CN120436779A - Duodenal mucosal surface replacement and electrotransfection integrated device and parameter feedback-based operation method - Google Patents
Duodenal mucosal surface replacement and electrotransfection integrated device and parameter feedback-based operation methodInfo
- Publication number
- CN120436779A CN120436779A CN202510899100.0A CN202510899100A CN120436779A CN 120436779 A CN120436779 A CN 120436779A CN 202510899100 A CN202510899100 A CN 202510899100A CN 120436779 A CN120436779 A CN 120436779A
- Authority
- CN
- China
- Prior art keywords
- tissue
- data
- state
- feedback
- electrotransfection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Apparatus Associated With Microorganisms And Enzymes (AREA)
Abstract
The invention belongs to the technical field of biomedical information analysis, and particularly relates to duodenal mucosa surface replacement and electrotransfection integrated equipment and a parameter feedback operation-based method. The invention firstly provides duodenal mucosa surface replacement and electrotransfection integrated equipment which comprises an endoscope guiding module, a radio frequency ablation module, an electrotransfection module and an intelligent terminal control module. The invention also provides a method for operating the DMR-EP integrated equipment based on the tissue parameter feedback. The invention provides the implementation of the personalized strategy generation and the data-driven treatment path selection in the DMR postoperative local gene therapy for the first time, and the safety, the effectiveness and the indication breadth of the treatment are obviously improved.
Description
Technical Field
The invention belongs to the technical field of biomedical information analysis, and particularly relates to duodenal mucosa surface replacement and electrotransfection integrated equipment and a parameter feedback operation-based method.
Background
In recent years, the incidence of metabolic diseases has increased year by year, and it has been found that diseases such as type 2 diabetes (T2 DM), obesity, and non-alcoholic fatty liver disease (NAFLD) are closely related to the physiological functions of the duodenal mucosa. Research shows that the duodenum is not only an important part of glucose absorption, but also plays a key role in the aspects of insulin sensitivity regulation, lipid metabolism regulation and the like. Abnormal duodenal mucosa function may lead to pathological conditions such as exacerbation of insulin resistance and glucose intolerance. Thus, intervention against the duodenal mucosa is an important strategy to improve metabolic diseases.
Duodenal mucosa surface replacement (DMR) is a novel minimally invasive treatment method, and lesion mucosa layers are removed through means such as radio frequency ablation and the like to regenerate the lesion mucosa layers, so that insulin sensitivity is improved, and secretion of metabolic hormones such as GLP-1 (glucagon-like peptide-1) is improved. However, there are individual differences in the repair process of mucosa after DMR surgery, and some patients may affect the efficacy due to slow regeneration of mucosa or incomplete functional recovery. In addition, the existing DMR technology mainly depends on passive repair of mucous membranes, and lacks the capability of actively regulating and controlling the expression of metabolic related genes. Therefore, how to optimize the mucosa repair and enhance the metabolic function after DMR surgery and improve the personalized treatment effect is still a technical problem to be solved.
Electrotransfection (Electroporation, EP) technology is a highly efficient non-viral gene delivery method, which promotes the entry of exogenous genes (e.g., mRNA, siRNA) into cells and achieves high expression by forming nanoscale transient pores in the cell membrane by short electrical pulses. EP has a higher safety than viral vectors, avoids the immune risk of virus-mediated gene delivery, and can be used for difficult transfected cell types (e.g. epithelial cells). Currently, EP technology has been widely applied to the fields of mRNA vaccine, cellular immunotherapy (such as CAR-T), gene editing (CRISPR-Cas 9) and the like, but has still less application in the aspect of local gene therapy of the digestive tract, and particularly has no mature scheme in the aspect of the gene regulation of a new mucosa after DMR operation.
However, the timing of the DMR post-operation electrotransfection is not clear, the electrotransfection parameter regulation has no physiological basis and the gene expression efficiency is lack of control, so that the three major core problems to be solved are urgent. In summary, there is a need to propose new strategies to alleviate at least one of the deficiencies in the prior art.
Disclosure of Invention
In view of the above, the present invention aims to provide a duodenal mucosa surface replacement and electrotransfection integrated device, system and method for operating DMR-EP, which partially solve or alleviate the above-mentioned drawbacks in the prior art, and the present invention specifically adopts the following technical scheme.
The invention provides a DMR-EP integrated system and equipment.
The integrated system for the duodenal mucosa surface replacement and the electric transfection comprises an endoscope guiding module, a radio frequency ablation module, an electric transfection module and an intelligent terminal control module, wherein an intelligent control system is arranged in the intelligent terminal control module and used for controlling the operation and the work of the endoscope guiding module, the radio frequency ablation module and the electric transfection module.
The intelligent control system comprises a multi-mode physiological parameter acquisition unit, a data processing and state identification unit, a feedback learning and strategy updating unit, a state sensing control unit and an execution control unit;
The multi-mode physiological parameter acquisition unit is used for acquiring data transmitted to the intelligent terminal control module during the operation of the duodenal mucosa surface replacement and electrotransfection integrated equipment and transmitting the data to the state sensing control unit and the feedback learning and strategy updating unit;
The state sensing control unit is used for carrying out iterative judgment on the collected data round by round and transmitting a judgment result to the execution control unit;
The execution control unit is arranged to send an operation instruction to the duodenal mucosa surface replacement and electrotransfection integrated equipment;
The execution control unit is also connected with the data processing and state identification unit and the feedback learning and strategy updating unit and used for analyzing data and organization states in real time and dynamically adjusting instructions of the execution control unit based on data operation of the feedback learning and strategy updating unit;
the intelligent control system is provided with an edge calculation model, a physiological state identification model and a state evaluation model.
