Pasc-related cardiovascular system symptom risk assessment application based on prdx4

Abstract

The present application relates to the technical field of biological medicine, and particularly relates to a PRDX4-based PASC-related cardiovascular system symptom risk assessment application.The present application provides a risk assessment system taking PRDX4 as a core molecular marker, and the system can provide objective and quantifiable molecular level reference basis for PASC-related cardiovascular system symptom risk assessment in the case that a conventional detection means cannot provide sufficient explanation.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to the application of PRDX4-based risk assessment of PASC-related cardiovascular symptoms. Background Technology

[0002] Post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a group of symptoms, signs, or health abnormalities that appear, persist, or recur after a previous suspected or confirmed SARS-CoV-2 infection. These symptoms typically persist for at least two months after the onset of acute infection and cannot be better explained by other diagnoses. This condition can affect one or more organ systems and can present with a persistent, fluctuating, or progressive course. Cardiovascular symptoms associated with PASC refer to symptoms or signs involving the cardiovascular system or presenting primarily with cardiovascular discomfort, including but not limited to palpitations, chest pain, shortness of breath, syncope / prodromal syncope, abnormal heart rate, or arrhythmias. The persistence of these symptoms not only affects the quality of life of individuals but also poses challenges to clinical identification, risk assessment, and intervention decisions.

[0003] Currently, existing technologies for the identification and assessment of PASC-related cardiovascular symptoms still have the following shortcomings.

[0004] First, conventional clinical testing methods have limited ability to identify some subjects. Some subjects with clear, long-term cardiovascular-related complaints do not show obvious abnormalities in routine electrocardiograms, echocardiograms, imaging examinations, and tests for myocardial injury-related indicators, leading to inconsistencies between subjective symptoms and objective examination results, thus increasing the difficulty of clinical assessment and risk stratification.

[0005] Secondly, the influence of basic biological covariates on the test results has not been adequately corrected. PASC-related manifestations exhibit strong individual heterogeneity, with differences among subjects in terms of gender, age, and body mass index (BMI). These factors themselves can affect the expression levels of immune, inflammatory, metabolic, and coagulation-related molecules. In existing technologies, some assessment methods rely primarily on a single indicator or fixed threshold for judgment, failing to adequately consider the interference of these covariates, thus limiting their stability and accuracy across different populations.

[0006] Secondly, current technologies lack comprehensive evaluation strategies targeting pathological changes across multiple pathways. The development of long-term cardiovascular symptoms related to COVID-19 may involve multiple biological processes, including persistent oxidative stress, persistent activation of chronic inflammation, vascular endothelial dysfunction, mitochondrial dysfunction, endoplasmic reticulum stress, and microcirculatory homeostasis imbalance. However, current technologies lack stable molecular biomarkers that can reflect these pathological changes and be used to assist in assessing the risk of related symptoms.

[0007] Furthermore, due to the lack of objective, stable, and quantifiable molecular markers, existing technologies still fall short in terms of individualized assessment and follow-up management, making it difficult to provide effective auxiliary decision-making support for clinical practice.

[0008] Previous studies have suggested that long-term PASC-related symptoms may be associated with a persistent state of low-level oxidative stress. Some subjects may still experience abnormal redox homeostasis, persistent inflammatory activation, and endothelial dysfunction after the acute infection has ended, leading to long-term cardiovascular-related discomfort. However, stable molecular indicators that can reflect these pathological states are currently lacking.

[0009] Therefore, developing a biomarker and its evaluation method that can be used to assess the risk of PASC-related cardiovascular symptoms is of great significance for improving the risk identification ability of relevant populations and assisting clinical decision-making. Summary of the Invention

[0010] This invention relates to the field of biomarker detection and molecular diagnostics, and in particular to a technical solution for risk assessment of PASC-related cardiovascular symptoms based on PRDX4 expression levels and its application.

[0011] This invention, based on molecular characterization analysis of individuals infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), reveals a correlation between PRDX4 expression levels and PASC-related cardiovascular symptoms. Therefore, it proposes using PRDX4 as a molecular biomarker for auxiliary risk assessment. Compared to methods relying on subjective symptoms or conventional cardiovascular indicators, this approach provides objective and quantifiable molecular-level assessment data.

[0012] In some embodiments, the present invention provides the use of reagents for quantitatively determining PRDX4 expression levels in the preparation of kits for assessing the risk of PASC-related cardiovascular symptoms. The PRDX4 expression level includes PRDX4 mRNA expression level and / or PRDX4 protein expression level; wherein the PRDX4 mRNA expression level may be derived from peripheral blood cells or blood-derived nucleic acid samples, and the PRDX4 protein expression level may be derived from serum, plasma, peripheral blood cells, or blood-derived extracellular vesicle components. The detection can be achieved through various technical pathways, including nucleic acid level detection methods and protein level detection methods. Nucleic acid detection methods may include sequencing, polymerase chain reaction, isothermal amplification reaction, microarray, or CRISPR-Cas detection systems, etc., while protein detection methods may include immunoassay, biomolecular mass spectrometry-related detection, or nucleic acid aptamer-based detection methods.

[0013] In some embodiments, the present invention further provides a PASC-related cardiovascular symptom risk assessment system based on PRDX4 expression levels. The system may include a sample information processing module, an assessment module, and an information output module. The sample information processing module is used to acquire the PRDX4 expression level information of the subject. The assessment module is used to analyze the PRDX4 expression level based on a regression model to obtain a risk assessment result. The information output module is used to output risk probability, risk score, or classification result. In some embodiments, the assessment model can be a regression model, preferably a logistic regression model, and can output the risk probability of the subject developing related symptoms.

[0014] In some implementations, the risk assessment can be further optimized by combining the subject's clinical characteristics with PRDX4 expression levels. These clinical characteristics may include gender, age, and / or body mass index (BMI). By jointly modeling molecular marker information with individual baseline physiological characteristics, the impact of individual differences on the assessment results can be reduced to some extent, thereby improving the stability and applicability of the risk assessment.

[0015] In some implementations, the risk assessment results can be expressed as continuous indicators (such as risk probability or risk score) and can be stratified or classified based on preset thresholds. These thresholds can be determined through subject operating characteristic curve analysis, for example, based on the maximum Yoden index, thereby distinguishing between high-risk and low-risk subjects.

