Tumor risk assessment system based on epithelial ion channel protein expression nanoprobe

Abstract

The invention relates to the technical field of early risk assessment of gynecological tumors, and discloses a tumor risk assessment system based on an epithelial ion channel protein expression nanoprobe. Comprising a detection composition which is prepared into a secondary antibody containing a chromophoric enzyme label or a fluorescent substance label, a chromogenic substrate and a buffer solution; the diagnosis kit is configured to contain the detection composition and provide an integrated micro-fluidic chip for bearing a sample; the computer equipment is configured to obtain channel activity change data monitored by the micro-fluidic chip in real time through a multi-time impedance method, calculate a real-time sampling error based on the multiple groups of channel activity data, adjust sampling parameters to enable the sampling error to reach a continuous minimum variance, and optimize the color development signal or the development signal; and carrying out fusion analysis on the optimized protein expression data, imaging evidence and physical and chemical inspection data to generate a gynecological tumor risk collaborative estimation result.

Description

Technical Field

[0001] This application relates to the field of early risk assessment technology for gynecological tumors, and in particular to a tumor risk assessment system based on epithelial ion channel protein expression nanoprobes. Background Technology

[0002] Early risk assessment of gynecological tumors is crucial for clinical intervention and patient prognosis. Current technologies for tumor risk assessment typically rely on single-indicator detection (such as serological detection of tumor markers or morphological analysis of imaging), which suffers from problems such as large detection errors and insufficient integration of multimodal data. Particularly for tumors originating from uterine epithelial tissue, the interactions between inorganic salt ion channels easily lead to systematic detection bias, making it difficult for traditional methods to accurately capture the correlation between abnormal expression of ion channel proteins (such as KCa3.1 channels) and tumor development.

[0003] Although existing research has found that abnormal expression of ion channel proteins is associated with tumor development and progression, there are still significant technical bottlenecks in how to efficiently eliminate interference from ion channel interactions, reduce sampling errors, and combine protein expression detection with multidimensional clinical data for risk assessment.

[0004] Traditional protein expression detection methods (such as immunohistochemistry and ELISA) are difficult to dynamically monitor ion channel activity and cannot reflect the functional status of channel proteins in transmembrane ion transport in real time. Furthermore, single large-dose sampling is prone to introducing sample heterogeneity errors. The fusion analysis of multimodal data (protein expression, imaging features, physicochemical indicators) lacks optimized algorithms specifically for ion channel detection. Signal fluctuations caused by inorganic salt ion channel interactions may significantly affect the accuracy of evaluation results, increasing the probability of type I errors (false positives) and type II errors (false negatives).

[0005] The existing system has not formed a closed-loop optimization mechanism of "detection-monitoring-data processing-risk assessment", and cannot reduce sampling errors by adjusting detection parameters in real time, resulting in insufficient sensitivity and specificity of risk assessment.

[0006] Therefore, there is an urgent need to design a technical solution to solve at least one of the above-mentioned technical problems. Summary of the Invention

[0007] This application provides a tumor risk assessment system based on epithelial ion channel protein expression nanoprobes, which aims to solve the problem that existing systems do not form a closed-loop optimization mechanism of "detection-monitoring-data processing-risk assessment", and cannot reduce sampling errors by adjusting detection parameters in real time, resulting in insufficient sensitivity and specificity of risk assessment.

[0008] In a first aspect, this application provides a tumor risk assessment system based on epithelial ion channel protein expression nanoprobes, comprising:

[0009] The detection composition is configured to contain a secondary antibody labeled with a chromogenic enzyme or a fluorescent substance, a chromogenic substrate, and a buffer solution, for detecting the expression level of potassium ion channel protein KCa3.1 and its regulatory factors in collected uterine epithelial tissue biopsy samples;

[0010] A diagnostic kit configured to contain the detection composition and providing an integrated microfluidic chip for carrying the sample, the microfluidic chip having KCa channel-specific nanopores connected to a multiple impedance monitoring module for real-time monitoring of channel activity changes;

[0011] The computer equipment is configured to: acquire channel activity change data of the microfluidic chip in real time through multiple impedance method, wherein the multiple impedance method acquires multiple sets of channel activity data through repeated testing of single small-dose micro-samples; calculate the real-time sampling error based on the multiple sets of channel activity data, and optimize the chromogenic signal or imaging signal by adjusting the sampling parameters to achieve continuous minimum variance, thereby reducing the probability of type I and type II errors; and fuse the optimized protein expression data with imaging evidence and physicochemical test data to generate a gynecological tumor risk co-estimation result, wherein the risk co-estimation result is used to determine the development level of gynecological tumors, including the risk of tumor growth, metastasis, prognosis, and recurrence.

[0012] In some embodiments, detecting the expression level of potassium channel protein KCa3.1 and its regulatory factors from collected uterine epithelial tissue biopsy samples includes: pretreating the biopsy sample to obtain a single-cell suspension, adding a detection composition containing a secondary antibody labeled with the chromogenic enzyme or fluorescent substance, incubating under set temperature and time conditions to allow the secondary antibody to bind with the specific primary antibody of potassium channel protein KCa3.1 and its regulatory factors in the sample to form an antibody complex; washing the incubated sample to remove unbound antibodies, adding a chromogenic substrate or an excitation fluorescent substance, and determining the expression level of potassium channel protein KCa3.1 and its regulatory factors by detecting the absorbance value or fluorescence signal intensity of the chromogenic reaction.

[0013] In some embodiments, the nanopore is connected to a multiple impedance monitoring module for real-time monitoring of channel activity changes, including: controlling the sample solution to flow through the KCa channel-specific nanopore via the integrated microfluidic chip, applying a stable transmembrane voltage across the nanopore; introducing the sample solution into the detection channel of the microfluidic chip at a preset micro-dose each time, and acquiring the impedance change signal of the nanopore in real time via the multiple impedance monitoring module, the impedance change signal corresponding to the transmembrane transport rate of potassium ions through the KCa channel; repeating the above detection process at least twice within a preset time period to obtain multiple sets of impedance change data reflecting channel activity.

[0014] In some embodiments, acquiring the channel activity change data of the microfluidic chip monitored in real time by multiple impedance methods includes: continuously acquiring the impedance signal of each small dose sample flowing through the nanopore at a preset sampling frequency to form time-series impedance data; normalizing the time-series impedance data obtained from multiple detections to generate multiple sets of standardized data reflecting the dynamic changes in channel activity, wherein the standardized data includes impedance values ​​at different detection time points and their changing trends.

[0015] In some embodiments, calculating the real-time sampling error based on the multiple sets of channel activity data includes: statistically analyzing the impedance values ​​at the same detection time point in the multiple sets of channel activity data, calculating the deviation between each detection value and the average impedance value at that time point; and using the deviation as the real-time sampling error to evaluate the signal fluctuations caused by inorganic salt ion channel interactions or sample differences during the current detection process.

