Polygonatum sibiricum bacteriostatic active component high-throughput screening system

Abstract

The invention relates to a high-throughput screening system for bacteriostatic active components of rhizoma polygonati. Firstly, polygonatum kingianum multi-component extraction and sample pretreatment are performed; performing component separation and preliminary screening based on high performance liquid chromatography; then, quickly mixing the separated components with target bacteria by utilizing a micro-fluidic chip technology to form a micro-droplet reaction unit; through a digital micro-fluidic platform, each micro-droplet represents a bacteriostatic test unit of one component, and real-time bacteriostatic effect monitoring is carried out in a micron-sized environment; after the preliminary bacteriostatic activity test is completed, high-throughput data processing and component activity analysis are carried out; through construction of a high-throughput screening platform and high-throughput screening of synergistic effects of antibacterial components, a component activity prediction model based on artificial intelligence is finally obtained.

Description

Technical Field

[0001] The invention relates to the technical field of antibacterial activity of polygonatum, and in particular to a high-throughput screening system for antibacterial active components of polygonatum. Background Art

[0002] Although the technology currently used for the high-throughput screening system of antibacterial active ingredients of Polygonatum sibiricum has certain prospects and application value, there are still many shortcomings and disadvantages in actual operation, which limit its efficiency and wide application in large-scale screening and development of new antibacterial ingredients. First of all, current screening technologies often rely on traditional microplate detection methods (such as 96-well plates or 384-well plates), which have obvious limitations in high-throughput screening. The test throughput of the microplate is limited, and only a fixed number of component combinations can be tested each time, which cannot meet the screening needs of large-scale and complex component systems. Moreover, microplate experiments usually require large amounts of reagents and samples, are time-consuming, require a lot of consumables, and are cumbersome to operate, especially when a large number of component combinations need to be screened, the operation is complicated and prone to errors. In addition, the reaction volume in the microplate experiment is large, and the mixing of components and bacteria may be uneven, affecting the accuracy and repeatability of the results.

[0003] Secondly, the current screening system for antibacterial components of Polygonatum sibiricum lacks flexibility in dynamically adjusting the concentration of components. Many traditional screening methods use fixed concentration combinations for testing. The disadvantage of this method is that it is impossible to adaptively adjust the proportion of components, and the screening process is too rigid, which often leads to missing the best synergistic effect combination. In complex biological systems, the interactions between different components are very complex, and their synergistic effects often change with changes in component concentrations. Fixed-concentration testing methods have difficulty capturing these complex nonlinear relationships, resulting in incomplete screening results. In addition, since traditional screening technology lacks a real-time feedback mechanism, it is impossible to make dynamic adjustments based on the data obtained during the experiment, resulting in blindness in the component screening process, high trial and error costs, and low screening efficiency.

[0004] Furthermore, existing screening technologies are also insufficient in evaluating the synergistic effects of ingredients. Many screening systems rely solely on simple linear models to evaluate the efficacy of ingredient combinations, assuming that the antibacterial effects of each ingredient are simply superimposed, while ignoring the complex nonlinear relationships that may exist between ingredients. However, in actual biological antibacterial experiments, complex synergistic effects often occur between ingredients, which may manifest as enhancement or inhibition. Linear models cannot accurately evaluate these nonlinear interactions, resulting in the loss of important ingredient combinations. Therefore, the lack of intelligent evaluation tools based on nonlinear optimization algorithms is a major drawback of current screening technology. In addition, due to the non-intelligence of the system, the screening system has difficulty processing massive amounts of experimental data and complex ingredient combinations, which makes it more difficult to quickly extract valuable information from large-scale experiments, resulting in a significant reduction in overall screening efficiency.

[0005] Finally, although the existing technology has made some progress in the automation and intelligence of screening systems, the overall level is still limited. Most of the screening systems for antibacterial components of Polygonatum sibiricum still rely on manual operation. Experimenters need to manually load samples, set up experiments and process data, which not only consumes a lot of manpower and time, but also easily leads to operational errors, affecting the stability and reliability of experimental results. Even if some systems have a certain degree of automation, the degree of intelligence is still insufficient, and the experimental parameters cannot be automatically adjusted according to the feedback of experimental data, especially when facing large-scale, multi-variable component screening, they lack sufficient flexibility and adaptability. Summary of the invention

[0006] The purpose of the present invention is to provide a high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum, thereby solving some of the drawbacks and shortcomings pointed out in the background technology.

[0007] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: A high-throughput screening system for antibacterial active components of polygonatum, comprising: S1, extraction of multiple components of polygonatum and sample pretreatment:

[0008] A multi-component extraction device based on ultrasonic-assisted extraction technology is used to extract multiple active ingredients in Polygonatum sibiricum; and by adjusting the ultrasonic frequency and temperature, components of different polarities are extracted step by step, and preliminary purification and concentration are performed at the same time;

[0009] S2. Component separation and preliminary screening based on high performance liquid chromatography:

[0010] Liquid chromatography HPLC is used to separate the components in the extract, and a preliminary identification of the active components is performed in combination with an ultraviolet detector UV and a mass spectrometer MS. A high-throughput screening process is then established to introduce the separated components into a downstream activity testing system.

[0011] S3. Application of microfluidics technology in antibacterial activity detection:

[0012] Using microfluidic chip technology, the separated components are quickly mixed with the target bacteria to form a microdroplet reaction unit. Through the digital microfluidic platform, each microdroplet represents an antibacterial test unit of a component, and the real-time antibacterial effect is monitored in a micron-level environment.