The integrated device for the duodenal mucosa surface replacement and the electric transfection comprises an endoscope guiding module, a radio frequency ablation module, an electric transfection module and an intelligent terminal control module;
The radio frequency ablation module is internally provided with radio frequency ablation equipment and a temperature sensor; the system comprises a radio frequency ablation device, a temperature sensor, an intelligent terminal control module, a control module and a control module, wherein the radio frequency ablation device is used for applying radio frequency ablation to target tissues in a certain temperature interval (according to a preset power curve), and the temperature sensor is used for acquiring temperature changes of the tissues in real time and transmitting data to the intelligent terminal control module;
The electrotransfection module comprises an electrode array, a microfluidic gene delivery device and a data acquisition and monitoring system, wherein the electrode array is used for applying electric pulses to target tissues to trigger cell membrane perforation, the microfluidic gene delivery device is used for delivering target genes to the target tissues, the data acquisition and monitoring system is used for receiving signals sent by the intelligent terminal control module to adjust the electrode array to apply the electric pulses to the target tissues and simultaneously transmit feedback indexes after the electric pulses to the intelligent terminal control module, and then the intelligent terminal control module dynamically adjusts the real-time gene delivery or transfection efficiency of the microfluidic gene delivery device based on the received feedback indexes, wherein the feedback indexes comprise tissue conductivity, tissue perfusion and microcirculation parameters, gene expression feedback and/or inflammatory factors (levels or concentrations).
Further, the real-time gene delivery includes a real-time gene delivery dose or a real-time gene delivery rate.
Further, the endoscope guiding module comprises an endoscope device, a high-definition visual camera and a positioning system, wherein the positioning system is used for guiding the endoscope device to a target tissue and fully contacting with the target tissue based on an image recognition and path planning algorithm.
Further, the intelligent terminal control module comprises an intelligent control system, wherein the intelligent control system comprises a multi-mode physiological parameter acquisition unit, a data processing and state identification unit, a feedback learning and strategy updating unit, a state sensing control unit and an execution control unit;
The multi-mode physiological parameter acquisition unit is arranged to acquire data transmitted to the intelligent terminal control module during the operation of the duodenal mucosa surface replacement and electrotransfection integrated equipment and transmit the data to the state perception control unit and the feedback learning and strategy updating unit, wherein in some specific embodiments, an edge calculation model and a physiological state identification model can be arranged in the multi-mode physiological parameter acquisition unit;
The state sensing control unit is used for carrying out iterative judgment on the collected data round by round and transmitting the judgment result to the execution control unit, and in some specific embodiments, a state evaluation model is arranged in the state sensing control unit;
The execution control unit is arranged to send an operation instruction to the duodenal mucosa surface replacement and electrotransfection integrated equipment;
The execution control unit is also connected with the data processing and state identification unit and the feedback learning and strategy updating unit and used for analyzing data and organization states in real time and dynamically adjusting instructions of the execution control unit based on data operation of the feedback learning and strategy updating unit;
the intelligent control system is provided with an edge calculation model, a physiological state identification model and a state evaluation model.
Further, the edge calculation model is a data preprocessing function, and the edge calculation model is:
;
Wherein, the Representing the original value of the ith physiological parameter acquired in real time;
representative parameters Is a historical average of (2);
Representative parameters Is a historical standard deviation of (c).
Further, the physiological state identification model is used for converting physiological data acquired by the system into different tissue state labels, and the physiological state identification model is as follows:
;
;
wherein x represents the normalized multidimensional feature vector and comprises n input indexes;
representing weight vectors obtained by training the physiological state recognition model, wherein the weight vectors are used for representing discrimination contribution of each feature;
representing a bias term;
y represents the tissue state label of the identification output.
In some embodiments, y= +1 means "enter transfection window", and y= -1 means "transfection condition has not been met".
Further, the state evaluation model is used for quantitatively scoring or evaluating safety of the identified state, and the state evaluation model is as follows:
;
Wherein, the
The rate of conductivity change (the difference between the current value and the baseline value, reflecting the change in cell permeability);
Tissue temperature rise (current temperature minus initial temperature reflecting thermal load);
inflammatory factor levels (e.g., TNF- α levels) in tissue or in perfusion fluid;
,, Representing weighting coefficients obtained from training or clinical experience, for adjusting the weights of the parameters in the decision.
The invention also provides a method for operating the DMR and the EP to work simultaneously/simultaneously based on parameter feedback.
A method of operating a duodenal mucosal surface replacement and electrotransfection integrated device based on tissue parameter feedback, the method being performed based on the DMR-EP integrated device described above, comprising the steps of:
S01, fully contacting duodenal mucosa surface replacement and electrotransfection integrated equipment with target tissues;
S02, performing radio frequency ablation on the target tissue;
s021, applying radio frequency ablation to target tissues by radio frequency ablation equipment and maintaining the radio frequency ablation temperature in a fixed interval according to a preset power curve;
S022, transmitting tissue temperature data acquired in real time to an intelligent control system by a temperature sensor, wherein the intelligent control system analyzes and processes the tissue temperature data and then regulates and controls the radio frequency energy intensity of the radio frequency ablation equipment;
S023, setting an edge calculation model, a physiological state identification model and a state evaluation model in the intelligent control system, wherein the physiological state identification model is used for converting the tissue temperature data into different tissue state labels, the tissue state labels comprise repair completion, transfection windows or active inflammation, the physiological state identification model normalizes the data and inputs the normalized data into a linear classifier (such as SVM) to judge whether the target tissue has/reaches an electric transfection condition;
S03, performing electric transfection on the target tissue which is subjected to radio frequency ablation and has/reaches an electric transfection condition, and delivering a target gene;
s031, the data acquisition and monitoring system receives the electric pulse parameter signal sent by the intelligent control system, and starts the electrode array to apply electric pulse to the target tissue and trigger the cell membrane to be perforated transiently;
s032, carrying out target gene delivery on the target tissue by using a microfluidic gene delivery device;
S033, the data acquisition and monitoring system transmits feedback indexes after electric pulse and gene delivery to the intelligent control system, wherein the feedback indexes comprise tissue conductivity, tissue perfusion and microcirculation parameters, gene expression feedback and/or inflammatory states;
the intelligent control system receives the feedback index and dynamically adjusts the real-time gene delivery and transfection efficiency of the microfluidic gene delivery device;
The edge computing model is used for preprocessing collected data, the physiological state identification model is used for converting the collected physiological data into different tissue state labels, the state evaluation model is used for quantitatively scoring or evaluating safety of the feedback indexes, weighting the change trend of a plurality of feedback indexes and outputting a quantitative scoring value for adjusting real-time gene delivery or transfection efficiency.
