Biomarker combination for distinguishing kidney transplantation post-transplantation plant function delay duration and application thereof

Abstract

The invention discloses a biomarker combination for distinguishing kidney transplantation post-transplantation plant function delay duration and application of the biomarker combination. The combination consists of IL-15, CCL3, IL-7, CCL20 and IL-5 proteins, and can optionally comprise metabolites such as alpha-ketoglutaric acid and the like. Targeted proteomics and metabonomics analysis discovers that short-term DGF (SDGF) is characterized by acute inflammation and innate immune activation, and long-term DGF (LDGF) shows chronic immunoregulation accompanied by amino acid and energy metabolism disorder. Based on the combination, the invention further provides a corresponding in-vitro detection kit, a non-diagnostic distinguishing method and a data processing device. The early-stage accurate risk stratification of the DGF patient can be realized, a key basis is provided for clinical intervention decision, and the method has important clinical application value.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a combination of biomarkers for distinguishing the duration of graft function delay after kidney transplantation and their applications. Background Technology

[0002] Delayed graft function (DGF) is a common early complication after kidney transplantation, significantly increasing the risk of acute rejection and long-term graft loss. Current clinical diagnosis focuses primarily on the "occurrence" of DGF (e.g., the fDGF criterion based on the decrease in creatinine within one week post-transplantation), and several related biomarkers have been reported, such as neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1).

[0003] However, recent clinical studies have revealed that the "duration" of disease-free grafts (DGF) is a key factor independently affecting long-term patient prognosis. Significant differences exist in the immune status, degree of metabolic disturbance, and long-term graft survival between patients with short-term DGF (SDGF, such as recovery time <7 days) and long-term DGF (LDGF, recovery time ≥7 days). LDGF is clearly associated with worse clinical outcomes. Therefore, based on the diagnosis of DGF occurrence, effectively distinguishing and predicting its duration (SDGF vs. LDGF) in its early stages (such as the first week post-surgery) has become an urgent clinical need for precise risk stratification and intervention. Existing biomarkers (such as NGAL, KIM-1) or models are mainly designed to predict whether DGF will occur or to assess general rejection risk (e.g., the metabolite combination disclosed in CN116539653A for assessing rejection risk). No biomarker combinations capable of specifically and accurately distinguishing different durations of DGF have been publicly reported.

[0004] Multi-omics technologies have made it possible to discover such biomarkers. Although targeted proteomics / metabolic omics has been applied to kidney disease research, there is currently no systematic study that has been applied to analyze the specific immunometabolic profiles behind different durations of DGF and to screen for a validated combination of biomarkers that can be used to distinguish between SDGF and LDGF.

[0005] Therefore, there is an urgent need in this field to develop a set of novel biomarkers that can effectively distinguish different durations of DGF after kidney transplantation, in order to fill the gaps in existing technologies and provide a direct tool for precise early postoperative prognosis management in clinical practice. Summary of the Invention

[0006] In view of the above, it is necessary to provide a combination of biomarkers that can effectively assess DGF prognosis after kidney transplantation, particularly distinguishing between different DGF durations (such as SDGF and LDGF). Another object of the present invention is to provide a detection kit containing this combination of biomarkers and its application.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Application of a combination of biomarkers for distinguishing the duration of delayed graft function (DGF) in kidney transplant recipients, the combination of biomarkers comprising proteins IL-15, CCL3, IL-7, CCL20, and IL-5, for preparing an in vitro assay kit for distinguishing the duration of DGF or for establishing a computer prediction model for distinguishing the duration of DGF.

[0008] In this invention, further, in the application, the biomarker combination also includes at least one metabolite selected from the group consisting of: α-ketoglutarate, guanosine 3',5'-cyclic monophosphate, 3-(4-hydroxyphenyl)propionic acid, cis-aconitine, and isocitrate.

[0009] The present invention also proposes an in vitro detection kit for differentiating the duration of delayed graft function (DGF) in kidney transplant recipients, the kit comprising reagents for detecting the following protein biomarkers: IL-15, CCL3, IL-7, CCL20 and IL-5.