[0013] S4. High-throughput data processing and component activity analysis:

[0014] After completing the preliminary antibacterial activity test, the data processing algorithm is used to analyze the antibacterial data points; the data processing algorithm associates the antibacterial efficacy with the structural characteristics of the ingredients through multi-dimensional data analysis, and labels and prioritizes the ingredients with significant activity;

[0015] S5. High-throughput screening of synergistic effects of antibacterial ingredients:

[0016] By building a high-throughput screening platform and adopting a combination screening strategy of different antibacterial ingredients, we evaluated whether the various ingredients in Polygonatum sibiricum have synergistic antibacterial effects; by mixing each ingredient in proportion, we tested the antibacterial effect of the combination to determine whether there is an enhanced effect;

[0017] S6. Ingredient activity prediction model based on artificial intelligence:

[0018] A machine learning algorithm was used to train a prediction model in combination with the antibacterial activity data obtained in S1-S6.

[0019] Furthermore, the application method of the microfluidic technology in antibacterial activity detection includes:

[0020] Design a fluid shear force control mechanism to adapt the droplet generation process to different fluid properties and adjust in real time according to changes in flow rate and reactant concentration:

[0021]

[0022] Among them, F(t) represents the instantaneous shear force exerted by the fluid during the droplet generation process, which is dynamically adjusted with the change of time t; α is the initial shear force intensity, which is related to the viscosity of the fluid and the geometric structure of the chip; β is used to adjust the amplitude change of the shear force, reflecting the response sensitivity of the fluid properties in the chip to the shear force fluctuation; ω is the fluid oscillation frequency, which determines the periodic change of the shear force over time.

[0023] Furthermore, the application method of the microfluidic technology in antibacterial activity detection includes:

[0024] A fluid shear force distributor is introduced in the droplet generation area; the distributor adjusts the flow rate and pressure gradient; the droplet generation rate is linked to the physical structure of the chip and the fluid properties, and the droplet generation rate can be expressed as:

[0025]

[0026] Where v(t) is the average rate of droplet generation, which changes with the continuation of time t, and T represents the time period of shear force control; γ is a constant related to the chip geometry and fluid properties, which determines the basic rate of droplet generation; δ is the shear force attenuation coefficient in the fluid, which is used to reflect the inhibitory effect of shear force on the droplet rate during the generation process.

[0027] Furthermore, the application method of the microfluidic technology in antibacterial activity detection includes:

[0028] The growth status of bacteria in the droplets is monitored in real time through fluorescent labeling technology. The fluorescent signal in the droplets is collected through an integrated optical sensor during the monitoring process, and the growth rate and mortality rate of bacteria are calculated in real time based on the changes in signal intensity and frequency. A calculus formula for regulating signal response is designed to describe the temporal changes in signal intensity:

[0029]

[0030] Among them, S(t) is the fluorescence signal intensity at time t, which represents the monitoring result of the bacterial growth status; η is the signal amplification factor, which is used to adjust the sensitivity of the detection system; θ is the phase modulation coefficient of the fluorescence signal, which controls the frequency of the signal response; k is the attenuation coefficient of the signal in the system, which reflects the attenuation rate of the signal over time; μ is the inhibition parameter of the signal response, which is used to describe the inhibition effect of the signal intensity under different experimental conditions.

[0031] Furthermore, the high-throughput screening method for the synergistic effect of antibacterial ingredients includes:

[0032] A dynamic ratio adjustment strategy is adopted to ensure that the concentration of each antibacterial component is optimized according to real-time feedback; and by intelligently adjusting the concentration of each component, from low concentration to high concentration, the synergistic effect between the components is gradually explored; the concentration adjustment of the components is affected by time and the synergistic effect between the components, and the adjustment process is described by the following function:

[0033]

[0034] Among them, P i (t) represents the concentration of the i-th component at time t; α i is the initial concentration value, representing the basic concentration of the component at the beginning of screening; β iis the concentration attenuation coefficient, which indicates the natural attenuation rate of the component over time t and reflects the stability of the component during the screening process; γ i is the oscillation amplitude coefficient, which is used to adjust the fluctuation range of component concentration over time; ω i is the frequency coefficient, which controls the oscillation period.

[0035] Furthermore, the high-throughput screening method for the synergistic effect of antibacterial ingredients includes:

[0036] The synergistic effect of the combined antibacterial ingredients was intelligently evaluated using a nonlinear optimization algorithm. By analyzing the experimental data, a nonlinear synergistic model for identifying the enhanced effect was established, and the multiplicative effect between the ingredients at a specific ratio was detected. The nonlinear optimization process of the synergistic effect was described by the following integral formula:

[0037]

[0038] Among them, E(t) is the synergistic effect index of the system at time t, which is used to quantify the enhancement effect between different component combinations; δ is the amplification coefficient of the synergistic effect intensity, which indicates the overall intensity of the synergistic effect of the components; λ is the response amplitude of the enhancement effect, which reflects the amplitude change of the enhancement effect between components in different time periods; μ is the attenuation coefficient, which controls the time attenuation of the synergistic effect of the components, that is, the trend of the synergistic effect gradually weakening over time; κ is the oscillation amplitude coefficient, which is used to indicate the fluctuation amplitude of the synergistic effect during the screening process; θ is the frequency adjustment parameter, which controls the fluctuation period of the synergistic effect.

[0039] Furthermore, the high-throughput screening method for the synergistic effect of antibacterial ingredients includes:

[0040] Through high-throughput microfluidic technology, large-scale, multi-component combination rapid parallel testing; the microfluidic platform mixes different antibacterial components into microdroplets in proportion, and each microdroplet acts as an independent reaction unit. Multiple combinations are tested simultaneously in a micrometer-scale environment, and the antibacterial effect in each microdroplet is detected in real time; the number of droplets generated is controlled by fluid dynamics, and the change of its generation rate over time and system parameters is described by the following equation:

[0041]

[0042] Where N(t) is the number of droplets generated at time t, representing the number of test units generated in different time periods; η is the initial generation rate coefficient, indicating the basic speed of droplet generation; t 1 / 2 It represents the square root relationship between the generation rate and time, indicating the natural increasing trend of the droplet generation rate with the extension of time; ζ is the resistance coefficient, reflecting the inhibitory effect of the internal resistance of the fluid on the droplet generation rate during the generation process; ρ is the time attenuation coefficient, indicating the natural attenuation trend of the generation rate with time.

