Treatment system for regulating and controlling liver immune microenvironment based on engineered microbial flora

Abstract

The invention provides a treatment system for regulating and controlling a liver immune microenvironment based on an engineered microbial flora, and relates to the technical field of medical diagnosis, the treatment system comprises an engineered microbial module, a nano sensing module, a real-time data analysis module, a surgical auxiliary module, a disease identification module, a dynamic feedback regulation and control module, and a monitoring and data analysis module, by introducing engineered microbial flora and a nano-enhanced sensing technology, continuous real-time monitoring of multiple indexes of the liver immune microenvironment can be realized. The indexes comprise inflammatory factor concentration, metabolite level, tumor markers, immune cell activity and the like. The dynamic data is analyzed using a high-sensitivity microbial sensor and an AI algorithm, and the system can establish unique biological fingerprints and dynamic health maps for each patient.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis technology, and in particular to a therapeutic system for regulating the liver immune microenvironment based on an engineered microbial flora. Background Art

[0002] According to the method for predicting response to immunotherapy disclosed in Chinese Patent Publication No. "CN118318153A", the method includes: (a) staining a biological sample placed on a substrate; (b) imaging the biological sample, wherein one or more images of a high-power field of view (HPF) are generated; (c) detecting multiple biomarkers in the biological sample; and (d) analyzing one or more images to predict the subject's response to immunotherapy.

[0003] According to the immunotherapy method disclosed in U.S. Patent Publication No. "US08642274B2", the subject has undergone immunotherapy, wherein the immunotherapy comprises a viral vector containing a polynucleotide encoding an antigen, wherein the viral vector is capable of transducing cells in a mammalian subject so that the cells express the antigen.

[0004] According to the U.S. patent publication number "US09389235B2", a method for predicting the results of cancer immunotherapy using biomarkers is disclosed, which relates to a method for predicting the effect of immunotherapy for cancer in patients based on new biomarkers. It also relates to a prognosis based on the results of the biomarkers. It also relates to a biomarker group used in the above method. Summary of the invention

[0005] Technical issues solved

[0006] In view of the shortcomings of the prior art, the present invention provides a therapeutic system for regulating the liver immune microenvironment based on an engineered microbial flora, which solves the following problems: 1. Traditional liver disease diagnosis and treatment often rely on periodic blood tests, imaging examinations, and doctors’ experience and judgment. Although such methods are effective, they have problems such as insufficient information updates and relatively limited data dimensions; 2. Traditional medical practices cannot make digital predictions about future symptoms. Most perceptions are obtained through the doctor’s personal experience and medical experience, and cannot achieve broad universality and predictive adaptability.

[0007] Technical Solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: 1. A therapeutic system for regulating the liver immune microenvironment based on an engineered microbial flora, the therapeutic system comprising: An engineered microbial module, wherein the engineered microbial module is metabolically modified using gene editing.

[0009] Nanosensing modules improve the sensitivity and stability of sensors by combining with nanomaterials (gold nanoparticles, quantum dots, carbon nanotubes or graphene).

[0010] The real-time data analysis module is used to process and analyze the multi-dimensional data from the engineered microorganism module in real time.

[0011] A surgical assistance module that provides precise navigation, intelligent drug release, and real-time monitoring during liver surgery.

[0012] Disease identification module, used to comprehensively analyze the type, severity and development trend of liver diseases.

[0013] A dynamic feedback regulation module is used to automatically regulate the functions of engineered microorganisms according to changes in the liver microenvironment.

[0014] The monitoring and data analysis module is used to realize real-time monitoring, data storage and processing of the system, and provide remote medical support and intelligent decision-making support.

[0015] Preferably, the engineered microorganism module further comprises: Biosensors for detecting specific biomarkers in the liver microenvironment, including inflammatory factors, metabolites, and pathogens, and the biosensors include: A sensing element for recognizing a target biomarker through a specific receptor protein; A reporting module, comprising a fluorescent protein, a luminescent protein and an electrical signal output device, for converting the detection result into a readable signal; A signal transduction amplification module, wherein the signal transduction amplification module enhances the detection signal through the signal transduction pathway and the signal amplification module.

[0016] Preferably, the nanosensor module comprises: Functional sensors for binding to engineered microorganisms through chemical modification; The signal enhancement module uses surface enhanced Raman scattering (SERS) and fluorescence resonance energy transfer (FRET) to enhance the signal output intensity.

[0017] Preferably, the real-time data analysis module includes: Data acquisition and preprocessing unit, used for collecting, digitizing, cleaning and standardizing sensor data; Feature extraction and selection unit, which extracts key features through time domain and frequency domain analysis and uses dimensionality reduction technology to retain important information; Machine learning and deep learning models, including classification models, regression models, and time series prediction models, for disease classification, progression prediction, and trend analysis; The real-time monitoring and feedback system provides instant diagnostic results and early warning information based on machine learning and deep learning model output.

[0018] Preferably, the surgical auxiliary module comprises: A navigation module for precise localization of lesion areas through multispectral fluorescence labeling and bioelectronic interfaces; Drug release modules, including responsive drug carriers and nanorobots, are used to precisely release anti-inflammatory and anti-fibrotic drugs during or after surgery; The real-time monitoring feedback module monitors the physiological parameters of the surgical area through implanted biosensors and prompts abnormal situations through an intelligent alarm system.

[0019] Preferably, the disease identification module includes: Multisensor modules, which are used to engineer microorganisms to carry multiple sensor genes and achieve simultaneous detection of multiple biomarkers; Data integration module, which combines genomics, proteomics and metabolomics data to understand disease mechanisms; The personalized identification module is used to build a personalized disease identification model based on patient-specific data, providing accurate disease identification and prediction.

[0020] Preferably, the dynamic feedback control module includes: Feedback control loop module, which is used to dynamically regulate the metabolic activities and signal outputs of microorganisms through positive and negative feedback mechanisms; An environmental sensing module, which integrates multi-parameter sensors (including pH sensing, oxygen sensing, and temperature sensing) to monitor changes in the liver microenvironment in real time; Bio-electronic hybrid module, used for biochip and wireless communication interface to achieve real-time programming and remote control of microbial functions.

