Duodenal mucosal surface replacement and electrotransfection integrated device and parameter feedback-based operation method

Abstract

The present invention belongs to the field of biomedical information analysis technology, and specifically relates to an integrated device for duodenal mucosal surface replacement and electrotransfection and a method for operation based on parameter feedback. The present invention first provides an integrated device for duodenal mucosal surface replacement and electrotransfection, which includes an endoscopic guidance module, a radiofrequency ablation module, an electrotransfection module, and an intelligent terminal control module. The present invention also provides a method for operating a DMR-EP integrated device based on tissue parameter feedback. The present invention proposes for the first time the realization of individualized strategy generation and data-driven treatment path selection in local gene therapy after DMR surgery, which significantly improves the safety, effectiveness, and breadth of indications of the treatment.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical information analysis, and in particular relates to an integrated device for duodenal mucosal surface replacement and electrotransfection and a method for operation based on parameter feedback. Background Art

[0002] In recent years, the incidence of metabolic diseases has steadily increased. Existing studies have found that conditions such as type 2 diabetes mellitus (T2DM), obesity, and non-alcoholic fatty liver disease (NAFLD) are closely related to the physiological functions of the duodenal mucosa. Studies have shown that the duodenum is not only a key site for glucose absorption but also plays a key role in regulating insulin sensitivity and lipid metabolism. Abnormal duodenal mucosal function can lead to pathological conditions such as increased insulin resistance and glucose intolerance. Therefore, intervention targeting the duodenal mucosa is an important strategy for improving metabolic disease outcomes.

[0003] Duodenal mucosal resurfacing (DMR) is a novel, minimally invasive treatment method that removes the diseased mucosal layer through methods such as radiofrequency ablation, allowing for regeneration. This improves insulin sensitivity and enhances the secretion of metabolic hormones such as GLP-1 (glucagon-like peptide-1). However, the mucosal repair process after DMR surgery varies among individuals, and some patients may experience slow regeneration or incomplete functional recovery, impacting treatment efficacy. Furthermore, existing DMR technology primarily relies on passive mucosal repair and lacks the ability to actively regulate the expression of metabolic genes. Therefore, optimizing mucosal repair, enhancing metabolic function, and improving personalized treatment outcomes after DMR surgery remain pressing technical challenges.

[0004] Electroporation (EP) is a highly efficient, non-viral gene delivery method that uses brief electrical pulses to create nanoscale, transient pores in the cell membrane, thereby facilitating the entry and efficient expression of exogenous genes (such as mRNA and siRNA) into cells. Compared to viral vectors, EP offers enhanced safety, avoiding the immunological risks of viral-mediated gene delivery, and can be used in difficult-to-transfect cell types (such as epithelial cells). Currently, EP technology has been widely used in mRNA vaccines, cellular immunotherapies (such as CAR-T), and gene editing (CRISPR-Cas9). However, its application in localized gene therapy in the digestive tract remains limited, particularly for gene regulation in the neomucosal mucosa following DMR surgery, where a mature approach is lacking.

[0005] However, three key challenges that need to be addressed include the unclear timing of electroporation after DMR surgery, the lack of physiological basis for adjusting electroporation parameters, and the lack of control over gene expression efficiency. Therefore, new strategies are needed to alleviate at least one of these shortcomings of existing technologies. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide an integrated device and system for duodenal mucosal surface replacement and electrotransfection, and a method for operating DMR-EP, to partially solve or alleviate the above-mentioned deficiencies in the prior art. The present invention specifically adopts the following technical solutions.

[0007] The first aspect of the present invention is to provide a DMR-EP integrated system and equipment.

[0008] A duodenal mucosal surface replacement and electrotransfection integrated system, the duodenal mucosal surface replacement and electrotransfection integrated system includes an endoscope guidance module, a radiofrequency ablation module, an electrotransfection module and an intelligent terminal control module; an intelligent control system is provided in the intelligent terminal control module for controlling the operation and work of the endoscope guidance module, the radiofrequency ablation module and the electrotransfection module.

[0009] The intelligent control system is provided with a multimodal physiological parameter acquisition unit, a data processing and state recognition unit, a feedback learning and strategy updating unit, a state perception control unit and an execution control unit;

[0010] The multimodal physiological parameter acquisition unit is configured to collect data transmitted to the intelligent terminal control module during the operation of the duodenal mucosal surface replacement and electrofection integrated device, and transmit the data to the state perception control unit and the feedback learning and strategy updating unit;

[0011] The state perception control unit is configured to perform iterative judgment on the collected data in rounds, and transmit the judgment result to the execution control unit;

[0012] The execution control unit is configured to issue an operation instruction to the duodenal mucosal surface replacement and electrotransfection integrated device;

[0013] The execution control unit is further configured to be connected to the data processing and state identification unit and the feedback learning and strategy updating unit, for analyzing data and tissue state in real time, and dynamically adjusting the instructions of the execution control unit based on the data operations of the feedback learning and strategy updating unit;

[0014] The intelligent control system is configured to include an edge computing model, a physiological state recognition model and a state assessment model.

