A method for improving the stability of a pigeon hyperuricemia model

Abstract

The present application relates to the field of experimental animal model construction, and discloses a method for improving the stability of a pigeon hyperuricemia model, comprising the following steps: selecting and monitoring: selecting healthy adult pigeons, obtaining individual metabolic parameters through pharmacokinetic experiments and circadian rhythm monitoring; feeding and calculating: feeding the pigeons in a controllable environment, and calculating the initial dose of an inducing agent based on the individual metabolic parameters through a space-time coupling model, wherein the inducing agent comprises a uric acid precursor and a uricase inhibitor; optimizing induction: according to the initial dose, giving the pigeons the inducing agent in stages, and combining a predictive control algorithm to optimize the dose of the inducing agent in real time, so as to induce the pigeons to develop hyperuricemia. Through the space-time coupling model in a controllable environment, the individual metabolic parameters are converted into an appropriate initial dose, avoiding the neglect of individual differences caused by a fixed dose, and through the staged administration and the predictive control algorithm, the dose is dynamically adjusted to compensate for metabolic fluctuations.

Description

Technical Field

[0001] This invention relates to the field of experimental animal model construction technology, specifically a method to improve the stability of a pigeon hyperuricemia model. Background Technology

[0002] Hyperuricemia is a metabolic disease caused by purine metabolism disorders or impaired uric acid excretion. It is not only a direct cause of gout but also closely related to various chronic diseases such as cardiovascular disease and chronic kidney disease. With the global incidence rate increasing year by year, the need for basic research and clinical translation in hyperuricemia is becoming increasingly urgent. As a key vehicle connecting basic research and clinical applications, the stability and reliability of experimental animal models directly affect the scientific validity and reproducibility of research conclusions. Therefore, constructing stable animal models with uric acid metabolism characteristics similar to humans has significant scientific research value.

[0003] In existing technologies, the construction of animal models of hyperuricemia mainly uses rodents such as rats and mice as experimental subjects, and is induced primarily through feeding high-purine diets, injecting uric acid precursors, or inhibiting uricase activity. However, rodents possess active uricase oxidase, and their uric acid metabolism pathway differs significantly from that of humans. This results in models that cannot fully mimic the pathophysiological characteristics of human hyperuricemia, such as typical phenotypes like urate crystal deposition and kidney damage. Therefore, pigeons, due to their extremely low uricase oxidase activity and uric acid excretion pathway similar to that of humans, have become ideal candidate animals for hyperuricemia models.

[0004] Existing methods for constructing pigeon hyperuricemia models mainly use fixed doses of inducers, ignoring the natural differences in uric acid production rate, transport efficiency, elimination capacity, and diurnal metabolic rhythm among individual pigeons. This results in a high coefficient of variation in serum uric acid levels under the same induction conditions, poor model stability and reproducibility, and difficulty in meeting the consistency requirements of hyperuricemia mechanism research and drug screening. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method to improve the stability of a pigeon hyperuricemia model, solving the problem that existing pigeon hyperuricemia models have poor stability and reproducibility, making it difficult to meet the consistency requirements of hyperuricemia mechanism research and drug screening.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for improving the stability of a pigeon hyperuricemia model, comprising the following steps:

[0007] Selection and monitoring: Healthy adult pigeons were selected, and individual metabolic parameters were obtained through pharmacokinetic experiments and circadian rhythm monitoring;

[0008] Feeding calculation: Pigeons are fed in a controlled environment, and the initial dose of inducer is calculated based on individual metabolic parameters using a spatiotemporal coupling model. The inducer includes uric acid precursors and uricase inhibitors.

[0009] Optimized induction: Based on the initial dose, inducers are given to pigeons in stages, and the dosage of inducers is optimized in real time using a predictive control algorithm to induce hyperuricemia in pigeons;

[0010] Detection and validation: The hyperuricemia model phenotype was confirmed through biochemical, behavioral and pathological tests, and the stability of the model was verified by combining chaotic dynamic indicators and statistical parameters.

[0011] By adopting the above technical solution, individual metabolic parameters are obtained to provide basic data for individualized induction. By combining a spatiotemporal coupling model in a controlled environment, individual metabolic parameters are transformed into an appropriate initial dose, avoiding the neglect of individual differences by fixed doses. By administering the drug in stages and combining predictive control algorithms, the dose is dynamically adjusted to compensate for metabolic fluctuations and maintain stable uric acid levels. The model phenotype is confirmed through multiple dimensions and stability is evaluated by combining chaotic dynamic indicators and statistical parameters to ensure model consistency. This achieves adaptation and dynamic regulation of individual metabolic heterogeneity in pigeons, solving the problem that the existing pigeon hyperuricemia models have poor stability and reproducibility, making it difficult to meet the model consistency requirements for hyperuricemia mechanism research and drug screening.

[0012] Preferably, in the step of selection and monitoring, the individual metabolic parameters include metabolic parameters and rhythm correction coefficients, and the pharmacokinetic experiment involves collecting blood samples at multiple time points, combining a multi-compartment model to analyze the transport and elimination characteristics of uric acid between blood and tissues, and obtaining metabolic parameters including transport rate constants and elimination rate constants.