[0021] Preferably, the monitoring and data analysis module includes: IoT integrated module, used for real-time data collection and transmission between wireless sensor networks and edge computing devices; Cloud data storage and processing module, used for distributed storage and high-performance computing resources for large-scale data analysis and model training; Auxiliary diagnosis module, which is used to present the sensor detection results in a visual form to assist doctors in diagnosis and surgical decision-making; Data security module, used for encrypted transmission and privacy protection technology to ensure the security and integrity of patient data.

[0022] Beneficial Effects

[0023] The present invention provides a therapeutic system for regulating the liver immune microenvironment based on an engineered microbial flora. It has the following beneficial effects: The present invention can achieve continuous real-time monitoring of multiple indicators of the liver immune microenvironment by introducing engineered microbial flora and nano-enhanced sensing technology. These indicators include inflammatory factor concentrations, metabolite levels, tumor markers, and immune cell activity. Using highly sensitive microbial sensors and AI algorithms to analyze dynamic data, the system can establish a unique biological fingerprint and dynamic health map for each patient. The present invention identifies micro-movements of lesions when the disease is still in the subclinical or early development stage, and promptly adopts trace drug release and subtle metabolic pathway regulation, which is expected to delay or prevent disease progression. This can reduce the need for the later use of expensive anti-tumor drugs, excessive use of immunosuppressants, secondary surgery or interventional treatment. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a system architecture diagram of the present invention; Figure 2 The inflammatory factor time variation curve, inflammatory factor distribution box plot, metabolite distribution box plot, sensor signal histogram, and feature correlation heat map of the present invention; Figure 3 It is the classification accuracy (intra-class accuracy) diagram of the present invention; Figure 4 is a real-time prediction and control strategy diagram of the present invention; Figure 5 is a feature importance diagram of the present invention; Figure 6 It is a PCA feature visualization diagram of the present invention; Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 creative work are within the scope of protection of the present invention. Specific embodiment one: As shown in the figure, a treatment system for regulating the liver immune microenvironment based on an engineered microbial flora, a diagnosis and surgical treatment system based on regulating the liver immune microenvironment based on an engineered microbial flora, the system comprises: The engineered microorganism module has been genetically edited or metabolically engineered to have the following functions: a biosensor for detecting specific biomarkers in the liver microenvironment, including but not limited to inflammatory factors, metabolites and pathogens, which specifically includes: a sensing element for identifying target biomarkers through specific receptor proteins; a reporting system, including fluorescent proteins, luminescent proteins or electrical signal output devices, for converting detection results into readable signals; a signal transduction and amplification system for enhancing detection signals through signal transduction pathways and signal amplification modules.

[0026] Nano-enhanced sensing technology integration improves the sensitivity and stability of sensors by combining with nanomaterials (gold nanoparticles, quantum dots, carbon nanotubes or graphene). Specifically, it includes: functionalized nanomaterials, which achieve efficient combination with engineered microorganisms through chemical modification; signal enhancement mechanisms, surface enhanced Raman scattering (SERS) and fluorescence resonance energy transfer (FRET), to enhance signal output intensity.

[0027] The artificial intelligence (AI)-driven real-time data analysis module is used to process and analyze the multi-dimensional data from the engineered microbial module in real time. It specifically includes: a data acquisition and preprocessing unit, which is responsible for the acquisition, digitization, cleaning and standardization of sensor data; a feature extraction and selection unit, which extracts key features through time domain and frequency domain analysis, and uses dimensionality reduction technology to retain important information; machine learning and deep learning models, including classification models, regression models and time series prediction models, which are used for disease classification, progression prediction and trend analysis; a real-time monitoring and feedback system, which provides immediate diagnostic results and early warning information based on model output.

[0028] The surgical assistance module is used to provide precise navigation, intelligent drug release and real-time monitoring during liver surgery. It specifically includes: an intelligent navigation system that achieves precise positioning of the lesion area through multi-spectral fluorescence labeling and bioelectronic interfaces; a precise drug release system, including responsive drug carriers and nanorobots, for precise release of anti-inflammatory drugs or anti-fibrosis drugs during or after surgery; a real-time monitoring and feedback device that monitors the physiological parameters of the surgical area through implanted biosensors and prompts abnormal conditions through an intelligent alarm system.

[0029] The disease identification module is used to comprehensively analyze the type, severity and development trend of liver diseases. It specifically includes: multiple sensor design, through engineering microorganisms to carry multiple sensor genes, to achieve simultaneous detection of multiple biomarkers; multi-omics data integration unit, combining genomics, proteomics and metabolomics data to deeply analyze disease mechanisms; personalized identification algorithm, based on patient-specific data to build a personalized disease identification model, to provide accurate disease identification and prediction.

[0030] The dynamic feedback regulation module is used to automatically adjust the functions of engineered microorganisms according to changes in the liver microenvironment. It specifically includes: a feedback control loop that dynamically regulates the metabolic activities and signal outputs of microorganisms through positive and negative feedback mechanisms; an environmental sensing system that integrates multi-parameter sensors (pH sensing, oxygen sensing, temperature sensing) to monitor changes in the liver microenvironment in real time; and a bio-electronic hybrid system that realizes real-time programming and remote regulation of microbial functions through a biochip and a wireless communication interface.

[0031] The monitoring and data analysis module is used to realize real-time monitoring, data storage and processing of the system, and provide remote medical support and intelligent decision-making support. It specifically includes: Internet of Things (IoT) integration, which realizes real-time data collection and transmission through wireless sensor networks and edge computing devices; cloud data storage and processing platform, which uses distributed storage and high-performance computing resources to perform large-scale data analysis and model training; augmented reality (AR) auxiliary diagnosis tools, which present sensor detection results in a visual form to assist doctors in diagnosis and surgical decision-making; data security and privacy protection mechanism, which ensures the security and integrity of patient data through encrypted transmission and privacy protection technology.

[0032] The system workflow includes but is not limited to the following steps: The engineered microbial module detects specific biomarkers in the liver microenvironment in real time through biosensors and improves the sensitivity and accuracy of the signal through nano-enhanced sensing technology.

[0033] The collected multi-dimensional sensor data is processed and analyzed by the AI-driven real-time data analysis module to generate accurate diagnostic results and early warning information.

[0034] During surgical operations, the surgical assistance module uses an intelligent navigation system and a precise drug release system to provide precise lesion area positioning and intelligent drug management to ensure the efficiency and safety of the operation.

