Targeted delivery method of plant-derived biological regulator

Abstract

The invention discloses a targeted delivery method of a plant-derived biological regulator, and relates to the technical field of bioengineering, and the targeted delivery method comprises the following steps: in the application process of an electroporation technology, monitoring and acquiring cell membrane lipid change data information in real time through a biosensor technology; the method comprises the following steps: preprocessing original data acquired by a biosensor, and organizing and storing the preprocessed data according to rules to form a structured data set; the system can accurately control the state of the plant cell membrane by monitoring the lipid change of the cell membrane in real time and combining intelligent evaluation and dynamic adjustment of the electric field parameters. When membrane lipid is oversaturated, the electric field intensity and the pulse frequency are dynamically adjusted, membrane damage is reduced, cell integrity is protected, and meanwhile effective delivery of allogenic materials is ensured. According to the method, the stability and the accuracy of an electroporation technology are improved, the targeted delivery of the plant-derived biological regulator is optimized, and the application potential of the plant-derived biological regulator in the fields of agriculture and biomedicine is expanded.

Description

Technical Field

[0001] The present invention relates to the field of bioengineering technology, and particularly relates to a method for targeted delivery of plant-derived biological regulators. Background Art

[0002] The targeted delivery of plant-derived biological regulators refers to the precise delivery of biological regulators derived from natural plants (such as glycine betaine, caffeine, melatonin, etc.) to the target parts or cells of crops through a specific delivery system, in order to promote the biosynthesis and accumulation of specific nutrients (such as amino acids, proteins, anthocyanins, flavonoids, etc.). Through this targeted delivery method, the accumulation level of the required nutrients in crops can be effectively increased, while avoiding the influence or waste on non-target areas. The core goal of this technology is to optimize the delivery system so that plant-derived biological regulators can play a role in specific parts of the crop body, promote the efficient synthesis of crop nutrients, and ultimately improve the nutritional value and yield of the crop. For example, the delivery of glycine betaine and caffeine can promote the synthesis of proteins and amino acids in rice and barley, while the delivery of melatonin and γ-aminobutyric acid helps the synthesis of anthocyanins and flavonoids in grapes and strawberries. The targeted delivery method can significantly improve the nutritional components of crops.

[0003] Glycine betaine is a quaternary ammonium compound with a positive charge in its structure, highly polar, and has good stability in an aqueous solution environment. It is widely present in various plants in nature and participates in plant stress responses as a typical osmolyte. However, due to its extremely strong polarity and ionic properties, it is difficult to naturally penetrate the hydrophobic lipid bilayer structure of plant cells, which limits its active absorption and directional transport efficiency in plants. Under conventional foliar spraying or root irrigation conditions, glycine betaine is often degraded by extracellular enzymes or retained by epidermal tissues and cannot effectively enter functional cells to exert its biological activity.

[0004] To break through this delivery barrier, electroporation technology has been introduced as an efficient delivery means. This technology applies a brief high-voltage electrical pulse to the cell membrane, instantaneously generating reversible nano-pores on the cell membrane, significantly improving the efficiency of glycine betaine crossing the membrane and entering the cytoplasm. After entering the cell, glycine betaine can act as an amphoteric osmolyte to maintain the cell osmotic pressure balance, and at the same time activate or enhance the expression of related genes, thereby promoting the synthesis and accumulation of proteins and amino acids in plants and improving the nutritional quality of crops.

[0005] This technical strategy is particularly prominent in cereal crops represented by barley and rice. Especially under stress environments (such as drought, high salinity or low temperature), the efficient targeted delivery of glycine betaine can significantly enhance the stress resistance of crops. At the same time, by regulating the nitrogen metabolism pathway, it can increase the protein content in grains, achieving a double improvement in crop nutrition and output value, and has broad application prospects and promotion value.

[0006] The existing technology has the following deficiencies: In the application of electroporation technology, although this technology generates reversible nanopores on the cell membrane by applying high-voltage electrical pulses so that exogenous substances can enter plant cells, it itself cannot adapt to the dynamic changes of the plant cell membrane. The properties and structure of the plant cell membrane change continuously with factors such as changes in the external environment, plant growth stages, and the physiological state of cells. For example, when plants encounter environmental stresses (such as high temperature, drought, salinity, etc.), the lipid composition of the cell membrane and the distribution of membrane proteins will be adjusted, thereby affecting the fluidity, permeability, and stability of the membrane.

[0007] Electroporation technology relies on preset fixed electric field intensities and pulse frequencies to generate membrane pores, but this technology cannot be dynamically adjusted according to the immediate changes of the cell membrane. In some cases, the cell membrane may change its response to the electric field due to changes in its structure or state (such as oversaturation of membrane lipids or aggregation of membrane proteins), resulting in the inability of electrical pulses to effectively generate the expected reversible pores and even potentially causing irreversible damage to the cell membrane. This irreversible damage not only destroys the integrity of the cell membrane but also may lead to the leakage of intracellular substances (such as ions, proteins, RNA, etc.), seriously interfering with the normal physiological functions of cells.

[0008] After the permanent rupture of the cell membrane, the basic functions of plant cells such as metabolism, information transmission, and material transport will be severely affected, thereby causing cell death or malfunction. With the accumulation of cell damage, the growth and development of the entire plant will be inhibited, especially in the early growth stage or key physiological processes of plants, which may lead to plant growth stagnation, reduced yields, or even death. Therefore, due to the inability of electroporation technology to adapt to the changes of the plant cell membrane under different physiological states, its application is subject to certain limitations and may cause unnecessary damage to plants in some cases.