[0043] The high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum of the present invention has the following beneficial effects: first, the system significantly improves the screening efficiency by introducing high-throughput microfluidics technology. By realizing rapid parallel testing of antibacterial ingredients at the micrometer scale, the system can complete the generation and monitoring of tens of thousands of antibacterial test units in a very short time, greatly shortening the experimental cycle. Secondly, the system adopts dynamic ratio adjustment and nonlinear optimization algorithm to realize the real-time optimization of antibacterial ingredient concentration and accurate evaluation of synergistic effect. This intelligent control mechanism can automatically adjust the ratio of each component combination to ensure that the screening process can capture the best antibacterial combination and enhance the accuracy of screening. Thirdly, the system can instantly evaluate the growth state of bacteria in each droplet during the experiment through real-time fluorescence signal monitoring technology to ensure high precision and high sensitivity of experimental data. Finally, the system has a high level of automation and intelligence, and can dynamically adjust experimental parameters according to real-time feedback, greatly reducing human intervention and operating errors. Overall, the system significantly improves the screening efficiency, accuracy and reliability of antibacterial active ingredients of Polygonatum sibiricum, and provides strong technical support for the discovery and application of new antibacterial ingredients. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention is a flow chart of the high-throughput screening system for the antibacterial active components of Polygonatum sibiricum.

[0045] Figure 2 The present invention is a flowchart of the application method of the microfluidic technology in the detection of antibacterial activity.

[0046] Figure 3 The present invention is a flow chart of the high-throughput screening method for the synergistic effect of antibacterial components. DETAILED DESCRIPTION

[0047] The specific implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0048] Combined with Figure 1, the high-throughput screening system for antibacterial active ingredients of polygonatum sibiricum of the present invention, the first step is the multi-component extraction of polygonatum sibiricum and sample pretreatment. In this process, the system uses ultrasonic assisted extraction technology to efficiently extract multiple active ingredients in polygonatum sibiricum. First, ultrasonic assisted extraction technology is a method for quickly separating active ingredients by interacting solvent molecules and target substances through high-frequency vibration waves. By adjusting the frequency and temperature of ultrasound, the extraction steps of components with different polarities can be accurately controlled. This means that the system can separate each component from the polygonatum sibiricum sample in turn according to the polarity difference of each component. In actual operation, the adjustment of frequency and temperature can optimize the solubility of components with different polarities and enhance their interaction with the solvent, thereby improving the extraction efficiency. At the same time, during the extraction process, the system will also perform preliminary purification and concentration treatment. Preliminary purification refers to obtaining relatively pure active ingredients by filtering out impurities and unnecessary substances. This process ensures the quality of the sample and provides a more accurate basis for subsequent high-throughput screening. Concentration treatment is to further increase the concentration of active ingredients by reducing the amount of solvent, so that the sample is more representative and effective in subsequent detection and analysis processes.

[0049] The second step is the separation and preliminary screening of components based on high performance liquid chromatography. First, high performance liquid chromatography (HPLC) is used to separate the components in the mixture extracted from Polygonatum sibiricum. HPLC uses liquid as the mobile phase under high pressure and passes the sample through a stationary phase column to separate different components according to retention time based on the polarity and molecular size of each chemical component. After separation, the system combines an ultraviolet detector (UV) and a mass spectrometer (MS) for preliminary identification of active ingredients. The UV detector helps identify the characteristic peaks of the components by detecting the absorbance of the separated components at different wavelengths, while the mass spectrometer (MS) provides more detailed molecular structure information by detecting the molecular mass and fragment information of the components. This dual detection method enables the system to quickly and accurately perform preliminary identification of the separated components. After the active ingredient identification is completed, the system will establish a high-throughput screening process to introduce these separated components into the downstream activity test system. This test system is usually a microfluidic or automated platform that can simultaneously test the antibacterial activity of a large number of samples. By combining HPLC separation with UV and MS detection, this step can effectively screen out the components of Polygonatum sibiricum with antibacterial activity and accurately and efficiently push them into the subsequent bioactivity detection process, laying a solid foundation for the next step of high-throughput screening.

[0050] The third step is the application of microfluidics in the detection of antibacterial activity. The system first uses microfluidics chip technology to quickly mix each active ingredient of Polygonatum sibiricum previously separated by high-performance liquid chromatography with the target bacteria. Microfluidics chip technology is a technology for processing fluids at the micrometer scale. Its precision and speed make it very suitable for high-throughput screening. The technology introduces antibacterial ingredients and bacterial solutions through tiny fluid channels and forms droplet reaction units inside the chip. Each droplet represents an independent antibacterial test unit. In this process, the flow and control of the liquid in the chip are very precise, ensuring that the ratio of ingredients to bacteria is consistent, so that each droplet can be evenly mixed and react independently. The key advantage of microfluidics technology lies in its efficiency and controllability. It can generate thousands of droplet reaction units in a short time, thereby greatly improving the experimental throughput. In addition, the system uses a digital microfluidics platform, which uses built-in sensors and detection devices to monitor the antibacterial reaction in each droplet in real time under a micrometer-level environment. Through real-time detection, the dynamic changes of bacterial growth or death in each droplet can be accurately tracked to evaluate the antibacterial effect. The system usually acquires data in real time through fluorescent labeling or other optical detection methods, quantifies the antibacterial effect and processes it through a digital platform. This method greatly improves the screening speed while ensuring the accuracy and sensitivity of the data.

