Burn wound repair system based on bionic nanomaterial

Abstract

The invention discloses a burn wound repair system based on a bionic nano-material, and relates to the technical field of intelligent wound therapy, the system comprises a bionic nano-material application layer used for covering a burn wound and providing a biocompatible stent, the surface of the bionic nano-material application layer is loaded with controllably released drugs or growth factors; the intelligent drug delivery module is connected with the bionic nano material application layer and used for releasing specific drugs or bioactive factors in a micro-dose mode under the control instruction; the multi-mode environment sensor array is arranged on the application layer or below the application layer and used for collecting wound surface environment data in real time, and the wound surface environment data comprise the pH value, the temperature, the humidity, percolate and the like; the closed-loop control and algorithm unit is in communication connection with the multi-mode environment sensor array and the intelligent drug delivery module, performs wound healing stage classification based on the collected environment data, and generates adjustment parameters and drug administration instructions through a reinforcement learning algorithm and a self-adaptive PID control strategy; the invention discloses a flexible integrated packaging and pasting module.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent wound treatment, and more specifically, to a burn wound repair system based on bionic nanomaterials. Background Art

[0002] Burn wounds refer to skin injuries caused by burns. Management and healing are key to burn wound treatment. During burn wound treatment, wound healing requires precise drug release at different stages. Traditional drug delivery systems often fail to respond to changes in the wound environment in real time, resulting in inaccurate regulation of drug release timing and dosage, which in turn affects healing outcomes. Therefore, developing an intelligent system that can adaptively adjust drug release based on the actual wound surface and healing stage has become a current research hotspot.

[0003] In existing technologies, general intelligent drug delivery systems can provide real-time drug delivery in some cases, but most systems cannot take into account wound environment data collection, stage classification, precise drug adjustment and personalized feedback control. In addition, their intelligent algorithms and feedback mechanisms are relatively simple and lack sufficient flexibility and adaptability. Summary of the Invention Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a burn wound repair system based on bionic nanomaterials. The system adopts a multimodal sensor array to collect wound environment data in real time, combines a lightweight classification algorithm with a reinforcement learning agent, and intelligently adjusts the release of drugs and growth factors through closed-loop control and feedback mechanism to achieve precise treatment and personalized care during the wound healing process.

[0005] The system of the present invention can dynamically adjust the drug delivery strategy based on the different stages of wound healing and environmental changes, significantly improving the treatment effect, and automatically perform safety protection when abnormalities occur to ensure the patient's treatment safety.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a burn wound repair system based on biomimetic nanomaterials, the system comprising: A burn wound repair system based on bionic nanomaterials, characterized in that the system comprises: A biomimetic nanomaterial dressing layer, used to cover the burn wound and provide a biocompatible scaffold, with its surface loaded with controlled-release drugs or growth factors; An intelligent drug delivery module, connected to the biomimetic nanomaterial application layer, for releasing specific drugs or bioactive factors in microdoses under control instructions; A multimodal environmental sensor array, disposed on or beneath the dressing layer, for real-time collection of wound environment data, including pH value, temperature, humidity, exudate, etc.; a closed-loop control and algorithm unit, communicating with the multimodal environmental sensor array and the intelligent drug delivery module, classifying wound healing stages based on the collected environmental data, and generating adjustment parameters and drug delivery instructions through a reinforcement learning algorithm and an adaptive PID control strategy; The flexible integrated packaging and application module integrates the above functional modules on a flexible substrate and adapts to the wound surface morphology to achieve application and fixation.

[0007] Preferably, the system can perform reinforcement learning and optimization in multiple consecutive cycles according to the patient's personalized needs.

[0008] Preferably, the closed-loop control and algorithm unit can adjust the amount, timing and frequency of drug release according to changes in the wound environment, healing stage and individual differences.

[0009] Preferably, the multimodal environmental sensor array includes at least the following sensors: a pH sensor, a temperature sensor, and a humidity sensor.

[0010] Preferably, the closed-loop control and algorithm unit uses the following reward function to optimize the control strategy:

[0011] The reward amount r is obtained based on the actual environmental change y and the expected environmental change Δy, where is the adjustment coefficient and ΔT is the dosage cycle adjustment. A continuous negative value is designed based on this. The smaller the error and the smaller the dosage adjustment range, the higher the reward amount r.

[0012] Technical effects and advantages of the present invention: This invention provides a burn wound repair system based on biomimetic nanomaterials, featuring intelligently regulated drug release, real-time feedback regulation, and personalized treatment. The system automatically adjusts drug release strategies based on wound environment data and changes in the healing stage. Through reinforcement learning, it continuously optimizes the control strategy, significantly improving treatment efficacy and ensuring patient safety. The system boasts a flexible design and easy deployment, promising broad clinical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flow chart for implementing a burn wound repair system based on bionic nanomaterials of the present invention. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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.