[0016] Through the above technical solution, the present invention provides a risk assessment system with PRDX4 as the core molecular marker. This system can provide objective and quantifiable molecular-level reference for the risk assessment of PASC-related cardiovascular symptoms when conventional detection methods cannot provide sufficient explanation. Attached Figure Description

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

[0018] Figure 1 Principal component analysis (PCA) results based on protein expression data.

[0019] Figure 2 Results of differentially expressed protein analysis.

[0020] Figure 3 A: A schematic diagram of the receiver operating characteristic (ROC) curve and area under the curve (AUC) of the univariate risk assessment model based on PRDX4 expression level; B: A schematic diagram of the receiver operating characteristic (ROC) curve and area under the curve (AUC) of the joint risk assessment model constructed by further incorporating age, sex, and body mass index (BMI) on the basis of PRDX4 expression level; C: A schematic diagram of the Youden index variation curve and the optimal judgment threshold of the joint risk assessment model under different prediction probability thresholds.

[0021] Figure 4 The results of the receiver operating characteristic (ROC) curve and area under the curve (AUC) of the validation set risk assessment model at the 1-year follow-up time. Detailed Implementation

[0022] Reference will now be made to detailed embodiments of the present invention, one or more of which are described below. Each example is provided for explanation and not for limitation of the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the invention without departing from its scope or spirit. For example, features described or illustrated as part of one embodiment may be used in another embodiment to produce further embodiments.

[0023] Unless otherwise stated, all terms used to disclose this invention (including technical and scientific terms) should be understood as having the meaning commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the scope of protection of this invention. Unless the context clearly defines otherwise, the scientific and technical terms used herein, as well as terms and laboratory procedures in related fields such as protein and nucleic acid chemistry, molecular biology, and immunology, are all conventional terms and standard methods well-known and widely used in the art. To facilitate understanding of the technical solutions of this invention, some related terms are further defined and explained below.

[0024] The terms “containing,” “comprising,” and “including” as used in this invention are synonyms and are inclusive or open-ended, and do not exclude additional, uncited members, elements, or method steps.

[0025] In this invention, the numerical range represented by endpoints includes all numerical values ​​and fractions contained within that range, as well as the endpoints mentioned.

[0026] This invention relates to concentration values, which include fluctuations within a certain range. For example, fluctuations are allowed within a corresponding precision range. For instance, for a concentration value of 2%, fluctuations within ±0.1% are permissible. For larger values ​​or values ​​that do not require overly precise control, even greater fluctuations are permitted.

[0027] As used in this invention, unless otherwise stated, the singular forms of the articles “a,” “an,” and “the” include plural referents.

[0028] In this invention, the terms "multiple" or "various" are used unless otherwise specified, referring to a quantity greater than or equal to 2.

[0029] In this invention, the technical features described in an open-ended manner include both closed-ended technical solutions composed of the listed features and open-ended technical solutions that include the listed features.

[0030] In this invention, terms such as "preferred," "better," "more suitable," and "ideal" merely describe implementation methods or embodiments with better effects and should be understood not to limit the scope of protection of this invention. In this invention, terms such as "optionally," "optionally," and "optional" mean that something is optional, that is, selected from either "with" or "without" a parallel solution. If multiple "optional" statements appear in a technical solution, unless otherwise specified and without contradiction or mutual constraint, each "optional" statement is independent.

[0031] In this invention, the term "PASC" (Post-acute sequelae of SARS-CoV-2 infection) refers to a type of symptom or health abnormality that persists, recurs, or recurs after the acute infection period following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This condition typically occurs several weeks or more after infection and can persist, potentially affecting multiple organ systems. In this invention, "PASC" can be understood as equivalent to or approximately similar to terms such as "post-COVID-19 condition," "post-COVID condition," "post-COVID syndrome," "Long COVID," "post-acute COVID-19 syndrome," "acute sequelae after SARS-CoV-2 infection," "persistent sequelae after SARS-CoV-2 infection," "long-term sequelae after SARS-CoV-2 infection," and "post-infection persistent symptom syndrome." These terms all characterize the same type of persistent clinical state after infection, and their differences lie only in naming conventions or time definition standards without affecting their technical connotation.

[0032] In this invention, the term "PASC-related cardiovascular symptoms" refers to symptoms or functional abnormalities within the scope of PASC and involving the cardiovascular system, including but not limited to palpitations, chest pain, chest tightness, bradycardia, tachycardia, decreased exercise tolerance, and post-exercise discomfort. These symptoms may exist alone or in combination and may persist even when routine electrocardiograms, imaging examinations, or myocardial injury markers show no significant abnormalities. In this invention, "PASC-related cardiovascular symptoms" can be understood as equivalent or approximately equivalent to "PASC cardiovascular symptoms," "PASC-CVS," "Long Covid with cardiovascular symptoms," "PASC-related long-term cardiovascular symptoms," "long-term cardiovascular symptoms," "long-term cardiovascular-related symptoms," and "persistent cardiovascular symptoms" when used to describe subjects who have previously been infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The differences between these terms only lie in naming conventions, level of detail, or emphasis on the duration of symptoms, and do not alter their technical meaning in this invention.

[0033] In this invention, the term "PRDX4" refers to peroxiredoxin 4, a member of the peroxiredoxin (PRDX) family. PRDX4 participates in processes related to reactive oxygen species (ROS) scavenging, peroxide reduction, and endoplasmic reticulum oxidative folding, and plays a role in the regulation of inflammatory responses, cellular damage responses, and the maintenance of vascular homeostasis. Unless otherwise stated, the term "PRDX4" includes its protein form and its encoding nucleic acid form, namely PRDX4 protein and / or PRDX4 mRNA, and includes its transcripts, complementary DNA (cDNA), and other related molecular forms that can reflect the expression status of PRDX4. In this invention, PRDX4 is primarily used as a molecular marker, and may be replaced by "marker" or "biochemical marker," to reflect changes in the relevant physiological or pathological state of the subject.

[0034] In this invention, the term "PRDX4 expression level" refers to the characterization of the quantity, abundance, concentration, or signal intensity of PRDX4-related molecules detected in a biological sample, and can be an absolute or relative quantitative value. In some embodiments, the PRDX4 expression level may include the PRDX4 protein expression level and / or the PRDX4 mRNA expression level. The PRDX4 protein expression level can be expressed as PRDX4 protein abundance, PRDX4 protein concentration, relative protein abundance, proteomics peak area, immunoassay signal intensity, or other protein quantitative indicators; the PRDX4 mRNA expression level can be expressed as PRDX4 mRNA abundance, relative expression level, cycle threshold (Ct value) related indicators, sequencing reads, or other nucleic acid quantitative indicators. The PRDX4 expression level can be obtained by comparison with control samples, reference intervals, or standardized signals, and can be normalized or standardized to improve the comparability between different samples.