[0051] In the fourth step, high-throughput data processing and component activity analysis, first, after completing the preliminary antibacterial activity test, the system will comprehensively process and analyze the large number of antibacterial data points obtained. These data points come from the antibacterial test previously conducted using microfluidic technology. The data generated by each test unit reflects the inhibitory effect of different components on bacteria. In order to ensure that the screened active ingredients are accurate and have biological effects, the system introduces a data processing algorithm. It can analyze the data from multiple dimensions. Specifically, it will correlate the antibacterial efficacy with the chemical structure characteristics of the ingredients. The structural information of each ingredient, such as molecular weight, polarity, functional groups, etc., is closely related to its biological activity. By analyzing this information through algorithms, the system can identify which structural features are associated with significant antibacterial effects and can further explore potential antibacterial mechanisms. The data processing algorithm is not just a simple screening of ingredients with higher activity, but through multi-dimensional analysis, it closely links the antibacterial activity with the characteristics of the ingredients and establishes an ingredient efficacy model. In this process, the algorithm will mark those active substances with significantly higher antibacterial efficacy than other ingredients, and prioritize these ingredients according to their activity intensity and structural characteristics. This prioritization mechanism helps to quickly identify the ingredients with the greatest development potential, thus providing guidance for subsequent in-depth research. Through high-throughput data processing, the system can greatly improve analysis efficiency, ensure that large amounts of data can be processed quickly, and accurately identify the most antibacterial active ingredients in Polygonatum sibiricum, providing a reliable basis for the next step of screening and development.

[0052] In the fifth step, during the high-throughput screening process of the synergistic effect of antibacterial ingredients, the system first constructs a high-throughput screening platform to simultaneously test the antibacterial effects of multiple ingredient combinations, with the aim of evaluating whether the multiple ingredients in Polygonatum have synergistic antibacterial effects. Synergistic effect means that when two or more ingredients are used in combination, their antibacterial effects far exceed the sum of the effects when each is used alone. In order to evaluate this enhanced effect, the system adopts a combined screening strategy to mix different antibacterial ingredients in different proportions and conduct rapid testing through a high-throughput screening platform. The platform is able to process a large number of ingredient combinations at the same time, greatly improving the screening efficiency. Specifically, the system tests the antibacterial effects of each ingredient by combining them in different proportions and monitors the growth or death of bacteria in real time. Through this test, the system can observe whether the combination of different ingredients produces a significant enhanced antibacterial effect at certain specific proportions, or a synergistic effect under specific conditions. The data analysis system then processes these test results to determine which ingredient combinations have the strongest synergistic effects and prioritizes these ingredient combinations for further research and development. The introduction of high-throughput platforms has greatly accelerated this screening process, making it possible to test thousands of ingredient combinations in a shorter period of time while ensuring the accuracy and repeatability of the data.

[0053] The sixth step is to build an artificial intelligence-based component activity prediction model. The system uses a machine learning algorithm to train a prediction model by comprehensively analyzing a large amount of antibacterial activity data obtained in the previous steps S1 to S6. First, the system has accumulated a large amount of experimental data in steps S1-S5. These data not only include the extraction, separation and test results of each active component of Polygonatum, but also cover multi-dimensional information such as the chemical structure characteristics, antibacterial efficacy, and synergistic effects of each component. By inputting these data into the machine learning algorithm, the system can gradually establish a complex relationship model between component structure and antibacterial activity through self-learning and optimization of data. Specifically, the machine learning algorithm analyzes the feature vector of each component, combines its actual antibacterial effect, mines out structural patterns that are highly correlated with activity intensity, and generates a prediction model. The model can predict the antibacterial efficacy of untested components, thereby significantly reducing the time and resource investment of blind experiments in screening work. Through continuous iteration and optimization, the prediction model will become more accurate and reliable as the amount of data increases, and can infer the potential antibacterial activity of other untested components in Polygonatum without conducting actual experiments. In addition, the model can also identify compounds with special structures that show stronger antibacterial effects under specific conditions. The introduction of machine learning has improved the intelligence level of the screening system, making ingredient screening more efficient and accurate, and ultimately providing strong data support and decision-making basis for subsequent research.

[0054] Embodiment 1:

[0055] See attached Figure 2 In this embodiment, in the high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum, the application of microfluidic technology in the detection of antibacterial activity is one of the key steps.

[0056] Several different active ingredients extracted from Polygonatum sibiricum were set up in the experiment, and their goal was to test the antibacterial effect on Escherichia coli. The system first mixed the extracted antibacterial ingredients with bacteria through a microfluidic chip, and in this process, the control of fluid shear force was crucial because it affects the generation of microdroplets, the uniformity of mixing, and the final antibacterial effect.

[0057] In this example, a fluid shear force control mechanism is designed in the microfluidic chip, so that the droplet generation process can adapt to different fluid properties and make real-time adjustments according to changes in flow rate and reactant concentration. During the set experiment, the goal is to keep the shear force within a small fluctuation range to ensure that the generated droplets are of uniform size and the antibacterial components are evenly mixed with the bacteria. The following shear force control formula is used:

[0058]

[0059] In this formula, the initial shear strength α is first set to 5N / m 2 , because the fluid flow rate in the microfluidic chip is low and the viscosity is high, and the chip geometry requires a high shear force to maintain the stability of droplet generation. The fluid viscosity η is set to about 0.001 Pa·s, which is the typical viscosity range of aqueous solutions.