[0013] Preferably, in the step of monitoring, the diurnal rhythm monitoring involves continuously and periodically collecting the basal uric acid data of pigeons, extracting the diurnal metabolic fluctuation characteristics using a rhythm cycle analysis method, and obtaining the rhythm correction coefficient.

[0014] Preferably, in the feeding calculation step, the parameters of the controllable environment include a temperature of 22-26℃, a relative humidity of 50%-60%, a 12h light / 12h dark cycle, and the feed is a standardized low-purine formula. The spatiotemporal coupling model constructs a dynamic equation of uric acid concentration over time by integrating metabolic parameters and rhythm correction coefficients, and calculates the initial dose based on the target uric acid level, so that the initial dose matches the individual's metabolic capacity.

[0015] Preferably, in the feeding calculation step, the uric acid precursor is xanthine, which is used to promote uric acid production, and the uricase inhibitor is potassium oxonate, which is used to inhibit uric acid decomposition. The ratio of xanthine to potassium oxonate is dynamically adapted according to individual metabolic parameters.

[0016] Preferably, in the optimized induction step, the phased stages include a basic induction stage, an enhanced induction stage, and a stable maintenance stage. The basic stage involves administering a dose lower than the initial dose, the enhanced stage involves administering a dose higher than the initial dose, and the maintenance stage involves administering the inducing agent based on the real-time optimized dose. The predictive control algorithm predicts future trends by analyzing historical uric acid data and minimizes the deviation between the predicted and target values, and optimizes the dose in real time based on the prediction results.

[0017] Preferably, in the optimized induction step, the administration of the inducing agent includes quantitative feeding of xanthine via a gastric tube and quantitative injection of potassium oxonate via intraperitoneal injection. The adjustment range of the real-time optimized inducing agent dose does not exceed 15% of the previous dose, and the inducing agent dose does not exceed the preset maximum daily dose.

[0018] Preferably, in the detection and verification steps, the biochemical detection includes periodically collecting venous blood to measure serum uric acid concentration, the behavioral detection includes recording activity frequency, food intake and stool morphology, and the pathological detection includes anatomical observation of renal tubules and joint synovial tissue.

[0019] Preferably, in the detection and verification step, the chaotic dynamics index is used to assess the stability of uric acid concentration fluctuations. By analyzing time series data, it is determined whether the hyperuricemia model is in a stable periodic state. This includes the maximum Lyapunov index. The statistical parameters include the coefficient of variation, which is used to quantify the consistency of uric acid concentration within and between batches.

[0020] A system for improving the stability of a pigeon hyperuricemia model, and a method for improving the stability of a pigeon hyperuricemia model as described above, comprising:

[0021] Selection and monitoring module: used to select healthy adult pigeons and obtain individual metabolic parameters through pharmacokinetic experiments and circadian rhythm monitoring;

[0022] Feeding calculation module: used to feed pigeons in a controlled environment and calculate the initial dose of inducer based on individual metabolic parameters through a spatiotemporal coupling model. The inducer includes uric acid precursors and uricase inhibitors.

[0023] Optimized induction module: It is used to administer inducers to pigeons in stages according to the initial dose, and combine predictive control algorithms to optimize the inducer dosage in real time to induce pigeons to develop hyperuricemia;

[0024] The detection and validation module is used to confirm the model phenotype of hyperuricemia through biochemical, behavioral, and pathological tests, and to verify the stability of the model by combining chaotic dynamic indicators and statistical parameters.

[0025] This invention provides a method to improve the stability of a pigeon hyperuricemia model. It has the following beneficial effects:

[0026] 1. This invention provides basic data for individualized induction by acquiring individual metabolic parameters. By combining a spatiotemporal coupling model in a controlled environment, individual metabolic parameters are transformed into suitable initial doses, avoiding the neglect of individual differences by fixed doses. By administering drugs in stages and combining predictive control algorithms, the dose is dynamically adjusted to compensate for metabolic fluctuations and maintain stable uric acid levels. By confirming the model phenotype in multiple dimensions and assessing stability by combining chaotic dynamic indicators and statistical parameters, the consistency of the model is ensured. This invention achieves adaptation and dynamic regulation of individual metabolic heterogeneity in pigeons, solving the problem that the existing pigeon hyperuricemia models have poor stability and reproducibility, making it difficult to meet the model consistency requirements for hyperuricemia mechanism research and drug screening.

[0027] 2. This invention obtains individual metabolic parameters through pharmacokinetic experiments and circadian rhythm monitoring, calculates the initial dose using a spatiotemporal coupling model, and optimizes the inducer dose in real time using a predictive control algorithm. This can compensate for individual differences in uric acid production, transport, elimination, and diurnal fluctuations, thereby maintaining serum uric acid levels stably within the target range, reducing fluctuation amplitude, and providing stable basic data for subsequent model research.

[0028] 3. This invention, through the synergistic induction of xanthine and potassium oxonate, combined with individualized dosage control, can not only stably increase serum uric acid levels, but also form typical urate crystal deposits in tissues such as renal tubules and synovial membranes of joints. At the same time, it is accompanied by behavioral changes similar to those in human hyperuricemia, thus more comprehensively simulating the pathophysiological process of the disease. This provides a more reliable experimental vehicle for the study of the pathogenesis of hyperuricemia, drug screening, and evaluation of treatment plans.

