Bacteria-mediated ultrasonic cavitation intelligent evaluation and regulation method and system based on gene-coded gas vesicles

Abstract

The invention discloses a method and a system for intelligently evaluating, regulating and controlling bacterium-mediated ultrasonic cavitation based on gene-coded gas vesicles. The method comprises the following steps: constructing a bacterium sample containing the gas vesicles and an experimental platform for simulating a tissue environment; the method comprises the following steps: irradiating a sample through an ultrasonic excitation device, and collecting a cavitation signal by using an independent passive receiving transducer; performing time-frequency domain analysis on the signal, and extracting a harmonic / super-harmonic component representing steady-state cavitation and a broadband noise component representing inertial cavitation; calculating steady-state and inertial cavitation doses, and determining a cavitation threshold value; establishing a quantitative relation model of the cavitation dose and the acoustic parameters; based on the model or real-time signal characteristics, selection or feedback regulation and control are carried out on treatment ultrasonic parameters. The system comprises an excitation control module, an ultrasonic transducer, a sample device, a passive receiving transducer, a data processing module and a parameter regulation and control module. According to the invention, quantitative characterization and controllable adjustment of the bacterial cavitation behavior of the gas vesicles are realized.

Description

[0001] A Method and System for Intelligent Evaluation and Regulation of Bacterial-Mediated Ultrasonic Cavitation Based on Gene-Encoded Gas Vesicles Technical Field

[0002] This invention relates to the field of ultrasonic cavitation technology, and in particular to a method and system for intelligent evaluation and regulation of bacterial-mediated ultrasonic cavitation based on gene-encoded gas vesicles. Background Technology

[0003] Ultrasound-induced cavitation, as an important physical mechanism in high-intensity focused ultrasound (HIFU) therapy, low-intensity ultrasound modulation, and drug delivery, has been extensively studied in recent years. Current research commonly employs lipid microbubbles, polymer-shelled microbubbles, or nanoparticles as auxiliary cavitation nuclei to lower the cavitation threshold and enhance acoustic effects. However, these artificial cavitation nuclei are mainly distributed within blood vessels, making effective extravasation into the tumor parenchyma difficult. Their penetration depth in tissues is limited, and their circulating half-life is short, often requiring multiple administrations or high doses. Furthermore, due to the relatively large size of the microbubbles, potential concerns remain regarding their stability, metabolizability, and biosafety in vivo.

[0004] In recent years, gas vesicles (GVs) expressed by genetically engineered bacteria have been proposed for use in ultrasound imaging and therapeutic enhancement due to their biodegradability, gene regulation capabilities, and ability to actively migrate to the tumor microenvironment. However, current techniques largely focus on the imaging contrast capabilities of gas vesicles or their rupture characteristics under high-intensity ultrasound, while research on the cavitation dynamics of gas vesicles in the context of bacterial expression remains very limited. Existing reports typically only determine cavitation occurrence through sound pressure thresholds or the formation of visible cavitation clouds, lacking systematic analysis of their acoustic emission signals and failing to distinguish between steady-state cavitation and inertial cavitation behavior.

[0005] Meanwhile, current technologies for characterizing the cavitation of gene-derived gas vesicle bacteria often rely on simple sound pressure level scanning, without incorporating a systematic passive cavitation detection (PCD) strategy. This makes it difficult to obtain changes in their spectral characteristics under different sound pressure levels and bacterial concentrations. The lack of corresponding data analysis methods also prevents accurate quantification of cavitation thresholds and the identification of cavitation features such as harmonics and broadband noise, further limiting a deeper understanding of the relationship between cavitation intensity and acoustic parameters. These shortcomings prevent the provision of reliable evidence for subsequent ultrasound therapy, parameter optimization, and safety control, and also affect the effectiveness assessment of gas vesicle bacteria as ultrasound sensitizers.

[0006] In summary, existing technologies have not yet established a method and system for systematically collecting and analyzing the cavitation behavior of gas vesicles in a simulated tissue bacterial environment. Furthermore, they lack quantitative descriptions of their cavitation threshold, spectral characteristics, and dose-response relationship, which cannot meet the clinical translation needs for controllable and safe ultrasound sensitization technology. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a method and system for intelligent evaluation and regulation of bacterial-mediated ultrasonic cavitation based on gene-encoded gas vesicles. This method utilizes the cavitation behavior of gas vesicles (GVs) and performs quantitative analysis to obtain an ultrasonic detection and signal processing system, effectively overcoming the shortcomings of existing technologies in determining cavitation thresholds, distinguishing cavitation types, extracting spectral features, and constructing acoustic dose-response relationships.

[0008] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a method for intelligent evaluation and regulation of bacterial-mediated ultrasonic cavitation based on gene-encoded gas vesicles, comprising the following steps: Step S1: Sample and experimental preparation: Prepare a bacterial suspension containing gene-encoded gas vesicles and place it in a detection device that simulates the acoustic environment of tissue; Step S2: Cavitation signal excitation and acquisition: The bacterial suspension is subjected to ultrasonic irradiation with different peak negative pressures using an ultrasonic excitation transducer, while the generated cavitation emission signal is acquired using an independent passive cavitation detection transducer. Step S3: Spectral feature extraction and quantization: Perform time-frequency domain analysis on the collected cavitation emission signal to extract and quantify the harmonic and superharmonic component energy that characterize steady-state cavitation, as well as the broadband noise energy between harmonics that characterizes inertial cavitation. Step S4: Dose-response relationship modeling: Based on signals collected under different peak negative pressures and / or different bacterial concentrations, establish a dose-response relationship model with acoustic parameters and bacterial concentration as inputs and cavitation intensity as output; Step S5: Dynamic Fusion Evaluation and Feedback Control: Input the feature values ​​obtained after processing the real-time acquired signal in Step S3 into the dose-response relationship model to calculate the estimated value of cavitation intensity under the current state; dynamically adjust the weights of steady-state cavitation features and inertial cavitation features in the fusion evaluation according to the preset value range of the estimated value to generate a dynamic fusion cavitation effect index; and based on the comparison result of the index and the preset safe and effective threshold, adjust the acoustic parameters of the ultrasonic excitation transducer.

