Brain microvascular endothelial cell injury assessment method

Abstract

The invention relates to the technical field of cell sensing, and discloses a brain microvascular endothelial cell injury assessment method, which comprises the following steps of: synchronously acquiring a metabolic vibration signal and a transcellular resistance signal of brain microvascular tissues through a sensor carrier integrating a piezoelectric sensing unit and an electrode unit, and calculating a correlation coefficient of time domain envelope of the signals; the cell damage state is judged based on continuous negative correlation, early warning of cell function instability is achieved through double-signal dynamic coupling analysis, a harmonic phase locking recognition mechanism can capture the recessive pathological state in the compensation period, and signal entropy change compensation enables the system to have the capacity of self-adaption to environment interference. And a real-time and accurate capillary function evaluation means is provided for clinic.

Description

TECHNICAL FIELD

[0001] The present application relates to a brain microvascular endothelial cell injury evaluation method, belonging to the field of cell sensing technology. BACKGROUND

[0002] In the field of cell sensing technology, the evaluation of brain microvascular endothelial cell injury mainly relies on ex vivo biomarker detection or medical imaging observation. Such methods indirectly judge the injury by capturing specific proteins (such as vWF) released after cell injury or tissue structure deformation (such as MRI edema band). There is a significant time lag in this method. In the clinical scenario of acute cerebral stroke reperfusion therapy, doctors need to know the state of endothelial barrier function in real time to prevent secondary injury. However, the existing technology can only provide evidence several hours later, resulting in the loss of the golden intervention window.

[0003] The single-parameter cell sensor developed in recent years, such as transendothelial resistance monitoring or piezoelectric metabolic vibration detection, has improved the dynamic monitoring capability. However, the isolated analysis of a single physical quantity has inherent limitations. When the cell is in a transitional state of stress compensation, the synergistic evolution characteristics of energy metabolism enhancement and barrier function attenuation are ignored, making it difficult to capture early functional instability.

[0004] The industry has tried to integrate multiple sensors through microfluidic chips to improve monitoring dimensions. However, such systems rely on constant temperature environments and precise fluid control, which cannot adapt to dynamic clinical scenarios such as operating rooms. Their signal processing often requires complex frequency domain transformation and machine learning algorithms, which are difficult to complete in real time on edge computing devices. The dynamic coupling relationship between cell energy and barrier function has not been converted into quantifiable sensing features, resulting in a systematic gap between high-precision sensing solutions and the need for immediate clinical diagnosis. The lack of adaptive correction ability when the monitoring reference drifts due to factors such as the decay of ex vivo samples and environmental disturbances. Therefore, how to construct a brain microvascular endothelial cell functional instability real-time monitoring method with multi-parameter dynamic coupling analysis capability, clinical scenario adaptability, and environmental robustness has become a technical problem to be solved by the present application. SUMMARY

[0005] The present application provides a brain microvascular endothelial cell injury evaluation method, which mainly aims to solve the problem of multi-parameter cooperative real-time monitoring of brain microvascular endothelial cell functional instability in clinical scenarios.

[0006] To achieve the above-mentioned purpose, the present application provides a brain microvascular endothelial cell injury evaluation method, comprising the following steps: Step a, obtaining a sensor carrier, the sensor carrier is integrated with a piezoelectric sensing unit for collecting cell metabolic vibration signals and an electrode unit for collecting transcellular resistance signals; Step b, placing the brain microvessel tissue to be tested on the sensor carrier and maintaining a wet environment for the brain microvessel tissue; Step c, synchronously collecting the vibration signal output by the piezoelectric sensing unit and the resistance signal output by the electrode unit; Step d, calculating the Pearson correlation coefficient between the time-domain envelope of the vibration signal and the resistance signal within a limited time window; Step e, determining the damage state of the brain microvessel endothelial cells based on the change in the Pearson correlation coefficient, wherein when the Pearson correlation coefficient continuously falls below a negative value threshold and the duration meets a time length requirement, it is determined that the brain microvessel endothelial cells are damaged.

[0007] Preferably, the negative value threshold in step e is negative zero point seven and the time length requirement is greater than sixty seconds.

[0008] Preferably, the following steps are further included between or in parallel with steps c and d: based on the collected vibration signal, calculating an entropy value representing the information complexity of the vibration signal in real time; and in step e, dynamically adjusting the negative value threshold according to the time rate of change of the entropy value, wherein the dynamically adjusted negative value threshold is determined by the following formula: wherein, is the dynamically adjusted negative value threshold, is the original negative value threshold, is the adjustment coefficient, is the time rate of change of the entropy value.

