AMH and sex hormone time-sharing detection method and system for trace sample

Abstract

The invention relates to the technical field of biomarker detection, in particular to an AMH and sex hormone time-sharing detection method and system for a trace sample, and the method comprises the following steps: receiving an in-vitro trace peripheral blood sample; dividing the trace peripheral blood sample into a first sub-sample flow and a second sub-sample flow; enabling the first sub-sample flow and the second sub-sample flow to respectively reach a first detection area and a second detection area at a preset time interval; when the first sub-sample flow reaches the first detection area, performing first quantitative detection on a target biomarker in the first sub-sample flow to obtain a first detection result; the trace peripheral blood sample is used for time-sharing detection, and the change rate of the biomarker is calculated, so that the hormone level under the action of the ovulation promoting drug can be quickly and high-frequency monitored, and the technical problems that the information is lagged and the dynamic change data of the hormone cannot be obtained in real time in the traditional detection method are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of in vitro biomarker detection technology, and in particular to a method and system for time-sequential detection of AMH and sex hormones in trace samples. It is suitable for dynamic monitoring of biomarkers and obtains dynamic change data of biomarkers in peripheral blood under the action of ovulation-inducing drugs through technical means, providing data support for dynamic analysis of biomarkers. Background Technology

[0002] Currently, in the field of assisted reproductive technology, controlled ovarian stimulation is a crucial step in obtaining a sufficient number of high-quality oocytes. Its core lies in precisely controlling the patient's endocrine environment to ensure treatment success and mitigate potential risks. In routine clinical practice, doctors typically assess ovarian reserve function by referring to the initial level of anti-Müllerian hormone (AMH) in the patient's sample. After the administration of ovulation-inducing drugs, venous blood samples are periodically (usually every few days) drawn from the patient and sent to a central laboratory to test the concentrations of sex hormones such as estradiol (E2) and luteinizing hormone (LH), combined with ultrasound examination to monitor follicle growth and adjust drug dosage. However, this monitoring model based on a relatively stable endocrine response has significant limitations for certain patient groups sensitive to changes in biomarkers. These patients may exhibit a non-linear response to ovulation-inducing drugs, leading to rapid changes in hormone levels within a short period. Traditional testing methods, due to their lengthy procedures, struggle to capture these dynamic changes in real time, resulting in delayed hormone level data and affecting the timeliness of treatment adjustments. The core technical drawback of traditional methods lies in their reliance on venous blood samples, long testing cycles, and inability to obtain high-frequency technical data on dynamic hormone changes, rather than addressing diagnostic issues specific to certain diseases.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for time-sequential detection of AMH and sex hormones in trace samples.

[0005] In a first aspect, the present invention provides a method for time-sequential detection of AMH and sex hormones in trace samples, for dynamic monitoring of biomarkers under the influence of ovulation-inducing drugs in assisted reproductive settings, the method comprising the following steps: Receive isolated, trace amounts of peripheral blood samples; The trace amount of peripheral blood sample is split into a first subsample stream and a second subsample stream; The first sub-sample stream and the second sub-sample stream arrive at the first detection area and the second detection area respectively at a preset time interval; When the first subsample stream arrives at the first detection area, a first quantitative detection is performed on the target biomarker in the first subsample stream to obtain a first detection result; and when the second subsample stream arrives at the second detection area, a second quantitative detection is performed on the target biomarker in the second subsample stream to obtain a second detection result. The rate of change of the target biomarker is calculated based on the first detection result and the second detection result; The rate of change is compared with a preset judgment threshold, and a technical data report is output based on the comparison result.

[0006] The core innovation of this application lies in combining the acquisition of trace peripheral blood samples with sample diversion technology, and using a preset time interval to perform two quantitative detections of the target biomarker in different detection areas. This allows the rate of change of the target biomarker to be calculated from a single sample collection and compared with a preset judgment threshold to output information. This achieves the effect of overcoming the information lag of traditional detection methods and realizing real-time dynamic monitoring of the response to ovulation-inducing drugs.

[0007] Secondly, a time-sequential detection system for AMH and sex hormones in trace samples is provided for dynamic monitoring of biomarkers under the influence of ovulation-inducing drugs in assisted reproductive settings. This system includes: The sample acquisition module is used to receive isolated micro-volume peripheral blood samples. The diversion module is used to divert the trace peripheral blood sample into a first sub-sample stream and a second sub-sample stream. The flow path control module is used to ensure that the first sub-sample stream and the second sub-sample stream arrive at the first detection area and the second detection area respectively at a preset time interval; The detection module is configured to perform a first quantitative detection on the target biomarker in the first sub-sample stream when the first sub-sample stream arrives at the first detection area, and to obtain a first detection result; and to perform a second quantitative detection on the target biomarker in the second sub-sample stream when the second sub-sample stream arrives at the second detection area, and to obtain a second detection result. The calculation module is used to calculate the rate of change of the target biomarker based on the first detection result and the second detection result; The output module is used to compare the rate of change with a preset judgment threshold and output a technical data report based on the comparison result.

[0008] Compared with the prior art, the present invention has the following beneficial effects: By using minute peripheral blood samples for time-series detection and calculating the rate of change of biomarkers, rapid and high-frequency monitoring of hormone levels under the influence of ovulation-inducing drugs can be achieved. This effectively solves the technical problems of information lag and inability to obtain real-time dynamic hormone change data in traditional detection methods, thereby providing technical support for timely adjustment of treatment plans and improving detection efficiency and data timeliness (this effect does not involve the prevention or treatment of diseases). Attached Figure Description

[0009] Figure 1 This is a flowchart of the method of the present invention.

[0010] Figure 2 This is a schematic diagram of the system structure of the present invention.