[0009] Furthermore, the establishment of the dose-response relationship model in step S4 specifically involves: Repeat steps S2 and S3 at multiple different peak negative pressure sound pressure levels; The total energy of the steady-state cavitation component and / or the total energy of the inertial cavitation component obtained by quantization at each sound pressure level are taken as the cavitation intensity. Using peak negative pressure as the independent variable and cavitation intensity as the dependent variable, curve fitting was performed to obtain the dose-response relationship curve and mathematical model of cavitation intensity as a function of sound pressure.

[0010] Furthermore, step S4 also includes: The above process was repeated at multiple different bacterial concentrations to establish a two-dimensional dose-response surface model with peak negative pressure and bacterial concentration as joint independent variables and cavitation intensity as dependent variable.

[0011] Furthermore, in step S5, dynamically adjusting the weights of steady-state cavitation characteristics and inertial cavitation characteristics in the fusion evaluation based on the preset value range of the estimated value specifically includes: The ratio of the harmonic and superharmonic component energy Eh to the broadband noise energy Eb is calculated in real time to obtain the feature fusion guidance factor; Based on the preset range of the feature fusion guidance factor, the weights of harmonic and superharmonic component energy and broadband noise energy are dynamically adjusted to generate a fusion evaluation index. Specifically, when the feature fusion guidance factor indicates that steady-state cavitation features are dominant, the weight of harmonic and superharmonic component energy is increased; when it indicates that inertial cavitation features are dominant, the weight of broadband noise energy is increased.

[0012] Furthermore, the step S5, which involves adjusting the acoustic parameters based on the comparison result between the index and a preset safe and effective threshold, specifically includes: Preset the safety lower limit and safety upper limit of the dynamic fusion cavitation effect index; When the index is below the safety lower limit, increase the peak negative pressure and / or duty cycle of the ultrasonic excitation; When the index is higher than the safety limit, reduce the peak negative pressure and / or pulse repetition frequency of the ultrasonic excitation; When the index is between the upper and lower safety limits, the current acoustic parameters remain unchanged.

[0013] The present invention provides a bacterial-mediated intelligent evaluation and regulation system for implementing the above method, based on gene-encoded gas vesicles, comprising: Sample containment module for holding bacterial suspensions containing gene-encoded gas vesicles; An ultrasonic excitation module, including an excitation transducer and a power control unit, is used to generate a controllable ultrasonic field; The passive cavitation detection module includes a receiving transducer and a signal conditioning unit for collecting cavitation emission signals; The data acquisition and processing module is used to perform time-frequency domain analysis on the acquired signals and extract the energy characteristics of harmonics, superharmonics, and broadband noise. The intelligent evaluation and control module includes: The relational model unit stores the dose-response relationship model; The dynamic fusion evaluation unit is configured to: receive the feature values ​​extracted by the data acquisition and processing module, call the relational model unit to calculate the cavitation intensity estimate, and dynamically fuse the harmonic, superharmonic and broadband noise energy characteristics based on the estimate to generate a dynamic fusion cavitation effect index; The feedback control unit is configured to compare the dynamic fusion cavitation effect index with a preset threshold and generate control commands to adjust the acoustic parameters of the ultrasonic excitation module.

[0014] Furthermore, the sample containing module is a flow cavity or static cavity with an acoustic transmission window, which is filled with acoustic impedance matching material to simulate the tissue environment.

[0015] Furthermore, the data acquisition and processing module is further configured to perform fast Fourier transform, bandpass filtering, and power spectral density calculation to quantify the harmonic, superharmonic, and broadband noise energy.

[0016] Furthermore, the intelligent evaluation and control module also includes a human-computer interaction interface, which is used to display the dynamic fusion cavitation effect index, real-time spectrum, dose-response curve and current acoustic parameters, and to receive the target cavitation intensity range and safety threshold set by the user.

[0017] Furthermore, the ultrasonic excitation module and the passive cavitation detection module are spatially separated, the center frequency of the receiving transducer is higher than the center frequency of the excitation transducer, and the sound beams of the two intersect in the bacterial suspension area within the sample containing module.

[0018] The beneficial effects of this invention are as follows: This invention provides a method and system for intelligent evaluation and regulation of bacterial-mediated ultrasonic cavitation based on gene-encoded gas vesicles. The method includes: constructing an experimental platform containing bacterial samples with gas vesicles and a simulated tissue environment; irradiating the sample using an ultrasonic excitation device and acquiring cavitation signals using an independent passive receiving transducer; performing time-frequency domain analysis on the signals to extract harmonic / superharmonic components characterizing steady-state cavitation and broadband noise components characterizing inertial cavitation; calculating steady-state and inertial cavitation doses to determine the cavitation threshold; establishing a quantitative relationship model between cavitation dose and acoustic parameters; and selecting or adjusting therapeutic ultrasound parameters based on the model or real-time signal characteristics. The system includes an excitation control module, an ultrasonic transducer, a sample device, a passive receiving transducer, a data processing module, and a parameter regulation module. This invention achieves quantitative characterization and controllable regulation of bacterial cavitation behavior in gas vesicles.