[0015] An integrated device for duodenal mucosal surface replacement and electrotransfection, comprising an endoscope guidance module, a radiofrequency ablation module, an electrotransfection module, and an intelligent terminal control module;

[0016] The radiofrequency ablation module is provided with a radiofrequency ablation device and a temperature sensor; the radiofrequency ablation device is used to perform radiofrequency ablation on the target tissue within a certain temperature range (according to a preset power curve); the temperature sensor is used to collect temperature changes of the tissue in real time and transmit the data to the intelligent terminal control module; the intelligent terminal control module controls the radiofrequency energy intensity of the radiofrequency ablation device based on the collected temperature data;

[0017] The electrotransfection module is configured to include an electrode array, a microfluidic gene delivery device, and a data acquisition and monitoring system; the electrode array is used to apply electric pulses to the target tissue to induce cell membrane perforation; the microfluidic gene delivery device is used to deliver the target gene to the target tissue; the data acquisition and monitoring system is configured to receive signals from the intelligent terminal control module to adjust the electrode array to apply electric pulses to the target tissue, and simultaneously transmit feedback indicators after the electric pulses to the intelligent terminal control module; the intelligent terminal control module then dynamically adjusts the real-time gene delivery or transfection efficiency of the microfluidic gene delivery device based on the received feedback indicators; the feedback indicators include tissue conductivity, tissue perfusion and microcirculation parameters, gene expression feedback, and / or inflammatory factors (levels or concentrations).

[0018] Furthermore, the real-time gene delivery includes real-time gene delivery dosage or real-time gene delivery speed.

[0019] Furthermore, the endoscopic guidance module is provided with an endoscopic device, a high-definition visual camera and a positioning system; the positioning system is configured to guide the endoscopic device to the target tissue and fully contact the target tissue based on image recognition and path planning algorithms.

[0020] Furthermore, the intelligent terminal control module includes an intelligent control system, which includes a multimodal physiological parameter acquisition unit, a data processing and state recognition unit, a feedback learning and strategy update unit, a state perception control unit and an execution control unit;

[0021] The multimodal physiological parameter acquisition unit is configured to collect data transmitted to the intelligent terminal control module during the operation of the duodenal mucosal surface replacement and electrofection integrated device, and transmit the data to the state perception control unit and the feedback learning and strategy update unit; in some specific embodiments, the multimodal physiological parameter acquisition unit may be provided with an edge computing model and a physiological state recognition model;

[0022] The state perception control unit is configured to perform iterative judgment on the collected data in rounds and transmit the judgment result to the execution control unit; in some specific embodiments, the state perception control unit is provided with a state assessment model;

[0023] The execution control unit is configured to issue an operation instruction to the duodenal mucosal surface replacement and electrotransfection integrated device;

[0024] The execution control unit is further configured to be connected to the data processing and state identification unit and the feedback learning and strategy updating unit, for analyzing data and tissue state in real time, and dynamically adjusting the instructions of the execution control unit based on the data operations of the feedback learning and strategy updating unit;

[0025] The intelligent control system is configured to include an edge computing model, a physiological state recognition model and a state assessment model.

[0026] Furthermore, the edge computing model is a data preprocessing function, and the edge computing model is:

[0027] ;

[0028] in, Represents the original value of the i-th physiological parameter collected in real time;

[0029] : represents the parameter The historical mean of

[0030] Representative parameters The historical standard deviation.

[0031] Furthermore, the physiological state recognition model is used to convert the physiological data collected by the system into different tissue state labels. The physiological state recognition model is:

[0032] ;

[0033] ;

[0034] Where x represents the normalized multidimensional feature vector, which contains n input indicators;

[0035] A weight vector representing the training of the physiological state recognition model, used to represent the discriminant contribution of each feature;

[0036] represents the bias term;

[0037] y represents the organizational state label of the recognition output.

[0038] In some embodiments, y = +1 indicates "entering the transfection window", and y = -1 indicates "transfection conditions have not yet been met".

[0039] Furthermore, the state assessment model is used to perform quantitative scoring or safety assessment on the identified state, and the state assessment model is:

[0040] ;

[0041] in,

[0042] Conductivity change rate (the difference between the current value and the baseline value, reflecting changes in cell permeability);

[0043] Tissue temperature rise (current temperature minus initial temperature, reflecting heat load);

[0044] Levels of inflammatory factors in tissues or perfusate (e.g., TNF-α levels);

[0045] , , It represents the weighting coefficient obtained from training or clinical experience, which is used to adjust the weight of each parameter in decision-making.

[0046] Another invention of the present invention further provides a method for operating DMR and EP simultaneously based on parameter feedback.