Further, the physiological state recognition model is:
;
;
wherein x represents the normalized multidimensional feature vector and comprises n input indexes;
representing weight vectors obtained by training the physiological state recognition model, wherein the weight vectors are used for representing discrimination contribution of each feature;
representing a bias term;
y represents the tissue state label of the identification output.
Further, the state evaluation model is:
;
Wherein, the
A rate of conductivity change;
Tissue temperature rise;
inflammatory factor levels in tissue or in perfusion fluid;
,, Representing weighting coefficients obtained from training or clinical experience, for adjusting the weights of the parameters in the decision.
Further, the physiological state recognition model is:
;
;
wherein x represents the normalized multidimensional feature vector and comprises n input indexes;
representing weight vectors obtained by training the physiological state recognition model, wherein the weight vectors are used for representing discrimination contribution of each feature;
representing a bias term;
y represents the tissue state label of the identification output.
The beneficial technical effects are as follows:
the invention firstly provides a device integrating duodenal mucosa surface replacement (DMR) and Electrotransfection (EP), wherein an intelligent control system based on physiological state real-time monitoring is configured in the device, so that the problems of undefined electrotransfection opportunity, parameter adjustment inadaptability and lack of control of gene expression efficiency after DMR operation can be relieved.
Specifically, the intelligent control system can judge whether the tissue is in a window period suitable for electric transfection and the expression condition of a target gene in the transfection process in real time by introducing tissue conductivity, tissue temperature, tissue perfusion and microcirculation parameters, gene expression feedback and/or inflammatory states, so as to feed back and dynamically regulate and control the operation of the equipment. Further, by constructing a closed loop model of state recognition, parameter optimization, execution control, expression feedback, EP parameters and delivered dose can be dynamically adjusted with individual differences. Finally, the invention provides the implementation of the personalized strategy generation and the data-driven treatment path selection in the DMR postoperative local gene treatment for the first time, and the safety, the effectiveness and the indication breadth of the treatment are obviously improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale. It will be apparent to those of ordinary skill in the art that the drawings in the following description are of some embodiments of the invention and that other drawings may be derived from these drawings without inventive faculty.
Fig. 1 is a schematic diagram of an operation module of a DMR-EP integrated device according to one embodiment of the present invention;
fig. 2 is a schematic diagram of main hardware modules in the DMR-EP integrated device according to one embodiment of the present invention;
fig. 3 is a schematic workflow diagram of a DMR-EP integrated device according to one embodiment of the present invention;
FIG. 4 is a functional logic block diagram of an intelligent control system in a DMR-EP integrated device according to one embodiment of the present invention;
FIG. 5 is a graph showing the effect of DMR-EP based integrated devices on blood glucose levels and insulin sensitivity in T2DM mice in one embodiment of the invention;
FIG. 6 is a graph showing the effect of DMR-EP based integrated devices on fat metabolism in obese mice according to one embodiment of the present invention;
Figure 7 is an illustration of the effect of DMR-EP based integrated devices on inflammation levels and intestinal integrity in an IBD model in one embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Herein, "and/or" includes any and all combinations of one or more of the associated listed items.
Herein, "plurality" means two or more, i.e., it includes two, three, four, five, etc.
As used in this specification, the term "about" is typically expressed as +/-5% of the value, more typically +/-4% of the value, more typically +/-3% of the value, more typically +/-2% of the value, even more typically +/-1% of the value, and even more typically +/-0.5% of the value.
In this specification, certain embodiments may be disclosed in a format that is within a certain range. It should be appreciated that such a description of "within a certain range" is merely for convenience and brevity and should not be construed as a inflexible limitation on the disclosed ranges. Accordingly, the description of a range should be considered to have specifically disclosed all possible sub-ranges and individual numerical values within that range. For example, the description of ranges 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, e.g., 1,2,3,4,5, and 6. The above rule applies regardless of the breadth of the range.
Example 1
The embodiment provides intelligent integrated equipment (hereinafter referred to as integrated equipment) integrating duodenal mucosa surface replacement (Duodenal Mucosal Resurfacing, DMR) and electrotransfection (Electroporation, EP) functions, and aims to improve quality and function recovery capability of mucosa remodeling after DMR operation, and further achieve accurate regulation and control of metabolic functions. The integrated equipment can carry out radio frequency ablation on target tissues, and can realize the effects of promoting the insulin sensitivity of the target tissues to be improved, improving the lipid metabolism, inhibiting inflammatory reaction and the like by targeting delivery of functional genes such as metabolic regulator mRNA (messenger ribonucleic acid) of GLP-1, FGF21 or PPAR-alpha and the like based on the window period that the newly-generated mucous membrane is in a transfectable and reparative active state within 24 to 48 hours after DMR operation.