[0011] In the diagram: 201, Sample Acquisition Module; 202, Flow Diversion Module; 203, Flow Path Control Module; 204, Detection Module; 205, Calculation Module; 206, Output Module. Detailed Implementation

[0012] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0013] like Figure 1 The method shown is a time-sequential detection method for AMH and sex hormones in trace samples, comprising the following steps: S101, Receive isolated micro-volume peripheral blood samples; S102. Divide the trace peripheral blood sample into a first subsample stream and a second subsample stream; S103, the first subsample stream and the second subsample stream arrive at the first detection area and the second detection area respectively at a preset time interval; S104. When the first subsample stream arrives at the first detection area, the target biomarker in the first subsample stream is subjected to a first quantitative detection to obtain a first detection result; and when the second subsample stream arrives at the second detection area, the target biomarker in the second subsample stream is subjected to a second quantitative detection to obtain a second detection result. S105. Calculate the rate of change of the target biomarker based on the first and second detection results; S106. Compare the rate of change with a preset judgment threshold, and output a technical data report based on the comparison result.

[0014] Among them, microcapillary blood samples refer to small blood samples collected from the extremities of the human body (such as fingertips or earlobes) through minimally invasive methods. These can be collected using lancets, micro-volume blood collection devices, or microfluidic blood collection systems. The aim is to reduce patient discomfort during blood collection and support high-frequency sample collection to meet the needs of real-time monitoring. Splitting refers to the process of dividing a received original sample into two or more independent sub-sample streams. This can be achieved using shunting channels, shunting valves, or pump-controlled shunting systems in microfluidic chips. The purpose is to obtain data from a single received sample at different "virtual" time points, thereby simulating the changing trends of biomarkers over a short period. A preset time interval refers to the set time difference between the arrival of two or more sub-sample streams at their respective detection areas during the detection process. This can be achieved by controlling the flow rate, adjusting the flow path length, or using delayed triggering detection. The purpose is to provide a clear time reference for calculating the rate of change of biomarkers, reflecting their dynamic changes within a specific time period. Target biomarkers are key biomolecules that need to be monitored in assisted reproductive technology (ART) to reflect a patient's physiological state or drug response. These may include AMH and sex hormones, estradiol, luteinizing hormone (LH), and anti-Müllerian hormone (AMH). The purpose is to assess a sample's response to ovulation-inducing drugs and predict potential risks through quantitative detection of their concentrations. The rate of change refers to how quickly the concentration of the target biomarker changes within a pre-defined time interval. It can be calculated by dividing the difference between two test results by the time interval. This method aims to more sensitively capture rapid fluctuations in biomarker levels, especially for patients exhibiting non-linear responses, providing more instructive dynamic information than a single concentration value. The technical data report refers to the quantitative data generated based on the calculated rate of change of the biomarker. This result only reflects the dynamic trend of hormone levels within a specific time period and is provided for healthcare professionals to refer to when comprehensively evaluating treatment plans in conjunction with other clinical information. This system does not make diagnostic conclusions or intervention recommendations for any disease.

[0015] This application's solution achieves rapid, dynamic monitoring of biomarker change rates through a series of collaborative steps. Due to the limitations of traditional venous blood collection, this solution utilizes collected micro-volume capillary blood samples, which can be transported via a delivery tube, providing a foundation for high-frequency monitoring. The received micro-volume capillary blood sample is then split into a first subsample stream and a second subsample stream. This splitting mechanism is crucial, enabling the simulation of detection scenarios at different time points from the same original sample without actually waiting for physiological time to pass. Next, a flow path control mechanism ensures that the first and second subsample streams arrive at the first and second detection areas, respectively, at preset time intervals. This time interval is carefully set, representing the time window of observation required in actual physiological changes, such as several minutes or tens of minutes, thereby capturing rapid fluctuations in hormone levels. When the first subsample stream arrives at the first detection area, the system immediately performs a first quantitative detection of the target biomarker contained within, obtaining its initial concentration and yielding the first detection result. Following this, when the second subsample stream arrives at the second detection area after a preset time interval, a second quantitative detection of the target biomarker is performed, yielding a second detection result. It is precisely because of these two detection results obtained at different "virtual" time points that it becomes possible to calculate the rate of change of the target biomarker. By comparing the second detection result with the first detection result and combining it with the preset time interval, the concentration change trend of the target biomarker within this short period can be accurately calculated. This rate of change calculation, compared to concentration values ​​at a single time point, can more sensitively and intuitively reflect the dynamic changes of biomarkers in a patient's body, especially for individuals who are sensitive to ovulation-inducing drugs and whose hormone levels may experience explosive increases. Finally, the calculated rate of change is compared in real time with a preset judgment threshold. If the rate of change exceeds the safety threshold, the system will immediately output a technical data report. This real-time technical data report only reflects the dynamic change trend of hormone levels within a specific time period, for medical staff to refer to when comprehensively evaluating treatment plans in conjunction with other clinical information. This system does not make diagnostic conclusions or intervention recommendations for any disease.

[0016] In one embodiment of the present invention, after performing the step of causing the first sub-sample stream and the second sub-sample stream to arrive at the first detection region and the second detection region respectively at a preset time interval, the method further includes: Record the instantaneous change trajectory of the target biomarker signal; Extract shape features reflecting the diffusion behavior of target biomarkers within microfluidic channels from the instantaneous change trajectory of the signal; Based on shape characteristics, determine the effective diffusion coefficient of the target biomarker in the current sample; Based on the effective diffusion coefficient, the geometric parameters of the microfluidic channel, and the preset time interval, the concentration decay of the target biomarker due to the physical diffusion effect is calculated. The second detection result is corrected based on the concentration decay to obtain a second corrected detection result that truly reflects the physiological changes.

