Kidney transplantation rejection reaction serum polypeptide detection device and method

Abstract

The invention provides a kidney transplantation rejection reaction serum polypeptide detection device and method, and the method comprises the steps: obtaining a kidney transplantation serum sample, and extracting a polypeptide characteristic signal group of the serum sample from a polypeptide mass spectrum of the serum sample in a preset mass-to-charge ratio range; the polypeptide characteristic signal group comprises three characteristic polypeptide peak signals corresponding to different protein fragments, and further determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group; carrying out fusion judgment on the rejection risk of the serum sample according to the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group to obtain a risk index representing the rejection state in the serum sample; and comparing the risk index with a rejection risk threshold value of the serum sample so as to make multi-level judgment on the rejection risk level to which the serum sample belongs. Based on the scheme, rejection reaction monitoring based on kidney transplantation serum polypeptide fingerprints can be realized.

Description

Technical Field

[0001] This application relates to the field of rejection detection technology, and more specifically, to a device and method for detecting serum polypeptides in kidney transplant rejection. Background Technology

[0002] Rejection is an immune attack initiated by the recipient's immune system after kidney transplantation, which recognizes the transplanted kidney as a foreign body. Based on the mechanism, it can be divided into cellular rejection mediated by T lymphocytes and humoral rejection mediated by antibodies. Its pathological process involves the activation of various immune cells, the release of cytokines and the activation of the complement system, ultimately leading to tissue damage and functional decline of the transplanted kidney.

[0003] Current non-invasive detection methods rely on the singularity and low dimensionality of biomarkers, depending on only one or a few pre-selected biomarkers for concentration measurement. In the complex pathophysiological environment after kidney transplantation, this single indicator is easily affected by non-specific factors such as drug interference, individual metabolic differences, concurrent infections, or other tissue damage, leading to a significant reduction in signal stability and specificity. Detection results often exhibit high false positive or false negative rates, making it difficult to simultaneously improve sensitivity and specificity to ideal levels. Furthermore, rejection is a complex event involving multiple immune pathways and cellular processes; changes in only one or two biomarkers cannot comprehensively capture this complex immune state, thus hindering accurate and reliable non-invasive assessment and risk stratification of the transplanted kidney's immune status. Therefore, how to achieve rejection monitoring based on kidney transplant serum peptide fingerprinting has become a major challenge for the industry. Summary of the Invention

[0004] This application provides a device and method for detecting serum polypeptides in kidney transplant rejection, which can realize the monitoring of rejection based on the serum polypeptide fingerprint of kidney transplant.

[0005] In a first aspect, this application provides a method for detecting serum polypeptides in kidney transplant rejection, comprising: Serum samples from kidney transplants were obtained, and low molecular weight peptides in the serum samples were enriched and purified using magnetic beads. The enriched and purified peptide samples were then spotted onto a mass spectrometry target plate to obtain the peptide mass spectra of the serum samples within a preset mass-to-charge ratio range. The polypeptide feature signal set of serum sample is extracted from the polypeptide mass spectrum. The polypeptide feature signal set contains three feature polypeptide peak signals corresponding to different protein fragments, thereby determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each feature polypeptide peak in the polypeptide feature signal set. The risk of rejection reaction in serum samples is determined by fusing the mass-to-charge ratio of each protein fragment and the relative intensity of each characteristic polypeptide peak in the polypeptide characteristic signal group, thus obtaining a risk index characterizing the rejection reaction status in serum samples. The risk index is compared with the rejection risk threshold of the serum sample, and then a multi-level determination is made on the rejection risk level of the serum sample.

[0006] In some embodiments, obtaining the peptide mass spectrum of a serum sample within a preset mass-to-charge ratio range specifically includes: The spotted mass spectrometer target plate was placed in a matrix-assisted laser desorption / ionization time-of-flight mass spectrometer, and each peptide sample spot was excited using a nitrogen laser in linear positive ion mode. Set the mass spectrometer's acquisition parameters so that the mass-to-charge ratio of the mass spectrometer covers the preset mass-to-charge ratio range; The peptide sample spots are bombarded with lasers a specified number of times using a mass spectrometer, and the average mass spectrum is obtained by merging the signals to obtain the peptide mass spectrum of the serum sample.