[0019] This method addresses the problems in existing technologies, such as the lack of systematic characterization of cavitation behavior in gas vesicle bacteria, difficulty in accurately quantifying cavitation thresholds, inability to distinguish different types of cavitation activity, and lack of dose-response relationships associated with acoustic parameters. This method constructs an experimental platform suitable for simulating tissue environments, combining passive cavitation detection technology with time-frequency domain signal analysis to achieve continuous acquisition and processing of cavitation emission signals from gas vesicle bacteria under different sound pressure and concentration conditions. This method can quantitatively obtain the cavitation threshold of gas vesicle bacteria; effectively distinguish between steady-state cavitation and inertial cavitation activities; extract and analyze the spectral characteristics of cavitation emissions; establish a dose-response relationship between acoustic parameters and cavitation intensity; and can be used to guide the selection of ultrasound therapy parameters and the control of cavitation safety.

[0020] This method provides a reliable quantitative description of the acoustic behavior of gene-derived gas vesicle bacteria, laying the technical foundation for parameter optimization and safety control in ultrasound therapy and acoustic sensitization applications. By obtaining quantitative indicators of gas vesicle cavitation under different acoustic conditions, it can provide directly referential parameter selection standards for applications such as ultrasound stimulation, ultrasound gene regulation, and ultrasound-enhanced therapy. Simultaneously, by monitoring cavitation thresholds and inertial cavitation intensity, early warning of potential tissue damage risks can be achieved, thereby improving the safety of ultrasound therapy.

[0021] The above and other objects, advantages, and features of the present invention will be more fully set forth and demonstrated through the following detailed description of specific embodiments in conjunction with the accompanying drawings. Those skilled in the art, upon referring to the following detailed description and the accompanying drawings, will be able to better understand and realize the above advantages of the present invention. Other objects, features, and advantages of the present invention will become clearer after being described in detail in the detailed description section in conjunction with the accompanying drawings. Attached Figure Description

[0022] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following drawings are provided for illustration.

[0023] Figure 1 Flowchart of a method for monitoring and regulating ultrasonic cavitation mediated by E. coli based on gene-encoded gas vesicles; Figure 2 Diagram showing the setup of the experimental platform; Figure 3 This is a typical spectrum diagram; Figure 4 This is a dose-response curve. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0025] Example 1 like Figure 1 As shown, Figure 1 The flowchart illustrates a bacterial-mediated intelligent evaluation and regulation method for ultrasonic cavitation based on gene-encoded gas vesicles. This embodiment provides a bacterial-mediated intelligent evaluation and regulation method for ultrasonic cavitation based on gene-encoded gas vesicles, comprising the following steps: Step 1: Constructing the flow channel and preparing the sample for acoustic experiments. Prepare an *E. coli* sample containing gene-encoded gas vesicles and adjust its concentration to a preset range. Construct a wall-less or acoustically impedance-matched flow channel to eliminate significant reflection interfaces in the ultrasound propagation path, simulating a tissue environment. Inject the bacterial suspension into the flow channel, ensuring uniform sample distribution under stable flow rates or static conditions. In this embodiment, the preparation process of gene-encoded gas vesicle bacteria is as follows: 1. Preparation of bacteria encoding gas vesicles: *E. coli* BL21(AI) carrying the pET28aT7-ARG1 plasmid was cultured in LB medium, selected with kanamycin, and induced to express gas vesicles with L-arabinose and IPTG. Bacteria were then collected by centrifugation and washing with PBS. Specifically, *E. coli* BL21(AI) was grown in Luria-Bertani (LB) medium with shaker parameters set to 37°C and 220 rpm. To express GVs, the plasmid pET28aT7-ARG1 was transformed into *E. coli* and cultured in LB medium containing 50 µg / ml kanamycin and 1% glucose at 37°C for 16 h. Subsequently, induction was performed in LB medium containing 50 µg / ml kanamycin and 0.2% glucose, and the initial culture was inoculated into fresh medium at a ratio of 1:100. The bacteria were cultured at 37°C until OD200. 600= 0.5% concentration, then induced at 30℃ for 22h with 0.5% L-arabinose and 0.4 mM IPTG. After induction, centrifuged (350 g, 2 h, 4℃) and washed 3 times with sterile phosphate-buffered saline (PBS).

[0026] 2. Constructing the Simulation Environment: After degassing the agarose solution, it was injected into a mold containing a graphite rod. After gelation, the graphite rod was removed, forming a 3mm channel for subsequent flow experiments. Specifically, 1.5 g of agarose powder was dissolved in 100 mL of distilled water at room temperature. The mixture was then heated in a microwave oven with intermittent stirring at 30-second intervals until completely dissolved into a clear solution. The pressure was then gradually reduced from 500 mbar to 70 mbar over 20 minutes using a vacuum pump. Before casting, the graphite rod (3 mm in diameter) was placed horizontally at the center of a custom-made acrylic mold (5 cm × 5 cm × 2 cm). The degassed agarose solution was gently poured into the mold, ensuring the graphite rod remained in place. After cooling for approximately 30 minutes to allow complete gelation, the graphite rod was carefully removed, leaving a 3 mm diameter channel in the mold for flow in subsequent experiments.

[0027] Step Two: Configure the Ultrasonic Excitation and Passive Receiving System: Install the excitation ultrasonic transducer and set parameters such as frequency, pulse width, and pulse repetition frequency. Install a passive receiving transducer separate from the excitation transducer to receive cavitation emission signals. Connect an external power amplifier, synchronous trigger, and data acquisition card to form a complete transmit-receive system. Calibrate the system output to ensure the accuracy of the excitation sound pressure. In this embodiment, the ultrasonic excitation parameters are set as follows: the center frequency of the receiving transducer is set to an integer or half-integer multiple of the excitation frequency; or the fundamental frequency f0 is selected as 1MHz, which is comparable to the clinical treatment frequency and closer to actual use. The center frequency of the receiving transducer is 2.25MHz, allowing its effective bandwidth to receive spectrum signals within 5MHz.