[0047] A method for operating an integrated duodenal mucosal surface replacement and electrofection device based on tissue parameter feedback, the method being based on the above-mentioned DMR-EP integrated device and comprising the following steps:

[0048] S01: Fully contact the duodenal mucosal surface replacement and electrofection integrated device with the target tissue;

[0049] S02: performing radiofrequency ablation on the target tissue;

[0050] S021: The radiofrequency ablation device applies radiofrequency ablation to the target tissue and maintains the radiofrequency ablation temperature within a fixed range according to a preset power curve;

[0051] S022: The temperature sensor transmits the real-time collected tissue temperature data to the intelligent control system; the intelligent control system analyzes and processes the tissue temperature data and then adjusts the radiofrequency energy intensity of the radiofrequency ablation device;

[0052] S023: The intelligent control system is configured to include an edge computing model, a physiological state recognition model, and a state assessment model; the physiological state recognition model is configured to convert the tissue temperature data into different tissue state labels, wherein the tissue state labels include repair completion, transfection window, or active inflammation; the physiological state recognition model normalizes the data and then inputs the data into a linear classifier (e.g., SVM) to determine whether the target tissue meets / has reached the conditions for electrotransfection;

[0053] S03: performing electrotransfection on the target tissue that has completed radiofrequency ablation and has met / reached electrotransfection conditions, and delivering the target gene;

[0054] S031: The data acquisition and monitoring system receives the electric pulse parameter signal sent by the intelligent control system, activates the electrode array to apply electric pulses to the target tissue and induces transient perforation of the cell membrane;

[0055] S032: a microfluidic gene delivery device delivers a target gene to the target tissue;

[0056] S033: The data acquisition and monitoring system transmits feedback indicators after the electrical pulse and gene delivery to the intelligent control system; the feedback indicators include tissue conductivity, tissue perfusion and microcirculation parameters, gene expression feedback and / or inflammatory status;

[0057] After receiving the feedback indicator, the intelligent control system dynamically adjusts the real-time gene delivery and transfection efficiency of the microfluidic gene delivery device;

[0058] Among them, the edge computing model set in the intelligent control system is used to preprocess the collected data; the physiological state recognition model is used to convert the collected physiological data into different tissue state labels; the state assessment model is used to quantitatively score or safety assess the feedback indicators, and weight the changing trends of multiple feedback indicators to output a quantitative score value for adjusting real-time gene delivery or transfection efficiency.

[0059] Furthermore, the physiological state recognition model is:

[0060] ;

[0061] ;

[0062] Where x represents the normalized multidimensional feature vector, which contains n input indicators;

[0063] A weight vector representing the training of the physiological state recognition model, used to represent the discriminant contribution of each feature;

[0064] represents the bias term;

[0065] y represents the organizational state label of the recognition output.

[0066] Furthermore, the state assessment model is:

[0067] ;

[0068] in,

[0069] conductivity change rate;

[0070] Tissue temperature rise;

[0071] Levels of inflammatory factors in tissues or perfusate;

[0072] , , It represents the weighting coefficient obtained from training or clinical experience, which is used to adjust the weight of each parameter in decision-making.

[0073] Furthermore, the physiological state recognition model is:

[0074] ;

[0075] ;

[0076] Where x represents the normalized multidimensional feature vector, which contains n input indicators;

[0077] A weight vector representing the training of the physiological state recognition model, used to represent the discriminant contribution of each feature;

[0078] represents the bias term;

[0079] y represents the organizational state label of the recognition output.

[0080] Beneficial technical effects:

[0081] The present invention first provides a device that integrates duodenal mucosal surface replacement (DMR) and electrotransfection (EP). The device is equipped with an "intelligent control system" based on real-time monitoring of physiological status, which can alleviate the problems of unclear timing of electrotransfection, non-adaptability of parameter adjustment, and lack of control over gene expression efficiency after DMR surgery.

[0082] Specifically, the intelligent control system, by introducing tissue conductivity, tissue temperature, tissue perfusion and microcirculatory parameters, gene expression feedback, and / or inflammatory status, can determine in real time whether the tissue is in the window period suitable for electrotransfection and the expression of the target gene during the transfection process, thereby providing feedback and dynamically regulating the operation of the equipment. Furthermore, by constructing a closed-loop model of state recognition-parameter optimization-execution control-expression feedback, the EP parameters and delivery dose can be dynamically adjusted according to individual differences. Finally, this invention proposes for the first time the realization of personalized strategy generation and data-driven treatment path selection in local gene therapy after DMR surgery, significantly improving the safety, effectiveness, and breadth of indications of the treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the embodiments or the description of the prior art. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the various elements or parts are not necessarily drawn according to the actual scale. Obviously, the drawings described below are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without inventive work.