[0009] Preferably, the following steps are further included in parallel before step c determines the damage state of the brain microvessel endothelial cells: based on the collected vibration signal, extracting the phase of the fundamental frequency signal and the phase of at least one harmonic signal of the vibration signal; monitoring the phase difference between the phase of the fundamental frequency signal and the phase of the harmonic signal; and when the phase difference is maintained within a range of less than or equal to fifteen degrees for a set duration, it is determined that the brain microvessel endothelial cells are in a functional compensation state, and a warning signal is generated.

[0010] Preferably, the sensor carrier is a piece of disposable slide glass, and the piezoelectric sensing unit and the electrode unit are integrated on the disposable slide glass.

[0011] Preferably, the piezoelectric sensing unit is attached below the center of the electrode array of the disposable slide glass through acoustic coupling glue.

[0012] Preferably, the calculation of the Pearson correlation coefficient in step d is completed by a microcontroller electrically connected to the sensor carrier.

[0013] Preferably, the microcontroller is an 8-bit microcontroller, and the calculation of the Pearson correlation coefficient comprises digital envelope detection of the vibration signal, and sliding window calculation of the time domain envelope of the vibration signal and the resistance signal.

[0014] Preferably, the frequency of synchronously collecting the vibration signal and the resistance signal is at least ten times per second.

[0015] Preferably, the brain microvascular tissue is a small piece of brain tissue with a size less than one cubic millimeter removed during surgery.

[0016] Compared with the prior art, the present application has the following beneficial effects: 1. By synchronously analyzing the dynamic coupling relationship between the time domain envelope of the metabolic vibration signal and the transcellular resistance signal, the system can capture the synergistic instability characteristics between cell energy metabolism and barrier function. The sustained negative correlation between the two signals under certain pathological conditions enables the identification of the critical state of functional compensation before the occurrence of cell structure damage, creating a key time window for clinical intervention. In the signal processing path, the complexity entropy value change of the metabolic vibration signal is converted into a dynamic scale of tissue activity attenuation, and the real-time linkage mechanism of the entropy change rate and the damage judgment threshold enables the system to autonomously perceive the physiological baseline drift of the sample after being removed from the body, thereby maintaining the stability of the damage criterion in the time-varying environment and avoiding the risk of misjudgment caused by a single quantitative threshold.

[0017] 2. Based on the phase locking phenomenon of the fundamental frequency and harmonics in the vibration signal, the system can detect the transient functional compensation behavior of cells before energy depletion. This analytical ability of the signal microstructure avoids the limitations of traditional binary judgment, forming a three-state recognition system of normal-compensation-damage, and providing deeper insight into the pathological process for clinical practice. The parallel processing of the four parameters of vibration signal envelope, resistance signal, entropy value, and harmonic phase constructs a cross-validation technical closed loop. When the main criterion temporarily fails due to the compensation state, the harmonic phase analysis automatically enhances the system sensitivity. When the sample activity decays, the entropy change mechanism dynamically corrects the judgment benchmark. This mutual calibration of multi-dimensional signals enables the system to have endogenous stability against environmental disturbances.

[0018] 3. The miniaturized integration of the piezoelectric sensing unit and the electrode unit on the disposable slide, combined with the lightweight implementation of complex algorithms by the edge computing module, integrates multi-parameter synchronous acquisition, signal analysis, and state judgment into a bedside operable detection process. The collaborative design of the sensor carrier and the microcontroller enables the laboratory-level cell monitoring capability to be transformed into an instant diagnosis tool in a clinical scenario. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The flowchart of the brain microvascular endothelial cell damage evaluation system of the present application; Figure 2Signal change diagram for evaluating brain microvascular endothelial cell damage of the present application; Figure 3 Signal change and correlation analysis diagram for evaluating brain microvascular endothelial cell damage of the present application; Figure 4 Signal processing and judgment flow chart for evaluating brain microvascular endothelial cell damage of the present application.