[0007] In some embodiments, the peptide characteristic signal set extracted from the peptide mass spectrum of the serum sample specifically includes: The peptide mass spectrum is preprocessed, including baseline correction and noise filtering, to identify all candidate peptide peaks in the peptide mass spectrum with a signal-to-noise ratio greater than a preset candidate threshold. Three characteristic peptide peaks, whose mass-to-charge ratios correspond to the mass numbers of different protein fragments, were screened from all candidate peptide peaks from a pre-established database of peptides related to rejection reactions. The characteristic peptide signal set of serum samples was determined by identifying all characteristic peptide peaks.

[0008] In some embodiments, determining the mass-to-charge ratio of each protein fragment and the relative intensity values ​​of each characteristic polypeptide peak in the polypeptide characteristic signal set specifically includes: Obtain the mass-to-charge ratio of each protein fragment and the absolute signal intensity of each characteristic polypeptide peak in the polypeptide characteristic signal group; The relative intensity values ​​of each characteristic polypeptide peak in the characteristic signal group of the polypeptide are calculated using all absolute signal intensities.

[0009] In some embodiments, the risk of rejection in serum samples is determined by fusing the mass-to-charge ratio of each protein fragment and the relative intensity of each characteristic peptide peak in the peptide characteristic signal group to obtain a risk index characterizing the rejection status in the serum sample. Specifically, this includes: Initialize a fusion discriminant model based on linear weighting; The mass-to-charge ratio of each protein fragment is used as a feature identifier in the fusion discrimination model; The relative intensity values ​​of each characteristic peptide peak in the peptide characteristic signal group are used as the weighted calculation input values ​​in the fusion discrimination model; The fusion discriminant model is used to determine the risk of rejection reaction status in serum samples, resulting in a risk index characterizing the rejection reaction status in serum samples.

[0010] In some embodiments, comparing the risk index with the rejection risk threshold of the serum sample to make a multi-level determination of the rejection risk level of the serum sample specifically includes: Obtain the preset low-risk threshold and high-risk threshold to obtain the rejection risk threshold of the serum sample; The risk index is compared with the rejection risk threshold, and then the rejection risk level of the serum sample is determined in three levels based on the comparison result.

[0011] In some embodiments, the magnetic beads are superparamagnetic silica magnetic beads based on the principle of hydrophobic interaction chromatography.

[0012] Secondly, this application provides a serum polypeptide detection device for kidney transplant rejection, comprising: The acquisition module is used to acquire serum samples from kidney transplantation, enrich and purify low molecular weight peptides in the serum samples using magnetic beads, and then spot the enriched and purified peptide samples onto a mass spectrometry target plate to acquire the peptide mass spectrum of the serum samples within a preset mass-to-charge ratio range. The processing module is used to extract the polypeptide feature signal set of the serum sample from the polypeptide mass spectrum. The polypeptide feature signal set contains three feature polypeptide peak signals corresponding to different protein fragments, thereby determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each feature polypeptide peak in the polypeptide feature signal set. The processing module is also used to fuse and discriminate the risk of rejection reaction in serum samples by using the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group, so as to obtain a risk index characterizing the rejection reaction status in serum samples. The execution module is used to compare the risk index with the rejection risk threshold of the serum sample, and then make a multi-level determination of the rejection risk level of the serum sample.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described method for detecting serum polypeptides in kidney transplant rejection.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to perform the aforementioned method for detecting serum polypeptides in kidney transplant rejection.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application provides a device and method for detecting serum peptides in kidney transplant rejection. The method involves obtaining a serum sample from a kidney transplant recipient, enriching and purifying low-molecular-weight peptides in the serum sample using magnetic beads, spotting the enriched and purified peptide sample onto a mass spectrometry target plate, and obtaining a peptide mass spectrum of the serum sample within a preset mass-to-charge ratio range. From the peptide mass spectrum, a peptide characteristic signal group of the serum sample is extracted. This peptide characteristic signal group contains three characteristic peptide peak signals corresponding to different protein fragments, thereby determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic peptide peak in the peptide characteristic signal group. The risk of rejection in the serum sample is fused and judged using the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic peptide peak in the peptide characteristic signal group to obtain a risk index characterizing the rejection status in the serum sample. The risk index is compared with a rejection risk threshold for the serum sample to make a multi-level determination of the rejection risk level of the serum sample.