Fig. 1 to 3 show a schematic block diagram and a schematic workflow diagram of a DMR-EP integrated device, respectively. The DMR-EP integrated equipment system adopts a modularized architecture design and mainly comprises a plurality of core functional units, namely (1) an endoscope guiding module with high-definition visual navigation capability and used for guiding equipment to accurately reach a duodenal target area and monitoring the tissue state of the target area in real time, (2) a radio frequency ablation module used for removing a lesion or a mucous membrane area with abnormal functions with high precision and promoting regeneration of new tissues, (3) an adjustable electric transfection module which is provided with an annular or multipolar electrode array and can apply electric pulses to trigger temporary perforation of cell membranes under set conditions, and meanwhile, a microfluidic gene delivery equipment based on a nano-upgrading control mechanism and a data acquisition and monitoring system are also arranged and used for accurately conveying nucleic acid medicines such as target mRNA or siRNA to target cells in a transfection window period, and (4) an intelligent terminal control module used for controlling the operation/work of the modules is arranged in the intelligent terminal control module, so that dynamic regulation and real-time feedback capability is provided for the whole flow of the equipment, a multi-parameter closed-loop control structure is formed, and the stability, safety and individuation response capability of the whole treatment process are ensured.
Fig. 4 shows the working logic of a DMR-EP device intelligent control (feedback) system. In order to realize the accurate and adaptive regulation of gene delivery after DMR operation, the system consists of a multi-mode physiological parameter acquisition unit, a data processing and state identification unit, a feedback learning and strategy updating unit, a state sensing control unit and an execution control unit, and a closed-loop control framework taking data driving as a core is formed. The multimode physiological parameter acquisition unit of the system is embedded in the endoscope terminal, the annular electrode structure and the microfluidic device interface, and can monitor the physiological parameters of the target tissue in real time and in multiple modes in a whole process on the premise of not additionally increasing wounds. Specific acquisition metrics include, but are not limited to, the following:
(1) Tissue conductivity (Tissue Conductivity) the parameter reflects the electrical response state of local tissue and is an important basis for judging cell membrane permeability and electroporation completion. The system monitors conductivity changes in real time before and after the application of the electrical pulse and establishes a conductivity response curve to infer whether the tissue has reached an optimal state of transfection.
(2) Tissue Temperature (Temperature) is a key indicator reflecting the risk of electrical stimulation side effects. The system adopts a thermistor or an infrared sensing unit to collect the surface and deep temperature data of the tissue, corrects the output of the radio frequency ablation equipment in real time, and ensures that the temperature is maintained in a physiological safety zone (generally 37+/-1.5 ℃).
(3) Local Perfusion and microcirculation parameters (Local Perfusion) by detecting Local blood flow change by spectroscopy or laser Doppler imaging, judging the course of mucosa neogenesis repair and blood supply recovery ability of the tissue after electric stimulation, and providing support for the rhythm and dosage evaluation of subsequent gene delivery.
(4) Gene expression feedback (Gene Expression Feedback) in some embodiments, target mRNA is quantitatively read by a data acquisition and monitoring system embedded in the device by modifying probe markers (e.g., fluorescent markers or electrochemical markers) to effect in situ monitoring of the delivered gene expression level. These data may be used as key indicators for the system to determine whether to continue or suspend delivery.
(5) Inflammatory State index (Inflammatory Signals) the system may also incorporate electrochemical sensors to monitor local tissue pH, NO, TNF-alpha or IL-6, etc. markers of inflammation to assess the susceptibility or degree of stress of the tissue microenvironment to therapeutic intervention.
The data are collected after each working module runs, and then are transmitted into an intelligent terminal control module of the equipment in real time, which is also called a main control processing module. And constructing a physiological state feature vector based on the collected feedback parameters, and forming a multi-dimensional feature model of the tissue functional state by fusing multi-source perception data, wherein the multi-dimensional feature model comprises an edge calculation model, a physiological state identification model, a state evaluation model and the like. The method can be used for rapidly identifying the arrival time of the transfection window and can also be used as an input variable of a subsequent regulation algorithm to drive the parameter decision and regulation behavior of the rear end. The data acquisition frequency, the filtering algorithm and the data cleaning mechanism can be flexibly configured according to application scenes so as to consider the response speed and the discrimination accuracy.
After completing the multi-parameter acquisition and modeling of the tissue state, the intelligent control (feedback) system of the embodiment will enter a feedback logic decision and parameter decision link. The link forms a feedback closed loop of 'perception-discrimination-regulation' based on a pre-constructed model and a machine learning enhancement discrimination mechanism.
Specifically, the intelligent control (feedback) system inputs parameters such as the current acquired tissue conductivity, tissue temperature, tissue perfusion and microcirculation parameters, gene expression feedback and inflammation state indexes into the embedded state evaluation model. The model is constructed by combining Rule reasoning (Rule-Based Inference) with a data driving algorithm (such as a support vector machine, a random forest, a shallow neural network and the like), and can judge the following key physiological states under millisecond response:
(1) Whether the optimal electrotransfection window period (i.e., conductivity rises to a threshold, tissue permeability is maximized, temperature stabilizes) has been entered.
(2) Whether the current electrical stimulation parameters are in a safe stimulation interval.
(3) Whether the expression level of the delivered mRNA reaches a therapeutic reference level.
(4) Whether there is a sustained inflammatory stress, pH imbalance or metabolic abnormality suggests that intervention should be delayed or discontinued.
The state evaluation model is as follows:
;
Wherein, the
A rate of conductivity change;
Tissue temperature rise;
inflammatory factor levels in tissue or in perfusion fluid;
,, Representing weighting coefficients obtained from training or clinical experience, for adjusting the weights of the parameters in the decision.