[0020] The purposes, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0022] The embodiment of the application provides a brain microvascular endothelial cell damage evaluation method, which realizes instant and multidimensional evaluation of the function of an isolated brain microvascular tissue by a microsystem integrating sensing, collecting and edge computing, and the whole operation process of the system starts from a disposable sensor carrier, the carrier is integrated with a sensing unit for synchronously capturing two key physiological signals of cells, the collected double signals are sent into a microcontroller for parallel processing, the microcontroller executes three sets of core algorithms: one is a main damage judgment logic based on double-signal correlation analysis, the second is an adaptive threshold compensation logic based on vibration signal entropy rate, and the third is an implicit compensation state early warning logic based on harmonic phase locking, the three work together, and finally output a three-state evaluation conclusion about cell normal-compensation-damage, in a typical clinical application scenario, for example, in the process of cerebral stroke reperfusion treatment surgery, a piece of brain tissue smaller than one cubic millimeter is taken from the brain of a patient as a sample to be measured, and the specific execution steps of the evaluation method are as follows: first, a sensor carrier is prepared, the carrier is a piece of pretreated disposable glass slide, and the carrier is integrated with an interdigital electrode array for collecting transcellular resistance signals and a piezoelectric sensing unit for collecting metabolic vibration signals of cells through a microelectromechanical system process, the piezoelectric sensing unit, for example, a piece of piezoelectric ceramic, is accurately attached below the center of the electrode array through acoustic coupling glue, so as to ensure that the two signals are obtained from the center area of the same cell colony, realize physical alignment at the source, and the whole carrier is electrically connected with an external eight-bit microcontroller selected by considering low power consumption and real-time computing capability through a standard interface, then, the brain microvascular tissue sample obtained is gently placed on the electrode array area of the sensor carrier, and an appropriate amount of physiological saline or artificial cerebrospinal fluid is added by using a dropper to maintain the wet physiological environment of the tissue sample during the whole test process, once the tissue is properly placed, the microcontroller is started to synchronously collect the vibration signal output by the piezoelectric sensing unit and the resistance signal output by the electrode unit, in order to accurately capture the dynamic changes of cell function, the synchronous signal collection frequency is set to be not less than ten times per second.

[0023] After the signal collection is started, three processing threads in the microcontroller are started in parallel, the main damage judgment thread is responsible for executing the core damage state evaluation, since the weakening of cell energy metabolism is usually accompanied by the destruction of cell tight junction, that is, the loss of barrier function, the two processes present a quantifiable coupling relationship when the function is unstable, in order to capture the relationship, the microcontroller first performs digital envelope detection on the collected high-frequency metabolic vibration signal to extract the time-domain envelope representing the vibration intensity, and then, in a continuously sliding limited time window, for example, a two-second window, the Pearson correlation coefficient between the time-domain envelope of the vibration signal and the transcellular resistance signal collected synchronously is calculated in real time , while the barrier function is destroyed leading to a decrease in electrical resistance, both showing a significant and persistent negative correlation, thus the system sets a damage determination procedure: when the calculated Pearson correlation coefficient is persistently below a negative value threshold, and the persistence time meets a time length requirement, the system determines that the brain microvascular endothelial cells are damaged, where the negative value threshold is set to negative zero point seven, and the time length requirement is set to greater than sixty seconds, the determination of these two parameters is based on statistical analysis of a large number of experimental data of in vitro samples from healthy to necrosis, and the combination value with the highest specificity and sensitivity in distinguishing irreversible damage is selected, at the same time, the adaptive compensation thread is activated to cope with the measurement baseline drift caused by the natural decay of in vitro sample activity, if not corrected, this physiological baseline drift may lead the system to misjudge the normal decay as pathological damage.

[0024] To cope with the above challenges, the system adopts the following procedure: the microcontroller calculates an entropy value representing the information complexity of the vibration signal in real time based on the collected vibration signal , for example, using the sample entropy algorithm with low computational overhead; further, the system calculates the time variation rate of the entropy value , a smooth decreasing entropy rate represents the natural activity decay of the sample, while a sharp change in the entropy rate may indicate acute damage, the entropy rate is used to dynamically adjust the negative value threshold in the main determination thread, and the dynamically adjusted negative value threshold is determined by the following formula: , wherein is the original negative value threshold, i.e. negative zero point seven, and is a preset adjustment coefficient, the adjustment coefficient is calibrated as follows: in a controlled in vitro environment, a plurality of healthy tissue samples are monitored for a long time, and the entropy rate of the samples without external damage stimulation and the drift amount of the Pearson correlation coefficient threshold required to maintain the health determination are recorded, and the optimal value is determined by linear fitting, so that the system can effectively filter out slow and smooth baseline drift from the final damage determination, while the main determination and adaptive compensation are running, the implicit pathological state early warning thread also works in parallel, its goal is to capture a short-term functional compensation behavior before the cell's energy is exhausted, this state is easily ignored in traditional measurement, to identify this state, the system is configured to perform the following steps: the microcontroller performs a lightweight fast Fourier transform on the collected vibration signal, extracts the fundamental frequency signal phase and at least one harmonic signal, for example, the phase of the second harmonic signal Subsequently, the system continuously monitors the phase difference between the fundamental frequency signal phase and the harmonic signal phase. Under normal physiological conditions, this phase difference fluctuates randomly. However, when cells consume excessive energy to maintain barrier function, their internal metabolic oscillations exhibit a phenomenon of enhanced nonlinear coupling, manifested as phase locking between the fundamental frequency and harmonics. Therefore, the system has a built-in early warning procedure: when the phase difference is maintained within a very small range of less than or equal to 15 degrees, and the duration of this locking state reaches a set duration, such as 30 seconds, the system determines that the brain microvascular endothelial cells are in a state of functional compensation and immediately generates an early warning signal. This early warning signal informs clinicians that although the cells have not yet met the damage criteria, they are on the verge of instability, providing a valuable time window for ultra-early intervention.

