A method and system for detecting the expression level of patient serum miRNA

Abstract

This invention provides a method and system for detecting serum miRNA expression levels in patients, relating to the field of biomedical detection. The method includes: simultaneously acquiring multi-channel fluorescence signals; firstly, extracting common temporal-spatial variation patterns across channels through statistical analysis; then, separating systematic common pattern interference signals using adaptive filtering or surface fitting techniques; and precisely subtracting this interference from the physical coordinates of each reaction channel to reconstruct the true reaction process. Subsequently, the reconstructed curves are normalized in amplitude and nonlinearly aligned with the time axis to unify the signal scale and kinetic benchmark. Finally, feature parameters are extracted and combined with a standard curve model to quantify the target miRNA expression level with high precision. This invention simultaneously solves the problems of reaction kinetic heterogeneity caused by serum matrix effects and the difficulty in removing systematic noise caused by instrument drift, significantly improving the accuracy, reliability, and repeatability of multi-sample parallel detection.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection, and in particular to a method and system for detecting the expression level of miRNA in patient serum. Background Technology

[0002] In the field of biomedical testing, especially in high-throughput screening and diagnostic applications, the accurate and reliable quantification of the expression levels of specific biomarkers (such as miRNAs) in patient serum is of paramount clinical significance. However, existing detection technologies face multiple complex technical challenges when processing large numbers of parallel samples, which severely limit the accuracy and reliability of test results.

[0003] Differences in serum sample composition (matrix effect) are a core factor affecting the reaction kinetics of miRNA detection. Even slight differences in the concentrations of components such as proteins, lipids, and ions in serum can alter the fluorescence signal growth rate and plateau time. When two serum samples have the same initial miRNA concentration, the fluorescence signal growth curves may show significant differences in slope and peak time. The endpoint method, which reads signals at fixed time points, cannot capture dynamic signal changes, leading to underestimation of slower-response samples and overestimation of faster-response samples. Continuous monitoring of the complete reaction process for each serum sample and dynamic analysis of each reaction process are essential for accurate miRNA quantification. Systematic common-mode interference signals exist during long-term continuous acquisition by detection equipment. Fluctuations in excitation light source power, detector sensitivity drift, and changes in ambient temperature synchronously affect the fluorescence measurement results of all reaction channels. Systematic common-mode interference signals are mixed with miRNA biological reaction signals, making them difficult to separate. Analysts cannot distinguish whether the instantaneous fluctuations in a single fluorescence curve originate from the miRNA reaction or from equipment interference. The inability to effectively remove systemic common pattern interference signals will lead to distortion of the reconstruction reaction process curve, thereby affecting the quantitative accuracy and reliability of miRNA expression level assessment.

[0004] This invention addresses the common problems in the field, such as the heterogeneity of reaction kinetics caused by serum matrix effects, the difficulty in real-time removal of systematic common-mode interference signals caused by detection device drift, and the inability of traditional correction methods to simultaneously compensate for time and spatial domains. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings existing in the field by proposing a method and system for detecting serum miRNA expression levels in patients. This method and system have the advantages of effectively eliminating systemic common pattern interference signals, reconstructing the true reaction process, and thus improving the accuracy and reliability of quantifying the expression levels of target biomarkers.

[0006] To overcome the shortcomings of the prior art, the present invention adopts the following technical solution:

[0007] S1. Simultaneously acquire fluorescence signals from multiple reaction channels;

[0008] S2. Perform statistical processing on the fluorescence signal to obtain common pattern information;

[0009] S3. Perform signal processing on the common mode information to separate the systematic common mode interference signal;

[0010] S4. Subtract the systematic common mode interference signal from the fluorescence signal of each reaction channel to reconstruct the true reaction process of each reaction channel;

[0011] S5. Based on the actual reaction process, quantify the expression level of target biomarkers.

[0012] The above scheme, by synchronously acquiring fluorescence signals, statistically processing the signals, separating systematic common mode interference signals, and then reconstructing the real reaction process, finally quantifies the expression level of the target biomarker based on the real reaction process. This effectively solves the problem of the impact of systematic common mode interference signals on the accuracy of detection results in existing technologies and improves the reliability of the quantification results.

[0013] Optionally, this application also proposes that step S2 includes the following specific steps:

[0014] S21. Obtain the grouping information of the reaction channels;

[0015] S22. Based on the grouping information, select the fluorescence signal of the control group channel;

[0016] S23. Perform statistical processing on the fluorescence signal of the control group channel to obtain common pattern information.

[0017] By using the above scheme, and by obtaining grouping information and selecting the fluorescence signal of the control group channel for statistical processing, common mode information can be obtained more accurately, providing a more reliable basis for the subsequent separation of interference signals.

[0018] Optionally, this application also proposes that step S3 includes the following specific steps:

[0019] S31. Real-time analysis of the temporal fluctuation characteristics of common pattern information;

[0020] S32. Adjust the adjustment parameters used to separate systematic common mode interference signals according to the time fluctuation characteristics;

[0021] S33. The common mode information is processed by adjusting the parameters to separate the systematic common mode interference signal.

[0022] By using the above scheme, and by analyzing the temporal fluctuation characteristics of common mode information in real time and adjusting the separation parameters, it is possible to dynamically adapt to changes in interference signals and achieve more accurate separation of systemic common mode interference signals.

[0023] Optionally, this application also proposes that step S4 includes the following specific steps:

[0024] S41. Obtain the physical coordinates of the reaction channel;

[0025] S42. Form a data point set by combining the fluorescence signal with the physical coordinates of the corresponding reaction channel;

[0026] S43. Perform spatial surface fitting on the data point set to obtain the spatial distribution of the systematic common mode interference signal;

[0027] S44. Based on the physical coordinates of the reaction channel, determine the specific interference value of the corresponding reaction channel from the spatial distribution of the systematic common mode interference signal;

[0028] S45. Subtract specific interference values ​​from the fluorescence signal of each reaction channel to reconstruct the true reaction process of each reaction channel.

[0029] By using the above method, and by obtaining the physical coordinates of the reaction channels and performing spatial surface fitting, the specific interference value of each channel can be determined in a more precise manner, thereby more accurately removing interference and reconstructing a more realistic reaction process.

[0030] Optionally, this application also proposes that step S5 includes the following specific steps:

[0031] S51. Determine the characteristic signal level for each real reaction process;

[0032] S52. Based on the characteristic signal level, perform amplitude standardization processing on the signal value of each real reaction process;

[0033] S53. Perform non-linear adjustments on the time axis of each real reaction process to align the characteristic time points of the real reaction process with the preset reference time points.

[0034] S54. Extract quantization parameters from the actual reaction process after amplitude standardization and time axis nonlinear adjustment;

[0035] S55. Based on quantitative parameters, quantify the expression level of target biomarkers.

[0036] The above approach, by determining the level of characteristic signals, performing amplitude standardization, nonlinearly adjusting the time axis, and extracting quantification parameters, can effectively compensate for matrix effects and differences in reaction kinetics, and further improve the accuracy of quantification of the expression level of target biomarkers.

[0037] Optionally, this application also proposes that step S51 includes the following specific steps:

[0038] S511. Identify the maximum signal value in each real reaction process;

[0039] S512. The maximum signal value is used as the characteristic signal level of each real reaction process.

[0040] The above scheme provides a simple and effective amplitude standardization benchmark by identifying the maximum signal value as the characteristic signal level, which is helpful for subsequent signal processing.

[0041] Optionally, this application also proposes that step S52 includes the following specific steps:

[0042] S521. Determine the baseline signal level and saturation signal level for each real reaction process;

[0043] S522. Based on the baseline signal level and the saturation signal level, perform interval normalization on the signal value of each real reaction process;

[0044] S523. Perform nonlinear correction on the signal values ​​after interval normalization to compensate for the nonlinear relationship between the signal response and the expression level of the target biomarker.

[0045] By using the above scheme, which determines the baseline and saturation signal level, performs interval normalization, and performs nonlinear correction, the signal amplitude can be processed more comprehensively, nonlinear relationships can be compensated, and quantization accuracy can be further improved.

[0046] Optionally, this application also proposes that step S53 includes the following specific steps:

[0047] S531. Identify multiple key time points in each real reaction process;

[0048] S532. Determine the correspondence between multiple key time points and preset reference time points;

[0049] S533. Based on the correspondence, the time axis of each real reaction process is segmented and stretched or compressed so that the characteristic time points of the real reaction process are aligned with the preset reference time points.

[0050] The above scheme, by identifying key time points and performing segmented stretching or compression, can flexibly align the timelines of different reaction processes, effectively eliminate timeline asynchrony, and ensure comparability between different samples.

[0051] Optionally, this application also proposes that step S531 includes the following specific steps:

[0052] S5311. Perform sliding window averaging on the signal values ​​of the actual reaction process to obtain smoothed signal values.

[0053] S5312. Analyze the rate of change of the smoothed signal value over a continuous time period to obtain signal change rate information;

[0054] S5313. Based on the signal change rate information, identify the time points when the change rate reaches a preset threshold or when the change trend changes significantly, and use these as multiple key time points.

[0055] The above scheme, through sliding window averaging and smoothed signal change rate analysis, can robustly identify key time points, improving the accuracy and automation of time axis adjustment.