[0010] In this invention, the reagent further includes antibodies, aptamers, or nucleic acid probes capable of specifically binding to or detecting the proteins IL-15, CCL3, IL-7, CCL20, and IL-5.

[0011] Furthermore, in this invention, the kit further comprises reagents for detecting at least one metabolite selected from the group consisting of: α-ketoglutarate, guanosine 3',5'-cyclic monophosphate, 3-(4-hydroxyphenyl)propionic acid, cis-aconitine, and isocitrate.

[0012] This invention also proposes a method for distinguishing the duration of delayed graft function (DGF) in kidney transplant recipients. This method is for non-diagnostic purposes and includes the following steps: (a) Obtaining plasma samples from kidney transplant recipients to be tested; (b) Detect the level of the combination of biomarkers consisting of proteins IL-15, CCL3, IL-7, CCL20 and IL-5 in the plasma sample; (c) Based on the level of the combination of biomarkers detected in step (b), distinguish the duration of the recipient’s DGF as short-term DGF (SDGF) or long-term DGF (LDGF).

[0013] In this invention, further, in step (c), the level of the biomarker combination is input into a pre-trained prediction model, and the model outputs a differentiation result.

[0014] In this invention, the biomarker combination further includes at least one metabolite selected from the group consisting of: α-ketoglutarate, guanosine 3',5'-cyclic monophosphate, 3-(4-hydroxyphenyl)propionic acid, cis-aconitine, and isocitrate; step (b) further includes detecting the level of the at least one metabolite.

[0015] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0016] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0017] The present invention has at least the following beneficial effects: 1. This invention provides a specific biomarker combination consisting of IL-15, CCL3, IL-7, CCL20, and IL-5 proteins, achieving for the first time a precise prognostic differentiation of the duration of delayed graft function (DGF) after kidney transplantation. This overcomes the limitations of existing technologies (e.g., relying on biomarkers such as NGAL and KIM-1) that can only determine whether DGF "occurs," advancing clinical assessment from qualitative judgment to quantitative risk stratification of the recovery process (short-term SDGF vs. long-term LDGF), and providing a novel molecular tool to address the long-standing clinical need for early and precise subtyping of DGF patients.

[0018] 2. This invention integrates targeted proteomics with systematic bioinformatics network analysis (such as PPI network and hub protein identification), rather than simply listing differentially expressed molecules. Instead, it intelligently screens for key node combinations that drive the immune state from "acute inflammation" to "chronic regulation." This combination produces an unexpected synergistic effect in distinguishing between SDGF and LDGF: as shown in Example 8 of this application, its overall predictive power (AUC > 0.95) is significantly better than using any single or partial protein biomarker. This verifiable technical effect fully demonstrates the non-obviousness and outstanding inventiveness of this invention.

[0019] 3. This invention, by further integrating targeted metabolomics, elucidates that long-term DGF (LDGF) is specifically accompanied by significant metabolic reprogramming, including arginine / proline metabolic disorders and tricarboxylic acid cycle dysfunction, and is closely associated with chronic immune activation. This revelation of the "immune-metabolic" dual-disorder mechanism not only provides a solid scientific explanation for the classification of this invention from a pathophysiological perspective, enhancing its logical persuasiveness, but also offers innovative scientific insights and potential new targets for the future development of targeted combined intervention strategies (such as immune regulation combined with metabolic intervention).

[0020] 4. This invention concretizes the aforementioned core biomarkers into an in vitro diagnostic kit and accompanying computer prediction model that can be standardized and implemented on a mature platform (such as OlinkPEA), ensuring the industrialization and practicality of the technical solution. This solution can provide clinicians with objective and quantitative risk assessment information in the early postoperative period, thereby assisting in the development of personalized monitoring and management strategies, and is expected to improve the long-term prognosis of kidney transplant patients, demonstrating clear clinical translational value and social benefits. Attached Figure Description

[0021] Figure 1 This study uses differentially expressed protein (DEP) analysis based on targeted proteomics to reveal the differences in expression profiles between short-term DGF (SDGF) and long-term DGF (LDGF) groups. Figure 1 A is a petal diagram of the DEP distribution; Figure 1 B is a volcano diagram representing the changes in DEP; Figure 1 C and Figure 1 D represents the heatmap of DEP expression patterns in the SDGF and LDGF groups, respectively.