[0025] Example 1: In a preclinical research scenario for assessing reperfusion injury in acute ischemic stroke, the technical solution of this invention operates as follows. In this scenario, the challenge lies in real-time differentiation between brain tissue that survives due to restored blood flow and tissue that has suffered irreversible reperfusion injury. Given the inherent activity decay characteristics of ex vivo brain tissue samples, any single-parameter monitoring method faces the inherent risk of misinterpreting physiological decay as pathological damage. When a piece of brain microvascular tissue smaller than one cubic millimeter from the ischemic area of ​​an experimental animal is placed on the sensor carrier and monitoring is initiated, the microcontroller simultaneously activates its three built-in processing logics. Initially, the Pearson correlation coefficient between the tissue's metabolic vibration signal and transcellular resistance signal is... The value may exhibit disordered fluctuations. In this case, the system's latent pathological state early warning thread responds first. This thread continuously monitors the phase of the fundamental frequency signal through a fast Fourier transform of the vibration signal. Phase with the second harmonic signal phase difference between When cells excessively consume energy to combat hypoxic stress, the nonlinear coupling of their metabolic oscillations is enhanced. Once the temperature stabilizes within a range of less than 15 degrees for 30 seconds, the system generates an early warning signal. This signal identifies a critical point of functional instability, transforming the cellular compensatory state into a quantifiable sensing event. This provides a pre-judgment state benchmark for subsequent main damage assessment. As monitoring continues, the adaptive compensation thread runs synchronously. The natural decay of the activity of the ex vivo sample causes a gradual decrease in the complexity of the metabolic vibration signal, which is reflected in the entropy value. rate of change over time It exhibits a stable negative value if a fixed original negative value threshold is used. This physiological baseline drift will be confused with the actual reperfusion injury signal, and the system will calculate in real time... And according to the formula The decision threshold is dynamically adjusted to resolve this contradiction, where the adjustment coefficient... The determination of the Pearson correlation coefficient does not rely on on-site adjustments, but rather on long-term monitoring of multiple groups of healthy tissues in a controlled in vitro environment. This involves recording the entropy change rate and the threshold drift of the correlation coefficient required to maintain health, and then using linear fitting to calibrate a fixed value. In this way, the system architecture achieves a mechanism to separate acute pathological changes from the background of slow physiological decline. When reperfusion injury occurs, and cellular energy metabolism and barrier function collapse simultaneously, the Pearson correlation coefficient... It will fall below the dynamic negative threshold that has been corrected in real time by the entropy change rate. If this state continues for more than sixty seconds, the system will output a damage assessment conclusion.

[0026] The fundamental reason for this assessment process lies in a design that structurally integrates multi-source information. The system does not rely on any single algorithm in isolation, but rather constructs a mutually verifying decision chain. Harmonic phase analysis, with its high sensitivity, is responsible for capturing the precursors of functional instability, providing a dynamic reference for judgment. The main judgment logic based on Pearson correlation confirms the damage based on this reference, ensuring the reliability of the conclusion. Meanwhile, the entropy adaptive compensation mechanism continuously calibrates the judgment benchmark of the entire system, making it unaffected by the time-varying characteristics of the sample itself. This collaborative analysis of the cell's life state from three dimensions—macroscopic coupling correlation, microscopic structural phase, and signal complexity—ultimately transforms a complex biomedical problem into a robust and deterministic assessment procedure that can be executed in real time on edge computing devices.