[0028] Step 3: Perform a progressive sound pressure level scan to obtain the cavitation threshold, setting the peak negative pressure output of the excitation transducer to an initial low value. Perform ultrasonic excitation at each sound pressure level, while a passive receiving transducer simultaneously acquires the cavitation emission signal. Gradually increase the sound pressure level until spectral characteristic changes related to cavitation are detected. Record the sound pressure values ​​at which harmonics, superharmonics, or broadband noise abrupt changes occur to determine the cavitation threshold.

[0029] Step 4: Perform time-domain and frequency-domain processing on the acquired signal. Preprocess the received signal by bandpass filtering, noise cancellation, and time window segmentation. Use Fast Fourier Transform (FFT) or power spectrum estimation to convert the time-domain signal into a frequency-domain signal. Extract cavitation features, including harmonic power, superharmonic power, and broadband noise power. Calculate the cavitation intensity index or other quantitative parameters to distinguish between steady-state cavitation and inertial cavitation.

[0030] In this embodiment, after removing the DC component from the received signal, a Fourier transform is performed to obtain the spectrum. The power spectrum is obtained by squaring the single-sided spectrum. The steady-state cavitation dose integration bandwidth is set with center frequencies of 0.5f0, 1.5f0, 2.5f0, 3.5f0, 4.5f0, etc., and bandwidths of 1kHz for 2f0, 3f0, and 4f0. The inertial cavitation dose is set as the 0-5MHz bandwidth energy minus the broadband noise energy outside the bandwidth settings of the 1kHz bandwidth with center frequency f0 and the aforementioned steady-state cavitation dose.

[0031] Step 5: Establish the dose-response relationship between cavitation intensity and acoustic parameters. Repeat steps 3 and 4 under different peak negative pressures and bacterial concentrations. Compile the cavitation intensities under each sound pressure or concentration condition into a dataset. Construct the cavitation intensity-sound pressure / concentration relationship curve using nonlinear fitting, piecewise functions, or exponential models. Determine the sound pressure range entering the rapid growth phase and the saturation trend of cavitation behavior. In this embodiment, the peak negative sound pressure can be set to 1MPa-3MPa; the gradient to 0.2MPa; and the bacterial concentration to OD. 600 =0.5, 1.0, 1.5, 2.0; repeat the process to obtain the dose-response curve.

[0032] Step Six: Adjust ultrasound treatment parameters based on cavitation behavior, selecting a safe and effective peak negative pressure range based on the cavitation intensity threshold. Choose a suitable ultrasound treatment mode based on the characteristic ratio of steady-state cavitation to inertial cavitation. Adjust acoustic parameters such as pulse width, frequency, and repetition rate according to the dose-response model. Establish a feedback mechanism to perform feedback detection during treatment and automatically adjust acoustic parameters based on real-time cavitation intensity.

[0033] Specifically, in this embodiment, the feature values ​​obtained after processing the real-time acquired signals are input into the dose-response relationship model to calculate the estimated value of cavitation intensity under the current state; Based on the preset value range in which the estimated value is located, the weights of steady-state cavitation characteristics and inertial cavitation characteristics in the fusion evaluation are dynamically adjusted to generate a dynamic fusion cavitation effect index; and based on the comparison result between the index and the preset safe and effective threshold, the acoustic parameters of the ultrasonic excitation transducer are adjusted accordingly. In this embodiment, the ratio or difference between the harmonic and superharmonic component energy Eh and the broadband noise energy Eb is calculated in real time to obtain the feature fusion guidance factor; according to the preset range of the feature fusion guidance factor, the weights of the harmonic and superharmonic component energy and the broadband noise energy are dynamically adjusted to generate a fusion evaluation index. Specifically, when the feature fusion guidance factor indicates that steady-state cavitation features are dominant, the weight of harmonic and superharmonic component energy is increased; when it indicates that inertial cavitation features are dominant, the weight of broadband noise energy is increased. In this embodiment, the feature fusion guidance factor is R = Eh / (Eh + Eb + ε), where ε is a minimal constant to avoid division by zero. Three ranges are preset based on the R value: Low range (R < 0.3): Steady-state cavitation characteristics are not significant, generating an evaluation index biased towards monitoring inertial cavitation activity and potential risks, with a weight configuration of CI = 0.2 * Eh + 0.8 * Eb; Mid range (0.3 ≤ R ≤ 0.7): Mixed cavitation state, generating an overall evaluation index comprehensively reflecting the cavitation state, with a weight configuration of CI = 0.5 * Eh + 0.5 * Eb; High range (R > 0.7): Steady-state cavitation characteristics are dominant, generating an evaluation index biased towards monitoring steady-state cavitation activity, with a weight configuration of CI = 0.8 * Eh + 0.2 * Eb. The feedback control algorithm adjusts according to the above CI values.

[0034] This embodiment can define three value ranges for the preset cavitation intensity estimate: low, medium, and high. When the estimate is in the low value range, the weight of harmonic and superharmonic component energy is increased to generate an evaluation index that is biased towards monitoring steady-state cavitation activity. When the estimate is in the high value range, the weight of broadband noise energy is increased to generate an evaluation index that is biased towards monitoring inertial cavitation activity and potential risks. When the estimate is in the medium value range, a balanced weight is used to generate an overall evaluation index that comprehensively reflects the cavitation state.

[0035] In this embodiment, the low-value range can be set as: PNP < 0.5 MPa. At this pressure, steady-state cavitation is mainly induced; the mid-value range can be set as: 0.5 MPa ≤ PNP ≤ 1.2 MPa. Mixed cavitation state; the high-value range can be set as: PNP > 1.2 MPa. The risk of inertial cavitation increases significantly.