[0029] 4. This invention establishes a standardized model construction process through standardized and controllable breeding environment, phased induction strategy and multi-dimensional verification. This ensures that different batches and different operators can obtain consistent model phenotypes and stability indicators when using this method, thus meeting the core requirement of reproducible results in scientific research experiments. Attached Figure Description

[0030] Figure 1 This is a flowchart of a method for improving the stability of a pigeon hyperuricemia model proposed in this invention;

[0031] Figure 2This is a system architecture diagram of a pigeon hyperuricemia model proposed in this invention. Detailed Implementation

[0032] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see the appendix Figure 1 This invention provides a method for improving the stability of a pigeon hyperuricemia model, comprising the following steps:

[0034] Selection and monitoring: Healthy adult pigeons were selected, and individual metabolic parameters were obtained through pharmacokinetic experiments and circadian rhythm monitoring;

[0035] Furthermore, in the step selection monitoring, individual metabolic parameters include metabolic parameters and rhythm correction coefficients. The pharmacokinetic experiment involves collecting blood samples at multiple time points and combining them with a multi-compartment model to analyze the transport and elimination characteristics of uric acid between blood and tissues, and to obtain metabolic parameters including transport rate constants and elimination rate constants.

[0036] Furthermore, in the selected monitoring steps, the diurnal rhythm monitoring involves continuously and periodically collecting the basal uric acid data of pigeons, using a rhythm cycle analysis method to extract diurnal metabolic fluctuation characteristics, and obtaining the rhythm correction coefficient.

[0037] Specifically, the monitoring steps involved experimental design to obtain individual metabolic characteristics of pigeons, laying the foundation for individualized calculation of the inducer dosage. Generally, healthy adult white-feathered king pigeons were selected, typically weighing between 250 and 300 grams, with an equal number of males and females. These pigeons underwent at least three days of acclimatization to eliminate the impact of stress on basal metabolism. Alternatively, health status was assessed by ensuring a stable basal serum uric acid concentration of 5.0 ± 0.5 mg / dL and the absence of clinical symptoms such as joint swelling and abnormal kidney function, ensuring physiological homogeneity among the experimental individuals.

[0038] Specifically, pharmacokinetic experiments involve collecting blood samples at multiple time points to analyze the transport and elimination patterns of uric acid in the body. In one possible approach, pigeons are given a single intraperitoneal injection of a uricase inhibitor before the experiment. The injection dose is determined based on the pigeon's weight, typically 5 mg / kg. Following injection, venous blood is collected from the wings at 0.5, 1, 2, 4, 6, 8, 12, and 24 hours, with each collection volume ranging from 0.5 to 1 mL. After centrifugation, serum is separated and used to determine serum uric acid concentrations at different time points.

[0039] In some embodiments, the serum uric acid concentration data at the above time points will be used to fit a multicompartment model, which reflects the dynamic distribution of uric acid in blood and tissues. The equation for the multicompartment model is as follows:

[0040]

[0041] Where C1 represents the uric acid concentration in the central compartment (i.e., blood), C2 represents the uric acid concentration in the peripheral compartment (i.e., tissues such as kidneys and joints), and k a The absorption rate constant of a drug, k, reflects the rate at which the uricase inhibitor enters the bloodstream. 10 The elimination rate constant of the central compartment, k, reflects the rate at which uric acid is cleared from the blood. 12 The k-value represents the rate constant of uric acid transport from the central compartment to the peripheral compartment, reflecting the ability of uric acid to accumulate in tissues. 21 The constant representing the rate of uric acid transport from the peripheral compartment to the central compartment reflects the rate at which uric acid reabsorbs from the tissue into the bloodstream, and D represents the dose of a single uricase inhibitor. By fitting this model, k can be output. 10 k 12 These metabolic parameters can quantify individual differences among pigeons in the process of uric acid transport and elimination.

[0042] Generally, circadian rhythm monitoring needs to be completed before pharmacokinetic experiments to obtain the metabolic fluctuation characteristics of pigeons under basal conditions. Specifically, basal uric acid concentration of pigeons needs to be measured for three consecutive days, with a measurement interval of 4 hours. That is, blood samples are collected at 0:00, 4:00, 8:00, 12:00, 16:00 and 20:00 every day. After centrifugation, serum uric acid concentration is measured to form a continuous uric acid concentration time series.

[0043] As an alternative, the circadian rhythm analysis method uses Fourier transform to analyze the above time series to extract characteristic parameters of diurnal metabolic fluctuations. The formula for the Fourier transform is as follows:

[0044]

[0045] Where U0(t) represents the basal uric acid concentration at time t, U base This represents the baseline value of uric acid concentration, i.e., the average value over 24 hours. (A) n f represents the amplitude of the nth periodic component, reflecting the fluctuation range of uric acid concentration within that period. n This represents the frequency of the nth period, where the main period f1 is 1 / 24h. -1 Corresponding to the diurnal rhythm, φ n The phase of the nth cycle is represented by t, which reflects the time when the peak of the fluctuation occurs. The amplitude A1 of the main cycle can be extracted through this formula, and then the rhythm correction coefficient can be calculated.