[0009] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, so it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0010] The object of the present invention is to provide a targeted delivery method for plant-derived biological regulators. By real-time monitoring of cell membrane lipid changes and combining intelligent evaluation and dynamic adjustment of electric field parameters, the system can accurately control the state of plant cell membranes. When the membrane lipids are oversaturated, the electric field strength and pulse frequency are dynamically adjusted to reduce membrane damage, protect cell integrity, and ensure the effective delivery of exogenous substances. This method improves the stability and accuracy of the electroporation technique, optimizes the targeted delivery of plant-derived biological regulators, and expands their application potential in the fields of agriculture and biomedicine to solve the problems in the above-mentioned background technology.

[0011] To achieve the above object, the present invention provides the following technical solution: A targeted delivery method for plant-derived biological regulators, comprising the following steps:

[0012] During the application of the electroporation technique, data information on cell membrane lipid changes is obtained through real-time monitoring by biosensor technology;

[0013] The original data obtained by the biosensor is preprocessed, and the preprocessed data is organized and stored according to rules to form a structured data set;

[0014] Key indicators reflecting the oversaturation of cell membrane lipids are extracted from the data set, and the extracted key indicators are analyzed in detail to evaluate the trend of cell membrane lipid changes and quantify the state of cell membrane lipid changes;

[0015] The key indicators after analysis are input into a pre-trained machine learning model, and the model is used for intelligent evaluation to determine whether the membrane lipids are in an oversaturated state;

[0016] When it is recognized that the membrane lipids are oversaturated, based on the evaluation results, the electric field strength is dynamically reduced to reduce the membrane voltage difference generated during the electroporation process, and at the same time, the pulse frequency is reduced to avoid the membrane lipids from being subjected to high-frequency high-voltage pulses in a short time.

[0017] Preferably, during the application of the electroporation technique, the specific steps for real-time monitoring and obtaining cell membrane lipid changes through biosensor technology are as follows:

[0018] A highly sensitive biosensor is installed in contact with the plant tissue to detect the physical and chemical properties of the cell membrane;

[0019] The sensor tracks the state of the cell membrane in real time through non-destructive detection means and collects data on changes in membrane lipids;

[0020] The collected data will be transmitted to the data processing system to provide basic information for subsequent analysis and decision-making.

[0021] Preferably, key indicators reflecting the excessive saturation of cell membrane lipids are extracted from the dataset. The extracted key indicators include the elongation length of fatty acid chains in the cell membrane and the transition temperature of cell membrane lipids from the liquid crystal phase to the gel phase. After analyzing the elongation length of fatty acid chains in the cell membrane and the transition temperature of cell membrane lipids from the liquid crystal phase to the gel phase under the detection window, a fatty acid chain elongation reference value and a lipid phase transition temperature reference value are generated respectively. The change trend of cell membrane lipids is evaluated through the fatty acid chain elongation reference value and the lipid phase transition temperature reference value, and the change state of cell membrane lipids is quantified.

[0022] Preferably, the specific steps for analyzing the elongation length of fatty acid chains in the cell membrane under the detection window to generate a fatty acid chain elongation reference value are as follows:

[0023] Detect the fatty acid chains in the cell membrane to obtain detailed information of each fatty acid molecule. Based on the obtained information, an elongation length parameter is defined. The elongation length parameter represents the actual elongation length of each fatty acid chain. The calculation expression of the elongation length parameter is as follows:

[0024] , where is the elongation length of the fatty acid chain, is the number of carbon chains of the th fatty acid, is an indicator variable indicating whether the th fatty acid contains a double bond,

[0025] By analyzing the elongation length of the fatty acid chain, a fatty acid chain elongation reference value is generated to quantify the saturation state of cell membrane lipids. The generation formula of the fatty acid chain elongation reference value is as follows:

[0026] , where is the fatty acid chain elongation reference value, is the elongation length of the th fatty acid chain, is a regulatory factor.

[0027] Preferably, the specific steps for analyzing the transition temperature of cell membrane lipids from the liquid crystal phase to the gel phase under the detection window to generate a lipid phase transition temperature reference value are as follows:

[0028] Detect the phase transition temperature of cell membrane lipids, that is, the transition temperature of the membrane from the liquid crystal phase to the gel phase. Heat or cool the cell membrane sample within different temperature ranges and record the heat flow response of the membrane. Capture the transition temperature characteristics of the membrane through the following transition temperature function;

[0029] , where is the transition temperature of the membrane lipids, is the rate of change of heat flow, is the thermal response sensitivity coefficient, is the structural coefficient, is the temperature scan start temperature;

[0030] Obtaining the transition temperature of membrane lipids After that, the lipid phase transition temperature reference value is generated, and the calculation expression is as follows:

[0031] , where is the reference value of membrane lipid transition temperature, is the reference transition temperature, is the saturation effect constant, is the adjustment coefficient, It is the temperature difference between the actual transition temperature of the membrane lipids and the starting temperature of the temperature scan.

[0032] Preferably, the analyzed fatty acid chain extension reference value and lipid phase transition temperature reference value are input into a pre-trained machine learning model, a lipid saturation risk coefficient is generated by the machine learning model, and the lipid saturation risk coefficient is used to intelligently evaluate the change state of cell membrane lipids to determine whether the membrane lipids are in an oversaturated state.

[0033] Preferably, the lipid saturation risk coefficient generated when the cell membrane lipid change state is intelligently evaluated by a pre-trained machine learning model is compared with a pre-set lipid saturation risk coefficient reference threshold to determine whether the membrane lipid is in an oversaturated state. The judgment logic is as follows:

[0034] If the lipid saturation risk coefficient is greater than the pre-set lipid saturation risk coefficient reference threshold, it is judged that the cell membrane lipid is in an oversaturated state; if the lipid saturation risk coefficient is less than or equal to the pre-set lipid saturation risk coefficient reference threshold, it is judged that the cell membrane lipid is not in an oversaturated state.