[0015] Reference Figure 1 The present invention relates to a burn wound repair system based on bionic nanomaterials. The system includes a bionic nanomaterial application layer 1, an intelligent drug delivery module 2, a multimodal environmental sensor array 3, a closed-loop control and algorithm unit 4, and a flexible integrated packaging and application module 5.

[0016] The intelligent drug delivery module 2 includes a plurality of independently controlled microfluidic drug release channels and MEMS microvalve components, which are used to realize time-sharing or synchronous release of multiple drugs.

[0017] Example 1 This system uses an integrated flexible patch material as a carrier, embedded with a multi-layer drug-releasing membrane made of biomimetic nanomaterials, combined with microsensors and microfluidic structures. It can intelligently identify the wound healing stage and dynamically release different types of drugs to assist in the treatment of burns.

[0018] Example 2 The bionic nanomaterial dressing layer 1 controls drug release based on a hierarchical microenvironment that mimics the structure of the human epidermis. Specifically, the bionic nanomaterial dressing layer 1 includes an inner layer, an intermediate layer, and a core drug-loaded phase. The inner layer material is a chitosan / polyacrylic acid composite, which is sensitive to the pH of the wound surface. The intermediate layer is poly N-isopropylacrylamide (PNIPAM), which is temperature-sensitive with a thermal transition point of 32°C. The core drug-loaded phase is embedded with anti-inflammatory drugs, antimicrobial peptides, or recombinant human epidermal growth factor (rhEGF). When in a low pH (specifically <6.2) and high temperature (specifically >37°C), the shell is triggered to swell and fall off, and the core releases the drug.

[0019] In this embodiment, the chitosan / polyacrylic acid complex is a multi-responsive core-shell structured nanoparticle. The nanocarrier is evenly distributed in the microfluidic channel and fixed to the inner wall of the channel by electrostatic adsorption or chemical bonding to ensure localized responsive release and avoid diffusive loss.

[0020] Example 3 The intelligent drug delivery module consists of a three-cavity drug chamber, a piezoelectric micropump, a MEMS electrothermal microvalve and a Y-shaped microchannel.

[0021] Each drug chamber has a volume of 120 μL and is used to store anti-infective drugs, growth factors and vasoactive peptides. The control principle is implemented by a 32-bit MCU controller, and the release amount is adjusted every 10 minutes based on environmental feedback. The micropump and valve response time is <100ms, supporting precise micro-drug mixing and directional release. By integrating with the multimodal environmental sensor array 3, the system can achieve differentiated drug delivery based on the differences in the inflammatory phase, proliferation phase and remodeling phase.

[0022] Example 4 The multimodal environmental sensor array 3 can sense the wound surface status, which relies on three types of flexible micro sensors: ① pH sensor: Based on a potentiometric microelectrode structure, with a response time of <5s and an accuracy of ±0.05, it is used to monitor changes in the pH value of the wound area, reflecting changes in the acidity and alkalinity of the wound surface and helping to identify the occurrence of inflammatory reactions; ② Temperature sensor: uses Pt thin film thermistor with an accuracy of ±0.1 degrees Celsius, which is used to monitor the temperature of the wound area. Temperature changes directly affect the healing speed of the wound; ③ Humidity sensor: It adopts a capacitive structure and responds to humidity changes in the range of 30%-95%RH. It is used to monitor wound humidity and exudate volume in real time. Changes in exudate can reflect whether the wound is in the proliferative stage or remodeling stage.

[0023] The sensor communicates with the MCU through the FPC line to form a closed-loop control network. The data is uploaded to the control unit or Bluetooth terminal in real time and stored in the EEPROM for subsequent analysis.

[0024] Example 5 The closed-loop control and algorithm unit 4 collects wound environment data in real time through the multimodal environmental sensor array 3, achieving precise adjustment and adaptive optimization of the intelligent drug delivery module. It can not only quickly respond to environmental changes, but also continuously adjust control parameters according to historical healing curves to ensure the accuracy and safety of drug release at different healing stages.

[0025] Step 1: The multimodal environmental sensor array 3 transmits the raw data to the closed-loop control and algorithm unit 4. The sampling frequency is kept above 1 Hz. The raw data is denoised by Kalman filtering, and the sensor error is corrected based on the initial zero point and temperature drift compensation using an online calibration model; Step 2: Ensure that the drug release rate converges quickly to the set target value based on indicators such as pH and temperature. Based on the system transfer function, the Ziegler-Nichols method is used for initial adjustment. The initial settings are Kp=0.8, Ki=0.05, Kd=0.2, and the error is calculated every minute. , and output to the piezoelectric micropump and MEMS electrothermal microvalve according to the discrete PID formula to achieve precise regulation of the step flow at a micro level.