[0046] In one possible implementation, the rhythm correction coefficient includes a diurnal correction coefficient α. d With nighttime correction factor α n The calculation method is as follows:

[0047] α d =1+A1 / U base

[0048] α n =1-A1 / U base

[0049] Where, α d α is used to characterize the enhanced metabolic effect during the daytime (8 am to 8 pm). n Used to characterize the attenuated metabolic effect during the night (8 PM to 8 AM the following day). This was achieved by obtaining α... d With α n This can quantify the degree of fluctuation in metabolic rate over different time periods, providing a basis for subsequent time-segmented calculation of inducer dosage.

[0050] In some embodiments, metabolic parameters obtained through pharmacokinetic experiments and rhythm correction coefficients obtained through circadian rhythm monitoring together constitute individual metabolic parameters. These parameters can comprehensively reflect the individual characteristics of pigeons in terms of uric acid production, transport, elimination, and temporal fluctuations, thereby providing input for the construction of a spatiotemporal coupling model and ensuring that the subsequent calculation of the initial dose can be adapted to the metabolic characteristics of different pigeons.

[0051] Feeding calculation: Pigeons are fed in a controlled environment, and the initial dose of inducers is calculated based on individual metabolic parameters using a spatiotemporal coupling model. The inducers include uric acid precursors and uricase inhibitors.

[0052] Furthermore, in the feeding calculation steps, the parameters of the controllable environment include a temperature of 22-26℃, a relative humidity of 50%-60%, a 12h light / 12h dark cycle, and the feed is a standardized low-purine formula. The spatiotemporal coupling model constructs a dynamic equation of uric acid concentration over time by integrating metabolic parameters and rhythm correction coefficients, and calculates the initial dose based on the target uric acid level, so that the initial dose matches the individual's metabolic capacity.

[0053] Furthermore, in the feeding calculation step, the uric acid precursor is xanthine, which is used to promote uric acid production, and the uricase inhibitor is potassium oxonate, which is used to inhibit uric acid decomposition. The ratio of xanthine to potassium oxonate is dynamically adapted according to individual metabolic parameters.

[0054] Specifically, the feeding calculation step involves selecting monitoring steps to obtain individual metabolic parameters, constructing a controllable feeding environment and a precise dosage calculation model, to provide stable basic conditions and initial dosage basis for subsequent induction. It is necessary to take into account both the control of environmental variables and the adaptation of individual metabolic characteristics to ensure the controllability of the induction process.

[0055] Generally, the controllable environment requires strict control of external factors that may affect pigeon metabolism. Temperature is typically maintained between 22 and 26 degrees Celsius, and relative humidity between 50% and 60%. This temperature and humidity range helps reduce stress responses and prevent metabolic disorders. A 12-hour light-dark cycle is used to simulate natural physiological rhythms and match the rhythmic characteristics obtained from circadian rhythm monitoring, ensuring that metabolic fluctuations conform to their inherent cycle. Standardized low-purine feed is used, with its main components being corn, wheat, soybean meal, and mineral premix. Purine content is typically below 50 mg per 100g. This formula reduces the interference of exogenous purines on uric acid production, making changes in uric acid levels more easily attributed to the effects of inducers. Alternatively, feed is provided in fixed quantities daily, approximately 30g per pigeon, along with ample clean drinking water to ensure adequate hydration and prevent dehydration that could hinder uric acid excretion.

[0056] Specifically, the spatiotemporal coupling model establishes a dynamic relationship between uric acid concentration and time by fusing metabolic parameters obtained from selected monitoring steps with rhythm correction coefficients. The core of this model is the dynamic equation for uric acid concentration, expressed as follows:

[0057]

[0058] Where U(t) represents the serum uric acid concentration at time t, and α(t) represents the rhythm correction coefficient at time t, with α taken as the daytime value. d Take α at night nThe formula is used to characterize the differences in metabolic rates at different time points. X(t) represents the dose of uric acid precursor (xanthine) administered at time t, O(t) represents the dose of uricase inhibitor (potassium oxonate) administered at time t, k1 is the rate constant for the conversion of xanthine to uric acid, reflecting the efficiency of xanthine in promoting uric acid production, and k2 is the rate constant for potassium oxonate in inhibiting uric acid breakdown, reflecting the strength of potassium oxonate in inhibiting uric acid metabolism. 10 k is the elimination rate constant of the central chamber. 12 U2(t) represents the transport rate constant from the central compartment to the peripheral compartment, which are all metabolic parameters obtained from pharmacokinetic experiments. U2(t) represents the uric acid concentration in the peripheral compartment at time t, reflecting the accumulation state of uric acid in the tissue.

[0059] In one possible implementation, the input to the above dynamic equation is the individual metabolic parameter (k). 10 k 12 ), rhythm correction coefficient (α) d α n The output is the rate of change of uric acid concentration over time, along with the doses of the inducing agent (X(t), O(t)) and the dosage of the inducing agent. This equation can predict the dynamic changes in uric acid concentration under different dose combinations, providing a theoretical basis for the calculation of the initial dose.