[0084] Figure 1 This is a schematic diagram of the working modules of a DMR-EP integrated device according to one embodiment of the present invention;

[0085] Figure 2 This is a schematic diagram of the main hardware modules in a DMR-EP integrated device according to one embodiment of the present invention;

[0086] Figure 3 This is a schematic diagram of the workflow of a DMR-EP integrated device according to one embodiment of the present invention;

[0087] Figure 4 This is a working logic block diagram of the intelligent control system in the DMR-EP integrated device according to one embodiment of the present invention;

[0088] Figure 5 The effect of the DMR-EP integrated device on blood glucose levels and insulin sensitivity in T2DM mice according to one embodiment of the present invention is shown;

[0089] Figure 6 This is the effect of the DMR-EP integrated device on fat metabolism in obese mice in one embodiment of the present invention;

[0090] Figure 7This is one of the embodiments of the present invention based on the effect of the DMR-EP integrated device on the inflammation level and intestinal integrity in an IBD model. DETAILED DESCRIPTION

[0091] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0092] As used herein, "and / or" includes any and all combinations of one or more of the associated listed items.

[0093] Herein, "plurality" means two or more than two, ie, it includes two, three, four, five, etc.

[0094] As used in this specification, the term "about" typically means + / - 5% of the stated value, more typically + / - 4% of the stated value, more typically + / - 3% of the stated value, more typically + / - 2% of the stated value, even more typically + / - 1% of the stated value, and even more typically + / - 0.5% of the stated value.

[0095] In this specification, certain embodiments may be disclosed in a format that is within a range. It should be understood that this description of "within a range" is merely for convenience and brevity and should not be interpreted as a rigid limitation on the disclosed range. Therefore, the description of a range should be considered to have specifically disclosed all possible subranges and individual numerical values ​​within this range. For example, the description of a range of 1-6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., as well as individual numbers within this range, such as 1, 2, 3, 4, 5, and 6. Regardless of the breadth of the range, the above rules apply.

[0096] Example 1

[0097] This embodiment provides an intelligent, integrated device (hereinafter referred to as the integrated device) that integrates duodenal mucosal resurfacing (DMR) and electroporation (EP) functions. This device aims to improve the quality of mucosal remodeling and functional recovery after DMR surgery, thereby enabling precise regulation of metabolic function. This integrated device can perform radiofrequency ablation of target tissues. Furthermore, within 24 to 48 hours after DMR surgery, when the newly formed mucosa is in a transfectable and repairable state, it delivers functional genes, such as mRNAs for metabolic regulators like GLP-1, FGF21, or PPAR-α, to target tissues for improved insulin sensitivity, lipid metabolism, and inflammatory responses.

[0098] Figure 1-Figure 3 The module diagram and workflow diagram of a DMR-EP integrated device are shown respectively. The DMR-EP integrated device system adopts a modular architecture design and mainly includes several core functional units: (1) an endoscopic guidance module with high-definition visual navigation capability, which is used to guide the device to accurately reach the duodenal target area and monitor the target area tissue status in real time; (2) a radiofrequency ablation module, which is used to remove the diseased or dysfunctional mucosal area with high precision and promote the regeneration of new tissue; (3) an adjustable electrotransfection module, which is equipped with a ring or multipolar electrode array and can apply electric pulses to induce transient perforation of the cell membrane under set conditions; at the same time, a microfluidic gene delivery device based on a nanoliter control mechanism and a data acquisition and monitoring system are also provided to accurately deliver nucleic acid drugs such as target mRNA or siRNA to target cells within the transfection window period; (4) an intelligent terminal control module for controlling the operation / work of the above modules, which has an internal intelligent control (feedback) system to provide dynamic regulation and real-time feedback capabilities for the entire process of the device, forming a multi-parameter closed-loop control structure to ensure the stability, safety and personalized response capabilities of the entire treatment process.

[0099] Figure 4 The working logic of an intelligent control (feedback) system for DMR-EP equipment is shown. To achieve precise and adaptive regulation of gene delivery after DMR surgery, the system consists of a "multimodal physiological parameter acquisition unit - data processing and state recognition unit - feedback learning and strategy update unit - state perception control unit - execution control unit", forming a closed-loop control architecture with data-driven as the core. The system's multimodal physiological parameter acquisition unit is embedded in the endoscope terminal, annular electrode structure and microfluidic device interface, and can perform real-time, multimodal physiological parameter monitoring of the target tissue throughout the entire process without additional trauma. Specific acquisition indicators include but are not limited to the following categories:

[0100] (1) Tissue Conductivity: This parameter reflects the electrical response state of the local tissue and is an important basis for judging cell membrane permeability and electroporation completion. The system monitors the conductivity changes in real time before and after the application of the electric pulse and establishes a conductivity response curve to infer whether the tissue has reached the optimal transfection state.

[0101] (2) Tissue temperature: Temperature is a key indicator of the risk of adverse reactions to electrical stimulation. The system uses a thermistor or infrared sensor unit to collect tissue surface and deep temperature data, and calibrates the output of the radiofrequency ablation device in real time to ensure that the temperature is maintained within the physiological safety range (generally 37 ± 1.5°C).