[0035] Preferably, when the membrane lipids are identified to be oversaturated, the electric field strength is dynamically reduced based on the evaluation results, and the pulse frequency is reduced at the same time, so as to prevent the membrane lipids from being subjected to high-frequency and high-voltage pulses in a short period of time. The specific steps are as follows:

[0036] After confirming that the membrane is oversaturated with lipids, the electric field strength needs to be adjusted according to the lipid saturation risk factor. The relationship with the reference threshold of lipid saturation risk coefficient is adjusted nonlinearly, and the adjustment formula is as follows:

[0037] , where is the adjusted electric field strength, is the current electric field strength, is the non - linear adjustment coefficient, is the reference threshold of the lipid saturation risk coefficient, is the maximum value of the lipid saturation risk coefficient, which is used for normalization and controlling the adjustment amplitude;

[0038] In order to further reduce the damage caused by excessive membrane lipid saturation, on the basis of the electric field strength adjustment, the pulse frequency is dynamically reduced. The adjustment formula of the pulse frequency is realized through the reciprocal relationship, and the formula is as follows:

[0039] , where, is the adjusted pulse frequency, is the current pulse frequency, is the pulse frequency adjustment coefficient.

[0040] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0041] By real - time monitoring the changes of cell membrane lipids, combining intelligent evaluation and dynamically adjusting the electric field parameters, the present invention realizes the precise control of the state of plant cell membranes. When it is identified that the membrane lipids are oversaturated, the system can dynamically adjust the electric field strength and pulse frequency according to the evaluation results, reduce the impact on the cell membrane, and avoid irreversible damage to the membrane. This not only protects the integrity of the cells, reduces unnecessary cell damage, but also ensures the effective delivery of exogenous substances, improves the stability and accuracy of the delivery process, thereby enhancing the overall effect of the targeted delivery of plant - derived biological regulators. This method realizes the adaptive adjustment of the electroporation technology, enabling it to optimize its application under different physiological states, and broadening the application potential of the electroporation technology in the fields of agriculture and biomedicine. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0043] Figure 1 is the method flow chart of a method for targeted delivery of a plant - derived biological regulator of the present invention. Detailed Embodiments

[0044] Now, the exemplary embodiments will be described more comprehensively with reference to the drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided so that the present disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0045] The present invention provides a targeted delivery method of a plant-derived bioregulator as shown in Figure 1 below, comprising the following steps:

[0046] During the application of electroporation technology, data information on the change of cell membrane lipids is monitored and obtained in real time through biosensor technology;

[0047] The biosensor can use various technical means (such as spectral analysis, mass spectrometry analysis or electrochemical sensors) to obtain data on the lipid composition, fatty acid ratio, membrane fluidity, etc. of the cell membrane. The function of this step is to dynamically master the state of the cell membrane during electroporation through real-time monitoring of the membrane lipid components, especially when membrane lipids are oversaturated due to environmental stress or other factors. Through this process, the sensor can accurately capture the changes in membrane lipids, providing the necessary data basis for subsequent analysis and intelligent adjustment.

[0048] During the application of electroporation technology, the specific steps for monitoring and obtaining the change of cell membrane lipids through biosensor technology are as follows: First, install a highly sensitive biosensor (such as a spectral sensor, an electrochemical sensor or a mass spectrometry sensor) in contact with plant cells or tissues to detect the physical and chemical properties of the cell membrane. These sensors can capture the compositional changes of membrane lipids in real time, such as parameters such as the ratio of saturated fatty acids to unsaturated fatty acids, membrane fluidity, and membrane potential changes. The sensor tracks the state of the cell membrane in real time through non-destructive detection means and collects data on the changes of membrane lipids. The collected data will be transmitted to a data processing system to provide the necessary basic information for subsequent analysis and decision-making. The core purpose of this process is to ensure that the change of membrane lipids can be dynamically monitored during electroporation by obtaining the lipid information of the cell membrane in real time, and to promptly identify the oversaturation or other abnormal states of the membrane.

[0049] Preprocess the raw data obtained by the biosensor, and organize and store the preprocessed data according to rules to form a structured data set;

[0050] Data preprocessing is a process of cleaning and standardizing the raw data obtained from the biosensor. The preprocessing steps include removing noise, handling missing values, data smoothing, standardization and normalization, etc., to ensure the high quality and consistency of the data. The purpose of doing this is to reduce the interference of noise and outliers on subsequent analysis and make the data more reliable. The function of this step is to ensure that the data input in the subsequent steps has high credibility, thereby improving the accuracy and effect of the machine learning model.

[0051] The construction of the data set is to organize and store the preprocessed data according to certain rules to form a structured database. This data set contains the data of the changes in cell membrane lipids under different states, such as the membrane lipid data in the normal state and the oversaturated state. Through the integration of these data, the system can establish a global view of the changes in membrane lipid states. In this process, it is necessary to ensure the representativeness and diversity of the data so as to cover various possible membrane lipid change situations and provide comprehensive information support for subsequent analysis.

[0052] Extract the key indicators reflecting the oversaturation of cell membrane lipids from the data set, and conduct a detailed analysis of the extracted key indicators to evaluate the change trend of cell membrane lipids and quantify the change state of cell membrane lipids;

[0053] Extract the key indicators reflecting the oversaturation of cell membrane lipids from the data set. The extracted key indicators include the extension length of fatty acid chains in the cell membrane and the transition temperature of cell membrane lipids from the liquid crystal phase to the gel phase. After analyzing the extension length of fatty acid chains in the cell membrane and the transition temperature of cell membrane lipids from the liquid crystal phase to the gel phase under the detection window, a fatty acid chain extension reference value and a lipid phase transition temperature reference value are generated respectively. The change trend of cell membrane lipids is evaluated through the fatty acid chain extension reference value and the lipid phase transition temperature reference value, and the change state of cell membrane lipids is quantified.