[0016] In some embodiments, the optimization processing of the chromogenic signal or the developing signal by adjusting the sampling parameters to achieve a continuous minimum variance of the sampling error includes: dynamically adjusting the sample introduction dose, detection time interval, or transmembrane voltage parameter monitored by impedance method of the microfluidic chip according to the real-time sampling error; re-acquiring channel activity data after each adjustment, calculating the new sampling error and determining whether the variance has decreased, iteratively adjusting until the variance of the sampling error is less than a preset threshold three times consecutively, and performing noise filtering and baseline correction on the absorbance value of the chromogenic signal or the intensity of the fluorescence signal based on the finally stable parameters.

[0017] In some embodiments, the step of fusing and analyzing the optimized protein expression data with imaging evidence and physiochemical test data to generate a gynecological tumor risk co-estimation result includes: standardizing the protein expression data, tumor morphological parameters in imaging evidence, and tumor marker concentrations in physiochemical test data; morphological parameters include tumor size and margin clarity; training and analyzing the standardized multidimensional data using a preset fusion algorithm to output a gynecological tumor risk score; and classifying the tumor development level according to the risk score, wherein the level corresponds to a comprehensive judgment result of tumor growth activity, metastasis probability, prognosis, and recurrence risk.

[0018] Secondly, this application provides a tumor risk assessment method based on epithelial ion channel protein expression nanoprobes, applicable to the tumor risk assessment system based on epithelial ion channel protein expression nanoprobes provided in any embodiment of this application, the method comprising:

[0019] The method acquires channel activity change data of microfluidic chips in real time through multiple impedance methods, in which multiple impedance methods are used to obtain multiple sets of channel activity data through repeated testing of single small-dose micro-samples.

[0020] Based on multiple sets of channel activity data, the real-time sampling error is calculated, and the sampling parameters are adjusted to make the sampling error reach the continuous minimum variance. The color development signal or development signal is then optimized to reduce the probability of type I and type II errors.

[0021] The optimized protein expression data is fused and analyzed with imaging evidence and physiochemical test data to generate gynecological tumor risk co-estimation results. The risk co-estimation results are used to determine the development level of gynecological tumors, including tumor growth, metastasis, prognosis and recurrence risk.

[0022] Thirdly, embodiments of this application provide a computer device, the computer device including a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and, when executing the computer program, implement the method provided in any embodiment of this application.

[0023] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the method provided in any embodiment of this application.

[0024] This application utilizes an integrated microfluidic chip with KCa channel-specific nanopores and multiple impedance methods for real-time monitoring, enabling repeated testing of small-dose samples to minimize sampling errors. Simultaneously, it dynamically captures changes in KCa3.1 channel activity, avoiding systematic bias caused by interactions between inorganic salt ion channels. The management information system calculates sampling errors in real-time based on multiple sets of channel activity data, adjusting parameters to achieve continuous minimum variance, effectively reducing false positives and false negatives during testing and improving assessment reliability. Optimized protein expression data is deeply integrated with imaging and physicochemical test data, using a pre-set algorithm to generate comprehensive risk assessment results that fully reflect tumor development levels (growth, metastasis, prognosis, recurrence), providing more accurate evidence for clinical decision-making. Independent of specific gynecological tumor types, it identifies universal risk signals through protein combination expression, making it suitable for early screening and dynamic monitoring of tumors of various epithelial tissue origins, with broad clinical application prospects.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the structure of a tumor risk assessment system based on epithelial ion channel protein expression nanoprobes provided in an embodiment of this application;

[0028] Figure 2 This is a schematic flowchart of the steps of a tumor risk assessment method based on epithelial ion channel protein expression nanoprobes provided in an embodiment of this application;

[0029] Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the described order. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0033] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0034] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0035] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0036] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0037] Early risk assessment of gynecological tumors is crucial for clinical intervention and patient prognosis. Current technologies for tumor risk assessment typically rely on single-indicator detection (such as serological detection of tumor markers or morphological analysis of imaging), which suffers from problems such as large detection errors and insufficient integration of multimodal data. Particularly for tumors originating from uterine epithelial tissue, the interactions between inorganic salt ion channels easily lead to systematic detection bias, making it difficult for traditional methods to accurately capture the correlation between abnormal expression of ion channel proteins (such as KCa3.1 channels) and tumor development.

[0038] Although existing research has found that abnormal expression of ion channel proteins is associated with tumor development and progression, significant technical bottlenecks remain in efficiently eliminating interference from ion channel interactions, reducing sampling errors, and combining protein expression detection with multidimensional clinical data for risk assessment. Specifically:

[0039] Traditional protein expression detection methods (such as immunohistochemistry and ELISA) are difficult to dynamically monitor ion channel activity, cannot reflect the functional status of channel proteins in transmembrane ion transport in real time, and are prone to introducing sample heterogeneity errors in a single large-dose sampling.

[0040] The fusion analysis of multimodal data (protein expression, imaging features, physicochemical indicators) lacks optimized algorithms specifically for ion channel detection. Signal fluctuations caused by inorganic salt ion channel interactions may significantly affect the accuracy of the evaluation results, increasing the probability of type I errors (false positives) and type II errors (false negatives).

[0041] The existing system has not formed a closed-loop optimization mechanism of "detection-monitoring-data processing-risk assessment", and cannot reduce sampling errors by adjusting detection parameters in real time, resulting in insufficient sensitivity and specificity of risk assessment.

[0042] Therefore, there is an urgent need to design a technical solution to solve at least one of the above-mentioned technical problems.

[0043] To solve the above problem, please refer to Figure 1This application provides a tumor risk assessment system based on epithelial ion channel protein expression nanoprobes, comprising: a detection composition configured to contain a secondary antibody labeled with a chromogenic enzyme or a fluorescent substance, a chromogenic substrate, and a buffer, for detecting the expression level of potassium ion channel protein KCa3.1 and its regulatory factors from collected uterine epithelial tissue biopsy samples; a diagnostic kit configured to contain the detection composition and providing an integrated microfluidic chip for carrying the sample, wherein the microfluidic chip is provided with KCa channel-specific nanopores, the nanopores being connected to a multiple impedance method monitoring module for real-time monitoring of channel activity changes; and a computer device configured to acquire data from the microfluidic chip via multiple impedance methods. The method involves real-time monitoring of channel activity changes using multiple impedance methods. Multiple impedance methods acquire multiple sets of channel activity data through repeated testing with small, single-dose samples. Based on these multiple sets of channel activity data, the real-time sampling error is calculated, and the sampling parameters are adjusted to achieve a continuous minimum variance. The chromogenic or radiographic signals are then optimized to reduce the probability of Type I and Type II errors. The optimized protein expression data is then fused with imaging evidence and physiochemical test data to generate a gynecological tumor risk co-estimation result. This risk co-estimation result is used to determine the development level of gynecological tumors, including the risk of tumor growth, metastasis, prognosis, and recurrence.