[0060] In some embodiments, the initial dose is calculated based on the dynamic equation described above, using the target uric acid level as a baseline. The target uric acid level is typically set at 10 to 16 mg / dL, a range that stably induces hyperuricemia while avoiding acute damage caused by excessively high uric acid levels. During calculation, the dynamic equation must reach a steady state (i.e.,...). At this point, the uric acid concentration stabilizes at the target level, from which the formula for calculating the initial dose can be derived.

[0061] The initial dose of xanthine, a uric acid precursor, is calculated separately for daytime and nighttime use, as shown in the following expressions:

[0062]

[0063] in, This indicates the initial dose of xanthine during the day. Indicates the initial dose of xanthine at night, U target The target uric acid level is indicated by O0, which represents the initial dose of potassium oxonate.

[0064] As an alternative, the initial dose of potassium oxonate (O0) is typically kept constant, while the initial dose of xanthine is dynamically adjusted based on individual metabolic parameters to adapt the xanthine-to-potassium oxonate ratio to the metabolic characteristics of different pigeons. For example, for k 10 Larger pigeons (those that eliminate uric acid faster) were calculated as follows and This will increase accordingly to offset the effect of rapid elimination on uric acid concentration; for α d Larger pigeons (with stronger diurnal metabolism) will have higher diurnal xanthine doses than at night to match their enhanced diurnal metabolism.

[0065] Specifically, through the above calculation process, an initial dose matching the individual's metabolic capacity can be obtained. Xanthine works by promoting uric acid production, while potassium oxonate reduces uric acid breakdown by inhibiting uricase activity. The synergistic effect of the two helps stabilize the uric acid concentration to the target level. This dose calculation method based on a spatiotemporal coupling model fully integrates individual metabolic parameters and rhythmic characteristics, helping to reduce initial dose deviations caused by individual differences and laying a good foundation for subsequent optimization of induction steps.

[0066] Optimized induction: Based on the initial dose, inducers are given to pigeons in stages, and the dosage of inducers is optimized in real time using a predictive control algorithm to induce hyperuricemia in pigeons;

[0067] Furthermore, the optimized induction process is divided into three phases: a basic induction phase, an enhanced induction phase, and a stable maintenance phase. The basic phase involves administering a dose lower than the initial dose, the enhanced phase involves administering a dose higher than the initial dose, and the maintenance phase involves administering the inducing agent based on the real-time optimized dose. The predictive control algorithm analyzes historical uric acid data to predict future trends and minimizes the deviation between the predicted and target values, and optimizes the dose in real time based on the prediction results.

[0068] Furthermore, in the optimized induction process, the inducing agents include quantitative feeding of xanthine via gastric tube and quantitative injection of potassium oxonate via intraperitoneal injection. The adjustment range of the inducing agent dosage in real time does not exceed 15% of the previous dosage, and the inducing agent dosage does not exceed the preset maximum daily dosage.

[0069] Specifically, the optimization induction process involves gradually inducing stable hyperuricemia in pigeons by combining phased administration with real-time dose optimization, based on the initial dose obtained from the feeding calculation process. It is necessary to balance the safety of the induction with the controllability of uric acid levels in order to achieve stable model construction.

[0070] Generally, the design of phased administration of inducers is based on the pigeons' tolerance to inducers and their metabolic adaptation patterns. It is divided into a basic induction phase, a intensive induction phase, and a stabilization phase. The dosage adjustments in each phase are closely related to the initial dosage obtained from the feeding calculation steps. The basic induction phase usually lasts for the first three days. During this phase, the inducer dosage is lower than the initial dosage, aiming to allow the pigeons to gradually adapt to the inducer and reduce acute metabolic stress. As an option, the xanthine dosage in the basic phase can be 70% to 80% of the initial daytime dosage, and the potassium oxonate dosage can be 60% to 70% of the initial dosage. For example, if the initial daytime xanthine dosage is 0.2 g / kg, the basic phase can administer 0.14 to 0.16 g / kg to reduce gastrointestinal irritation and kidney burden.

[0071] Specifically, the intensive induction phase typically lasts from day four to seven. During this phase, the inducer dosage is gradually increased to the initial dosage obtained from the feeding calculation steps. The increase can be done in a stepwise manner, for example, increasing the previous day's dosage by 15% to 20% each day until the initial dosage is reached, to avoid drastic fluctuations in uric acid levels caused by sudden dosage changes. In one possible implementation, if the initial daytime xanthine dose is 0.2 g / kg and the dose on the last day of the basal phase is 0.16 g / kg, then the dose can be increased to 0.19 g / kg on the first day of the intensive phase and to 0.2 g / kg on the second day, ensuring a steady rise in uric acid levels.