[0017] The instantaneous signal change trajectory refers to the real-time record of the concentration or related physical quantity of the target biomarker changing over time as it flows through the detection area. Specifically, this can be achieved by continuously monitoring the signal intensity in the detection area, such as fluorescence intensity, electrochemical signal, or light absorbance, to obtain signal values ​​at a series of time points. The purpose is to provide raw data on the dynamic behavior of the target biomarker within the microfluidic channel. The shape characteristics reflecting the diffusion behavior of the target biomarker within the microfluidic channel refer to the geometric or mathematical properties in the instantaneous signal change trajectory that quantify the diffusion characteristics of the target biomarker. Specifically, this can be characterized by analyzing parameters such as the rise rate, fall rate, peak width, or half-width at half-maximum (HWHM) of the signal trajectory. The purpose is to transform the complex diffusion process into a calculable and comparable numerical indicator. The effective diffusion coefficient refers to the diffusion capacity parameter of the target biomarker in a specific microfluidic channel and sample matrix environment. Specifically, it can be determined through experimental calibration, theoretical model calculation, or combined with signal trajectory characteristic analysis. The purpose is to quantify the diffusion rate of the target biomarker in the actual flow environment. The physical diffusion effect refers to the phenomenon within a microfluidic channel where, due to uneven fluid velocity distribution and the Brownian motion of molecules, the target biomarker undergoes axial diffusion in the flow direction and radial mixing. This manifests as broadening of the sample peak shape and a decrease in peak height, aiming to describe changes in the concentration distribution of the target biomarker within the channel. Concentration attenuation refers to the degree to which the concentration of the target biomarker decreases within the microfluidic channel due to the physical diffusion effect. This can be calculated using a mathematical model combining the effective diffusion coefficient, channel geometry parameters, and flow time, aiming to quantify the impact of diffusion on the target biomarker concentration. The second calibration detection result refers to the value closer to the true physiological concentration of the target biomarker obtained after compensating for the concentration attenuation in the second detection result. This is obtained by mathematically calculating the concentration attenuation between the original second detection result and the calculated concentration attenuation, aiming to eliminate measurement errors caused by the physical diffusion effect and improve the accuracy of the detection results.

[0018] This application's solution introduces a mechanism to correct for physical diffusion effects after performing the step of having the first and second subsample flows arrive at the first and second detection regions respectively at preset time intervals. Specifically, firstly, the instantaneous signal change trajectory of the target biomarker is recorded. This trajectory captures the real-time signal response of the target biomarker flowing within the microfluidic channel, providing basic data for subsequent diffusion behavior analysis. Subsequently, shape features reflecting the diffusion behavior of the target biomarker within the microfluidic channel are extracted from this instantaneous signal change trajectory. These features are a quantitative representation of the diffusion process, such as the broadening of the signal peak or the rate of rise or fall. Based on these shape features, the effective diffusion coefficient of the target biomarker in the current sample can be determined. This coefficient comprehensively reflects the diffusion ability of the target biomarker under specific sample matrix and channel conditions. Furthermore, based on the determined effective diffusion coefficient, the geometric parameters of the microfluidic channel, and the preset time interval, the concentration decay of the target biomarker due to physical diffusion effects can be accurately calculated. Finally, based on this calculated concentration decay, the second detection result is corrected to obtain a second corrected detection result that truly reflects physiological changes. The introduction of this series of steps effectively eliminates measurement errors caused by physical diffusion effects within the microfluidic channel when calculating the rate of change of the target biomarker. In the original scheme, although samples at different time points were detected to obtain the rate of change, the second subsample stream spent a longer time within the channel, causing the target biomarker concentration to decrease due to diffusion. This resulted in the second detection result being lower than the actual value, thus affecting the accuracy of the rate of change. This new scheme precisely corrects the second detection result, ensuring that both detection results used for rate of change calculation accurately reflect the physiological concentration of the target biomarker. This significantly improves the accuracy of biomarker rate of change detection, providing a more solid data foundation for subsequent physiological change assessment.

[0019] As one embodiment of the present invention, the step of extracting shape features reflecting the diffusion behavior of target biomarkers in microfluidic channels from the instantaneous change trajectory of signals includes: Preprocessing is performed on the instantaneous trajectory of the signal. The preprocessing includes: Analyze the instantaneous change trajectory of the signal before the appearance of the target biomarker signal or in the initial stage when its signal intensity is below a preset threshold, in order to determine the local background signal level or the offset trend of the background signal level of the instantaneous change trajectory of the signal. Furthermore, based on the local background signal level or the offset trend of the background signal level, the instantaneous change trajectory of the signal is calibrated or the background is subtracted to obtain the calibrated signal trajectory; Identify the main rising and falling segments of the calibrated signal trajectory; Based on the characteristics of the rising and falling segments, the rising rate, falling rate, time required to reach a specific proportion of its maximum intensity, or half-width of the signal trajectory are calculated as shape features reflecting the diffusion behavior of the target biomarker within the microfluidic channel. Perform a consistency check on the calculated shape features to eliminate erroneous features caused by abnormal fluctuations or transient disturbances.

[0020] Preprocessing refers to a series of operations performed on the original signal before subsequent analysis to improve its quality and eliminate noise and interference. These operations may include filtering, smoothing, and baseline correction, aiming to ensure the accuracy and reliability of subsequent feature extraction. Shape features are parameters that quantify specific geometric or dynamic characteristics of the instantaneous trajectory of a signal. These may include the signal's slope, peak width, symmetry, or signal intensity ratio at a specific time point. The purpose is to characterize the diffusion behavior of the target biomarker within the microfluidic channel using these quantitative indicators. Furthermore, consistency checking is the process of validating the effectiveness or reliability of the calculated shape features. This can be done by comparing them with preset standards, model predictions, or multiple measurement results. The aim is to identify and eliminate invalid or erroneous features caused by measurement errors, environmental interference, or sample anomalies, thereby improving the accuracy of the final results.