[0027] Example 2: The technical solution of this invention is applied to a high-throughput drug screening process, aiming to rapidly identify the potential toxicity of candidate compounds to the brain microvascular endothelial barrier. The core challenge of this application scenario is to accurately distinguish between irreversible cytotoxic damage caused by drugs and non-damaging effects that only cause temporary stress responses within a timescale of minutes. Simultaneously, it is necessary to avoid the evaluation benchmark drift problem caused by the inherent attenuation of sample activity in the in vitro screening platform. To address this challenge, this example uses a test platform composed of a 16-channel sensor array. Each channel replicates a sensor carrier integrating a piezoelectric sensing unit and an electrode unit, and is connected to a central data processing unit. This unit executes the evaluation algorithm in parallel, thereby simulating a drug screening application scenario. One of the key steps in the parameter calibration stage of this test platform is adjusting the coefficients. The determination of this coefficient is directly related to the effectiveness of the adaptive compensation mechanism. The setting aims to achieve an optimal balance between the robustness of filtering physiological attenuation noise and the sensitivity of capturing real slow injury signals. To this end, the following deterministic calibration procedure is established: first, at least ten healthy brain microvessel tissue samples with uniform source, size and preparation conditions are obtained, which are placed on the sensor channels respectively, and continuously monitored for four hours in a culture environment of 37 degrees Celsius and 5% carbon dioxide without any drug stimulation; second, for each sample, the sample entropy value of the vibration signal is recorded simultaneously and the Pearson correlation coefficient between the metabolic vibration signal time domain envelope and the transcellular resistance signal are calculated to obtain the respective entropy rate of change and the threshold compensation required to maintain the healthy state determination is not less than minus zero point two; finally, the linear regression analysis of and its corresponding threshold compensation of all samples is carried out, and the slope of the obtained fitting straight line is determined as the final value of the adjustment coefficient After the start of the test, two brain microvessel tissue samples with consistent physiological state are placed on two adjacent sensor channels as the control group and the experimental group. After five minutes of stable operation of the system, it is confirmed that the basic physiological signals of the two groups of samples, including the Pearson correlation coefficient , entropy and phase difference are within the normal fluctuation range. The same volume of drug solvent is added to the control group, and the same solvent containing a known concentration of apoptosis inducer is added to the experimental group. The microcontroller immediately analyzes the two parallel data streams in real time. In the experimental group, the system first captures the early signal of cells entering the stress state. About three minutes after drug administration, the phase difference between the fundamental frequency phase and the second harmonic phase of the vibration signal quickly converges and remains stable within a range of less than fifteen degrees. At this time, the warning logic of the system is triggered, and a signal of functional compensation state is output. However, the core injury indicator value has not yet deteriorated significantly and still maintains above minus zero point four.

[0028] With the continuation of the drug effect, the cell state of the experimental group further evolves. About ten minutes after the appearance of the functional compensation warning, the energy metabolism and barrier integrity of the cells begin to collapse synchronously, which is manifested as a sharp decline in the metabolic vibration signal intensity and the transcellular resistance value, resulting in a rapid drop in the Pearson correlation coefficient between the two below the dynamic negative threshold value corrected in real time by the entropy rate, and remaining below this threshold for more than sixty seconds. The system outputs an accurate cell injury determination accordingly. During the entire process, the indicators of the control group remain relatively stable, and The value is always within the dynamic threshold. The above only shows the entropy value due to natural decay. The slow decline was effectively filtered out by the adaptive compensation mechanism, without generating any warnings or misjudgments of damage. The following embedded table records the core data of the two groups of samples at three key time points during this process: see Table 1: Experimental Parameter Settings and Statistical Analysis Table.

[0029]

[0030] Data shows that after damage was detected, the phase difference in the experimental group returned to a state of random fluctuation, indicating that the nonlinear coupling lock-in state had been released. This sequence of data clearly demonstrates the complete identification chain of the method of this invention from functional compensation warning to irreversible damage determination, wherein the phase difference... The initial locking transforms the compensatory behavior of cells before energy depletion into a quantifiable early warning event, while the Pearson correlation coefficient... With dynamic threshold The comparison provides a reliable basis for the final damage assessment.

[0031] Example 3: This example combines Figures 1 to 4 This document describes the implementation of a method for assessing brain microvascular endothelial cell injury, such as... Figure 1 As shown in the diagram, the signal acquisition module first collects the raw signal envelope, acquiring the metabolic vibration signal provided by the pressure sensor and the transcellular resistance signal provided by the electrode unit. The acquired signal information is then transmitted to the system calculation module, where the Pearson correlation coefficient is calculated to assess the cell damage state. Next, the calculated signal is sent to the threshold comparison module for threshold judgment to determine whether cell damage exists. When the system enters the timer phase, it starts the initial state and reports the state every second. The state machine module executes corresponding logical operations based on the real-time updated signal state and calculation results. Through these operations, the alt module, after starting the timer, iterates through each step according to the system's needs. Within the loop module, the system updates the signal and calculates parameter values ​​each time. Every sixty counts, it requests new data and uses it to maintain a normal state. When the signal state is abnormal, the system enters a timeout trigger state, switching to the damage judgment state to respond to the real-time signal. Ultimately, the system ensures accurate assessment of the cell state through continuous monitoring to maintain a normal state.

