to overcome the shortcomings of existing technologies in early warning capabilities, dynamism, and individualized assessment, thereby achieving early, dynamic, and individualized monitoring of the risk of enamel demineralization in orthodontic patients.

[0007] This invention provides a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles. The method first obtains saliva samples from the orthodontic patient, with the acquisition performed at least twice during the entire orthodontic treatment process. Then, the collected saliva samples are processed to obtain standardized saliva samples for subsequent analysis. Next, the standardized saliva samples are analyzed to obtain at least two different enzymatic properties of at least one selected enzyme or enzyme system in the saliva. These properties are designed to reflect multiple molecular characteristics of the enzyme or enzyme system or its response pattern to changes in the oral microenvironment. Finally, based on the changing trends of the enzymatic properties obtained at different time points (i.e., the time points corresponding to at least two sample acquisitions), the risk of enamel demineralization in orthodontic patients is dynamically assessed. In this process, the enzymatic properties obtained in step S3 directly serve as the basis for risk assessment in step S4. The core of step S4 is to dynamically track and reflect the changes in the patient's enamel demineralization risk status over time by comparing the enzymatic properties obtained at different time points that reflect the response patterns of their salivary enzymes or enzyme systems under changes in the oral microenvironment, thereby achieving dynamic monitoring.

[0008] The core of the method described above lies in utilizing the diverse and dynamically changing characteristics of enzymes or enzyme systems in saliva to reflect the patient's risk status. Step S1 is the sample acquisition step, which provides the raw material required for analysis—a saliva sample. The key is that "at least two acquisitions must be performed during orthodontic treatment," which provides the basis for subsequent "dynamic" monitoring. Without at least two sample acquisitions, it is impossible to observe changes in enzymatic properties over time, and thus dynamic assessment cannot be achieved. Step S2 is the sample processing step, which preprocesses the original saliva sample to obtain a "standardized saliva sample." Saliva has a complex composition, containing cells, microorganisms, food debris, various proteins (including enzymes), and small molecules. Standardization processing (such as centrifugation to remove solid components, aliquoting, cryopreservation, and protein concentration homogenization) aims to reduce the impact of non-essential differences between samples and external interference factors (such as sampling method, saliva flow rate, storage conditions, etc.) on subsequent enzymatic analysis results, ensuring comparability between samples from different time points and different patients, thereby enabling the subsequently measured enzymatic properties to more accurately reflect the intrinsic properties of the enzyme itself and its response to the oral microenvironment. Standardization is fundamental to achieving reliable "analysis" and "dynamic assessment." Step S3 is the enzymatic property index analysis step. This step does not simply measure the activity level of a single enzyme, but emphasizes the analysis of "at least two different enzymatic property indices of at least one enzyme or enzyme system." This reflects the key innovation of this invention, which differs from existing technologies—exploring the behavior and response of enzyme molecules from multiple dimensions and perspectives. These "different enzymatic property indices" may include, but are not limited to, those listed in the appendix technical disclosure, such as the inactivation rate of "sentinel enzymes," substrate selectivity, stability, allosteric activation threshold, interfacial kinetic parameters, cascade reaction signal flow distribution, and flux redistribution ability. These indices are often more sensitive to capturing subtle changes in the oral microenvironment (such as slight pH decreases, ion concentration fluctuations, oxidative stress, etc.) during the early stages of demineralization risk, because these microenvironmental changes can directly affect the enzyme's conformation, stability, catalytic efficiency, and interaction with other molecules. By measuring "at least two different" indices, a more comprehensive and three-dimensional understanding of the enzyme's functional state can be obtained, avoiding false positives or false negatives that may result from a single indicator, and improving the accuracy of risk assessment. This step directly provides the "basis" for subsequent risk assessment. Step S4 is the risk assessment step, the core of which lies in assessing the changes in enzymatic characteristic indicators obtained at different time points, and achieving "dynamic tracking" by comparing indicators at different time points. This means that the assessment does not only rely on the absolute indicator values ​​at a certain time point, but more importantly, it observes the "trend of change" and "degree of deviation" of these indicators over time.For example, whether the inactivation rate of a certain enzyme accelerates over time, or whether the substrate selectivity spectrum of a certain enzyme drifts from its baseline state in a specific direction, these dynamic changes can reflect the stability of the patient's oral microenvironment and its resistance or adaptation to risk factors. Through this dynamic tracking, the emergence, aggravation, or mitigation of risks can be detected in a timely manner, thus achieving "dynamic monitoring." The indicator data provided by S3 is the input for S4 to perform calculations, comparisons, and judgments. The repeated execution of S1, S2, and S3 constitutes the process of acquiring dynamic data, while S4 uses this dynamic data to analyze and output risk information.

[0009] Therefore, S1 and S2 form the foundation for data acquisition and preprocessing, S3 is crucial for information extraction, and S4 serves as the decision-making layer for risk assessment and early warning, utilizing the dynamic information generated by the repeated execution of S1-S3. Together, they work in close coordination to achieve the goal of early risk monitoring based on the dynamic characteristics of salivary enzymes.

[0010] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles, step S1 includes:

[0011] Collect either non-irritating or irritating saliva. For non-irritating saliva, the procedure is as follows: after fasting for 2 hours, the patient rinses their mouth for 5 minutes and collects at least 2 mL of naturally flowing saliva in a sterile container. For irritating saliva, the procedure is as follows: after chewing a tasteless paraffin block for 1 minute, the patient spits out the first mouthful of saliva, continues chewing for 5 minutes, spits out saliva into a sterile container every 30 seconds, and collects at least 5 mL of saliva.

[0012] It is understandable that the oral microenvironment is a complex and dynamic system, with saliva being a crucial component. The properties of saliva (such as flow rate, pH, ion concentration, and enzyme content) are influenced by various factors, including physiological state, diet, medication, and the presence of pathological processes. Different types of saliva collection (non-irritating vs. irritating) can reflect saliva composition under different physiological states. Non-irritating saliva better reflects the basic oral microenvironment at rest, with a relatively stable composition; irritating saliva reflects the oral cavity's response to stimulation, exhibiting a high flow rate, which facilitates sufficient sample collection, and certain enzymes (such as salivary α-amylase) show increased secretion upon stimulation. Standardized sample collection is essential to ensure accurate and reliable monitoring of salivary enzyme activity profiles and to enable effective comparisons between samples from different time points. Detailed pre-collection preparation guidelines (fasting, rinsing the mouth, abstaining from smoking and alcohol, etc.) are designed to eliminate the transient influence of external factors on saliva composition. The standardized collection procedures specify the collection time (e.g., starting after 5 minutes of non-irritating sitting), method (natural flow or specific chewing stimulation), duration, and saliva volume requirements. This aims to minimize sample variability caused by the collection method itself, ensuring comparability in type and quantity between each collection. This allows subsequent detection of enzymatic changes to more accurately reflect changes in the patient's physiological state or oral microenvironment, rather than artifacts introduced by the collection method. Using sterile containers ensures sample purity, preventing external microbial contamination from affecting enzyme activity. The technical advantages of this approach are twofold: First, it provides stable and representative samples. Whether collecting non-irritating saliva reflecting a resting state or irritating saliva reflecting a stimulus response, the standardized process ensures consistent sample type and better comparability in physiological state, composition, and flow rate. Second, it reduces individual variability and experimental error. The standardized collection steps eliminate various potential interfering factors, such as diet, oral hygiene product use, and uneven collection time, significantly reducing batch-to-batch variations and improving the accuracy and reliability of subsequent enzymatic index measurements. It also supports the effectiveness of dynamic monitoring. By using the same standardized method to collect samples at different time points, the changes in monitored enzymatic properties accurately reflect the changes in the patient's oral microenvironment or physiological state over time, providing a solid foundation for dynamically assessing the risk of enamel demineralization. In summary, the above-mentioned saliva collection method, through standardized procedures, effectively controls the variability of sample collection, providing the prerequisites for highly sensitive enzymatic property analysis and dynamic risk monitoring in subsequent steps of this invention, significantly improving the reliability and clinical applicability of the method.

[0013] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles, step S2 includes:

[0014] The collected saliva was centrifuged at 4000×g (g is the acceleration due to gravity) for 10 minutes at 4°C for 30 minutes to remove cellular components and food residue, obtaining the supernatant. The supernatant was aliquoted into multiple 200μL / tube aliquots, and a mixture of protease inhibitors was added to a final concentration of 1mM. The aliquots were then rapidly frozen to -80°C for storage. Before use, the samples were thawed slowly in a 4°C water bath. The protein concentration was measured immediately after thawing and then standardized to a uniform protein concentration.

[0015] It is understandable that raw saliva samples contain a large number of non-enzymatic components, such as oral epithelial cells, leukocytes, bacteria, and food debris. These components may not only interfere with enzyme activity measurements, but may also contain proteases or other active substances that degrade the target enzyme or affect the balance of the enzyme system. Therefore, centrifugation is crucial to remove these solid components and cells to obtain a supernatant primarily containing soluble proteins (including the target enzyme and endogenous inhibitors). Operating at 4°C and limiting the processing time (within 30 minutes) is to minimize changes in and degradation of the inherent enzyme activity in saliva, especially some temperature-sensitive enzymes and inhibitors. After obtaining the supernatant, aliquoting and rapid freezing are necessary for long-term preservation and to ensure sample stability. Rapid freezing to -80°C minimizes the damage to protein structure caused by ice crystal formation and inhibits biochemical reactions. Adding a protease inhibitor mixture (such as PMSF, EDTA, ampicillin) prevents degradation of the target enzyme or other proteins (such as endogenous inhibitors) by endogenous or exogenous proteases during sample processing and preservation. This is crucial for maintaining the integrity of salivary enzyme activity profiles and protein composition, especially for detecting enzyme-inhibitor balance-related indicators or indicators involving protein stability. Slow thawing (4°C water bath) reduces protein aggregation and denaturation compared to rapid thawing. Finally, determining protein concentration and standardizing dilution to a uniform concentration (e.g., 1.0 mg / mL) is a core step in achieving comparability between different samples. The total protein concentration of saliva varies significantly across individuals and over time due to factors such as flow rate and fluid balance. Directly measuring enzyme activity can lead to results affected by the dilution or concentration effect of total protein concentration. Diluting all samples to the same total protein concentration effectively standardizes based on total protein content, ensuring that subsequent measurements of specific enzyme activities or enzymatic properties better reflect the relative abundance of the enzyme or enzyme system or its inherent characteristics under specific microenvironments, rather than simply fluctuations in saliva flow rate or total protein content. The above approach first yields pure, soluble protein-enriched samples because centrifugation effectively removes cells and solid impurities, reducing interference and allowing subsequent analysis to focus on the bioactive soluble components in saliva. It also improves sample stability and storability, as the addition of protease inhibitors and rapid freezing significantly reduce changes in enzyme activity and protein structure during storage, ensuring long-term sample stability and supporting the need for multiple, dynamic monitoring. Furthermore, it eliminates the influence of variations in total protein concentration. By measuring and standardizing saliva total protein concentration, it ensures that the relative enzyme content is comparable across samples from different time points or patients for enzymatic characterization analysis. This makes the measurement results more reflective of changes in the intrinsic properties of enzyme molecules (such as conformational stability, substrate affinity, and sensitivity to inhibitors) rather than being affected by fluctuations in total protein concentration.Furthermore, it enhances the sensitivity and accuracy of detection because standardized sample processing reduces variability introduced by non-biological factors, improving the precision and reproducibility of enzymatic property measurements, enabling the method to capture early, subtle changes in enzyme activity profiles. In summary, the above-described approach, through meticulous sample pretreatment and standardized procedures, ensures that samples used for enzymatic analysis are stable, pure, and comparable. This is crucial for accurately measuring the multidimensional characteristics of salivary enzymes and conducting effective dynamic monitoring, and is a key guarantee for achieving high sensitivity and reliability of the method.

[0016] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles, the enzymatic characteristic indicators in step S3 include at least one of the following:

[0017] Inactivation rate of salivary enzymes; competitive enzyme inhibition release effect of salivary enzymes; changes in the selectivity of salivary enzymes for non-natural substrates; changes in the stability of salivary enzymes under sublethal denaturant concentrations; changes in the allosteric switch trigger threshold required for salivary enzyme activation; changes in interfacial kinetic parameters of the interaction between salivary enzymes and solidified substrates or substrate analogs; changes in signal flow distribution preferences in enzyme-catalyzed cascade reactions; changes in the flux redistribution capacity within the enzyme system after the introduction of exogenous predatory substrate analogs.

[0018] Understandably, traditional enzymological analysis typically only measures the enzyme's maximum reaction rate (Vt). max ) or catalytic efficiency (k cat / K m This primarily reflects the quantity of enzymes and their catalytic ability under optimal conditions. However, the function of enzymes goes far beyond this. The structure, conformational stability, interactions with other molecules, responsiveness to environmental changes, and coordination within complex networks of enzyme molecules can more comprehensively reflect their state and function in the real physiological microenvironment. Early enamel demineralization risk is often accompanied by subtle changes in the oral microenvironment (such as local pH fluctuations, ion imbalances, oxidative stress, metabolite changes caused by altered microbiota, and the appearance of inflammatory factors). These changes may not be sufficient to cause complete enzyme inactivation or drastic concentration changes, but they are enough to affect the conformation, stability, and interaction patterns of enzyme molecules, thereby altering their behavior under suboptimal or perturbed conditions. The eight enzymatic property indicators listed in the above scheme each probe the state of salivary enzymes or enzyme systems from different molecular levels and functional perspectives:

[0019] Indicator 1, inactivation rate: measures the stability of enzyme molecules under specific stress conditions (such as low pH, high temperature). Decreased stability is a direct manifestation of damage to the enzyme molecule's structure or conformational instability.