[0054] A longer extension length and a dense arrangement of fatty acid chains in the cell membrane usually indicate that the cell membrane lipids are in an oversaturated state. The carbon chain of saturated fatty acids does not contain double bonds, and its structure is straight-chain, with good stacking properties. When the fatty acid chains are long and exist in a saturated form, their arrangement is more compact, resulting in a reduction in the gap between membrane lipid molecules and forming a highly ordered and dense arrangement structure. This arrangement will significantly reduce the fluidity and flexibility of the cell membrane, making it show higher rigidity and lower permeability. Under the action of external stimuli such as electroporation, it is more difficult for such membrane structures to form reversible nanopores, and at the same time, the responsiveness to the electric field becomes poor, and irreversible damage is likely to occur. Therefore, the characteristics of longer fatty acid chain extension and dense arrangement are one of the important physical manifestations of membrane lipid oversaturation, reflecting that the membrane is in a state of low fluidity and high rigidity, which poses potential risks to technical means such as electroporation delivery that require membrane flexibility.

[0055] The specific steps for analyzing the extension length of fatty acid chains in the cell membrane under the detection window to generate a fatty acid chain extension reference value are as follows:

[0056] Detect the fatty acid chains in the cell membrane (such as by mass spectrometry, infrared spectroscopy or NMR) to obtain detailed information about each fatty acid molecule, especially the length, saturation degree of the fatty acid chain and the distribution of double bonds in the chain. Define the extension length parameter based on the obtained information. The extension length parameter represents the actual extension length of each fatty acid chain. Specifically, for each fatty acid, the extension length corresponds to the length of its carbon chain. If the fatty acid is in an unsaturated form, the extension length will be adjusted accordingly according to the presence of double bonds. The calculation expression of the extension length parameter is as follows:

[0057] , where is the extension length of the fatty acid chain, is the number of carbon chains of the th fatty acid, is an indicator variable indicating whether the th fatty acid contains double bonds, is the number of fatty acid molecules;

[0058] The above steps provide basic data for generating the reference value of fatty acid chain extension, and further help to analyze whether the membrane lipids are in an over-saturated state.

[0059] By analyzing the extension length of the fatty acid chain, generate the reference value of fatty acid chain extension to quantify the saturation state of the cell membrane lipids. The reference value of fatty acid chain extension takes into account the extension length and the saturation degree of the fatty acids, reflects the density and rigidity of the membrane lipids, and further indicates whether the membrane is in an over-saturated state. The generation formula of the reference value of fatty acid chain extension is as follows:

[0060] , where is the reference value of fatty acid chain extension, is the extension length of the th fatty acid chain, is a regulatory factor used to adjust the weight of the influence of unsaturated fatty acids on the membrane lipid structure.

[0061] This step generates the reference value of fatty acid chain extension by taking the square sum average of the extension lengths of the fatty acid chains and combining the attenuation factor of unsaturated fatty acids . The larger this reference value is, the more saturated the cell membrane is, the lower the fluidity of the membrane is, and the more likely it is to be in an over-saturated state.

[0062] The larger the fatty acid chain extension reference value generated after analyzing the extension length of fatty acid chains in the cell membrane under the detection window, the more it means that the cell membrane lipids are in an oversaturated state. Conversely, it indicates that the lipids are unsaturated. In the cell membrane, saturated fatty acid chains have longer carbon chains. And due to the absence of double bonds, their molecular structure is relatively straight and easy to arrange closely, resulting in a reduced gap between lipid molecules, a decrease in membrane fluidity, and an increase in rigidity. Therefore, the longer the extension length of the fatty acid chain and the closer its arrangement, the larger the fatty acid chain extension reference value, indicating that the membrane lipids exhibit a high saturation state. In this state, the flexibility and adaptability of the membrane are poor, which easily leads to an increased risk of membrane damage in technical applications such as electroporation. On the contrary, when the fatty acid chain contains more unsaturated double bonds, the chain will bend and form a looser arrangement, thereby increasing the membrane fluidity and flexibility. At this time, the fatty acid chain extension reference value is smaller, indicating that the membrane lipids are in an unsaturated state.

[0063] An increase in the transition temperature of cell membrane lipids from the liquid crystal phase to the gel phase generally indicates the oversaturation of cell membrane lipids. The phase transition temperature of membrane lipids (also known as the phase change temperature) reflects the fluidity of membrane lipids and the physical properties of the membrane. When the saturation of cell membrane lipids is relatively high, the proportion of saturated fatty acids increases, and the van der Waals forces between fatty acid molecules are enhanced, making the lipid molecules arrange more closely and restricting their fluidity. Since the molecules of saturated fatty acids do not have double bonds, they cannot form the flowing structure of the liquid crystal phase, and the membrane lipids are more likely to be stable in the gel phase. This change leads to a decrease in membrane fluidity, thereby increasing the phase transition temperature of the membrane. An increase in the phase transition temperature means that the membrane requires a higher temperature to change from the relatively fluid liquid crystal phase to the harder gel phase. In this state, the rigidity of the membrane increases, the formation of membrane pores becomes more difficult, and the permeability and flexibility of the membrane decrease. Therefore, the oversaturation of membrane lipids not only reflects the imbalance in the proportion of fatty acids but also indicates that the physical properties of the membrane have changed, which may affect the normal functions of cells and the effect of the electroporation process, and easily lead to irreversible damage to the membrane.