[0035] In this invention, the term "risk assessment" refers to the process of quantifying or classifying the probability of a subject experiencing a specific state or behavior based on the subject's biological information. The risk assessment can output risk probability, risk score, or classification results, and can be calculated using a mathematical model. In some embodiments, the risk assessment results can be continuous variables, or they can be divided into different risk levels using thresholds.

[0036] In this invention, the term "regression model" refers to a mathematical model used to describe the relationship between one or more independent variables and a dependent variable. In this invention, the regression model is used to map PRDX4 expression levels or combinations thereof with other variables to risk assessment results.

[0037] In this invention, the term "Logistic regression model" refers to a statistical model used to handle binary classification problems, which outputs a probability value between 0 and 1 by linearly combining independent variables and transforming them through a log-odds function.

[0038] In this invention, the term "subject" refers to an individual who can undergo the above-mentioned tests and evaluations, which may be a human or other mammal.

[0039] According to a first aspect of the invention, there is a use of reagents for quantitatively determining PRDX4 expression levels in the preparation of a kit for risk assessment of PASC-related cardiovascular symptoms.

[0040] In this invention, the risk of PASC-related cardiovascular symptoms is assessed by quantitatively measuring the expression level of PRDX4 in the biological samples of subjects. The detection can employ various technical approaches to analyze blood samples, including immunoassay, biomolecular mass spectrometry, and nucleic acid detection methods, offering advantages such as high operability, flexible detection methods, and suitability for clinical translation. This invention, through quantitative measurement of PRDX4 expression levels combined with a risk assessment model, achieves an objective assessment of the risk of PASC-related cardiovascular symptoms. Compared to methods relying on subjective symptoms or conventional testing indicators, this approach improves the stability and quantifiability of risk identification, thereby providing a reference for risk stratification and subsequent management of relevant populations.

[0041] In this invention, PRDX4 is used as a molecular marker for risk assessment of PASC-related cardiovascular symptoms and is detected.

[0042] In this invention, the PRDX4 mRNA used as a marker is expected to include its full-length ribonucleotide sequence, or naturally occurring variants, or fragments of the full-length sequence and variants, particularly fragments that can be detected and identified as having a specific sequence, more preferably fragments that can be distinguished from other RNA sequences in the blood. Preferably, it contains at least 7, 8, 9, 10, 11, 12, 15, or 20 consecutive ribonucleotides of the full-length ribonucleotide sequence.

[0043] In this invention, the PRDX4 protein used as a marker is expected to include naturally occurring variants of the protein as well as fragments of the protein or the variants, particularly immunologically detectable fragments. The immunologically detectable fragments preferably comprise at least 5, 6, 7, 8, 9, 10, 11, 12, 15, or 20 consecutive amino acids of the marker peptide. For example, the expression "PRDX4 protein" includes the complete protein sequence of PRDX4 and the marker peptide as defined above.

[0044] Those skilled in the art will recognize that ribonucleotides / proteins / peptides released by cells or present in the extracellular matrix can be damaged (e.g., during inflammation) and can be degraded or cleaved into such fragments. As those skilled in the art will understand, mRNA, proteins, or fragments thereof can also be present as part of a complex. Such complexes can also be used as biomarkers in the sense of this invention. Alternatively, the biomarker peptide or a variant thereof may carry post-translational modifications. Non-limiting examples of post-translational modifications are glycosylation, acylation, and / or phosphorylation. "Naturally present variants" should be understood to mean that genes in higher animals are typically accompanied by a high frequency of polymorphism. Many homotypes of molecules containing mutually different amino acid sequences also exist during splicing.

[0045] In some embodiments, the quantitative detection reagent includes a reagent for detecting PRDX4 mRNA expression levels, which is suitable for at least one of the following methods: Sequencing, polymerase chain reaction (PCR), isothermal amplification reaction, resonance light scattering, biomass spectrometry, electrochemical analysis, gel electrophoresis, capillary electrophoresis, microarray, CRISPR-Cas detection system.

[0046] Among them, nucleic acid sequencing can be Maxam Gilbert sequencing, chain termination sequencing, shotgun sequencing, bridge PCR, single-molecule real-time sequencing, ion semiconductor (ion-current sequencing), synthesis sequencing, ligation sequencing (SOLiD sequencing), chain termination (Sanger sequencing), massively parallel-tagged sequencing (MPSS), polymerase cloning sequencing, 454 pyrosequencing, Illumina (Solexa) sequencing, DNA nanosphere sequencing, Heliscope single-molecule sequencing, single-molecule real-time (SMRT) sequencing, nanopore DNA sequencing, tunneling current DNA sequencing, hybridization sequencing, mass spectrometry sequencing, microfluidic Sanger sequencing, microscopy-based techniques, RNAP sequencing, and in vitro viral high-throughput sequencing.

[0047] Among them, qRT-PCR and digital PCR are preferred for PCR.

[0048] In a broad sense, isothermal techniques can be further divided into the following methods: methods that rely on primer substitution to initiate repetitive template copies (examples below include HDA (helicase-dependent amplification), exonuclease-dependent amplification (EP1866434), recombinase polymerase amplification (RPA), recombinase-mediated amplification (RAA), loop-mediated amplification (LAMP), rolling circle amplification (RCA), multiple substitution amplification (MDA), and cross-primer amplification (CPA)); and methods that rely on the continuous reuse or de novo synthesis of single primer molecules, such as SDA (strand substitution amplification and nucleic acid-based amplification (NASBA and TMA)).

[0049] Based on the type of nucleic acid molecule being detected, CRISPR / Cas technology nucleic acid detection can be divided into DNA (CRISPR / Cas9, CRISPR / Cas12 and CRISPR / Cas12 recognize DNA sequences) and RNA (CRISPR / Cas13 recognizes RNA sequences) detection.

[0050] In some embodiments, the quantitative detection agent includes a probe and / or primer capable of specifically binding to PRDX4 mRNA or cDNA. In some embodiments, the probe and / or primer carries a detectable label.

[0051] In some embodiments, the quantitative detection reagent includes a reagent for detecting PRDX4 protein expression levels, which is suitable for at least one detection method, including immunoassay, mass spectrometry, and nucleic acid aptamer-based detection methods.