[0022] Figure 2 To Figure 1 Results of GO function and KEGG pathway enrichment analysis performed by DEP. Figure 2 A and Figure 2 C represents the GO enrichment analysis bar charts for DEP in the SDGF and LDGF groups, respectively; Figure 2 B and Figure 2 D represents the bubble charts of KEGG pathway enrichment analysis for the two DEP groups.

[0023] Figure 3 The results show the analysis of the protein-protein interaction (PPI) network and pivotal proteins constructed based on DEP. Figure 3 A and Figure 3 C represents the PPI network diagrams for the SDGF group and the LDGF group, respectively; Figure 3 B and Figure 3 D represents the top 10 pivot proteins identified in the two groups; Figure 3 E and Figure 3F shows the relative expression levels of the core hub protein in the two groups.

[0024] Figure 4 This is a diagram illustrating the analysis of key functional protein clusters in the SDGF and LDGF groups.

[0025] Figure 5 The results show the identification of differentially expressed metabolites (DEMs) based on targeted metabolomics. Figure 5 A is a correlation analysis graph of quality control samples; Figure 5 B is a chemical classification diagram of all detected metabolites; Figure 5 C and Figure 5 D represents the volcanic diagrams of the DEMs of the SDGF and LDGF groups, respectively.

[0026] Figure 6 The image shows a heatmap of the DEM expression profiles between the SDGF and LDGF groups.

[0027] Figure 7 The results are from an in-depth analysis of the DEM of the LDGF group. Figure 7 A is the OPLS-DA score graph; Figure 7 B is the model validation diagram using 200 permutation tests; Figure 7 C is the volcanic map of the LDGF group DEM; Figure 7 D is a chord diagram showing the correlation between DEMs.

[0028] Figure 8 The results of metabolic pathway enrichment analysis for DEM in the LDGF group. Figure 8 A is a graph showing the enrichment analysis of the KEGG pathway; Figure 8 B is a diagram illustrating the impact of the MetPA pathway. Figure 8 C is a schematic diagram showing the changes in the expression levels of DEM, which is involved in key pathways, before and after transplantation.

[0029] Figure 9 This is the result of a combined proteomics and metabolomics analysis of the LDGF group. Figure 9 A is a Venn diagram showing the overlap of enriched pathways between DEP and DEM; Figure 9 B is a heatmap of Spearman correlation analysis between DEP and DEM. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0031] Example 1: Establishment of a kidney transplant recipient cohort and definition of clinical phenotypes

[0032] The core objective of this study was to establish a cohort of kidney transplant recipients with clearly defined clinical endpoints for biomarker discovery. The study was approved by the ethics committee. Recipients who received allogeneic kidney transplants between September 2023 and November 2024 and experienced delayed graft function (DGF) post-transplantation were consecutively enrolled. All kidneys were derived from post-circulating death donation (DCD).

[0033] Strict plasma sample collection timeframes were set for: within 24 hours preoperatively (as an individualized baseline control), and on postoperative days 3 and 7. Collected peripheral blood was centrifuged to separate plasma, which was then immediately cryopreserved at -80°C to ensure biomolecular stability.

[0034] This study used the internationally recognized definition of functional DGF (fDGF) as the endpoint: the time required for a ≥10% daily decrease in serum creatinine level for three consecutive days from the transplant date. Based on this, and according to previous research and clinical consensus, recipients were clearly divided into two subgroups with different prognostic significance: the short-term DGF group (SDGF), defined as renal function recovery time < 7 days; and the long-term DGF group (LDGF), defined as renal function recovery time ≥ 7 days. Ultimately, this study included 3 SDGF recipients and 3 LDGF recipients, and all recipients signed informed consent forms. The two groups were comparable in key baseline characteristics such as age, primary disease, and cold ischemia time, as detailed in Table 1 and statistical analysis. Table 1. Baseline clinical characteristics of kidney transplant recipients (n=3 / group)

[0035] Note: SDGF: Short-term graft function delay; LDGF: Long-term graft function delay; ESRD: End-stage renal disease; HLA: Human leukocyte antigen. Continuous variables are expressed as mean ± standard deviation, and independent samples t-tests are used for comparisons between groups; categorical variables are expressed as number of cases (percentage). The p-value is used to assess the statistical significance of differences between groups.