[0021] This application's scheme employs a series of meticulous processing steps to ensure that the shape features extracted from the instantaneous signal trajectory accurately reflect the diffusion behavior of the target biomarker within the microfluidic channel, thus providing a reliable basis for subsequent calculation of the effective diffusion coefficient. Specifically, the instantaneous signal trajectory is first preprocessed. This step is crucial because it directly addresses interference issues such as background drift, matrix complexity, and instantaneous noise present in the original signal. By analyzing the signal in its initial stage before the appearance of the target biomarker or when its intensity is below a preset threshold, the local background signal level or its shift trend can be accurately determined. Based on this, the signal trajectory is calibrated or subtracted from the background signal level to obtain a calibrated signal trajectory. This preprocessing effectively improves the signal-to-noise ratio, allowing the true signal of the target biomarker to be clearly presented, laying the foundation for subsequent feature identification. After obtaining the calibrated signal trajectory, the scheme further identifies the main signal rising and falling segments. These two segments contain important information about the dynamic processes of diffusion, binding, and dissociation of the target biomarker within the microfluidic channel. The rising segment reflects the rapid increase in biomarker concentration, while the falling segment reflects its concentration decay or clearance. By accurately identifying these segments, the most informative parts of the signal can be focused on. Subsequently, based on the characteristics of the identified rising and falling segments, the rise rate, fall rate, time required to reach a specific proportion of its maximum intensity, or half-width at half-maximum (WHM) of the signal trajectory are calculated. These parameters are crucial indicators for quantifying the signal shape characteristics, directly related to the diffusion rate, response time, and distribution characteristics of the target biomarker within the channel. For example, the rise rate reflects the speed at which the biomarker enters the detection area, while the WHM reflects its diffusion degree within the channel. These calculated shape features, as a quantitative representation of the target biomarker's diffusion behavior, are directly used to determine its effective diffusion coefficient in the current sample. Finally, the calculated shape features undergo a consistency check. This step is crucial for ensuring the reliability of the extracted features, effectively eliminating erroneous features caused by occasional abnormal fluctuations or transient interference. Feature validation ensures that only features that truly reflect the biomarker's diffusion behavior are adopted, thus avoiding the negative impact of inaccurate feature input on the calculation of the effective diffusion coefficient. Through the aforementioned series of steps, this scheme can extract accurate and reliable shape features from the interfered signal, thereby enabling the precise determination of the effective diffusion coefficient of the target biomarker within the microfluidic channel. This accurate diffusion coefficient calculation further improves the accuracy of calculating the concentration decay of the target biomarker due to physical dispersion effects, thus making the correction of the second detection result more precise, ultimately obtaining a second corrected detection result that truly reflects physiological changes.This effectively improves the accuracy of biomarker change rate detection, especially in microfluidic detection scenarios, overcoming the impact of background interference and noise on detection accuracy, and providing more reliable data support for monitoring the response of ovulation-inducing drugs in assisted reproductive treatment.

[0022] As one embodiment of the present invention, the step of performing a consistency check on the calculated shape features to eliminate erroneous features caused by abnormal fluctuations or transient interference includes: Acquire background signal features or non-target signal features related to the instantaneous trajectory of the signal; The matrix type of the sample is determined based on background signal characteristics or non-target signal characteristics; Based on the matrix type, obtain the corresponding shape feature validity judgment criteria; The calculated shape features are compared with the validity criteria of the acquired shape features to confirm the validity of the shape features, thereby performing a consistency check on the calculated shape features to eliminate erroneous features caused by abnormal fluctuations or transient interference.

[0023] Background signal characteristics refer to signal information obtained from the instantaneous change trajectory of the signal before the target biomarker signal appears or when its intensity is below a preset threshold. Examples include the average value, fluctuation range, or noise level of the signal baseline. This can be achieved through statistical analysis of blank areas in the signal trajectory or noise assessment using specific filters. Its purpose is to reflect the inherent characteristics or interference level of the sample environment. Non-target signal characteristics refer to signals generated by substances other than the target biomarker in the sample during the detection process. Examples include the responses of proteins, lipids, or other endogenous substances in the sample under specific detection wavelengths or conditions. This can be achieved by pre-calibrating known non-target substances before detection or by using multi-wavelength / multi-parameter detection to distinguish between target and non-target signals. Its purpose is to provide additional information about sample complexity. The sample matrix type refers to the classification of the sample's physicochemical composition or characteristics, such as serum, plasma, whole blood, urine, or other body fluids. This can be identified using specific patterns based on background signal characteristics or non-target signal characteristics. For example, different matrices can be distinguished by analyzing viscosity-related changes in the signal baseline or the signal intensity of specific non-target substances. Its purpose is to provide a basis for subsequent determination of the effectiveness of shape features. Among them, the validity criteria for shape features refer to the set of rules used to evaluate whether the calculated shape features are reliable or conform to expectations. These criteria can be implemented using a pre-established database, machine learning model, or threshold range based on statistical analysis. These criteria are determined based on the typical diffusion behavior and signal characteristics of target biomarkers under different matrix types. Their purpose is to ensure that the shape features used can accurately reflect the diffusion behavior of target biomarkers and exclude abnormal data.

[0024] This application addresses the problem of shape feature errors caused by sample matrix complexity or transient interference in existing technologies by introducing a consistency check step for calculated shape features. Specifically, the solution first acquires background signal features or non-target signal features related to the transient trajectory of the signal. This is because these features reflect the inherent environment and potential interference of the sample, providing basic information for subsequent judgment. Based on these background signal features or non-target signal features, the system can determine the matrix type of the current sample. Different matrix types, such as serum or whole blood, have different effects on the diffusion behavior and signal performance of target biomarkers; therefore, identifying the matrix type is a prerequisite for accurate judgment. Once the matrix type of the sample is determined, the system can acquire shape feature validity judgment criteria corresponding to that matrix type. These criteria are pre-established for specific matrix environments and can more accurately reflect the expected performance of shape features under that environment. Finally, the calculated shape features are compared with the acquired shape feature validity judgment criteria. Through this comparison, it can be determined whether the calculated shape features meet the expectations under that matrix type, thereby confirming their validity. If the shape features do not meet the criteria, it indicates that they may have been affected by abnormal fluctuations or transient interference and need to be excluded.

[0025] As one embodiment of the present invention, the step of confirming the validity of the shape feature includes: Acquire auxiliary signal features related to the instantaneous change trajectory of the signal. These auxiliary signal features include the degree of fluctuation of the signal baseline, the signal strength of non-target substances, or the overall stability of the signal. The reliability of matrix type identification of samples is evaluated based on auxiliary signal characteristics; When the reliability level is below a preset threshold, or when the matrix type of the sample is not identified, The validity criteria for shape features are adjusted based on the auxiliary signal characteristics and the calculated shape features. The calculated shape features are compared with the adjusted shape feature validity criteria to confirm the validity of the shape features. Alternatively, an internal consistency criterion based on multiple shape features can be used to determine the validity of the calculated shape features, thereby confirming their validity.