Based on the above state recognition result, the system will trigger the regulation mechanism and perform adaptive optimization adjustment on the following parameters:
(1) Parameters of electric pulse (voltage, pulse width, frequency, pulse number) the system adjusts the electric transfection waveform according to the tissue reaction condition. For example, if the electrical conductivity is not as expected, the system may 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), and if the tissue temperature is increased, the system automatically decreases the stimulation frequency or activates the intermittent stimulation mode.
(2) Dose and rate delivered (Gene Dosing and Flow Rate) the single delivered mRNA dose (e.g., adjusted to the range of 10-60 ng) and flow rate (0.1-1.5 μl/min) were fine-tuned by the microfluidic injection system to match the current transfection capacity and local expression capacity of the tissue, avoiding local oversaturation or under dosing.
(3) Delivery cycle and schedule Scheduling Control when multiple rounds of delivery or delivery-pause alternating therapy are required, the system can dynamically program the next round of delivery time based on gene expression level feedback, creating a balance between minimum delivery unit and maximum effect.
(4) Safety threshold and emergency stop mechanism (Fail-safe Control) once the temperature is found to be out of standard, the gene expression is over high or the inflammation signal is rapidly increased, the system automatically stops the electric stimulation and delivery, and activates the cooling module and the alarm mechanism, thereby ensuring the biological safety.
The system is also provided with a physiological state identification model for converting physiological data acquired by the system into different tissue state labels, and the physiological state identification model is as follows:
;
;
wherein x represents the normalized multidimensional feature vector and comprises n input indexes;
representing weight vectors obtained by training the physiological state recognition model, wherein the weight vectors are used for representing discrimination contribution of each feature;
representing a bias term;
y represents the tissue state label of the identification output.
The edge calculation model is used for preprocessing the acquired data, and the edge calculation model is as follows:
;
Wherein, the Representing the original value of the ith physiological parameter acquired in real time;
representative parameters Is a historical average of (2);
Representative parameters Is a historical standard deviation of (c).
The operation mode of the DMR-EP integrated equipment based on parameter feedback is specifically shown below.
Example 2
The present embodiment provides a specific application example.
In a T2DM animal model experiment, DMR-EP integrated equipment is used for the duodenal region of a mouse, and the regulation effect of the DMR-EP integrated equipment on the aspect of sugar metabolism remodeling is verified. When the experiment starts, a positioning system in the endoscope guiding module accurately guides the treatment head of the equipment to reach the duodenal target area based on the image recognition and path planning algorithm, so that the follow-up spatial positioning accuracy of the DMR and the EP operation and sufficient contact with tissues are ensured.
After the positioning is completed, a radio frequency ablation module (DMR) is started according to a set power curve, and the running temperature is maintained at 65-75 ℃. And then, dynamically regulating and controlling energy output according to feedback of a temperature sensor, and precisely removing the lesion epithelial mucosa layer. The intelligent control system synchronously collects and records the temperature change, the electrical impedance response and the perfusion level of the target tissue, and the temperature change, the electrical impedance response and the perfusion level are used as initial condition parameters for judging the subsequent state. After DMR operation, the system enters a postoperative monitoring stage, an intelligent control system continuously collects the change of tissue conductivity, local blood flow perfusion indexes and the concentration change trend of inflammatory factors (such as TNF-alpha), and the data are subjected to preliminary feature extraction and trend modeling through an edge calculation model. The physiological state recognition model built in the device analyzes the multi-mode data in real time, judges whether the tissue enters a transfectable window period based on a conductivity-temperature combined prediction algorithm, and finally automatically establishes an optimal time point of transfection operation at 48 hours after operation.
After the transfection window is determined, the electrotransfection module automatically invokes a recommended parameter set in the system, adjusts the annular electrode array to apply electric pulses (0.8 kV/cm,7 ms), and enhances the membrane permeability of the duodenal neo-epithelial cells. In the process, the intelligent control system acquires the tissue electrical response, the temperature rise rate and the tissue stress feedback index after the electrical pulse in real time, and synchronously inputs the tissue electrical response, the temperature rise rate and the tissue stress feedback index into the state evaluation model to carry out iterative judgment round by round. In the transfection process, the microfluidic gene delivery module is started, and the intelligent control system dynamically sets the dose and the infusion rate of GLP-1 mRNA according to the individual tissue response state and the target therapeutic level, so that the dose accuracy and the region homogenization control are realized. Meanwhile, the intelligent control system quantifies GLP-1 expression intensity in real time by means of fluorescence signal feedback, and the GLP-1 expression intensity is used as feedback input for adjusting subsequent delivery rate and electric pulse parameters, so that a closed-loop treatment mechanism of 'expression feedback driving-parameter dynamic reconstruction' is constructed.
As shown in fig. 5, on postoperative day 7, the fasting blood glucose levels of the mice in the experimental group were reduced by about 30% from the baseline, significantly lower than the control group, and the insulin sensitivity index was increased by about 60%. Histological analysis further showed that GLP-1 mRNA expression was significantly up-regulated in the epithelial cells of the EP treatment region suggesting that the gene delivery efficiency was high and the tissue expression response was good. Through feedback adjustment driven by multiple rounds of data, the device realizes real-time sensing and fine regulation of the treatment process, and greatly improves the bioavailability of gene delivery and the stability of metabolic intervention.
The experimental result verifies the functional advantages of the system in T2DM treatment, especially the capability in the aspects of intelligent data processing, individual physiological state identification and self-adaptive parameter optimization, and lays an experimental foundation and model support for the subsequent development of accurate treatment schemes for individual human differences.
Example 3
The present embodiment provides a specific application example.