[0064] The specific steps for generating the lipid phase transition temperature reference value by analyzing the transition temperature of cell membrane lipids from the liquid crystal phase to the gel phase under the detection window are as follows:

[0065] Detect the phase transition temperature of cell membrane lipids, that is, the transition temperature of the membrane from the liquid crystal phase to the gel phase. Usually, differential scanning calorimetry (DSC) or other temperature scanning techniques are used for experiments. The cell membrane sample is heated or cooled within different temperature ranges, and the heat flow response of the membrane is recorded. These responses reveal the transition process of membrane lipids from the liquid crystal phase (liquid state) to the gel phase (solid state). The transition temperature is the temperature point at which the membrane lipids start to change from the liquid state to the solid state. The transition temperature characteristics of the membrane are captured through the following transition temperature function;

[0066] , where, is the transition temperature of membrane lipids, which represents the temperature at which membrane lipids begin to change from a relatively fluid liquid structure to a relatively rigid solid structure during the temperature change process. is the heat flow change rate, which represents the heat flow change rate of membrane lipids during heating or cooling. It represents the change in the heat flow of the membrane under a unit temperature change. is the heat flow. is the temperature. is the heat response sensitivity coefficient, which reflects the response sensitivity of membrane lipid types and structures to heat flow changes. is the structure coefficient, which reflects the influence of the structure of membrane lipids on the transition temperature. is the starting temperature of the temperature scan;

[0067] Through the above steps, the transition temperature of membrane lipids can be calculated. This temperature reflects the physical properties of the membrane and its saturation level.

[0068] Obtain the transition temperature of membrane lipids. After that, generate a reference value for the lipid phase transition temperature, and quantify the saturation state of membrane lipids through the reference value of the lipid phase transition temperature. The calculation formula is as follows:

[0069] , where, is the reference value of the membrane lipid transition temperature. is the reference transition temperature, which is used as a reference value to compare the actual transition temperature of the membrane. is the saturation influence constant, which reflects the degree of influence of lipid saturation on the transition temperature. is the adjustment coefficient, which reflects the influence of the change in membrane lipid fluidity on the transition temperature. is the temperature difference between the actual transition temperature of membrane lipids and the starting temperature of the temperature scan, which reflects the response change of membrane lipids in the experimental environment.

[0070] The above steps quantify by taking the ratio of the actual transition temperature of the membrane to the reference value, and adjust the saturation state of the membrane in combination with the temperature difference. The larger the value, the higher the saturation of membrane lipids, the lower the fluidity of the membrane, and the more obvious the over-saturated state. On the contrary, A smaller value indicates that the membrane lipids are in a lower saturation state and have better fluidity. This index can provide a quantitative basis for further optimization of the electroporation process and help dynamically adjust the electric field parameters.

[0071] The larger the reference value of the lipid phase transition temperature generated after analyzing the transition temperature of cell membrane lipids from the liquid crystal phase to the gel phase under the detection window, the more excessive the saturation of cell membrane lipids. The increase in the transition temperature of lipids from the liquid crystal phase to the gel phase reflects the decrease in the fluidity and the increase in the rigidity of membrane lipids. In the cell membrane, the higher the proportion of saturated fatty acids, the closer the arrangement of lipid molecules in the membrane, the stronger the van der Waals forces between fatty acid molecules, which limits the fluidity of the membrane and significantly raises the phase transition temperature. This is because saturated fatty acids lack double bonds and cannot provide the fluidity and flexibility like unsaturated fatty acids, resulting in the membrane being unable to maintain the liquid crystal phase structure at lower temperatures and transitioning to the gel phase. Conversely, if the content of unsaturated fatty acids in the lipids is high, the fluidity of the membrane increases, the phase transition temperature is lower, and the lipids tend to remain in the liquid crystal phase state. Therefore, an increase in the reference value of the lipid phase transition temperature means a higher proportion of saturated fatty acids in the lipids, excessive saturation of membrane lipids, and a decrease in the fluidity of the membrane; on the contrary, a smaller reference value of the lipid phase transition temperature indicates that the membrane lipids are not overly saturated and have good fluidity.

[0072] Input the key indicators after analysis into a pre-trained machine learning model, and use the model for intelligent evaluation to determine whether the membrane lipids are in an overly saturated state;

[0073] Input the reference value of fatty acid chain elongation and the reference value of lipid phase transition temperature after analysis into a pre-trained machine learning model, generate a lipid saturation risk coefficient through the machine learning model, and conduct intelligent evaluation on the change state of cell membrane lipids through the lipid saturation risk coefficient to determine whether the membrane lipids are in an overly saturated state.

[0074] A pre-trained machine learning model refers to a model that, during the application process, has been able to learn and extract patterns from data after being trained with a large amount of real data. This model can master the relationship between the key features of the reference value of fatty acid chain elongation and the reference value of lipid phase transition temperature and the saturation state of cell membrane lipids through learning a large amount of historical data. During the training process, machine learning algorithms (such as support vector machines, neural networks, decision trees, etc.) will extract features from the known dataset and optimize the prediction results based on these features. For example, in the case of excessive lipid saturation, the model will identify the relationship between the reference value of fatty acid chain elongation and the lipid phase transition temperature, and infer whether the membrane lipids are in an overly saturated state through this data. The training dataset usually includes data on the changes in cell membrane lipids in various different states and has been labeled with corresponding results (whether it is overly saturated), and the machine learning model learns through these labels to enable accurate evaluation and prediction in future data input.