[0026] Auxiliary signal features refer to additional information related to the instantaneous trajectory of signal changes, specifically the degree of fluctuation in the signal baseline, the signal intensity of non-target substances, or the overall stability of the signal. These features provide supplementary information about the sample matrix state to aid in assessing the accuracy of matrix type identification. The reliability of matrix type identification refers to the confidence level in determining the sample matrix type. This can be quantified based on the clarity, stability, or matching degree with known matrix features of the auxiliary signal features. Its purpose is to determine whether the current matrix type identification result is sufficient to support subsequent shape feature validity assessments. The preset threshold is a critical value used to determine whether the reliability of matrix type identification is sufficient. It can be set based on historical data, experimental validation, or clinical experience, aiming to define the usability boundary of the matrix type identification result. Adjusting the validity criteria for shape features refers to dynamically modifying the rules or parameters used to determine the validity of shape features based on the current auxiliary signal features and the calculated shape features. This can be achieved through weighted, modified, or redefined judgment intervals, aiming to make the judgment criteria more adaptable to uncertain matrix environments and improve the accuracy of the judgment. The internal consistency criterion refers to a rule that judges the validity of multiple shape features by analyzing the intrinsic correlation between them, without relying on external matrix type information. It can use statistical methods, machine learning models or expert system rules to evaluate whether multiple shape features support each other or conform to the expected pattern. Its purpose is to provide an alternative and robust validity judgment mechanism when matrix type identification is unreliable.

[0027] Specifically, when performing a consistency check on the calculated shape features to exclude erroneous features caused by abnormal fluctuations or transient interference, auxiliary signal features related to the instantaneous trajectory of the signal are first acquired. These auxiliary signal features, such as the degree of fluctuation of the signal baseline, the signal strength of non-target substances, or the overall stability of the signal, provide additional information about the sample matrix state. Based on these auxiliary signal features, the system can assess the reliability of the sample matrix type identification. This assessment is a crucial step, enabling the system to intelligently determine whether the current matrix type identification result is accurate and reliable enough to support subsequent shape feature validity judgments. When the assessment result shows that the reliability of matrix type identification is lower than a preset threshold, or the sample matrix type cannot be identified at all, the system no longer simply uses preset shape feature validity judgment criteria that may no longer be applicable. Instead, it dynamically adjusts the shape feature validity judgment criteria based on the auxiliary signal features and the calculated shape features. This adjustment allows the judgment criteria to adapt to the current uncertain matrix environment. For example, in cases of large signal fluctuations, the validity range of certain shape features can be appropriately broadened, or the weight of specific shape features can be adjusted when non-target substance interference is significant, thereby making the judgment more accurate.

[0028] As an alternative or supplementary solution, when matrix type identification is unreliable, the system can also employ an internal consistency criterion based on multiple shape features to determine the validity of the calculated shape features. This method does not rely on external matrix type information but analyzes the interrelationships between multiple shape features. For example, if multiple shape features reflecting diffusion behavior (such as rise rate and half-width at half-maximum) exhibit similar trends or fall within mutually corroborating ranges, these shape features can be considered valid even if the matrix type is uncertain. This internal consistency check provides a robust mechanism for effective judgment even with incomplete information. Through this mechanism, this application adds adaptive processing capabilities to the reliability of matrix type identification, building upon the original method of determining the validity of shape features based on matrix type. This enables accurate shape feature extraction and validity confirmation of the diffusion behavior of target biomarkers within microfluidic channels, even in complex real-world sample environments where accurate matrix type determination is difficult.

[0029] As one embodiment of the present invention, the step of adjusting the validity judgment criterion of shape features includes: Obtain the intensity of auxiliary signal features; Obtain the degree of deviation in the calculated shape features; Furthermore, the parameter values ​​in the validity judgment criteria of shape features are modified based on the intensity of the auxiliary signal features or the degree of deviation of the calculated shape features.

[0030] Among them, the intensity of auxiliary signal features refers to the result of quantitative evaluation of auxiliary signal features that reflect the reliability of sample matrix type identification. It can be quantified by the root mean square value of the signal baseline fluctuation amplitude, the absolute intensity of the peak value of the non-target substance signal, or the standard deviation of the overall signal stability. Its purpose is to provide an operable numerical value to reflect the reliability of matrix type identification. The deviation of the calculated shape features refers to the degree of deviation between the calculated shape features and the preset reference value or expected range. It can be quantified by the absolute difference between the calculated value and the reference value, the relative percentage deviation, or the statistical outlier index. Its purpose is to quantify the abnormal fluctuation or deviation of the shape features. Modifying the parameter values ​​in the validity judgment criteria of shape features refers to adjusting the specific values ​​used to define the validity range or threshold of shape features. It can be achieved by directly modifying the upper / lower limit value, adjusting the weight coefficient, or changing the constant term in the judgment function. Its purpose is to enable the judgment criteria to dynamically adapt to different signal quality and feature performance.

[0031] Specifically, after preprocessing the instantaneous trajectory of the signal and extracting shape features, a consistency check is required to ensure the validity of these shape features. During the consistency check, auxiliary signal features related to the instantaneous trajectory of the signal are first acquired; these features reflect the reliability of the sample matrix type identification. Simultaneously, the degree of deviation between the calculated shape features and the expected values ​​is also acquired. Given that the intensity of the auxiliary signal features directly relates to the reliability of matrix type identification (e.g., lower intensity may indicate insufficient identification reliability), the judgment criteria need to be more inclusive; while the degree of deviation of the calculated shape features directly reflects the stability of the features themselves or the presence of abnormal interference (e.g., a large deviation may indicate abnormal fluctuations), the judgment criteria need to be more stringent. Therefore, this scheme utilizes information from both aspects to modify the specific parameter values ​​in the shape feature validity judgment criteria. This modification is not a simple relaxation or tightening, but rather a quantitative adjustment of the specific values ​​of the judgment criteria (such as the upper and lower limits of the allowable range, weighting coefficients, etc.) based on the intensity of the auxiliary signal features and / or the degree of deviation of the calculated shape features. It is precisely because of this dynamic and precise parameter adjustment mechanism that the accuracy of shape feature consistency checks can still be ensured even when there is uncertainty in matrix type identification or insufficient reliability of auxiliary signal features. This effectively eliminates erroneous features caused by abnormal fluctuations or transient interference, thereby providing more reliable input for subsequent effective diffusion coefficient calculation and second detection result correction, and ultimately improving the overall accuracy of biomarker change rate detection.