[0052] As a method for determining the molecular markers of the present invention, any known method such as immunoassay or mass spectrometry can be used, by way of example. Among the reagents used to determine the marker proteins of the present invention, antibodies and aptamers can be used as detection agents.

[0053] As an immunoassay, there are no particular limitations; examples include various enzyme immunoassays, radioimmunoassays (RIA), enzyme-linked immunosorbent assays (ELISA), double monoclonal antibody sandwich immunoassays, monoclonal and polyclonal antibody sandwich immunoassays, immunostaining methods, immunofluorescence methods, Western blotting, and biotinylation methods. Avidin method, immunoprecipitation method, colloidal gold agglutination method, immunochromatography, latex agglutination (LA) method, and immunoturbidimetric assay (TIA), etc.

[0054] As a reagent used in immunoassays, commercially available anti-PRDX4 antibodies can be used, or antibodies can be prepared using conventional methods based on the known amino acid sequence of PRDX4.

[0055] There are no particular restrictions on the animal species or clones from which antibodies can detect the PRDX4 protein. Examples include antibodies derived from rabbits, goats, mice, rats, guinea pigs, horses, sheep, camels, and chickens; both monoclonal and polyclonal antibodies are acceptable. Furthermore, antibodies suitable for specifically binding to all subclasses of the PRDX4 protein can be used. Recombinant antibodies, Fab, Fab', or F(ab')2 fragments can also be used.

[0056] As a mass spectrometry method, there are no particular limitations. Mass spectrometers that combine ion sources using electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), surface-enhanced laser desorption / ionization (SELDI), etc., with time-of-flight (TOF), ion trap (IT), or Fourier transform (FT) analyzers can be used. LC (chromatograph-LC) systems, which connect a mass spectrometer to separation devices such as high-performance liquid chromatography (HPLC) or capillary electrophoresis (CE), can also be used. MS and CE MS, etc. In addition, methods for obtaining mass spectrometry data include: data-independent analysis (DIA), data-dependent analysis (DDA), and multiple reaction monitoring (MRM). Mass spectrometry also includes the use of stable isotope labeling of samples with reagents such as iTRAQ (SCIEX).

[0057] In some embodiments, the quantitative detection reagent includes an antibody.

[0058] In some embodiments, the antibody is a monoclonal antibody or a polyclonal antibody.

[0059] In some embodiments, the quantitative detection reagent is selected from: ELISA reagents, Western blotting reagents, immunofluorescence reagents, or immunohistochemical reagents.

[0060] In some embodiments, the subject is a subject who has previously been infected with Severe Acute Respiratory Syndrome Coronavirus 2 (PASC-CoV-2). In some embodiments, the subject is a subject who has previously been infected with PASC-CoV-2 and is in the recovery phase, follow-up phase, or chronic symptom persistence phase after acute infection. The previous infection can be confirmed by nucleic acid testing, antigen testing, or serological testing results, or can be determined in conjunction with epidemiological history or previous diagnostic records. In some embodiments, the subject is an individual who is still in the recovery phase, long-term follow-up phase, or has persistent symptoms after the acute infection phase. Detection of the expression levels of the biomarkers in their biosamples can be used to assess their risk of developing PASC-related cardiovascular symptoms, thereby providing a basis for subsequent follow-up management or risk stratification. In some embodiments, the subject is a primate; preferably a human.

[0061] In this invention, the test samples for the kit can be derived from the subject's blood samples, including peripheral blood cells, serum, plasma, or blood-derived extracellular vesicle components. In some embodiments, the blood-derived components may include peripheral blood mononuclear cells (PBMCs), buffy coat, circulating cell-free nucleic acid components, blood lysate components, or other blood-derived samples capable of obtaining PRDX4-related molecular information. Besides the above-mentioned samples, blood-derived extracellular vesicle components, exosome components, or other vesicle-related components may also be used for detection without departing from the technical concept of this invention.

[0062] In some implementations, when the target is the expression level of PRDX4 protein, plasma or serum is preferably used as the test sample. PRDX4 belongs to the peroxidase family and is closely related to the regulation of oxidative stress, peroxide clearance, and inflammation-related pathological processes, and can be present in the blood circulation system. Therefore, by detecting plasma or serum samples, a more stable PRDX4 protein-related signal can be obtained, which is beneficial to improving the stability and reproducibility of the test results.

[0063] In some embodiments, the samples may be pretreated, such as by centrifugation, membrane filtration, protein enrichment, immunoenrichment, or other treatments that improve detection sensitivity and result stability. In some embodiments, protein standardization, internal control calibration, or batch calibration may also be combined to improve the comparability between different samples.

[0064] In some embodiments, when the target for detection is PRDX4 mRNA expression level, peripheral blood cells or blood-derived nucleic acid samples are preferably used as the detection sample. The blood-derived nucleic acid sample may include total cellular RNA, circulating cell-free RNA, extracellular vesicle-derived RNA, or other blood-derived nucleic acid components. In some embodiments, PRDX4 mRNA expression level information for subsequent analysis can be obtained through nucleic acid extraction, reverse transcription, amplification, or standardization, thereby improving the comparability between different samples and the reproducibility of the detection results.

[0065] The second aspect of the present invention relates to a PASC-related cardiovascular system symptom risk assessment system, including a sample information processing module, an assessment module, and an information output module; The sample information processing module is used to acquire subject information, which includes at least PRDX4 expression level information. The assessment module is used to analyze the PRDX4 expression level information based on a regression model to calculate the risk assessment result; The information output module is used to output risk probability, risk score, or classification results.

[0066] In some implementations, the system can be integrated into a computing device, and a computer program can be used to perform functions such as data reception, model calculation and result output, thereby realizing an automated or semi-automated risk assessment process.

[0067] The risk assessment results can be expressed as risk probability, risk score, or classification results. Risk probability refers to the probability value calculated by the model of a subject experiencing PASC-related cardiovascular symptoms; it is typically a continuous value between 0 and 1, reflecting the likelihood of this condition occurring. Risk score is a numerical indicator obtained by transforming or standardizing the model output; it can be the result of a linear or non-linear transformation of the risk probability, used to characterize the degree of risk and facilitate comparison between different individuals. Classification results are discrete results obtained by comparing the risk probability or risk score with a preset threshold, used to classify subjects into different risk categories, such as high risk or low risk. In some embodiments, the system can output corresponding information based on the above different forms of results, thereby providing a reference for clinical risk identification, stratified management, or subsequent intervention.