[0060] Next, the coefficient β reflects the sensitivity of the fluid properties to shear force fluctuations. In this experiment, it was set to 0.8, which means that under different flow rates and concentrations, the shear force fluctuation amplitude will be relatively gentle, avoiding drastic changes that lead to unstable droplet generation. By setting this coefficient, the system can adaptively adjust according to the fluid properties of different antibacterial ingredients (such as the higher viscosity of Polygonatum odoratum extract).

[0061] Finally, the oscillation frequency ω is set to 2πrad / s, which means that the fluid undergoes a complete periodic fluctuation every second. This periodic change allows the shear force to be dynamically adjusted over time, ensuring that the fluid can maintain stable droplet generation at different time points. In actual operation, the time t is set to 0 to 10 seconds, and the periodic fluctuation of the fluid shear force can be observed throughout the process, which directly affects the generation speed and size of the droplets.

[0062] Set the time to observe the instantaneous value of shear force at 5 seconds. Substitute into the formula:

[0063]

[0064] Since sin(2π·5)=0, therefore:

[0065]

[0066] At this time, the shear force is 9N / m 2 , which is an increase compared to the initial shear force, indicating that the system adjusts the shear force to adapt to the current fluid state.

[0067] Through this regulation mechanism, the system can ensure that the size of the generated droplets and the concentration of the ingredients are consistent, effectively improve the mixing efficiency of the antibacterial ingredients and the target bacteria, ensure that the concentration of the antibacterial ingredients in each droplet is precisely controllable, and thus improve the accuracy and repeatability of the experimental data. This design is particularly suitable for high-throughput screening systems that test multiple ingredients at the same time. It can test hundreds of different ingredient combinations in a short period of time and ensure the quality and reliability of each test unit (droplet) through stable shear force control.

[0068] The setting of parameters α, β and ω enables the mechanism to be flexibly adjusted under different experimental conditions, ensuring the efficient and accurate operation of the high-throughput screening system.

[0069] The microfluidic technology in the system is further introduced into the key link, that is, the fluid shear force distributor is introduced into the droplet generation area. The function of this distributor is to ensure that the size and rate of the generated droplets are compatible with the fluid properties and the geometric structure of the chip by adjusting the flow rate and pressure gradient. Specifically, the fluid shear force distributor achieves precise regulation of the droplet generation rate by controlling the flow rate changes in the microfluidic system.

[0070] In an experiment, it is expected to generate droplets of different sizes to test the effect of the antibacterial components of Polygonatum odoratum. In order to ensure the stability of the droplet generation process, the shear force distributor needs to be regulated in real time according to the flow rate and pressure gradient of the fluid. The following formula is used to describe the average rate of droplet generation:

[0071]

[0072] In this formula, v(t) represents the average rate of droplet generation, which changes with the continuation of time t; T represents the time period of shear force control, which is set to 10 seconds in the experiment to ensure sufficient time to generate stable droplets. The parameter γ is a constant related to the chip geometry and fluid properties. This constant determines the basic rate of droplet generation. In this experiment, the geometry of the chip is set to be more complex, and the fluid requires a larger basic shear force, so γ is set to 12mm / s. In addition, δ is the shear force attenuation coefficient in the fluid, which reflects the inhibitory effect of shear force on the droplet generation rate. Since the fluid in the experiment is relatively viscous (its viscosity is set to 0.002Pa·s), δ is set to 0.3.

[0073] Next, substitute these parameters to calculate the droplet generation rate over a 10 second time period. First, calculate the integral part:

[0074]

[0075] The integral result is calculated using the numerical integration method, which is about 83.64. Then, the average rate of droplet generation is calculated by substituting it into the formula:

[0076]

[0077] This means that under this condition, the average rate of droplet generation is 8.364 mm / s. With this rate, the system can ensure that a sufficient number of droplets are generated within a certain period of time, and the mixing ratio of the antibacterial components and target bacteria in each droplet is precisely controllable.

[0078] The core innovation of this design is that by introducing a fluid shear force distributor, the system can adaptively adjust the droplet generation rate according to fluid properties (such as flow rate, viscosity, etc.). This control mechanism ensures the uniformity of the size of each generated droplet and the mixing of internal components, thereby improving the accuracy and repeatability of the experiment. Compared with the traditional droplet generation method, this system can accurately characterize the antibacterial efficacy of different Polygonatum components by regulating shear force and flow rate in real time. Especially in high-throughput screening, when multiple combined component tests are performed simultaneously, this design improves experimental efficiency and reliability.

[0079] The system further introduces technology for real-time monitoring of bacterial growth status. In order to evaluate the effect of antibacterial components in the droplet reaction unit, the system uses fluorescent labeling technology to track the growth and death process of bacteria in real time. This link is crucial to determining the antibacterial effect, because fluorescently labeled bacteria will show different fluorescence intensities under different conditions, reflecting their growth or death status.

[0080] To monitor these changes, the microfluidic chip integrates optical sensors that can accurately collect fluorescent signals in droplets. Each droplet represents an independent experimental unit, containing a mixture of antibacterial ingredients and target bacteria. When the metabolic activity of bacteria changes, especially under the action of antibacterial ingredients, the intensity and frequency of the fluorescent signal will change. The integrated optical sensor can capture these signal changes in real time and transmit the data to the system for analysis. Through the changes in signal intensity, the system can calculate the growth rate and mortality rate of bacteria in real time.