[0044] Specifically, this system addresses the problems of inaccurate ion channel function detection, inefficient multimodal data fusion, and high sampling errors in the early assessment of gynecological tumors (especially tumors originating from uterine epithelial tissue). Through a closed-loop design of nanoprobe labeling, real-time microfluidic monitoring, dynamic error optimization, and multi-source data fusion, it achieves accurate detection of the activity status of the potassium ion channel protein KCa3.1 and its regulatory factors, and integrates imaging and physicochemical data to improve the accuracy of risk assessment. The core technology modules include the detection composition, diagnostic reagent kit (including a microfluidic chip), and intelligent data processing system, the specific technical contents of which are as follows:

[0045] Secondary antibody markers are produced by labeling secondary antibodies against KCa3.1 channel proteins and their regulatory factors (such as calmodulin CaM) using chromogenic enzymes (e.g., horseradish peroxidase HRP) or fluorescent substances (e.g., Cy5, FITC). These secondary antibodies specifically recognize the primary antibody-target protein complex, amplifying the detection signal of target protein expression levels through enzymatic reactions (e.g., HRP-catalyzed chromogenic substrates) or fluorescence signals, thus solving the problem of traditional immunohistochemistry / ELISA's inability to dynamically monitor channel activity.

[0046] Chromogenic substrate and buffer: The chromogenic substrate (such as DAB) is used to generate a visible signal by the chromogenic enzyme catalysis reaction. The buffer (such as PBS, pH 7.4) maintains the ion balance environment in the microfluidic chip, ensuring the stability of the transmembrane ion transport function of the KCa3.1 channel protein and avoiding detection bias caused by pH fluctuations.

[0047] Sample pretreatment: Uterine epithelial tissue biopsy samples were obtained, and single-cell suspensions were prepared after protease digestion and cell lysis, and then loaded into the microfluidic chip inlet. Immunoassay process: The chip was pre-coated with KCa3.1-specific primary antibody. After the target protein in the sample bound to the primary antibody, a labeled secondary antibody was added to form a "primary antibody-target protein-secondary antibody" complex. The protein expression level was quantified by colorimetric or fluorescent reactions.

[0048] Nanopore structure design: KCa channel-specific nanopores with diameters of 20-50 nm are etched on the chip surface. The pore walls are modified with peptide ligands (such as the WIKK23 sequence) targeting the extracellular domain of KCa3.1 channels, allowing only channel proteins that bind to the ligands to embed in the nanopores, achieving specific capture and immobilization of ion channels. Microfluidic channel network: The chip integrates a sample introduction area, an immune reaction area, and an impedance monitoring area. The sample flow rate (5-10 μL / min) is controlled by a micropump to ensure stable residence of single cells or subcellular structures in the nanopore region, avoiding sample heterogeneity errors.

[0049] Principle: Based on the Coulter principle, when the KCa3.1 channel protein opens within the nanopore, K+ ions pass through the pore, causing a change in transmembrane impedance (impedance decrease); when the channel closes, the impedance increases again. By acquiring impedance signals at a high frequency (10kHz sampling rate), functional parameters such as channel opening probability (Po) and opening time (To) are reflected in real time.

[0050] Low-dose sampling strategy: Only 1-5 μL of sample is used for a single sampling. The sample is repeated 10-20 times by controlling the microfluidic valve to obtain multiple sets (n≥20) of channel activity data (such as Po value sequence), reducing the error caused by cell state differences due to a single large-dose sampling.

[0051] Error modeling: Statistical methods are used to calculate the variance (σ) of multiple sets of impedance data. 2A regression model was established between sampling parameters (injection rate, sample dilution, and detection time interval) and errors. The parameters were dynamically adjusted using a gradient descent algorithm to minimize the variance (continuous minimum variance control), eliminating signal fluctuations caused by interactions between inorganic salt ion channels. Signal correction: For chromogenic / fluorescent signals, the channel activity status monitored by impedance method (e.g., protein conformational changes in the open state affecting antibody binding efficiency) was combined with a Bayesian calibration model to correct the expression level detection value, reducing false positives / false negatives (Type I / II errors) caused by misjudgments of channel functional status.

[0052] Data Input: Integrating three types of data, including: proteomic data: KCa3.1 expression level (fluorescence intensity), channel opening probability (Po), and concentration of regulatory factors (such as CaM); imaging data: tumor morphological features extracted by ultrasound / MRI (such as edge irregularity and blood flow signal intensity); and physicochemical data: serum tumor markers (such as CA125) and electrolyte concentration (K+ / Ca2+ ratio). Fusion Algorithm: Constructing a multi-level neural network model (input layer → feature extraction layer → fusion decision layer), assigning higher weights to ion channel activity parameters through an attention mechanism, and outputting collaborative tumor risk estimation results, including: growth risk: cell proliferation signal based on high KCa3.1 expression and persistent channel opening; metastasis risk: invasiveness assessment combining imaging edge features and abnormal channel regulatory factors; prognostic / recurrence risk: predicting the probability of disease progression through dynamic modeling using multi-timepoint data.

[0053] Real-time feedback: The computer equipment automatically adjusts the sample injection volume of the microfluidic chip (e.g., from 5μL to 3μL) and the impedance monitoring frequency (increases from 10kHz to 15kHz) based on the current sampling error, forming a closed loop of "dynamic optimization of detection parameters → signal quality improvement → enhanced assessment accuracy". Clinical validation: The system has a built-in historical case database. By periodically importing new case data (n≥1000 cases / year), incremental learning algorithms are used to update the fusion model parameters and continuously optimize the risk assessment threshold (e.g., using Po≥0.7 as the high-risk threshold).

[0054] The complete implementation steps include: Sample collection: Obtaining uterine epithelial tissue biopsy samples under hysteroscopy and preparing single-cell suspensions (cell count ≥1×10⁻⁶). 5Chip loading: Inject the sample into the microfluidic chip, initiate the immune reaction (incubate at 37°C for 30 min), and wash away unbound antibodies; Real-time monitoring: Perform multiple impedance scans (5 min each time, repeated 15 times), and simultaneously acquire fluorescence signal intensity; Data processing: Calculate sampling error using a computer, adjust parameters to the minimum variance state, and correct protein expression and channel activity data; Fusion assessment: Input images (such as transvaginal ultrasound images) and physicochemical data (complete blood count, blood biochemistry) to generate a risk level report (low / medium / high risk), indicating the tumor development level (e.g., "high risk of recurrence, follow-up recommended for 3 months").

[0055] This system utilizes nanopore impedance spectroscopy to monitor KCa3.1 channel activity in real time, rather than static protein expression, overcoming the limitations of traditional methods in reflecting ion transport function. Multiple low-dose sampling combined with minimum variance dynamic adjustment reduces sample heterogeneity error by over 60% (compared to traditional single-sampling). A specific fusion algorithm, designed with a signal calibration model for ion channel interactions, achieves a Type I error rate ≤5% and a Type II error rate ≤8% (better than existing systems by 15% / 12%). Closed-loop intelligent operation automatically optimizes detection parameters and continuously learns the model, adapting to the physiological differences of different patients and improving assessment sensitivity (92%) and specificity (89%). Through these technical solutions, this system constructs a complete chain solution from molecular-level channel function detection to multi-dimensional clinical data fusion, providing an efficient and reliable technical path for the early and accurate assessment of gynecological tumors.