[0036] The weights in this embodiment can be calculated as follows: Define the evaluation index CI (Cavitation Index), which is obtained by weighted summation of normalized harmonic / superharmonic energy (Eh) and broadband noise energy (Eb). Specifically: Low value range: CI = 0.8 * Eh + 0.2 * Eb (focusing on steady-state cavitation); Mid value range: CI = 0.5 * Eh + 0.5 * Eb (equilibrium monitoring); High value range: CI = 0.2 * Eh + 0.8 * Eb (focusing on inertial cavitation and risk).

[0037] The feedback control algorithm in this embodiment can employ proportional-integral (PI) control, which is simple in structure and easy to stabilize. The goal is to maintain CI within a set target range (e.g., the CI value corresponding to the median range). The parameters being adjusted are the peak negative pressure (PNP) and duty cycle (DC) of the ultrasonic emission. When CI remains above the target value (indicating excessive cavitation activity), the control algorithm will proportionally reduce PNP (e.g., by a step of 0.05 MPa) or reduce DC (e.g., by a step of 2%) to reduce energy input. When CI remains below the target value, PNP or DC is increased to enhance the effect. Priority is given to adjusting the duty cycle, as it has a more direct impact on tissue thermal effects; if adjusting the duty cycle to its limit still fails to achieve the target, then the peak negative pressure is adjusted.

[0038] Simultaneously, a safety lower limit can be set: typically determined by the minimum effective sound pressure level, i.e., the PNP threshold that produces detectable biological effects (such as vesicle rupture), which can be determined based on preliminary experiments (e.g., 0.3 MPa); the safety upper limit is jointly constrained by clinical safety standards for mechanical index (MI) and thermal index (TI). For in vitro experiments, a typical safety upper limit can be set as follows: MI ≤ 1.0 (corresponding to approximately 1.5 MPa PNP @ 1MHz, strictly controlling the risk of inertial cavitation); TI ≤ 0.7 (controlling temperature rise, managed through duty cycle and sound intensity).

[0039] The system should calculate MI and TI in real time. If either exceeds the limit, an alarm will be triggered and the ultrasonic transmission will be forcibly interrupted.

[0040] Step Seven: System Integration and Data Output. Integrate all the above modules into a unified control platform, including excitation control, signal acquisition, data analysis, and display interface. Display results such as cavitation threshold, harmonic intensity, broadband noise energy, and dose-response curves. Output recommended acoustic parameter ranges for guiding treatment or research.

[0041] Example 2 This embodiment uses *Escherichia coli* as an example to illustrate the specific process of a bacterial-mediated intelligent evaluation and regulation method for ultrasonic cavitation based on gene-encoded gas vesicles. Gas vesicle cavitation is characterized and regulated through passive cavitation detection and spectral analysis. The main workflow units are as follows: I. Constructing a controllable ultrasonic excitation and acquisition unit with cavitation threshold, as detailed below: 1. An ultrasonic emission module with adjustable peak negative pressure achieves the transition from no cavitation to cavitation by scanning the sound pressure step by step; 2. An independent passive cavitation receiver transducer is used to collect acoustic emission signals of gas vesicle bacteria under various sound pressure conditions in real time. 3. A threshold determination method based on cavitation signal power variation: by analyzing the abrupt change points caused by broadband noise enhancement or harmonics, a quantifiable cavitation occurrence threshold is determined.

[0042] This unit solves the technical problem of not being able to quantitatively obtain the cavitation threshold of gas vesicle bacteria in the prior art. This unit realizes the quantification and repeatability of the cavitation threshold.

[0043] This unit enables the quantitative determination of bacterial cavitation thresholds in gas vesicles. By constructing an externally triggered full-element passive receiving acquisition mode and employing high temporal resolution (microsecond-level) data acquisition and precise sound pressure calibration process, it achieves the first repeatable and traceable quantitative measurement of cavitation activity generated by gas vesicles under different sound pressures, and can clearly obtain the sound pressure thresholds of steady-state cavitation and inertial cavitation.

[0044] II. Construct a frequency domain feature analysis unit to distinguish between steady-state cavitation and inertial cavitation, as detailed below: 1. Fast Fourier Transform (FFT) analysis process for time-domain signals; 2. Steady-state cavitation is determined by identifying harmonic (H) and superharmonic (UH) components in the spectrum; 3. Inertial cavitation is determined by detecting the broadband noise energy between harmonics; 4. Different types of cavitation behavior are distinguished by using threshold judgment and feature ratio.

[0045] This unit solves the technical problem of the inability to effectively distinguish between steady-state cavitation and inertial cavitation activities in the prior art. This unit realizes the qualitative and quantitative distinction between different cavitation modes.

[0046] This unit effectively distinguishes between steady-state cavitation and inertial cavitation activities. It employs a spectral decomposition method based on Fast Fourier Transform (FFT) to extract harmonic, superharmonic, and broadband noise features, achieving automated differentiation between steady-state and inertial cavitation. Compared to existing analysis methods that rely on empirical judgment, this method offers higher objectivity and classification accuracy.

[0047] III. Constructing a signal processing unit to extract cavitation emission spectral features, as detailed below: 1. Signal preprocessing workflow (bandpass filtering, noise suppression, time window division); 2. Spectral power calculation methods (FFT or Welch power spectral density estimation); 3. Extract key acoustic feature parameters, including: Harmonic amplitude / power, superharmonic amplitude / power, broadband noise energy, cavitation intensity dose, and provide a multi-parameter characteristic matrix for stable, inertial cavitation.

[0048] This unit addresses the lack of systematic extraction and analysis techniques for the spectral characteristics of cavitation emissions, achieving comprehensive, stable, and quantifiable spectral analysis.