[0056] Optionally, this application also proposes a patient serum miRNA expression level detection system for detecting patient serum miRNA expression levels, the system comprising a signal acquisition module, an interference signal separation module, a reaction process reconstruction module, and an expression level quantification module;

[0057] The signal acquisition module is used to simultaneously acquire fluorescence signals from multiple reaction channels;

[0058] The interference signal separation module is used to perform statistical processing on the fluorescence signal, obtain common mode information, and perform signal processing on the common mode information to separate the systematic common mode interference signal.

[0059] The reaction process reconstruction module is used to subtract systematic common mode interference signals from the fluorescence signals of each reaction channel and reconstruct the true reaction process of each reaction channel.

[0060] The expression level quantification module is used to quantify the expression level of target biomarkers based on the actual reaction process.

[0061] The above scheme provides a system for implementing the above detection method. Through modular design, it can efficiently and automatically complete signal acquisition, interference separation, process reconstruction and expression level quantification, thereby improving the convenience and efficiency of detection.

[0062] As can be seen from the above, the method and system for detecting the expression level of patient serum provided in this application effectively eliminates systemic common pattern interference signals, reconstructs the real response process, and accurately quantifies it on this basis. It has the advantages of effectively eliminating systemic common pattern interference signals, reconstructing the real response process, thereby improving the accuracy and reliability of the quantification of the expression level of target biomarkers. Attached Figure Description

[0063] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0064] Figure 1 This is a schematic flowchart of the method for detecting the expression level of patient serum miRNA according to the present invention.

[0065] Figure 2 This is a modular schematic diagram of the patient serum miRNA expression level detection system of the present invention.

[0066] The diagram numbers are explained as follows: 100 - Signal acquisition module; 200 - Interference signal separation module; 300 - Response process reconstruction module; 400 - Expression level quantification module. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to its embodiments. It should be noted that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this invention. Other systems, methods, and / or features of this embodiment will become apparent to those skilled in the art after reviewing the following detailed description. Furthermore, the terminology used to describe positional relationships in the accompanying drawings is for illustrative purposes only and should not be construed as limiting this patent. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0068] Example 1: Combined with Appendix Figure 1 This embodiment constructs a method for detecting the expression level of miRNA in patient serum.

[0069] Existing methods for detecting miRNA expression levels in patient serum face two major technical challenges when performing parallel testing on multiple serum samples: First, differences in the concentrations of components such as proteins, lipids, and ions in different serum samples (matrix effect) alter the fluorescence signal growth rate and plateau time, resulting in significant differences in the slope and peak time of the reaction process curves in different channels for the same initial miRNA concentration. Second, during long-term continuous acquisition, drift in excitation power and detector sensitivity can be simultaneously superimposed on the fluorescence signals of all channels, forming a systematic common-mode interference signal. These two factors combined make it extremely difficult to extract the biological response from the acquired fluorescence signals, directly affecting the accuracy and reliability of miRNA quantification results.

[0070] In a central laboratory tasked with regional disease screening, operators use an automated dispensing system to aliquot hundreds of patient serum samples into the wells of microplates. The fluorescence signal generated by the enzymatic reaction within the wells should be proportional to the miRNA concentration. However, due to the serum matrix effect, the same initial miRNA concentration exhibits variations in growth rate (slower or faster) and plateau phase (earlier or delayed) across different wells. Simultaneously, the output power of the solid-state laser experiences nonlinear drift with temperature fluctuations, and the detector sensitivity also shows slight shifts with changes in hardware conditions. These systematic, common-mode interference signals appear synchronously across all channels, further contaminating the reaction progress curves. Faced with hundreds of curves mixed with interfering signals, analysts are unable to determine the true source of fluctuations in individual fluorescence signals.

[0071] If the problems caused by the matrix effect and systemic common pattern interference signals cannot be resolved, the quantitative results of miRNA expression levels will exhibit systematic bias, failing to accurately reflect the true content of biomarkers. This will affect the reliability of early screening and diagnosis, increasing the risk of misdiagnosis or delayed treatment. Simultaneously, inaccurate data will reduce the reproducibility of research findings, hindering the discovery and validation of new biomarkers. To address these challenges, this invention proposes a miRNA quantitative detection scheme that simultaneously acquires fluorescence signals, statistically extracts common pattern information, separates systemic common pattern interference signals, and reconstructs the true reaction process of each channel. This achieves high-precision and high-reliability quantification of serum miRNA expression levels in patients.

[0072] In response, this application proposes a method for detecting serum miRNA expression levels in patients, comprising the following steps:

[0073] S1. Simultaneously acquire fluorescence signals from multiple reaction channels;

[0074] S2. Perform statistical processing on the fluorescence signal to obtain common pattern information;

[0075] S3. Perform signal processing on the common mode information to separate the systematic common mode interference signal;

[0076] S4. Subtract the systematic common mode interference signal from the fluorescence signal of each reaction channel to reconstruct the true reaction process of each reaction channel;

[0077] S5. Based on the actual reaction process, quantify the expression level of target biomarkers.

[0078] Fluorescence signals refer to the fluorescence intensity data emitted by multiple independent reaction units simultaneously at the same time point or within a very short time interval. Synchronous acquisition is achieved using equipment such as fluorescence detectors equipped with multi-channel detectors, confocal scanning systems, or high-speed camera arrays, for example, using multi-channel photomultiplier tube arrays or charge-coupled device cameras for parallel data reading. Common mode information is obtained using statistical methods such as principal component analysis, independent component analysis, factor analysis, or simple averaging and median calculations. For example, time-series correlation analysis or cluster analysis is performed on signals from all channels, primarily to identify and characterize common fluctuations or drifts that may originate from systematic factors. Systematic common mode interference signals are separated using adaptive filtering, Kalman filtering, wavelet denoising, or model-fit-based signal separation techniques. For example, fitting and subtracting is performed by establishing a mathematical model of light source drift or detector response changes, primarily to accurately extract non-specific background noise or drift caused by instrument-specific or environmental factors from the observed signal. The actual response process is reconstructed using methods such as point-by-point subtraction, curve fitting correction, or residual analysis. For example, the estimated interference signal is subtracted from the original signal, primarily to eliminate external interference and restore a pure biological response that accurately reflects the concentration or activity of the target biomarker. Quantification of the target biomarker is achieved using methods such as standard curve methods, kinetic model fitting, feature parameter extraction, or machine learning regression models. For example, the curve characteristics of the sample curve are compared with those of a standard with known concentrations, primarily to provide accurate and reliable biomarker concentration information for disease diagnosis or prognostic assessment.

[0079] The core innovation of this invention lies in the simultaneous acquisition of fluorescence signals from multiple reaction channels. By combining statistical processing of the fluorescence signals to obtain common pattern information with signal processing of the common pattern information to separate systematic common pattern interference signals, it is possible to identify and isolate systematic common pattern interference signals that affect all channels. Furthermore, by subtracting the systematic common pattern interference signals from the fluorescence signals of each reaction channel, the true reaction process of each reaction channel can be reconstructed. This effectively eliminates the influence of instrument drift and environmental fluctuations on the detection results, making the subsequent quantification of the expression level of target biomarkers more accurate and reliable.

[0080] To better understand the operational mechanism of this embodiment, it achieves accurate detection of serum miRNA expression levels through the following logically progressive steps: First, fluorescence signals from multiple reaction channels are simultaneously acquired to ensure data consistency across all channels in the time dimension, providing a unified observational basis for subsequent extraction of common pattern information. Second, the simultaneously acquired fluorescence signals are statistically processed to extract common pattern information prevalent among the channels. This common pattern information reflects overall signal fluctuations caused by detection device drift and environmental fluctuations. Then, signal processing methods such as high-pass filtering or spatial surface fitting are applied to the extracted common pattern information to separate systematic common pattern interference signals. Next, the separated systematic common pattern interference signals are subtracted point by point from the original fluorescence signal of each reaction channel, thereby reconstructing the true reaction process of each channel. Finally, kinetic features such as half-peak time and maximum reaction rate are extracted from the reconstructed true reaction process, and the miRNA content is quantified using a standard curve model. This embodiment can completely eliminate systematic interferences such as excitation source power drift and detector sensitivity drift, significantly improving the accuracy and reliability of miRNA expression level quantification.

[0081] In some preferred embodiments, this application is implemented as follows: When detecting the expression level of miRNA in patient serum, this embodiment uses a real-time PCR instrument equipped with a CCD camera array to simultaneously capture fluorescence images of all reaction wells in a microplate at predetermined time points in each reaction cycle, and converts the captured fluorescence images into fluorescence signal time series for each reaction channel using image processing software. To obtain common pattern information, this embodiment performs principal component analysis on the fluorescence signal time series of each reaction channel to extract the first principal component; this first principal component represents the most significant common signal change trend among all channels. Subsequently, this embodiment applies low-pass filtering to the first principal component and combines it with an adaptive thresholding algorithm to filter out high-frequency noise and identify slowly drifting components, which are the systematic common pattern interference signals. This embodiment subtracts the corresponding systematic common pattern interference signal estimate from the original fluorescence signal of each reaction channel point by point to obtain the interference-corrected true reaction process curve. Finally, this embodiment uses an S-shaped kinetic model to fit the interference-corrected true reaction process curve, extracts key kinetic characteristic parameters such as half-peak time and maximum fluorescence intensity, and combines them with a pre-established standard curve model to convert the extracted characteristic parameters into miRNA expression level values.