[0016] Therefore, in this application, the risk index and the rejection risk threshold of the serum sample are compared to make a multi-level judgment on the rejection risk level of the serum sample. First, the standardized peptide expression level can be obtained by determining the relative intensity value. By converting the absolute signal intensity obtained from the original mass spectrometry into a relative proportion based on the total intensity within the signal group, the systematic errors introduced by differences in sample pretreatment, fluctuations in sample loading, and the instantaneous instability of mass spectrometer ionization efficiency are effectively eliminated. This ensures that the peptide expression levels of serum samples from different batches and individuals are comparable, thus focusing on the real biological fluctuations of the biomarker itself, rather than technical variations. The resulting relative intensity value becomes a stable and reproducible feature variable, thereby improving the model's applicability in different scenarios. The robustness and repeatability of the technology enable accurate peptide fingerprinting and cross-center validation. Then, by determining the risk index, a comprehensive quantitative risk assessment value can be obtained, enabling objective and tiered early warning of rejection status. This approach reduces the dimensionality of multi-dimensional peptide fingerprint information and transforms it into a scalar that can be compared with a defined threshold. This not only overcomes the shortcomings of insufficient sensitivity and specificity when relying on a single biomarker, but also provides a continuous spectrum reflecting the severity of rejection, rather than a simple binary yes or no judgment. Determining the risk index shifts the assessment of rejection risk from qualitative description to quantitative analysis, providing data support for dynamic monitoring, risk stratification, and early intervention decisions. In summary, the above scheme enables rejection monitoring based on kidney transplant serum peptide fingerprints. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an exemplary flowchart of a method for detecting serum polypeptides in kidney transplant rejection according to some embodiments of this application; Figure 2 This is a flowchart illustrating the process of determining a risk index according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of a serum polypeptide detection device for kidney transplant rejection according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device for implementing a method for detecting serum polypeptides in kidney transplant rejection, according to some embodiments of this application. Detailed Implementation

[0019] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] refer to Figure 1 The figure is an exemplary flowchart of a method for detecting serum polypeptides in kidney transplant rejection according to some embodiments of this application. The method for detecting serum polypeptides in kidney transplant rejection mainly includes the following steps: In step 101, a serum sample from a kidney transplant is obtained, and low molecular weight peptides in the serum sample are enriched and purified using magnetic beads. The enriched and purified peptide sample is then spotted onto a mass spectrometry target plate to obtain a peptide mass spectrum of the serum sample within a preset mass-to-charge ratio range.

[0021] It should be noted that, in this application, the serum sample is a liquid biological matrix used for peptide mass spectrometry analysis; the magnetic beads are superparamagnetic microspheres that achieve selective adsorption and enrichment of peptides, and these magnetic beads are superparamagnetic silica magnetic beads based on the principle of hydrophobic interaction chromatography; the low molecular weight peptide is a protein fragment characterizing a specific biomarker of repulsion reaction, and the molecular weight of the low molecular weight peptide is between 1,000 and 10,000 Daltons; enrichment and purification is a sample pretreatment process used to separate and concentrate the target low molecular weight peptide from the complex serum matrix, while removing high-abundance proteins and interfering substances such as salts; the peptide sample is a solution containing the target low molecular weight peptide used for mass spectrometry analysis; the mass spectrometry target plate is a sample plate that carries and immobilizes the peptide sample and matrix co-crystal for laser desorption and ionization analysis by the mass spectrometer.

[0022] In practice, the process involves first obtaining a serum sample from a kidney transplant recipient. The enrichment and purification of low-molecular-weight peptides in the serum sample using magnetic beads can be achieved as follows: Peripheral blood is collected from the kidney transplant recipient via venipuncture. After allowing the blood to clot, it is centrifuged to obtain the clear upper layer of serum. The serum sample is then co-incubated with magnetic beads pretreated with a equilibration solution. Under these conditions, low-molecular-weight peptides in the serum sample specifically adsorb onto the surface of the magnetic beads. The magnetic beads are then fixed under an external magnetic field, and the supernatant is discarded to remove other impurities from the serum. The magnetic beads conjugated with low-molecular-weight peptides are washed with a specified washing solution, and the target low-molecular-weight peptides are removed using a low-ionic-strength acidic elution buffer. The peptides dissociate from the magnetic beads, thus completing the enrichment and purification of low molecular weight peptides. The resulting clear eluent is used as the peptide sample for analysis. The enriched and purified peptide sample can then be spotted onto a mass spectrometry target plate in the following manner: a saturated matrix solution is uniformly mixed with the peptide sample prepared in the previous steps at a fixed volume ratio; a precise pipette is used to aspirate a quantitative amount of this mixture and accurately deposit it at the designated spotting site on the mass spectrometry target plate; the solvent in the mixture is allowed to evaporate naturally at room temperature, forming a thin microcrystalline layer of uniform co-crystallized peptide sample and matrix. The dried mass spectrometry target plate then serves as a pre-treated detection carrier for mass spectrometry analysis.