[0036] As shown in Table 1, there were no significant differences between the two groups of recipients in continuous variables such as age and pre-transplant cardiac function (p>0.05). Regarding categorical variables, the sex composition differed, but the specific distribution of HLA mismatch sites (the proportion of patients with one mismatch versus two mismatches) was completely consistent. Furthermore, there were no statistically significant differences in the etiology of end-stage renal disease. These results indicate that the subsequent inter-group biological differences observed in this study are primarily attributable to the duration of DGF itself, rather than other baseline confounding factors listed except for sex. The rigorous cohort design and phenotypic definition in this embodiment laid the foundation for the subsequent discovery of clinically discriminative specific biomarkers.

[0037] Example 2: Panoramic Analysis of Differential Expression Profiling Based on Targeted Proteomics

[0038] To unbiasedly explore the immunomolecular characteristics behind different durations of DGF, the Olink® Immuno-Oncology Panel, with its high sensitivity and specificity, was used to perform targeted proteomics analysis on all plasma samples.

[0039] Using each recipient's own preoperative sample as a control, differentially expressed proteins (DEPs) were screened using strict statistical criteria (fold change FC > 1.2, p < 0.05). Figure 1 As shown in Figure A, the petal diagram visually reveals that the SDGF and LDGF groups each possess a large number of unique DEPs, while also sharing a small number of common DEPs, indicating that their immune statuses are both related and fundamentally different. (Volcano diagram) Figure 1 B) Quantified the magnitude and significance of changes in each DEP. (Heatmap) Figure 1 C and D) confirm, from an overall perspective, that the protein expression profiles of the SDGF group and the LDGF group show distinct clustering patterns. This is the first time from a systems biology perspective that proteomics data can effectively distinguish between the two clinical subtypes, SDGF and LDGF.

[0040] Example 3: Functional enrichment analysis elucidates differential immunopathological processes

[0041] GO function and KEGG pathway enrichment analyses were performed on the DEP identified in Example 2 to interpret its biological significance. The analysis showed ( Figure 2 SDGF-related DEP was significantly enriched in the "cellular response to interleukin-1", "IL-17 signaling pathway", and "chemokine signaling pathway". Figure 2 (A, B). These pathways are markers of acute inflammatory responses and innate immune activation, suggesting that SDGF primarily manifests as transient, intense tissue damage-related inflammation.

[0042] In stark contrast, LDGF-related DEP was significantly enriched in the "regulation of T cell differentiation," "α-β T cell differentiation," and "JAK-STAT signaling pathway." Figure 2 (C, D). This clearly points to the continuous activation and regulation of the adaptive immune system centered on T lymphocytes, marking the entry of the immune response into a more complex and persistent chronic immune regulation phase. This example explains the underlying reasons for the differences in clinical progression between SDGF and LDGF from a mechanistic perspective.

[0043] Example 4: Network pharmacology analysis identifies core hub protein combinatorial composition

[0044] To identify key regulatory molecules driving the aforementioned immune phenotypic shifts from a vast amount of DEPs, we constructed a protein-protein interaction (PPI) network and performed hub protein analysis.

[0045] like Figure 3 As shown, PPI network ( Figure 3 A, C) revealed complex interactions between DEPs. Pivot protein analysis ( Figure 3 (B, D) Further screening identified the top 10 most connected and influential proteins in the network. Analysis of the expression levels of these pivotal proteins ( Figure 3 E, F) is crucial: We discovered a specific set of proteins, consisting of IL-15, CCL3, IL-7, CCL20, and IL-5, that exhibited a highly consistent and statistically significant differential expression pattern between the SDGF and LDGF groups.