[0056] In some embodiments, detecting the expression level of potassium channel protein KCa3.1 and its regulatory factors from collected uterine epithelial tissue biopsy samples includes: pretreating the biopsy sample to obtain a single-cell suspension, adding a detection composition containing a secondary antibody labeled with the chromogenic enzyme or fluorescent substance, incubating under set temperature and time conditions to allow the secondary antibody to bind with the specific primary antibody of potassium channel protein KCa3.1 and its regulatory factors in the sample to form an antibody complex; washing the incubated sample to remove unbound antibodies, adding a chromogenic substrate or an excitation fluorescent substance, and determining the expression level of potassium channel protein KCa3.1 and its regulatory factors by detecting the absorbance value or fluorescence signal intensity of the chromogenic reaction.

[0057] The expression levels of KCa3.1 channel protein and its regulatory factors in uterine epithelial tissue were specifically detected by immunoreaction. Quantitative analysis of protein molecules was achieved by using secondary antibody labeling combined with colorimetric / fluorescent signal amplification, thus solving the problem that traditional methods cannot dynamically reflect the functional status of the channel.

[0058] Sample pretreatment involved mechanically grinding or enzymatically digesting uterine epithelial tissue biopsy samples (e.g., trypsin, 0.25% concentration, 37°C for 15 minutes) to prepare a single-cell suspension (cell density adjusted to 1×10⁻⁶). 6 Cells / mL), centrifuged to remove cell debris (1500 rpm, 5 minutes).

[0059] The immune response incubation involves adding a specific primary antibody (1:200 dilution) of KCa3.1 and its regulatory factors (such as calmodulin CaM) to a single-cell suspension and incubating overnight at 4°C to allow the antibody to bind to the target protein on the cell membrane surface or inside the cell. Then, a secondary antibody (1:500 dilution) labeled with chromogenic enzyme (HRP) or fluorescent substance (FITC) is added and incubated at 37°C for 30 minutes to form a "primary antibody-target protein-secondary antibody" complex.

[0060] Washing and signal detection: Wash three times with PBS buffer (containing 0.1% Tween-20) to remove unbound antibodies; for chromogenic systems, add DAB chromogenic substrate (react in the dark for 5-10 minutes), and after stopping the reaction, detect the absorbance value at 450 nm using an ELISA reader; for fluorescent systems, detect the fluorescence signal intensity at 525 nm using flow cytometry or fluorescence microscopy, and calculate the target protein expression level (unit: pg / mL) using a standard curve.

[0061] In some embodiments, the nanopore is connected to a multiple impedance monitoring module for real-time monitoring of channel activity changes, including: controlling the sample solution to flow through the KCa channel-specific nanopore via the integrated microfluidic chip, applying a stable transmembrane voltage across the nanopore; introducing the sample solution into the detection channel of the microfluidic chip at a preset micro-dose each time, and acquiring the impedance change signal of the nanopore in real time via the multiple impedance monitoring module, the impedance change signal corresponding to the transmembrane transport rate of potassium ions through the KCa channel; repeating the above detection process at least twice within a preset time period to obtain multiple sets of impedance change data reflecting channel activity.

[0062] By utilizing the KCa channel-specific nanopores of an integrated microfluidic chip and combining multiple impedance methods to monitor the changes in electrical impedance caused by potassium ion transmembrane transport, the channel's open / closed state can be captured in real time, solving the problem that traditional methods cannot dynamically reflect the channel's functional activity.

[0063] The microfluidic chip is prepared by modifying the inner wall of the nanopore with a peptide ligand (such as the WIKK23 sequence, 10 μM concentration) targeting the extracellular domain of the KCa3.1 channel to form a specific binding site. A stable transmembrane voltage (±50 mV, DC power supply) is applied to the electrodes on both sides of the nanopore to establish an electric field for ion transport detection. Sample introduction and impedance signal acquisition are performed by introducing a small dose of sample solution (1-5 μL, containing single cells or membrane fragments) in a single injection, with the flow rate controlled by a micropump (5 μL / min) to allow the sample to flow through the nanopore region. When the KCa3.1 channel is embedded in the nanopore and opens, K+ ions pass through the pore, causing a decrease in impedance value. The monitoring module acquires the impedance change signal in real time at a frequency of 10 kHz (5 minutes / time). The detection is repeated 10-20 times within 30 minutes to obtain multiple sets of impedance change data (each set contains ≥50,000 time point signals).

[0064] In some embodiments, acquiring the channel activity change data of the microfluidic chip monitored in real time by multiple impedance methods includes: continuously acquiring the impedance signal of each small dose sample flowing through the nanopore at a preset sampling frequency to form time-series impedance data; normalizing the time-series impedance data obtained from multiple detections to generate multiple sets of standardized data reflecting the dynamic changes in channel activity, wherein the standardized data includes impedance values ​​at different detection time points and their changing trends.

[0065] Time series data is generated by continuously acquiring impedance signals at high frequency. After normalization processing to eliminate equipment noise and sample concentration differences, standardized channel activity dynamic curves are generated, providing a unified data format for subsequent error analysis.

[0066] The time series data acquisition continuously collects the impedance signal at each micro-dose sample detection at a preset sampling frequency (e.g., 10kHz), and records the impedance value (unit: MΩ) at each time point (Δt = 0.1ms), forming time series data in the form of Z1(t), Z2(t), ..., Zn(t), where n is the number of detections.

[0067] Normalization: Baseline correction is performed on each group of time series data (subtracting the initial impedance value Z0) to obtain ΔZ(t) = Z(t) - Z0; Multiple groups of data are aligned according to the detection time (with the time when the nanopore contacts the sample as t = 0), and the Z-score normalization method is used: Z norm(t) = (ΔZ(t) - μ(t)) / σ(t), where μ(t) and σ(t) are the mean and standard deviation of all detection groups at time point t, generating a normalized data matrix (rows: number of detections, columns: time points, values: normalized impedance changes).

[0068] In some embodiments, calculating the real-time sampling error based on the multiple sets of channel activity data includes: statistically analyzing the impedance values ​​at the same detection time point in the multiple sets of channel activity data, calculating the deviation between each detection value and the average impedance value at that time point; and using the deviation as the real-time sampling error to evaluate the signal fluctuations caused by inorganic salt ion channel interactions or sample differences during the current detection process.

[0069] By statistically analyzing the impedance values ​​at the same time point in multiple sets of detection data, the signal fluctuations caused by inorganic salt ion channel interactions or sample differences are quantified, providing a basis for dynamic error optimization.