[0102] (3) Tissue perfusion and microcirculation parameters (Local Perfusion): Spectroscopy or laser Doppler imaging is used to detect local blood flow changes, to determine the progress of mucosal regeneration and repair, and the ability of tissues to recover blood supply after electrical stimulation, to provide support for the subsequent rhythm and dose assessment of gene delivery.

[0103] (4) Gene Expression Feedback: In some embodiments, the target mRNA is labeled with a modified probe (e.g., fluorescent or electrochemical), and a data acquisition and monitoring system embedded in the device performs quantitative readings, enabling in situ monitoring of the expression level of the delivered gene. This data can serve as a key indicator for the system to determine whether to continue or pause delivery.

[0104] (5) Inflammatory Signals: The system can also be embedded with electrochemical sensors to monitor inflammatory markers such as local tissue pH, NO, TNF-α or IL-6 to assess the sensitivity or stress level of the tissue microenvironment to therapeutic intervention.

[0105] The above data is collected after each working module runs and then transmitted in real time to the device's intelligent terminal control module, also known as the master processing module. Based on the collected feedback parameters, a physiological state feature vector is constructed. By integrating multi-source sensory data, a multidimensional feature model of tissue functional status is formed. This includes an edge computing model, a physiological state recognition model, and a state assessment model. This model can be used to quickly identify the arrival of the transfection window and also serves as an input variable for subsequent control algorithms, driving back-end parameter decisions and regulatory actions. The data collection frequency, filtering algorithm, and data cleaning mechanism can be flexibly configured according to the application scenario to balance response speed and judgment accuracy.

[0106] After completing the multi-parameter acquisition and modeling of tissue status, the intelligent control (feedback) system of this embodiment enters the feedback logic judgment and parameter decision phase. This phase, based on pre-built models and machine learning-enhanced discrimination mechanisms, forms a closed feedback loop of "perception-discrimination-control."

[0107] Specifically, the intelligent control (feedback) system inputs currently acquired parameters such as tissue conductivity, tissue temperature, tissue perfusion and microcirculatory parameters, gene expression feedback, and inflammatory status indicators into an embedded state assessment model. This model is constructed through a combination of rule-based inference and data-driven algorithms (such as support vector machines, random forests, and shallow neural networks), and can determine the following key physiological states with millisecond-level response:

[0108] (1) Whether the optimal electrotransfection window has been reached (i.e., conductivity rises to the threshold, tissue permeability is maximized, and temperature is stable).

[0109] (2) Whether the current electrical stimulation parameters are within the safe stimulation range.

[0110] (3) Whether the expression level of the delivered mRNA reaches the therapeutic reference level.

[0111] (4) Whether there is a persistent inflammatory stress response, pH imbalance, or metabolic abnormality, suggesting that the intervention needs to be postponed or terminated.

[0112] The state assessment model is:

[0113] ;

[0114] in,

[0115] conductivity change rate;

[0116] Tissue temperature rise;

[0117] Levels of inflammatory factors in tissues or perfusate;

[0118] , , It represents the weighting coefficient obtained from training or clinical experience, which is used to adjust the weight of each parameter in decision-making.

[0119] Based on the above state recognition results, the system will trigger the control mechanism and perform adaptive optimization adjustments on the following parameters:

[0120] (1) Electric pulse parameters (voltage, pulse width, frequency, number of pulses): The system adjusts the electroporation waveform according to the tissue response. For example, if the conductivity does not meet expectations, the system can automatically increase the field strength (e.g., from 0.8 kV / cm to 1.2 kV / cm) or extend the pulse width (e.g., from 5 ms to 8 ms); if the tissue temperature rises, the system automatically reduces the stimulation frequency or activates the intermittent stimulation mode.

[0121] (2) Gene Dosing and Flow Rate: The single-delivery mRNA dose (e.g., adjusted to the range of 10-60 ng) and flow rate (0.1-1.5 μL / min) are finely adjusted through the microfluidic injection system to match the current transfection capacity of the tissue and the local expression capacity, avoiding local oversaturation or insufficient dosage.

[0122] (3) Delivery cycle and timing arrangement (Scheduling Control): When multiple rounds of delivery or delivery-pause alternating treatment are required, the system can dynamically plan the next round of delivery time based on gene expression level feedback to form a balance between the minimum delivery unit and the maximum effect.

[0123] (4) Safety threshold and emergency stop mechanism (Fail-safe Control): Once the temperature exceeds the standard, gene expression is too high, or the inflammatory signal rises sharply, the system will automatically terminate electrical stimulation and delivery, and activate the cooling module and alarm mechanism to ensure biosafety.

[0124] The system also sets a physiological state recognition model for converting the physiological data collected by the system into different tissue state labels. The physiological state recognition model is:

[0125] ;

[0126] ;

[0127] Where x represents the normalized multidimensional feature vector, which contains n input indicators;

[0128] A weight vector representing the training of the physiological state recognition model, used to represent the discriminant contribution of each feature;

[0129] represents the bias term;

[0130] y represents the organizational state label of the recognition output.