[0075] Once the model is trained, it can be used as an intelligent decision-making tool during real-time monitoring to generate a lipid saturation risk coefficient based on the fatty acid chain elongation reference value and lipid phase transition temperature reference value obtained in real time. This risk coefficient reflects the probability that the cell membrane lipids are in an over-saturated state. The advantage of the machine learning model is that it can continuously self-adjust and optimize the prediction results from new data, avoiding the limitations of human intervention or fixed thresholds in traditional methods. Through intelligent evaluation, the model can dynamically judge the change state of cell membrane lipids based on real-time monitoring data, thereby providing timely feedback and adjustment suggestions. For example, when the lipid saturation risk coefficient reaches a certain threshold, the system can automatically adjust the electric field strength and pulse frequency during electroporation to avoid damage caused by over-saturation of membrane lipids and ensure the safety and effectiveness of the electroporation process. Therefore, the pre-trained machine learning model is not only a tool for data analysis but also the key to realizing the intelligent regulation of electroporation technology.

[0076] The machine learning model is not limited here, and it can realize the fatty acid chain elongation reference value and the lipid phase transition temperature reference value for comprehensive analysis to generate the lipid saturation risk coefficient Any machine learning model can be used. To implement the technical solution of the present invention, the present invention provides a specific implementation method;

[0077] The lipid saturation risk coefficient is generated by the following formula: , where and are the preset proportionality coefficients of the fatty acid chain elongation reference value and the lipid phase transition temperature reference value respectively, and and are both greater than 0.

[0078] The preset proportionality coefficients ( and ) refer to the coefficients used in the model to measure the relative importance of different factors on the final result. and represent the influence weights of the fatty acid chain elongation reference value and the lipid phase transition temperature reference value on the lipid saturation risk . By setting these two coefficients, the response sensitivity of the model to the two variables of the fatty acid chain elongation reference value and the lipid phase transition temperature reference value can be adjusted. Usually, these coefficients are determined in advance according to historical data or experimental results, reflecting the relative contribution degrees of these two parameters in predicting the lipid saturation risk.

[0079] In practical applications, and The values ​​of are usually derived from the data through regression analysis or machine learning methods. They determine how the model combines fatty acid chain extension and lipid phase transition temperature to comprehensively evaluate lipid saturation risk. Therefore, the preset proportionality coefficient is a key factor in adjusting and optimizing the model's prediction results, allowing the model to generate accurate evaluation results based on different input data.

[0080] It can be seen from the lipid saturation risk coefficient that the larger the fatty acid chain extension reference value generated after analyzing the extension length of the fatty acid chain in the cell membrane under the detection window, the larger the lipid phase transition temperature reference value generated after analyzing the transition temperature of the cell membrane lipid from the liquid crystal phase to the gel phase under the detection window, indicating that the larger the lipid saturation risk coefficient generated when the pre-trained machine learning model is used to intelligently evaluate the change state of cell membrane lipids, the greater the probability of over-saturation of cell membrane lipids, and vice versa, the smaller the probability of over-saturation of cell membrane lipids.

[0081] The lipid saturation risk coefficient generated by the intelligent evaluation of the cell membrane lipid change state by the pre-trained machine learning model is compared with the pre-set lipid saturation risk coefficient reference threshold to determine whether the membrane lipid is in an oversaturated state. The judgment logic is as follows:

[0082] If the lipid saturation risk coefficient is greater than the pre-set lipid saturation risk coefficient reference threshold, it is judged that the cell membrane lipid is in an oversaturated state; if the lipid saturation risk coefficient is less than or equal to the pre-set lipid saturation risk coefficient reference threshold, it is judged that the cell membrane lipid is not in an oversaturated state.

[0083] When the membrane lipids are identified to be oversaturated, the electric field strength is dynamically reduced based on the evaluation results to reduce the membrane voltage difference generated during the electroporation process, and the pulse frequency is reduced to prevent the membrane lipids from being subjected to high-frequency and high-voltage pulses in a short period of time;

[0084] When oversaturation of membrane lipids is identified, the electric field strength and pulse frequency are dynamically reduced based on the evaluation results. The main purpose is to reduce excessive physical impact on the cell membrane, thereby avoiding irreversible damage to the cell membrane during electroporation. Oversaturation of membrane lipids usually leads to reduced membrane fluidity and increased rigidity, making the membrane more fragile, less likely to form reversible pores, and more prone to permanent rupture. If a strong electric field or high-frequency pulse continues to be applied in this state, it will cause the membrane voltage difference to be too large, forming too many pores, leading to irreversible damage to the membrane and even cell death. Therefore, dynamically adjusting the electric field strength and pulse frequency to a milder level can effectively avoid excessive membrane perforation.

[0085] The effect of reducing the electric field strength is to reduce the membrane voltage difference, avoid excessive electric field force acting on the cell membrane, and place the membrane lipids in a milder stress environment, thereby reducing the damage to the membrane structure. At the same time, reducing the pulse frequency can avoid the membrane from being subjected to frequent electric field shocks in a short time, reduce the excessive oscillation and deformation of the membrane caused by high-frequency pulses, and thus effectively reduce the risk of membrane rupture caused by excessive membrane stress. In summary, dynamically adjusting the electric field and pulse frequency can ensure that the electroporation process can not only achieve effective delivery of exogenous substances, but also maintain the integrity of the membrane, avoid unnecessary cell damage, and ensure that the electroporation technology can still operate stably and safely under the condition of excessive membrane lipid saturation.