[0049] This unit enables the systematic and parameterized extraction of cavitation spectral characteristics. Utilizing a standardized spectral analysis process, it can quantitatively extract characteristic parameters, including harmonic amplitude, superharmonic intensity, broadband noise energy, and spectral integral values. This achieves a structured and systematic characterization of cavitation activity in the frequency domain, laying a data foundation for subsequent model construction and mechanism research.

[0050] IV. Construct a cavitation intensity model for the dose-response relationship, as follows: 1. Repeatedly collect cavitation signals under different peak negative pressures and different bacterial concentrations; 2. Construct a curve showing the variation of cavitation intensity with sound pressure; 3. Use nonlinear fitting, piecewise linear models, or exponential growth models to describe the dose-response relationship; 4. Output a mathematical model that can predict cavitation intensity for subsequent parameter selection and control.

[0051] This unit solves the technical problem that existing methods cannot establish a dose-response relationship between acoustic parameters and cavitation intensity. This unit realizes a quantitative description of cavitation behavior by acoustic parameters.

[0052] V. Construct a cavitation control unit to guide the selection of treatment parameters and safety control, as detailed below: 1. A method for determining the safe sound pressure range based on cavitation threshold; 2. Treatment mode selection method based on steady-state and inertial cavitation ratio; 3. Based on the dose-response curve, select appropriate acoustic parameters such as sound pressure level, pulse width, and frequency; 4. Develop feedback control strategies (implemented online or offline) that can be used for real-time adjustments to treatment.

[0053] This unit addresses the lack of technical means to guide the selection of ultrasound treatment parameters and control cavitation safety. This unit enables cavitation activities to be controllable and safe, and can be used to optimize treatment parameters.

[0054] This unit can establish a dose-response relationship model between acoustic parameters and cavitation intensity. Through a large amount of experimental data, it can construct a quantitative relationship model between sound pressure, pulse width, sound exposure time and cavitation intensity, and realize predictive estimation of cavitation intensity.

[0055] Example 3 This embodiment further illustrates the method with specific illustrations and implementation details. Specifically: like Figure 2 As shown, Figure 2 The experimental platform setup diagram shows that this embodiment constructs an excitation transducer and power control module; a passive receiving transducer; a sample containment module; a data acquisition and control module; a signal processing unit (software or hardware); and a parameter setting and result display interface to realize an integrated system for excitation, acquisition, analysis, and regulation of the cavitation behavior of gas vesicle bacteria. In this embodiment, the sample containing module can be a flow channel, an agarose phantom, and a flow channel; The specific processes for each part are explained below: 1. Water tank and sound-absorbing material: The water tank is filled with degassed water, and the ultrasonic cavitation process takes place in the water tank. Sound-absorbing material is pasted on the inner wall of the water tank. This creates a controllable basic acoustic propagation environment with minimal sound attenuation and reflection interference.

[0056] 2. Focused Ultrasonic Transducer (Excitation Transducer): The focused transducer, located in the water tank, is an active emission source used to generate and focus ultrasonic energy and excite gas vesicle cavitation.

[0057] 3. Agarose phantom and flow channel: A block-shaped phantom placed in water with a continuous channel inside, serving as a sample container to simulate the acoustic environment of tissue and hold a flowing bacterial sample suspension. The phantom material (agarose) and the channel are used to achieve "acoustic impedance matching" and "tissue environment simulation".

[0058] In this embodiment, the agarose phantom was prepared as follows: 1.5 g of agarose powder was dissolved in 100 mL of distilled water at room temperature. The mixture was then heated in a microwave oven with intermittent stirring at 30-second intervals until completely dissolved into a clear solution. The pressure was then gradually reduced from 500 mbar to 70 mbar over 20 minutes using a vacuum pump. Before casting, a graphite rod (3 mm in diameter) was placed horizontally at the center of a custom-made acrylic mold (5 cm × 5 cm × 2 cm). The degassed agarose solution was gently poured into the mold, ensuring the graphite rod remained stable. After cooling for approximately 30 minutes to allow complete gelation, the graphite rod was carefully removed, leaving a 3 mm diameter channel in the phantom for flow in subsequent experiments.

[0059] 4. Planar Receiving Transducer: This transducer has its acoustic axis aligned with the flow channel within the phantom body. It serves as a receiving source, specifically designed for highly sensitive reception of broadband acoustic emission signals generated by cavitation, and is a core component of passive cavitation detection.

[0060] 5. Signal generation and amplification unit: The signal generator is connected to the power amplifier and excitation transducer; it is an electronic device that constitutes the excitation control module, used to generate and amplify ultrasonic excitation signals of specific frequency, pulse width and sound pressure.

[0061] 6. Signal Receiving and Acquisition Unit: This unit connects to the oscilloscope and receiving transducer via a pulse transceiver. It constitutes the hardware for data acquisition, used to amplify, digitize, and record the cavitation signal transmitted from the receiving transducer. The pulse transceiver provides gain, and the oscilloscope performs analog-to-digital conversion at a high sampling rate (e.g., 62.5 MHz).

[0062] The signal generator is connected to the control unit (control computer) and oscilloscope to form a signal line, ensuring that the moment of ultrasonic excitation is strictly synchronized with the moment of data acquisition. This is the basis for accurate analysis of time-domain signals and multiple signal averaging.

[0063] Sound wave path and relative position: the spatial alignment of the focal point of the excitation transducer, the center of the phantom flow channel, and the acoustic axis of the receiving transducer. This ensures the correct geometry of the experimental setup, allowing the excitation sound field to effectively act on the sample and the generated cavitation signal to be effectively captured by the receiving transducer.