[0020] Indicator 2, competitive enzyme inhibition relief effect: This detects the dynamic balance between an enzyme and its endogenous inhibitors. This balance may be disrupted by changes in the abundance or activity of the inhibitor or the enzyme's sensitivity to the inhibitor, reflecting the health of the broader protein-protein interaction network.

[0021] Indicator 3, Selectivity for Non-Natural Substrates: This indicator sensitively reflects subtle conformational changes in the enzyme's active site or substrate-binding region. These changes may not affect the high efficiency of catalysis for natural substrates, but they can alter the relative affinity or catalytic efficiency for structurally similar or differently bound non-natural substrates.

[0022] Indicator 4: Stability changes under sublethal denaturant concentrations: This indicator probes the intrinsic stability or flexibility of the enzyme's molecular structure under mild stress conditions (insufficient to completely denature). Some enzymes may more readily lose activity under sublethal stress, reflecting their vulnerability to environmental disturbances.

[0023] Indicator 5: Changes in the trigger threshold of the allosteric switch required for activation: This measures the sensitivity of the enzyme's allosteric regulation mechanism. The activity of some enzymes is regulated by specific small molecules (allosteric effectors). Changes in the concentration or conditions of the effector required for activation reflect the responsiveness of the enzyme's allosteric site state or overall conformation to regulatory signals.

[0024] Index 6: Changes in interfacial kinetic parameters of interaction with solid-phase substrates or substrate analogs: Simulating the behavior of enzymes at solid-liquid interfaces such as biofilms or tooth enamel surfaces. Changes in kinetic parameters such as binding rate and dissociation rate reflect the functional state of enzymes and their interaction characteristics with surfaces in complex biological interfacial environments more effectively than simple solution-phase activity.

[0025] Indicator 7: Changes in signal flow distribution preferences in enzyme cascade reactions: This detects the coordination of enzymes in multi-step, branched, or competitive pathways. Changes in the microenvironment may disproportionately affect the activity of different enzymes or substrate competition in cascade reactions, leading to characteristic changes in the proportion of intermediate or branched products, reflecting the adaptation or dysregulation of the overall pathway function.

[0026] Index 8: Changes in the flux redistribution capacity within the enzyme system after the introduction of exogenous predatory substrate analogs: This is an "active probing" method. By introducing a disruptor, it tests whether an enzyme system (usually a metabolic pathway) can maintain downstream product production by regulating enzyme activity or expression when disturbed. Flux redistribution capacity reflects the system's reliability and its ability to buffer against stress.

[0027] By measuring and analyzing at least one (preferably multiple) of the enzymatic properties listed above, this invention firstly provides multi-dimensional information on the molecular state of enzymes, overcoming the limitations of traditional single-enzyme activity measurements. It comprehensively characterizes the molecular behavior patterns of the salivary enzyme system from multiple levels, including conformational stability, molecular flexibility, interactions with other molecules, allosteric regulation, interfacial behavior, and functional coordination in complex systems. Simultaneously, it improves the sensitivity and specificity of early risk detection. These complex enzymatic properties are often more sensitive to subtle changes in the microenvironment than total enzyme activity, enabling the detection of the subtle impact of oral microenvironment deterioration on salivary enzyme system function in the early stages of enamel demineralization, before the appearance of clinical symptoms. Different indicators may have specific responses to different types of microenvironmental stress; combining multiple indicators can improve diagnostic specificity. Furthermore, it can reveal underlying molecular mechanisms. Changes in these indicators not only indicate risk but also reflect potential structural or functional changes in enzyme molecules under demineralization risk microenvironments, contributing to the understanding of the biochemical processes of demineralization and providing a theoretical basis for risk intervention. Furthermore, it enables personalized risk assessment, as the composition and characteristics of salivary enzyme systems differ among individuals, leading to varying response patterns to risk factors. Multidimensional indicators provide richer individual characteristic information, laying the foundation for establishing personalized risk assessment models. In summary, the multidimensional enzymatic characteristic indicators listed above are the core technical content enabling early, highly sensitive, and mechanistic risk monitoring in this invention. Together, they construct an "enzyme activity spectrum" that surpasses traditional enzyme activity measurements, providing a more comprehensive and in-depth reflection of the functional status of the salivary enzyme system as a barometer of the oral microenvironment.

[0028] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles, step S3, determining the inactivation rate of salivary enzymes, includes the following steps: mixing a standardized saliva sample with an inactivation buffer and incubating it at a preset temperature of 37°C for a predetermined time; removing the reaction solution, adding an activity assay buffer and substrate, and terminating the reaction after a predetermined time; measuring the absorbance of the product and calculating the percentage of residual activity; plotting a curve of the percentage of residual activity against time, and obtaining the apparent inactivation rate constant through linear regression; wherein the salivary enzyme is salivary alkaline phosphatase (SAP), the pH of the inactivation buffer is 6.2-6.6, and it contains 0.5-2.5 mM Ca. 2+ .

[0029] It is understood that the above scheme defines the specific method for determining the inactivation rate of salivary enzyme in step S3, and specifies the target enzyme as salivary alkaline phosphatase (SAP). The inactivation reaction (sample + inactivation buffer, preset temperature 37°C, predetermined incubation time), reaction termination, activity assay (addition of activity assay buffer and substrate, reaction terminated after predetermined time), product absorbance measurement, calculation of residual activity percentage, and plotting curves and obtaining the apparent inactivation rate constant through linear regression are described in detail. The key limitations are that the salivary enzyme is SAP, the pH of the inactivation buffer is 6.2-6.6, and it contains 0.5-2.5 mM Ca. 2+ This approach focuses on the aforementioned indicator of "salivary enzyme inactivation rate," specifically targeting an important enzyme in saliva—salivary alkaline phosphatase (SAP). SAP participates in various physiological processes in saliva, including the hydrolysis of phosphate groups, and may be related to local phosphate balance. Studies have shown that the activity and stability of SAP are affected by the pH and ion concentration of the oral microenvironment, and are particularly sensitive to acidic environments. Early enamel demineralization is accompanied by a decrease in the local pH of the tooth surface. Although the overall pH of saliva may not change significantly, the pH of the microenvironment of dental plaque or the tooth surface can be significantly reduced. The above approach selects SAP as one of the "sentinel enzymes" and designs specific inactivation experimental conditions. Saliva samples are exposed to a pH of 6.2-6.6 (simulating the slightly acidic environment during early demineralization) and contain a certain concentration of Ca. 2+ In a buffer simulating the dynamic changes of calcium ions during demineralization / remineralization, SAP was incubated for different times at near physiological temperature (37°C), and its residual activity was rapidly measured. By measuring the residual activity at different incubation times, the inactivation rate of SAP under this simulated stress condition can be calculated. The inactivation rate of SAP under this mild stress condition can sensitively reflect the stability of the enzyme in the face of challenges similar to the oral microenvironment. Furthermore, pH 6.2–6.6 was chosen as the pH range for the inactivation buffer because this range represents the transition region between the neutral pH (~7.0) of a healthy oral cavity and the low pH (<5.5) during significant demineralization, better capturing the effect of early, subtle pH changes on enzyme stability. A certain concentration of Ca was added. 2+ ions are because of Ca 2+ It may play a role in the structural stability and activity regulation of SAP, and Ca 2+ The concentration itself also changes during demineralization / remineralization, and incorporating it into the deactivation conditions is closer to physiological relevance.

[0030] The above approach first quantifies the stability of SAP under simulated early demineralization stress conditions, obtaining a specific numerical value—the apparent inactivation rate constant—which directly reflects the tolerance of SAP in patient saliva to a weakly acidic / specific ionic environment. Simultaneously, it provides a sensitive early risk indicator; compared to measuring the total activity of SAP, measuring its inactivation rate under specific stress conditions can more early and sensitively detect signs of decreased salivary buffering capacity or the stability of the SAP molecule itself. Under early demineralization risk conditions, SAP in patient saliva may exhibit a faster inactivation rate. Furthermore, it supports dynamic monitoring and individualized assessment; by repeating this measurement at different time points, the trend of SAP inactivation rate changes in the same patient can be tracked. An increasing inactivation rate trend indicates that the patient faces a higher demineralization risk, requiring intervention. Combined with an individual baseline, the change in SAP stability relative to the patient's "normal" state can be assessed. In summary, the above scheme provides a method for quantitatively measuring salivary SAP stability (inactivation rate) through standardized, physiologically relevant experimental conditions, providing an important single-dimensional risk signal for early enamel demineralization risk.

[0031] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles, the inactivation rate is characterized by an improved second-order inactivation mechanics model, which is as follows:

[0032]

[0033] Where, k inact The apparent inactivation rate constant (min) -1 );

[0034] k0 represents the standard conditions (pH 7.0, no Ca). 2+ The basic deactivation rate constant;

[0035] α is the pH sensitivity coefficient, which characterizes the sensitivity of inactivation to pH changes;

[0036] pH c This is the critical pH value; below this value, inactivation is significantly accelerated (value taken as 6.5).

[0037] β is Ca 2+ Protection coefficient, characterizing Ca 2+ Protective effect against inactivation;

[0038] [Ca 2+ [Ca in the environment] 2+ concentration;

[0039] K d For salivary alkaline phosphatase (SAP) and Ca 2+The apparent dissociation constant.

[0040] The α and β parameters extracted from the experimental data were used as individualized risk indicators, with high-risk patients exhibiting larger α values ​​and smaller β values.

[0041] It is understandable that the above scheme has limitations at specific pH and Ca... 2+ The inactivation rate of SAP was measured at a specific concentration. However, the pH and Ca of the oral microenvironment also played a role. 2+ Concentrations fluctuate, and different individuals may have different sensitivities to these factors. Simply measuring a specific point (e.g., pH 6.4, 1.0 mCa) is less effective. 2+ While the deactivation rate of SAP is meaningful, it is insufficient to fully characterize the response characteristics of SAP to different microenvironmental conditions. Therefore, an improved second-order deactivation mechanics model is introduced, which is a more advanced data analysis and characterization method. It uses the SAP deactivation rate (k...) as the basis for characterization. inact ) are considered as pH and Ca 2+ The concentration is a function, and key parameters reflecting SAP's intrinsic characteristics and environmental sensitivity are introduced:

[0042] k0: Basal inactivation rate. Reflects the inherent instability of the enzyme under standard non-stress conditions.

[0043] α: pH sensitivity coefficient. This parameter quantifies the sensitivity of SAP inactivation rate to pH changes. The larger the α, the faster the inactivation rate accelerates for every slight decrease in pH. pH c =6.5 is the threshold at which inactivation begins to accelerate significantly.

[0044] β: for Ca 2+ Protection factor. This parameter quantifies Ca. 2+ The protective effect of ions on SAP inactivation: the larger the β, the stronger the effect of the same concentration of Ca. 2+ The better the protection provided, the better K d It is SAP and Ca 2+ The apparent dissociation constant reflects the affinity between them.

[0045] At multiple different pH and Ca 2+ The inactivation rate of SAP was measured at various concentration combinations. These experimental data were then fitted into the mathematical model described above to extract the individualized parameters k0, α, and β of SAP in the patient's saliva (assuming pH). c and K d(Population averages or further experimental determinations can be used). These parameters reflect the stability characteristics of a patient's SAP more essentially than a single inactivation rate value. The risk assessment criteria proposed in the above model—larger α values ​​and smaller β values—are biologically significant and associated with high risk: a larger α value means that the patient's SAP inactivates rapidly with a slight decrease in pH, indicating poor acid tolerance in their salivary buffer / enzyme system; a smaller β value means that the patient's SAP is more sensitive to Ca2+. 2+ The protective response is poor, or there is calcium in saliva. 2+ Insufficient contribution to enzyme stability. Both of these points directly or indirectly point to an increased risk of enamel demineralization.

[0046] Therefore, the above-mentioned approach can first achieve quantitative and mechanistic characterization of SAP inactivation behavior: extracting key parameters (α and β) reflecting the intrinsic properties of enzyme molecules from complex experimental data. These parameters have greater diagnostic value than simple inactivation rate values. It can also provide more refined and personalized risk indicators, as parameters α and β directly quantify the effect of individual saliva SAP on pH and Ca2+. 2+ The sensitivity and responsiveness to change allow risk assessment to be based not only on the "speed" of inactivation, but also on the "why" inactivation occurs quickly or slowly, thus providing a more in-depth, individualized risk profile. Furthermore, it can improve the accuracy of risk prediction. Compared to a single inactivation rate, model-based risk assessment can more comprehensively consider the impact of oral microenvironment fluctuations on SAP function, potentially identifying individuals highly susceptible to early demineralization risk factors more accurately. In summary, the above approach, by introducing an advanced enzyme kinetic model, elevates the aforementioned experimental data into risk parameters with greater biological significance and individualized characteristics, significantly enhancing the depth and accuracy of this invention in quantitatively assessing SAP stability. This is a crucial support for the method's high sensitivity and individualization.

[0047] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles, the competitive enzyme inhibition relief effect of salivary enzymes is assessed by measuring the apparent activity of salivary cathepsin D, which is inhibited by salivary cysteine ​​protease inhibitors. Under the risk of enamel demineralization, when the activity of cysteine ​​protease inhibitors decreases, the inhibitory effect on cathepsin D weakens, thereby increasing the apparent activity of cathepsin D.