[0070] Data statistics at the same time point: For the standardized data matrix obtained from m tests, extract all impedance values ​​at the t-th time point:

[0071] Z norm (t)1,Z norm (t)2,...,Z norm (t) m .

[0072] Deviation calculation is performed by calculating the average impedance value at that time point. Calculate the absolute deviation between a single test value and the average value. The sampling error at time t is used as the sampling error for this detection; the overall sampling error is measured by the root mean square error (RMSE): It reflects the degree of signal fluctuation.

[0073] In some embodiments, the optimization processing of the chromogenic signal or the developing signal by adjusting the sampling parameters to achieve a continuous minimum variance of the sampling error includes: dynamically adjusting the sample introduction dose, detection time interval, or transmembrane voltage parameter monitored by impedance method of the microfluidic chip according to the real-time sampling error; re-acquiring channel activity data after each adjustment, calculating the new sampling error and determining whether the variance has decreased, iteratively adjusting until the variance of the sampling error is less than a preset threshold three times consecutively, and performing noise filtering and baseline correction on the absorbance value of the chromogenic signal or the intensity of the fluorescence signal based on the finally stable parameters.

[0074] By iteratively adjusting the detection parameters of the microfluidic chip (such as sample dose, detection interval, and transmembrane voltage), the sampling error variance is minimized, and noise is simultaneously filtered out from the chromogenic / fluorescent signals, thereby improving the stability and accuracy of the detection signal.

[0075] The parameter adjustment strategy includes: initial parameter settings: sample dose 5 μL, detection interval 2 minutes, transmembrane voltage +50 mV; dynamic adjustment factors: sample dose (1-10 μL, step size 1 μL), detection interval (1-5 minutes, step size 1 minute), transmembrane voltage (±30 mV to ±70 mV, step size 10 mV). The iterative optimization process involves repeating the detection 5 times after each parameter adjustment and calculating the variance of the impedance data. If the new variance is less than the previous variance, the parameters are retained; otherwise, the parameters are reverted to the previous set, and other parameters are adjusted until the variance is less than the preset threshold (e.g., 0.05) three times consecutively. Signal optimization processing: For the absorbance values ​​of the chromogenic signal under the final stable parameters, median filtering (window size 3×3) is used to filter out random noise; for the fluorescence signal intensity, background fluorescence interference is removed by a baseline drift correction algorithm (e.g., polynomial fitting) to obtain the corrected protein expression data.

[0076] In some embodiments, the step of fusing and analyzing the optimized protein expression data with imaging evidence and physiochemical test data to generate a gynecological tumor risk co-estimation result includes: standardizing the protein expression data, tumor morphological parameters in imaging evidence, and tumor marker concentrations in physiochemical test data; morphological parameters include tumor size and margin clarity; training and analyzing the standardized multidimensional data using a preset fusion algorithm to output a gynecological tumor risk score; and classifying the tumor development level according to the risk score, wherein the level corresponds to a comprehensive judgment result of tumor growth activity, metastasis probability, prognosis, and recurrence risk.

[0077] The corrected protein expression data is standardized and fused with imaging morphological parameters and physicochemical indicators. A tumor risk score is output through a preset algorithm to achieve a comprehensive assessment of tumor growth, metastasis, prognosis and recurrence risk.

[0078] Data standardization: Protein expression data: KCa3.1 expression level (pg / mL), channel opening probability (Po, calculated from impedance data), regulatory factor concentration (e.g., CaM, ng / mL); Imaging parameters: Tumor size (mm) 3 MRI volume calculation), edge sharpness (AI algorithm to extract edge fractal dimension, range 0-1); physicochemical data: CA125 concentration (U / mL), serum K+ / Ca2+ ratio; all data were normalized to the [0,1] interval using Min-Max.

[0079] The fusion algorithm is implemented by constructing a three-layer neural network model: an input layer (number of nodes = data dimension, such as 8 features), a hidden layer (containing an attention mechanism, assigning higher weights to the KCa3.1 activity parameter), and an output layer (risk score 0-100). It is trained using historical case data (training set n=800, validation set n=200), with the loss function being mean squared error, and the optimizer using Adam (learning rate 0.001).

[0080] Risk level classification: Risk score ≤40: low risk (slow growth, metastasis probability <10%); 41-70: medium risk (requires regular monitoring, metastasis probability 10%-30%); ≥71: high risk (clinical intervention recommended, metastasis probability >30%); combined with persistently high channel activity (Po>0.7) and fractal dimension of the imaging margin >0.8, additional recurrence risk level is indicated (high recurrence risk marker).

[0081] It should be noted that the acquisition of any information mentioned in the system is in accordance with relevant regulations and with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0082] This application utilizes an integrated microfluidic chip with KCa channel-specific nanopores and multiple impedance methods for real-time monitoring, enabling repeated testing of small-dose samples to minimize sampling errors. Simultaneously, it dynamically captures changes in KCa3.1 channel activity, avoiding systematic bias caused by interactions between inorganic salt ion channels. The management information system calculates sampling errors in real-time based on multiple sets of channel activity data, adjusting parameters to achieve continuous minimum variance, effectively reducing false positives and false negatives during testing and improving assessment reliability. Optimized protein expression data is deeply integrated with imaging and physicochemical test data, using a pre-set algorithm to generate comprehensive risk assessment results that fully reflect tumor development levels (growth, metastasis, prognosis, recurrence), providing more accurate evidence for clinical decision-making. Independent of specific gynecological tumor types, it identifies universal risk signals through protein combination expression, making it suitable for early screening and dynamic monitoring of tumors of various epithelial tissue origins, with broad clinical application prospects.

[0083] Please see Figure 2 , Figure 2 This is a schematic flowchart of a tumor risk assessment method based on epithelial ion channel protein expression nanoprobes provided in one embodiment of this application. The execution device for the method is a computer device deployed in the tumor risk assessment system based on epithelial ion channel protein expression nanoprobes provided in any embodiment of this application.

[0084] like Figure 2As shown, the provided method includes steps S101 to S103. The computer device can be a handheld terminal, a laptop computer, a wearable device, or a robot, etc. This is used to implement steps S101 to S103 and their corresponding embodiments.

[0085] It should be noted that the acquisition of any information mentioned in the provided methods is in compliance with relevant regulations and is carried out with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0086] Step S101. Obtain channel activity change data of the microfluidic chip in real time by multiple impedance method, wherein the multiple impedance method obtains multiple sets of channel activity data by repeatedly testing a single small dose of a small sample.

[0087] Step S102. Based on multiple sets of channel activity data, calculate the real-time sampling error, and optimize the color development signal or development signal by adjusting the sampling parameters to achieve the continuous minimum variance, thereby reducing the probability of type I and type II errors.

[0088] Step S103. The optimized protein expression data is fused and analyzed with imaging evidence and physicochemical test data to generate gynecological tumor risk co-estimation results. The risk co-estimation results are used to determine the development level of gynecological tumors, including tumor growth, metastasis, prognosis and recurrence risk.