[0068] In some implementations, the risk assessment can be further analyzed based on the PRDX4 expression level and combined with the subject's clinical characteristics, which may include one or more of gender, age, and body mass index. The assessment module is used to perform risk assessment based on the PRDX4 expression level information and further combined with gender, age, and / or body mass index information.

[0069] In some implementations, the evaluation module uses a regression model.

[0070] In some implementations, the evaluation module uses a Logistic regression model.

[0071] In some implementations, the expression of the Logistic regression model is: Logit(P) = β0 + β1 × PRDX4 + β2 × Age + β3 × Sex + β4 × BMI, where P represents the risk probability of a subject developing PASC-related cardiovascular symptoms, β0 is the intercept term, β1, β2, β3 and β4 are the regression coefficients corresponding to PRDX4 expression level, age, sex and body mass index, respectively, PRDX4 represents the PRDX4 expression level in the subject sample, Age represents age, Sex represents the binary encoded variable of sex, and BMI represents body mass index.

[0072] In some implementations, the Sex can be encoded using binary variables. For example, one sex can be encoded as 0 and the other as 1; for instance, male can be encoded as 1 and female as 0, or other equivalent encoding methods can be used.

[0073] In some implementations, the model parameters can be determined by fitting the training dataset using the maximum likelihood estimation method, and can be further applied to independent sample sets to evaluate the model's discriminative and generalization abilities.

[0074] In some implementations, the model parameters can be obtained based on the training dataset and applied to the PRDX4 expression level and clinical characteristics of the subjects to be tested, thereby calculating the risk probability value and using it for risk assessment.

[0075] In some implementations, the risk probability, risk score, or classification result of the subject is obtained based on the model output. The classification result can be determined based on a preset threshold. For example, when the risk probability P is greater than or equal to the preset threshold, it is determined to be a high risk of PASC-related cardiovascular symptoms; when the risk probability P is lower than the preset threshold, it is determined to be a low risk.

[0076] In some specific implementations, the expression for the Logistic regression model is: Logit(P) = -9.1571 + 0.0470×PRDX4 + 0.0261×Age + 0.3219×Sex + 0.0535×BMI In some implementations, the risk assessment result is determined based on a preset threshold, which is determined according to the maximum Youden index based on the analysis of the subject's operating characteristic curve.

[0077] In some implementations, the preset threshold may be 0.5. In some implementations, the Youden index may be calculated as Sensitivity + Specificity - 1.

[0078] The present invention also relates to a computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets that, when run on a computer, cause the computer to perform the functions of the system described above.

[0079] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0080] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0081] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0082] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, C++, Swift, and Python—as well as conventional procedural programming languages—such as C or similar programming languages. Scripting languages, such as Python and R, can also be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0083] The present invention also relates to an electronic device, comprising: One or more processors; and A computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets that, when executed on a computer, cause the one or more processors to perform the functions of the system as described above.

[0084] In some embodiments, the electronic device may also include a transceiver. The processor and the transceiver are connected, such as via a bus. It should be noted that in practical applications, the transceiver is not limited to one unit, and the structure of the electronic device does not constitute a limitation on the embodiments of this application.

[0085] The processor can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0086] A bus can include a pathway for transmitting information between the aforementioned components. The bus can be a PCI bus or an EISA bus, etc. Buses can be categorized as address buses, data buses, control buses, etc.

[0087] The embodiments of the present invention will be described in detail below with reference to the examples. It should be understood that these embodiments are only used to illustrate the technical content of the present invention and are not intended to limit the scope of protection of the present invention. Unless otherwise specified, the specific experimental conditions in the following embodiments are given priority reference to the guidelines provided in this specification, or may be carried out according to generally accepted experimental manuals or conventional experimental conditions, or other experimental methods known in the art, or according to the conditions recommended by the relevant reagent or instrument manufacturers. In specific embodiments, unless otherwise specified, minor deviations within the weighing accuracy range are allowed for the measurement parameters involving raw material components; reasonable deviations due to instrument detection accuracy or operational accuracy are also allowed for parameters such as temperature and time.

[0088] Example 1: Association analysis of PRDX4 screening and PASC-related cardiovascular symptoms based on cross-sectional cohort 1. Research Subjects and Sample Collection This study is based on a community-based follow-up cohort conducted in Beijing, China. The target population consisted of participants aged ≥18 years who reported a positive SARS-CoV-2 test between December 1, 2022 and January 31, 2023, including positive antigen test or RT-PCR test. Individuals meeting any of the following criteria were excluded: (1) those unable to cooperate with the survey due to severe mental disorders, dementia, or other reasons; (2) those with limited mobility due to severe musculoskeletal disease, stroke, or other reasons; (3) those who were not permanent residents of Beijing; and (4) those who refused to participate in the study for other reasons. A stratified multi-stage random sampling method was used to recruit participants from 16 administrative districts in Beijing. All participants had no prior history of SARS-CoV-2 infection before infection with the Omicron variant, which was confirmed through the Beijing SARS-CoV-2 pneumonia epidemiological investigation system. Based on the above criteria, a parent cohort of 12,789 participants was ultimately formed.

[0089] Based on the aforementioned parent cohort, participants were followed up for one year after SARS-CoV-2 infection. According to follow-up completion and plasma sample availability, 161 participants were ultimately included in the analysis. The median age of these 161 participants was 43.27 years, with an interquartile range of 37.93–54.51 years. 128 participants (79.50%) were female. Among them, 95 participants still reported persistent PASC-related cardiovascular symptoms, including palpitations, chest pain, and bradycardia, one year after Omicron infection; the remaining 66 participants recovered from SARS-CoV-2 infection during the same period without developing PASC-related symptoms and were considered the non-PASC group, matched with the PASC-related cardiovascular symptoms group in age and sex distribution. Peripheral blood samples were collected from these participants, and plasma samples were obtained after centrifugation for subsequent proteomics analysis and candidate biomarker screening.

[0090] 2. Experimental Methods 2.1 Plasma sample processing and enrichment of low-abundance proteins In this embodiment, peripheral blood samples were collected from the subjects, and plasma samples were obtained by centrifugation. To improve the detection capability of low-abundance proteins, the plasma samples underwent low-abundance protein enrichment treatment.

[0091] Specifically, high-abundance proteins in plasma samples are removed to reduce interference from high-abundance proteins such as albumin and immunoglobulins in subsequent detection; then, the processed samples are enriched with low-abundance proteins to improve the detection sensitivity of potential biomarkers.

[0092] The samples processed in the above manner were used for subsequent proteomics testing.