[0023] In some embodiments, obtaining a peptide mass spectrum of a serum sample within a preset mass-to-charge ratio range can be achieved using the following steps: The spotted mass spectrometer target plate was placed in a matrix-assisted laser desorption / ionization time-of-flight mass spectrometer, and each peptide sample spot was excited using a nitrogen laser in linear positive ion mode. Set the mass spectrometer's acquisition parameters so that the mass-to-charge ratio of the mass spectrometer covers the preset mass-to-charge ratio range; The peptide sample spots are bombarded with lasers a specified number of times using a mass spectrometer, and the average mass spectrum is obtained by merging the signals to obtain the peptide mass spectrum of the serum sample.

[0024] It should be noted that, in this application, the peptide mass spectrum is mass spectrometry data characterizing all peptide components and their signal intensities in a peptide sample; the matrix-assisted laser desorption / ionization time-of-flight mass spectrometer is a large-scale precision analytical instrument used to convert peptide samples on a spotting target plate into gaseous ions; the peptide sample spot is the microcrystalline region to be analyzed formed by the co-crystallization of the peptide sample and matrix on the mass spectrometry target plate; the nitrogen laser is an energy source used to generate pulsed lasers of a specified wavelength (337 nm) to excite the peptide sample spot to cause desorption and ionization; the linear positive ion mode is a mass spectrometer working configuration used to detect positively charged peptide ions and achieve mass separation based on time of flight in a field-free flight tube; the preset mass-to-charge ratio range is a range covering all target characteristic peptide peak signals, with a preset minimum (1000 Daltons) to maximum (10000 Daltons) mass-to-charge ratio.

[0025] In practice, firstly, the spotted mass spectrometry target plate is placed in a matrix-assisted laser desorption / ionization time-of-flight mass spectrometer. In linear positive ion mode, excitation of each peptide sample spot using a nitrogen laser can be achieved as follows: The spotted and dried mass spectrometry target plate is placed on a dedicated target plate holder in the mass spectrometer's sample chamber, ensuring good electrical contact; the mass spectrometer is started, and linear positive ion mode is selected as the detection mode; the position of the peptide sample spot to be measured is selected in the instrument control software, and then a pulsed laser with a wavelength of 337 nm is triggered. This laser beam, after being focused by the optical system, precisely irradiates the peptide sample spot, causing the peptide molecules in the co-crystallization to absorb energy, desorb, and ionize into positively charged ions. The resulting peptide ion cloud is then used as the target for mass analysis. Next, the mass spectrometer's acquisition parameters are set so that the mass-to-charge ratio covers a preset range. This can be achieved as follows: In the mass spectrometer's control software interface, settings including accelerating voltage and delayed extraction time are configured. Key acquisition parameters, including detector voltage, are selected. Through coordinated optimization and setting of these parameters, the mass spectrometer's detection system is ensured to effectively record and cover the entire preset mass-to-charge ratio range from 1000 Daltons to 10000 Daltons. This optimized instrument state serves as a technical guarantee for obtaining complete peptide mass spectrometry information. Finally, the mass spectrometer bombards each peptide sample point with a specified number of laser strikes, and the average mass spectrum is obtained by merging the signals. The peptide mass spectrum of the serum sample can be obtained as follows: For each peptide sample point, a nitrogen laser is continuously and stably bombarded with a specified number of strikes, ranging from 300 to 1000. The mass spectrometer's detection system records all the instantaneous mass spectrometry signals acquired from each laser strike and uses software algorithms to superimpose and average these signals, ultimately generating an average mass spectrum with significantly reduced noise and a more stable signal. This average mass spectrum is used as the peptide mass spectrum of the serum sample.