[0048] Understandably, this approach focuses on the aforementioned indicator of "competitive enzyme inhibition relief effect of salivary enzymes." Saliva contains a complex system of proteases and their endogenous inhibitors. Catepsin D, an aspartic protease, is present in saliva and participates in protein degradation. Saliva also contains various cysteine ​​protease inhibitors, especially Cystatin S, which are effective inhibitors of cysteine ​​proteases such as catepsin K and L, but can also non-specifically affect the activity of other proteases. This technical approach utilizes the fact that Cystatin S has a certain degree of inhibitory effect on Catepsin D (although the Cystatin family primarily inhibits cysteine ​​proteases). The key technical logic lies in the fact that under early enamel demineralization risk conditions, the oral microenvironment may change, such as due to inflammation, oxidative stress, or enhanced metabolic activity of specific bacterial flora. These factors may lead to a decrease in the concentration or activity of endogenous protease inhibitors such as Cystatin S in saliva. When the inhibitory effect of Cystatin S weakens, the activity of Catepsin D, which was previously partially inhibited, becomes "revealed," manifested as an increase in the measured apparent activity of Catepsin D. Measuring the apparent activity of Catepsin D indirectly reflects the inhibitory level or effectiveness of its endogenous inhibitors (mainly Cystatins). This "competitive enzyme inhibition deactivation" mechanism assesses the status of the endogenous inhibitors interacting with it by detecting changes in the apparent activity of an indicator enzyme (Cathepsin D). This reflects whether protein homeostasis and the protective inhibitory system in saliva are affected. In summary, this protocol firstly provides an indirect indicator of the status of endogenous protease inhibitors; an increase in the apparent activity of Catepsin D directly suggests a decrease in the activity or concentration of inhibitors such as Cystatin in saliva, revealing that saliva's defense against abnormal proteolytic activity may be weakening. It can also capture risk signals associated with protein homeostasis imbalance; a decrease in protease inhibitor activity may be related to inflammation, tissue damage, or specific pathological processes, which may be linked to the occurrence and development of enamel demineralization. This indicator provides a risk signal based on the state of the protein-protein interaction network, distinct from enzyme stability or catalytic efficiency. It also contributes to improving the comprehensiveness of risk detection. By assessing enzyme-inhibitor balance, this invention monitors oral microenvironment health from another dimension (protein interaction state), enhancing the comprehensiveness and sensitivity of risk assessment.In summary, this scheme cleverly reflects the status of its endogenous inhibitors (Cystatins) by monitoring the apparent activity of a specific indicator enzyme (Cathepsin D), providing a risk assessment indicator based on the "competitive enzyme inhibition relief" effect, and is an important component of the aforementioned multidimensional indicator system.

[0049] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity spectra, the selectivity changes of salivary enzymes to non-natural substrates are assessed by measuring the changes in the relative catalytic efficiency spectra of salivary lysozyme to a group of structurally related but differently catalytically efficient non-natural substrates; wherein, the non-natural substrates include oligosaccharide substrates containing different glycosidic bonds, and the ratio of their relative catalytic efficiencies exhibits characteristic differences between healthy states and early demineralization risk states; the selectivity changes are quantified by calculating the substrate selectivity index (SSI) using the following formula:

[0050]

[0051] SSI is the substrate selectivity index, a dimensionless value between 0 and 1.

[0052] n is the number of substrates in the substrate library;

[0053] (k cat / K m ) i The catalytic efficiency of the enzyme for the i-th substrate;

[0054] (k cat / K m ) j The catalytic efficiency of the enzyme for the j-th substrate;

[0055] w i is the weighting coefficient of the i-th substrate (the weighting coefficient calculated by the entropy weighting method).

[0056] This scheme defines the variation in the selectivity of the salivary enzyme for non-natural substrates as being assessed by measuring changes in the relative catalytic efficiency spectra of salivary lysozyme for a group of structurally related but differently catalytically efficient non-natural substrates. These non-natural substrates include oligosaccharide substrates containing different glycosidic bonds (e.g., β-1,4, β-1,3, β-1,6). Characteristic differences exist between the relative catalytic efficiency ratios under healthy and early demineralization risk conditions. This change is quantified by calculating the substrate selectivity index (SSI) using the formula described above.

[0057] This approach understandably focuses on the aforementioned indicator of "selective changes in salivary enzymes towards non-natural substrates," specifically targeting salivary lysozyme. Lysozyme is an important antibacterial enzyme whose main function is to hydrolyze peptidoglycan in bacterial cell walls. Peptidoglycan is a polysaccharide chain containing alternating N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid linked by β-1,4 glycosidic bonds. Lysozyme exhibits high specificity for β-1,4 glycosidic bonds. However, subtle conformational adjustments to the enzyme's active site and substrate-binding region can affect its affinity or catalytic efficiency for structurally similar but not identical substrates (especially non-natural substrates). Under the influence of early demineralization risk microenvironments (such as slight pH decreases, changes in ionic environment, oxidative stress, etc.), the three-dimensional structure of salivary lysozyme molecules may undergo subtle changes. These changes may not be sufficient to significantly alter the catalytic efficiency for native substrates (β-1,4 bonds), but they may change the relative catalytic efficiency for non-native substrates (e.g., oligosaccharides containing other glycosidic bonds such as β-1,3 or β-1,6). For example, a slight contraction or expansion of the active site inlet or a region of the substrate binding groove may result in a tighter binding to a certain non-native substrate (K... m Decreasing or loosening the transition state energy of the catalytic reaction, or altering the transition state energy, thus affecting k. cat / K m Different non-natural substrates exhibit varying sensitivities to enzyme conformational changes due to structural differences. This approach constructs a library of non-natural oligosaccharide substrates containing different glycosidic bonds and measures the relative catalytic efficiency spectra of lysozyme for these substrates. For example, under healthy conditions, the relative k values ​​of lysozyme for substrates S1 (β-1,4 enriched), S2 (containing β-1,3), and S3 (containing β-1,6) are shown. cat / K m The ratio might be 4:2:1. However, in early risk states, due to enzyme conformational changes, this ratio might change to 2:3:1 or other patterns. This change in the relative catalytic efficiency ratio, i.e., the change in the "substrate selectivity profile," is a sensitive indicator of minute structural changes in the enzyme molecule. To quantify this profile change into a single index, this scheme introduces a formula for calculating the substrate selectivity index (SSI). This formula weights and sums the relative catalytic efficiencies of the enzyme for all tested substrates, yielding a dimensionless value between 0 and 1. The weighting coefficient w... iThe SSI index is calculated from data of a healthy reference population using the entropy weighting method. This means that the weighting is based on the variability and discriminative power of various substrate catalytic efficiencies in healthy individuals, making the SSI index more prominent for substrates that show the greatest differences between healthy and risk states. In summary, this approach can firstly detect subtle conformational changes in enzyme molecules with high sensitivity. Using non-natural substrates as probes, it captures subtle structural adjustments in salivary lysozyme caused by early changes in the oral microenvironment, affecting only its catalytic ability towards non-natural substrates—something difficult to achieve when measuring total activity. Furthermore, it provides a comprehensive index based on a "selectivity profile," analyzing the relative catalytic efficiency of the enzyme on a set of substrates to obtain information on the enzyme's performance under different binding modes, providing a richer and more discriminative portrait of enzyme functional status than single-substrate activity. Simultaneously, it can quantify changes in enzyme substrate selectivity. The SSI index integrates complex relative catalytic efficiency profiles into a single value, making the results easy to interpret and compare, and optimizing the clinical discriminative power of the index through weighting coefficients. Significant deviations in the SSI value (such as decreases or increases) directly indicate abnormalities in the enzyme molecule state, suggesting a risk of demineralization. This facilitates dynamic monitoring and individualized assessment, allowing for the dynamic tracking of changes in patients' SSI values. It enables timely detection of shifts in the selectivity profile of salivary lysozyme towards a risk pattern, and, combined with individual baseline data, assesses the impact of these changes on individual demineralization risk. In summary, this approach, through the innovative use of non-natural substrate selectivity profile analysis and SSI index quantification, provides a sensitive indicator reflecting subtle structural and functional changes in salivary lysozyme molecules, forming an important component of the aforementioned multidimensional enzymatic property indicator system.

[0058] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity spectrum, the dynamic assessment in step S4 includes: performing enzyme index testing on the patient before the start of orthodontic treatment, which serves as the individual baseline reference value for the patient; performing testing every 1-3 months after the start of treatment, and plotting curves of the changes of each index over time; when any index deviates from the individual baseline by more than 25% and is biased towards the risk direction, it is considered an early risk signal; integrating multi-dimensional enzyme indexes and calculating a risk score using a weighted scoring system.

[0059] This protocol defines the dynamic assessment method in step S4. Specifically, it includes: conducting enzyme index testing before orthodontic treatment begins as an individual baseline reference; testing every 1-3 months after treatment begins, plotting the curves of index changes over time; when any index deviates from the individual baseline by more than 25% and leans towards a risk direction, it is considered an early risk signal; integrating multi-dimensional enzyme indicators and using a weighted scoring system to calculate a risk score. In other words, this protocol applies the individualized, multi-dimensional enzyme characteristic indicators obtained in the preceding steps to dynamic monitoring and early risk assessment. Enamel demineralization risk is a dynamic process influenced by various factors (such as oral hygiene, dietary habits, appliance type, changes in saliva function, and microbial composition). A single test can only reflect the risk status at a specific point in time and cannot capture the evolution trend of the risk. Establishing an individual baseline (before treatment begins) is the foundation of dynamic monitoring. Each patient's physiological state, saliva composition, enzyme activity profile, and inherent responsiveness to the microenvironment differ. Comparing measurements during treatment to the patient's baseline, rather than to the population mean, eliminates interference from inherent individual differences, allowing monitoring to better reflect the true changes in a patient's demineralization risk during treatment. Regular monitoring (every 1-3 months) and plotting indicator changes are key to achieving "dynamic" monitoring. By observing fluctuations or trends in indicators, it's possible to determine whether a patient's risk status is improving, stabilizing, or worsening. This trend analysis is more predictive than single-point values. For example, the SAP inactivation rate may increase initially (adaptation phase), then decrease and stabilize (good risk control), or continue to increase (persistent or worsening risk). Setting a 25% deviation threshold as an early risk signal is an empirically or clinically validated quantitative standard. This threshold aims to balance sensitivity (timely detection of changes) and specificity (avoiding false positives). Emphasizing that the deviation is "biased towards risk" ensures the clinical significance of the signal (e.g., an increased inactivation rate indicates risk, a decrease does not; a decrease in SSI may indicate risk, an increase may indicate another risk pattern or health). Finally, integrating multi-dimensional indicators and employing a weighted scoring system (detailed later) is key to overcoming the limitations of single indicators. The oral microenvironment is complex, and demineralization risk is the result of the combined effects of multiple factors. Different enzymatic indicators may be more sensitive to different risk factors (acid, oxidative stress, inflammation, microbial metabolites, etc.). By combining multiple independent or synergistic indicators and assigning different weights based on their sensitivity in patients or their contribution to prediction, a more comprehensive and robust overall risk assessment can be obtained. This approach enables truly dynamic and individualized risk monitoring, comparing against the patient's own baseline and regularly tracking the changing trends of multi-dimensional indicators, overcoming the limitations of traditional methods and providing personalized risk profiles and evolution curves.Furthermore, it can provide early, quantifiable risk warning signals and set clear deviation thresholds, enabling the method to issue early warnings before enamel demineralization is even visible to the naked eye, alerting clinicians and patients. This improves the accuracy and reliability of risk assessment. Integrating multi-dimensional indicators and utilizing their complementarity reduces the risk of misjudgment that may arise from a single indicator, making the overall risk assessment more comprehensive and accurate. The dynamically changing risk curve and early warning signals provide clinicians with important objective evidence to guide them in timely adjustments to patients' oral hygiene guidance, dietary recommendations, or the implementation of local preventative measures. Therefore, this approach, by establishing individual baselines, conducting regular dynamic tracking, setting warning thresholds, and integrating multi-dimensional indicators, constructs a scientific and systematic framework for dynamic risk monitoring and early warning. This is a key step in the present invention's transformation of enzymatic characterization analysis into a clinically practical tool.

[0060] According to a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles, the risk score is calculated using the following formula:

[0061]

[0062] Among them, I i This represents the current value of the i-th indicator.

[0063] I i,0 This represents the baseline value of the i-th indicator for this patient;

[0064] w i The weighting coefficient for the i-th indicator (with a value between 0.5 and 2.0);

[0065] sign i The value is +1 or -1, depending on whether the direction of the indicator change increases the risk.

[0066] The risk score is divided into three levels:

[0067] Low risk: Risk Score < 0.5;

[0068] Medium risk: 0.5 ≤ Risk Score < 1.0;

[0069] High risk: Risk Score > 1.0;

[0070] When the Risk Score is ≥0.5, an early warning is activated, and it is recommended to strengthen oral hygiene and preventive measures. When the Risk Score is ≥1.0, it is recommended to take active intervention measures.

[0071] It is understandable that this scheme is the specific mathematical implementation and clinical application standard of the aforementioned weighted scoring system.

[0072] The risk scoring formula is designed based on the following logic:

[0073] 1. Relative deviation: using This calculates the relative change of the current indicator value compared to its individual baseline value. The advantage of this approach is that the absolute value ranges of different indicators can vary considerably (e.g., inactivation rate may range from 0.01 to 0.1 min). -1 While SSI may be between 0.3 and 0.6, using relative changes allows us to compare and accumulate them on the same scale.

[0074] 2. Directionality: sign i The factor ensures that the contribution of an indicator is added to the total risk score only when the direction of the indicator change is to increase the risk of demineralization (e.g., an increase in the SAP deactivation rate and a decrease in the SSI to the low-risk range). If the indicator change is to reduce the risk, its contribution is negative, thereby reducing the total risk score and accurately reflecting the improvement in the risk status.

[0075] 3. Weighted summation: w i The coefficients allow for assigning different levels of importance based on the predictive power of different indicators for demineralization risk or their sensitivity in a particular patient. For example, in a patient, the SAP inactivation rate may be particularly sensitive to pH changes, while SSI changes may be less pronounced. In such cases, a higher weight (>1.0) could be given to the SAP inactivation rate, and a lower weight (<1.0) to the SSI, making the scoring system more reflective of the patient's specific risk pattern. The weight range of 0.5–2.0 provides flexibility for adjustment.