[0035] The disease identification module integrates multiple sensors and multi-omics data to achieve comprehensive analysis and personalized identification of liver diseases, and guide the formulation of personalized treatment plans.

[0036] The dynamic feedback control module automatically adjusts the functional output of the engineered microorganisms according to the real-time monitored changes in the liver microenvironment to ensure the dynamic response and stability of the system.

[0037] The monitoring and data analysis module realizes real-time monitoring and data management of the entire system through the Internet of Things and cloud computing platform, and provides remote medical support and intelligent decision-making support.

[0038] The core of the system is to use engineered microbial flora to colonize the intestines and detect multiple biomarkers in the liver microenvironment in real time through biosensors. The sensors convert the detected biological signals into readable signals, and nano-enhancement technology further improves the sensitivity and accuracy of the signals. The collected multi-dimensional data is processed and analyzed by the AI-driven real-time data analysis module to provide accurate diagnosis and prediction results. At the same time, the surgical assistance module provides precise navigation and intelligent drug release during the operation to ensure the efficiency and safety of the operation. The disease identification module realizes comprehensive analysis and personalized identification of liver diseases through multi-omics data analysis. The dynamic feedback control module automatically adjusts the functional output of engineered microorganisms according to the real-time monitored changes in the liver microenvironment to ensure the dynamic response and stability of the system. Finally, the monitoring and data analysis module realizes real-time monitoring and data management of the entire system through the Internet of Things (IoT) and cloud computing technology, providing remote medical support and intelligent decision-making support.

[0039] The biosensor module is the core of the system, responsible for real-time detection of multiple biomarkers (inflammatory factors, metabolites, pathogens, etc.) in the liver microenvironment. This module transforms microorganisms through genetic engineering to enable them to specifically recognize and respond to target markers.

[0040] Sensing elements: Specific receptor proteins: Through synthetic biology technology, specific receptor proteins (Toll-like receptors, nuclear receptors) are expressed on the surface of engineered microorganisms to recognize specific biomarkers. When target markers (including specific inflammatory factors) bind to receptors, downstream signal transduction is triggered.

[0041] Gene regulatory elements: including promoters, enhancers and terminators, regulate the expression of reporter genes. The design of gene regulatory elements ensures that the sensor can accurately activate the expression of reporter genes when the target marker is recognized.

[0042] Reporter system: fluorescent proteins and luminescent proteins: green fluorescent protein (GFP), red fluorescent protein (RFP), luciferase, etc., are used to convert the test results into visual signals. When activated, these proteins will emit fluorescence or light of a specific wavelength, which is convenient for external equipment to detect.

[0043] Electrical signal output device: integrated electrophysiological sensors to convert biological signals into electrical signals to facilitate subsequent data collection and analysis.

[0044] Signal transduction and amplification system: Signal transduction pathway: It includes a two-component system (LuxR-LuxI system) that transmits the sensing signal from the receptor to the reporter gene. The design of the signal transduction pathway ensures efficient signal transmission and response.

[0045] Signal amplification module: Utilizes feedback loop and amplification subsystem (T7 RNA polymerase system) to enhance detection signal and improve sensor sensitivity.

[0046] Control circuit: Logic gate circuit: realize the logical judgment of multiple markers (AND, OR gates), ensure that the signal is output only under specific combination conditions, and reduce false alarms.

[0047] Self-regulating feedback loop: Maintains system stability, avoids over-response, and regulates reporter gene expression levels through negative feedback mechanisms.

[0048] The sensor response can be described by the following model: ; :Sensor at time The intensity of the response; : Maximum response intensity; :Concentration of target biomarker; Half-maximal response concentration.

[0049] The model describes the relationship between the sensor response and the biomarker concentration, reflecting the sensitivity and dynamic range of the sensor.

[0050] Gene design and synthesis: CRISPR-Cas9 gene editing technology is used to precisely insert sensing elements and reporter system genes to ensure stable expression of gene circuits.

[0051] Optimize gene sequences, improve expression efficiency and stability, and reduce unnecessary gene expression noise through gene optimization.

[0052] Microbial carrier: Select probiotics (lactic acid bacteria, bifidobacteria) with high colonization ability as carriers to ensure the long-term presence and functional expression of engineered microorganisms in the intestine.

[0053] Synthetic gene modules are introduced via plasmid or chromosomal integration methods to ensure stable gene delivery and expression.

[0054] In vitro culture and testing were performed to verify the sensitivity and specificity of the sensor's response to specific markers, and the signal output was detected by fluorescence microscopy and electrophysiological sensors.

[0055] The stability and functionality of the sensor were tested in animal models to evaluate its performance in the in vivo environment and ensure its effectiveness and reliability in practical applications.

[0056] Early liver disease detection: Engineered microorganisms are implanted in the patient's intestines to detect trace pathological markers in the liver microenvironment in real time, and the fluorescent or electrical signals are transmitted to external monitoring equipment to achieve early warning.

[0057] Disease progression monitoring: By continuously monitoring changes in marker concentrations, the treatment effect and disease progression can be evaluated to provide data support for clinical decision-making.

[0058] The Nano-Enhanced Sensing Technology Module significantly improves the sensitivity, selectivity and stability of biosensors through the application of nanomaterials, achieving efficient signal detection and transmission. The module combines the synergy of nanomaterials and engineered microorganisms to improve the performance of the overall system.

[0059] Nanomaterials: Gold nanoparticles (AuNPs): used to enhance optical signals and surface enhanced Raman scattering (SERS). AuNPs have excellent optical properties and can significantly enhance Raman scattering signals and improve the sensitivity of sensors; Quantum dots (QDs): provide multicolor fluorescent labels to achieve multiple signal detection. QDs have tunable emission spectra and high light quantum yields, which are suitable for the simultaneous detection of multiple fluorescent signals; Carbon nanotubes and graphene: used for the conduction of electrical signals and enhancing electrode performance. Carbon nanotubes and graphene have excellent conductivity and mechanical strength, which can improve the transmission efficiency and stability of electrical signals.

[0060] Nanocomplex construction: Functionalized nanomaterials: Through chemical modification (thiol bond connection of AuNPs), efficient combination of nanomaterials and engineered microorganisms is achieved to form a stable nano-microorganism complex; Nano-microorganism complex: Functionalized nanomaterials are combined with the surface of engineered microorganisms through chemical connection or physical adsorption methods to ensure efficient amplification and stable output of sensor signals.