[0032] As one embodiment of the present invention, the step of modifying the parameter values ​​in the validity judgment criteria of shape features includes: The fluctuations in the intensity of the auxiliary signal features and the degree of deviation in the calculated shape features; Based on the fluctuation situation, determine the effective range of the intensity of the auxiliary signal characteristics and the degree of deviation of the calculated shape characteristics; The intensity of the auxiliary signal features and the degree of deviation of the calculated shape features are limited to an effective range; Based on the strength of the constrained auxiliary signal features or the degree of deviation of the calculated shape features after constraining, modify the parameter values ​​in the validity judgment criteria of the shape features.

[0033] Among them, fluctuation refers to the change pattern or trend of the intensity of auxiliary signal features and the degree of deviation of calculated shape features over a period of time. Specifically, it can be obtained through continuous sampling, statistical analysis or time series analysis. The purpose is to fully understand the dynamic characteristics of these parameters and avoid making judgments based solely on the values ​​at a single moment.

[0034] The effective range refers to the numerical interval between the intensity of the auxiliary signal features and the deviation of the calculated shape features under normal or acceptable operating conditions. Specifically, it can be determined through historical data analysis, statistical methods (such as mean plus or minus standard deviation), or expert experience. Its purpose is to filter out abnormal fluctuations and retain representative values, thereby improving the accuracy of subsequent judgments. Limiting the intensity of the auxiliary signal features and the deviation of the calculated shape features within the effective range involves pruning or normalizing the values ​​of these parameters to ensure they do not exceed the preset effective range boundaries. This can be achieved by setting upper and lower limits, truncating values ​​to boundary values ​​when they exceed the range, or using a nonlinear mapping function to map them into the effective range. The purpose is to ensure that subsequent parameter adjustments are based on stable and reliable data, avoiding misjudgments caused by extreme or outlier values.

[0035] Specifically, the system first acquires information on the fluctuations of these parameters, enabling it to comprehensively grasp the dynamic changes in signal characteristics, rather than relying solely on values ​​at a single moment. Then, based on these fluctuations, the system intelligently determines the effective range of the intensity of auxiliary signal features and the deviation of calculated shape features. This step is equivalent to preprocessing the raw data, effectively filtering out abnormal fluctuations that may be caused by noise or transient interference, thus retaining more representative and stable data. Next, the intensity of auxiliary signal features and the deviation of calculated shape features are limited to the determined effective range. This operation ensures that the data used to subsequently modify the judgment criteria are filtered and optimized, avoiding interference from extreme or outlier values ​​in parameter adjustments. Finally, based on the intensity of these limited auxiliary signal features or the deviation of the limited calculated shape features, the system can more reasonably and robustly modify the parameter values ​​in the validity judgment criteria of shape features. It is precisely because of this refined processing of data fluctuations that this scheme allows the validity judgment criteria of shape features to adapt to the dynamic changes in signal and feature calculations in practical applications, avoiding frequent adjustments due to transient fluctuations, thereby improving the stability of the judgment criteria and the reliability of the detection results. This method, combined with the previous method of adjusting the judgment criteria based on auxiliary signal features and calculated shape features, forms a more complete and adaptive judgment mechanism. This allows for accurate confirmation of the validity of shape features even when the reliability of sample matrix type identification is insufficient or not identified. This, in turn, ensures the accuracy of subsequent effective diffusion coefficient calculation and second detection result correction, ultimately improving the overall accuracy of biomarker change rate detection.

[0036] As one embodiment of the present invention, the step of determining the effective range of the intensity of the auxiliary signal feature and the degree of deviation of the calculated shape feature includes: Continuously monitor the changes in the intensity of auxiliary signal features and the degree of deviation in calculated shape features over a specific time period; Adjust the boundaries of the effective range based on the trend or magnitude of change.

[0037] Continuous monitoring refers to the continuous or periodic observation and data collection of target parameters to obtain their dynamic information over time. This can be achieved through real-time data stream analysis, timed sampling, or event-triggered recording, with the aim of comprehensively understanding the actual fluctuations of the parameters. A specific time period refers to a clearly defined time window for data monitoring and analysis, such as the most recent minutes, hours, or several monitoring cycles. This can be flexibly set according to the needs of the actual application scenario and the rate of parameter change, with the aim of ensuring the timeliness and representativeness of the analyzed data. A trend refers to the overall direction of the parameter over a period of time, such as rising, falling, or remaining stable. This can be achieved using linear regression analysis. Statistical methods such as analysis, moving averages, or exponential smoothing are used to identify parameters, aiming to reveal the long-term or short-term evolution patterns of parameters. The magnitude of change refers to the maximum range or degree of deviation of a parameter's fluctuations within a specific time period, such as the difference between the maximum and minimum values, standard deviation, or coefficient of variation. It can be quantified using statistical calculations or by setting thresholds for comparison, aiming to measure the stability or instability of the parameter. Adjusting the boundaries of the effective range refers to dynamically modifying the upper and lower limits of the parameter's acceptable values ​​based on the monitored parameter changes. This can be achieved using automatic adjustment based on preset algorithms, expert system rules, or machine learning models, aiming to make the effective range better adapt to the actual fluctuations of the parameter, thereby improving the accuracy of the judgment.

[0038] This application's solution ensures the stability and accuracy of subsequent judgment criteria by dynamically managing the effective range of the intensity of auxiliary signal features and the deviation of calculated shape features. Specifically, the solution first continuously monitors the actual changes of these parameters over a specific time period, enabling the system to grasp their fluctuations in real time, including the frequency, amplitude, and potential trends of the changes. This continuous data acquisition provides a solid foundation for subsequent decision-making. Based on this, the system intelligently adjusts the boundaries of the effective range according to the monitored trends or amplitudes of change. This means that if a parameter exhibits a continuous upward or downward trend, or if its fluctuation amplitude exceeds a preset threshold, the upper and lower limits of the effective range will be expanded or contracted accordingly to better adapt to these dynamic changes. This dynamic adjustment mechanism is closely integrated with the method in previous solutions that limits the intensity of auxiliary signal features and the deviation of shape features to modify the judgment criteria. In previous solutions, to improve the accuracy of judgment, the intensity of auxiliary signal features and the deviation of calculated shape features were acquired, and the validity judgment criteria of shape features were modified based on this information. However, if the effective range of these features themselves is fixed, when the actual parameters fluctuate significantly, the fixed effective range may not accurately reflect the current state, resulting in poor effectiveness of the limitation and modification. This approach ensures that the parameters used to modify the judgment criteria remain accurately limited to reflect the true situation, even when there are natural fluctuations in the degree of deviation of auxiliary signal features and shape features. This makes subsequent modifications to the judgment criteria more reliable, avoiding errors introduced by parameter fluctuations, thereby improving the accuracy and reliability of the entire biomarker change rate detection method. Especially when facing complex or variable sample matrices, it can more effectively confirm the validity of shape features, thus ensuring the accuracy of the final biomarker change rate calculation results.