[0093] 2.2 Proteomics Detection In this embodiment, proteomics detection was performed on plasma samples after low-abundance protein enrichment treatment.

[0094] Specifically, the sample proteins are denatured, reduced, alkylated, and enzymatically digested, preferably using trypsin, to obtain a peptide mixture. The peptides are then separated and detected by liquid chromatography-tandem mass spectrometry (LC-MS / MS) to obtain proteomics data of the sample.

[0095] The mass spectrometry detection can be achieved using conventional data acquisition modes, including data-dependent acquisition mode (DDA) or data-independent acquisition mode (DIA).

[0096] 2.3 Proteomics Data Processing and Candidate Biomarker Screening In this embodiment, the raw data obtained from proteomics detection is processed to obtain protein expression information.

[0097] Specifically, the raw data underwent quality control, protein identification, and quantitative expression analysis, and the data were normalized to improve the comparability between different samples. Subsequently, the differences in protein expression between the long-term cardiovascular symptom group and the non-PASC group were compared and analyzed.

[0098] Differentially expressed proteins are screened according to preset screening criteria, and candidate biomarkers are determined by combining statistical significance and biological relevance.

[0099] In this embodiment, PRDX4 was selected as one of the candidate biomarkers associated with the risk of PASC-related cardiovascular symptoms.

[0100] 2.4 Risk Assessment Model Construction In this embodiment, based on the candidate biomarkers obtained through screening, the expression level of PRDX4 in the subject samples is further analyzed, and a long-term cardiovascular symptom risk assessment model is constructed.

[0101] Specifically, the modeling analysis used the presence or absence of PASC-related cardiovascular symptoms in the subjects as the dependent variable and the PRDX4 expression level as the independent variable.

[0102] In this embodiment, a univariate model was first constructed to assess the association between PRDX4 expression levels and PASC-related cardiovascular symptoms. Based on this, the subjects' age, gender, and body mass index (BMI) were further included as covariates to construct a multivariate logistic regression model to correct for the impact of individual basic biological differences on the assessment results.

[0103] The multivariate logistic regression model can be expressed as: Logit(P) = β0 + β1×PRDX4 + β2×Age + β3×Sex + β4×BMI Where P represents the predicted probability or risk assessment value of the subject belonging to the PASC-related cardiovascular symptoms group output by the model, β0 is the intercept term, and β1 to β4 are the regression coefficients corresponding to each variable; PRDX4 represents the expression level of PRDX4 protein and / or PRDX4 mRNA in the subject sample, Age represents age, Sex represents gender, and BMI represents body mass index.

[0104] The model is used to output the risk probability or risk score of subjects developing long-term cardiovascular symptoms, and can be further used for risk stratification and to assist in clinical management decisions.

[0105] 2.5 Model Performance Evaluation In this embodiment, the performance of the constructed risk assessment model is evaluated.

[0106] Specifically, the model's discriminative ability is evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC); and further, the model's classification performance at different thresholds is analyzed by combining the optimal decision threshold corresponding to the maximum Yangen index.

[0107] In some implementations, model performance evaluation metrics may also include one or more of sensitivity, specificity, positive predictive value, negative predictive value, and Youden index.

[0108] 3. Experimental Results 3.1 Baseline Characteristics Analysis of Study Subjects Table 1: Baseline characteristics of the cohort of participants

[0109] As shown in Table 1, this embodiment included a total of 161 subjects, of which 66 were in the non-PASC group and 95 were in the PASC-related cardiovascular symptoms group. The overall median age was 43.27 years, and the proportion of female subjects was relatively high.

[0110] The results showed that the two groups were generally comparable in terms of age, sex, place of residence, education level, income level, smoking status, and most underlying diseases. Some clinical characteristics differed between the two groups, including hyperlipidemia and a history of anxiety or depression; furthermore, among the characteristics related to the acute infection phase, the incidence of pneumonia also differed between the two groups.

[0111] Overall, the baseline characteristics of the two groups of subjects in the cohort were relatively balanced. Although routine demographic and clinical characteristics can provide some reference, their role in identifying the risk of PASC-related cardiovascular symptoms remains limited.

[0112] 3.2 Analysis of routine laboratory test results Table 2: Routine laboratory test results of cohort subjects

[0113] The results showed no significant differences in several routine laboratory indicators between the non-PASC group and the PASC-related cardiovascular symptoms group. Specifically, there were no significant differences between the two groups in myocardial injury-related indicators, including CK-MB, cTnI, myoglobin, and BNP; coagulation-related indicator D-dimer; lipid metabolism indicators, including total cholesterol, triglycerides, HDL, LDL, small dense LDL, and lipoprotein(a); and glucose metabolism indicators, including blood glucose and HbA1c.

[0114] The above results indicate that conventional laboratory test indicators have limited ability to distinguish between the PASC-related cardiovascular symptoms group and the non-PASC group, suggesting the need to further screen for more discriminative candidate biomarkers using molecular detection methods such as proteomics.

[0115] 3.3 Quality Control of Proteomics Detection The quality control results of the proteomics detection data showed that the protein detection signals of each sample were stable, the data distribution was consistent, and no obvious abnormal samples were found. This indicates that the proteomics data obtained in this embodiment has high reliability and repeatability and can be used for subsequent differential protein screening and model construction analysis.

[0116] 3.4 Overall Difference Analysis of Protein Expression Profiles Figure 1 The results of principal component analysis (PCA) based on protein expression data are presented. The results show a certain degree of separation between the non-PASC group and the PASC-related cardiovascular symptoms group in the principal component space, suggesting a systematic difference in the overall protein expression profiles between the two groups, providing a basis for subsequent candidate biomarker screening.

[0117] 3.5 Screening for Differentially Expressed Proteins Figure 2 The results of differentially expressed protein analysis are shown. The results indicate that multiple differentially expressed proteins exist between the two groups. Further screening yielded a set of candidate biomarkers, among which PRDX4 showed high expression levels in the PASC-related cardiovascular symptoms group, suggesting its association with related pathological processes and its potential as a candidate molecular biomarker for subsequent risk assessment model construction.

[0118] 3.6 Performance Evaluation of the Joint Discovery Set Model Figure 3 The results of the receiver operating characteristic (ROC) curve and area under the curve (AUC) of the multivariate logistic regression risk assessment model based on PRDX4 expression level and further combined with age, sex and body mass index (BMI) are shown in the discovery set.