[0072] In some embodiments, the stabilization maintenance phase lasts from the eighth to the tenth day. The dosage during this phase is determined based on the real-time optimization results of the predictive control algorithm to maintain uric acid levels within the target range. The method of administering the inducer must be adapted to the physicochemical properties of the inducer. Xanthine is usually administered quantitatively via a gastric tube. Before administration, xanthine should be dissolved in a 0.5% sodium carboxymethyl cellulose solution to improve its palatability and absorption efficiency. The gastric tube is generally inserted to a depth of 5 to 6 cm to avoid accidental intubation. Potassium oxonate is administered quantitatively via intraperitoneal injection at a site 1 cm to the right of the midline of the lower abdomen. The injection rate is controlled within 0.1 ml per second. After injection, the abdomen is gently massaged to promote drug diffusion.

[0073] Specifically, the predictive control algorithm is the core of dosage optimization during the stabilization phase. It predicts future trends by analyzing historical uric acid data and then adjusts the inducer dosage to stabilize uric acid levels within the target range. The algorithm's inputs include serum uric acid concentrations from the previous three days, the inducer dosage at the corresponding time points, and the rhythm correction coefficient obtained from the feeding calculation steps. The output is the optimized dosage for the current day. The objective function of the predictive control algorithm is as follows:

[0074]

[0075] Where J is the objective function value, reflecting the quality of the optimization effect. The predicted uric acid concentration for the next i hours is obtained from historical data using a long short-term memory network model. target The target uric acid level is λ, which is a smoothing coefficient, typically 0.1, used to constrain the dose adjustment range and avoid drastic fluctuations. X(t) is the xanthine dose at the current moment, and X(t-1) is the xanthine dose at the previous moment.

[0076] In one possible implementation, if it is predicted that the uric acid concentration will drop to 9 mg / dL in the next 6 hours (below the target value of 10 mg / dL), then the objective function calculation shows that the current xanthine dose needs to be increased to reduce the deviation between the predicted value and the target value; if the predicted concentration will rise to 17 mg / dL (above the target value), then the dose needs to be reduced to ensure that the uric acid level returns to the target range.

[0077] In some embodiments, the dose adjustment of the predictive control algorithm must meet constraints to ensure the safety of induction. The real-time optimized inducer dose adjustment range does not exceed 15% of the previous dose. For example, if the previous xanthine dose was 0.2 g / kg, the adjusted dose range is 0.17 to 0.23 g / kg. At the same time, the inducer dose does not exceed the preset maximum daily dose. The maximum daily dose of xanthine is usually no more than 0.3 g / kg, and potassium oxonate is no more than 15 mg / kg, to avoid kidney damage and intestinal dysfunction caused by excessive induction.

[0078] Alternatively, the predictive control algorithm can be optimized daily at 8:00 AM, at which time daily serum uric acid concentration data is collected. This data is then combined with historical data to complete predictions and dosage calculations, ensuring timely and consistent dosage adjustments. This real-time optimization mechanism compensates for the impact of individual metabolic fluctuations and minor environmental changes on uric acid levels, making the induction process more adaptive and laying the foundation for the stable presentation of the model phenotype in the detection and validation steps.

[0079] Detection and validation: The hyperuricemia model phenotype was confirmed through biochemical, behavioral and pathological tests, and the stability of the model was verified by combining chaotic dynamic indicators and statistical parameters.

[0080] Furthermore, in the testing and verification steps, biochemical testing includes periodically collecting venous blood to measure serum uric acid concentration, behavioral testing includes recording activity frequency, food intake and stool morphology, and pathological testing includes anatomical observation of renal tubules and joint synovial tissue.

[0081] Furthermore, in the testing and verification steps, chaotic dynamics indicators are used to assess the stability of uric acid concentration fluctuations. By analyzing time series data, it is determined whether the hyperuricemia model is in a stable periodic state. These indicators include the maximum Lyapunov index, and statistical parameters such as the coefficient of variation are used to quantify the consistency of uric acid concentration within and between batches.

[0082] Specifically, the detection and verification steps are used to confirm whether the optimized induction steps have successfully induced a stable hyperuricemia model phenotype. By combining multi-dimensional detection and multi-index analysis, the effectiveness and stability of the model are comprehensively evaluated. It needs to form a closed-loop verification with the optimized induction steps to ensure that the induction effect meets expectations.

[0083] Generally, biochemical testing involves periodically collecting venous blood to measure serum uric acid concentration to confirm whether the uric acid level meets the requirements of a hyperuricemia model. Blood collection is typically scheduled for days 3, 7, and 10 of the optimized induction process, using the wing vein as the collection site. Each collection yields 0.5 to 1 ml of blood, which is immediately centrifuged to separate the serum. Serum uric acid concentration is then measured using the uricase method. Alternatively, the assay process must be strictly followed according to the kit instructions, with the reaction temperature controlled at 37 degrees Celsius and the reaction time at 15 minutes to ensure accurate results.

[0084] Specifically, behavioral monitoring assesses the impact of the induction process on the physiological state of pigeons by recording their activity frequency, food intake, and fecal form. Activity frequency is recorded by installing infrared sensors in the cages, automatically counting the number of activities every hour and summarizing and analyzing the data daily. Food intake is obtained by accurately weighing the difference between the daily feed and the remaining feed, with recording time fixed at every evening. Fecal form is observed visually, recording its shape, color, and water content. Normal feces should be formed into strips and grayish-black in color; loose stools or abnormal colors indicate possible metabolic abnormalities.