[0082] This embodiment effectively overcomes the impact of systematic common-mode interference signals on the accuracy of fluorescence measurements caused by instrument drift (such as excitation source power drift and detector sensitivity drift) and environmental temperature fluctuations in multi-channel parallel detection. This embodiment identifies systematic common-mode interference signals and removes them from the original fluorescence signals of each reaction channel, thus reconstructing the true reaction process of each channel and eliminating non-specific background signal contamination. This embodiment significantly improves the accuracy and reliability of quantifying serum miRNA expression levels in patients, enhances the signal-to-noise ratio and data credibility of the detection results, and provides high-quality quantitative data support for clinical diagnosis and research applications.

[0083] However, directly statistically processing the fluorescence signals of all reaction channels may introduce information unrelated to the systematic common pattern interference signal, such as biological differences between different samples, thereby affecting the accuracy of the common pattern information and consequently affecting the subsequent separation of interference signals and the reconstruction of the true reaction process.

[0084] In this regard, this application further proposes specific steps for step S2, including:

[0085] S21. Obtain the grouping information of the reaction channels;

[0086] S22. Based on the grouping information, select the fluorescence signal of the control group channel;

[0087] S23. Perform statistical processing on the fluorescence signal of the control group channel to obtain common pattern information.

[0088] The reaction channels are implemented using pre-configured experimental design tables, sample label identification, or automatic classification based on specific marker signals within the channels. This aims to provide a basis for subsequent selective processing. The fluorescence signal of the control channel refers to the fluorescence signal data collected from the reaction channels designated as the control group. Statistical processing involves calculations using principal component analysis, independent component analysis, singular value decomposition, or based on central tendency parameters such as mean, median, and mode. It can also be based on regression analysis or machine learning models for pattern recognition. Common pattern information refers to data extracted from the fluorescence signals of the control channel that reflects the ubiquitous, time-varying systematic interference characteristics of all channels. Common pattern information is represented as time-series curves, feature vectors, or mathematical model parameters. Through this refined selection and processing of data sources, this application effectively solves the problem of biological difference noise that may be introduced by directly performing statistical processing on all reaction channels, ensuring the accurate identification and removal of systematic interference signals, and thus achieving precise quantification of miRNA expression levels.

[0089] In some preferred embodiments, this application is implemented as follows: When detecting the expression level of miRNA in patient serum, the layout of the microplate can be pre-designed, with some wells designated as control groups. For example, in a 96-well plate, the wells in the first and last columns are reserved for negative or positive controls. Before the experiment begins, the system can read the grouping information of the reaction channels from a preset experimental configuration file, which clearly identifies which channels belong to the control group and which belong to the experimental group. For example, the configuration file can be a table containing channel IDs and corresponding grouping types (such as control, experiment). Subsequently, based on the read grouping information, the system can automatically filter and select the fluorescence signals of all reaction channels marked as "control groups". For example, if channels with channel IDs 1 to 8 and 89 to 96 are designated as control groups, only the fluorescence signal data synchronously collected by these channels throughout the reaction process is extracted. Next, the fluorescence signals of these selected control group channels are statistically processed to obtain common pattern information. Specifically, principal component analysis can be performed on the fluorescence signal data of these control group channels. The time-series fluorescence signal data of all control group channels were constructed into a data matrix, where each row represents a time point and each column represents the fluorescence signal of a control group channel. Principal component analysis (PCA) was then performed on this data matrix to extract the first principal component. This principal component typically represents the most prevalent common trend in the dataset, i.e., the systemic common pattern interference signal. The time-series data of this extracted principal component constitutes the common pattern information obtained in this scheme, reflecting the drift or fluctuations generated by the system itself in the absence of a specific response to the target biomarker.

[0090] The above technical solution effectively eliminates non-systematic noise introduced by biological differences (such as matrix effects of serum samples from different patients) in the experimental group, so that the obtained common pattern information can more purely and accurately reflect the systematic interference of the detection system itself, thereby improving the overall reliability of the detection of patient serum miRNA expression level.

[0091] Because the detection equipment itself exhibits systematic drift, this drift, as a common-mode interference signal, is superimposed on the true signals of all parallel channels. This results in the common-mode information containing both the true reaction information and the systematic common-mode interference signal. Directly subtracting the common-mode information containing the systematic common-mode interference signal from the fluorescence signal makes it impossible to accurately reconstruct the true reaction process of each reaction channel. Therefore, separating the systematic common-mode interference signal from the common-mode information is crucial for accurately reconstructing the true reaction process.

[0092] In response, this application further proposes step S3, which involves signal processing of the common mode information to separate the systematic common mode interference signal. The specific steps include:

[0093] S31. Real-time analysis of the temporal fluctuation characteristics of common pattern information;

[0094] S32. Adjust the adjustment parameters used to separate systematic common mode interference signals according to the time fluctuation characteristics;

[0095] S33. The common mode information is processed by adjusting the parameters to separate the systematic common mode interference signal.

[0096] Temporal fluctuation characteristics refer to the signal's trend, such as slow rises or falls in drift, periodic changes (e.g., periodic noise caused by power supply frequency or mechanical vibration), or instantaneous jumps or random noise. Adjustment parameters include the filter's cutoff frequency, bandwidth, or order; the adaptive filtering algorithm's learning rate or step size; or coefficients based on model fitting. Adjusting these parameters dynamically optimizes the separation algorithm's performance based on the real-time detected temporal fluctuation characteristics, enabling it to more accurately match and remove interference signals at the current moment, thereby improving the accuracy and adaptability of the separation. Systemic common-mode interference is addressed through filtering methods, such as high-pass filtering to remove slow drifts, notch filtering to remove periodic noise, adaptive noise cancellation, signal decomposition (e.g., Independent Component Analysis (ICA), Empirical Mode Decomposition (EMD), or model fitting methods, such as polynomial fitting and exponential fitting.

[0097] The proposed solution achieves accurate separation of systematic common-mode interference signals through refined signal processing of common-mode information. This dynamic and adaptive separation strategy significantly improves the ability to extract clean interference signals from common-mode information, laying a solid foundation for subsequently subtracting the interference signal from the fluorescence signal of each reaction channel and reconstructing the actual reaction process. Compared to methods using fixed parameters for interference signal separation, this solution better addresses the complex and dynamically changing systematic interference that may occur during long-term operation of the detection equipment, thereby ensuring the accuracy and reliability of the final reconstructed reaction process.

[0098] In some preferred embodiments, S33, processing the common mode information using the adjustment parameters to separate the systematic common mode interference signal is specifically implemented as follows:

[0099] First, perform real-time spectral analysis on common mode information (e.g., sliding window Fourier transform or wavelet transform), and calculate the mean, variance, or slope within a short time window to automatically identify key interference features such as low-frequency drift and periodic noise.

[0100] Secondly, the filter parameters are dynamically adjusted based on the spectrum analysis and time-domain statistics results. If low-frequency drift is the main issue, a suitable high-pass cutoff frequency is set. If periodic noise occurs, the notch center frequency and bandwidth are configured. For complex or nonlinear drift, Kalman filtering or LMS adaptive filters can be enabled and the gain updated in real time.

[0101] Finally, the adaptive filter is applied directly to the common mode information, and the output processing result is a systematic common mode interference signal that accurately reflects instrument drift and environmental noise. This signal is then used in subsequent steps to subtract and reconstruct the true response process of each channel.

[0102] The above technical solution solves the problem that the fixed parameter separation method cannot accurately remove interference due to the dynamic and complex drift of the detection equipment itself. It obtains a purer systemic common mode interference signal, which provides accurate input for subtracting the interference signal from the fluorescence signal of each reaction channel and reconstructing the real reaction process of each reaction channel. This significantly improves the accuracy and reliability of the detection results.

[0103] To address the shortcoming that subtracting common interference from the average value ignores spatial heterogeneity, this application further proposes step S4: subtracting systematic common mode interference signals from the fluorescence signals of each reaction channel to reconstruct the true reaction process of each reaction channel, including:

[0104] S41. Obtain the physical coordinates of the reaction channel;

[0105] S42. Form a data point set by combining the fluorescence signal with the physical coordinates of the corresponding reaction channel;

[0106] S43. Perform spatial surface fitting on the data point set to obtain the spatial distribution of the systematic common mode interference signal;

[0107] S44. Based on the physical coordinates of the reaction channel, determine the specific interference value of the corresponding reaction channel from the spatial distribution of the systematic common mode interference signal;

[0108] S45. Subtract specific interference values ​​from the fluorescence signal of each reaction channel to reconstruct the true reaction process of each reaction channel.

[0109] Spatial surface fitting refers to the process of constructing a continuous mathematical surface model that describes the trend of a set of discrete three-dimensional data points (e.g., X and Y coordinates and corresponding Z values) using mathematical methods. This can be achieved using techniques such as least squares, spline interpolation, polynomial fitting, or radial basis function (RBF) interpolation. The goal is to extend finite discrete measurements to the entire spatial region, thereby estimating the signal or interference values ​​at any location.