[0076] 4. Comprehensive Score: A single, comprehensive risk score is obtained by summing the weighted, directional-adjusted relative deviations of all included indicators. This score comprehensively integrates information from multiple dimensions, providing a concise and clear overall risk measurement.

[0077] The risk score-based system, with its three risk levels and thresholds, transforms continuous risk scores into discrete classifications with clear clinical guidance. These thresholds (0.5, 1.0) need to be determined through clinical research and validation to maximize its effectiveness in differentiating low, intermediate, and high-risk patients. Corresponding clinical recommendations (strengthening prevention, proactive intervention) allow the risk assessment results to directly guide subsequent clinical management, achieving a transformation from detection to decision-making.

[0078] The above-described approach first provides an objective and quantitative comprehensive risk assessment tool. It integrates multi-dimensional, dynamically monitored enzymatic indicators into a single, easily understood and comparable risk score using a mathematical model. Furthermore, it enables tiered risk management, clearly classifying patients into low, medium, and high-risk levels, helping clinicians develop personalized prevention and intervention plans based on each patient's specific risk level. Simultaneously, it establishes clear clinical decision trigger points, providing specific guidelines based on risk score thresholds (e.g., 0.5 and 1.0) for when to initiate early warnings, strengthen prevention, or implement more proactive clinical interventions, improving the standardization and effectiveness of clinical management. This further enhances the method's clinical applicability, transforming complex enzymatic data into scores and recommendations with direct clinical guidance, making the method easily promoted and applied in orthodontic clinical practice. Therefore, this approach provides a specific mathematical description and clinical decision-making process for the core risk assessment model of this invention, which is crucial for transforming advanced molecular biology detection technology into a risk management tool with practical clinical value.

[0079] According to the present invention, a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles is provided: the stability changes of salivary enzymes under sublethal denaturant concentrations are assessed by measuring the residual activity of salivary peroxidase in the presence of sublethal urea concentrations; the change in the allosteric switch trigger threshold required for salivary enzyme activation is assessed by measuring the Zn required for salivary carbonic anhydrase VI to reach 50% of its maximum activity under pH gradient conditions. 2+ The concentration was used for evaluation; the changes in interfacial kinetic parameters of the interaction between the salivary enzyme and the immobilized substrate or substrate analog were evaluated by measuring the binding rate constant and dissociation rate constant of salivary thiocyanate peroxidase with the substrate or substrate analog immobilized on the carrier surface using surface plasmon resonance (SPR) technology; the changes in signal flux distribution preference in the enzyme-catalyzed cascade reaction were evaluated by measuring the ratio of the rates of the two reaction pathways in the cascade system composed of glutathione peroxidase and glutathione S-transferase; the changes in the flux redistribution capacity within the enzyme system were evaluated by introducing a competitive substrate analog into the glycolysis pathway in saliva and measuring the changes in the accumulation rate of intermediate products and the formation rate of final products before and after the introduction.

[0080] The above scheme further specifies the detailed determination methods for the other enzymatic property indicators listed above, including:

[0081] Stability changes of salivary peroxidase (SPO) at sublethal concentrations of urea: determination of residual activity at specific urea concentrations.

[0082] Changes in the allosteric switch trigger threshold required for activation of salivary carbonic anhydrase VI (CA-VI): Determination of the Zn required to achieve 50% maximum activity at a pH gradient.2+ Concentration (EC) 50 ).

[0083] Variations in interfacial kinetic parameters of the interaction between salivary thiocyanate peroxidase (STPO) and immobilized substrates / analogs: Binding / dissociation rate constants with immobilized substrates / analogs determined using surface plasmon resonance (SPR) technique.

[0084] Changes in signal flow distribution preference in GPx / GST enzymatic cascade reaction: The ratio of the generation rates of the two reaction pathway products (GSSG and CDNB-SG) in the cascade system composed of GPx and GST was determined.

[0085] Changes in flux redistribution capacity of glycolysis pathways: Introduce competitive substrate analogs (such as 2-deoxy-glucose-6-phosphate, 2dG6P) into pathways (such as the glycolysis part of the pathway) and measure the changes in the accumulation of intermediate products and the formation rate of final products before and after the introduction.

[0086] This scheme provides specific technical means and experimental methods for determining the aforementioned multiple multidimensional enzymatic properties. The selection and design of each method are based on the specific biological characteristics of the enzyme or enzyme system and their potential impact on changes in the oral microenvironment.

[0087] SPO stability (urea): SPO participates in the antioxidant defense system of saliva. Urea is a weak denaturant, and at sub-lethal concentrations (e.g., 0.5-1.0 M), the stability of different enzymes varies considerably. In the oxidative stress or inflammatory microenvironment associated with early demineralization risk, the structure of SPO may become more fragile and more easily inactivated in the presence of mild denaturants. Measuring its residual activity in the presence of urea is a sensitive method for probing its intrinsic structural stability.

[0088] CA-VI allosteric activation threshold (Zn) 2+ CA-VI is an important carbonic anhydrase in saliva, involved in regulating local pH. Its activity is affected by Zn. 2+ Allosteric activation by metal ions. Changes in the ion balance of the oral microenvironment (such as ion loss during demineralization) may affect the response of CA-VI to activators. The required Zn concentration to reach half-maximal activity was determined under different pH conditions. 2+ Concentration (EC) 50 EC can reflect changes in the sensitivity of enzyme allosteric sites or overall conformation to activation signals. 50 An increase may indicate that enzyme activation requires higher levels of Zn. 2+ Concentration indicates impaired allosteric regulation of the enzyme or an unfavorable ionic environment.

[0089] STPO interfacial kinetics (SPR): STPO is also a member of the salivary antioxidant system, catalyzing the oxidation of thiocyanate. Many enzymes in vivo function at biological interfaces (such as tooth surfaces and bacterial biofilm surfaces). SPR technology enables label-free, real-time measurement of the binding and dissociation processes between molecules in solution and molecules immobilized on the chip surface, obtaining the binding rate constant (k... on ) and dissociation rate constant (k off The changes in the interfacial kinetic parameters between STPO and the immobilized substrate analog can reflect the behavioral characteristics of the enzyme in the microenvironment near the tooth surface, and may be related to whether the enzyme can effectively exert a local protective effect on the tooth surface.

[0090] GPx / GST cascade signaling: GPx and GST are enzymes involved in detoxification and antioxidant processes, both using reduced glutathione (GSH) as a substrate, forming a competitive branched pathway. In early demineralization risks accompanied by oxidative stress or metabolic disorders, the activity ratio of GPx and GST, or their competitive ability against GSH, may change, leading to variations in the proportions of the products (oxidized glutathione GSSG and glutathione conjugate CDNB-SG). Measuring the product proportions (signal flow allocation ratio) reflects the overall coordination of this complex system's function and the preference of metabolic fluxes, rather than focusing solely on the activity of a single enzyme.

[0091] Glycolysis pathway flux redistribution capacity (2dG6P perturbation): The glycolysis pathway in saliva and plaque biofilms is a key process in acid production. In early demineralization risk microenvironments, microbial metabolism or host cell metabolism may change, affecting pathway function. Introducing a competitive analogue with a structure similar to the pathway intermediates (e.g., 2dG6P as a G6P analogue and inhibiting PGI) can create a controllable perturbation. Under healthy conditions, the pathway may have some resilience, able to partially compensate for this inhibition by regulating the activity of other enzymes, maintaining downstream flux. Under risk conditions, this compensatory / redistribution capacity may decline, manifested as excessive accumulation of upstream intermediates and more severe inhibition of downstream product formation. By measuring changes in metabolites before and after perturbation, the overall reliability and regulatory capacity of the pathway can be assessed.

[0092] In summary, the above-described scheme enables the practical measurement of the aforementioned multi-dimensional indicators, providing detailed experimental protocols for each complex enzymatic characteristic indicator (SPO stability, CA-VI allosteric variation, STPO interfacial kinetics, GPx / GST cascade, glycolysis flux), allowing these innovative indicators to be practically applied to clinical sample testing. Furthermore, it captures risk signals from more diverse perspectives. These indicators provide rich risk information from different and complementary angles, such as enzyme antioxidant stability, ion sensitivity, interfacial behavior, pathway coordination, and metabolic system reliability, further improving the comprehensiveness and sensitivity of this method. It also enhances the scientific basis and reproducibility of the method. Detailed experimental procedures and parameter settings ensure the operability and reproducibility of these complex measurements, providing technical support for the promotion, application, and clinical validation of the method. This supports mechanistic research and indicator optimization. The measurement and analysis of these specific indicators contribute to a deeper understanding of the biochemical mechanisms of early demineralization risk and may guide the future discovery or optimization of more predictive combinations of enzymatic indicators. Therefore, this scheme provides the core experimental technical details required to realize the multidimensional enzymatic characterization analysis of this invention, and is an important technical support to ensure that the aforementioned methods can be practically implemented and produce reliable risk assessment results. Each specific method has been carefully designed to maximize the capture of subtle and clinically significant changes in the salivary enzyme system that occur in the early demineralization risk microenvironment.

[0093] The method of the present invention for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profile can achieve the following technical effects:

[0094] 1. Achieving Early Warning: Step S3 obtains enzymatic indicators reflecting the multidimensional molecular characteristics of enzymes or enzyme systems, or their response to environmental changes. These indicators are often more sensitive than macroscopic clinical manifestations (such as visible enamel demineralization plaques) in capturing early biochemical changes in the oral microenvironment (such as slight pH decreases, local ion imbalances, oxidative stress, etc.). Step S4 performs dynamic assessment based on changes in these early indicators, enabling the identification of risk signals before clinically visible enamel damage occurs, thereby achieving early warning of enamel demineralization.

[0095] 2. Providing multi-dimensional information: Step S3 emphasizes obtaining "at least two different enzymatic property indicators," which enables the method to comprehensively assess the state and response of the enzyme system from multiple molecular behavioral levels (such as enzyme stability, substrate affinity, regulatory mechanisms, and interactions with other molecules). This multi-dimensional analysis can more comprehensively reflect the complex changes in the oral microenvironment than a single indicator, reducing the possibility of misjudgment and improving the accuracy and reliability of risk assessment.

[0096] 3. Supports dynamic monitoring: Step S1 requires obtaining at least two saliva samples, which, combined with the assessment in Step S4 based on "changes in enzymatic properties obtained at different time points," enable this method to track the dynamic evolution of a patient's risk status over time. This not only determines the current risk level but also monitors whether the risk is rising, falling, or stable, thus providing dynamic evidence for clinical decisions (such as adjusting oral hygiene guidelines and preventive measures).

[0097] 4. Non-invasive: Step S1 explicitly uses "obtaining saliva samples from orthodontic patients", which is a completely non-invasive collection method. It is painless and trauma-free for patients, and facilitates multiple and regular sample collections throughout the orthodontic treatment process. It meets the needs of dynamic monitoring and improves patient compliance.

[0098] 5. Achieving Personalized Assessment: Although not explicitly mentioned as an "individual baseline," assessment based on "changes in enzymatic properties obtained at different time points" naturally supports personalized monitoring. Indicators prior to orthodontic treatment can be used as an individual baseline, with subsequent monitoring results compared to this baseline. This assessment, based on individual trends, avoids the potential biases of simply comparing individual data with group averages, more accurately reflecting an individual's unique risk profile and trajectory, thus achieving personalized risk assessment.

[0099] 6. Strong Mechanistic Correlation: The enzymatic properties obtained in step S3 reflect the behavior and response at the enzyme molecular level. These behavioral changes are intrinsically linked to the biochemical alterations in the oral microenvironment (directly affecting the demineralization process). For example, an increased inactivation rate of enzymes sensitive to low pH, or changes in the activity or stability of enzymes sensitive to changes in ion concentration, directly reflect the formation of the demineralization microenvironment. These mechanism-based indicators have strong biological significance, making the risk assessment results more interpretable and persuasive. Detailed Implementation

[0100] This invention provides a method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles. By analyzing the multidimensional molecular behavioral characteristics of specific enzymes or enzyme systems in saliva and their response patterns to specific perturbations, it achieves early, non-invasive, dynamic, and individualized monitoring of enamel demineralization risk. This invention overcomes the shortcomings of existing technologies where enamel demineralization monitoring primarily diagnoses existing demineralization, lacks early warning capabilities, and suffers from insufficient sensitivity of traditional single salivary biochemical indicators, making it difficult to achieve dynamic, accurate, and individualized assessment. The invention will be described in detail below with specific embodiments.

[0101] Example 1: A general method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles

[0102] This embodiment discloses a general method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles. The method includes the following core steps:

[0103] Step S1: Obtain saliva samples from orthodontic patients

[0104] This step aims to obtain saliva samples that reflect the physiological and biochemical state of the patient's oral microenvironment. For dynamic monitoring, samples should be collected at least twice during orthodontic treatment, for example, the first collection before the start of treatment (as a baseline), and subsequent collections periodically during treatment (e.g., every 1-3 months at follow-up appointments). The collection method can be either non-irritating or irritating saliva collection, depending on the purpose of the study or clinical practice. Non-irritating saliva better reflects the oral microenvironment at a basal state, while irritating saliva reflects the body's maximum secretory capacity and the total amount of certain enzymes.

[0105] Regarding non-irritating saliva collection: For example, patients are required to fast for at least 2 hours between 9 and 11 am. Before collection, patients should rinse their mouth with water for 5 minutes, then sit quietly for 5 minutes with their head slightly tilted forward, and gently spit out the naturally flowing saliva into a sterile collection container. The collection time should be controlled within 5-10 minutes, ensuring that at least 2 mL of saliva is collected.