[0085] In some embodiments, pathological examination involves dissecting and observing the renal tubules and synovial tissue of the joints to confirm the deposition of urate crystals and verify the pathological features of hyperuricemia. Dissection is typically performed on the eleventh day after induction. The tissue integrity must be maintained during the dissection process. The kidney and synovial tissues are immediately fixed in 10% neutral formalin solution for 24 hours after removal, followed by paraffin embedding, sectioning, and hematoxylin-eosin staining.

[0086] Specifically, chaotic dynamics indices are used to assess the stability of uric acid concentration fluctuations. The core of these indices is the maximum Lyapunov index, which is calculated by analyzing time-series data of serum uric acid concentration. The input is daily serum uric acid concentration data collected during the optimization induction phase, and the output is an index value quantifying system stability. The formula for calculating the maximum Lyapunov index is as follows:

[0087]

[0088] Where, λ maxδU(0) represents the maximum Lyapunov exponent; δU(0) is the initial perturbation, i.e., the artificially introduced small change in uric acid concentration, typically taken as 0.1 mg / dL; δU(t) is the evolution of the perturbation after time t, reflecting the change of the initial perturbation over time, where t is time and ln is the natural logarithm function. In this embodiment, if the calculated λ... max A value less than 0 indicates that the fluctuation of uric acid concentration is in a stable periodic state, and the system has convergence to the initial disturbance; a value greater than 0 indicates that the fluctuation is in a chaotic state, and the system has poor stability.

[0089] In some embodiments, the statistical parameter includes the coefficient of variation (CV), used to quantify the consistency of uric acid concentration within and between batches. The input is serum uric acid concentration data from the same or different batches of pigeons, and the output is a coefficient value reflecting the degree of data dispersion. The formula for calculating the CV is as follows:

[0090]

[0091] Wherein, CV is the coefficient of variation, σ is the standard deviation of serum uric acid concentration, reflecting the dispersion of the data, and μ is the mean of serum uric acid concentration, reflecting the central tendency of the data. Generally, the coefficient of variation within a batch should not exceed 15%, and the coefficient of variation between batches should not exceed 20%, indicating that the model uric acid levels of different individuals or different batches have good consistency.

[0092] As an alternative, joint verification of chaotic dynamics indices and statistical parameters can be achieved by first calculating the maximum Lyapunov exponent to determine the stability trend of the system, and then quantifying the dispersion of the data using the coefficient of variation. Combining these two methods provides a more comprehensive reflection of the model's stability. For example, if the maximum Lyapunov exponent for a batch of pigeons is -0.2 (less than 0) and the coefficient of variation is 12% (less than 15%), it indicates that the model for that batch is in a stable periodic state and exhibits good individual consistency, meeting the stability requirements of a hyperuricemia model.

[0093] Specifically, through the aforementioned multi-dimensional detection and multi-index analysis, the effectiveness of the model can be verified from two aspects: phenotypic characteristics and stability. Biochemical tests confirm that uric acid levels are within the target range, behavioral tests ensure that the model animals have no serious physiological abnormalities, pathological tests verify typical pathological features, and the combined application of chaotic dynamic indicators and statistical parameters assesses stability from both dynamic and static perspectives. These detection and analysis methods complement each other, jointly ensuring the reliability and repeatability of the model.

[0094] By selecting healthy adult pigeons, pharmacokinetic experiments were used to analyze the transport and elimination characteristics of uric acid between blood and tissues. Simultaneously, circadian rhythm monitoring was employed to capture the diurnal fluctuations in metabolic activity, comprehensively obtaining key parameters reflecting individual metabolic differences. Based on this, the pigeons were raised in an environment with controlled temperature, humidity, and light to minimize external interference with metabolism. A spatiotemporal coupling model was constructed based on the aforementioned individual metabolic parameters. This model integrates metabolic characteristics and circadian rhythms to calculate the appropriate initial dose of inducer for different individuals. The inducer includes uric acid precursors that promote uric acid production and uricase inhibitors that inhibit uric acid breakdown, achieving a match between the initial dose and individual metabolic capacity. Subsequently, a phased strategy of basal induction, intensive induction, and stable maintenance was implemented. The model incorporates an inducer and a predictive control algorithm. By analyzing historical uric acid data to predict future trends, the inducer dosage is optimized in real time to compensate for dynamic fluctuations during metabolism. Biochemical tests confirm that uric acid levels are within target ranges, behavioral tests assess physiological status, and pathological tests verify typical phenotypes such as urate crystal deposition. Chaotic kinetic indicators and statistical parameters are used to jointly evaluate the model's stability and consistency. This approach achieves adaptation and dynamic regulation to the metabolic heterogeneity of individual pigeons, enabling stable induction of hyperuricemia while effectively reducing uric acid level fluctuations caused by individual differences and metabolic variations. This addresses the problem of poor stability and reproducibility in existing pigeon hyperuricemia models, which are insufficient to meet the consistency requirements of hyperuricemia mechanism research and drug screening.