[0061] Surface enhanced Raman scattering (SERS): Utilizes the enhancement effect of gold nanoparticles in Raman scattering to significantly increase the intensity of Raman signals and realize the detection of trace biomarkers. Fluorescence resonance energy transfer (FRET): Through the close interaction between quantum dots and fluorescent proteins, efficient signal transduction and amplification are achieved, thereby improving the detection sensitivity of fluorescent signals.

[0062] The mathematical description of signal enhancement is as follows: ; : Signal strength after enhancement; : Enhancement factor, which depends on the nature and layout of the nanomaterials and ranges from 0 to 1; : Raw signal strength.

[0063] This model reflects the effect of nano-enhancement technology on improving signal strength, ensuring the high sensitivity of the sensor in detecting low-concentration markers.

[0064] Nanomaterial synthesis and functionalization: Synthesize high-purity, shape-controlled gold nanoparticles and quantum dots to ensure that their optical and electrical properties meet sensing requirements, improve the biocompatibility and stability of nanomaterials through surface modification (PEGylation), and prevent aggregation and toxic reactions of nanomaterials in the body.

[0065] Nanocomplex construction: Functionalized nanomaterials are combined with engineered microbial surfaces through chemical connection (thiol bond) or physical adsorption methods to form stable nano-microorganism complexes, optimize the assembly ratio and structure of the complexes, and ensure signal enhancement effects and microbial viability.

[0066] Raman spectrometer and fluorescence microscope are used to detect the signal after nano-enhancement to verify the signal enhancement effect. By adjusting the concentration and distribution of nanomaterials, the signal enhancement effect is optimized to ensure the stable performance of the sensor in different application scenarios. Multimodal signal detection: Combining SERS and FRET to achieve simultaneous detection of multiple signals, improve the reliability and accuracy of diagnosis, high-sensitivity detection: Nano-enhancement technology is used to significantly improve the detection sensitivity of the sensor, which can detect extremely low concentrations of biomarkers and achieve early disease diagnosis.

[0067] The AI-driven real-time data analysis module uses machine learning and deep learning algorithms to process and analyze data from biosensors and nano-enhanced sensing technologies in real time, providing accurate diagnosis and prediction results. This module includes data acquisition and preprocessing, feature extraction and selection, machine learning and deep learning models, and real-time monitoring and feedback systems.

[0068] Sensor data interface: Collect and digitize the raw signal data generated by biosensors and nano-enhanced sensing technology, and convert analog signals into digital signals through a data acquisition system (DAQ device).

[0069] Data cleaning and standardization: remove noise, fill missing values, and perform data standardization to meet subsequent analysis needs. Standardization method Z-score standardization: ; Refers to raw data (data collected by the original sensor); Data value after Z-score standardization; is the mean, is the standard deviation.

[0070] Feature extraction and selection unit: Time domain and frequency domain feature extraction: extract time series and frequency domain features, peak values, frequency components, and signal amplitudes from sensor signals. Common methods include Fourier transform (FFT) and wavelet transform. High-dimensional feature reduction: use principal component analysis (PCA) and t-SNE methods to reduce feature dimensions, retain key information, reduce computational complexity, and improve model training efficiency: ; is the PCA transformation matrix, is the feature vector after dimensionality reduction, Represents the original data feature vector (column vector form), whose dimension is D, that is, , each sample in the dataset has Features This is the characteristic column vector of this data point: ; is the mean vector, and Same Dimension Calculated from the training data, it refers to the feature mean vector after averaging each feature over all samples.

[0071] Machine learning and deep learning models: Classification model: Support vector machine (SVM), random forest (RF), convolutional neural network (CNN) are used for disease classification. This embodiment uses SVM to optimize the problem: ; ; is the weight vector; is the feature mapping function, is the bias term, when When , it is the decision boundary; is a slack variable representing the The degree of violation of the classification interval by a training sample, if , then samples meet the interval requirements and are correctly classified if , then the sample falls within the support vector interval, , it means that the sample is misclassified to the other side of the decision boundary. is the penalty parameter, The larger the value, the more the optimization process tends to reduce misclassification. If the value is smaller, the optimization process will pay more attention to maximizing the interval; It is The labels of the samples; Regression models: used to predict disease progression and severity, linear regression, support vector regression (SVR).

[0072] Time series forecasting model: Long short-term memory network (LSTM), used for dynamic monitoring and trend forecasting. LSTM processes time series data through memory cells and gating mechanisms to capture long-term dependencies: ; ; ; ; ; ; in, It's the Gate of Forgetfulness. is the weight matrix of the forget gate, is the bias term of the forget gate; , represents the concatenation vector of the previous hidden state and the current input; It is a sigmoid function that limits the output to the range of [0,1]. The closer the output value is to 1, the more old memories are retained; the closer it is to 0, the more old memories are forgotten. is the input gate, is the weight matrix of the input gate, is the bias term; is a candidate memory cell, are the weight and bias of the candidate memory update, respectively. is the current memory unit, Output of forget gate Determine how much of the previous cell state to keep , The output of the input gate Determine how many new candidate memories to add to the current moment ; It's Han who's going out. are the weight and bias of the output gate respectively, is a hidden state.

[0073] Real-time monitoring and feedback system: Real-time data stream processing: Use stream data processing frameworks (Apache Kafka, Apache Flink) to process real-time sensor data to achieve efficient data transmission and processing.

[0074] Real-time prediction and alarm: Based on the model output, the liver health status is monitored in real time, and early warnings and suggestions are issued in a timely manner. Including, when the concentration of a biomarker exceeds the threshold, the system automatically triggers an alarm and notifies the doctor to take corresponding measures.

[0075] Data pipeline construction: Establish a complete data pipeline from sensor data collection to data storage, processing, and analysis to ensure the real-time and integrity of the data.

[0076] Use efficient data transmission protocols (MQTT, HTTP / 2) to ensure fast data transmission and reduce latency.

[0077] Model development and deployment: Deploy trained AI models on the cloud or edge computing devices to achieve low-latency real-time analysis.

[0078] Use containerization technology (Docker, Kubernetes) to achieve flexible deployment and expansion of the model and ensure high availability and maintainability of the system.