[0131] The edge computing model is used to pre-process the collected data. The edge computing model is:

[0132] ;

[0133] in, represents the original value of the 𝑖th physiological parameter collected in real time;

[0134] : represents the parameter The historical mean of

[0135] Representative parameters The historical standard deviation.

[0136] The following is a detailed demonstration of the DMR-EP integrated device and its operation based on parameter feedback.

[0137] Example 2

[0138] This embodiment provides a specific application example.

[0139] In a T2DM animal model experiment, the integrated DMR-EP device was applied to the duodenum of mice to verify its regulatory effect on glucose metabolism remodeling. At the beginning of the experiment, the positioning system in the endoscope guidance module, using image recognition and path planning algorithms, precisely guided the device's treatment head to the target area in the duodenum, ensuring accurate spatial positioning and adequate tissue contact during subsequent DMR and EP procedures.

[0140] After positioning is complete, the radiofrequency ablation module (DMR) is activated according to the set power curve and maintains the operating temperature at 65-75°C. Subsequently, energy output is dynamically adjusted based on feedback from the temperature sensor to precisely remove the diseased epithelial mucosal layer. The intelligent control system simultaneously collects and records the temperature changes, electrical impedance response, and perfusion level of the target tissue, which serve as initial condition parameters for subsequent status judgment. After the DMR procedure, the system enters the postoperative monitoring phase. The intelligent control system continuously collects changes in tissue conductivity, local blood perfusion indicators, and concentration trends of inflammatory factors (such as TNF-α), and performs preliminary feature extraction and trend modeling on this data using an edge computing model. The device's built-in physiological state recognition model analyzes this multimodal data in real time and determines whether the tissue has entered the transfection window based on a conductivity-temperature joint prediction algorithm. Ultimately, the optimal time point for transfection is automatically determined 48 hours after surgery.

[0141] After the transfection window is determined, the electrotransfection module automatically invokes the recommended parameter set within the system and adjusts the circular electrode array to apply electrical pulses (0.8 kV / cm, 7 ms) to enhance the membrane permeability of newly formed duodenal epithelial cells. During this process, the intelligent control system collects real-time tissue electrical responses, temperature rise rates, and tissue stress feedback indicators after the electrical pulses, and simultaneously inputs these into the state assessment model for iterative judgment. During the transfection process, the microfluidic gene delivery module is activated, and the intelligent control system dynamically sets the GLP-1 mRNA dose and infusion rate based on the individual tissue response state and target therapeutic level, achieving precise dosing and regional uniformity. Furthermore, the intelligent control system uses fluorescence signal feedback to quantify GLP-1 expression intensity in real time, which serves as feedback input for adjusting subsequent delivery rates and electrical pulse parameters, establishing a closed-loop therapeutic mechanism of "expression feedback-driven and parameter dynamic reconfiguration."

[0142] like Figure 5 As shown, on day 7 after surgery, fasting blood glucose levels in the experimental group of mice decreased by approximately 30% compared to baseline, significantly lower than those in the control group, and the insulin sensitivity index increased by approximately 60%. Histological analysis further revealed significant upregulation of GLP-1 mRNA expression in epithelial cells in the EP-treated area, indicating high gene delivery efficiency and a good tissue expression response. Through multiple rounds of data-driven feedback adjustments, the device achieves real-time perception and precise control of the treatment process, significantly improving the bioavailability of gene delivery and the stability of metabolic intervention.

[0143] The experimental results verified the functional advantages of this system in the treatment of T2DM, especially its capabilities in intelligent data processing, individual physiological state identification and adaptive parameter optimization, laying an experimental foundation and model support for the subsequent development of precise treatment plans for human individual differences.

[0144] Example 3

[0145] This embodiment provides a specific application example.

[0146] In an obese mouse model, the integrated DMR-EP device was used to modulate the metabolic state of the gut-fat axis to validate its potential for lipid metabolism intervention. During the initial phase of the experiment, the positioning system within the endoscope guidance module utilized image recognition and path optimization algorithms to precisely deliver the device to the target area in the duodenum, achieving high-precision spatial positioning of the treatment area and providing real-time visual support for subsequent procedures.

[0147] After precise positioning, the device activates the radiofrequency ablation module (DMR), maintaining the ablation temperature between 65-75°C according to a preset power curve. Simultaneously, a thermal sensor collects real-time tissue temperature change data to ensure effective removal of the diseased mucosal layer while avoiding thermal damage. After ablation is complete, the system enters a brief postoperative repair monitoring phase. The intelligent feedback system continuously collects tissue conductivity, local microcirculatory perfusion indicators, and metabolic-related tissue impedance change trends. Using a built-in state recognition algorithm, it dynamically models the repair progress of the new mucosa and predicts and confirms the optimal timing of electrotransfection intervention 24 hours after surgery.