[0119] The results showed that the univariate model based solely on PRDX4 expression levels could, to some extent, distinguish between the non-PASC group and the PASC-related cardiovascular symptoms group, with an area under the ROC curve (AUC) of 0.923 and a 95% confidence interval of [0.880–0.965]. The discriminative ability of the combined model was further improved after incorporating age, sex, and BMI.

[0120] In this embodiment, the multivariate logistic regression model obtained based on the fitting of the discovery set samples is specifically as follows: Logit(P) = -9.1571 + 0.0470×PRDX4 + 0.0261×Age + 0.3219×Sex +0.0535×BMI Where P is the predicted probability of a subject developing PASC-related cardiovascular symptoms, and Sex is a binary coded variable with a regression coefficient of 0.3219 for Sex=1 relative to Sex=0.

[0121] Furthermore, the area under the ROC curve (AUC) of the joint model is 0.9249, and the 95% confidence interval is 0.8822–0.9676, indicating that the model has high discriminative power.

[0122] In this embodiment, the optimal judgment threshold is determined by ROC curve analysis and the maximum Yoden index. Specifically, the Yoden index is calculated as Sensitivity + Specificity - 1, and the predicted probability threshold corresponding to the maximum Yoden index is 0.6786. When the predicted probability P ≥ 0.6786, it is judged as a high risk of long-term cardiovascular symptoms; when the predicted probability P < 0.6786, it is judged as a low risk.

[0123] At this threshold, the model's sensitivity was 0.8105, specificity was 0.9848, positive predictive value was 0.9872, negative predictive value was 0.7831, and Youden index was 0.7954.

[0124] The above results indicate that the combined model based on PRDX4 and clinical characteristics can be used to output the risk probability or risk score of subjects developing PASC-related cardiovascular symptoms, and can be further used for risk stratification and to assist in clinical management decisions.

[0125] Example 2: Validation Analysis Based on a 1-Year Follow-up Cohort 1. Research subjects and sample sources The study subjects in this embodiment were drawn from a long-term follow-up cohort study of discharged COVID-19 patients. The cohort included laboratory-confirmed COVID-19 patients discharged between January 7, 2020 and May 29, 2020. All patients meeting the inclusion criteria were eligible for follow-up; those who died before the first follow-up after discharge, resided in nursing homes or welfare facilities, or had mental disorders, dementia, musculoskeletal diseases, or limited mobility were excluded.

[0126] The cohort was followed up at 6 months, 1 year, and 2 years after symptom onset, and proteomics analysis was performed on plasma samples from hospitalized COVID-19 recovered patients. This embodiment selected data from the 1-year follow-up point for validation analysis, including subjects who completed the 1-year follow-up and had relevant clinical data and plasma samples.

[0127] In this embodiment, the PASC-related cardiovascular symptom group is defined as COVID-19 survivors who previously contracted SARS-CoV-2 and who, at a follow-up of approximately one year after symptom onset, still have at least one cardiovascular-related symptom or sign. These cardiovascular-related symptoms or signs include, but are not limited to, palpitations, chest pain, chest tightness, and bradycardia. The non-PASC control group is defined as COVID-19 survivors who were infected with SARS-CoV-2 at the same or similar timeframe and underwent follow-up assessment approximately one year after symptom onset, and who did not report any persistent, recurrent, or new PASC-related symptoms at the time of the follow-up assessment.

[0128] 2. Experimental Methods 2.1 Follow-up assessment and plasma sample processing In this embodiment, subjects underwent face-to-face assessments at the one-year follow-up, including symptom questionnaires, physical examinations, physical fitness assessments, and quality of life questionnaires. Venous blood samples were collected at this follow-up time point, and plasma samples were separated for subsequent proteomics testing and model validation analysis.

[0129] 2.2 Evaluation Methods for Validation Set Models In this embodiment, a multivariate logistic regression model was constructed in the validation queue based on the expression level of PRDX4 and in combination with age, gender, and body mass index (BMI).

[0130] Specifically, the PRDX4 expression level, age, gender, and BMI of the subjects in the validation cohort were used as independent variables, and the presence or absence of long-term cardiovascular symptoms was used as the dependent variable for modeling analysis to obtain the risk probability, risk score, or classification results for each subject.

[0131] In this embodiment, the model's discriminative ability on the validation set is evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC).

[0132] 3. Experimental Results 3.1 Baseline Features of the Validation Set Table 3: Baseline characteristics of validation set participants at 1-year follow-up

[0133] This study included 101 participants, with 24 in the PASC-related cardiovascular symptoms group and 77 in the non-PASC group. Results showed that the two groups were generally comparable in age; however, there were some differences between the two groups in terms of gender and BMI. The PASC-related cardiovascular symptoms group had a higher proportion of women and a lower overall BMI than the non-PASC group.

[0134] The above results indicate that there are differences among the subjects in the validation set in some demographic and physical characteristics, further suggesting that incorporating clinical characteristics such as age, gender, and BMI into the risk assessment process for correction can help improve the applicability and stability of the model in independent samples.

[0135] 3.2 Performance Evaluation of the Validation Set Model Figure 4 The receiver operating characteristic (ROC) curves and area under the curve (AUC) results of the validation set risk assessment model at the 1-year follow-up time are shown. The results show that, in the validation set, the single-indicator model based solely on PRDX4 expression levels has limited discriminative ability; after further incorporating age, sex, and body mass index (BMI), the multivariate logistic regression combined model based on PRDX4 and clinical characteristics significantly improved its discriminative ability.

[0136] The results suggest that combining PRDX4 expression levels with clinical characteristics can help improve the assessment of the risk of PASC-related cardiovascular symptoms. Furthermore, the combined model demonstrates good stability and application value in independently validated samples with a 1-year follow-up period. It can be used to output the risk probability or risk score of subjects developing long-term cardiovascular symptoms, and further for risk stratification and to assist in clinical management decisions.

[0137] discuss Compared to existing technologies, this invention obtains PRDX4 through proteomics screening and further combines it with clinical characteristics such as age, gender, and body mass index (BMI) to construct a risk assessment model, providing a technical solution for assessing the risk of PASC-related cardiovascular symptoms. The technical solution has the following beneficial effects: 1. Objective molecular biomarkers for risk assessment of PASC-related cardiovascular symptoms are provided.

[0138] The results of this embodiment indicate that although there were no significant differences in conventional myocardial injury indicators, coagulation-related indicators, and metabolic-related indicators among the groups, PRDX4 was still able to differentiate the risk of PASC-related cardiovascular symptoms. This suggests that the pathological information reflected by PRDX4 may differ from traditional clinical test indicators and may involve related pathological processes such as persistent oxidative stress, chronic inflammatory response, or abnormal intravascular environment.