[0026] In step 102, a polypeptide characteristic signal set of serum sample is extracted from the polypeptide mass spectrum. The polypeptide characteristic signal set contains three characteristic polypeptide peak signals corresponding to different protein fragments, thereby determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal set.

[0027] In some embodiments, the extraction of peptide characteristic signal groups from the peptide mass spectrum of a serum sample can be achieved by the following steps: The peptide mass spectrum is preprocessed, including baseline correction and noise filtering, to identify all candidate peptide peaks in the peptide mass spectrum with a signal-to-noise ratio greater than a preset candidate threshold. Three characteristic peptide peaks, whose mass-to-charge ratios correspond to the mass numbers of different protein fragments, were screened from all candidate peptide peaks from a pre-established database of peptides related to rejection reactions. The characteristic peptide signal set of serum samples was determined by identifying all characteristic peptide peaks.

[0028] It should be noted that in this application, the polypeptide feature signal set is a combined dataset that characterizes the risk of rejection reaction in serum samples; the candidate polypeptide peak is a potential polypeptide signal whose signal intensity is higher than the noise level and meets the basic peak shape requirements; and the feature polypeptide peak is a set of three target polypeptide signals used to construct the final discrimination model.

[0029] In specific implementation, firstly, the peptide mass spectrum is preprocessed, including baseline correction and noise filtering. Then, all candidate peptide peaks with a signal-to-noise ratio (SNR) greater than a preset candidate threshold in the peptide mass spectrum can be identified in the following way: The peptide mass spectrum is preprocessed, where baseline correction is estimated using an iterative fitting algorithm and nonlinear background in the spectrum is subtracted, and noise filtering uses a sliding window averaging method to smooth the signal curve to suppress random fluctuations. On the smoothed spectrum after baseline correction and noise filtering, a peak detection algorithm is used to identify all local maxima and calculate their SNR. All signal peaks with an SNR greater than a preset candidate threshold (default 5) are initially identified as candidate peptide peaks, thus using the list of selected candidate peptide peaks as input for specific screening. Then, from a pre-established rejection-related peptide database, three characteristic peptide peaks with mass-to-charge ratios corresponding to the mass number of different protein fragments are selected from all candidate peptide peaks. This can be achieved in the following way: adjusting... A pre-established database of rejection-related peptides, storing the mass numbers of specific protein fragments associated with rejection reactions as verified by previous studies, is used. The measured mass-to-charge ratios of all candidate peptide peaks are compared with the theoretical mass numbers of different protein fragments in the database, with the matching rule being that the measured values ​​fall within a mass tolerance range of ±0.1% of the theoretical values. Three characteristic peptide peaks that successfully match three specified theoretical mass numbers in the database are precisely selected from all candidate peptide peaks, and these three selected characteristic peptide peaks are used as biomarkers for constructing a discriminant model. Finally, the peptide characteristic signal set of the serum sample is determined by associating and integrating the measured mass-to-charge ratio values ​​of the three successfully selected characteristic peptide peaks with the normalized signal intensity values ​​to form a structured dataset characterizing the specified molecular phenotype of the sample. This structured dataset is then used as the peptide characteristic signal set of the serum sample.

[0030] In some embodiments, determining the mass-to-charge ratio of each protein fragment and the relative intensity of each characteristic polypeptide peak in the polypeptide characteristic signal set can be achieved by the following steps: Obtain the mass-to-charge ratio of each protein fragment and the absolute signal intensity of each characteristic polypeptide peak in the polypeptide characteristic signal group; The relative intensity values ​​of each characteristic polypeptide peak in the characteristic signal group of the polypeptide are calculated using all absolute signal intensities.

[0031] It should be noted that, in this application, the mass-to-charge ratio is a physical identifier parameter used to uniquely locate and identify a specified characteristic peptide peak in a mass spectrum; the relative intensity value is a standardized signal intensity value used to eliminate the influence of total ion intensity fluctuations between samples, and this relative intensity value can be used for cross-sample comparison; the absolute signal intensity is the raw response value of the characteristic peptide peak in the mass spectrum without normalization, and the value of the absolute signal intensity is related to the abundance of the peptide in the sample.