[0046] It is particularly important to emphasize that existing technologies (including the metabolite combination for assessing rejection risk disclosed in Chinese Patent Document CN116539653A) have never taught or suggested any association between the specific protein combination of "IL-15, CCL3, IL-7, CCL20, and IL-5" and the duration of DGF after kidney transplantation. This invention, for the first time, through systematic multi-omics network analysis, discovered and validated this specific combination as a core biomarker combination distinguishing SDGF from LDGF. Its value lies in its precise capture, at the systemic level, of the key node in the transition from "acute innate immune activation" to "chronic adaptive immune regulation."

[0047] Example 5: Targeted metabolomics reveals LDGF-related metabolic reprogramming

[0048] To more comprehensively elucidate the pathophysiological characteristics of LDGF, we supplemented the analysis with targeted metabolomics. The quality control results were excellent. Figure 5 A). Compared with the SDGF group, the LDGF group exhibited a wider range of metabolic disorders, with a significantly greater number of differentially expressed metabolites (DEMs). Figure 5 C, D), and the overall metabolic spectrum has been fundamentally reshaped ( Figure 6 In-depth analysis of the LDGF group ( Figure 7 Its unique metabolic characteristics were confirmed, and key metabolic nodes such as α-ketoglutarate were identified.

[0049] Example 6: Metabolic Pathway Analysis Coupled with Immune and Metabolic Mechanisms

[0050] Pathway analysis of DEM in the LDGF group ( Figure 8The study revealed that the most significantly disrupted pathways involved were arginine / proline metabolism, alanine / aspartate / glutamate metabolism, and the TCA cycle. This indicates that LDGF status not only involves chronic immune activation but is also deeply coupled with abnormal amino acid metabolism and cellular energy metabolism disorders. Figure 8 C). This finding provides a new "immune-metabolic" dual perspective for understanding the difficulty in restoring renal function caused by LDGF.

[0051] Example 7: Multi-omics integration and biomarker combination validation

[0052] Multi-omics joint analysis ( Figure 9 The results showed a significant correlation between the immune molecules (DEP) and metabolic molecules (DEM) of LDGF. Figure 9 B) confirms that the interaction between immunity and metabolism (i.e., "immunometabolic crosstalk") is an important characteristic of LDGF.

[0053] Based on Examples 2-6, we identified IL-15, CCL3, IL-7, CCL20, and IL-5 as the core biomarker combination for distinguishing SDGF / LDGF. The biological rationale for this combination lies in the fact that it simultaneously represents acute inflammatory mediators that cause short-term damage (such as IL-15 and CCL3) and key cytokines involved in long-term immune regulation (such as CCL20 and IL-5), thereby enabling precise differentiation between two distinct clinicopathological states.

[0054] Example 8: Validation of the discriminative power of a combination of core biomarkers

[0055] To quantitatively assess the discriminative power of the 5-protein combination, we conducted a retrospective validation. Standardized expression data of the five proteins from postoperative samples of six recipients were used to construct a classification model using logistic regression.

[0056] Performance analysis showed that the 5-protein combination exhibited excellent performance in distinguishing between SDGF and LDGF. Receiver operating characteristic (ROC) curve analysis showed an area under the curve (AUC) exceeding 0.95, demonstrating extremely high discriminative power. More importantly, comparative experiments confirmed that the predictive accuracy (AUC) using the complete 5-protein combination was significantly higher than using any single protein or a subset (2-4 proteins) of the combination. This demonstrates the synergistic effect of this specific combination, and its technical effect is unexpected, not simply the sum of the effects of individual biomarkers. This embodiment provides direct data support demonstrating that the biomarker combination claimed in this invention solves the specific technical problem of "accurately distinguishing DGF duration."

[0057] Example 9: Construction of in vitro diagnostic kit

[0058] Based on the above findings, an in vitro detection kit for implementing the present invention can be constructed. The core of this kit is the inclusion of reagents capable of specifically detecting the five proteins (IL-15, CCL3, IL-7, CCL20, and IL-5). These detection reagents include, but are not limited to: specific antibody pairs against each protein (for ELISA, chemiluminescence, or immunoturbidimetry), probe pairs based on adjacent extension analysis (PEA) (such as Olink technology), or aptamers, nucleic acid probes, etc. The kit may also include calibrators, quality controls, universal buffers, and detailed operating instructions.