[0106] Regarding irritant saliva collection: The patient chews a tasteless paraffin block (approximately 1.5g) for 1 minute to initiate saliva production. The first spit of saliva, containing a significant amount of oral epithelial cells and food debris, is discarded. The patient then continues chewing the paraffin block for 5 minutes, spitting out the secreted saliva into a sterile collection container every 30 seconds. A total of at least 5mL of saliva should be collected.

[0107] To ensure sample quality and comparability, the collection process must be strictly standardized: record the collection time, specific collection method (non-irritating / irritating), and saliva flow rate (calculated by dividing the collection volume by the time). Do not eat, smoke, drink alcohol, or use any oral care products (such as toothpaste or mouthwash) within 2 hours prior to collection. For female subjects, it is recommended to avoid the menstrual period and the 3 days before and after to reduce the potential impact of hormonal fluctuations on saliva composition.

[0108] Technical benefits: The standardized saliva collection process ensures the biological validity of the samples and the reproducibility of experimental results, minimizing errors introduced during collection and providing a foundation for accurate subsequent analysis and dynamic comparison. Saliva, as a non-invasive and readily available biological sample, makes this method well-tolerated by patients and suitable for long-term dynamic monitoring.

[0109] Step S2: Process the saliva sample to obtain a standardized saliva sample.

[0110] The collected saliva samples need to be pre-processed immediately to preserve the activity of the enzymes and remove interfering components.

[0111] Preliminary processing: Collected saliva samples should be immediately placed at 4°C and preliminarily processed within 30 minutes. First, centrifuge the saliva sample at a relative centrifugal force of 4,000 × g at 4°C for 10 minutes. This step aims to effectively remove cellular components (such as oral epithelial cells and leukocytes), food debris, and bacteria that may affect enzyme activity. The supernatant after centrifugation is the saliva sample after particulate matter removal.

[0112] Standardization: Carefully aliquot the supernatant into multiple equal aliquots, approximately 200 μL per tube. To prevent degradation of the target enzyme by endogenous or exogenous proteases during subsequent storage and processing, add a 1 mM mixture of protease inhibitors to each aliquot. Commonly used combinations include PMSF (serine protease inhibitor), EDTA (metalloproteinase inhibitor), and ampicillin (a broad-spectrum antibiotic that inhibits bacterial growth and enzyme activity). After treatment, the samples should be immediately subjected to rapid freezing (e.g., immersion in liquid nitrogen or a dry ice / ethanol bath) and then transferred to a -80°C freezer for long-term storage. For long-term storage with minimal freeze-thaw cycles, 10% (v / v) glycerol can be added as a cryoprotectant to further improve enzyme stability.

[0113] Pre-treatment: When removing samples from the -80°C freezer, they should be slowly thawed in a 4°C water bath to avoid protein aggregation and inactivation caused by rapid thawing. After thawing, the total protein concentration in the sample should be determined immediately using the Bradford method or BCA method. To ensure the comparability of enzymatic indicators between samples from different batches and at different time points, the samples should be diluted to a uniform protein concentration, such as 1.0 mg / mL, using a suitable buffer (e.g., neutral physiological saline or specific enzyme reaction buffer), based on the measured protein concentration.

[0114] Technical benefits: The standardized sample processing procedure maximizes the preservation of the original activity of salivary enzymes and eliminates potential interference from cells, food residues, and microorganisms. By measuring total protein concentration and performing standardized dilution, the influence of fluctuations in total protein concentration caused by differences in saliva secretion on enzyme activity measurement is eliminated. This ensures that enzymatic indicators can more accurately reflect the relative content and functional state of enzymes in saliva, and improves the accuracy of data comparison between samples at different time points.

[0115] Step S3: Analyze the standardized saliva samples to obtain multi-dimensional enzymatic characteristics.

[0116] This step is the core innovation of this invention. Unlike measuring only the total activity of a single enzyme, this invention analyzes at least two different enzymatic properties of at least one enzyme or enzyme system in saliva. These properties reflect not only the molecular characteristics of the enzyme (such as conformational stability and substrate binding preference) but also its response pattern to changes in the oral microenvironment (such as pH, ion concentration, and oxidative stress). This multidimensional information collectively constitutes the "activity profile" of salivary enzymes, providing a sensitive indication of early, subtle changes in risk.

[0117] Specific enzymatic property indicators may include, but are not limited to, at least two of the following eight indicators. These indicators capture the dynamic changes of the enzyme system from different perspectives:

[0118] 1. Inactivation rate of "sentinel enzymes": reflects the sensitivity and stability of enzymes to microenvironmental stresses (such as low pH, abnormal ion concentrations).

[0119] 2. "Competitive enzyme inhibition relief" effect: This reflects a change in the balance between endogenous regulatory factors (such as inhibitors) and enzymes, and may be related to inflammation or tissue degradation.

[0120] 3. Selective alteration of non-natural substrates: This reflects the fine-tuning of the conformation or flexibility of the enzyme's active site caused by environmental changes, which may affect its catalytic efficiency in complex mixed substrate environments.

[0121] 4. Changes in stability spectrum under sublethal denaturant concentrations: This reflects the inherent structural stability or folding state of the enzyme and is sensitive to early environmental stresses.

[0122] 5. Changes in the trigger threshold of the required "allosteric switch": This reflects changes in the enzyme's responsiveness to physiological regulatory signals (such as allosteric effectors) and is related to the function of the enzyme's regulatory network.

[0123] 6. Changes in interfacial kinetic parameters of interaction with solid substrates / analogs: reflecting changes in the ability and properties of enzymes to interact with solid surfaces (such as tooth enamel surfaces and biofilms).

[0124] 7. Changes in the allocation preference of "signal flow" in enzyme cascade reactions: reflecting the changes in the relative activity of each link in the enzyme synergistic system, revealing the functional state of metabolic or signaling pathways.

[0125] 8. Changes in the "flux redistribution capacity" within the enzyme system after the introduction of exogenous "competitive" substrate analogs: This reflects the enzyme system's ability to cope with external disturbances and maintain homeostasis, demonstrating the system's reliability.

[0126] This step involves analyzing standardized saliva samples using the detailed detection methods described in Examples 2-9 to obtain quantitative data on the aforementioned enzymatic characteristics.

[0127] Technical Advantages: By analyzing multi-dimensional enzymatic properties, this method can capture subtle functional and conformational changes in enzymes that are difficult to detect with a single total activity measurement. These changes often precede clinically visible tissue damage, providing the possibility of early warning. The comprehensive analysis of multiple indicators also improves the specificity and accuracy of the detection, reducing the false alarm rate.

[0128] Step S4: Based on the changes in enzymatic properties obtained at different time points, dynamically assess the risk of enamel demineralization in orthodontic patients.

[0129] This step utilizes enzymatic characteristic data obtained from multiple collections and tests for longitudinal comparison and comprehensive evaluation to dynamically track changes in the patient's enamel demineralization risk, as detailed below:

[0130] Establishing an individual baseline: The results of the first test before the start of orthodontic treatment are used as the individual baseline reference value for that patient (I i,0 Considering the individual differences in the oral microenvironment and physiological state among different patients, establishing an individual baseline is key to achieving accurate individualized risk assessment. Based on the patient's oral condition, genetic background, or baseline test results, 3-5 core indicators with the highest sensitivity and specificity can be selected for focused monitoring to form their individualized monitoring indicator combination.

[0131] Dynamic tracking of changes: During treatment, samples are collected periodically (e.g., every 1-3 months) and selected enzyme indicators are tested, and curves of each indicator changing over time are plotted. The changes are then compared with current test values ​​(I...). i ) and individual baseline value (I i,0 This can quantify the magnitude of changes in indicators. and direction.

[0132] Risk assessment and early warning: Single indicator early warning: If any selected key indicator deviates from its individual baseline value by a preset threshold (e.g., more than 25%), and the direction of change is consistent with the direction of known risk increase (e.g., increased sentinel enzyme inactivation rate, decreased stability dependence index, etc.), it is considered an early risk signal.

[0133] Comprehensive Assessment: To obtain a more comprehensive risk assessment, this method integrates changes in multiple dimensions of enzymatic indicators and uses a weighted scoring system to calculate a comprehensive risk score.

[0134]

[0135] in:

[0136] I i It is the value of the i-th indicator in the current (this test).

[0137] Ii,0 This is the individual baseline value of the corresponding indicator ii for this patient.

[0138] w i This is the weighting coefficient for the i-th indicator, with a value ranging from, for example, 0.5 to 2.0. The weight can be set based on the indicator's sensitivity to risk changes and its predictive value in previous studies or in the patient's own baseline data analysis. Indicators showing significant changes in the patient can be assigned higher weights.

[0139] sign i It is a directional factor, taking a value of +1 or -1. If an increase in the i-th indicator indicates an increase in risk, then when I... i >I i,0 At that time, sign i =+1 when I i i,0 At that time, sign i =-1; If a decrease in the i-th indicator indicates an increase in risk, then when I i i,0 At that time, sign i =+1 when I i >I i,0 At that time, sign i =-1. For example, an increase in the SAP inactivation rate indicates an increased risk, so its sign... i The sign is +1 when the inactivation rate increases; a decrease in SDI indicates an increased risk, so its sign... i It is +1 when SDI decreases.

[0140] n is the number of indicators used to calculate the overall risk score.

[0141] Risk stratification and intervention recommendations: Based on the calculated comprehensive risk score, the patient's risk status is divided into different levels:

[0142] Low risk: Risk Score < 0.5; It is recommended to maintain current oral hygiene habits and have regular follow-up visits.

[0143] Medium risk: 0.5≤Risk Score<1.0; At this time, the early warning mechanism should be activated, and it is recommended to strengthen oral hygiene education for patients, guide them to use preventive measures such as fluoride toothpaste or mouthwash, and increase the frequency of follow-up visits.

[0144] High risk: Risk Score > 1.0; more aggressive interventions are recommended, such as topical application of high-concentration fluoride (fluorine protective varnish, fluorine foam), use of anti-demineralization agents (such as CPP-ACP), or adjustment of the orthodontic treatment plan.

[0145] ​​When the Risk Score is ≥0.5, an early warning is activated, and it is recommended to strengthen oral hygiene and preventive measures. When the Risk Score is ≥1.0, it is recommended to take active intervention measures.

[0146] Technical Benefits: Dynamic tracking and individualized baseline comparison enable this method to capture early, patient-specific risk changes, rather than relying on universal thresholds. The multi-indicator weighted scoring system provides more comprehensive and robust risk assessment results, reducing false positives and false negatives. Clear risk stratification and corresponding intervention recommendations directly translate laboratory test results into clinically actionable guidance, achieving true risk management and personalized prevention.

[0147] Example 2: Monitoring the inactivation of "sentinel enzymes"

[0148] This embodiment details how to determine the inactivation rate of the "sentinel enzyme" and uses an improved second-order inactivation kinetic model for quantification and risk assessment.

[0149] The selected primary "sentinel enzyme" is salivary alkaline phosphatase (SAP). SAP is highly sensitive to pH changes and calcium ion concentrations, and is prone to irreversible inactivation under weakly acidic conditions, making it an ideal indicator enzyme for reflecting changes in the microenvironment of early demineralization risk.

[0150] Specific testing methods:

[0151] 1. Construction and incubation of the inactivation reaction system: Take 50 μL of standardized saliva sample prepared according to step S2 of Example 1 (e.g., protein concentration adjusted to 1.0 mg / mL). Then add 50 μL of pre-prepared 2× inactivation buffer. This buffer consists of 100 mM PIPES (piperazine-N,N′-bis(2-ethanesulfonic acid)) buffer, pH 6.4, and contains 2.0 mM CaCl2. The PIPES buffer has good buffering capacity within this pH range, the pH of 6.4 simulates the weakly acidic environment under early demineralization risk, and the 2.0 mM CaCl2 simulates the effect of different calcium ion concentrations on enzyme activity. The mixture is then incubated in a 37°C constant temperature water bath. Multiple incubation time points are set, such as 0, 10, 20, 30, 40, and 60 minutes. At each predetermined time point, 10 μL of the reaction solution is quickly taken for subsequent residual activity determination.

[0152] 2. Residual SAP Activity Assay: First, transfer 10 μL of the inactivated reaction solution to a well containing 190 μL of SAP activity assay buffer. This buffer consists of 100 mM Tris-HCl, pH 9.0, and 1 mM MgCl2. pH 9.0 was chosen because SAP exhibits optimal activity under these alkaline conditions, maximizing the reflection of the remaining enzyme amount. MgCl2 2+ It is an essential cofactor for SAP. Then, 10 μL of SAP substrate solution, such as 20 mM p-nitrophenyl phosphate (pNPP), is added. pNPP hydrolyzes under SAP catalysis to generate yellow p-nitrophenol (pNP), and the product can be quantified by changes in absorbance. The reaction system is then incubated at 37°C for a predetermined time (e.g., 10 minutes). Then, 50 μL of stop solution, such as 2 M NaOH solution, is added to increase the pH, terminate the enzyme reaction, and stabilize the color of the generated pNP. The absorbance of the reaction system at 405 nm is then measured using a spectrophotometer. This absorbance value is proportional to the amount of pNP generated, thus reflecting the residual SAP activity in the saliva sample at that time point.

[0153] Data analysis and risk assessment:

[0154] 1. Calculate the percentage of residual activity:

[0155] For each incubation time point t, calculate the percentage of residual activity:

[0156] Residual activity (%) = (At / A0) × 100%

[0157] Where At is the SAP activity measured in the reaction solution after incubation time t (e.g., reflected by the rate of change of absorbance per unit time or the total absorbance over a fixed time), and A0 is the initial SAP activity measured at incubation time 0 minutes (i.e., without inactivation incubation and immediately before activity determination).