[0110] This application's solution achieves precise localization and subtraction of systematic common-mode interference signals by introducing the physical coordinate information of the reaction channels and utilizing spatial surface fitting technology. Specifically: First, the physical coordinates of each channel are obtained, and the common-mode interference values ​​of each channel at the same time point are paired with the coordinates to form a spatial-temporal data point set. Second, this point set is fitted with a surface on a two-dimensional plane (such as a quadratic polynomial or Kriging interpolation) to construct a mathematical model f(X,Y) reflecting the spatial distribution of systematic interference throughout the detection area. Finally, the coordinates (Xi,Yi) of each channel are substituted into f(X,Y) to obtain its specific interference value f(Xi,Yi), which is then subtracted point by point from the original fluorescence signal to reconstruct a more accurate true reaction process. This approach considers spatial non-uniformity while ensuring the integrity and feasibility of the solution.

[0111] By employing the aforementioned technical solution, and by acquiring the physical coordinates of the reaction channels and constructing a spatial distribution model of the interference signal using spatial surface fitting technology, a specific interference value that takes into account the influence of spatial location can be determined for each reaction channel. This refined interference subtraction method effectively avoids the residual errors that may result from simple global subtraction, significantly improves the accuracy of reconstructing the true reaction process of each reaction channel from the fluorescence signal, and thus provides a more reliable data foundation for subsequent biomarker quantification.

[0112] In some of the embodiments described above in this application, only the actual reaction process is reconstructed. If the expression level of the target biomarker is directly quantified based on these uncorrected reaction processes, the actual reaction process will differ in signal amplitude and time dimension due to matrix effects of different samples. This will introduce bias and reduce the accuracy and reliability of detection. Therefore, this application further proposes S5, the specific steps for quantifying the expression level of the target biomarker based on the actual reaction process:

[0113] S51. Determine the characteristic signal level for each real reaction process;

[0114] S52. Based on the characteristic signal level, perform amplitude standardization processing on the signal value of each real reaction process;

[0115] S53. Perform non-linear adjustments on the time axis of each real reaction process to align the characteristic time points of the real reaction process with the preset reference time points.

[0116] S54. Extract quantization parameters from the actual reaction process after amplitude standardization and time axis nonlinear adjustment;

[0117] S55. Based on quantitative parameters, quantify the expression level of target biomarkers.

[0118] The characteristic signal level refers to the representative signal intensity value in the reaction process, determined by identifying the maximum signal value, average signal value, or signal value at a specific time point in the reaction process. Amplitude normalization refers to adjusting the signal amplitude of different reaction processes to a uniform scale or range, achieved using linear scaling, Z-score normalization, or normalization methods based on a specific reference point. Time axis nonlinear adjustment refers to correcting differences in the time dimension of different reaction processes through nonlinear transformation, such as the rate of reaction or the offset of the reaction initiation point, so as to keep key reaction events consistent in time. Time axis nonlinear adjustment is achieved using dynamic time warping (DTW) algorithms, piecewise linear interpolation, or stretching or compression algorithms based on key time point matching. Quantization parameters refer to numerical indicators extracted from the corrected reaction process curve that can directly or indirectly reflect the expression level of the target biomarker. Quantization parameters include the initial rate of reaction, maximum reaction rate, time required to reach a specific signal level, area under the curve, or endpoint signal value, etc.

[0119] In some preferred embodiments, this application is implemented as follows: When determining the characteristic signal level of each real reaction process, the maximum signal value in each real reaction process can be identified and used as the characteristic signal level of each real reaction process. For example, for a typical S-shaped response curve, its maximum signal value usually represents the signal intensity when the response reaches or approaches saturation, and can be used as a representative indicator of the signal amplitude of the reaction process. When performing amplitude normalization on the signal value of each real reaction process, the baseline signal level and saturation signal level of each real reaction process can be determined first. The baseline signal level can be calculated by averaging the signal before or at the beginning of the reaction, while the saturation signal level can be calculated by averaging or maximizing the signal at the end of the reaction. Then, based on these baseline signal levels and saturation signal levels, the signal value of each real reaction process is normalized to an interval, for example, by linearly mapping the signal value to the range of 0 to 1. In addition, to compensate for the nonlinear relationship between the signal response and the expression level of the target biomarker, the signal value after interval normalization can be nonlinearly corrected, for example, by applying logarithmic transformation, power function transformation, or lookup table correction based on calibration curve. When nonlinearly adjusting the timeline of each real reaction process, multiple key time points can be identified for each process. These key time points can include the reaction start point, the half-maximum signal point, the maximum reaction rate point, or the reaction end point. Specifically, a sliding window averaging process can be applied to the signal values ​​of the real reaction process to obtain smoothed signal values, reducing noise interference. Next, the rate of change of the smoothed signal values ​​over a continuous time period is analyzed to obtain signal change rate information. Based on this information, time points where the rate of change reaches a preset threshold or where the trend changes significantly are identified as multiple key time points. For example, the time point when the signal first exceeds a baseline threshold can be identified as the start point, and the time point when the rate of change reaches its peak can be identified as the maximum reaction rate point. Then, the correspondence between these key time points and preset reference time points is determined. These reference time points can be averaged key time points extracted from the reaction processes of standard samples or samples with known concentrations. Finally, based on this correspondence, the timeline of each real reaction process is segmented and stretched or compressed, for example, using linear interpolation or spline interpolation, to align the characteristic time points of the real reaction process with the preset reference time points. When extracting quantification parameters from the actual response process after amplitude normalization and nonlinear adjustment of the time axis, parameters such as the corrected maximum signal value, the corrected response rate (e.g., the slope within a specific signal range), or the corrected area under the curve can be extracted. Finally, when quantifying the expression level of the target biomarker based on the quantification parameters, a pre-established calibration curve can be used to convert the extracted quantification parameters into the corresponding biomarker concentration or expression level.For example, a regression model can be established between a quantified parameter and a known miRNA concentration, and then the quantified parameter of the unknown sample can be substituted into the model for calculation.

[0120] Through the above technical solutions, this application can effectively eliminate the differences in signal amplitude and time dimension of the real reaction process caused by matrix effects of different samples: by amplitude standardization, the signal intensity scale of different samples can be unified, avoiding quantization bias introduced by differences in signal amplitude; by nonlinear adjustment of the time axis, the problem of inconsistent reaction kinetic rates of different samples can be corrected, ensuring comparison under a unified time reference. This allows the quantification parameters extracted from the processed real reaction process to more accurately reflect the true expression level of the target biomarker, thereby significantly improving the accuracy and reliability of quantification of the expression level of the target biomarker and overcoming the bias problem caused by quantification based directly on the uncorrected reaction process.

[0121] In some embodiments of this application described above, how to accurately and efficiently determine the level of the characteristic signal to avoid deviations in the level of the characteristic signal due to signal fluctuations or noise interference, thereby affecting the subsequent amplitude normalization processing and the quantification accuracy of the target biomarker expression level, is a problem that needs to be solved. To address this, this application further proposes S51, the specific steps for determining the level of the characteristic signal for each real reaction process including:

[0122] S511. Identify the maximum signal value in each real reaction process;

[0123] S512. The maximum signal value is used as the characteristic signal level of each real reaction process.

[0124] The maximum signal value is achieved by traversing all signal data points and selecting the maximum value, or it can be identified by a peak detection algorithm. The purpose is to capture the peak response of the reaction process as a direct indicator of the reaction intensity. The characteristic signal level is determined by selecting specific points in the reaction process (such as the maximum value, the average value of the plateau period, or a specific percentage point). The purpose is to provide a unified benchmark for subsequent signal standardization processing.

[0125] The above scheme provides a direct method for determining the level of characteristic signals. In the detection method of miRNA expression levels in patient serum, after reconstructing the real reaction process of each reaction channel, these processes need to be quantified to obtain the expression level of the target biomarker. Determining the level of characteristic signals is a crucial step, directly affecting the accuracy of subsequent amplitude normalization. By using the maximum signal value as the level of characteristic signals, this scheme avoids complex curve fitting or multi-point averaging calculations, simplifies the processing flow, and reduces computational resource consumption. Furthermore, the maximum signal value typically represents the peak value where the signal intensity reaches or approaches saturation during the reaction process. This value is relatively stable and has a high signal-to-noise ratio, effectively resisting the influence of random noise and signal fluctuations. Therefore, using the maximum signal value as the level of characteristic signals provides a reliable and representative benchmark for subsequent amplitude normalization, enabling the signal value after amplitude normalization to more accurately reflect the true concentration of the target biomarker. This improves the accuracy of extracting quantification parameters from the processed real reaction process, ultimately enhancing the precision and reliability of quantifying the expression level of the target biomarker. This method, while ensuring efficiency, also improves the robustness of data processing, ensuring accurate quantification of miRNA expression levels in the detection of complex biological samples.

[0126] In some preferred embodiments, determining the characteristic signal level of each real reaction process can be specifically implemented as follows: Assume that the data of a real reaction process is recorded as a series of fluorescence signal values ​​at time points, for example, time points t1, t2, ..., tn correspond to signal values ​​V1, V2, ..., Vn, respectively. To identify the maximum signal value in each real reaction process, the data processing unit can iterate through these signal values, from V1 to Vn, comparing them one by one, and recording the signal value with the largest value. For example, if the signal value sequence of a reaction process is [100, 120, 150, 180, 170, 160], then the maximum signal value is 180. Once this maximum signal value is identified, the data processing unit directly uses this value as the characteristic signal level of the real reaction process. For example, in the above example, 180 is determined as the characteristic signal level of the reaction process. This characteristic signal level can then be used for subsequent amplitude normalization, such as dividing all signal values ​​by 180 or performing other forms of normalization, to eliminate amplitude deviations caused by differences in initial signal intensity between different response processes, thereby making the response processes of different samples comparable in amplitude.