[0081] In order to quantify these changes, the system designed a calculus formula to describe the trend of signal strength over time. The formula is:

[0082]

[0083] In this formula, S(t) represents the fluorescence signal intensity at time t, which represents the growth state of bacteria in the droplet reaction unit. The change in signal intensity is a direct reflection of bacterial growth or death. The parameter η is the signal amplification factor, which determines the sensitivity of the detection system. In the experiment, η=1.5 was set to ensure that the signal can be amplified enough to detect subtle fluorescence changes. The parameter θ is the phase modulation coefficient of the fluorescence signal, which controls the frequency of the signal response. In order to ensure that the frequency of signal acquisition is high enough, θ=2rad / s is set, so that multiple data acquisitions can be performed per second to capture the periodic changes of the fluorescence signal.

[0084] Next, the parameter κ represents the attenuation coefficient of the signal, reflecting the trend of the signal gradually weakening over time. During the growth or death of bacteria, the fluorescence signal will gradually weaken, and κ=0.7 is set to simulate the effect of this natural attenuation. Finally, the parameter μ is the inhibition parameter of the signal response, which describes the inhibition of the signal intensity under different experimental conditions. Since different antibacterial ingredients have different effects on bacteria, the inhibitory effect of the signal will also be different. Setting μ=0.4 can effectively capture the inhibitory effect of the antibacterial ingredient on the signal.

[0085] Next, set the data collection of fluorescence signals within 10 seconds and substitute the parameters to calculate the signal intensity. Set t = 10 seconds and substitute the formula:

[0086]

[0087] Through the numerical integration method, the integral result is about 6.28. Substituting it into the formula, we get:

[0088] S(10)=1.5·6.28=9.42

[0089] This means that at 10 seconds, the fluorescence signal intensity detected by the system is 9.42, which can be used to analyze the growth status of bacteria. If the signal intensity continues to decrease, it means that the bacteria are strongly inhibited by the antibacterial ingredients and are in a dead state; if the signal remains stable or increases, it means that the bacteria have developed resistance to the antibacterial ingredients or are still growing.

[0090] Embodiment 2:

[0091] Combined with Figure 3 In this embodiment, in the high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum, a high-throughput screening method for the synergistic effect of antibacterial ingredients designs a set of dynamic ratio adjustment strategies to ensure that the concentration of each antibacterial ingredient can be optimized according to real-time feedback. This process is crucial for accurately evaluating the synergistic effects of different combinations of ingredients. It is set to screen the active ingredients in three types of Polygonatum sibiricum, which are ingredient A, ingredient B and ingredient C, respectively, which have potential synergistic antibacterial effects in the experiment. In order to find the optimal antibacterial combination ratio, the system adopts a dynamic ratio adjustment strategy, gradually increasing the concentration of each ingredient from a low concentration, detecting their antibacterial effects in real time, and dynamically adjusting the concentration according to feedback information.

[0092] In this process, the concentration of each component P i (t) is adjusted as time t changes, and the change in concentration is controlled by the following formula:

[0093]

[0094] Each parameter in the formula has a specific physical meaning. First, α i It represents the initial concentration value of component i, which reflects the basic concentration of the component at the beginning of the screening. The initial concentration of component A in the experiment is set to α A =10 mg / mL, the initial concentration of component B is α B =8 mg / mL, the initial concentration of component C is α C = 6 mg / mL. These initial concentrations were determined based on the experimental design and represent the starting points for the components. Next, the parameter β i is the concentration decay coefficient, which indicates the rate at which the concentration of each component decays naturally over time. Set the decay coefficient of component A to β A =0.05, the attenuation coefficient of component B is β B =0.03, the attenuation coefficient of component C is β C =0.04. These values ​​reflect the stability of each component during the experiment. The components with slower decay are more persistent in the experiment.

[0095] In addition, γ iis the oscillation amplitude coefficient, which is used to adjust the fluctuation range of the component concentration over time. Set the oscillation amplitude of component A γ A = 0.6, the oscillation amplitude of component B is γ B = 0.4, the oscillation amplitude of component C is γ C = 0.5. This parameter allows the concentration of the component to be fine-tuned during the experiment with feedback information to avoid concentrations that are too high or too low. Finally, ω i is the frequency coefficient, which controls the oscillation period of the component concentration. In this experiment, we set ω A =2rad / s,ω B =1.5rad / s,ω C =1.8rad / s, which means that the concentration change of each component will show periodic fluctuations to adapt to different experimental stages and feedback.

[0096] It is assumed that during the screening process, the concentration change of each component is monitored after 10 seconds of the experiment. Substitute the formula for calculation:

[0097] For Ingredient A:

[0098]

[0099] Among them, e -0.5 ≈0.6065, and sin(20)≈0.9129, after substituting into the equation:

[0100]

[0101] For Ingredient B:

[0102]

[0103] Among them, e -0.3 ≈0.7408, and sin(15)≈0.6503, after substituting into the equation:

[0104]

[0105] For Ingredient C:

[0106]

[0107] Among them, e -0.4 ≈0.6703, and sin(18)≈0.309, after substituting into the equation:

[0108]

[0109] Through these calculations, we can see the concentration of each component at 10 seconds, component A is 3.92 mg / mL, component B is 4.7 mg / mL, and component C is 3.48 mg / mL. During the experiment, the system dynamically adjusts the concentration of each component based on the antibacterial effect monitored in real time to ensure the best synergistic effect. Over time, the system will continue to adjust the concentration of each component based on feedback, so that the entire screening process can dynamically optimize the combination ratio and quickly identify the antibacterial combination with the strongest synergy.

[0110] Through this dynamic ratio adjustment strategy, the high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum can screen out the most synergistic combination of ingredients in a short period of time, and greatly improve the efficiency and accuracy of the experiment through real-time adjustment and feedback.