[0032] like Figure 2 As shown in the figure, the solid line represents the change in the vibration signal envelope, and the dashed line represents the change in transcellular resistance. The horizontal axis represents time (minutes), and the vertical axis represents the changes in vibration signal envelope intensity and transcellular resistance, respectively. The data in the figure show the changes under normal conditions ( =-0.15) and the injury period ( Significant changes in the signal at -0.85°C were observed. During the normal phase, the vibration signal envelope intensity remained relatively stable, while the transcellular resistance showed minimal change. However, during the injury phase, the vibration signal envelope decreased significantly, and the transcellular resistance change intensified. The correlation between the dashed line (transcellular resistance) and the solid line (vibration signal envelope) in the figure further illustrates the assessment indicators of cell damage. The horizontal axis in the figure indicates time (minutes), while the vertical axis displays the values ​​of vibration signal envelope intensity (au) and transcellular resistance. In addition, the figure also provides information on different stages (normal phase and injury phase). The change in the value is used to quantify the association between the signal and damage progression.

[0033] like Figure 3 As shown in the figure, the curves illustrate the changes in three signals: metabolic vibration intensity (solid line), transcellular resistance (dashed line), and a phase-locked index related to cell state (dotted line). The horizontal axis represents time (minutes), the left side of the vertical axis represents the normalized signal intensity, and the right side represents the change in the phase-locked index. Within the time period shown in the figure, the metabolic vibration intensity starts from an initial value close to 1, gradually decreases over time, and shows a sharp decline during the injury phase (approximately after 10 minutes). The transcellular resistance value shows a similar trend, gradually decreasing as cell damage intensifies. Meanwhile, the phase-locked index shows a significant negative correlation with the other two curves, especially during the injury phase, where the correlation increases significantly. (Values ​​close to or below 0.7) This figure clearly shows the different rates of change of different signals during cell damage, and by comparing the changes in metabolic vibration signals and transcellular resistance signals, it can effectively reflect the normal, compensatory and damaged states of cells.

[0034] like Figure 4 As shown in the diagram, the system first has a dual-modal sensing unit, which includes a piezoelectric sensing unit and an electrode unit. These units are responsible for acquiring metabolic vibration signals and transcellular resistance signals, respectively. The signals are transmitted from the sensing unit to the signal processing module, which performs frequency domain analysis, including FFT transformation, to obtain high-frequency signals. Next, the signals are further processed through fundamental frequency-harmonic correlation measurement, time-domain envelope, and digital envelope of the vibration signal. After signal processing, the system calculates the Pilton correlation coefficient and dynamically adjusts the signal using a sliding window calculation. The results of the signal processing provide a basis for system judgment, including a 30-second warning strategy based on a phase difference <15° calculated using a sliding window of the Pilton correlation coefficient, and real-time monitoring of the signal status. Finally, all signals are transmitted to the output judgment section through the signal processing module, based on a set threshold (…). The cell damage state is determined, and a damage determination conclusion is output. The determination result includes normal, compensation, and damage states. This process effectively combines various signal analysis methods, and through three-state output, further improves the accurate monitoring and early warning of the cell damage state.

[0035] In a first deployment in a specific clinical research center or laboratory environment, to ensure the accuracy of the evaluation results and the comparability between different devices, a standardized system-level offline calibration and algorithm parameter determination procedure is required. The core challenge of this application scenario is how to convert a general algorithm model into a special evaluation system with a deterministic metrological reference for the current specific hardware batch, reagent environment, and potential operator habits, thereby avoiding systematic errors introduced by individual sensor differences or environmental baseline drift. The execution of this procedure begins with the preparation of two groups of standardized test samples. The first group is at least ten brain microvessel tissue samples taken from healthy experimental animals, with consistent size and preparation conditions, serving as the negative control group. The second group is the same number and source of tissue samples, but treated with a chemical inducer, i.e., 10 micromolar of staurosporine per milliliter, for two hours to induce irreversible apoptosis, serving as the positive control group.