[0032] In specific implementation, firstly, obtaining the mass-to-charge ratio of each protein fragment and the absolute signal intensity of each characteristic peptide peak in the peptide characteristic signal group can be achieved in the following way: directly read the measured values ​​of the mass-to-charge ratio of each protein fragment recorded in the previous step from the peptide characteristic signal group; simultaneously, obtain the absolute signal intensity of each characteristic peptide peak corresponding to the mass-to-charge ratio of each protein fragment from the raw data of the peptide mass spectrum. This absolute signal intensity is quantified by the peak height or peak area of ​​the mass spectrum, thereby combining the obtained paired datasets of the mass-to-charge ratio of each protein fragment and its corresponding absolute signal intensity into the subsequent normalization. The calculation uses a baseline input; then, the relative intensity values ​​of each characteristic peptide peak in the polypeptide characteristic signal group can be calculated using the following method: sum the absolute signal intensities of all characteristic peptide peaks in the polypeptide characteristic signal group to obtain the total absolute signal intensity; for each characteristic peptide peak in the polypeptide characteristic signal group, divide its absolute signal intensity by the calculated total absolute signal intensity, and the quotient is the relative intensity value of that characteristic peptide peak; this calculation process is performed sequentially on all characteristic peptide peaks in the polypeptide characteristic signal group, and the relative intensity values ​​of each characteristic peptide peak in the polypeptide characteristic signal group can be obtained in the above manner.

[0033] In step 103, the risk of rejection reaction in serum samples is determined by fusing the mass-to-charge ratio of each protein fragment and the relative intensity values ​​of each characteristic polypeptide peak in the polypeptide characteristic signal group, thus obtaining a risk index characterizing the rejection reaction status in serum samples.

[0034] In some embodiments, the risk of rejection in serum samples is determined by fusing the mass-to-charge ratio of each protein fragment with the relative intensity of each characteristic peptide peak in the polypeptide characteristic signal set, resulting in a risk index characterizing the rejection status in the serum samples. Figure 2 The diagram is a flowchart illustrating the process of determining the risk index in some embodiments of this application. In this embodiment, the risk index can be determined using the following steps: In step 1031, a fusion discrimination model based on linear weighting is initialized; In step 1032, the mass-to-charge ratio of each protein fragment is used as a feature identifier in the fusion discrimination model; In step 1033, the relative intensity values ​​of each characteristic peptide peak in the peptide characteristic signal group are used as the weighted calculation input values ​​in the fusion discrimination model; In step 1034, the fusion discriminant model is used to determine the risk of rejection reaction status in serum samples, and a risk index characterizing the rejection reaction status in serum samples is obtained.

[0035] It should be noted that in this application, the fusion discriminant model is a pre-defined mathematical computational framework for converting peptide signals into a risk index. This fusion discriminant model linearly integrates and quantitatively weights multiple biomarker information (i.e., the relative intensity values ​​of each characteristic peptide peak) from different protein fragments that are independent of each other. Specifically, the fusion discriminant model treats the relative intensity value of each characteristic peptide peak as an independent input variable and assigns a specified weight coefficient to each variable. This weight coefficient reflects the contribution of the biomarker to the rejection risk. Through the weighted summation formula, i.e., the risk index = Σ (weight coefficient × relative intensity value), the fusion discriminant model integrates information from multiple dimensions into a single, continuous comprehensive score. In this way, a mapping relationship from a multi-dimensional feature space to a one-dimensional risk scale is constructed, thereby transforming the complex differences in peptide expression profiles into an intuitive risk index that can be used for subsequent judgment, realizing an objective and quantitative assessment of the rejection status of serum samples.

[0036] In step 104, the risk index and the rejection risk threshold of the serum sample are compared, and then a multi-level determination is made on the rejection risk level of the serum sample.

[0037] In some embodiments, comparing the risk index with the rejection risk threshold of the serum sample to make a multi-level determination of the rejection risk level of the serum sample can be achieved by the following steps: Obtain the preset low-risk threshold and high-risk threshold to obtain the rejection risk threshold of the serum sample; The risk index is compared with the rejection risk threshold, and then the rejection risk level of the serum sample is determined in three levels based on the comparison result.

[0038] It should be noted that, in this application, the low-risk threshold is the lower limit of the range into which the risk index falls without significant rejection reaction risk; the high-risk threshold is the upper limit of the range into which the risk index falls with high rejection reaction risk; and the rejection risk threshold is a set of judgment criteria that includes the low-risk threshold and the high-risk threshold, used to discretize and classify continuous risk indices.