[0026]

[0027] In step three, a lightweight decision tree or SVM is used to quickly classify the inflammatory phase, hyperplasia phase, and remodeling phase using the sliding window of pH, temperature, and humidity in the last 10 minutes as features. A reinforcement learning agent is used to dynamically fine-tune the PID parameters and drug administration sequence by comparing the differences between "actual environmental changes" and "expected changes" to cope with individual differences and dynamic environmental changes.

[0028] Specifically, in step 301, the sensor collects pH, temperature, and humidity data at a frequency of 1 Hz. The system forms a sliding window with the most recent 600 samples at each moment for subsequent feature calculation and stage classification.

[0029] In step 302, the statistical features of each sensing channel in the window are calculated: mean, standard deviation, extreme value, slope and kurtosis. Based on this, the short-term rate of change, exponentially weighted moving average and short-term frequency domain energy are further calculated. These can be obtained through a small window fast Fourier transform, which will not be described in detail here. Finally, a 25-dimensional feature vector is obtained.

[0030] In step 303 , a linear kernel function is used to divide the above features into two or three categories (including inflammation stage, proliferation stage, and remodeling stage). The model parameters are solidified on the MCU after completing cross-validation offline.

[0031] Step 304, agent environment and state definition: ① State s: It consists of the classification result at the end of the current window, the statistical features of the sliding window, and the "expected environment trajectory" vector, with a total of 25 dimensions; ②Action a: includes three-dimensional continuous variables - PID gain adjustment Kp, Ki, Kd and drug administration cycle adjustment ΔT; ③ Reward amount r: r is calculated based on the actual environmental change y and the expected environmental change Δy. The expected environmental change Δy is generated by establishing a three-stage ideal healing curve (pH slow drop, temperature rise, humidity drop) based on large-scale clinical data and literature;

[0032] is the adjustment coefficient, and a continuous negative value is designed based on it. The smaller the error, the smaller the dosage adjustment range, and the higher the reward amount r.

[0033] Step 305, training and updating, uses the TD3 algorithm, uses a simulation environment to collect experience, trains until basic convergence, learns the unique healing response of each patient through continuous online updates, and stores historical errors and control parameters.

[0034] Through the “actual-expected” error reward, the system is ensured to not only follow the threshold but also approach the ideal healing trajectory, significantly improving the level of personalized precision medicine. The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A burn wound repair system based on biomimetic nanomaterials, characterized by: The system includes: A biomimetic nanomaterial dressing layer (1) is used to cover the burn wound surface and provide a biocompatible scaffold, and the surface of the biomimetic nanomaterial dressing layer is loaded with controlled-release drugs or growth factors; An intelligent drug delivery module (2), connected to the biomimetic nanomaterial application layer, for releasing specific drugs or bioactive factors in a micro-dose manner under control instructions; A multimodal environmental sensor array (3) is provided on or below the dressing layer and is used to collect real-time wound environment data, including pH value, temperature, humidity, exudate, etc.; A closed-loop control and algorithm unit (4) is connected to the multimodal environmental sensor array (3) and the intelligent drug delivery module (2), classifies the wound healing stage based on the collected environmental data, and generates adjustment parameters and drug delivery instructions through a reinforcement learning algorithm and an adaptive PID control strategy; The flexible integrated packaging and application module (5) integrates the above-mentioned functional modules on a flexible substrate and adapts to the wound surface morphology to achieve application and fixation.

2. The burn wound repair system based on biomimetic nanomaterials according to claim 1, characterized in that: The system can perform reinforcement learning and optimization in multiple consecutive cycles based on the patient's personalized needs.

3. The burn wound repair system based on biomimetic nanomaterials according to claim 1, characterized in that: The closed-loop control and algorithm unit (4) can adjust the amount, timing and frequency of drug release according to changes in the wound environment, healing stage and individual differences.

4. The burn wound repair system based on biomimetic nanomaterials according to claim 1, characterized in that: The multimodal environmental sensor array (3) comprises at least the following sensors: a pH sensor, a temperature sensor, and a humidity sensor.

5. The burn wound repair system based on biomimetic nanomaterials according to claim 1, characterized in that: The closed-loop control and algorithm unit (4) uses the following reward function to optimize the control strategy: The reward amount r is obtained based on the actual environmental change y and the expected environmental change Δy, where is the adjustment coefficient and ΔT is the dosage cycle adjustment. A continuous negative value is designed based on this. The smaller the error and the smaller the dosage adjustment range, the higher the reward amount r.

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