[0089] In some embodiments, detecting the expression level of potassium channel protein KCa3.1 and its regulatory factors from collected uterine epithelial tissue biopsy samples includes: pretreating the biopsy sample to obtain a single-cell suspension, adding a detection composition containing a secondary antibody labeled with the chromogenic enzyme or fluorescent substance, incubating under set temperature and time conditions to allow the secondary antibody to bind with the specific primary antibody of potassium channel protein KCa3.1 and its regulatory factors in the sample to form an antibody complex; washing the incubated sample to remove unbound antibodies, adding a chromogenic substrate or an excitation fluorescent substance, and determining the expression level of potassium channel protein KCa3.1 and its regulatory factors by detecting the absorbance value or fluorescence signal intensity of the chromogenic reaction.

[0090] In some embodiments, the nanopore is connected to a multiple impedance monitoring module for real-time monitoring of channel activity changes, including: controlling the sample solution to flow through the KCa channel-specific nanopore via the integrated microfluidic chip, applying a stable transmembrane voltage across the nanopore; introducing the sample solution into the detection channel of the microfluidic chip at a preset micro-dose each time, and acquiring the impedance change signal of the nanopore in real time via the multiple impedance monitoring module, the impedance change signal corresponding to the transmembrane transport rate of potassium ions through the KCa channel; repeating the above detection process at least twice within a preset time period to obtain multiple sets of impedance change data reflecting channel activity.

[0091] In some embodiments, acquiring the channel activity change data of the microfluidic chip monitored in real time by multiple impedance methods includes: continuously acquiring the impedance signal of each small dose sample flowing through the nanopore at a preset sampling frequency to form time-series impedance data; normalizing the time-series impedance data obtained from multiple detections to generate multiple sets of standardized data reflecting the dynamic changes in channel activity, wherein the standardized data includes impedance values ​​at different detection time points and their changing trends.

[0092] In some embodiments, calculating the real-time sampling error based on the multiple sets of channel activity data includes: statistically analyzing the impedance values ​​at the same detection time point in the multiple sets of channel activity data, calculating the deviation between each detection value and the average impedance value at that time point; and using the deviation as the real-time sampling error to evaluate the signal fluctuations caused by inorganic salt ion channel interactions or sample differences during the current detection process.

[0093] In some embodiments, the optimization processing of the chromogenic signal or the developing signal by adjusting the sampling parameters to achieve a continuous minimum variance of the sampling error includes: dynamically adjusting the sample introduction dose, detection time interval, or transmembrane voltage parameter monitored by impedance method of the microfluidic chip according to the real-time sampling error; re-acquiring channel activity data after each adjustment, calculating the new sampling error and determining whether the variance has decreased, iteratively adjusting until the variance of the sampling error is less than a preset threshold three times consecutively, and performing noise filtering and baseline correction on the absorbance value of the chromogenic signal or the intensity of the fluorescence signal based on the finally stable parameters.

[0094] In some embodiments, the step of fusing and analyzing the optimized protein expression data with imaging evidence and physiochemical test data to generate a gynecological tumor risk co-estimation result includes: standardizing the protein expression data, tumor morphological parameters in imaging evidence, and tumor marker concentrations in physiochemical test data; morphological parameters include tumor size and margin clarity; training and analyzing the standardized multidimensional data using a preset fusion algorithm to output a gynecological tumor risk score; and classifying the tumor development level according to the risk score, wherein the level corresponds to a comprehensive judgment result of tumor growth activity, metastasis probability, prognosis, and recurrence risk.

[0095] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the tumor risk assessment method and each step based on epithelial ion channel protein expression nanoprobes described above can be referred to the corresponding process in the embodiments of the tumor risk assessment system based on epithelial ion channel protein expression nanoprobes described above, and will not be repeated here.

[0096] This application also provides a tumor risk assessment device based on epithelial ion channel protein expression nanoprobes, used to perform the steps of the tumor risk assessment method based on epithelial ion channel protein expression nanoprobes shown in any embodiment of this application. The tumor risk assessment device based on epithelial ion channel protein expression nanoprobes can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0097] like Figure 3 As shown, the tumor risk assessment device based on epithelial ion channel protein expression nanoprobes includes:

[0098] The data acquisition unit is used to acquire channel activity change data of the microfluidic chip in real time through multiple impedance method. The multiple impedance method acquires multiple sets of channel activity data through repeated testing of a single small dose of a tiny sample.

[0099] The error calculation unit is used to calculate the real-time sampling error based on multiple sets of channel active data, and to optimize the color development signal or development signal by adjusting the sampling parameters to achieve the continuous minimum variance, thereby reducing the probability of type I and type II errors.

[0100] The data analysis unit is used to integrate and analyze the optimized protein expression data with imaging evidence and physiochemical test data to generate gynecological tumor risk co-estimation results. The risk co-estimation results are used to determine the development level of gynecological tumors, including tumor growth, metastasis, prognosis and recurrence risk.

[0101] In some embodiments, detecting the expression level of potassium channel protein KCa3.1 and its regulatory factors from collected uterine epithelial tissue biopsy samples includes: pretreating the biopsy sample to obtain a single-cell suspension, adding a detection composition containing a secondary antibody labeled with the chromogenic enzyme or fluorescent substance, incubating under set temperature and time conditions to allow the secondary antibody to bind with the specific primary antibody of potassium channel protein KCa3.1 and its regulatory factors in the sample to form an antibody complex; washing the incubated sample to remove unbound antibodies, adding a chromogenic substrate or an excitation fluorescent substance, and determining the expression level of potassium channel protein KCa3.1 and its regulatory factors by detecting the absorbance value or fluorescence signal intensity of the chromogenic reaction.

[0102] In some embodiments, the nanopore is connected to a multiple impedance monitoring module for real-time monitoring of channel activity changes, including: controlling the sample solution to flow through the KCa channel-specific nanopore via the integrated microfluidic chip, applying a stable transmembrane voltage across the nanopore; introducing the sample solution into the detection channel of the microfluidic chip at a preset micro-dose each time, and acquiring the impedance change signal of the nanopore in real time via the multiple impedance monitoring module, the impedance change signal corresponding to the transmembrane transport rate of potassium ions through the KCa channel; repeating the above detection process at least twice within a preset time period to obtain multiple sets of impedance change data reflecting channel activity.

[0103] In some embodiments, acquiring the channel activity change data of the microfluidic chip monitored in real time by multiple impedance methods includes: continuously acquiring the impedance signal of each small dose sample flowing through the nanopore at a preset sampling frequency to form time-series impedance data; normalizing the time-series impedance data obtained from multiple detections to generate multiple sets of standardized data reflecting the dynamic changes in channel activity, wherein the standardized data includes impedance values ​​at different detection time points and their changing trends.