[0036] First, the parameters of the core algorithm are fixed. To ensure computational efficiency and result stability, the low-pass filter cutoff frequency used in the digital envelope detection implemented in the microcontroller is set to zero point five hertz. Correspondingly, the sample entropy algorithm used to represent signal complexity has its internal parameters set deterministically to an embedding dimension of two and a tolerance of zero point two times the standard deviation of the data within the analysis time window. Subsequently, the calibration process of the core determination threshold is started. The negative and positive control groups are placed on the sensor carrier for continuous monitoring, and at least thirty minutes of vibration signal and resistance signal data are collected. Based on this data set, for each pre-set potential negative threshold, the sensitivity and specificity of distinguishing the two groups of samples are calculated, and by constructing the receiver operating characteristic curve, the threshold corresponding to the maximum Youden index is selected. This value is calibrated as the original negative threshold of the system. Using the same statistical analysis method, by analyzing the phase difference data randomly fluctuating in the negative control group and the phase-locked data experienced by the positive control group before damage, the phase difference threshold for determining the functional compensation state is determined, i.e., less than or equal to fifteen degrees, and the corresponding minimum duration. Finally, the key adjustment coefficient deterministic calibration, which is the cornerstone of the effective operation of the entire adaptive compensation mechanism, all untreated healthy tissue samples, i.e. negative control group, are continuously monitored in the incubation environment for up to four hours, during which the samples will experience a slow physiological activity decay due to ex vivo effect, for each sample, the microcontroller records the natural drift trajectory of its Pearson correlation coefficient and the synchronous entropy time rate of change, in order to maintain these healthy samples from being misjudged by the system as damage, it is necessary to calculate the minimum compensation amount applied to the original negative value threshold which is necessary to maintain the corrected threshold value of the healthy samples at all times not higher than the real-time correlation coefficient , thus, for each sample, a two-dimensional data set composed of hundreds of data points can be obtained, with the horizontal coordinate and the vertical coordinate being the necessary compensation amount , the data sets of all samples are summarized and subjected to least squares linear regression analysis, the slope of the obtained fitting straight line is rigorously determined as the only fixed adjustment coefficient of the system, by performing the above systematic calibration and verification procedures, all core parameters of the evaluation method, including and , are transformed from reference values relying on universality statistics to internal benchmarks with clear metrology source generated by the current system under controlled conditions through deterministic experimental procedures, which not only fundamentally ensures the accuracy and reproducibility of the evaluation conclusion, but also enables the system to make judgments based on a strictly verified and solid internal logic when facing real and uncertain clinical samples, thereby realizing the transformation of a multi-parameter dynamic coupling complex biological phenomenon into an engineered evaluation process that can be robust and accurately executed in the bedside environment.

[0037] Example 5: In this embodiment, in order to ensure the accuracy and repeatability of the evaluation method, all core algorithm parameters need to be determined by a standardized system-level offline calibration procedure, rather than relying on on-site empirical adjustment, among which the determination of the key adjustment coefficient in the adaptive compensation mechanism is particularly important, as it directly determines the system's ability to distinguish between physiological decay and pathological damage, the calibration process begins with the continuous monitoring of multiple groups of healthy brain microvessel tissue samples with uniform source, size and preparation conditions for up to several hours, during which the microcontroller synchronously records the natural drift trajectory of its Pearson correlation coefficient and the time rate of change of entropy , in order to maintain these healthy samples from being misjudged by the system, the minimum compensation amount necessary to maintain the corrected threshold value of the healthy samples at all times not higher than the real-time correlation coefficient is calculated, and the compensation amount of all samples and the corresponding The values are summarized, and the slope of the fitted straight line is determined as the only fixed adjustment coefficient of the system by linear regression analysis .

[0038] Similarly, the harmonic selection and determination threshold for functional compensation warning also follows the deterministic optimization process. In the calibration stage, we analyze the fast Fourier transform of multiple sample vibration signals that have experienced the process from health to stress compensation, identify the harmonic with the smallest standard deviation and the most stable locking phenomenon in the phase difference between the fundamental frequency and each harmonic during compensation, and select it as the monitoring object of the system, which is usually the second harmonic. Subsequently, based on a large number of phase difference data of healthy samples and compensation samples, we use the receiver operating characteristic curve analysis to select the phase difference and duration corresponding to the maximum Youden index as the determination threshold, thereby achieving the optimal balance between sensitivity and specificity. In the specific signal processing hardware implementation, the piezoelectric sensing unit is acoustically coupled with the glass slide, and an acoustic medium that matches the acoustic impedance of biological tissue is selected to maximize the vibration signal transmission efficiency. The digital envelope detection executed in the microcontroller precisely extracts the vibration signal by applying a second-order Butterworth low-pass filter after full-wave rectification of the original vibration signal, and the algorithm implementation fully considers the computational power limitation of the edge computing device to ensure the real-time performance and stability of the entire evaluation process.