[0095] A system for improving the stability of a pigeon hyperuricemia model, and a method for improving the stability of a pigeon hyperuricemia model as described above, comprising:

[0096] Selection and monitoring module: used to select healthy adult pigeons and obtain individual metabolic parameters through pharmacokinetic experiments and circadian rhythm monitoring;

[0097] Feeding calculation module: used to raise pigeons in a controlled environment and calculate the initial dose of inducers based on individual metabolic parameters through a spatiotemporal coupling model. The inducers include uric acid precursors and uricase inhibitors.

[0098] The optimization induction module is used to administer inducers to pigeons in stages based on the initial dose, and to optimize the inducer dosage in real time in combination with predictive control algorithms to induce hyperuricemia in pigeons.

Claims

1. A method for improving the stability of a pigeon hyperuricemia model, characterized in that, Includes the following steps: Selection and monitoring: Healthy adult pigeons were selected, and individual metabolic parameters were obtained through pharmacokinetic experiments and circadian rhythm monitoring; Feeding calculation: Pigeons are fed in a controlled environment, and the initial dose of inducer is calculated based on individual metabolic parameters using a spatiotemporal coupling model. The inducer includes uric acid precursors and uricase inhibitors. Optimized induction: Based on the initial dose, inducers are given to pigeons in stages, and the dosage of inducers is optimized in real time using a predictive control algorithm to induce hyperuricemia in pigeons; Detection and validation: The hyperuricemia model phenotype was confirmed through biochemical, behavioral, and pathological tests, and the stability of the model was verified by combining chaotic dynamic indicators and statistical parameters. In the step selection monitoring, the individual metabolic parameters include metabolic parameters and rhythm correction coefficients. The pharmacokinetic experiment involves collecting blood samples at multiple time points and combining them with a multi-compartment model to analyze the transport and elimination characteristics of uric acid between blood and tissues, and to obtain metabolic parameters including transport rate constant and elimination rate constant. In the selected monitoring steps, the diurnal rhythm monitoring involves continuously and periodically collecting the basal uric acid data of pigeons, extracting diurnal metabolic fluctuation characteristics using a rhythm cycle analysis method, and obtaining the rhythm correction coefficient. In the feeding calculation step, the parameters of the controllable environment include a temperature of 22-26℃, a relative humidity of 50%-60%, a 12h light / 12h dark cycle, and the feed is a standardized low-purine formula. The spatiotemporal coupling model constructs a dynamic equation of uric acid concentration over time by integrating metabolic parameters and rhythm correction coefficients, and calculates the initial dose based on the target uric acid level to match the initial dose with the individual's metabolic capacity. In the feeding calculation step, the uric acid precursor is xanthine, which is used to promote uric acid production, and the uricase inhibitor is potassium oxonate, which is used to inhibit uric acid decomposition. The ratio of xanthine to potassium oxonate is dynamically adapted according to individual metabolic parameters. In the optimized induction process, the phased stages include a basic induction stage, an enhanced induction stage, and a stable maintenance stage. The basic induction stage involves administering a dose lower than the initial dose, the enhanced induction stage involves administering a dose higher than the initial dose, and the stable maintenance stage is performed based on the real-time optimized induction dose. The predictive control algorithm predicts future trends by analyzing historical uric acid data and minimizes the deviation between the predicted and target values, and optimizes the dose in real time based on the prediction results. In the optimized induction step, the administration of the inducing agent includes feeding xanthine quantitatively through a gastric tube and injecting potassium oxonate quantitatively through the peritoneal cavity. The adjustment range of the real-time optimized inducing agent dose does not exceed 15% of the previous dose, and the inducing agent dose does not exceed the preset maximum daily dose. In the detection and verification steps, the chaotic dynamics index is used to assess the stability of uric acid concentration fluctuations. By analyzing time series data, it is determined whether the hyperuricemia model is in a stable periodic state, including the maximum Lyapunov index. The statistical parameters include the coefficient of variation, which is used to quantify the consistency of uric acid concentration within and between batches.

2. The method for improving the stability of a pigeon hyperuricemia model according to claim 1, characterized in that: In the detection and verification steps, the biochemical detection includes periodically collecting venous blood to measure serum uric acid concentration, the behavioral detection includes recording activity frequency, food intake and stool morphology, and the pathological detection includes anatomical observation of renal tubules and joint synovial tissue.

3. A system for improving the stability of a pigeon hyperuricemia model, characterized in that: A method for improving the stability of a pigeon hyperuricemia model as described in claim 1 or 2, comprising: Selection and monitoring module: used to select healthy adult pigeons and obtain individual metabolic parameters through pharmacokinetic experiments and circadian rhythm monitoring; Feeding calculation module: used to feed pigeons in a controlled environment and calculate the initial dose of inducer based on individual metabolic parameters through a spatiotemporal coupling model. The inducer includes uric acid precursors and uricase inhibitors. Optimized induction module: It is used to administer inducer to pigeons in stages according to the initial dose, and combine predictive control algorithm to optimize the inducer dose in real time to induce pigeons to develop hyperuricemia; The detection and validation module is used to confirm the model phenotype of hyperuricemia through biochemical, behavioral, and pathological tests, and to verify the stability of the model by combining chaotic dynamic indicators and statistical parameters.

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