[0158] 2. Calculate the apparent deactivation rate constant:

[0159] Plot ln (residual activity %) against incubation time t. In a simple exponential decay model, this curve should show a linear relationship, and the negative value of its slope is the apparent inactivation rate constant k(min). -1 ).

[0160] 3. Application of an improved second-order dynamic model of kinetic loss:

[0161] To more accurately describe the deactivation behavior of SAP under different microenvironments, this invention introduces an improved second-order deactivation mechanics model:

[0162]

[0163] The model uses the apparent deactivation rate constant k inact With regard to the pH and calcium ion concentration [Ca] of the microenvironment 2+ Connect them.

[0164] Where, k inact It is the measured apparent inactivation rate constant (min) -1 );

[0165] k0 represents the standard conditions (e.g., pH 7.0, no Ca). 2+ The baseline inactivation rate constant under the given conditions represents the inherent stability of the enzyme.

[0166] α is the pH sensitivity coefficient, characterizing how sensitive SAP inactivation is to pH changes. A larger α value means that SAP inactivation is accelerated more significantly when the pH decreases;

[0167] pH c The critical pH value is typically 6.5; below this value, SAP inactivation is significantly accelerated.

[0168] β is Ca 2+ Protection coefficient, characterizing Ca 2+ A larger β value indicates that calcium ions have a stronger protective effect against inactivation of SAP.

[0169] [Ca 2+ [ ] represents the calcium ion concentration in the inactivation buffer solution;

[0170] K d For salivary alkaline phosphatase (SAP) and Ca 2+ The apparent dissociation constant;

[0171] By conducting deactivation mechanics experiments at multiple pH and calcium ion concentration combinations, or by fitting the above model to optimized conditions (pH 6.4, [Ca ion concentration]), 2+ Inactivation data at 1.0 mM can be used to extract parameters reflecting individual characteristics, especially the pH sensitivity coefficient α and Ca2+. 2+ Protection coefficient β. These parameters serve as individualized risk indicators.

[0172] Technical Results: High-risk patients typically exhibit larger α values ​​(increased sensitivity of SAP to pH decreases) and smaller β values ​​(weakened protective effect of calcium ions on SAP). This indicates that SAP in their saliva is more prone to irreversible inactivation when faced with microenvironment acidification and ion imbalance caused by early demineralization. By dynamically monitoring changes in α and β over time, risk signals such as decreased salivary buffering capacity and calcium ion homeostasis imbalance can be detected early, thus achieving dynamic early warning of enamel demineralization risk. Quantifying complex biomolecular behaviors into comparable parameters significantly improves the sensitivity and specificity of the method and provides a theoretical basis for standardized comparison of data between individuals.

[0173] Example 3: Monitoring the effect of "competitive enzyme inhibition relief"

[0174] This embodiment illustrates how to assess the "competitive enzyme inhibition relief" effect by monitoring the apparent activity of a specific indicator enzyme.

[0175] This embodiment screened and identified salivary cathepsin D as an indicator enzyme, whose activity is inhibited by endogenous cysteine ​​protease inhibitors (Cystatin) family members (especially Cystatin S) in salivary. Under early demineralization risk conditions, the concentration or activity of Cystatin S may decrease due to inflammatory responses or changes in the microenvironment, leading to a weakened inhibitory effect on cathepsin D and thus an increase in the apparent activity of cathepsin D. Monitoring changes in the apparent activity of cathepsin D can indirectly reflect alterations in the Cystatin S / Cathepsin D balance.

[0176] Specific testing methods:

[0177] 1. Cathepsin D Activity Assay Reaction System: First, take 50 μL of standardized saliva sample prepared according to step S2 of Example 1. Then add 140 μL of Cathepsin D activity assay buffer. This buffer consists of 100 mM sodium acetate and has a pH of 3.8. Cathepsin D is an aspartic protease and exhibits optimal activity under weakly acidic conditions. Then add 10 μL of fluorescent substrate solution. A specific fluorescent substrate for Cathepsin D is selected, such as 1 mM MOCAc-Gly-Lys-Pro-Ile-Leu-Phe-Phe-Arg-Leu-Lys(Dnp)-D-Arg-NH2. This substrate contains a fluorophore MOCAc and a quencher group Dnp. When Cathepsin D hydrolyzes the substrate, the fluorophore separates from the quencher group, generating a fluorescent signal. Then, the reaction system is placed at a constant temperature of 37°C for reaction. Using a fluorescence microplate reader, with the excitation wavelength set to 328 nm and the emission wavelength to 393 nm, the fluorescence intensity changes were continuously monitored over 30 minutes.

[0178] 2. Data Analysis: The slope (ΔF / min) of the linear portion of the fluorescence intensity curve obtained through continuous monitoring was calculated. This slope reflects the enzymatic reaction rate of Catepsin D. By comparing with a fluorescent product standard of known concentration, the reaction rate can be converted into the apparent activity of Catepsin D (e.g., nmol / min / mg total protein).

[0179] Technical Effects: Increased apparent activity of cathepsin D can serve as an indicator of early demineralization risk. This increase is not due to an increase in the amount of cathepsin D enzyme, but rather to a weakened inhibitory effect of its endogenous inhibitor, cystatin S. Dynamic monitoring of changes in the apparent activity of cathepsin D can reveal the proteolytic / inhibition balance in the oral microenvironment. Disruptions in this balance may be associated with early inflammation or tissue degradation processes, providing a perspective on demineralization risk that differs from changes in enzyme stability.

[0180] Example 4: Monitoring of "Substrate Selectivity Changes"

[0181] This embodiment illustrates how to assess “substrate selectivity alteration” by measuring changes in the relative catalytic efficiency spectrum of an indicator enzyme on a set of non-natural substrates, and quantify it using the substrate selectivity index (SSI).

[0182] This invention screened and identified salivary lysozyme as an indicator enzyme. Lysozyme is a glycoside hydrolase whose active site is selective for substrate structure and glycosidic bond type. Under early demineralization risk conditions, changes in the oral microenvironment may cause fine-tuning of lysozyme conformation, thereby affecting its binding and catalytic efficiency for substrates with different structures, manifested as characteristic changes in substrate selectivity profiles.

[0183] Specific testing methods:

[0184] 1. Construction of Non-Natural Substrate Libraries: Constructing a series of oligosaccharide fluorescent substrate libraries containing different glycosidic bonds. For example, selecting the following three classes of oligosaccharide substrates labeled with 4-methylumbelliferone (4-MU):

[0185] Substrate S1: 4-MU-(GlcNAc)3, mainly containing β-1,4 glycosidic bonds.

[0186] Substrate S2: 4-MU-(GlcNAc)2-(GlcNAc-β-1,3-GlcNAc), containing β-1,3 glycosidic bonds.

[0187] Substrate S3: 4-MU-(GlcNAc-β-1,6-GlcNAc)-(GlcNAc)2, containing a β-1,6 glycosidic bond. These substrates have similar structures, but differ in the type or mode of glycosidic bond linkage, resulting in variations in the catalytic efficiency of lysozyme on them.

[0188] 2. Lysozyme activity assay for multiple substrates: Lysozyme activity was assayed for each substrate (S1, S2, S3) in the substrate library.

[0189] Reaction system: Take 50 μL of standardized saliva sample and add 140 μL of lysozyme activity assay buffer (e.g., 50 mM sodium phosphate buffer, pH 6.2, 100 mM NaCl). pH 6.2 was chosen to simulate the environment under early demineralization risk, while maintaining high lysozyme activity at this pH. NaCl was used to maintain ionic strength.

[0190] Add 10 μL of substrate solution of different concentrations (S1, S2 or S3), with at least 5 concentration points set for each substrate, ranging from, for example, 0.1 mM to 2.0 mM.

[0191] The reaction system was kept at a constant temperature of 37°C. The release of 4-methylumbelliferone (4-MU) was monitored using a fluorescence microplate reader, with the excitation wavelength set to 360 nm and the emission wavelength to 460 nm.

[0192] 3. Data Analysis and Substrate Selectivity Index Calculation: For each substrate and each concentration, enzyme activity was calculated based on the 4-MU release rate. The enzyme activity data were plotted against substrate concentration, and the Michaelis-Menten kinetic analysis (e.g., Lineweaver-Burk plot or nonlinear regression) was used to obtain the Michaelis-Menten constant (K0) for each substrate. m ) and catalytic constant (k cat ), and then calculate the catalytic efficiency (k cat / K m ).

[0193] Then, a substrate selectivity index (SSI) calculation model based on the entropy weight method is introduced to integrate catalytic efficiency data on multiple substrates into a single index:

[0194]

[0195] SSI is the substrate selectivity index, which is a dimensionless value between 0 and 1.

[0196] n is the number of substrates in the substrate library (in this example, n = 3);

[0197] (k cat / K m ) i The catalytic efficiency of lysozyme on the i-th substrate;

[0198] (k cat / K m ) j The catalytic efficiency of lysozyme on the j-th substrate;

[0199] It is the sum of the total catalytic efficiency of lysozyme for all substrates.

[0200] w i Let be the weighting coefficient for the i-th substrate. These weights can be calculated using the entropy weighting method by analyzing lysozyme substrate selectivity data from a large number of healthy reference populations. The entropy weighting method can objectively reflect the dispersion of the catalytic efficiency of each substrate in healthy individuals. The greater the dispersion, the greater the potential contribution of the substrate to distinguishing individual differences, and the higher its weight.

[0201] Technical Effects: Under healthy conditions, the relative catalytic efficiency ratio of lysozyme for substrates S1, S2, and S3 is approximately 4:2:1, corresponding to SSI values ​​within a normal range (e.g., 0.40-0.55). In the early demineralization risk state, lysozyme undergoes conformational changes, leading to disproportionate changes in its binding to and catalytic ability for different glycosidic bonds. For example, the relative catalytic efficiency ratio characteristically changes to 2:3:1, causing SSI values ​​to deviate significantly from the healthy range (e.g., dropping below 0.30 or rising above 0.65). Dynamically monitoring SSI values ​​over time can capture changes in the fine structure and function of lysozyme, providing a sensitive and specific early biomarker for demineralization risk. Integrating multi-substrate data into a single index simplifies result interpretation, while the entropy weighting method improves the index's discriminative power.

[0202] Example 5: Monitoring of "Stability Dependence Index"

[0203] This example illustrates how to assess changes in the stability of an indicator enzyme by measuring its residual activity in the presence of a sublethal concentration of denaturant.

[0204] This invention screened and identified salivary peroxidase (SPO) as an indicator enzyme. SPO is an important antioxidant enzyme in saliva, and its structural stability can be challenged by changes in the oral microenvironment. Enzymes with weaker structural stability are more easily inactivated in the presence of low concentrations of denaturing agents (such as urea). Early demineralization risk factors may make the structure of SPO more fragile, thus exhibiting lower residual activity under sublethal concentrations of denaturing agents.

[0205] Specific testing methods:

[0206] 1. Construction and incubation of the inactivation reaction system:

[0207] Set up two sets of reaction tubes:

[0208] Tube A (Control): Take 50 μL of standardized saliva sample and add 50 μL of buffer (e.g., 50 mM sodium phosphate buffer, pH 7.0).

[0209] Tube B (Experiment): Take 50 μL of standardized saliva sample and add 50 μL of urea solution (e.g., 1.6 M urea solution prepared in 50 mM sodium phosphate buffer, pH 7.0) to bring the final urea concentration to 0.8 M. According to Technical Disclosure 3.4.3, 0.8 M urea is the optimized sublethal concentration. Incubate both tubes A and B at a constant temperature of 37°C for a predetermined time, e.g., 15 minutes.

[0210] 2. Determination of residual SpO activity:

[0211] After incubation, quickly take 10 μL of reaction solution from tube A and 10 μL of reaction solution from tube B and add them to different activity assay systems.

[0212] Add 180 μL of SPO activity assay buffer (e.g., 50 mM sodium phosphate buffer, pH 7.0) to each sample.

[0213] Add 5 μL of H2O2 solution (e.g., 2 mM, as an oxidizing substrate for SPO).

[0214] Add 5 μL of TMB substrate solution (e.g., 20 mM, 3,3',5,5'-tetramethylbenzidine, which is oxidized to a colored product by SPO). Measure the rate of change of absorbance of the reaction system at 650 nm. This rate reflects the residual activity of SPO.

[0215] 3. Data Analysis: Calculate the Stability Dependence Index (SDI), where SDI = Activity B / Activity A, where Activity A is the residual SpO activity in the control tube (without urea), and Activity B is the residual SpO activity in the experimental tube (containing urea). The SDI value reflects the relative stability of the enzyme under sublethal urea concentrations.

[0216] Technical Effects: In healthy individuals, the SDI value typically falls within a high range (e.g., 0.7-0.9), indicating that SPO retains most of its activity even at low urea concentrations. However, in high-risk patients, the structural stability of SPO decreases, resulting in a significantly lower SDI value (e.g., <0.6). Dynamic monitoring of SDI changes allows for direct assessment of whether the intrinsic structural stability of key enzymes in saliva is reduced by early demineralization risk factors, providing a basis for predicting the functional durability of enzymes in the complex oral cavity environment.

[0217] Example 6: Monitoring of "Allosteric Activation Threshold"

[0218] This example illustrates how to assess changes in the "allosteric activation threshold" of an indicator enzyme by measuring the concentration of allosteric effector required to reach a specific activation level.

[0219] This invention screened and identified salivary carbonic anhydrase VI (CA-VI) as an indicator enzyme. CA-VI is an important carbonic anhydrase isoenzyme in saliva, involved in regulating saliva pH and calcium-phosphorus balance. Its activity is affected by zinc ions (Zn). 2 + Allosteric regulation of Zn concentration 2+ It is an essential cofactor or activator. Under early demineralization risk conditions, CA-VI and Zn... 2+ The binding affinity or allosteric activation efficiency of Zn may change, manifesting as the amount of Zn required to achieve the same activation level.2+ The concentration threshold changes.