[0148] The electrotransfection module then automatically adjusts the electrode array configuration based on the current tissue state and applies electrical pulses (0.8 kV / cm, 7 ms) within the optimal window to induce a temporary increase in epithelial cell membrane permeability. The system collects multimodal data during the transfection process, including tissue electrical response, conductivity slope, temperature fluctuations, and tissue tension feedback, in real time. It then uses a multi-feature fusion algorithm to determine the state of the transfection. Once perforation is detected to be satisfactory and the tissue response is stable, the system initiates the microfluidic gene delivery module. This module utilizes a nanoliter syringe pump to precisely control the single dose and delivery flow rate of FGF21 mRNA (e.g., within the range of 20-60 ng, 0.2-1.0 μL / min) to match epithelial absorptive capacity and gene expression rhythms.

[0149] Throughout the delivery process, the intelligent control system uses real-time mRNA expression level feedback (obtained through fluorescence signal integration or electrochemical sensing modules) to dynamically evaluate delivery effectiveness. If expression levels are low, the system triggers a parameter optimization process, automatically adjusting subsequent electrical pulse intensity or extending delivery time. It also updates the strategy cache, providing training samples for subsequent model iterations, thus establishing a dynamic closed loop of delivery-expression-feedback-reconstruction.

[0150] like Figure 6 As shown, two weeks after surgery, the experimental group mice lost approximately 15% of their baseline body weight, significantly better than the control group. Regarding blood lipid profiles, total cholesterol (TC) decreased by 25%, triglycerides (TG) decreased by 20%, and high-density lipoprotein (HDL-C) levels increased significantly. Histological examinations revealed significantly increased expression of FGF21 mRNA in intestinal epithelial cells in the transfected areas, suggesting that its function in regulating lipid breakdown and transport was effectively activated.

[0151] The experimental results show that the method provided by the present invention for operating the integrated duodenal mucosal surface replacement and electrofection equipment based on tissue parameter feedback can accurately deliver and efficiently express FGF21 mRNA after DMR surgery through data-driven individual state recognition and treatment strategy adaptive control mechanism, significantly improving the lipid metabolism ability of mice, and verifying that the DMR-EP integrated operation technology path based on specific parameter feedback has potential applicability and high controllability in the treatment of obesity and related metabolic diseases.

[0152] Example 4

[0153] This embodiment provides a specific application example.

[0154] In a mouse model of inflammatory bowel disease (IBD), the DMR-EP integrated device was used in diseased intestinal segments to validate its therapeutic potential for alleviating inflammation and repairing the intestinal barrier. At the onset of treatment, the device utilizes the positioning system within the endoscopic guidance module to identify and precisely locate the damaged intestinal segment. Image enhancement and path-tracking algorithms guide the treatment tip to the target area, ensuring efficient ablation and transfection procedures within the affected area.

[0155] After the device activates the radiofrequency ablation module, the intelligent control system maintains the DMR output temperature at 65-75°C. Simultaneously, a temperature sensor is activated to monitor local tissue thermal response in real time, ensuring precise removal of the epithelial layer of the lesion while avoiding secondary inflammation or deep mucosal damage caused by overheating. After the DMR procedure is completed, the device enters a postoperative physiological monitoring state. The system continuously records the conductivity trends of the newly formed tissue, the local microcirculatory perfusion characteristics, and the expression dynamics of key inflammatory factors (such as TNF-α and IL-1β). These multi-source physiological parameters undergo multi-scale filtering and feature extraction before being input into the system's embedded state recognition model. The model then assesses whether the current tissue state is in the "transfection sensitive period" or the "inflammatory suppression window."

[0156] At 48 hours post-operatively, the intelligent control system determined that the tissue was ready for transfection based on the slope of TNF-α decline, conductivity recovery, and perfusion parameters. It then automatically triggered the EP module. The device modulated the circular electrode array structure and applied electrical pulses (0.8 kV / cm, 7 ms), inducing temporary electroporation of intestinal epithelial cells to enhance permeability. During this process, the system monitored the tissue's electrical response and temperature rise feedback in real time, dynamically assessing the impact of stimulation intensity on tissue tolerance. Based on these predictions, the system dynamically adjusted stimulation parameters to ensure efficient transfection while avoiding overstimulation.

[0157] Microfluidic gene delivery is initiated simultaneously with electrotransfection. IL-10 mRNA is slowly delivered via a nanoliter, high-precision injection device, with an initial dose range of 15-45 ng. A feedback mechanism automatically adjusts the flow rate to achieve spatial uniformity and temporal continuity in delivery. The system also integrates fluorescence signals or microsensor technology to monitor IL-10 expression levels, which serve as input for subsequent delivery strategy adjustments. If expression is insufficient, the system automatically relaxes the pulse frequency and extends the delivery cycle. When expression reaches the upper threshold, it enters a protection mode, reducing the stimulation frequency and prematurely terminating delivery.