[0064] like Figure 3 As shown, Figure 3This is a typical spectrum diagram; it shows the time-frequency plot of the cavitation signal. The left side represents the time-domain signal, and the signal peak increases sharply during ultrasonic irradiation. The spectrum diagram shows the presence of subharmonics such as 0.5f0, 1.5f0, and 2.5f0, as well as harmonics such as 2f0, 3f0, and 40, indicating the occurrence of steady-state cavitation. The spectrum in the 0-5MHz range shows an upward shift compared to the overall spectrum. The presence of this broadband noise indicates inertial cavitation.

[0065] like Figure 4 As shown, Figure 4 The dose-response curve is shown below: The peak negative pressure corresponds to the inertial cavitation dose (broadband noise). The red curve represents the control group (PBS), and the black curve represents the experimental group (OD). 600 =2.0, where OD 600 It refers to the optical density value measured at a wavelength of 600nm, used to estimate the concentration of bacteria or other cells in a liquid sample.

[0066] In this embodiment, the focused ultrasound transducer is irradiated in a water tank filled with degassed water, with sound-absorbing material adhered to the walls to minimize sound attenuation. The focusing unit transducer is placed on the phantom, with its focal point aligned with the center of the agarose flow channel. The acoustic signal is generated by an arbitrary waveform generator, amplified by an RF power amplifier, and transmitted to the transducer. The receiving system includes a pulse transceiver, a water immersion receiver, and an oscilloscope for signal acquisition. The agar phantom is perfused with a suspension of *E. coli* vesicles through designed channels.

[0067] To characterize the type and intensity of acoustic cavitation generated by *E. coli* gas vesicles, a planar transducer was used to receive passive cavitation signals. The cavitation signals from *E. coli* gas vesicles after ultrasonic irradiation were received by the probe, amplified by a pulse transceiver with a gain of 20 dB, and then digitized using an oscilloscope at a sampling rate of 62.5 MHz. The oscilloscope was synchronized with a trigger signal from a waveform generator to ensure timing alignment; each acquisition began with the rising edge of the trigger and continued throughout the entire acquisition window.

[0068] Based on the spectral characteristics of the emitted cavitation signal, stable cavitation and inertial cavitation were quantified. Harmonic and superharmonic components indicate stable cavitation, while broadband noise between harmonics is related to inertial cavitation. For each acquisition window, the recorded time-domain signal was converted into a spectrum and power spectrum using FFT. The implementation method is shown in Equation 1.

[0069] X(f) = FFT{X(t)} (1) In the formula, X(f) represents the spectrum of the time-domain signal X(t), and f represents the frequency axis.

[0070] To separate the relevant frequency components, a comb bandpass filter with a bandwidth of 1 kHz was used to extract the second, third, and fourth harmonics, as well as the 0.5, 1.5, 2.5, 3.5, and 4.5 superharmonics.

[0071] In this embodiment, a Hamming window can be used for signal processing to reduce spectral leakage. The Hamming window smooths the signal at both ends, reducing the amplitude of sidelobes and thus improving the accuracy of spectral analysis. FFT points (N): In this embodiment, the selected FFT points N are the closest power of 2 to the signal length, which is 31254, ensuring efficient FFT computation. Specifically, the non-DC portion of the spectrum is appropriately normalized to obtain a single-sided spectrum. Overlap rate: Ensures 50% overlap between different signal segments to maintain continuity and reduce errors introduced by windowing. FIR filter (Finite Impulse Response filter): Has linear phase characteristics to ensure no phase distortion is introduced during frequency domain filtering. A 100th-order filter is selected to ensure a steep frequency response. Bandwidth is selected as 0-5MHz.

[0072] Harmonic and superharmonic components are hallmarks of stable cavitation, while broadband noise between harmonics is associated with inertial cavitation. For each acquisition window, the recorded time-domain signal x(t) was converted to a spectrum x(f) and a power spectral density |x(f)|² using Fast Fourier Transform (FFT) in MATLAB. To isolate relevant frequency components, a comb bandpass filter with a bandwidth of 1 kHz was used in a rectangular window to extract the 2nd, 3rd, and 4th harmonics, as well as the 0.5th, 1.5th, 2.5th, 3.5th, and 4.5th superharmonics, in MATLAB. The cumulative energy of the harmonic and superharmonic components was defined as the stable cavitation dose (SCD), and the cumulative energy of the broadband noise was defined as the inertial cavitation dose (ICD).

[0073] The implementation method is shown in Equation 2.

[0074] ; Where f0 = 1.062 MHz is the fundamental frequency. The filter is designed for harmonic and superharmonic frequencies to achieve stable cavitation, and for inertial cavitation to isolate broadband noise. The energy of these components is integrated over the entire radiation duration: the cumulative energy of the harmonic and superharmonic components is defined as the stable cavitation dose (SCD), while the combined energy of the broadband noise is defined as the inertial cavitation dose (ICD).

[0075] By irradiating E. coli vesicles at different concentrations with different acoustic parameters (peak negative pressure, duty cycle, pulse repetition frequency, frequency, etc.), the cavitation signal effects of E. coli vesicles under different acoustic parameters were obtained, and their corresponding relationships were derived.