[0220] Specific testing methods:

[0221] 1. Construction of CA-VI activity assay system: First, take 50 μL of standardized saliva sample, then add 100 μL of CA-VI activity assay buffer (e.g., 50 mM HEPES buffer, pH 6.8), and then add 40 μL of Zn at different concentrations. 2+ Solution. Set up a series of Zn 2+ A concentration gradient, for example, from 0 μM to 100 μM, should be set at least 8 concentration points to construct enzyme activity pairs against Zn. 2+ The dose-response curve was calculated at a specific concentration, followed by the addition of 10 μL of substrate solution, such as 20 mM p-nitrophenylacetic acid (pNPA). pNPA is the esterase substrate of carbonic anhydrase, which hydrolyzes to p-nitrophenol (pNP) under CA-VI catalysis. The absorbance change was monitored, and the rate of absorbance change at 400 nm was measured. This rate reflects the absorption rate at a specific Zn concentration. 2+ CA-VI activity at various concentrations.

[0222] 2. Data Analysis and Allosteric Activation Index Calculation: First, the measured CA-VI activity (e.g., the rate of change in absorbance per unit time) is compared with the corresponding Zn... 2+ Concentration plotting to show enzyme activity vs. [Zn] 2+ The dose-response curve is typically S-shaped or a sigmoidal curve. Then, nonlinear regression analysis is used to fit the dose-response curve to obtain the Zn required to achieve 50% of maximum enzyme activity. 2+ Concentration (i.e., Zn) 2+ -EC 50 Value). EC 50 The value reflects the effect of CA-VI on Zn 2+ The sensitivity or activation threshold required is then determined. The allosteric activation index (AAI) is then calculated: AAI = patient EC 50 / Average EC of the healthy reference population 50 Among them, the patient's EC 50 The Zn measured in the current patient 2+- EC 50 Value, average EC value of the healthy reference population 50 The average EC was determined based on data from a large number of healthy individuals. 50 value.

[0223] Technical Results: In healthy reference populations, AAI values ​​typically fluctuate around 1.0 (e.g., 0.8–1.2). CA-VI was found to increase Zn content in patients at early demineralization risk. 2+ -EC 50The value increased significantly, resulting in an AAI value greater than 1.0 (e.g., >1.5). This means that a higher concentration of Zn is required. 2+ Only when CA-VI reaches the same activation level can the enzyme's responsiveness to physiological activation signals decrease. Dynamic monitoring of AAI over time can assess whether the regulatory status of key pH-regulating enzymes in saliva is affected, providing another dimension of early warning signals for demineralization risk.

[0224] Example 7: Monitoring of "Interfacial Dynamics Parameters"

[0225] This embodiment illustrates how to determine the kinetic parameters of the interaction between an indicator enzyme and a solidified substrate / analyte using surface plasmon resonance (SPR) technology to assess changes in its "interfacial kinetics" characteristics.

[0226] This invention screens and identifies salivary thiocyanate peroxidase (STPO) as an indicator enzyme. STPO participates in the salivary antibacterial system, and its activity requires effective binding with substrates or cofactors at interfaces, such as on bacterial surfaces or tooth enamel surfaces. Early demineralization leading to changes in the physicochemical properties of the tooth enamel surface may affect the interaction kinetics between STPO and these interfaces.

[0227] Specific testing methods:

[0228] 1. Solid-state substrate / analog chip fabrication:

[0229] A surface plasmon resonance (SPR) sensor chip is used as a solid-phase support. The substrate or substrate analogue of STPO is immobilized on the chip surface. For example, STPO substrate analogues, such as taurine derivatives or compounds containing thiocyanate structures, can be immobilized on the surface of a pre-activated (e.g., carboxymethylated or aminated) SPR chip via chemical crosslinking methods (e.g., NHS / EDC coupling, glutaraldehyde crosslinking). It is crucial to ensure that the substrate analogue is immobilized on the surface at an appropriate density and orientation, preserving its binding activity with STPO.

[0230] 2. SPR interfacial bonding kinetics determination:

[0231] The prepared immobilized substrate / analyte chip was mounted into an SPR instrument. Standardized saliva samples prepared according to step S2 of Example 1 were used as the analytical stream, passing through the chip surface at different flow rates and concentrations. The SPR instrument monitored changes in chip surface mass in real time during the association and dissociation of STPO with the immobilized substrate / analyte in the saliva sample, generating a binding / dissociation curve (sensorgram) of resonance units (RU) over time. At least one control channel was set up, such as a channel immobilized with irrelevant proteins or a channel activated but not immobilized with ligands, to eliminate non-specific binding signals.

[0232] 3. Data Analysis and Interface Dynamics Index Calculation:

[0233] Kinetic fitting was performed on the STPO binding / dissociation curves after background subtraction (e.g., using the Langmuir 1:1 binding model or a more complex model). The binding rate constant k of STPO to the immobilized ligand was obtained through curve fitting. on ) and dissociation rate constant (k off These parameters reflect the rate and strength of enzyme binding to the surface. The epigenetic affinity constant K can be calculated. A =k on / k off This reflects the overall bonding strength. For ease of risk assessment, the interfacial dynamic index IKI is defined as IKI = k on ×10 -3 / k off (Multiply by 10 here) -3 This is merely an adjustment of the numerical range; the actual definition can be determined based on the data distribution. on The usual unit is M. -1 S -1 k off The unit is S -1 The unit of IKI is M. -1 .

[0234] Technical effect: IKI values ​​in healthy reference populations typically fall within a relatively high range (e.g., 2.0-5.0M). -1 This indicates that STPO has a strong binding affinity to substrates / analogs on simulated tooth enamel surfaces. Under early demineralization risk conditions, changes in enamel surface properties may lead to a decrease in the binding affinity of STPO. on Decrease or k off Increased levels can significantly reduce IKI values ​​(e.g., <1.5M). -1 Dynamic monitoring of IKI changes over time can directly reflect the interaction state between STPO and the enamel surface, providing a unique perspective for assessing early changes in the enamel surface and the function of enzymes at the interface.

[0235] Example 8: Monitoring of "Enzyme-catalyzed cascade signal flow distribution ratio"

[0236] This example illustrates how to assess changes in "signal flow allocation preference" by monitoring the product ratio of two competing pathways in an enzyme cascade reaction.

[0237] This invention identifies a cascade system composed of glutathione peroxidase (GPx) and glutathione S-transferase (GST) from saliva. Both GPx and GST use reduced glutathione (GSH) as a substrate and catalyze different reactions, respectively.

[0238] GPx-catalyzed reactions: Its function is to remove reactive oxygen species (H2O2).

[0239] GST-catalyzed reactions: It participates in the metabolism of exogenous substances.

[0240] GPx is responsible for scavenging reactive oxygen species (H2O2), while GST is involved in the metabolism of exogenous substances (such as CDNB). Under early demineralization risk conditions, oxidative stress levels may increase, or certain components in saliva may affect the relative activities of GPx and GST, leading to changes in their competitive use of GSH, thereby altering the ratio of GPx pathway products (GSSG) to GST pathway products (CDNB-SG).

[0241] Specific testing methods:

[0242] 1. Construction of the GPx-GST cascade system activity assay system:

[0243] A coupled enzyme reaction system for simultaneously monitoring GPx and GST activities was constructed. First, 50 μL of standardized saliva sample was taken, followed by 100 μL of activity assay buffer (e.g., 100 mM sodium phosphate buffer, pH 7.0, 2 mM EDTA), then 10 μL of GSH solution (e.g., 20 mM), then 10 μL of H2O2 solution (e.g., 2 mM, GPx substrate), then 10 μL of LCDNB solution (e.g., 10 mM, 1-chloro-2,4-dinitrobenzene, GST substrate), and finally 10 μL of NADPH solution (e.g., 2 mM). To monitor GPx activity, a coupled glutathione reductase (GR) reaction was required. GR utilizes NADPH to reduce GSSG to GSH, and the NADPH consumption rate is correlated with GPx activity. Then, 10 μL of glutathione reductase (GR) solution (e.g., 2 U / mL) was added. The system was incubated at 37 °C. The absorbance changes at two wavelengths were monitored simultaneously using a spectrophotometer: 340 nm (where NADPH depletion and CDNB-SG formation both absorb) and 412 nm (where CDNB-SG exhibits specific absorption).

[0244] 2. Data Analysis and Signal Flow Allocation Ratio Calculation

[0245] Monitor the linear change rates (ΔA340 / min and ΔA412 / min) of absorbance at 340 nm and 412 nm over a period of time (e.g., 10-20 minutes). The formation rate V of the GST pathway product CDNB-SG. gst It can be calculated directly from the change in absorbance at 412nm:

[0246] V gst =ΔA412 / min / ε412

[0247] Where ε412 is the molar absorptivity of CDNB-SG at 412 nm. The rate of GSH consumption in the GPx pathway is coupled with the rate of NADPH consumption in GR, and the NADPH consumption rate is measured at 340 nm. The total absorbance change rate at 340 nm is the superposition of NADPH consumption and CDNB-SG. Therefore, the rate of GSH consumption in the GPx pathway (V gst This can be derived from data at 340nm and 412nm (considering the absorption of CDNB-SG at 340nm and the molar absorptivity of NADPH at 340nm). A simplified calculation method is to monitor the NADPH consumption rate and subtract the contribution from CDNB-SG formation.

[0248] First, calculate the total 340nm change rate ΔA340 / min. Then, calculate the rate at which CDNB-SG formation contributes to 340nm: (ΔA412 / min / ε412)×ε340.

[0249] The rate at which the GPx pathway consumes NADPH = ΔA340 / min - (ΔA412 / min * ε340 / ε412)

[0250] V gst It is proportional to the rate of NADPH consumption.

[0251] Next, calculate the signal flow distribution ratio (SFR):

[0252] SFR = V gpx / V gst

[0253] This ratio reflects the relative preference for signaling to the GPx pathway (antioxidant) and the GST pathway (detoxification) under common GSH substrate competition.

[0254] Technical Effects: In healthy reference populations, SFR values ​​typically fall within a specific range (e.g., 1.2–2.5), indicating that the GPx pathway is generally slightly dominant or maintains a specific balance with the GST pathway. Under early demineralization risk conditions, such as increased oxidative stress or relative inhibition of GST activity, SFR may decrease (e.g., <1.0), indicating that signaling flow is more biased towards the GST pathway or that GPx pathway function is impaired. Dynamic monitoring of SFR changes over time can reveal the functional balance of important redox and detoxification systems in saliva; imbalances in these systems are important markers of the oral microenvironment's health.

[0255] Example 9: Monitoring of "flux redistribution capability"

[0256] This embodiment illustrates how to monitor the system's ability to respond to disturbances by introducing a predatory substrate analog into the glycolysis pathway in order to assess its "flux redistribution capability".

[0257] This embodiment selects the glycolysis pathway present in saliva as the research object:

[0258] Glucose (G) → hexapeptide (HK) → glucose-6-phosphate (G6P) → glucose phosphate isomerase (PGI) → fructose-6-phosphate (F6P).

[0259] The structural analogue of G6P, 2-deoxy-glucose-6-phosphate (2dG6P), was designed and synthesized. 2dG6P cannot be efficiently metabolized by downstream enzymes (such as G6PDH), but it can act as a competitive inhibitor or fault substrate of PGI. By introducing 2dG6P into the reaction system to perturb the PGI enzyme and monitoring the rates of change in upstream and downstream products (G6P and F6P), the flux redistribution capacity of the entire pathway system in the face of PGI limitation can be assessed. A robust and healthy enzyme system may maintain a relatively stable F6P yield by compensatingly increasing PGI activity or regulating upstream HK activity. An impaired system, however, may exhibit G6P accumulation and a significant decrease in F6P yield.

[0260] Specific testing methods:

[0261] 1. Construction of a system for measuring the activity of the glycolysis pathway:

[0262] Construct two reaction systems:

[0263] Reaction system A (control): used to determine normal pathway flux.

[0264] Take 50 μL of standardized saliva sample. Add 100 μL of activity assay buffer (e.g., 50 mM Tris-HCl buffer, pH 7.4, 5 mM MgCl2). Add 10 μL of glucose solution (e.g., 100 mM, as a starting substrate). Add 10 μL of ATP solution (e.g., 20 mM, HK requires ATP). Add 10 μL of NADP. + Solution (e.g., 10 mM). For flux monitoring, a downstream indicator enzyme needs to be coupled. Add 10 μL of a mixed solution of G6P dehydrogenase (G6PDH, 2 U / mL) and 6-phosphoglucose dehydrogenase (6PGDH, 1 U / mL). G6PDH catalyzes the conversion of G6P to 6-phosphoglucose, while simultaneously converting NADP... + G6P is reduced to NADPH, which can be monitored at 340 nm. 6PGDH catalyzes the further reaction of 6-phosphogluconic acid, ensuring effective consumption of G6P and indirectly reflecting the flux. The system is reacted at a constant temperature of 37°C, and the rate of change in absorbance at 340 nm is monitored; this rate reflects the total flux of G6P converted to downstream products.

[0265] Reaction system B (experiment): A predatory analogue is introduced based on the control system.

[0266] The composition is the same as that of reaction system A, except that 10 μL of 2dG6P solution (e.g., 5 mM) is added.

[0267] In reaction systems A and B, the total flux through the HK and PGI pathways can be reflected by monitoring the change in absorbance at 340 nm over time. A more refined approach is to incubate the system for a period before adding G6PDH / 6PGDH and measure the accumulation of G6P and F6P, or to track flux using isotopically labeled substrates. Based on the technical disclosure, a coupled enzyme is used here to measure the NADPH production rate to reflect the flux.