In the obese mouse model, DMR-EP integrated devices were used to regulate the metabolic state of the gut-fat axis to verify its potential for application in lipid metabolism intervention. In the initial stage of the experiment, a positioning system in an endoscope guiding module accurately delivers equipment to a duodenal target area by means of an image recognition and path optimization algorithm, high-precision spatial positioning of a treatment area is realized, and real-time visual field support is provided for subsequent operation.
After accurate positioning, the device starts a radio frequency ablation module (DMR), maintains the ablation temperature between 65 ℃ and 75 ℃ according to a preset power curve, and simultaneously combines a thermal sensor to collect tissue temperature change data in real time, so that the occurrence of thermal damage is avoided while the lesion mucous membrane layer is effectively cleared. After ablation is completed, the system enters a short post-operative repair monitoring phase. The intelligent feedback system continuously collects tissue conductivity, local microcirculation perfusion indexes and tissue impedance change trend related to metabolism, dynamically models the repair progress of the newly-generated mucosa through a built-in state recognition algorithm, and predicts and confirms the optimal electrotransfection intervention time point 24 hours after operation.
Subsequently, the electrotransfection module automatically adjusts the electrode array configuration according to the current tissue state, applying an electrical pulse (0.8 kV/cm,7 ms) within the optimal window to induce temporary permeability enhancement of the epithelial cell membrane. The system collects multi-mode data such as tissue electrical response, conductivity change slope, temperature fluctuation, tissue tension feedback and the like in the transfection process in real time, and performs state discrimination through a multi-feature fusion algorithm. Once the perforation effect is detected to reach the standard and the tissue reaction is stable, the system starts the microfluidic gene delivery module. The module uses a nanoliter syringe pump to finely control single dose and delivery flow rate (e.g., in the range of 20-60ng, 0.2-1.0 μl/min) of FGF21 mRNA to match epithelial uptake capacity and gene expression rhythms.
Throughout the delivery process, the intelligent control system utilizes real-time mRNA expression level feedback (which may be obtained by fluorescent signal integration or electrochemical sensing modules) to dynamically evaluate the delivery effect. If the expression level is lower, the system triggers a parameter optimization path, automatically adjusts the strength of subsequent electric pulses or prolongs the delivery time, updates a strategy buffer, provides training samples for subsequent model iteration, and constructs a dynamic closed loop of delivery, expression, feedback and reconstruction.
As shown in fig. 6, two weeks after surgery, the body weight of the mice in the experimental group was reduced by about 15% from the baseline, which is significantly superior to the control group. In terms of blood lipid index, total Cholesterol (TC) is reduced by 25%, triglyceride (TG) is reduced by 20%, and high density lipoprotein (HDL-C) level is remarkably increased. Histological examination showed that FGF21 mRNA was significantly elevated in expression levels in intestinal epithelial cells in the transfected area, suggesting that its function in regulating lipid breakdown and transport was effectively activated.
The experimental result shows that the method for operating the duodenal mucosa surface replacement and electrotransfection integrated equipment based on tissue parameter feedback can accurately deliver and efficiently express the DMR postoperative FGF21 mRNA through a data-driven individual state identification and treatment strategy self-adaptive control mechanism, remarkably improves the lipid metabolism capability of mice, and verifies that the DMR-EP integrated operation technical path based on specific parameter feedback has potential applicability and high controllability in obesity and related metabolic disease treatment.
Example 4
The present embodiment provides a specific application example.
In an Inflammatory Bowel Disease (IBD) mouse model experiment, DMR-EP integrated devices were used on focal intestinal segment areas to verify their therapeutic potential in terms of inflammation relief and intestinal barrier repair. At the beginning of treatment, the equipment identifies and accurately positions the damaged intestinal section by means of a positioning system in an endoscope guiding module, and guides the treatment head to a target area by an image enhancement and path tracking algorithm, so that the subsequent ablation and transfection operation is ensured to be efficiently implemented in a lesion area.
After the equipment starts the radio frequency ablation module, the DMR output temperature is maintained at 65-75 ℃ under the control of the intelligent control system, and the temperature sensor is synchronously started to monitor the local tissue thermal reaction in real time, so that the accurate removal of the focal epithelial layer is ensured, and meanwhile, the secondary inflammation or the excessive deep mucous membrane injury caused by overheating is avoided. After the DMR operation is completed, the equipment enters a postoperative physiological monitoring state, and the system continuously records the change trend of the conductivity of the new tissue, the local microcirculation perfusion characteristics and the expression dynamics of key inflammatory factors (such as TNF-alpha and IL-1 beta). The multisource physiological parameters are input into a state identification model embedded in the system after multiscale filtering and feature extraction, and whether the current tissue state is in a 'transfection sensitive period' or an 'inflammation inhibition window' is estimated by the model.
At 48 hours after the operation, the intelligent control system jointly judges that the tissue has transfection conditions according to the descending slope of TNF-alpha, the conductivity recovery degree and the perfusion parameters, and then automatically triggers the EP module. The device induced temporary electroporation of intestinal epithelial cells to enhance permeability by modulating the ring electrode array structure, applying an electrical pulse (0.8 kV/cm, 7 ms). In the process, the system monitors the tissue electrical response and temperature rise feedback in real time, dynamically evaluates the influence of the stimulation intensity on the tissue tolerance, dynamically adjusts the stimulation parameters according to the prediction result, ensures efficient transfection and simultaneously avoids excessive stimulation.
The microfluidic gene delivery is synchronously started after the electrotransfection is started, IL-10 mRNA is slowly delivered by a nano-upgrading high-precision injection device, the initial dose setting range is 15-45ng, and the flow rate is automatically regulated according to a feedback mechanism so as to realize the spatial uniformity and the time continuity of the delivery. The system also integrates fluorescent signaling or microsensor technology to monitor IL-10 expression levels as a criterion input for subsequent delivery strategy modulation. When the expression reaches the upper threshold, the system enters a protection mode, reduces the stimulation frequency and stops delivery in advance.