[0086] When it is recognized that the membrane lipids are oversaturated, based on the evaluation results, the specific steps to dynamically reduce the electric field strength and at the same time reduce the pulse frequency to avoid the membrane lipids from being subjected to high-frequency and high-voltage pulses in a short time are as follows:

[0087] After confirming that the membrane lipids are oversaturated, the electric field strength needs to be non-linearly adjusted according to the relationship between the lipid saturation risk coefficient and the reference threshold of the lipid saturation risk coefficient. The adjustment formula is as follows:

[0088] , where is the adjusted electric field strength, is the current electric field strength, is the non-linear adjustment coefficient, which controls the intensity of the adjustment and is usually set as a constant less than 1, is the reference threshold of the lipid saturation risk coefficient, is the maximum value of the lipid saturation risk coefficient, which is used for normalization and control of the adjustment amplitude;

[0089] This step adjusts the electric field strength using an exponential decay method , and its purpose is to make the adjustment of the electric field strength not only proportional to the degree of oversaturation of the membrane lipids, but also as the lipid saturation risk coefficient increases, the reduction of the electric field strength will intensify, so as to avoid excessive pressure on the membrane during oversaturation and ensure that the membrane can safely withstand the electroporation operation.

[0090] In order to further reduce the damage caused by oversaturation of membrane lipids, on the basis of adjusting the electric field strength, the pulse frequency is dynamically reduced. The adjustment formula of the pulse frequency is achieved through a reciprocal relationship, and the formula is as follows:

[0091] , where is the adjusted pulse frequency, is the current pulse frequency, is the pulse frequency adjustment coefficient, which controls the sensitivity of the frequency reduction and is usually set as a constant less than 1.

[0092] The above steps adjust the pulse frequency and, based on and the difference between them, slow down the frequency decline through the reciprocal relationship. When the membrane lipids are oversaturated, the pulse frequency will decline at a higher rate, avoiding excessive rapid pulses on the membrane when it is oversaturated, thereby reducing the risk of membrane damage. This reciprocal method can rapidly reduce the pulse frequency when the oversaturation risk is high, while changing slowly when the risk is low, providing a smooth adjustment process.

[0093] To verify the actual effect of the "targeted delivery method of a plant-derived bioregulator" proposed in the present invention, the following experiment was designed. Comparative verification was carried out around three major indicators: delivery efficiency, membrane integrity protection, and cell activity maintenance, to evaluate the feasibility and technical advantages of the method of the present invention in the plant cell environment.

[0094] I. This experiment aims to verify whether, based on adjusting the electroporation parameters (electric field strength and pulse frequency), the described targeted delivery method can effectively improve the delivery efficiency of exogenous bioregulators (taking glycine betaine as an example) in plant cells under the condition of real-time recognition of the cell membrane lipid state, while reducing the membrane breakage rate and maintaining a high cell physiological activity.

[0095] II. Experimental materials and methods:

[0096]

[0097]

[0098] III. Brief description of the experimental process

[0099] 1. Cells in each group first contacted the glycine betaine-FITC mixed solution;

[0100] 2. Group A collected membrane lipid indicators in real time through a sensor and calculated the lipid saturation risk coefficient through a model;

[0101] 3. The system dynamically adjusted the electric field strength and pulse frequency according to the risk coefficient;

[0102] 4. Group B was treated with traditional fixed voltage + frequency parameters (800V / 1kHz);

[0103] 5. Group C was not subjected to any perforation treatment and was only used to evaluate the necessity of perforation in delivery;

[0104] 6. Immediately after the treatment, the fluorescence intensity, membrane integrity, and cell activity indicators of each group were detected.

[0105] IV. Experimental data and analysis

[0106] Index Group A (the method of the present invention) Group B (fixed perforation method) Group C (non-perforated) Fluorescence intensity (AU) 1625 ± 110 940 ± 85 205 ± 30 Membrane integrity index (%) 92.4 ± 3.2 68.7 ± 5.1 97.3 ± 2.4 Cell viability retention rate (%) 89.6 ± 4.1 63.2 ± 6.0 98.5 ± 2.1

[0107] Result analysis:

[0108] The delivery efficiency (fluorescence intensity) of group A was increased by about 73% compared with the traditional method, which was significantly better than that of group B;

[0109] The membrane integrity and cell activity were also significantly higher than those in group B, indicating that the method of the present invention effectively reduced cell membrane damage while improving delivery efficiency;

[0110] Group C had almost no delivery effect, confirming that electroporation is a necessary means of delivery, but the fixed parameter method has the risk of cell damage;

[0111] In summary, the method of the present invention not only ensures delivery efficiency but also takes cell protection into consideration, thus achieving efficient and safe targeted delivery under intelligent regulation.

[0112] V. Technical Conclusion

[0113] This experiment verified that the method of the present invention has significant delivery efficiency advantages and membrane protection effects in plant cells. Its technical advantages are mainly reflected in:

[0114] 1. Ability to adjust electroporation parameters in real time according to the lipid status of cell membrane, with strong adaptability;

[0115] 2. Effectively improve the delivery rate of exogenous regulators and is suitable for the delivery of highly polar active substances;

[0116] 3. Significantly reduce the physical damage of traditional electroporation to cell membrane and protect the physiological activity of cells.

[0117] The present invention realizes precise control of the state of plant cell membranes by real-time monitoring of cell membrane lipid changes, combined with intelligent evaluation and dynamic adjustment of electric field parameters. When the membrane lipids are identified to be oversaturated, the system can dynamically adjust the electric field strength and pulse frequency according to the evaluation results, reduce the impact on the cell membrane, and avoid irreversible damage to the membrane. This not only protects the integrity of the cells and reduces unnecessary cell damage, but also ensures the effective delivery of exogenous substances, improves the stability and accuracy of the delivery process, and thus enhances the overall effect of targeted delivery of plant-derived bioregulators. This method realizes the adaptive adjustment of electroporation technology, enables it to optimize its application under different physiological conditions, and broadens the application potential of electroporation technology in agriculture and biomedicine.