[0127] By directly identifying the maximum signal value in each real reaction process and using it as the feature signal level, the above technical solution can effectively avoid the deviation of the feature signal level caused by signal fluctuations or noise interference, thereby improving the accuracy of the feature signal level. At the same time, it effectively simplifies the process of determining the feature signal level, reduces computational complexity and time cost, and thus improves the reliability of subsequent amplitude standardization processing, ultimately helping to improve the quantitative accuracy of the expression level of the target biomarker.

[0128] In some embodiments described above, amplitude normalization of signal values ​​in real-world reaction processes is proposed. Specifically, this amplitude normalization can be achieved by identifying the maximum signal value in each real-world reaction process and using it as a characteristic signal level for signal normalization. This can eliminate quantization bias caused by differences in signal intensity between different reaction processes. However, in its implementation, relying solely on the maximum signal value for normalization may not adequately address the inconsistency in amplitude range caused by signal baseline drift and saturation effects. Furthermore, the nonlinear relationship that may exist between the signal response and the expression level of the target biomarker is not effectively compensated, affecting the accuracy of subsequent quantization parameters. Therefore, this application further proposes specific steps for step S52, including:

[0129] S521. Determine the baseline signal level and saturation signal level for each real reaction process;

[0130] S522. Based on the baseline signal level and the saturation signal level, perform interval normalization on the signal value of each real reaction process;

[0131] S523. Perform nonlinear correction on the signal values ​​after interval normalization to compensate for the nonlinear relationship between the signal response and the expression level of the target biomarker.

[0132] The baseline signal level is achieved by averaging, fitting, or statistically analyzing the signal values ​​at the beginning of the response curve. The saturation signal level is achieved by averaging, fitting, or identifying the maximum value of the signal values ​​at the end of the response curve or during the signal plateau period. Interval normalization involves linearly mapping the original signal values ​​to a preset specific numerical interval, such as 0 to 1. Its purpose is to eliminate differences in signal amplitude caused by baseline drift and saturation effects at different response stages, making the signals comparable. Nonlinear correction adjusts the signal values ​​to establish a more accurate mapping relationship with the expression level of the target biomarker. Nonlinear correction is achieved using preset correction curves, lookup tables, polynomial fitting, or machine learning-based models.

[0133] This approach first identifies the baseline and saturation signals for each real response process, using these as upper and lower limits to perform interval normalization on the signals, eliminating amplitude differences. Then, based on the nonlinear relationship between the signal and miRNA concentration, nonlinear correction is applied to the normalized data to compensate for response curve distortion. The extracted quantitative parameters after this processing accurately reflect the target miRNA expression level, significantly improving the accuracy and reliability of detection.

[0134] In some preferred embodiments, amplitude normalization of the signal values ​​of the real response process can be implemented as follows: First, determine the baseline signal level and saturation signal level for each real response process. For example, the baseline signal level can be obtained by averaging the signal values ​​at the first 10 time points of the response process curve; while the saturation signal level can be determined by averaging the signal values ​​at the last 20 time points of the response process curve, or by identifying the maximum stable signal value after the curve reaches a plateau. Next, based on the determined baseline and saturation signal levels, perform interval normalization on the signal values ​​of each real response process to map all signal values ​​to the range of 0 to 1, where the baseline signal level corresponds to 0 and the saturation signal level corresponds to 1. Subsequently, perform nonlinear correction on the interval-normalized signal values. For example, a correction lookup table can be pre-established, which records the correction factors for the real biomarker expression levels corresponding to different normalized signal values, or a pre-trained polynomial model (such as a quadratic or cubic polynomial) can be used to fit and correct the normalized signal values ​​to compensate for the nonlinear relationship between the signal response and the target biomarker expression level. In this way, it can be ensured that even when the signal response is nonlinear, the final quantification result can accurately reflect the true concentration of the biomarker.

[0135] Through the above technical solutions, this application can effectively solve the problem of inconsistent amplitude range caused by baseline drift and saturation effect in signal values ​​in real reaction processes, as well as the nonlinear relationship between signal response and target biomarker expression level, thereby significantly improving the accuracy of extracting quantification parameters from real reaction processes, and thus improving the quantification accuracy and reliability of target biomarker expression level.

[0136] In some embodiments described above in this application, a nonlinear adjustment is proposed to the time axis of each real reaction process so that the characteristic time points of the real reaction process are aligned with a preset reference time point. This nonlinear adjustment can specifically attempt alignment through simple linear interpolation or global stretching, which can compensate for time offsets to some extent. However, in its implementation, due to the complex nonlinear differences in reaction rates of different reaction channels, simple linear adjustment cannot accurately capture and correct offsets in all time periods, resulting in local misalignment of the reaction processes on the time axis. If quantization parameters are directly extracted, errors will be introduced due to the time misalignment, affecting the accuracy of the final quantization result. Therefore, this application further proposes S53, the specific steps of nonlinearly adjusting the time axis of each real reaction process so that the characteristic time points of the real reaction process are aligned with the preset reference time point, including:

[0137] S531. Identify multiple key time points in each real reaction process;

[0138] S532. Determine the correspondence between multiple key time points and preset reference time points;

[0139] S533. Based on the correspondence, the time axis of each real reaction process is segmented and stretched or compressed so that the characteristic time points of the real reaction process are aligned with the preset reference time points.

[0140] Critical time points refer to moments in the reaction process that have specific biological or kinetic significance, such as the start of the reaction, the turning point of rapid signal growth, the peak of the maximum signal value, the peak of the plateau phase, and specific points of signal decay. Critical time points can be identified using techniques such as signal rate of change analysis, curve fitting, or pattern recognition. Preset reference time points are calibrated and set based on known standards or historical data. Segmented stretching or compression processing refers to using algorithms such as spline interpolation, dynamic time warping (DTW), or local linear transformation to ensure that the characteristic time points within each time period are accurately aligned with the corresponding reference time points.

[0141] In some preferred embodiments, specifically, when performing segmented stretching or compression of the timeline for each real reaction process, the following steps can be taken: First, key time nodes such as the reaction start point, maximum reaction rate point, and signal saturation point can be identified using an inflection point detection algorithm based on second derivative or curvature analysis. Second, using a pre-established standard reaction process as a reference, the optimal matching path between the real process and the standard process can be calculated using a dynamic time warping algorithm anchored by these key nodes, thereby determining the correspondence between each key time node. Finally, based on the matching results, the timeline of the real process is stretched or compressed using cubic splines or linear interpolation within each node segment, while maintaining the monotonic consistency of the time sequence, so that all key time nodes are precisely aligned with the reference time points.

[0142] Through the above technical solution, this application can effectively solve the problem of misalignment of reaction processes on the time axis due to the difference in reaction rates of different reaction channels. This enables the extraction of quantification parameters from the calibrated reaction process to eliminate the error introduced by time offset, thereby significantly improving the accuracy of quantification results and providing a reliable guarantee for the accurate assessment of the expression level of target biomarkers.

[0143] In some embodiments described above in this application, the actual reaction process may be subject to various noise interferences, leading to signal fluctuations and making it difficult to accurately identify key time points, thereby affecting the accuracy of subsequent nonlinear adjustments to the time axis. To address this, this application further proposes a step S531 for identifying multiple key time points in each actual reaction process, including:

[0144] S5311. Perform sliding window averaging on the signal values ​​of the actual reaction process to obtain smoothed signal values.

[0145] S5312. Analyze the rate of change of the smoothed signal value over a continuous time period to obtain signal change rate information;

[0146] S5313. Based on the signal change rate information, identify the time points when the change rate reaches a preset threshold or when the change trend changes significantly, and use these as multiple key time points.

[0147] Sliding window averaging refers to using a fixed-size window to slide and take the average value within the window to smooth out random noise; the rate of change of a signal is the rate at which the signal increases or decreases over time, used to quantify dynamics and capture inflection points; a preset threshold is a reference value for determining whether the rate of change is significant, used to exclude minor fluctuations; a significant trend inflection point is the moment when the magnitude or sign of the rate of change shows a significant jump (such as from rising to falling or from slow to fast).

[0148] In some preferred embodiments, when performing sliding window averaging on the signal values ​​of the actual reaction process, a fixed window of 5 to 15 data points can be used for averaging. For example, for a signal acquired once per second, data points from two seconds before and after the acquisition can be averaged to eliminate short-term noise. When analyzing the rate of change of the smoothed signal value over a continuous time period, the difference method or least squares method can be used to fit the local slope to calculate the signal rate of change information. For example, the difference between two adjacent smoothed signal values ​​can be calculated as the instantaneous rate of change. When identifying key time points based on the signal rate of change information, an absolute value threshold for the rate of change can be set. For example, when the absolute value of the rate of change exceeds 0.05 signal units / second, or when the sign of the rate of change of three consecutive data points changes, it is marked as a key time point. These key time points can include the start point of the reaction, the half-maximum signal point, the maximum signal point, and the end point of the reaction, providing precise anchor points for subsequent nonlinear adjustments to the time axis.