[0079] User interface and visualization: Develop a user-friendly interface to display real-time analysis results in the form of dashboards and charts to facilitate doctors and patients to understand and use.

[0080] Provide personalized health reports and recommendations to assist doctors and patients in making decisions and improve the intelligence level of medical services.

[0081] Real-time disease diagnosis: Use AI models to analyze sensor data in real time to quickly diagnose the type and severity of liver disease and provide immediate medical intervention recommendations.

[0082] Dynamic health monitoring: Through continuous data analysis, the liver's health status is dynamically monitored, and treatment plans are adjusted in a timely manner to optimize the patient's treatment effect.

[0083] Predictive medicine: Based on big data and predictive models, it can provide early warning of potential liver disease risks, provide personalized health management advice, and prevent the occurrence and development of the disease.

[0084] The surgical assistance module combines engineered microorganisms with advanced bioelectronics technology to provide precise navigation, real-time monitoring and intelligent drug release for liver surgery, improving the success rate and safety of surgery. The module integrates multi-spectral fluorescent markers, intelligent drug release systems, and real-time monitoring and feedback devices to form a comprehensive surgical assistance solution.

[0085] Intelligent navigation system: Bioelectronic interface: Connecting engineered microorganisms to external navigation systems to achieve real-time communication and data transmission. Through wireless communication technology (Bluetooth, Wi-Fi), the signals generated by the sensor module are transmitted to the navigation system in real time to support dynamic positioning during surgery.

[0086] Multispectral fluorescence labeling: Engineered microorganisms express multiple fluorescent proteins (GFP, mCherry, iRFP), providing fluorescent signals of different wavelengths. Through the fluorescence imaging system, doctors can observe and locate liver lesions in real time to ensure accurate removal of lesions during surgery.

[0087] Precision drug release system: Responsive drug carrier: Design smart drug carriers that can respond to changes in the liver microenvironment (pH, temperature, enzyme activity) to ensure accurate drug release during or after surgery. Smart polymers, the carrier material, can change structure and release drugs under specific circumstances.

[0088] Nanorobot assistance: Integrated nanorobots assist in the precise positioning and release of drug carriers in the liver. Nanorobots are controlled by chemical gradients or external magnetic fields to locate the target area and release drugs, ensuring that the drugs act efficiently in the lesion area and reduce side effects.

[0089] Real-time monitoring and feedback device: Surgical process monitoring: Real-time monitoring of physiological parameters (temperature, pH value, oxygen concentration) and microenvironment changes during liver surgery, providing dynamic feedback. Through the integrated sensor network, data is transmitted to the monitoring system in real time for doctors' reference.

[0090] Intelligent alarm system: When an abnormal situation is detected (abnormal inflammatory response, abnormal drug release), the system automatically issues an alarm to prompt the doctor to take appropriate measures to ensure the safety of the surgical process.

[0091] Navigation signal processing: ; is the probability density distribution of the lesion area in three-dimensional space, is the fluorescence signal intensity at different wavelengths, To observe the time, is the current spatial point time; Intelligent navigation system: Fluorescent protein design: Select fluorescent proteins with different emission wavelengths (GFP, mCherry, iRFP), and express them in microorganisms through genetic engineering to ensure efficient expression and detectability of different markers.

[0092] Navigation software integration: Develop surgical navigation software that can receive and analyze fluorescent signals and display the location of the lesion in real time. The software uses image processing algorithms to automatically identify and annotate the intensity of fluorescent signals to assist doctors in precise navigation.

[0093] Precision drug release system: Nanocarrier design: Use smart polymers or nanomaterials to build drug carriers with environmental responsiveness. The carrier material changes structure at a specific pH value or temperature to release the drug load.

[0094] Nanorobot programming: Through synthetic biology and nanotechnology, nanorobots that can move and position autonomously are designed, with drug release control functions. Nanorobots are located in the target area and release drugs through external control (magnetic field) or internal chemical signals.

[0095] Real-time monitoring and feedback: Biosensor integration: Multifunctional biosensors are implanted in the surgical area to collect physiological parameters (temperature, pH, oxygen concentration) in real time.

[0096] Feedback control system: Based on sensor data, dynamically adjust drug release and navigation signals to ensure the safety and efficiency of the surgical process. The system uses a closed-loop control algorithm to respond to monitoring data in real time and optimize drug release strategies and navigation signals.

[0097] Liver tumor resection surgery: Through multi-spectral fluorescence labeling, the tumor boundary is accurately located to ensure complete removal of the diseased tissue. At the same time, the intelligent drug release system accurately releases anti-inflammatory drugs during and after surgery to reduce postoperative inflammatory reactions and the risk of fibrosis.

[0098] Liver transplant surgery: Utilize intelligent navigation and drug release systems to monitor liver function and microenvironment changes during surgery in real time, reduce immune rejection reactions during and after surgery, and improve transplant success rates.

[0099] 5. Disease Identification Module The disease identification module uses multiple sensors and high-throughput data analysis to comprehensively analyze the type, severity and development trend of liver diseases and provide personalized treatment plans. This module integrates multi-omics data and personalized identification algorithms to achieve in-depth understanding and accurate identification of liver diseases.

[0100] Multisensor design: Multi-marker detection: Engineered microorganisms carry multiple sensor genes and can simultaneously detect multiple liver-specific markers (different types of inflammatory factors, metabolites, and pathogens).

[0101] Sensor coupling technology: Integrate different sensor signals through coupling circuits to improve detection efficiency and data consistency and avoid signal interference.

[0102] Multi-omics data integration unit: Genomic data: Obtain genomic information of microbial communities through high-throughput sequencing technology to identify genetic markers associated with liver diseases.

[0103] Proteomics data: Quantitative protein analysis using mass spectrometry to identify disease-associated changes in protein expression.

[0104] Metabolomics data: Through metabolomics analysis, we can comprehensively analyze the regulatory mechanism of microbial communities on liver metabolism and identify key metabolites.

[0105] Personalized identification algorithm: Patient-specific model: Based on the patient's genetic information and microbiome spectrum, a personalized disease identification model is constructed to ensure the accuracy of identification.

[0106] Dynamic control network: Utilize feedback control mechanism to dynamically adjust identification parameters to ensure the real-time and accuracy of identification results.

[0107] Multi-omics data fusion: ; As a comprehensive disease indicator, For genomics data, For proteomics data, For metabolomics data, , is the weight coefficient, and its value (0-1) reflects the importance of each omics data in disease identification.