[0118] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0119] Only some exemplary embodiments of the present invention are described by way of illustration. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0120] It should be noted that in this article, if there are relational terms such as first and second, they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0121] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution is prior or subsequent. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0122] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0123] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, may exist separately as individual physical units, or two or more units may be integrated into one unit.

[0126] As described above, only the specific embodiments of the present application are provided, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0127] Only some exemplary embodiments of the present invention have been described by way of illustration above. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A targeted delivery method for a plant-derived biological regulator, characterized in that, The following steps are involved: During the application of electroporation technology, biosensor technology is used to monitor and obtain data on changes in cell membrane lipids in real time; Preprocess the raw data obtained by the biosensor, and organize and store the preprocessed data according to rules to form a structured data set; Extract key indicators reflecting the oversaturation of cell membrane lipids from the data set, and conduct detailed analysis on the extracted key indicators to evaluate the changing trend of cell membrane lipids and quantify the changing state of cell membrane lipids; The analyzed key indicators are input into the pre-trained machine learning model, and the model is used for intelligent evaluation to determine whether the membrane lipids are in an oversaturated state; When it is identified that the membrane lipids are oversaturated, the electric field strength is dynamically reduced based on the evaluation results to alleviate the membrane voltage difference generated during the electroporation process, while the pulse frequency is reduced to prevent the membrane lipids from being subjected to high-frequency and high-voltage pulses in a short period of time.

2. The targeted delivery method of a plant-derived biological regulator according to claim 1, wherein During the application of electroporation technology, the specific steps for real-time monitoring and obtaining changes in cell membrane lipids through biosensor technology are as follows: Install highly sensitive biosensors in contact with plant tissues to detect physical and chemical properties of cell membranes; The sensor uses non-destructive testing to track the state of the cell membrane in real time and collect data on changes in membrane lipids; The collected data will be transmitted to the data processing system to provide basic information for subsequent analysis and decision-making.

3. The targeted delivery method of a plant-derived biological regulator according to claim 1, characterized in that, Key indicators reflecting the supersaturation of cell membrane lipids were extracted from the data set. The extracted key indicators included the extension length of the fatty acid chain in the cell membrane and the transition temperature of the cell membrane lipids from the liquid crystal phase to the gel phase. The extension length of the fatty acid chain in the cell membrane and the transition temperature of the cell membrane lipids from the liquid crystal phase to the gel phase were analyzed under the detection window to generate the fatty acid chain extension reference value and the lipid phase transition temperature reference value, respectively. The fatty acid chain extension reference value and the lipid phase transition temperature reference value were used to evaluate the change trend of cell membrane lipids and quantify the change state of cell membrane lipids.

4. The targeted delivery method of a plant-derived biological regulator according to claim 3, wherein The specific steps for analyzing the extension length of the fatty acid chain in the cell membrane under the detection window to generate the fatty acid chain extension reference value are as follows: Detecting fatty acid chains in the cell membrane, obtaining detailed information of each fatty acid molecule, and defining an extension length parameter based on the obtained information, wherein the extension length parameter represents the actual extension length of each fatty acid chain; By analyzing the extension length of fatty acid chains, a reference value for fatty acid chain extension is generated to quantify the saturation state of cell membrane lipids.

5. The targeted delivery method of a plant-derived biological regulator according to claim 3, characterized in that, The specific steps for analyzing the transition temperature of cell membrane lipids from liquid crystal phase to gel phase under the detection window to generate the lipid phase transition temperature reference value are as follows: Detect the phase transition temperature of cell membrane lipids, that is, the transition temperature from liquid crystal phase to gel phase of the membrane, heat or cool the cell membrane sample in different temperature ranges, and record the thermal flow response of the membrane, and capture the transition temperature characteristics of the membrane through the transition temperature function; After obtaining the transition temperature of membrane lipids, a lipid phase transition temperature reference value is generated.

6. The targeted delivery method of a plant-derived biological regulator according to claim 3, wherein The analyzed fatty acid chain extension reference value and lipid phase transition temperature reference value are input into the pre-trained machine learning model, and the lipid saturation risk coefficient is generated by the machine learning model. The lipid saturation risk coefficient is used to intelligently evaluate the change state of cell membrane lipids to determine whether the membrane lipids are in an oversaturated state.

7. The targeted delivery method of a plant-derived biological regulator according to claim 6, characterized in that, The lipid saturation risk coefficient generated by the intelligent evaluation of the cell membrane lipid change state by the pre-trained machine learning model is compared with the pre-set lipid saturation risk coefficient reference threshold to determine whether the membrane lipid is in an oversaturated state. The judgment logic is as follows: If the lipid saturation risk coefficient is greater than the pre-set lipid saturation risk coefficient reference threshold, it is judged that the cell membrane lipid is in an oversaturated state; if the lipid saturation risk coefficient is less than or equal to the pre-set lipid saturation risk coefficient reference threshold, it is judged that the cell membrane lipid is not in an oversaturated state.

8. The targeted delivery method of a plant-derived biological regulator according to claim 7, characterized in that, When the membrane lipids are identified to be oversaturated, the electric field strength is dynamically reduced based on the evaluation results, and the pulse frequency is reduced at the same time to prevent the membrane lipids from being subjected to high-frequency and high-voltage pulses in a short period of time. The specific steps are as follows: After confirming that the membrane lipids are oversaturated, the electric field strength needs to be nonlinearly adjusted according to the relationship between the lipid saturation risk coefficient and the lipid saturation risk coefficient reference threshold; In order to further reduce the damage caused by oversaturation of membrane lipids, the pulse frequency is dynamically reduced based on the adjustment of the electric field strength, and the adjustment formula of the pulse frequency is achieved through an inverse relationship.

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