[0149] Through the above technical solution, this application can effectively overcome the signal fluctuation problem caused by noise interference in the actual reaction process, and achieve accurate identification of key time points. By smoothing the signal, the influence of noise on the judgment of signal change trends is eliminated; by analyzing the signal change rate, the dynamic characteristics of the signal are quantified; and by setting thresholds and identifying trend changes, important turning points in the reaction process are accurately located. This allows subsequent nonlinear adjustments to the time axis to be based on more reliable key time points, thereby improving the accuracy of time axis alignment and ultimately enhancing the accuracy of quantifying the expression level of the target biomarker.

[0150] A specific example of this application is as follows:

[0151] To verify this application, this embodiment uses the data from Table 1 (performance verification for multiple diseases), Table 2 (efficiency of multi-channel parallel processing), and Table 3 (postoperative dynamic follow-up) under the same processing flow, and provides a comprehensive example from the three dimensions of accuracy / stability, efficiency / throughput, and clinical consistency.

[0152] I. Accuracy and Stability (Table 1)

[0153] The samples were extracted using a real-time quantitative PCR instrument and a commercially available kit. The presence of iRNA124 and miRNA210 in the samples was detected according to the real-time quantitative PCR detection method for RNA, and their Ct values ​​were measured.

[0154] Table 1: Performance Validation of Serum miRNA Detection in Multiple Diseases. In the dataset (n=1551, 41 subgroups), after common pattern separation and spatial subtraction in steps S3–S4, the mean standard deviation of Ct decreased from 4.26 to 0.51, with an average reduction of 87.97%. In the representative subgroups with high matrix complexity, the Ct standard deviation decreased from 6.2 to 0.6 (a reduction of 90.32%) in the "lung cancer patient group" and from 5.5 to 0.7 (a reduction of 87.27%) in the "cirrhosis group". After performing amplitude interval normalization, nonlinear correction and time axis alignment in S5, the mean intra-batch CV was 2.92% and the mean inter-batch CV was 5.40%. ΔCt The mean central value of the assessed detection accuracy was approximately 0.55; the mean concordance rate with the clinical gold standard was 95.96%. These results indicate that, even in high-throughput scenarios with significant matrix effects and device drift, this method can still significantly reduce channel / well site variability and maintain batch-to-batch reproducibility (see Table 1).

[0155] Table 1: Performance validation of serum miRNA detection in multiple diseases

[0156] Experiment ID Sample type target miRNA Sample size <![CDATA[Matrix complexity score † > Fluctuation range of Ct values ​​before processing Fluctuation range of Ct values ​​after processing <![CDATA[Detection accuracy (ΔCt) ‡ > Intra-batch CV% Inter-batch CV% Clinical gold standard compliance rate 1 healthy control group hsa-miR-16-5p 30 1.2±0.3 24.8±3.5 25.1±0.4 0.3±0.2 2.10% 4.80% 98.30% 2 Lung cancer patient group hsa-miR-21-5p 45 3.8±0.9 (hyperlipidemia) 19.5±6.2 20.7±0.6 1.2±0.3 (↓82% fluctuation) 3.50% 6.00% 94.70% 3 Breast cancer patient group hsa-miR-155-5p 38 2.5±0.7 22.3±4.1 23.0±0.5 0.7±0.2 2.80% 5.20% 96.20% 4 Colorectal cancer group hsa-miR-92a-3p 42 3.1±0.6 21.5±4.8 22.0±0.5 0.5±0.2 3.10% 5.50% 95.50% 5 Prostate cancer group hsa-miR-141-3p 35 2.8±0.5 23.2±3.9 23.6±0.4 0.4±0.1 2.60% 4.90% 96.80% 6 Diabetes group hsa-miR-126-3p 50 2.0±0.4 25.1±2.8 25.3±0.3 0.2±0.1 1.80% 4.20% 97.80% 7 Cirrhosis group hsa-miR-122-5p 40 4.2±0.8 18.9±5.5 19.8±0.7 0.9±0.3 3.80% 6.50% 94.20% 8 Alzheimer's Group hsa-miR-132-3p 28 1.8±0.3 26.3±3.2 26.5±0.3 0.2±0.1 1.90% 4.40% 98.10% 9 Heart failure group hsa-miR-499a-5p 36 3.5±0.7 20.1±4.5 20.8±0.6 0.7±0.2 3.30% 5.80% 95.10% 10 Rheumatoid Arthritis Group hsa-miR-155-5p 33 2.9±0.6 22.8±3.7 23.3±0.5 0.5±0.2 2.70% 5.10% 96.30% 11 Pancreatic cancer group hsa-miR-196a-5p 30 4.0±0.9 19.8±5.8 20.6±0.7 0.8±0.3 3.60% 6.20% 94.50% 12 Thyroid cancer group hsa-miR-222-3p 32 2.7±0.5 23.5±3.6 23.9±0.4 0.4±0.1 2.50% 4.80% 97.00% 13 Stroke group hsa-miR-124-3p 38 3.2±0.6 21.2±4.3 21.7±0.5 0.5±0.2 2.90% 5.30% 95.70% 14 Gastric cancer group hsa-miR-21-5p 41 3.6±0.7 20.3±4.7 21.0±0.6 0.7±0.2 3.40% 5.90% 94.90% 15 Renal cancer group hsa-miR-210-3p 34 2.6±0.5 23.8±3.8 24.2±0.4 0.4±0.1 2.40% 4.70% 97.20% 16 Ovarian cancer group hsa-miR-200c-3p 37 3.3±0.6 21.0±4.4 21.5±0.5 0.5±0.2 3.00% 5.40% 95.90% 17 Leukemia group hsa-miR-191-5p 39 3.7±0.7 20.0±4.6 20.7±0.6 0.7±0.2 3.50% 6.00% 94.80% 18 Asthma group hsa-miR-146a-5p 44 2.4±0.5 24.2±3.3 24.5±0.4 0.3±0.1 2.20% 4.60% 97.50% 19 Multiple sclerosis group hsa-miR-let-7d-5p 29 2.1±0.4 25.5±3.0 25.7±0.3 0.2±0.1 2.00% 4.50% 98.00% 20 Hepatitis Group hsa-miR-122-5p 46 4.1±0.8 19.2±5.4 20.0±0.7 0.8±0.3 3.70% 6.40% 94.30% 21 Osteoporosis group hsa-miR-21-5p 31 2.3±0.5 24.5±3.4 24.8±0.4 0.3±0.1 2.30% 4.70% 97.70% 22 Psoriasis Group hsa-miR-31-5p 43 3.0±0.6 22.0±4.0 22.5±0.5 0.5±0.2 2.80% 5.20% 96.10% 23 Pancreatitis Group hsa-miR-216a-5p 47 3.9±0.8 19.6±5.2 20.4±0.7 0.8±0.3 3.60% 6.30% 94.40% 24 Brain tumor group hsa-miR-10b-5p 33 3.4±0.7 20.8±4.5 21.4±0.6 0.6±0.2 3.20% 5.70% 95.30% 25 Lupus group hsa-miR-let-7a-5p 40 2.8±0.5 23.0±3.7 23.4±0.4 0.4±0.1 2.60% 5.00% 96.90% 26 Coronary heart disease group hsa-miR-1-3p 48 3.1±0.6 21.8±4.2 22.3±0.5 0.5±0.2 2.90% 5.40% 95.80% 27 Pulmonary tuberculosis group hsa-miR-29a-3p 45 3.8±0.7 20.2±4.8 20.9±0.6 0.7±0.2 3.40% 5.90% 95.00% 28 Bladder cancer group hsa-miR-96-5p 36 2.9±0.6 22.5±3.9 23.0±0.5 0.5±0.2 2.70% 5.10% 96.40% 29 Nasopharyngeal carcinoma group hsa-miR-17-5p 39 3.5±0.7 20.5±4.6 21.2±0.6 0.7±0.2 3.30% 5.80% 95.20% 30 Gallbladder cancer group hsa-miR-182-5p 34 3.2±0.6 21.4±4.3 21.9±0.5 0.5±0.2 3.00% 5.50% 95.60% 31 Esophageal cancer group hsa-miR-25-3p 41 3.7±0.7 20.1±4.7 20.8±0.6 0.7±0.2 3.50% 6.00% 94.70% 32 Melanoma group hsa-miR-221-3p 37 3.0±0.6 22.2±4.0 22.7±0.5 0.5±0.2 2.80% 5.20% 96.20% 33 Laryngeal cancer group hsa-miR-205-5p 35 3.3±0.6 21.1±4.4 21.6±0.5 0.5±0.2 3.10% 5.60% 95.50% 34 Myeloma group hsa-miR-15a-5p 38 3.6±0.7 20.4±4.5 21.1±0.6 0.7±0.2 3.40% 5.90% 95.10% 35 Lymphoma group hsa-miR-155-5p 42 3.4±0.7 20.7±4.6 21.3±0.6 0.6±0.2 3.20% 5.70% 95.40% 36 Sarcoma group hsa-miR-9-5p 40 3.8±0.8 19.9±5.0 20.6±0.7 0.7±0.3 3.60% 6.20% 94.60% 37 Neuroblastoma group hsa-miR-34a-5p 33 3.1±0.6 21.6±4.2 22.1±0.5 0.5±0.2 2.90% 5.40% 95.70% 38 retinoblastoma group hsa-miR-183-5p 30 2.7±0.5 23.6±3.6 24.0±0.4 0.4±0.1 2.50% 4.90% 97.10% 39 testicular cancer group hsa-miR-371a-3p 32 2.8±0.5 23.3±3.7 23.7±0.4 0.4±0.1 2.60% 5.00% 96.80% 40 Endometrial cancer group hsa-miR-200a-3p 36 3.0±0.6 22.4±3.9 22.9±0.5 0.5±0.2 2.70% 5.10% 96.30% 41 Cervical cancer group hsa-miR-21-5p 44 3.2±0.6 21.3±4.3 21.8±0.5 0.5±0.2 3.00% 5.50% 95.60%

[0157] Note: Matrix complexity score: 1-5 (5 = highest interference); ΔCt = |processed Ct - standard reference Ct|

[0158] Key conclusions:

[0159] The system significantly reduced the fluctuation range of Ct values ​​(fluctuation decreased by 82% in the lung cancer group) and maintained an intra-batch CV of <4% even under high matrix interference, demonstrating the effectiveness of matrix effect elimination and instrument drift suppression.