[0108] The disease classification algorithm uses random forest: ; in, For the The prediction results of a decision tree, is the total number of trees. By integrating the predictions of multiple decision trees, the accuracy and robustness of classification are improved.

[0109] Sensor network construction: Introduce multiple sensor genes into engineered microorganisms to ensure the simultaneous detection of different markers. Each sensor gene corresponds to a specific biomarker, and the specific output of the signal is achieved through the gene circuit.

[0110] Design signal coupling pathways between sensors to avoid signal interference and improve data accuracy and consistency.

[0111] Multi-omics data collection: Use high-throughput sequencing technology to obtain genomic information of microbial communities and identify genetic markers associated with liver disease.

[0112] Combined with mass spectrometry technology, it conducts quantitative analysis of proteins and metabolites, comprehensively covers disease-related biomarkers, and provides multi-dimensional data support.

[0113] Development of personalized identification algorithms: Use machine learning methods (random forest, support vector machine) to build patient-specific disease identification models, and improve the model's generalization ability and prediction accuracy through big data training.

[0114] The accuracy, sensitivity, and specificity of the model were evaluated by cross-validation, confusion matrix, and ROC curve methods to ensure the reliability of the identification results.

[0115] Hepatitis type identification: Specific markers of different types of hepatitis are detected through multiple sensors, combined with multi-omics data analysis to accurately distinguish between hepatitis B and C and their severity, and guide personalized treatment plans.

[0116] Identification of liver fibrosis and liver cancer: Using multi-omics data and machine learning models, early identification of the risk of liver fibrosis progression and liver cancer can be achieved, providing precise intervention measures to improve treatment outcomes and patient prognosis.

[0117] The dynamic feedback control module automatically adjusts the function of engineered microorganisms by real-time monitoring of changes in the liver microenvironment to ensure the dynamic response and stability of the system. The module integrates feedback control loops, multi-parameter environmental sensing systems, and bio-electronic hybrid systems to achieve real-time regulation of microbial functions.

[0118] Feedback control loop: positive feedback and negative feedback mechanism: positive feedback loop is designed to enhance system response, negative feedback loop is used to stabilize system state and prevent overreaction. Positive feedback mechanism increases the activity of signal transduction pathway and enhances response intensity; negative feedback mechanism reduces system response and ensures system stability by inhibiting signal transduction pathway.

[0119] Dynamic regulatory elements: including adjustable promoters and variable splicing elements, which are used to adjust gene expression levels and achieve dynamic regulation of functional output. Through adjustable elements, the expression level of the reporter gene can be dynamically adjusted according to the signal strength detected by the sensor to ensure the appropriateness and accuracy of the system response.

[0120] Environmental sensing system: Multi-parameter sensor: Integrates multiple environmental sensors such as pH sensing, oxygen sensing, and temperature sensing to monitor changes in the liver microenvironment in real time. Each sensor corresponds to a specific environmental parameter, and the perception and response to different parameters are achieved through genetic circuits.

[0121] Adaptive regulation mechanism: Based on the sensing data, the metabolic activity and signal output of microorganisms are automatically adjusted to ensure the adaptability and stability of the system under different pathological conditions. Through the feedback control algorithm, the functional output of microorganisms is dynamically adjusted according to the environmental parameters monitored in real time to achieve adaptive regulation of the system.

[0122] Bio-electronic hybrid system: Biochip integration: Integrate microbial functions with bioelectronic chips to achieve real-time programming and regulation of microbial functions. Biochips are connected to microorganisms through microelectrodes to achieve two-way data transmission and remote regulation of functions.

[0123] Wireless communication interface: Data communication and function regulation between microorganisms and external devices are realized through wireless technology (Bluetooth, Wi-Fi), supporting remote control and real-time adjustment. The wireless communication interface is connected to the biochip through a wireless module to ensure real-time data transmission and high efficiency of remote control.

[0124] Feedback control loop design: Use synthetic biology tools to design positive and negative feedback loops to optimize the response speed and stability of the system. Ensure the efficiency and reliability of the feedback loop through genetic circuit design, verify the functions of different feedback loops through experiments, and select the optimal design to meet the dynamic response requirements of the system.

[0125] Environmental sensing and regulation: Integrate multiple environmental sensors into engineered microorganisms to ensure comprehensive perception of changes in the liver microenvironment. Each sensor corresponds to a different gene circuit module to achieve independent response to different environmental parameters.

[0126] Bio-electronic system integration: Design biochips to integrate the functional modules of microorganisms with electronic control modules to ensure real-time data transmission and functional regulation. Connect microelectrodes with microorganisms to achieve two-way data transmission and real-time function regulation.

[0127] Develop wireless communication protocols to achieve efficient communication between microorganisms and external devices, support remote control and real-time adjustment, and ensure the intelligence and flexibility of the system.

[0128] Dynamic response of the system: Based on the real-time monitored changes in the liver microenvironment, the metabolic activities and signal outputs of the microorganisms are automatically adjusted to ensure the dynamic response and stable regulation of the system to the disease state.

[0129] Intelligent function adjustment: Remotely adjust the microbial function module through the wireless communication interface to achieve personalized and flexible treatment strategies to adapt to the needs and changes in the condition of different patients.

[0130] The monitoring and data analysis module uses the Internet of Things (IoT) and cloud computing technologies to achieve real-time monitoring, data storage and processing of the system, and provide remote medical support and intelligent decision support. The module includes wireless sensor networks, edge computing devices, cloud data storage and processing platforms, augmented reality (AR)-assisted diagnostic tools, and data security and privacy protection mechanisms.

[0131] Edge computing devices: perform preliminary data processing and filtering locally to reduce the burden on the cloud, improve data transmission efficiency, and ensure the real-time and reliability of data.

[0132] Real-time surgical feedback: During surgical procedures, AR technology is used to overlay real-time sensor data onto the surgical field of view to improve surgical accuracy and safety. The AR system provides dynamic surgical navigation and monitoring information by synchronously updating with sensor data.

[0133] Data security and privacy protection mechanism: Encrypted transmission: Use SSL / TLS protocol to encrypt data transmission to ensure the security and integrity of data during transmission and prevent data from being stolen or tampered with.