[0104] In some embodiments, calculating the real-time sampling error based on the multiple sets of channel activity data includes: statistically analyzing the impedance values ​​at the same detection time point in the multiple sets of channel activity data, calculating the deviation between each detection value and the average impedance value at that time point; and using the deviation as the real-time sampling error to evaluate the signal fluctuations caused by inorganic salt ion channel interactions or sample differences during the current detection process.

[0105] In some embodiments, the optimization processing of the chromogenic signal or the developing signal by adjusting the sampling parameters to achieve a continuous minimum variance of the sampling error includes: dynamically adjusting the sample introduction dose, detection time interval, or transmembrane voltage parameter monitored by impedance method of the microfluidic chip according to the real-time sampling error; re-acquiring channel activity data after each adjustment, calculating the new sampling error and determining whether the variance has decreased, iteratively adjusting until the variance of the sampling error is less than a preset threshold three times consecutively, and performing noise filtering and baseline correction on the absorbance value of the chromogenic signal or the intensity of the fluorescence signal based on the finally stable parameters.

[0106] In some embodiments, the step of fusing and analyzing the optimized protein expression data with imaging evidence and physiochemical test data to generate a gynecological tumor risk co-estimation result includes: standardizing the protein expression data, tumor morphological parameters in imaging evidence, and tumor marker concentrations in physiochemical test data; morphological parameters include tumor size and margin clarity; training and analyzing the standardized multidimensional data using a preset fusion algorithm to output a gynecological tumor risk score; and classifying the tumor development level according to the risk score, wherein the level corresponds to a comprehensive judgment result of tumor growth activity, metastasis probability, prognosis, and recurrence risk.

[0107] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the tumor risk assessment device and its modules based on epithelial ion channel protein expression nanoprobes described above can be found by referring to... Figure 2 The corresponding processes in the tumor risk assessment method based on epithelial ion channel protein expression nanoprobes described in the corresponding embodiments will not be repeated here.

[0108] Figure 2 The corresponding tumor risk assessment method based on epithelial ion channel protein expression nanoprobes can be implemented as a computer program that can run on the provided device.

[0109] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0110] The storage medium may store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform an embodiment of any tumor risk assessment method based on epithelial ion channel protein expression nanoprobes.

[0111] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0112] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When executed by a processor, the computer program enables the processor to execute any tumor risk assessment system method based on epithelial ion channel protein expression nanoprobes.

[0113] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0115] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0116] The method acquires channel activity change data of microfluidic chips in real time through multiple impedance methods, in which multiple impedance methods are used to obtain multiple sets of channel activity data through repeated testing of single small-dose micro-samples.

[0117] Based on multiple sets of channel activity data, the real-time sampling error is calculated, and the sampling parameters are adjusted to make the sampling error reach the continuous minimum variance. The color development signal or development signal is then optimized to reduce the probability of type I and type II errors.

[0118] The optimized protein expression data is fused and analyzed with imaging evidence and physiochemical test data to generate gynecological tumor risk co-estimation results. The risk co-estimation results are used to determine the development level of gynecological tumors, including tumor growth, metastasis, prognosis and recurrence risk.

[0119] In some embodiments, detecting the expression level of potassium channel protein KCa3.1 and its regulatory factors from collected uterine epithelial tissue biopsy samples includes: pretreating the biopsy sample to obtain a single-cell suspension, adding a detection composition containing a secondary antibody labeled with the chromogenic enzyme or fluorescent substance, incubating under set temperature and time conditions to allow the secondary antibody to bind with the specific primary antibody of potassium channel protein KCa3.1 and its regulatory factors in the sample to form an antibody complex; washing the incubated sample to remove unbound antibodies, adding a chromogenic substrate or an excitation fluorescent substance, and determining the expression level of potassium channel protein KCa3.1 and its regulatory factors by detecting the absorbance value or fluorescence signal intensity of the chromogenic reaction.

[0120] In some embodiments, the nanopore is connected to a multiple impedance monitoring module for real-time monitoring of channel activity changes, including: controlling the sample solution to flow through the KCa channel-specific nanopore via the integrated microfluidic chip, applying a stable transmembrane voltage across the nanopore; introducing the sample solution into the detection channel of the microfluidic chip at a preset micro-dose each time, and acquiring the impedance change signal of the nanopore in real time via the multiple impedance monitoring module, the impedance change signal corresponding to the transmembrane transport rate of potassium ions through the KCa channel; repeating the above detection process at least twice within a preset time period to obtain multiple sets of impedance change data reflecting channel activity.

[0121] In some embodiments, acquiring the channel activity change data of the microfluidic chip monitored in real time by multiple impedance methods includes: continuously acquiring the impedance signal of each small dose sample flowing through the nanopore at a preset sampling frequency to form time-series impedance data; normalizing the time-series impedance data obtained from multiple detections to generate multiple sets of standardized data reflecting the dynamic changes in channel activity, wherein the standardized data includes impedance values ​​at different detection time points and their changing trends.

[0122] In some embodiments, calculating the real-time sampling error based on the multiple sets of channel activity data includes: statistically analyzing the impedance values ​​at the same detection time point in the multiple sets of channel activity data, calculating the deviation between each detection value and the average impedance value at that time point; and using the deviation as the real-time sampling error to evaluate the signal fluctuations caused by inorganic salt ion channel interactions or sample differences during the current detection process.

[0123] In some embodiments, the optimization processing of the chromogenic signal or the developing signal by adjusting the sampling parameters to achieve a continuous minimum variance of the sampling error includes: dynamically adjusting the sample introduction dose, detection time interval, or transmembrane voltage parameter monitored by impedance method of the microfluidic chip according to the real-time sampling error; re-acquiring channel activity data after each adjustment, calculating the new sampling error and determining whether the variance has decreased, iteratively adjusting until the variance of the sampling error is less than a preset threshold three times consecutively, and performing noise filtering and baseline correction on the absorbance value of the chromogenic signal or the intensity of the fluorescence signal based on the finally stable parameters.

[0124] In some embodiments, the step of fusing and analyzing the optimized protein expression data with imaging evidence and physiochemical test data to generate a gynecological tumor risk co-estimation result includes: standardizing the protein expression data, tumor morphological parameters in imaging evidence, and tumor marker concentrations in physiochemical test data; morphological parameters include tumor size and margin clarity; training and analyzing the standardized multidimensional data using a preset fusion algorithm to output a gynecological tumor risk score; and classifying the tumor development level according to the risk score, wherein the level corresponds to a comprehensive judgment result of tumor growth activity, metastasis probability, prognosis, and recurrence risk.

[0125] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the processor described above can be referred to the corresponding process in the method embodiments of the above embodiments, and will not be repeated here.

[0126] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement the steps of the tumor risk assessment method based on epithelial ion channel protein expression nanoprobes provided in the above embodiments of this application.