[0158] like Figure 7 As shown, on day 14 after surgery, TNF-α levels in the serum and tissues of mice in the experimental group decreased by approximately 60% compared to those in the control group. Histopathology revealed a significant decrease in intestinal mucosal inflammatory cell infiltration, regularized villus structure, and significantly enhanced barrier integrity. This experiment clearly demonstrates that through data-driven inflammation identification and strategy-driven dynamic regulation, this system successfully achieved precise delivery and stable expression of IL-10 mRNA in the local inflammatory environment, effectively alleviating the inflammatory response and significantly promoting tissue repair and functional recovery.

[0159] In summary, the DMR-EP integrated device and method of the present invention fully verified the full-process control system of "multi-parameter monitoring - state discrimination - individualized delivery - expression feedback - strategy optimization" in the IBD model. It not only achieved the functional reconstruction of the mucosa after DMR surgery, but also improved the specificity, safety and controllability of gene therapy with data decision-making as the core, providing an innovative intervention technology path for IBD and other local inflammatory diseases.

[0160] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0161] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. An integrated device for duodenal mucosal surface replacement and electrotransfection, characterized in that: The duodenal mucosal surface replacement and electrotransfection integrated device includes an endoscope guidance module, a radiofrequency ablation module, an electrotransfection module and an intelligent terminal control module; The radiofrequency ablation module is provided with a radiofrequency ablation device and a temperature sensor; the radiofrequency ablation device is configured to perform radiofrequency ablation on the target tissue within a certain temperature range; the temperature sensor is configured to collect temperature changes of the tissue in real time and transmit the data to the intelligent terminal control module; the intelligent terminal control module controls the radiofrequency energy intensity of the radiofrequency ablation device based on the collected temperature data; The electrotransfection module is provided with an electrode array, a microfluidic gene delivery device, and a data acquisition and monitoring system; the electrode array is used to apply electric pulses to the target tissue to induce cell membrane perforation; the microfluidic gene delivery device is used to deliver the target gene to the target tissue; the data acquisition and monitoring system is configured to receive signals from the intelligent terminal control module to adjust the electrode array to apply electric pulses to the target tissue, and transmit feedback indicators after the electric pulses to the intelligent terminal control module; The intelligent terminal control module then dynamically adjusts the real-time gene delivery or transfection efficiency of the microfluidic gene delivery device based on the received feedback indicators, wherein the feedback indicators include tissue conductivity, tissue perfusion and microcirculation parameters, gene expression feedback and / or inflammatory status; The intelligent terminal control module includes an intelligent control system, which is provided with a multimodal physiological parameter acquisition unit, a data processing and state recognition unit, a feedback learning and strategy update unit, a state perception control unit and an execution control unit; The multimodal physiological parameter acquisition unit is configured to collect data transmitted to the intelligent terminal control module during the operation of the duodenal mucosal surface replacement and electrofection integrated device, and transmit the data to the state perception control unit and the feedback learning and strategy updating unit; The state perception control unit is configured to perform iterative judgment on the collected data in rounds, and transmit the judgment result to the execution control unit; The execution control unit is configured to issue an operation instruction to the duodenal mucosal surface replacement and electrotransfection integrated device; The execution control unit is further configured to be connected to the data processing and state identification unit and the feedback learning and strategy updating unit, for analyzing data and tissue state in real time, and dynamically adjusting the instructions of the execution control unit based on the data operations of the feedback learning and strategy updating unit; The intelligent control system is configured to include an edge computing model, a physiological state recognition model and a state assessment model.

2. The integrated device for duodenal mucosal surface replacement and electrotransfection according to claim 1, characterized in that: The endoscope guidance module is provided with an endoscope device, a high-definition visual camera and a positioning system; the positioning system is configured to guide the endoscope device to the target tissue and fully contact the target tissue based on image recognition and path planning algorithms.

3. The integrated device for duodenal mucosal surface replacement and electrotransfection according to claim 1, characterized in that: The edge computing model is a data preprocessing function, and the edge computing model is: ； in, Represents the original value of the i-th physiological parameter collected in real time; : represents the parameter The historical mean of Representative parameters The historical standard deviation.

4. The integrated duodenal mucosal surface replacement and electrotransfection device according to claim 1, characterized in that: The physiological state recognition model is used to convert the physiological data collected by the system into different tissue state labels. The physiological state recognition model is: ； ； Where x represents the normalized multidimensional feature vector, which contains n input indicators; A weight vector representing the training of the physiological state recognition model, used to represent the discriminant contribution of each feature; represents the bias term; y represents the organizational state label of the recognition output.

5. The integrated device for duodenal mucosal surface replacement and electrotransfection according to claim 1, characterized in that: The state assessment model is used to perform quantitative scoring or safety assessment on the identified state. The state assessment model is: ； in, conductivity change rate; Tissue temperature rise; Levels of inflammatory factors in tissues or perfusate; , , It represents the weighting coefficient obtained from training or clinical experience, which is used to adjust the weight of each parameter in decision-making.

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