[0111] The high-throughput screening system for antibacterial active ingredients in Polygonatum sibiricum has further entered the stage of evaluating synergistic effects. In order to effectively evaluate the effects of multiple antibacterial ingredient combinations, the system uses a nonlinear optimization algorithm, which aims to establish a nonlinear synergistic model by analyzing experimental data, thereby identifying the enhanced effects in different ingredient combinations. Specifically, when ingredients A, B, and C in Polygonatum sibiricum are mixed, the system will detect whether they show a multiplied synergistic antibacterial effect in different proportions. Through feedback from experimental data, the system continuously optimizes the combination of ingredients and ultimately locks in the ratio with the strongest synergy.

[0112] The optimization process of the synergistic effect is quantified by the following integral formula:

[0113]

[0114] In this formula, E(t) represents the synergistic effect index of the system at time t. The higher the value, the stronger the synergistic effect between the components. First, δ is the amplification factor of the synergistic effect strength, which reflects the overall strength of the synergistic effect between the combined components. In this experiment, δ=2.0 is set, which means that the synergistic effect strength is large, and the antibacterial effect between the component combinations will be significantly enhanced. Next, λ is the response amplitude of the enhancement effect, which reflects the amplitude change of the synergistic effect in different time periods. λ=1.2 is set, which indicates that the system will experience a certain enhancement amplitude in different time periods.

[0115] In addition, μ is the attenuation coefficient, which controls the tendency of the synergistic effect to gradually weaken over time. In the experiment, the synergistic effect does not remain strong all the time, but gradually decays over time. Set μ = 0.5 to reflect this natural decay. κ represents the oscillation amplitude of the synergistic effect, which is used to illustrate the fluctuation range of the synergistic effect during the screening process. Setting κ = 0.8 means that the synergistic effect fluctuates greatly over time, and the combination of ingredients shows a strong antibacterial effect at certain time points, but weaker at other time periods. Finally, θ is the frequency adjustment parameter that controls the fluctuation period of the synergistic effect. Setting θ = 1.5rad / s means that the synergistic effect will fluctuate periodically at a fixed frequency.

[0116] The synergistic effect of components A, B and C is evaluated within 20 seconds after the start of the experiment. Substitute the above parameters for calculation, first considering the integral part of the formula:

[0117]

[0118] The calculated result by numerical integration method is about 27.54. This means that at 20 seconds, the combination of components A, B and C showed a high synergistic effect, and the synergistic effect index was 27.54. This value shows that the combination of components showed a significant enhanced antibacterial effect on the target bacteria during this period of time, and the multiplication effect was significant.

[0119] As the experiment progresses, the system adjusts the combination ratio of ingredients based on real-time feedback to further optimize the synergistic effect. Through an automated nonlinear optimization algorithm, the system can identify the most effective combination of ingredients and continuously optimize the experimental conditions using a feedback loop. This intelligent nonlinear optimization process not only improves experimental efficiency, but also ensures that the selected ingredient combination can stably exhibit a strong antibacterial effect under different conditions.

[0120] The high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum continues to conduct large-scale, rapid parallel testing of multi-component combinations through high-throughput microfluidic technology. In this stage, the system uses the microfluidic platform to mix different antibacterial ingredients into microdroplets in proportion, and each microdroplet acts as an independent reaction unit. The system conducts multiple combination tests simultaneously in a micrometer-scale environment and detects the antibacterial effect in each microdroplet through real-time monitoring.

[0121] In this scheme, the speed of droplet generation is one of the key factors. The system controls the number and rate of droplet generation through fluid dynamics. The change of generation rate over time and system parameters is described by the following equation:

[0122]

[0123] In this equation, N(t) represents the number of droplets generated at time t, that is, the number of independent test units generated in different time periods of the experiment. In order to better understand the equation and verify its practical feasibility, specific data and parameter values ​​will be introduced for illustration. First, η is the initial generation rate coefficient, which represents the basic speed of droplet generation. In this experiment, η=500 is set, which means that 500 droplets are generated per second at the beginning of the experiment. As the experiment progresses, the generation rate will increase with time. The t in the formula 1 / 2 Represents the square root relationship between the generation rate and time, indicating that the generation rate will naturally increase over time.

[0124] Next, ζ is the drag coefficient, which reflects the inhibitory effect of the internal resistance of the fluid on the droplet generation rate during the generation process. Setting ζ = 0.05 indicates that the resistance during the generation process is small, so the inhibitory effect on the generation rate is limited. Finally, ρ is the time decay coefficient, which represents the natural decay trend of the generation rate over time. Setting ρ = 0.02 reflects that the generation rate will gradually slow down over time, but the overall generation number will remain at a high level.

[0125] Substituting these parameters and setting the experiment to run for 30 seconds, calculate the number of droplets generated at this time point. According to the formula:

[0126]

[0127] First calculate 30 1 / 2 =5.477 and e -0.6 ≈0.5488, so substituting into the formula we get:

[0128]

[0129] The results showed that the system generated approximately 2,665 droplets at 30 seconds into the experiment. Each droplet represents an independent antibacterial test unit, and through the real-time monitoring system, the experimenter can track the antibacterial effect within each unit. The system captures the growth or inhibition status of bacteria in the droplets through an integrated optical sensor, and feeds this data back to the high-throughput screening platform for analysis.

[0130] The core innovation of this design is that it uses microfluidics technology to achieve parallel testing of thousands of droplets at the same time, greatly improving the screening efficiency. In just a few tens of seconds, the system can generate thousands of independent test units, and the antibacterial effect of each unit can be monitored in real time. This high-throughput parallel screening method greatly shortens the experimental time and ensures that the experimental results of each component combination are highly consistent and repeatable.