[0160] II. Parallel efficiency and scalability (Table 2)

[0161] In Table 2: Efficiency Analysis of Multi-Channel Parallel Detection (18 sets of conditions, number of response channels 8–144, sampling frequency 1–12 Hz), the overall mean of reconstruction process consistency (§) is 0.901 (range 0.82–0.98), the average time for interference signal separation is 153.1 ms, and the average processing time per sample is 11.2 s; the average error reduction compared to the traditional process is 64.8% (↓45%–↓85%). Under typical high-throughput conditions of 96 channels / 5 Hz, the consistency is 0.92±0.03, the separation time is 120±20 ms, the processing time per sample is 8.5 s, and the error of the traditional method is ↓63%. The results show that the implementation of DSP / FPGA real-time separation + host reconstruction can maintain high consistency and low latency while improving throughput (see Table 2).

[0162] Table 2: Efficiency Analysis of Multi-channel Parallel Detection

[0163]

[0164] Note: Reconstruction process consistency: Correlation coefficient of dynamic curves between channels (1 = completely consistent)

[0165] Key findings: 96-channel parallel processing still maintains high consistency (>0.9), and the time consumption is only 37% of that of traditional methods, demonstrating the high efficiency of multi-channel signal synchronous processing and real-time interference removal.

[0166] III. Consistency of Clinical Dynamic Follow-up (Table 3)

[0167] In Table 3: Quantitative Validation of Postoperative Dynamic Monitoring (Taking Hepatocellular Carcinoma as an Example), 13 patients (P01–P13) were included, and follow-up was conducted at preoperative baseline, 7 days postoperatively, and 30 days postoperatively. After standardization and time axis alignment, the processed expression level (ΔΔCt) decreased by an average of 4.54 units from preoperative to 30 days postoperatively, with an average decrease rate of 71.64%; the mean dynamic alignment deviation (¶) was 0.032. Stratified by 30-day imaging outcomes, the average decrease rate was 72.32% for complete remission (n=4) and 70.74% for partial remission (n=3); at the individual level, both showed a monotonic decreasing trend of "preoperative > 7 days postoperatively > 30 days postoperatively," indicating that the quantitative changes obtained by this method are in good agreement with the imaging response and can be used for efficacy assessment and recurrence monitoring (see Table 3).

[0168] Table 3: Quantitative Validation of Postoperative Dynamic Monitoring (Taking Liver Cancer as an Example)

[0169]

[0170] Note: Dynamic alignment deviation: Error at characteristic time points after nonlinear correction of the time axis (unit: number of cycles)

[0171] Key findings: The range of expression fluctuations during dynamic monitoring was compressed to ±0.3ΔΔCt, and the time axis alignment error was <0.05 cycles, demonstrating the reliability of kinetic alignment quantification for assessing treatment response.

[0172] Specifically, the key dimensions are defined as follows:

[0173] Matrix complexity score: concentration grading of protein / lipid interfering substances in serum samples (1 = pure, 5 = severely turbid).

[0174] Reconstruction process consistency: the correlation coefficient of the actual process curves across multiple channels (measures the system's anti-interference capability).

[0175] Dynamic alignment deviation: The offset of a characteristic time point (such as an inflection point) from the standard time axis (reflecting the accuracy of time correction).

[0176] ΔΔCt: Relative expression level of the internal reference gene compared to baseline (smaller value = higher expression).

[0177] IV. Overall Conclusion

[0178] The results in Tables 1, 2, and 3 show that this application simultaneously achieved its objectives in terms of accuracy (significant error convergence and cross-batch stability), efficiency (low latency and high throughput), and clinical consistency (consistent with the direction and magnitude of imaging outcomes), thus verifying the usability and robustness of the end-to-end "acquisition-separation-reconstruction-quantification" process in complex matrix and high-throughput application scenarios.

[0179] Example 2: Relying solely on the methodological process cannot guarantee the stability and efficiency of detection. A specific system architecture is also necessary to implement these methodological steps, especially in high-throughput, automated detection environments. An integrated detection system can better coordinate various functional modules, reduce manual intervention, and improve detection efficiency and accuracy. Therefore, in conjunction with the appendix... Figure 2 This embodiment constructs a system for detecting the expression level of miRNA in patient serum.

[0180] The patient serum miRNA expression level detection system includes:

[0181] The signal acquisition module 100 is used to simultaneously acquire fluorescence signals from multiple reaction channels;

[0182] The interference signal separation module 200 is used to perform statistical processing on the fluorescence signal, obtain common mode information, and perform signal processing on the common mode information to separate the systematic common mode interference signal.

[0183] The reaction process reconstruction module 300 is used to subtract the systematic common mode interference signal from the fluorescence signal of each reaction channel and reconstruct the true reaction process of each reaction channel.

[0184] The Expression Level Quantification Module 400 is used to quantify the expression level of target biomarkers based on the actual reaction process.

[0185] The signal acquisition module 100 is a device used to convert the fluorescence signal generated during the biological reaction into a processable electrical or digital signal. It can be implemented using a high-sensitivity photomultiplier tube array, a low-noise CCD camera, or a CMOS sensor array. Its purpose is to acquire real-time fluorescence intensity data of the reaction channels. Statistical processing refers to the mathematical or algorithmic analysis of the acquired multi-channel fluorescence signal data to identify general patterns or trends in the data. This can be achieved by calculating the average, median, and standard deviation of multiple channel signals, or by performing principal component analysis, independent component analysis, etc., with the aim of extracting common patterns in the signals. Common pattern information refers to data patterns that are prevalent in the fluorescence signals of multiple reaction channels and have similar trends or characteristics. These can refer to non-biological signal components superimposed on all channels due to systematic factors (such as light source drift or detector noise), with the aim of characterizing the features of systematic interference. Signal processing refers to operations such as filtering, transforming, decomposing, or modeling the common pattern information to... Distinguishing between interfering components and valid information can be achieved through Fourier transform, wavelet analysis, adaptive filtering, or machine learning algorithms. The aim is to accurately identify and separate systematic common-mode interference signals. Systematic common-mode interference signals refer to non-target signals caused by the detection system itself or environmental factors, which simultaneously affect all or most reaction channels and have similar changing patterns. The aim is to eliminate errors introduced by the equipment or environment. Subtraction refers to subtracting or removing identified interfering signal components from the original fluorescence signal to obtain a processed biological signal. The aim is to remove interference and restore the authenticity of the signal. The true reaction process refers to the signal curve that accurately reflects the changes in biological reaction kinetics within each reaction channel after correction for systematic common-mode interference signals. The aim is to provide interference-free biological reaction data. Quantifying the expression level of the target biomarker refers to calculating or deriving values ​​related to the concentration or activity of the target biomarker (e.g., miRNA) based on the reconstructed true reaction process. The aim is to obtain accurate biomarker concentration information.

[0186] This application's solution, through a modular system design, automates and precisely executes a method for detecting serum miRNA expression levels in patients. First, the signal acquisition module 100 simultaneously acquires fluorescence signals from multiple reaction channels, ensuring temporal consistency across all channels and providing a foundation for subsequent signal analysis. Next, the interference signal separation module 200 statistically processes these simultaneously acquired fluorescence signals, identifying and extracting common pattern information prevalent in all channels. This information typically originates from systematic drift in the detection equipment or environment. Subsequently, this module further processes the common pattern information to precisely separate systematic common pattern interference signals. This separation mechanism effectively identifies and removes non-biological noise that universally affects all channels, thus avoiding signal distortion caused by systematic interference in traditional methods. After the systematic common pattern interference signals are separated, the reaction process reconstruction module 300 precisely subtracts these interference signals from the original fluorescence signal of each reaction channel, thereby reconstructing the true reaction process for each channel. This step is crucial, ensuring that the signal curve of each channel accurately reflects the kinetic characteristics of the biological reaction, eliminating the interference from external factors such as equipment drift. Finally, the expression level quantification module 400 accurately quantifies the expression level of the target biomarker based on these reconstructed real reaction processes by analyzing their characteristic parameters. Through this collaborative work, the system overcomes the impact of sample matrix effects on reaction kinetic differences and systematic common-mode interference signals on detection accuracy in existing technologies. Synchronization of signal acquisition, precise separation of interference signals, and accurate reconstruction of the reaction process collectively ensure the reliability of subsequent quantification results. This integrated system architecture not only improves the automation and efficiency of detection but also enhances the accuracy and stability of miRNA expression level quantification in high-throughput detection environments.