[0039] In specific implementation, firstly, preset low-risk and high-risk thresholds are obtained. The rejection risk threshold for serum samples can be achieved as follows: Two pre-calculated and stored fixed values, namely the low-risk threshold and the high-risk threshold, are read from the system configuration file or the judgment criteria determined through retrospective clinical studies, with the high-risk threshold being greater than the low-risk threshold. The low-risk and high-risk thresholds together constitute a complete judgment criterion, and the value pair composed of the low-risk and high-risk thresholds is used as the rejection risk threshold for subsequent comparison and judgment. Then, the risk index is compared with the rejection risk threshold. The three-level determination of the rejection risk level of a serum sample based on the comparison results can be achieved as follows: the risk index is compared with two specific values ​​in the rejection risk threshold; that is: if the risk index is lower than the low-risk threshold, it is determined to be "low risk"; if the risk index is between the low-risk threshold and the high-risk threshold (including the case of being equal to either threshold), it is determined to be "medium risk"; if the risk index is higher than the high-risk threshold, it is determined to be "high risk"; and the conclusion of "low risk", "medium risk" or "high risk" obtained after performing this logical judgment is used as the three-level determination result of the rejection risk level of the serum sample.

[0040] In another aspect, in some embodiments, this application provides a serum polypeptide detection device for kidney transplant rejection, as referenced. Figure 3 The figure is a schematic diagram of a kidney transplant rejection serum polypeptide detection device according to some embodiments of this application. The kidney transplant rejection serum polypeptide detection device includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire serum samples from kidney transplantation, and to enrich and purify low molecular weight peptides in the serum samples using magnetic beads. The enriched and purified peptide samples are then spotted onto a mass spectrometry target plate to acquire the peptide mass spectrum of the serum samples within a preset mass-to-charge ratio range. Processing module 202, in this application, is used to extract a set of peptide characteristic signals from the peptide mass spectrum of the serum sample. The set of peptide characteristic signals includes three characteristic peptide peak signals corresponding to different protein fragments, thereby determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic peptide peak in the set of peptide characteristic signals. It should be noted that the processing module 202 is also used to fuse and judge the risk of rejection reaction of serum sample by the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group, so as to obtain a risk index characterizing the rejection reaction status in serum sample. The execution module 203 in this application is mainly used to compare the risk index and the rejection risk threshold of the serum sample, and then make a multi-level determination of the rejection risk level of the serum sample.

[0041] The foregoing detailed examples of the kidney transplant rejection serum polypeptide detection device and method provided in this application. It is understood that, to achieve the aforementioned functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specified application, but such implementation should not be considered beyond the scope of this application.

[0042] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described method for detecting serum polypeptides in kidney transplant rejection.

[0043] In some embodiments, reference Figure 4 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the structure of a computer device for implementing a method for detecting serum peptides in kidney transplant rejection according to an embodiment of this application. The method for detecting serum peptides in kidney transplant rejection described in the above embodiments can be implemented through… Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0044] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0045] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0046] For example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0047] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.

[0048] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.

[0049] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0050] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to perform the above-described method for detecting serum polypeptides in kidney transplant rejection.

[0052] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0053] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for detecting serum polypeptides in kidney transplant rejection, characterized in that, Includes the following steps: Serum samples from kidney transplants were obtained, and low molecular weight peptides in the serum samples were enriched and purified using magnetic beads. The enriched and purified peptide samples were then spotted onto a mass spectrometry target plate to obtain the peptide mass spectra of the serum samples within a preset mass-to-charge ratio range. The polypeptide feature signal set of serum sample is extracted from the polypeptide mass spectrum. The polypeptide feature signal set contains three feature polypeptide peak signals corresponding to different protein fragments, thereby determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each feature polypeptide peak in the polypeptide feature signal set. The risk of rejection reaction in serum samples is determined by fusing the mass-to-charge ratio of each protein fragment and the relative intensity of each characteristic polypeptide peak in the polypeptide characteristic signal group, thus obtaining a risk index characterizing the rejection reaction status in serum samples. The risk index is compared with the rejection risk threshold of the serum sample, and then a multi-level determination is made on the rejection risk level of the serum sample.