[0268] 2. Data Analysis and Flux Redistribution Index Calculation

[0269] In reaction systems A and B, the linear rate of change of absorbance at 340 nm over time (ΔA340 / min) was calculated. This rate is proportional to the NADPH formation rate and indirectly reflects the flux through the HK-PGI pathway into subsequent reactions.

[0270] Calculate the flux redistribution index (FRI): FRI = (ΔA340 / min in reaction system B) / (ΔA340 / min in reaction system A).

[0271] Technical Results: Reaction system A measures the flux of the normal pathway. In reaction system B, the presence of 2dG6P inhibits PGI activity. If the enzyme system lacks effective flux redistribution, the total flux (reflected in ΔA340 / min) will significantly decrease, resulting in a very low FRI value. The enzyme system in healthy saliva may have some compensatory capacity, partially maintaining pathway flux by regulating other mechanisms (e.g., increasing PGI expression or activity), resulting in a relatively high FRI value (e.g., 0.6-0.8). However, the salivary enzyme system of high-risk patients may be functionally impaired, lacking this redistribution capacity, leading to a significantly lower FRI value (e.g., <0.4). Dynamically monitoring changes in FRI over time can assess the ability of the salivary enzyme system to cope with disturbances and maintain homeostasis. This is an important indicator reflecting the overall health and reliability of the system and is closely related to the homeostasis imbalance and demineralization risk of the oral environment.

[0272] Example 10: Case study on comprehensive evaluation and dynamic monitoring of multi-dimensional indicators.

[0273] This embodiment combines the various enzymatic characteristic index data obtained in Examples 1-9 to illustrate how to conduct comprehensive evaluation and dynamic monitoring.

[0274] Suppose that saliva samples were collected and analyzed from an orthodontic patient before treatment (baseline, T0), at 3 months (T1) and 6 months (T2) of treatment. The following four indicators were selected as the patient's individualized monitoring combination: pH sensitivity coefficient (α) of SAP, apparent activity of Cathepsin D, substrate selectivity index (SSI) of lysozyme, and stability dependence index (SDI) of SpO.

[0275] 1. Establish individual baselines (T0 data)

[0276] index T0 baseline value Weight (wi) Risk direction Health Reference Range SAP pH sensitivity coefficient (α) 0.5 1.2 ↑Risk 0.3-0.4 Cathepsin D apparent activity (units) 10 1.0 ↑Risk 5-8 Lysozyme SSI 0.48 1.5 Deviation risk 0.40-0.55 SPOSDI 0.85 1.0 ↓Risk 0.7-0.9

[0277] Note: Weights are determined based on the patient's historical data or clinical presentation. Risk orientation is determined based on the technical effects of the aforementioned embodiments (α high risk, Cathepsin D high risk, SSI deviation high risk, SDI low risk).

[0278] 2. Dynamic tracking and data acquisition (T1 and T2 data)

[0279]

[0280]

[0281] 3. Risk Assessment and Early Warning

[0282] T1 time point assessment (3 months after treatment):

[0283] Calculate the rate of change of each indicator relative to the baseline:

[0284] SAPα change rate: (0.55-0.5) / 0.5 = +10%

[0285] Cathepsin D change rate: (11-10) / 10 = +10%

[0286] SSI change rate: (0.45-0.48) / 0.48 = -6.25%

[0287] SDI change rate: (0.80-0.85) / 0.85 = -5.88%

[0288] Checking for single indicator warnings: All indicator change rates are less than 25%, and there are no single indicator warnings.

[0289] Calculate the overall risk score:

[0290] The sign is determined according to the formula in Example 1 and based on the risk direction of the indicator. i

[0291] SAPα: The direction of change is ↑ (risk), sign i =+1;

[0292] Cathepsin D: The direction of change is ↑ (risk), sign i =+1;

[0293] SSI: A downward change (deviation from the healthy range, considered a risk), sign i=+1;

[0294] SDI: The direction of change is ↓ (risk), sign i =+1.

[0295]

[0296] Risk assessment: Risk Score (T1) = 0.37, <0.5. The patient is at low risk. Continued routine oral hygiene care and regular follow-up appointments are recommended.

[0297] T2 time point assessment (6 months of treatment):

[0298] Calculate the rate of change of each indicator relative to the baseline:

[0299] SAPα change rate: (0.6-0.5) / 0.5 = +20%

[0300] Cathepsin D change rate: (14-10) / 10 = +40%

[0301] SSI change rate: (0.35-0.48) / 0.48 = -27.08%

[0302] SDI change rate: (0.70-0.85) / 0.85 = -17.65%

[0303] Checking single indicator warnings: Cathepsin D change rate +40% (>25%), SSI change rate -27.08% (absolute value >25%). Cathepsin D and SSI are issuing early risk signals.

[0304] Calculate the overall risk score:

[0305] SAPα: The direction of change is ↑ (risk), sign i =+1;

[0306] Cathepsin D: The direction of change is ↑ (risk), sign i =+1;

[0307] SSI: A downward change (deviation from the healthy range, considered a risk), sign i =+1;

[0308] SDI: The direction of change is ↓ (risk), sign i =+1

[0309]

[0310] Risk assessment: Risk Score (T2) = 1.22, ≥1.0. The patient is at high risk. Recommended aggressive interventions include immediate enhanced oral hygiene instruction, topical fluoride application, consideration of anti-demineralization agents, and close monitoring of oral condition and risk indicators.

[0311] Technical Effectiveness: Through dynamic monitoring and a comprehensive risk score based on individual baselines, this method can promptly detect increasing trends in enamel demineralization risk during orthodontic treatment. Significant changes in a single indicator provide early warning, while multi-indicator weighted scoring offers a more comprehensive risk assessment, guiding clinicians to implement tiered interventions based on risk levels, thereby effectively preventing clinically observable enamel demineralization. This dynamic and individualized monitoring and assessment process is a significant advantage of this method compared to existing technologies.

Claims

1. A method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles, characterized in that, Includes the following steps: S1. Obtain saliva samples from orthodontic patients at least twice during orthodontic treatment; S2. Process the saliva sample to obtain a standardized saliva sample; S3. Analyze standardized saliva samples to obtain at least two different enzymatic property indicators of at least one enzyme or enzyme system in saliva, and use these enzymatic property indicators to reflect the multiple molecular properties of the enzyme or enzyme system or its response to environmental changes. S4. Based on the changes in enzymatic properties obtained at different time points, dynamically assess the risk of enamel demineralization in orthodontic patients. In this step, the enzymatic characteristics obtained in step S3 serve as the basis for risk assessment in step S4. Step S4 compares the enzymatic characteristics obtained at different time points to dynamically track the response of salivary enzymes or enzyme systems to changes in the patient's oral microenvironment, thereby reflecting the changing status of the patient's enamel demineralization risk and achieving dynamic monitoring.

2. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 1, characterized in that, Step S1 includes: collecting non-irritating or irritating saliva; wherein, The method for collecting non-irritating saliva is as follows: After fasting for 2 hours, the patient rinses their mouth for 5 minutes and collects at least 2 mL of naturally flowing saliva in a sterile container. The method for collecting irritating saliva is as follows: After the patient chews a tasteless paraffin block for 1 minute, spit out the first mouthful of saliva, continue chewing for 5 minutes, spit the saliva into a sterile container every 30 seconds, and collect at least 5 mL of saliva.

3. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 1, characterized in that, Step S2 includes: The collected saliva was centrifuged at 4000×g for 10 minutes at 4°C for 30 minutes to remove cellular components and food residue, obtaining a supernatant. The supernatant was aliquoted into multiple 200μL / tube aliquots, and a mixture of protease inhibitors with a final concentration of 1mM was added. The aliquots were then rapidly frozen to -80°C for storage. Before use, the samples were thawed slowly in a 4°C water bath. The protein concentration was measured immediately after thawing and then standardized to a uniform protein concentration.

4. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 1, characterized in that, The enzymatic property indicators in step S3 include at least one of the following: The inactivation rate of salivary enzymes; The competitive enzyme inhibition relief effect of salivary enzymes; Changes in the selectivity of salivary enzymes for non-natural substrates; Stability changes of salivary enzymes under sublethal denaturant concentrations; Changes in the allosteric switch trigger threshold required for salivary enzyme activation; Changes in interfacial kinetic parameters of the interaction between salivary enzymes and solidified substrates or substrate analogs; Changes in signal flow distribution preference in enzyme-catalyzed cascade reactions; Changes in the flux redistribution capacity within the enzyme system after the introduction of exogenous predatory substrate analogs.

5. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 4, characterized in that, The determination of the inactivation rate of salivary enzyme in step S3 includes the following steps: mixing a standardized saliva sample with an inactivation buffer and incubating at a preset temperature of 37°C for a predetermined time; removing the reaction solution, adding an activity assay buffer and substrate, and terminating the reaction after a predetermined time; measuring the absorbance of the product and calculating the percentage of residual activity; plotting a curve of the percentage of residual activity against time, and obtaining the apparent inactivation rate constant through linear regression; wherein the salivary enzyme is salivary alkaline phosphatase (SAP), the pH of the inactivation buffer is 6.2-6.6, and it contains 0.5-2.5 mM Ca. 2+ .

6. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 5, characterized in that, The deactivation rate is characterized by an improved second-order deactivation mechanics model, which is as follows: Where, k inact The apparent inactivation rate constant (min) -1 ); k0 represents the standard conditions (pH 7.0, no Ca). 2+ The basic deactivation rate constant; α is the pH sensitivity coefficient, which characterizes the sensitivity of inactivation to pH changes; pH c This is the critical pH value; below this value, inactivation is significantly accelerated (value taken as 6.5). β is Ca 2+ Protection coefficient, characterizing Ca 2+ Protective effect against inactivation; [Ca 2+ [Ca in the environment] 2+ concentration; K d For salivary alkaline phosphatase (SAP) and Ca 2+ The apparent dissociation constant; The α and β parameters extracted from the experimental data were used as individualized risk indicators, with high-risk patients exhibiting larger α values ​​and smaller β values.

7. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 4, characterized in that, The competitive enzyme inhibition relief effect of the salivary enzyme was assessed by measuring the apparent activity of salivary cathepsin D, which is inhibited by salivary cysteine ​​protease inhibitors. Under the risk of enamel demineralization, the inhibitory effect on cathepsin D is weakened when the activity of cysteine ​​protease inhibitors decreases, thereby increasing the apparent activity of cathepsin D.

8. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 4, characterized in that, The selectivity changes of the salivary enzyme to non-natural substrates were assessed by measuring the changes in the relative catalytic efficiency spectra of salivary lysozyme to a group of structurally related but differently catalytically efficient non-natural substrates; wherein the non-natural substrates included oligosaccharide substrates containing different glycosidic bonds, and the ratio of their relative catalytic efficiencies showed characteristic differences in healthy and early demineralization risk states; the selectivity changes were quantified by calculating the substrate selectivity index (SSI) using the following formula: SSI is the substrate selectivity index, a dimensionless value between 0 and 1. n is the number of substrates in the substrate library; (k cat / K m ) i The catalytic efficiency of the enzyme for the i-th substrate; (k cat / K m ) j The catalytic efficiency of the enzyme for the j-th substrate; w i is the weighting coefficient for the i-th substrate.

9. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 1, characterized in that, The dynamic assessment in step S4 includes: before orthodontic treatment begins, enzyme indicators are tested on the patient as the individual baseline reference value; after treatment begins, tests are performed every 1-3 months, and curves of each indicator changing over time are plotted; when any indicator deviates from the individual baseline by more than 25% and is biased towards the risk direction, it is considered an early risk signal; multi-dimensional enzyme indicators are integrated, and a weighted scoring system is used to calculate the risk score.

10. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 9, characterized in that, The formula for calculating the risk score is as follows: Among them, I i This represents the current value of the i-th indicator. I i,0 This represents the baseline value of the i-th indicator for this patient; w i The weighting coefficient for the i-th indicator (with a value between 0.5 and 2.0); sign i The value is +1 or -1, depending on whether the direction of the indicator change increases the risk. The risk score is divided into three levels: Low risk: Risk Score < 0.5; Medium risk: 0.5 ≤ Risk Score < 1.0; High risk: Risk Score > 1.0; When the Risk Score is ≥0.5, an early warning is activated, and it is recommended to strengthen oral hygiene and preventive measures. When the Risk Score is ≥1.0, it is recommended to take active intervention measures.

11. The method for dynamically monitoring the risk of enamel demineralization in orthodontic patients based on salivary enzyme activity profiles according to claim 4, characterized in that: The stability changes of the salivary enzyme under sublethal denaturant conditions were assessed by measuring the residual activity of salivary peroxidase in the presence of sublethal urea. The change in the allosteric switch trigger threshold required for salivary enzyme activation is determined by measuring the Zn required for salivary carbonic anhydrase VI to reach its maximum activity of 50% under pH gradient conditions. 2+ Assess using concentration; The changes in the interfacial kinetic parameters of the interaction between the salivary enzyme and the immobilized substrate or substrate analogue were assessed by measuring the binding rate constant and dissociation rate constant of the salivary thiocyanate peroxidase with the substrate or substrate analogue immobilized on the carrier surface using surface plasmon resonance technology. The changes in signal flow distribution preference in the enzyme-catalyzed cascade reaction are assessed by measuring the ratio of the rates of the two reaction pathways in the cascade system composed of glutathione peroxidase and glutathione S-transferase. The changes in the flux redistribution capacity within the enzyme system were assessed by introducing a competitive substrate analog into the glycolysis pathway in saliva and measuring the changes in the accumulation rate of intermediate products and the rate of final product formation before and after the introduction.

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