[0131] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum, characterized in that The steps include: S1. Polygonatum multi-component extraction and sample pretreatment: A multi-component extraction device based on ultrasonic-assisted extraction technology is used to extract multiple active ingredients in Polygonatum sibiricum; and by adjusting the ultrasonic frequency and temperature, components of different polarities are extracted step by step, and preliminary purification and concentration are performed at the same time; S2. Component separation and preliminary screening based on high performance liquid chromatography: Liquid chromatography HPLC is used to separate the components in the extract, and a UV detector UV and mass spectrometer MS are used to perform preliminary identification of the active components. A high-throughput screening process is then established to introduce the separated components into a downstream activity testing system. S3. Application of microfluidics technology in antibacterial activity detection: Using microfluidic chip technology, the separated components are quickly mixed with the target bacteria to form a microdroplet reaction unit. Through the digital microfluidic platform, each microdroplet represents an antibacterial test unit of a component, and the real-time antibacterial effect is monitored in a micron-level environment. S4. High-throughput data processing and component activity analysis: After completing the preliminary antibacterial activity test, the data processing algorithm is used to analyze the antibacterial data points; the data processing algorithm associates the antibacterial efficacy with the structural characteristics of the ingredients through multi-dimensional data analysis, and labels and prioritizes the ingredients with significant activity; S5. High-throughput screening of synergistic effects of antibacterial ingredients: By building a high-throughput screening platform and adopting a combination screening strategy of different antibacterial ingredients, we evaluated whether the various ingredients in Polygonatum sibiricum have synergistic antibacterial effects; by mixing each ingredient in proportion, we tested the antibacterial effect of the combination to determine whether there is an enhanced effect; S6. Ingredient activity prediction model based on artificial intelligence: A machine learning algorithm was used to train a prediction model in combination with the antibacterial activity data obtained in S1-S6.

2. The high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum according to claim 1, characterized in that The application method of the microfluidic technology in antibacterial activity detection includes: Design a fluid shear force control mechanism to adapt the droplet generation process to different fluid properties and adjust in real time according to changes in flow rate and reactant concentration: Among them, F(t) represents the instantaneous shear force exerted by the fluid during the droplet generation process, which is dynamically adjusted with the change of time t; α is the initial shear force intensity, which is related to the viscosity of the fluid and the geometric structure of the chip; β is used to adjust the amplitude change of the shear force, reflecting the response sensitivity of the fluid properties in the chip to the shear force fluctuation; ω is the fluid oscillation frequency, which determines the periodic change of the shear force over time.

3. The high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum according to claim 2, characterized in that The application method of the microfluidic technology in antibacterial activity detection includes: A fluid shear force distributor is introduced into the droplet generation area; the distributor adjusts the flow rate and pressure gradient; and the rate of droplet generation is linked to the physical structure of the chip and the fluid properties.

4. The high-throughput screening system for antibacterial active ingredients of Polygonatum sibiricum according to claim 3, characterized in that The application method of the microfluidic technology in antibacterial activity detection includes: The growth status of bacteria in the droplets is monitored in real time through fluorescent labeling technology. The fluorescent signal in the droplets is collected through an integrated optical sensor during the monitoring process, and the growth rate and mortality rate of bacteria are calculated in real time based on the changes in signal intensity and frequency. A calculus formula for regulating signal response is designed to describe the temporal changes in signal intensity: Among them, S(t) is the fluorescence signal intensity at time t, which represents the monitoring result of the bacterial growth status; η is the signal amplification factor, which is used to adjust the sensitivity of the detection system; θ is the phase modulation coefficient of the fluorescence signal, which controls the frequency of the signal response; κ is the attenuation coefficient of the signal in the system, which reflects the attenuation rate of the signal over time; μ is the inhibition parameter of the signal response, which is used to describe the inhibition effect of the signal intensity under different experimental conditions.

5. The high-throughput screening system for antibacterial active ingredients of polygonatum according to claim 1, characterized in that The high-throughput screening method for the synergistic effect of antibacterial ingredients comprises: A dynamic ratio adjustment strategy is adopted to ensure that the concentration of each antibacterial ingredient is optimized according to real-time feedback; and the synergistic effect between ingredients is explored by intelligently adjusting the concentration of each ingredient from low concentration to high concentration; the concentration adjustment of ingredients is affected by time and the synergistic effect between ingredients.

6. The high-throughput screening system for antibacterial active ingredients of polygonatum according to claim 5, characterized in that The high-throughput screening method for the synergistic effect of antibacterial ingredients comprises: A nonlinear optimization algorithm was used to intelligently evaluate the synergistic effect of the combined antibacterial ingredients. By analyzing the experimental data, a nonlinear synergistic model for identifying the enhanced effect was established, and the multiplicative effect between the ingredients at a specific ratio was detected.

7. The high-throughput screening system for antibacterial active ingredients of polygonatum according to claim 6, characterized in that The high-throughput screening method for the synergistic effect of antibacterial ingredients comprises: Through high-throughput microfluidic technology, large-scale, multi-component combination rapid parallel testing; the microfluidic platform mixes different antibacterial components into microdroplets in proportion, and each microdroplet acts as an independent reaction unit. Multiple combinations are tested simultaneously in a micrometer-scale environment, and the antibacterial effect in each microdroplet is detected in real time; the number of droplets generated is controlled by fluid dynamics, and the change of its generation rate over time and system parameters is described by the following equation: Where N(t) is the number of droplets generated at time t, representing the number of test units generated in different time periods; η is the initial generation rate coefficient, indicating the basic speed of droplet generation; t 1 / 2 It represents the square root relationship between the generation rate and time, indicating the natural increasing trend of the droplet generation rate with the extension of time; ζ is the resistance coefficient, reflecting the inhibitory effect of the internal resistance of the fluid on the droplet generation rate during the generation process; ρ is the time attenuation coefficient, indicating the natural attenuation trend of the generation rate with time.

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