[0059] As a preferred embodiment, the kit may further include reagents (such as internal standards for mass spectrometry and extraction reagents) for detecting one or more metabolites (such as α-ketoglutarate) disclosed in Examples 5 and 6, to provide richer auxiliary information.

[0060] Example 10: Non-diagnostic prognostic risk assessment method

[0061] This invention also provides a method for prognostic risk assessment, which is not for diagnostic purposes but aims to provide auxiliary information for clinical decision-making. The steps are as follows: Obtain plasma samples from recipients who developed DGF after kidney transplantation.

[0062] The concentrations of IL-15, CCL3, IL-7, CCL20 and IL-5 in a sample were quantitatively detected using the kit described in Example 9 or other equivalent methods in the art.

[0063] The obtained concentration data is fed into a pre-trained classification model built using machine learning algorithms such as logistic regression, random forest, or support vector machine. This model has been trained and optimized using sample data with known clinical outcomes (specifically SDGF or LDGF).

[0064] The model outputs a risk assessment value (such as the probability of belonging to LDGF). Researchers or clinicians can use this risk value, combined with other clinical information, to assess the risk level of delayed renal function recovery in the patient, thereby providing a reference for the selection of individualized follow-up intensity or intervention strategies.

[0065] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. 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 modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. An application of a combination of biomarkers for differentiating the duration of graft function delay in kidney transplant recipients, characterized in that, The biomarker combination consists of proteins IL-15, CCL3, IL-7, CCL20, and IL-5, and is used to prepare an in vitro detection kit to distinguish the duration of DGF or to establish a computer prediction model to distinguish the duration of DGF.

2. The application according to claim 1, characterized in that, The biomarker combination also includes at least one metabolite selected from the group consisting of: α-ketoglutarate, guanosine 3',5'-cyclic monophosphate, 3-(4-hydroxyphenyl)propionic acid, cis-aconitine, and isocitrate.

3. An in vitro diagnostic kit for differentiating the duration of graft function delay in kidney transplant recipients, characterized in that, The kit contains reagents for detecting the following protein biomarkers: IL-15, CCL3, IL-7, CCL20, and IL-5.

4. The in vitro diagnostic kit according to claim 3, characterized in that, The reagents include antibodies, aptamers, or nucleic acid probes capable of specifically binding to or detecting the proteins IL-15, CCL3, IL-7, CCL20, and IL-5.

5. The in vitro diagnostic kit according to claim 3, characterized in that, The kit also contains reagents for detecting at least one metabolite selected from the group consisting of: α-ketoglutarate, guanosine 3',5'-cyclic monophosphate, 3-(4-hydroxyphenyl)propionic acid, cis-aconitine, and isocitrate.

6. A method for distinguishing the duration of graft function delay in kidney transplant recipients, characterized in that, The method is for non-diagnostic purposes and includes the following steps: (a) Obtaining plasma samples from kidney transplant recipients to be tested; (b) Detect the level of the combination of biomarkers consisting of proteins IL-15, CCL3, IL-7, CCL20 and IL-5 in the plasma sample; (c) Based on the level of the combination of biomarkers detected in step (b), distinguish the duration of the recipient’s DGF as short-term DGF (SDGF) or long-term DGF (LDGF).

7. The method according to claim 6, characterized in that, In step (c), the levels of the biomarker combination are input into a pre-trained prediction model, and the model outputs the discrimination result.

8. The method according to claim 6 or 7, characterized in that, The biomarker combination further includes at least one metabolite selected from the group consisting of: α-ketoglutarate, guanosine 3',5'-cyclic monophosphate, 3-(4-hydroxyphenyl)propionic acid, cis-aconitine, and isocitrate; step (b) further includes detecting the level of the at least one metabolite.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 6 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 6 to 8.

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