2. The method as described in claim 1, characterized in that, Obtaining peptide mass spectra from serum samples within a preset mass-to-charge ratio range specifically includes: The spotted mass spectrometer target plate was placed in a matrix-assisted laser desorption / ionization time-of-flight mass spectrometer, and each peptide sample spot was excited using a nitrogen laser in linear positive ion mode. Set the mass spectrometer's acquisition parameters so that the mass-to-charge ratio of the mass spectrometer covers the preset mass-to-charge ratio range; The peptide sample spots are bombarded with lasers a specified number of times using a mass spectrometer, and the average mass spectrum is obtained by merging the signals to obtain the peptide mass spectrum of the serum sample.

3. The method as described in claim 1, characterized in that, The specific peptide characteristic signal set extracted from the serum sample from the peptide mass spectrometry includes: The peptide mass spectrum is preprocessed, including baseline correction and noise filtering, to identify all candidate peptide peaks in the peptide mass spectrum with a signal-to-noise ratio greater than a preset candidate threshold. Three characteristic peptide peaks, whose mass-to-charge ratios correspond to the mass numbers of different protein fragments, were screened from all candidate peptide peaks from a pre-established database of peptides related to rejection reactions. The characteristic peptide signal set of serum samples was determined by identifying all characteristic peptide peaks.

4. The method as described in claim 1, characterized in that, Determining the mass-to-charge ratio of each protein fragment and the relative intensity values ​​of each characteristic polypeptide peak in the polypeptide characteristic signal set specifically includes: Obtain the mass-to-charge ratio of each protein fragment and the absolute signal intensity of each characteristic polypeptide peak in the polypeptide characteristic signal group; The relative intensity values ​​of each characteristic polypeptide peak in the characteristic signal group of the polypeptide are calculated using all absolute signal intensities.

5. The method as described in claim 1, characterized in that, The risk of rejection in serum samples is determined by fusing the mass-to-charge ratio of each protein fragment and the relative intensity of each characteristic peptide peak in the peptide characteristic signal group, resulting in a risk index characterizing the rejection status in the serum sample. Specifically, this index includes: Initialize a fusion discriminant model based on linear weighting; The mass-to-charge ratio of each protein fragment is used as a feature identifier in the fusion discrimination model; The relative intensity values ​​of each characteristic peptide peak in the peptide characteristic signal group are used as the weighted calculation input values ​​in the fusion discrimination model; The fusion discriminant model is used to determine the risk of rejection reaction status in serum samples, resulting in a risk index characterizing the rejection reaction status in serum samples.

6. The method as described in claim 1, characterized in that, The risk index is compared with the rejection risk threshold of the serum sample, and a multi-level determination of the rejection risk level of the serum sample is made, specifically including: Obtain the preset low-risk threshold and high-risk threshold to obtain the rejection risk threshold of the serum sample; The risk index is compared with the rejection risk threshold, and then the rejection risk level of the serum sample is determined in three levels based on the comparison result.

7. The method as described in claim 1, characterized in that, The magnetic beads are superparamagnetic silica magnetic beads based on the principle of hydrophobic interaction chromatography.

8. A serum polypeptide detection device for kidney transplant rejection, characterized in that, include: The acquisition module is used to acquire serum samples from kidney transplantation, enrich and purify low molecular weight peptides in the serum samples using magnetic beads, and then spot the enriched and purified peptide samples onto a mass spectrometry target plate to acquire the peptide mass spectrum of the serum samples within a preset mass-to-charge ratio range. The processing module is used to extract the polypeptide feature signal set of the serum sample from the polypeptide mass spectrum. The polypeptide feature signal set contains three feature polypeptide peak signals corresponding to different protein fragments, thereby determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each feature polypeptide peak in the polypeptide feature signal set. The processing module is also used to fuse and discriminate the risk of rejection reaction in serum samples by using the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group, so as to obtain a risk index characterizing the rejection reaction status in serum samples. The execution module is used to compare the risk index with the rejection risk threshold of the serum sample, and then make a multi-level determination of the rejection risk level of the serum sample.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory for storing computer programs, and the processor for calling and running the computer programs from the memory, causing the computer device to perform the method for detecting serum polypeptides in kidney transplant rejection as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to perform the method for detecting serum polypeptides in kidney transplant rejection as described in any one of claims 1 to 7.

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