As shown in fig. 7, on day 14 after surgery, the serum and tissue TNF- α levels in the mice of the experimental group were reduced by about 60% compared to the control group, and histopathology showed a significant decrease in inflammatory cell infiltration of the intestinal mucosa, with a regular villus structure and a significant increase in barrier integrity. The experiment clearly shows that through a data-driven inflammation recognition and strategy dynamic regulation mechanism, the system successfully realizes the accurate delivery and stable expression of IL-10 mRNA in a local inflammation environment, effectively relieves inflammation reaction, and remarkably promotes tissue repair and functional recovery.
In summary, the DMR-EP integrated device and method of the present invention fully verifies the whole-flow control system of "multiparameter monitoring-state discrimination-personalized delivery-expression feedback-policy optimization" in the IBD model, not only implements functional reconstruction of the mucosa after DMR surgery, but also improves specificity, safety and controllability of gene therapy with data decision as a core, and provides an innovative intervention technical path for IBD and other local inflammatory diseases.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510899100.0A CN120436779B (en) | 2025-07-01 | 2025-07-01 | Duodenal mucosal surface replacement and electrotransfection integrated device and parameter feedback-based operation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510899100.0A CN120436779B (en) | 2025-07-01 | 2025-07-01 | Duodenal mucosal surface replacement and electrotransfection integrated device and parameter feedback-based operation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN120436779A true CN120436779A (en) | 2025-08-08 |
| CN120436779B CN120436779B (en) | 2025-09-30 |
Family
ID=96620560
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510899100.0A Active CN120436779B (en) | 2025-07-01 | 2025-07-01 | Duodenal mucosal surface replacement and electrotransfection integrated device and parameter feedback-based operation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN120436779B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080091135A1 (en) * | 2006-10-17 | 2008-04-17 | Ruxandra Draghia-Akli | Electroporation devices and methods of using same for electroporation of cells in mammals |
| CN102277297A (en) * | 2011-08-16 | 2011-12-14 | 中国科学院深圳先进技术研究院 | Implanted in vivo electrotransfection device |
| US20160184003A1 (en) * | 2013-08-06 | 2016-06-30 | Memorial Sloan Kettering Cancer Center | System, method and computer-accessible medium for in-vivo tissue ablation and/or damage |
| CN106137381A (en) * | 2015-03-31 | 2016-11-23 | 四川锦江电子科技有限公司 | Radiofrequency melting instrument, the control method of radiofrequency melting instrument and device |
| CN110074857A (en) * | 2018-09-14 | 2019-08-02 | 杭州堃博生物科技有限公司 | Lung's radio frequency ablation system and control method, control device and computer-readable medium and radio frequency ablation catheter |
| US20210259765A1 (en) * | 2020-02-20 | 2021-08-26 | The Board Of Trustees Of The Leland Stanford Junior University | System and method for guiding direction to and treating targets for abnormal biological rhythms |
| CN115312194A (en) * | 2022-08-08 | 2022-11-08 | 上海英医达医疗器械用品有限公司 | Physiological data analysis system, method, device and storage medium |
| CN115426968A (en) * | 2020-02-21 | 2022-12-02 | 直观外科手术操作公司 | Systems and methods for delivering targeted therapies |
| CN118395342A (en) * | 2024-05-10 | 2024-07-26 | 广西电网有限责任公司 | Artificial intelligence-based electricity price anomaly user analysis method and system |
| WO2024192223A1 (en) * | 2023-03-15 | 2024-09-19 | Neurovigil, Inc. | Control of computer operations via translation of biological signals and traumatic brain injury prediction based on sleep states |
| CN119138999A (en) * | 2024-09-11 | 2024-12-17 | 天津市鹰泰利安康医疗科技有限责任公司 | Voltage control method and system for electroporation ablation based on pulse sequence |
| CN119955855A (en) * | 2025-02-05 | 2025-05-09 | 天津市鹰泰利安康医疗科技有限责任公司 | A cartilage regeneration and repair method and system using irreversible electroporation technology |
| CN119950008A (en) * | 2025-02-05 | 2025-05-09 | 天津市鹰泰利安康医疗科技有限责任公司 | An intelligent control method and system for irreversible electroporation regeneration of cartilage tissue |
| CN120132230A (en) * | 2025-05-16 | 2025-06-13 | 北京北琪医疗科技股份有限公司 | Surface-mounted electrode system and method for auxiliary positioning of knee joint nerve treatment |
-
2025
- 2025-07-01 CN CN202510899100.0A patent/CN120436779B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080091135A1 (en) * | 2006-10-17 | 2008-04-17 | Ruxandra Draghia-Akli | Electroporation devices and methods of using same for electroporation of cells in mammals |
| CN102277297A (en) * | 2011-08-16 | 2011-12-14 | 中国科学院深圳先进技术研究院 | Implanted in vivo electrotransfection device |
| US20160184003A1 (en) * | 2013-08-06 | 2016-06-30 | Memorial Sloan Kettering Cancer Center | System, method and computer-accessible medium for in-vivo tissue ablation and/or damage |
| CN106137381A (en) * | 2015-03-31 | 2016-11-23 | 四川锦江电子科技有限公司 | Radiofrequency melting instrument, the control method of radiofrequency melting instrument and device |