[0139] The PRDX4 sample selected in this embodiment was derived from proteomics differential analysis. It serves as an objective and detectable molecular marker to aid in identifying the risk of PASC-related cardiovascular symptoms, thus compensating for the limitations of conventional detection methods in risk assessment for this population. PRDX4 belongs to the peroxidase family and is closely related to redox homeostasis regulation, reactive oxygen species scavenging, and inflammation-related pathological processes. In this embodiment, PRDX4 expression was elevated in the PASC-related cardiovascular symptom group, suggesting its potential involvement in long-term persistent oxidative stress or related pathophysiological processes. Therefore, PRDX4 can serve not only as a risk assessment marker but also as a molecular indicator reflecting changes in related pathological states.

[0140] 2. A combined assessment model that takes into account both molecular markers and individual clinical differences has been established.

[0141] This embodiment does not rely solely on a single indicator or fixed threshold for judgment, but rather incorporates PRDX4 expression levels in conjunction with clinical characteristics such as age, gender, and BMI into a multivariate logistic regression model for analysis.

[0142] The results of the examples show that, in the discovery set, the combined model based on PRDX4 and clinical features has good discriminative ability; in the independent validation set, the model performance was further improved after incorporating age, gender, and BMI. These results indicate that combining PRDX4 with clinical features in modeling helps reduce the impact of individual baseline differences on assessment results, thereby improving the model's stability and application value. The improved performance of the combined model in the independent 1-year follow-up validation set further suggests that PRDX4-related molecular information does not only reflect changes during the acute infection phase but may also be associated with long-term persistent oxidative stress or chronic pathological changes.

[0143] 3. It has achieved the transformation from symptom description to risk quantification output.

[0144] In current clinical practice, the diagnosis of PASC-related cardiovascular symptoms relies heavily on patient complaints, lacking quantifiable and objective assessment tools.

[0145] This embodiment constructs a risk assessment model based on PRDX4 and clinical characteristics, which can output the risk probability, risk score or classification results of subjects developing long-term cardiovascular symptoms, thereby providing a basis for high-risk and low-risk stratification and providing a reference for subsequent follow-up management and auxiliary clinical decision-making.

[0146] 4. It has good stability and potential for clinical application.

[0147] The joint model constructed in this embodiment still exhibits good discriminative ability on the independent validation set, indicating that the model has a certain degree of stability and generalization ability.

[0148] Furthermore, the test samples involved in this embodiment are plasma or serum samples derived from peripheral blood. The sampling method is relatively simple, with good operability and repeatability, making it suitable for widespread application in clinical settings.

[0149] 5. It provides technical clues for further research on related abnormal states.

[0150] This study found differential expression of PRDX4 in individuals with PASC-related cardiovascular symptoms, suggesting that PRDX4 can be used as a candidate molecular biomarker in risk assessment. These results provide technical clues for further research into the pathogenesis, molecular subtyping, and intervention strategies of PASC-related cardiovascular symptoms.

[0151] In summary, this embodiment, by screening molecular biomarkers and constructing a joint risk assessment model, achieves an objective assessment of the risk of PASC-related cardiovascular symptoms when conventional clinical testing is insufficient for effective identification. Compared to existing technologies, this embodiment has advantages in assessment methods, information integration capabilities, risk stratification capabilities, and clinical application potential, and can be used for risk identification and auxiliary management of PASC-related populations.

[0152] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims, and the specification and drawings can be used to interpret the content of the claims.

Claims

1. Application of reagents for quantitative determination of PRDX4 expression levels in the preparation of kits for risk assessment of PASC-related cardiovascular symptoms.

2. The application according to claim 1, wherein the reagent comprises a reagent for detecting PRDX4 mRNA expression levels, and is suitable for at least one of the following methods: Sequencing, polymerase chain reaction (PCR), isothermal amplification reaction, resonance light scattering, biomolecular mass spectrometry, electrochemical analysis, gel electrophoresis, capillary electrophoresis, microarrays, CRISPR-Cas detection system.

3. The application according to claim 1, wherein the reagent comprises a reagent for detecting PRDX4 protein expression levels, and is suitable for at least one of the following detection methods: Immunoassay, biomolecular mass spectrometry, and nucleic acid aptamer-based detection methods.

4. In the application according to claim 1, the subject is a subject who has been previously infected with severe acute respiratory syndrome coronavirus 2 and is in the recovery period, follow-up period or chronic symptom persistence period after acute infection.

5. The application according to any one of claims 1 to 4, wherein the test sample of the kit is selected from peripheral blood cells, serum, plasma or blood-derived extracellular vesicle components.

6. PASC-related cardiovascular symptoms risk assessment system, including sample information processing module, assessment module, and information output module; The sample information processing module is used to obtain subject information, which includes at least PRDX4 expression level information. The assessment module is used to analyze the PRDX4 expression level information based on a regression model to calculate the risk assessment result; The information output module is used to output risk probability, risk score, or classification results.

7. The PASC-related cardiovascular symptoms risk assessment system according to claim 6, wherein the subject information further includes one or more of gender, age, and body mass index information, and the assessment module is used to perform risk assessment based on PRDX4 expression level information and further combined with gender, age, and / or body mass index information; Optionally, the evaluation module uses a regression model, preferably a logistic regression model.

8. The PASC-related cardiovascular symptom risk assessment system according to claim 7, wherein the expression of the logistic regression model is: Logit(P) = β0 + β1 × PRDX4 + β2 × Age + β3 × Sex + β4 × BMI, where, P represents the risk probability of a subject developing PASC-related cardiovascular symptoms, β0 is the intercept term, and β1, β2, β3 and β4 are the regression coefficients corresponding to PRDX4 expression level, age, sex and body mass index, respectively. PRDX4 represents the PRDX4 expression level in the subject sample, Age represents age, Sex represents the binary coded variable for sex, and BMI represents body mass index. Optionally, the risk assessment result is determined based on a preset threshold, which is determined according to the maximum Youden index determined by the analysis of the subject's working characteristic curve.

9. A computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets that, when executed on a computer, cause the computer to perform the functions of the system as described in any one of claims 6 to 8.

10. An electronic device, comprising: One or more processors; as well as A computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets that, when executed on a computer, cause the one or more processors to perform the functions of the system as described in any one of claims 6 to 8.

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