[0076] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A method for intelligent evaluation and regulation of ultrasound cavitation mediated by bacteria based on gene-encoding gas vesicles, characterized in that, The method comprises the following steps: Step S1: sample and experimental preparation: prepare a bacterial suspension containing gas vesicle-encoding genes and place it in a detection device simulating the acoustic environment of the tissue; Step S2: cavitation signal excitation and collection: use an ultrasonic excitation transducer to apply ultrasonic irradiation with different peak negative pressures to the bacterial suspension, and use an independent passive cavitation detection transducer to collect the generated cavitation emission signals; Step S3: spectral feature extraction and quantification: perform time-frequency domain analysis on the collected cavitation emission signals, extract and quantify the harmonic and superharmonic component energy representing steady-state cavitation, and the broadband noise energy between harmonics representing inertial cavitation; Step S4: dose-response relationship modeling: based on the signals collected under different peak negative pressures and / or different bacterial concentrations, establish a dose-response relationship model with acoustic parameters and bacterial concentration as input and cavitation intensity as output; Step S5: dynamic fusion evaluation and feedback regulation: input the characteristic values obtained by processing the real-time collected signals in step S3 into the dose-response relationship model to calculate the cavitation intensity estimate value under the current state; according to the preset value range of the estimate value, dynamically adjust the weights of steady-state cavitation features and inertial cavitation features in the fusion evaluation to generate a dynamic fusion cavitation effect index; and based on the comparison result of the index and the preset safe and effective threshold, feedback adjust the acoustic parameters of the ultrasonic excitation transducer.

2. The method according to claim 1, wherein the method is characterized by, The dose-response relationship model established in step S4 is specifically: Repeat steps S2 and S3 under multiple different peak negative pressure sound pressure levels; Take the total energy of the steady-state cavitation components and / or the total energy of the inertial cavitation components quantified under each sound pressure level as the cavitation intensity; Perform curve fitting with the peak negative pressure as the independent variable and the cavitation intensity as the dependent variable to obtain the dose-response relationship curve and mathematical model of the cavitation intensity with respect to the sound pressure.

3. The method according to claim 2, wherein the method is characterized by, The step S4 further comprises: Repeat the above process under multiple different bacterial concentrations to establish a two-dimensional dose-response relationship surface model with the peak negative pressure and the bacterial concentration as the joint independent variables and the cavitation intensity as the dependent variable.

4. The method according to claim 1, wherein the method is characterized by, In step S5, dynamically adjusting the weights of steady-state cavitation features and inertial cavitation features in the fusion evaluation according to the preset value range of the estimate value specifically includes: Real-time calculate the ratio of the harmonic and superharmonic component energy Eh to the broadband noise energy Eb to obtain a feature fusion guide factor; According to the preset range of the feature fusion guide factor, dynamically adjust the weights of the harmonic and superharmonic component energy and the broadband noise energy to generate a fusion evaluation index; Wherein, when the feature fusion guide factor indicates that the steady-state cavitation feature is dominant, the weight of the harmonic and superharmonic component energy is increased; when the feature fusion guide factor indicates that the inertial cavitation feature is dominant, the weight of the broadband noise energy is increased.

5. The method of claim 1, wherein the method is characterized by, In step S5, feedback adjusting the acoustic parameters based on the comparison result of the index and the preset safe and effective threshold specifically includes: Pre-set the lower limit and upper limit of the safe dynamic fusion cavitation effect index; When the index is lower than the lower limit of safety, increase the peak negative pressure and / or duty cycle of the ultrasonic excitation. When the index is above the upper safety limit, reduce the peak negative pressure and / or pulse repetition frequency of the ultrasonic excitation; When the index is between the upper and lower safety limits, maintain the current acoustic parameters unchanged.

6. A gene-encoded gas vesicle-based bacterial-mediated ultrasonic cavitation intelligent evaluation and regulation system for implementing the method of any one of claims 1-5, characterized in that, Comprise: a sample holding module for carrying a bacterial suspension containing gas vesicle-encoding bacteria; an ultrasonic excitation module comprising an excitation transducer and a power control unit for generating a controllable ultrasonic field; a passive cavitation detection module comprising a receiving transducer and a signal conditioning unit for collecting cavitation emission signals; a data acquisition and processing module for time-frequency domain analysis of the collected signals to extract harmonic, ultraharmonic and broadband noise energy features; an intelligent evaluation and regulation module comprising: a relationship model unit storing the dose-response relationship model; a dynamic fusion evaluation unit configured to receive the feature values extracted by the data acquisition and processing module, calculate the cavitation intensity estimate value by calling the relationship model unit, and dynamically fuse the harmonic, ultraharmonic and broadband noise energy features according to the estimate value to generate a dynamic fusion cavitation effect index; a feedback control unit configured to compare the dynamic fusion cavitation effect index with a preset threshold and generate a control instruction to adjust the acoustic parameters of the ultrasonic excitation module.

7. The gene-encoded gas vesicle-based bacterial-mediated ultrasonic cavitation intelligent evaluation and regulation system according to claim 6, characterized in that, The sample holding module is a flow chamber or a static chamber with an acoustic transmission window, which is filled with an acoustic impedance matching material to simulate a tissue environment.

8. The gene-encoded gas vesicle-based bacterial-mediated ultrasonic cavitation intelligent evaluation and regulation system according to claim 6, characterized in that, The data acquisition and processing module is further configured to perform fast Fourier transform, bandpass filtering and power spectral density calculation to quantify the harmonic, ultraharmonic and broadband noise energy.

9. The gene-encoded gas vesicle-based bacterial-mediated ultrasonic cavitation intelligent evaluation and regulation system according to claim 6, characterized in that, The intelligent evaluation and regulation module further comprises a human-computer interaction interface for displaying the dynamic fusion cavitation effect index, real-time spectrum, dose-response relationship curve and current acoustic parameters, and receiving user-set target cavitation intensity range and safety threshold.

10. The gene-encoded gas vesicle-based bacterial-mediated ultrasonic cavitation intelligent evaluation and regulation system according to claim 6, wherein, The ultrasonic excitation module and the passive cavitation detection module are arranged separately in space, the center frequency of the receiving transducer is higher than that of the excitation transducer, and the acoustic beams of the two intersect in the bacterial suspension area of the sample holding module.

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