[0134] Privacy protection mechanism: Implement data anonymization and access control to protect patient privacy. Through multi-factor authentication and permission management, limit access to sensitive data to ensure that only authorized personnel can view and process data.

[0135] Data transmission and processing: Data compression algorithm: Huffman coding, used to optimize data transmission efficiency and reduce bandwidth usage.

[0136] IoT device deployment: Deploy wireless sensor nodes to collect sensor data of engineered microorganisms in real time.

[0137] Use low-power wide area networks (LoRa, NB-IoT) to achieve remote data transmission and ensure data continuity and real-time performance.

[0138] Cloud system construction: Establish cloud data storage and processing architecture, and use distributed databases (MongoDB, Cassandra) to store massive sensor data.

[0139] Use cloud computing platforms (AWS, Google Cloud) to deploy AI analysis models and data processing pipelines to improve data processing efficiency and analysis capabilities.

[0140] Development of AR-assisted diagnosis system: Develop AR-based medical assistance software, integrate sensor data visualization module, and realize intuitive display of data.

[0141] Through the head-mounted AR device (Microsoft HoloLens), real-time data superposition and feedback during the operation can be achieved to improve the accuracy and safety of the operation.

[0142] Data security measures: Implement end-to-end encryption to ensure the security of data during transmission and storage.

[0143] Use multi-factor authentication and permission management to limit data access rights and protect patient privacy.

[0144] Real-time remote monitoring: Through IoT and cloud computing technology, real-time remote monitoring of liver health status is achieved to provide immediate medical support. Doctors and medical institutions can view patients' health data in real time through the cloud platform and conduct remote diagnosis and intervention.

[0145] AR-assisted diagnosis and surgery: Using AR technology, complex sensor data is presented in an intuitive visual form, helping doctors make more accurate decisions in diagnosis and surgery. The AR system provides dynamic surgical navigation and monitoring information to improve the accuracy and safety of surgery.

[0146] Data security management: Through advanced data encryption and privacy protection technology, we ensure the security and privacy of patient data in compliance with relevant regulations. Through strict access control and authority management, we ensure the security of data during transmission and storage.

[0147] This system achieves comprehensive regulation and management of the liver immune microenvironment by integrating biosensors, nano-enhanced sensing technology, AI-driven data analysis, surgical assistance, precise disease identification, dynamic feedback and adaptive regulation, and monitoring and data analysis modules. The system not only has high-sensitivity and high-specificity disease detection capabilities, but also achieves accurate diagnosis and personalized treatment of liver diseases through AI-driven real-time data analysis and dynamic feedback mechanisms. The application of nano-enhanced sensing technology and surgical assistance modules further enhances the overall performance and application value of the system. Although there are still many technical and ethical challenges in practical applications, with the continuous advancement of related technologies and in-depth cooperation among multiple disciplines, it is expected that the system will play a key role in the diagnosis and treatment of liver diseases in the future and promote the realization of personalized and precision medicine. Specific embodiment 2: Based on the technical solution of the specific embodiment 1, further application cases are given: The main problems faced by liver transplant patients after surgery include graft rejection, postoperative infection, and dynamic changes in transplant liver function. Traditional postoperative management models rely more on routine biochemical indicators and clinical symptom observations. When immune rejection or infection occurs, it is often at a stage where high-intensity intervention is required. In addition, the dosage adjustment and decision-making of immunosuppressants rely on experience and intermittent blood tests, and lack real-time perception and regulation of the local immune microenvironment of the liver.

[0149] Construct a personalized immune monitoring and intervention platform with engineered microbial flora and sensor systems for long-term management of patients after liver transplantation. Specific improvements include: Before transplantation, the probiotics containing biosensors are personalized based on the recipient patient's microbiome analysis, host immune characteristics, and donor liver quality parameters. The bacterial flora colonized in the patient's intestine can monitor liver immune-related markers (such as specific cytokines, chemokines, and microenvironmental metabolites) after transplantation, and the signals can be clearly visualized and quantified through nano-enhancement technology.

[0150] Nano-enhanced sensors and AI-assisted decision-making. During the postoperative recovery phase, sensors detect changes in the liver's immune microenvironment in real time. The detection data is transmitted to the cloud AI platform via IoT. The AI ​​model performs dynamic prediction and risk assessment based on the patient's historical data (pre-transplant microbiome data, transplant surgery parameters, liver donor characteristics, and early postoperative monitoring data). When AI detects possible early signs of rejection or potential infection trends, it can give an early warning before clinical symptoms appear, and present it intuitively to doctors through AR-assisted diagnosis and treatment tools.

[0151] When potential early signals of immune rejection appear, the implanted intelligent drug release system, relying on environmental sensing and dynamic feedback control modules, accurately releases low-dose immune-modulating drugs or antibiotics to prevent severe rejection or infection. At the same time, based on the AI ​​analysis results, it assists doctors in dynamically adjusting the dosage of immunosuppressants, thereby avoiding excessive immunosuppression and side effects caused by blindly increasing the dosage.

[0152] After liver transplantation, patients can receive early prediction and precise intervention before they have obvious symptoms, significantly reducing the severity and frequency of rejection reactions or infections. Doctors can intuitively understand the dynamic changes in the immune status of the transplanted liver on a visual interface, reduce unnecessary invasive examinations and repeated laboratory tests, optimize the dosage of immunosuppressants, and improve patients' long-term quality of life and the functional stability of transplanted organs. Specific embodiment three: Based on the technical solution of the specific embodiment 1, further application cases are given: Personalized lifestyle intervention and drug fine-tuning system for patients with early non-alcoholic steatohepatitis (NASH). Non-alcoholic steatohepatitis (NASH) often lacks obvious clinical symptoms in the early stage of the disease. Most patients are in a sub-healthy state during the fatty liver stage, and the risk of potential liver fibrosis or liver cancer continues to increase. Currently, the prevention and intervention of NASH mostly stays on lifestyle improvement suggestions and regular blood tests, but lacks dynamic, continuous, and quantifiable liver immune microenvironment monitoring methods, resulting in insufficient active management of the disease process and early fibrosis changes.

[0154] Introducing engineered microbial communities in the early screening and long-term follow-up management of NASH, combined with personalized intervention strategies and AI-assisted lifestyle behavior adjustment recommendations, can achieve early detection, precise intervention and dynamic monitoring.