[0039] In an extended embodiment, to ensure the accuracy and comparability of the evaluation results, a set of system-level offline calibration procedures are performed to determine the core decision threshold when first deployed in a specific experimental environment. The procedures first require the preparation of two sets of standardized test samples, namely healthy brain microvessel tissue samples as negative controls, and positive control samples that have undergone irreversible damage after treatment with a chemical inducer such as staurosporine. Subsequently, both sets of samples are continuously monitored under the same environmental conditions, and their metabolic vibration signals and transcellular resistance signals are completely collected. Based on the collected data sets, the threshold values are systematically determined by constructing a receiver operating characteristic curve (ROC curve). Specifically, by traversing and testing a series of candidate parameter combinations, the combination that maximizes the Youden index, which is the sum of sensitivity and specificity minus one, is selected to determine the original negative threshold Toriginal and the minimum duration for primary damage determination, as well as the phase difference threshold and the corresponding duration for functional compensation state warning. At the same time, the establishment of the key adjustment coefficient k in the adaptive compensation mechanism can follow a deterministic calibration process that accurately quantifies the physiological decay effect of the sample. This process selects multiple healthy tissue samples and continuously monitors them in a standard culture environment for several hours to capture the natural decline of their activity after being removed from the body. During this period, the system synchronously records the real-time drift trajectory of the Pearson correlation coefficient of each sample, as well as the time rate of change of the vibration signal complexity entropy value. To maintain these healthy samples from being misjudged as damaged by the system, the minimum compensation required to make the corrected dynamic threshold just below the real-time correlation coefficient at each time point must be calculated. Finally, all the entropy rate data generated by the samples during the monitoring period and the corresponding necessary compensation data are aggregated, and a least squares linear regression analysis is performed on the two-dimensional data set. The slope of the fitted straight line, which is rigorously determined as the only fixed adjustment coefficient k of the evaluation system, belongs to the extended embodiments known to those skilled in the art.

[0040] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0041] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application.

Claims

1. A method for evaluating brain microvascular endothelial cell damage, characterized by, The method comprises the following steps: Step a, obtaining a sensor carrier, the sensor carrier is integrated with a piezoelectric sensing unit for collecting cell metabolic vibration signals and an electrode unit for collecting transcellular resistance signals; Step b, placing the brain microvessel tissue to be tested on the sensor carrier and maintaining a humid environment for the brain microvessel tissue; Step c, synchronously collecting the vibration signals output by the piezoelectric sensing unit and the resistance signals output by the electrode unit; Step d, calculating the Pearson correlation coefficient between the time-domain envelope of the vibration signals and the resistance signals within a limited time window; Step e, determining the damage state of the brain microvessel endothelial cells based on the change of the Pearson correlation coefficient, wherein when the Pearson correlation coefficient continuously falls below a negative value threshold and the continuous time meets a time length requirement, it is determined that the brain microvessel endothelial cells are damaged.

2. The method of claim 1, wherein the method is for evaluating the damage of brain microvascular endothelial cells. The negative value threshold in step e is negative zero point seven, and the time length requirement is greater than sixty seconds.

3. The method of claim 1, wherein the method is for evaluating the damage of brain microvascular endothelial cells. Further comprising the following steps between or in parallel with step c and step d: calculating an entropy value representing the complexity of the vibration signal information in real time based on the collected vibration signal; and in step e, dynamically adjusting the negative value threshold according to the time rate of change of the entropy value, wherein the dynamically adjusted negative value threshold is determined by the following formula: wherein, is the dynamically adjusted negative value threshold, is the original negative value threshold, is the adjustment coefficient, is the time rate of change of the entropy value.

4. The method of claim 1, wherein the method is for evaluating brain microvascular endothelial cell damage. Further comprising, before step c determines the damage state of the brain microvessel endothelial cells, performing the following steps in parallel: based on the collected vibration signals, extracting the phase of the fundamental frequency signal of the vibration signals and the phase of at least one harmonic signal; monitoring the phase difference between the phase of the fundamental frequency signal and the phase of the harmonic signal; and when the phase difference is maintained within a range less than or equal to fifteen degrees for a set time length, it is determined that the brain microvessel endothelial cells are in a functional compensation state, and a warning signal is generated.

5. The method of claim 1, wherein the method is for evaluating brain microvascular endothelial cell damage. The sensor carrier is a piece of disposable slide glass, and the piezoelectric sensing unit and the electrode unit are integrated on the disposable slide glass.

6. The method of claim 5, wherein the brain microvascular endothelial cells are human brain microvascular endothelial cells. The piezoelectric sensing unit is attached below the center of the electrode array of the disposable slide glass through acoustic coupling glue.

7. The method of claim 1, wherein the method is for evaluating brain microvascular endothelial cell damage. The calculation of the Pearson correlation coefficient in step d is completed by a microcontroller electrically connected to the sensor carrier.

8. The method of claim 7, wherein the brain microvascular endothelial cells are human brain microvascular endothelial cells. The microcontroller is an eight-bit microcontroller, and the calculation of the Pearson correlation coefficient includes digital envelope detection of the vibration signals and sliding window calculation of the time-domain envelope of the vibration signals and the resistance signals.

9. The method of claim 1, wherein the method is for evaluating brain microvascular endothelial cell damage. The frequency of synchronously collecting the vibration signals and the resistance signals is at least ten times per second.

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