[0039] As one embodiment of the present invention, the step of adjusting the boundary of the effective range according to the changing trend or the magnitude of the change includes: To obtain multiple observations of the trend or magnitude of change over a period of time; Process multiple observations to identify components that reflect persistent changes; Calculate the offset of the effective range boundary based on the continuously changing components; Update the boundaries of the valid range based on the offset.

[0040] The component exhibiting sustained change refers to a trend of change with a stable direction or amplitude over a period of time, rather than instantaneous fluctuations or random noise. It can be identified using various signal processing techniques, such as moving average filtering, exponential smoothing, Kalman filtering, or wavelet analysis, to extract low-frequency or long-term trend components from the original observations. This effectively suppresses high-frequency noise and short-term disturbances, ensuring that subsequent boundary adjustments are based on real and stable physiological or systemic changes, avoiding misjudgments or over-adjustments caused by transient interference. Processing multiple observations involves performing a series of calculations or transformations on the acquired trend or amplitude data to extract meaningful information. Specifically, this can involve applying statistical methods, digital signal processing algorithms, or machine learning models to perform denoising, smoothing, and feature extraction on the original observations. The aim is to separate components reflecting sustained change from the original data, providing accurate and stable input for subsequent boundary adjustments.

[0041] This application improves the accuracy and reliability of biomarker change rate detection by optimizing the adjustment mechanism of the effective range boundary. In biomarker change rate detection methods, to ensure the accuracy of the consistency check of the shape characteristics of the target biomarker's diffusion behavior within the microfluidic channel, it is necessary to modify the parameter values ​​in the shape feature validity judgment criteria based on the intensity of auxiliary signal features and the degree of deviation in the calculated shape features. To avoid interference from fluctuations in these auxiliary signal features and the degree of deviation in the judgment criterion modification, it is necessary to determine its effective range and limit it within this range. Based on this, this application further optimizes the adjustment method of the effective range boundary. Specifically, the scheme no longer relies solely on instantaneous or short-term change trends or magnitudes to adjust the effective range boundary, but first acquires multiple observations of the change trend or magnitude over a period of time. By collecting data over a period of time, more comprehensive dynamic information of the change can be captured, avoiding the influence of accidental fluctuations at a single moment on the judgment. Subsequently, the scheme processes these multiple observations to identify components reflecting continuous changes. This step is crucial; by applying appropriate signal processing techniques, such as filtering or smoothing, it effectively removes short-term fluctuations and noise interference from the data, thereby extracting components that truly represent long-term or stable trends. It is precisely this identification of persistent changes that allows subsequent boundary adjustments to be based on more stable and reliable input. Next, based on the identified persistently changing components, the scheme calculates the offset of the effective range boundary. This offset is determined based on a true and stable trend, thus accurately reflecting the direction and magnitude of the boundary adjustment needed. Finally, based on the calculated offset, the effective range boundary is updated.

[0042] This adjustment method, based on continuously changing components, allows the effective range boundary to more accurately reflect actual physiological or systemic changes, avoiding frequent and unnecessary adjustments caused by short-term fluctuations. It is precisely this improvement in the effective range boundary adjustment mechanism that allows the intensity of auxiliary signal features and the degree of deviation in calculated shape features to be more stably limited within a reasonable range. This ensures that the modification of the shape feature validity judgment criteria is more accurate, thereby improving the reliability of shape feature validity judgment.

[0043] like Figure 2 The system shown is a time-sequential detection system for AMH and sex hormones in trace samples, used for dynamic monitoring of biomarkers under the influence of ovulation-inducing drugs in assisted reproductive technology settings. The system includes: The sample acquisition module 201 is used to receive isolated micro-volume peripheral blood samples. The diversion module 202 is used to divert a small amount of capillary blood sample into a first subsample stream and a second subsample stream. The flow path control module 203 is used to ensure that the first sub-sample stream and the second sub-sample stream arrive at the first detection area and the second detection area respectively at a preset time interval; The detection module 204 is used to perform a first quantitative detection on the target biomarker in the first subsample stream when the first subsample stream arrives at the first detection area, and to obtain a first detection result; and to perform a second quantitative detection on the target biomarker in the second subsample stream when the second subsample stream arrives at the second detection area, and to obtain a second detection result. The calculation module 205 is used to calculate the rate of change of the target biomarker based on the first detection result and the second detection result; The output module 206 is used to compare the rate of change with a preset judgment threshold and output a technical data report based on the comparison result.

[0044] The technical solutions provided in this invention are only used for the quantitative detection and data analysis of biomarkers in in vitro samples, and do not involve the diagnosis, prediction, or treatment recommendations for any disease. All output data are for technical reference only and should not be used as the sole basis for clinical decision-making.

[0045] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for time-sequential detection of AMH and sex hormones in trace samples, used for dynamic monitoring of biomarkers under the influence of ovulation-inducing drugs in assisted reproductive technology settings, characterized in that... The method includes the following steps: Receive isolated, trace amounts of peripheral blood samples; The trace amount of peripheral blood sample is split into a first subsample stream and a second subsample stream; The first sub-sample stream and the second sub-sample stream arrive at the first detection area and the second detection area respectively at a preset time interval; When the first subsample stream arrives at the first detection area, a first quantitative detection is performed on the target biomarker in the first subsample stream to obtain a first detection result; and when the second subsample stream arrives at the second detection area, a second quantitative detection is performed on the target biomarker in the second subsample stream to obtain a second detection result. The rate of change of the target biomarker is calculated based on the first detection result and the second detection result; The rate of change is compared with a preset judgment threshold, and a technical data report is output based on the comparison result.