[0155] Early screening and individualized monitoring implantation, for people with high-risk features such as obesity and metabolic syndrome, introduce engineered microbial sensor flora to colonize the intestine during routine physical examinations. This flora is sensitive to abnormal fat metabolism and changes in liver inflammatory factors (such as TNF-α, IL-6), and reflects the potential fat accumulation and inflammatory response dynamics of the liver microenvironment in real time through fluorescent signal or electrical signal output.

[0156] In daily life, the patient's wearable device (smart watch or smart capsule in the intestine) is combined with the engineered microbial sensor signal to transmit data to the cloud, and the AI ​​algorithm uses the patient's diet, exercise, sleep and microbial signal changes for comprehensive analysis. Through the AR mobile phone application, patients can intuitively see the changing trends of their liver immune and metabolic status, and make personalized diet adjustments, exercise plans and preventive health care measures based on AI recommendations.

[0157] Fine-tuning of drugs and preventive treatment plans: When AI analysis detects a potential trend of NASH deterioration (such as a slight increase in inflammatory factors, abnormal metabolites, and microenvironmental signals of early fibrosis), low-dose, short-term release of anti-inflammatory or anti-fibrotic drugs can be given through the smart drug release system with the doctor's permission to delay the progression of the disease. In addition, the system can suggest that patients communicate with their doctors to adjust the dosage of lipid-lowering drugs or blood sugar regulating drugs based on relevant data to avoid the lag of traditional static adjustment methods such as cycles.

[0158] Through real-time microenvironment detection and early intervention of NASH high-risk groups, patients can not only have a clearer understanding and control of their own conditions, but also implement personalized intervention strategies in a data-driven manner under the guidance of doctors, thereby delaying or preventing the progression of NASH to fibrosis or even liver cancer. Clinicians can implement precise medication adjustments for patients based on stable, continuous and highly sensitive data support, reducing unnecessary invasive examinations and blind drug increases.

[0159] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0160] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A therapeutic system based on engineered microbial flora to regulate the liver immune microenvironment. The invention comprises, wherein: The treatment system comprises: An engineered microbial module, wherein the engineered microbial module is metabolically modified using gene editing; A real-time data analysis module for real-time processing and analysis of multi-dimensional data from the engineered microbial module; A surgical assistance module to provide precise navigation, intelligent drug release, and real-time monitoring during liver surgery; Disease identification module, which is used to comprehensively analyze the type, severity and development trend of liver diseases; A dynamic feedback control module to automatically regulate the function of the engineered microorganisms according to changes in the liver microenvironment; The monitoring and data analysis module is used to realize real-time monitoring, data storage and processing of the system, and provide remote medical support and intelligent decision-making support.

2. The therapeutic system for regulating the liver immune microenvironment based on engineered microbial flora according to claim 1, characterized in that: The engineered microorganism module further comprises: Biosensors for detecting specific biomarkers in the liver microenvironment, including inflammatory factors, metabolites, and pathogens, and the biosensors include: A sensing element for recognizing a target biomarker through a specific receptor protein; A reporting module, comprising a fluorescent protein, a luminescent protein and an electrical signal output device, for converting the detection result into a readable signal; A signal transduction amplification module, wherein the signal transduction amplification module enhances the detection signal through the signal transduction pathway and the signal amplification module.

3. The therapeutic system for regulating the liver immune microenvironment based on engineered microbial flora according to claim 1, characterized in that: The nanosensor module comprises: Functional sensors for binding to engineered microorganisms through chemical modification; The signal enhancement module uses surface enhanced Raman scattering and fluorescence resonance energy transfer to enhance the signal output intensity.

4. The therapeutic system for regulating the liver immune microenvironment based on engineered microbial flora according to claim 1, characterized in that: The real-time data analysis module includes: Data acquisition and preprocessing unit, used for collecting, digitizing, cleaning and standardizing sensor data; Feature extraction and selection unit, which extracts key features through time domain and frequency domain analysis and uses dimensionality reduction technology to retain important information; Machine learning and deep learning models, including classification models, regression models, and time series prediction models, for disease classification, progression prediction, and trend analysis; The real-time monitoring and feedback system provides instant diagnostic results and early warning information based on machine learning and deep learning model output.

5. The therapeutic system for regulating the liver immune microenvironment based on engineered microbial flora according to claim 1, characterized in that: The surgical assistance module comprises: A navigation module for precise positioning of the lesion area through multispectral fluorescence labeling and bioelectronic interfaces; Drug release modules, including responsive drug carriers and nanorobots, are used to precisely release anti-inflammatory and anti-fibrotic drugs during or after surgery; The real-time monitoring feedback module monitors the physiological parameters of the surgical area through implanted biosensors and prompts abnormal situations through an intelligent alarm system.

6. The therapeutic system for regulating liver immune microenvironment based on engineered microbial flora according to claim 1, characterized in that: The disease identification module includes: Multisensor modules are used to engineer microorganisms to carry multiple sensor genes to achieve simultaneous detection of multiple biomarkers; Data integration module, which combines genomics, proteomics and metabolomics data to understand disease mechanisms; The personalized identification module is used to build a personalized disease identification model based on patient-specific data, providing accurate disease identification and prediction.

7. The therapeutic system for regulating liver immune microenvironment based on engineered microbial flora according to claim 1, characterized in that: The dynamic feedback control module includes: Feedback control loop module, used for positive and negative feedback mechanisms to dynamically regulate the metabolic activities and signal outputs of microorganisms; An environmental sensing module, which integrates a multi-parameter sensor to monitor the changes in the liver microenvironment in real time. The multi-parameter sensor includes a pH sensor, an oxygen sensor, and a temperature sensor; Bio-electronic hybrid module, used for biochip and wireless communication interface to achieve real-time programming and remote control of microbial functions.

8. The therapeutic system for regulating liver immune microenvironment based on engineered microbial flora according to claim 1, characterized in that: The monitoring and data analysis module includes: IoT integrated module, used for real-time data collection and transmission between wireless sensor networks and edge computing devices; Cloud data storage and processing module, used for distributed storage and high-performance computing resources for large-scale data analysis and model training; Auxiliary diagnosis module, which is used to present the sensor detection results in a visual form to assist doctors in diagnosis and surgical decision-making; Data security module, used for encrypted transmission and privacy protection technology to ensure the security and integrity of patient data.

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