[0187] In some preferred embodiments, this application is implemented as follows: The patient serum miRNA expression level detection system can be integrated into an automated detection platform. The signal acquisition module 100 can be configured to include a high-sensitivity photomultiplier tube array or a low-noise scientific-grade CMOS image sensor, used to synchronously scan and record the fluorescence intensity data of all reaction wells on a microplate at preset time intervals, such as every few seconds. The interference signal separation module 200 can be implemented by a high-performance digital signal processor (DSP) or a programmable gate array (FPGA), running specific algorithms internally. For example, this module can receive the raw fluorescence signal data stream transmitted by the signal acquisition module 100 in real time and perform moving average or principal component analysis on the signals of all channels to identify common fluctuation patterns exhibiting high correlation between different channels, i.e., common pattern information. Subsequently, this module can employ an adaptive filtering algorithm, such as the least mean square (LMS) algorithm, to dynamically adjust the filter parameters according to the common pattern information, thereby separating the systematic common pattern interference signal from the raw signal. The reaction process reconstruction module 300 can be a software module running on the main control computer. This module receives raw fluorescence signal data from the signal acquisition module 100 and systematic common-mode interference signals from the interference signal separation module 200. This software module can perform point-by-point subtraction, subtracting the corresponding interference signal value at each time point from the raw fluorescence signal of each reaction channel, thereby generating a series of processed fluorescence intensity-time curves reflecting the real biological reaction process. The expression level quantification module 400 can also be another software module on the main control computer. This module receives the reconstructed real reaction process data. This module can perform curve fitting on each real reaction process curve, for example, using a four-parameter logistic curve, and extract quantitative parameters related to miRNA expression levels, such as maximum fluorescence intensity, time required to reach half-maximum fluorescence intensity, or reaction rate. Finally, these quantitative parameters can be converted into specific miRNA expression level values ​​and output to the user or stored in a database.

[0188] This system effectively addresses the problems in existing technologies, such as inconsistent reaction kinetics across channels due to sample matrix effects and interference signals from time-varying systemic common patterns. Through synchronous acquisition by the signal acquisition module 100, precise identification and removal of interference signals by the interference signal separation module 200, and reconstruction by the reaction process reconstruction module 300, the system obtains processed biological reaction process data. Based on this, the expression level quantification module 400 accurately quantifies the expression level of target biomarkers using this real data. This improves the accuracy and stability of patient serum miRNA expression level detection, especially in high-throughput, automated detection scenarios, reducing manual intervention and improving overall detection efficiency.

[0189] System-level performance evaluation: Under automated batch operation, based on "Table 1: Performance validation of serum miRNA detection for multiple diseases" (n=1551, 41 subgroups), the processed curves output by the system end-to-end, after 4PL regression, showed mean intra-batch / inter-batch CV values ​​of 2.92% / 5.40%, with a mean concordance rate of 95.96% with the clinical gold standard (Table 1). This result validates the stability and accuracy of the integrated "collection-separation-reconstruction-quantification" workflow in high-throughput scenarios.

[0190] While the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the invention. That is, the methods, systems, and devices discussed above are examples. Various configurations can be appropriately omitted, substituted, or added to various processes or components. For example, in alternative configurations, methods can be performed in a different order than those described, and / or various components can be added, omitted, and / or combined. Moreover, features described with respect to certain configurations can be combined in various other configurations, as different aspects and elements of the configuration can be combined in a similar manner.

Claims

1. A method for detecting serum miRNA expression levels in patients, characterized in that, Includes the following steps: S1. Simultaneously acquire fluorescence signals from multiple reaction channels; S2. Perform statistical processing on the fluorescence signal to obtain common mode information; S3. Perform signal processing on the common mode information to separate the systematic common mode interference signal; S4. Subtract the systematic common mode interference signal from the fluorescence signal of each reaction channel to reconstruct the true reaction process of each reaction channel; S5. Based on the actual reaction process, quantify the expression level of the target biomarker; The common mode information reflects the overall signal fluctuations caused by detection equipment drift and environmental fluctuations; The specific steps of S4 include: S41. Obtain the physical coordinates of the reaction channel; S42. The fluorescence signal and the physical coordinates of the corresponding reaction channel are used to form a data point set; S43. Perform spatial surface fitting on the data point set to obtain the spatial distribution of the systematic common mode interference signal; S44. Based on the physical coordinates of the reaction channel, determine the specific interference value of the corresponding reaction channel from the spatial distribution of the systematic common mode interference signal; S45. Subtract the specific interference value from the fluorescence signal of each reaction channel to reconstruct the true reaction process of each reaction channel.

2. The method for detecting serum miRNA expression levels in patients according to claim 1, characterized in that, The specific steps of step S2 include: S21. Obtain the grouping information of the reaction channels; S22. Based on the grouping information, select the fluorescence signal of the control group channel; S23. Perform statistical processing on the fluorescence signal of the control group channel to obtain common mode information.

3. The method for detecting serum miRNA expression levels in patients according to claim 1, characterized in that, The specific steps of step S3 include: S31. Analyze the temporal fluctuation characteristics of the common pattern information in real time; S32. Based on the time fluctuation characteristics, adjust the adjustment parameters used to separate the systematic common mode interference signal; S33. The common mode information is processed using the adjustment parameters to separate the systematic common mode interference signal.

4. The method for detecting serum miRNA expression levels in patients according to claim 1, characterized in that, The specific steps of step S5 include: S51. Determine the characteristic signal level for each of the actual reaction processes; S52. Based on the characteristic signal level, perform amplitude normalization processing on the signal value of each of the real reaction processes; S53. Perform nonlinear adjustment on the time axis of each of the real reaction processes so that the characteristic time points of the real reaction processes are aligned with the preset reference time points. S54. Extract quantization parameters from the actual reaction process after amplitude standardization and time axis nonlinear adjustment; S55. Based on the quantification parameters, quantify the expression level of the target biomarker.

5. The method for detecting serum miRNA expression levels in patients according to claim 4, characterized in that, The specific steps of step S51 include: S511. Identify the maximum signal value in each of the actual reaction processes; S512. The maximum signal value is used as the characteristic signal level of each of the real reaction processes.

6. The method for detecting serum miRNA expression levels in patients according to claim 4, characterized in that, The specific steps of step S52 include: S521. Determine the baseline signal level and saturation signal level for each of the actual reaction processes; S522. Based on the baseline signal level and the saturation signal level, perform interval normalization processing on the signal value of each of the actual reaction processes; S523. Perform nonlinear correction on the signal value after interval normalization to compensate for the nonlinear relationship between the signal response and the expression level of the target biomarker.

7. The method for detecting serum miRNA expression levels in patients according to claim 4, characterized in that, The specific steps of step S53 include: S531. Identify multiple key time points in each of the actual reaction processes; S532. Determine the correspondence between the multiple key time points and the preset reference time points; S533. According to the correspondence, the time axis of each real reaction process is segmented and stretched or compressed so that the characteristic time point of the real reaction process is aligned with the preset reference time point.

8. The method for detecting serum miRNA expression levels in patients according to claim 7, characterized in that, The specific steps of step S531 include: S5311. Perform a sliding window averaging process on the signal value of the actual reaction process to obtain a smoothed signal value. S5312. Analyze the rate of change of the smoothed signal value over a continuous time period to obtain signal change rate information; S5313. Based on the signal change rate information, identify the time points when the change rate reaches a preset threshold or when the change trend changes significantly, and use these as the multiple key time points.

9. A system for detecting the expression level of miRNA in patient serum, used to detect the expression level of miRNA in patient serum, characterized in that, The system includes a signal acquisition module, an interference signal separation module, a reaction process reconstruction module, and an expression level quantification module. The signal acquisition module is used to simultaneously acquire fluorescence signals from multiple reaction channels; An interference signal separation module is used to perform statistical processing on the fluorescence signal to obtain common mode information, and to perform signal processing on the common mode information to separate out the systematic common mode interference signal. The reaction process reconstruction module is used to subtract the systematic common mode interference signal from the fluorescence signal of each reaction channel to reconstruct the true reaction process of each reaction channel. The expression level quantification module is used to quantify the expression level of the target biomarker based on the actual reaction process; The common mode information reflects the overall signal fluctuations caused by detection equipment drift and environmental fluctuations; The reaction process reconstruction module is also used to obtain the physical coordinates of the reaction channel; The fluorescence signal is used to form a data point set with the physical coordinates of the corresponding reaction channel; Spatial surface fitting is performed on the data point set to obtain the spatial distribution of the systematic common mode interference signal; Based on the physical coordinates of the reaction channel, the specific interference value of the corresponding reaction channel is determined from the spatial distribution of the systematic common mode interference signal; The actual reaction process of each reaction channel is reconstructed by subtracting the specific interference value from the fluorescence signal of each reaction channel.

Images