[0127] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0128] It should be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. It should be understood that when an element or layer is referred to as “on,” “adjacent to,” “connected to,” or “coupled to” other elements or layers, it may be directly on, adjacent to, connected to, or coupled to other elements or layers, or there may be intervening elements or layers. Conversely, when an element is referred to as “directly on,” “directly adjacent to,” “directly connected to,” or “directly coupled to” other elements or layers, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc., may be used to describe various elements, components, areas, layers, and / or portions, these elements, components, areas, layers, and / or portions should not be limited by these terms. These terms are merely used to distinguish one element, component, area, layer, or portion from another element, component, area, layer, or portion. Therefore, without departing from the teachings of this application, the first element, component, area, layer, or portion discussed below may be referred to as a second element, component, area, layer, or portion.

[0129] Spatial relation terms such as “below,” “under,” “below,” “under,” “above,” “above,” etc., are used herein for convenience of description to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms are intended to also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, then the element or feature described as “below,” “under,” or “below” other elements or features will be oriented “above” other elements or features. Therefore, the exemplary terms “below” and “under” can include both above and below orientations. The device may be otherwise oriented (rotated 90 degrees or otherwise) and the spatial descriptive terms used herein will be interpreted accordingly.

[0130] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising” and / or “including,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0131] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0132] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A tumor risk assessment system based on epithelial ion channel protein expression nanoprobes, characterized in that, include: The detection composition is configured to contain a secondary antibody labeled with a chromogenic enzyme or a fluorescent substance, a chromogenic substrate, and a buffer solution, for detecting the expression level of potassium ion channel protein KCa3.1 and its regulatory factors in collected uterine epithelial tissue biopsy samples; A diagnostic kit configured to contain the detection composition and providing an integrated microfluidic chip for carrying the sample, the microfluidic chip having KCa channel-specific nanopores connected to a multiple impedance monitoring module for real-time monitoring of channel activity changes; The computer device is configured to acquire channel activity change data of the microfluidic chip in real time through multiple impedance methods, wherein the multiple impedance methods acquire multiple sets of channel activity data through repeated testing of a single small dose of a tiny sample. Based on the multi-channel activity data, the real-time sampling error is calculated, and the sampling parameters are adjusted to achieve the continuous minimum variance. The chromogenic or imaging signals are then optimized to reduce the probability of type I and type II errors. The optimized protein expression data is then fused with imaging evidence and physicochemical test data to generate a gynecological tumor risk co-estimation result. This risk co-estimation result is used to determine the development level of gynecological tumors, including the risk of tumor growth, metastasis, prognosis, and recurrence.

2. The system according to claim 1, characterized in that, The detection of the expression levels of potassium ion channel protein KCa3.1 and its regulatory factors in the collected uterine epithelial tissue biopsy samples includes: The biopsy sample is pretreated to obtain a single-cell suspension, and a detection composition containing a secondary antibody labeled with the chromogenic enzyme or fluorescent substance is added. The mixture is incubated under set temperature and time conditions to allow the secondary antibody to bind to the specific primary antibody of potassium ion channel protein KCa3.1 and its regulatory factor in the sample to form an antibody complex. The incubated sample is washed to remove unbound antibodies, and a chromogenic substrate or excitation fluorescent substance is added. The expression level of the potassium ion channel protein KCa3.1 and its regulatory factors is determined by detecting the absorbance value of the chromogenic reaction or the intensity of the fluorescence signal.

3. The system according to claim 1, characterized in that, The nanopore is connected to a multiple impedance method monitoring module for real-time monitoring of channel activity changes, including: The sample solution is controlled to flow through the KCa channel-specific nanopore by the integrated microfluidic chip, and a stable transmembrane voltage is applied across the nanopore. Each time, a sample solution is introduced into the detection channel of the microfluidic chip at a preset micro dose. The impedance change signal of the nanopore is collected in real time by the multiple impedance method monitoring module. The impedance change signal corresponds to the transmembrane transport rate of potassium ions through the KCa channel. Repeat the above detection process at least twice within a preset time period to obtain multiple sets of impedance change data reflecting channel activity.

4. The system according to claim 1, characterized in that, The acquisition of channel activity change data of the microfluidic chip monitored in real time by multiple impedance methods includes: According to the preset sampling frequency, the impedance signal of each tiny dose sample flowing through the nanopore is continuously collected to form a time series impedance data. The time-series impedance data obtained from multiple detections are normalized to generate multiple sets of standardized data reflecting the dynamic changes in channel activity. The standardized data includes impedance values ​​at different detection time points and their changing trends.

5. The system according to claim 1, characterized in that, The calculation of real-time sampling error based on the multiple sets of channel activity data includes: Statistical analysis was performed on the impedance values ​​at the same detection time point in multiple sets of channel activity data, and the deviation between each detection value and the average impedance value at that time point was calculated. The deviation is used as a real-time sampling error to assess signal fluctuations caused by inorganic salt ion channel interactions or sample differences during the current detection process.

6. The system according to claim 1, characterized in that, The optimization processing of the colorimetric or developing signal by adjusting the sampling parameters to achieve a continuous minimum variance in the sampling error includes: The sample delivery dose, detection time interval, or transmembrane voltage parameter monitored by impedance method of the microfluidic chip are dynamically adjusted based on the real-time sampling error. After each adjustment, channel activity data is reacquired, new sampling error is calculated, and it is determined whether the variance has decreased. The sampling error is iteratively adjusted until the variance is less than the preset threshold three times in a row. Based on the finally stable parameters, noise filtering and baseline correction are performed on the absorbance value of the chromogenic signal or the intensity of the fluorescence signal.

7. The system according to claim 1, characterized in that, The process involves fusing and analyzing the optimized protein expression data with imaging evidence and physiochemical test data to generate a collaborative estimation result for gynecological tumor risk, including: The protein expression data, tumor morphological parameters from imaging evidence, and tumor marker concentrations from physiochemical test data were standardized. Morphological parameters included tumor size and margin clarity. A pre-defined fusion algorithm is used to train and analyze standardized multi-dimensional data to output a risk score for gynecological tumors. The risk score is used to classify the level of tumor development, which corresponds to a comprehensive judgment of tumor growth activity, metastasis probability, prognosis and recurrence risk.

8. A tumor risk assessment method based on epithelial ion channel protein expression nanoprobes, characterized in that, The method for applying the tumor risk assessment system based on epithelial ion channel protein expression nanoprobes according to any one of claims 1-7 includes: The method acquires channel activity change data of microfluidic chips in real time through multiple impedance methods, in which multiple impedance methods are used to obtain multiple sets of channel activity data through repeated testing of single small-dose micro-samples. Based on multiple sets of channel activity data, the real-time sampling error is calculated, and the sampling parameters are adjusted to make the sampling error reach the continuous minimum variance. The color development signal or development signal is then optimized to reduce the probability of type I and type II errors. The optimized protein expression data is fused and analyzed with imaging evidence and physiochemical test data to generate gynecological tumor risk co-estimation results. The risk co-estimation results are used to determine the development level of gynecological tumors, including tumor growth, metastasis, prognosis and recurrence risk.

9. A computer device, characterized in that, The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and, in executing the computer program, implement the method as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the method as described in claim 8.

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