| CN110074857A (en) * | 2018-09-14 | 2019-08-02 | 杭州堃博生物科技有限公司 | Lung's radio frequency ablation system and control method, control device and computer-readable medium and radio frequency ablation catheter |
| US20210259765A1 (en) * | 2020-02-20 | 2021-08-26 | The Board Of Trustees Of The Leland Stanford Junior University | System and method for guiding direction to and treating targets for abnormal biological rhythms |
| CN115426968A (en) * | 2020-02-21 | 2022-12-02 | 直观外科手术操作公司 | Systems and methods for delivering targeted therapies |
| CN115312194A (en) * | 2022-08-08 | 2022-11-08 | 上海英医达医疗器械用品有限公司 | Physiological data analysis system, method, device and storage medium |
| WO2024192223A1 (en) * | 2023-03-15 | 2024-09-19 | Neurovigil, Inc. | Control of computer operations via translation of biological signals and traumatic brain injury prediction based on sleep states |
| CN118395342A (en) * | 2024-05-10 | 2024-07-26 | 广西电网有限责任公司 | Artificial intelligence-based electricity price anomaly user analysis method and system |
| CN119138999A (en) * | 2024-09-11 | 2024-12-17 | 天津市鹰泰利安康医疗科技有限责任公司 | Voltage control method and system for electroporation ablation based on pulse sequence |
| CN119955855A (en) * | 2025-02-05 | 2025-05-09 | 天津市鹰泰利安康医疗科技有限责任公司 | A cartilage regeneration and repair method and system using irreversible electroporation technology |
| CN119950008A (en) * | 2025-02-05 | 2025-05-09 | 天津市鹰泰利安康医疗科技有限责任公司 | An intelligent control method and system for irreversible electroporation regeneration of cartilage tissue |
| CN120132230A (en) * | 2025-05-16 | 2025-06-13 | 北京北琪医疗科技股份有限公司 | Surface-mounted electrode system and method for auxiliary positioning of knee joint nerve treatment |
Non-Patent Citations (3)
| Title |
|---|
| LIU, F ; SU, RT ; JIANG, XR ; WANG, SQ ; MU, W ; CHANG, LQ: "Advanced micro/nano-electroporation for gene therapy: recent advances and future outlook", NANOSCALE, 24 May 2024 (2024-05-24) * |
| 王晓晨，迟坤，杜军霞，宋晨雯，丁潇楠，冀雨薇，张可颖，张益帆，韩秋霞，傅博，洪权，朱晗玉: "胰岛素样生长因子结合蛋白2对高血糖环境诱导人足细胞凋亡的影响及机制研究", 医药卫生科技, 24 June 2024 (2024-06-24) * |
| 陈晓;万芝清;韩根成;王济东;赵智;周平;: "六君子汤合左金丸治疗小鼠急性放射性十二指肠炎的疗效及机制研究", 中国中药杂志, no. 02, 15 January 2014 (2014-01-15) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN120436779B (en) | 2025-09-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20240299748A1 (en) | Devices, systems, and methods for delivering therapy to a sacral nerve | |
| CN102019040B (en) | Biofeedback photoelectric therapeutic device | |
| Zhang et al. | Hypothermia evoked by stimulation of medial preoptic nucleus protects the brain in a mouse model of ischaemia | |
| JP6594332B2 (en) | System and method for therapeutic management of cough | |
| JP2013510590A (en) | Bioreactor, system and method for forming and / or analyzing organs | |
| WO2014151931A1 (en) | Systems for selectively migrating cells using electric fields | |
| CN119386383A (en) | A multifunctional magnetic stimulation treatment system and its application method | |
| WO2019122819A1 (en) | Nerve stimulation device for unidirectional stimulation and current steering | |
| CN120436779B (en) | Duodenal mucosal surface replacement and electrotransfection integrated device and parameter feedback-based operation method | |
| US20250135209A1 (en) | Detection and treatment of neotissue | |
| CN119908672A (en) | A transcutaneous nerve field vagus nerve stimulation diagnostic and therapeutic device and a diagnostic and therapeutic method thereof | |
| US12042629B2 (en) | Bio-electroceutical device using cell cluster | |
| CN106725399A (en) | A kind of light regulates and controls animal blood pressure real-time monitoring system | |
| CA2395492A1 (en) | Method and apparatus for targeting localised electroporation | |
| Andersen et al. | Using fiber photometry in mice to estimate fluorescent biosensor levels during sleep | |
| CN114668409A (en) | Human neural interface system and method based on optogenetic regulation | |
| Sweeten et al. | Optogenetics in sleep and integrative systems research | |
| CN103705945A (en) | Application of CUG-BP1 (cytosine-uridineguanine-binding protein 1) and gene thereof in preparation of products for controlling and treating muscle atrophy diseases | |
| Li et al. | Engineering Logevity and the Reversibility of Aging (Interview with Dr. Irina Conboy) | |
| CN121237449B (en) | A medical data processing method and system for reversible adhesion of hydrogel brain electrodes | |
| Lee et al. | Motor cortex somatostatin interneurons adaptively shape the structure of action sequences | |
| US20190201692A1 (en) | Treatment of conditions associated with impaired glucose control | |
| WO2024256942A1 (en) | Devices and systems for chronic optical neuromodulation | |
| Burguière | Closed-loop recruitment of striatal parvalbumin interneurons prevents the onset of compulsive behaviours | |
| CN121081101A (en) | Laser dehairing method based on multi-parameter feedback |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20260114 Address after: No.393, Songling Road, Laoshan District, Qingdao City, Shandong Province 266104 Patentee after: QINGDAO RESEARCH INSTITUTE OF BEIHANG University Country or region after: China Patentee after: BEIHANG University Address before: 266100 No. 393, Songling Road, Laoshan District, Qingdao, Shandong Patentee before: QINGDAO RESEARCH INSTITUTE OF BEIHANG University Country or region before: China |