2. The method according to claim 1, characterized in that, After performing the step of causing the first sub-sample stream and the second sub-sample stream to arrive at the first detection region and the second detection region respectively at a preset time interval, the method further includes: Record the instantaneous change trajectory of the target biomarker signal; Shape features reflecting the diffusion behavior of the target biomarker within the microfluidic channel are extracted from the instantaneous change trajectory of the signal; Based on the shape characteristics, determine the effective diffusion coefficient of the target biomarker in the current sample; Based on the effective diffusion coefficient, the geometric parameters of the microfluidic channel, and the preset time interval, the concentration decay of the target biomarker due to physical diffusion effect is calculated. Based on the concentration decay, the second detection result is corrected to obtain a second corrected detection result that truly reflects the physiological changes.

3. The method according to claim 2, characterized in that, The step of extracting shape features reflecting the diffusion behavior of the target biomarker within the microfluidic channel from the instantaneous change trajectory of the signal includes: The instantaneous change trajectory of the signal is preprocessed, and the preprocessing includes: Analyze the instantaneous change trajectory of the signal before the appearance of the target biomarker signal or in the initial stage when its signal intensity is below a preset threshold, in order to determine the local background signal level or the offset trend of the background signal level of the instantaneous change trajectory of the signal; Furthermore, based on the local background signal level or the offset trend of the background signal level, the instantaneous change trajectory of the signal is calibrated or the background is subtracted to obtain the calibrated signal trajectory; Identify the main rising and falling segments of the calibrated signal trajectory; Based on the characteristics of the rising and falling segments, the rising rate, falling rate, time required to reach a specific proportion of its maximum intensity, or half-width of the signal trajectory are calculated as shape features reflecting the diffusion behavior of the target biomarker within the microfluidic channel. Perform a consistency check on the calculated shape features to eliminate erroneous features caused by abnormal fluctuations or transient disturbances.

4. The method according to claim 3, characterized in that, The step of performing a consistency check on the calculated shape features to eliminate erroneous features caused by abnormal fluctuations or transient interference includes: Acquire background signal features or non-target signal features related to the instantaneous change trajectory of the signal; The matrix type of the sample is determined based on the background signal characteristics or non-target signal characteristics. Based on the matrix type, obtain the corresponding shape feature validity judgment criteria; The calculated shape features are compared with the validity criteria of the acquired shape features to confirm the validity of the shape features, thereby performing a consistency check on the calculated shape features to eliminate erroneous features caused by abnormal fluctuations or transient interference.

5. The method according to claim 4, characterized in that, The step of confirming the validity of the shape feature includes: Obtain auxiliary signal features related to the instantaneous change trajectory of the signal, including the degree of fluctuation of the signal baseline, the signal strength of non-target substances, or the overall stability of the signal; The reliability of matrix type identification of the sample is evaluated based on the auxiliary signal characteristics. When the reliability level is below a preset threshold, or when the matrix type of the sample is not identified, Based on the auxiliary signal features and the calculated shape features, adjust the validity criteria for the shape features; The calculated shape features are compared with the adjusted shape feature validity judgment criteria to confirm the validity of the shape features. Alternatively, an internal consistency criterion based on multiple shape features can be used to determine the validity of the calculated shape features, thereby confirming the validity of the shape features.

6. The method according to claim 5, characterized in that, The steps for determining the effectiveness of the adjusted shape features include: Obtain the intensity of the auxiliary signal features; Obtain the degree of deviation in the calculated shape features; Furthermore, the parameter values ​​in the validity judgment criteria of the shape feature are modified according to the intensity of the auxiliary signal feature or the degree of deviation of the calculated shape feature.

7. The method according to claim 6, characterized in that, The steps for modifying the parameter values ​​in the validity judgment criteria of the shape feature include: The fluctuation of the intensity of the auxiliary signal features and the degree of deviation of the calculated shape features is obtained; Based on the fluctuation situation, determine the effective range of the intensity of the auxiliary signal feature and the degree of deviation of the calculated shape feature; The degree of deviation between the intensity of the auxiliary signal feature and the calculated shape feature is limited within the effective range; Based on the strength of the limited auxiliary signal feature or the degree of deviation of the calculated shape feature after limitation, the parameter values ​​in the validity judgment criteria of the shape feature are modified.

8. The method according to claim 7, characterized in that, The step of determining the effective range of the intensity of the auxiliary signal feature and the degree of deviation of the calculated shape feature includes: Continuously monitor the changes in the intensity of the auxiliary signal features and the degree of deviation of the calculated shape features over a specific time period; Adjust the boundaries of the effective range according to the trend or magnitude of change.

9. The method according to claim 8, characterized in that, The step of adjusting the boundary of the effective range according to the trend or magnitude of change includes: Obtain multiple observations of the change trend or the change magnitude over a period of time; Process the multiple observations to identify components that reflect persistent changes; Calculate the offset of the effective range boundary based on the continuously changing components; Update the boundary of the effective range based on the offset.

10. A time-sequential detection system for AMH and sex hormones in trace samples, used for dynamic monitoring of biomarkers under the influence of ovulation-inducing drugs in assisted reproductive technology settings, characterized in that... The system includes: The sample acquisition module is used to receive isolated micro-volume peripheral blood samples. The diversion module is used to divert the trace peripheral blood sample into a first sub-sample stream and a second sub-sample stream. The flow path control module is used to ensure that the first sub-sample stream and the second sub-sample stream arrive at the first detection area and the second detection area respectively at a preset time interval; The detection module is configured to perform a first quantitative detection on the target biomarker in the first sub-sample stream when the first sub-sample stream arrives at the first detection area, and to obtain a first detection result; and to perform a second quantitative detection on the target biomarker in the second sub-sample stream when the second sub-sample stream arrives at the second detection area, and to obtain a second detection result. The calculation module is used to calculate the rate of change of the target biomarker based on the first detection result and the second detection result; The output module is used to compare the rate of change with a preset judgment threshold and output a technical data report based on the comparison result.

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