Application of blood biomarker CFP in preparation of acute pancreatitis diagnostic reagent

Abstract

The invention relates to the technical field of medical biology, in particular to application of a blood biomarker CFP to preparation of an acute pancreatitis diagnostic reagent. The invention proves that the CFP in the blood can be used as a sensitive and effective biomarker with prediction value for evaluating the severity of acute pancreatitis, and the detection convenience and the result repeatability of the CFP have more clinical application advantages.

Description

Technical Field

[0001] The present invention relates to the field of medical biotechnology, and specifically to the application of a blood biomarker properdin (CFP) in the preparation of a diagnostic reagent for acute pancreatitis. Background Art

[0002] Acute pancreatitis (AP) is an inflammatory disease triggered by abnormal activation of pancreatic enzymes. Its complications can include local or systemic infections, systemic inflammatory response syndrome (SIRS), and multiple organ failure (MOF). The mortality rate of severe patients is as high as 20%-30%. The disease progression of patients varies greatly. Predicting the disease severity in the early stage (within 48 hours) helps to implement rapid and effective interventions.

[0003] Currently, the biomarkers used in clinical practice include C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6). Other biochemical indices and hematological parameters in the research stage, such as lipopolysaccharide-binding protein, interleukin-8, neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR), have not fully met the clinical needs. Clinical scoring systems such as APACHE II score, Ranson score, and BISAP also have limitations. Since their analysis requires more than 48 hours of hospitalization and involves multiple complex indices, their clinical application is restricted. An ideal biomarker should have the characteristics of rapid detection, simple operation, high accessibility, strong economy, high sensitivity, and high specificity.

[0004] As a powerful research method, proteomics has been widely applied to the screening of biomarkers in different categories of biological samples of AP. However, when screening for candidate biomarkers derived from the detection results, it is necessary to fully consider the inherent non-linear dynamic characteristics during the inflammatory process. This characteristic mainly stems from the multi-level regulatory mechanism resulting from the combined action of pathogens and the host immune system (pro-inflammatory and anti-inflammatory responses). In this context, the limitations of traditional methods that focus on first-order statistical information (such as differential protein expression levels) are gradually emerging, as they are difficult to effectively capture dynamic network interaction information. In response to this challenge, the theory of Dynamic Network Biomarker (DNB) proposed by researchers can identify early warning signals of disease stage transitions and the rapid deterioration of complex diseases by mining higher-order statistical information or differential correlations (such as the correlation coefficient / covariance of the proteome).

[0005] Based on previous research, we used the DNB method to perform proteomic analysis on peripheral blood samples of healthy individuals and patients with different AP subtypes, and found that the key positive regulatory protein CFP of the complement cascade reaction was significantly correlated with disease progression. CFP is mainly derived from neutrophils, and its expression level is not only related to the severity of the disease, but can also reflect changes in the number and functional status of peripheral neutrophils, thereby revealing the immunosuppressive state of the patient. Current immunosuppressive treatment strategies face major challenges in patient stratification and determining the optimal intervention time due to the lack of reliable biomarkers. The present invention discovers that CFP has the potential of a dual biomarker for both patient stratification and immunotherapy guidance, highlighting its good clinical application prospects in the precision management of AP. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art and provide an application of the blood biomarker CFP in the preparation of a diagnostic reagent for acute pancreatitis.

[0007] The purpose of the present invention is achieved through the following technical solutions: The present invention provides an application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis.

[0008] Furthermore, the diagnosis is performed by measuring the concentration of properdin in the blood of the subject. When the reduction amplitude of the properdin concentration in the blood relative to the properdin concentration in the blood of the normal group is -40% to 50% (a negative reduction amplitude indicates an increase in the properdin concentration in the subject's blood relative to the control group), it is judged as non-severe acute pancreatitis; when the reduction amplitude of the properdin concentration in the blood relative to the properdin concentration in the blood of the normal group is more than 50%, it is judged as severe acute pancreatitis. Among them, the subject is a patient with acute pancreatitis.

[0009] Further, the normal group refers to a group with the same racial background as the subject and matching some demographic characteristics. Demographic characteristics include, for example, age, gender, place of residence, etc.

[0010] Further, the diagnosis is carried out by measuring the concentration of properdin in the subject's blood. The reduction amplitude of the properdin concentration in the blood relative to the subject's own properdin concentration in the blood in the early stage of the disease or before getting sick is negatively correlated with the severity of acute pancreatitis. Among them, the subject is a patient with acute pancreatitis.

[0011] Further, the properdin concentration in the blood of the subject and the normal group or the subject himself / herself in the early stage of the disease or before getting sick is measured by the same detection method.

[0012] Further, the detection methods include electrochemiluminescence technology, enzyme-linked immunosorbent assay (ELISA), liquid chip technology (xMAP), single molecule immune array technology (SiMoA), fully automated capillary digital Western Blot, proximity extension assay (PEA), immunoelectrophoresis, high performance liquid chromatography tandem mass spectrometry or microfluidic chip.

[0013] Further, the types of properdin in the blood include one or more of plasma properdin, serum properdin and whole blood properdin. Existing studies have shown that the expression levels of properdin molecules in whole blood, serum and plasma are significantly correlated. After their detection values are corrected by hematocrit and processed by standard centrifugation procedures, they show equivalent diagnostic efficacy among the three sample types. Given that whole blood samples can achieve point-of-care testing, serum samples avoid anticoagulant interference, and plasma samples maintain the continuity of routine testing, they can be selected according to the actual situation.

[0014] Further, the uses of the reagent for diagnosing the severity of acute pancreatitis include judging the severity of the condition of patients with acute pancreatitis, judging the progress or recovery degree of the condition of patients with acute pancreatitis, being used alone or in combination to assist in guiding the formulation of the drug administration plan for acute pancreatitis, judging the rationality of the drug administration plan for acute pancreatitis, and judging the effect of the therapeutic drugs for acute pancreatitis.

[0015] The present invention also provides an application of a reagent for detecting the concentration of properdin in the blood in the preparation of a product for diagnosing the severity of acute pancreatitis. The reagent includes a kit.

[0016] The present invention also provides a detection system for judging the severity of acute pancreatitis. The detection system includes a biochemical index detection part and a calculation and analysis part; The biochemical index detection part includes measuring the concentration of properdin in the blood; The calculation and analysis part includes the classification of biochemical indexes and / or the evaluation of treatment effects and / or the formulation of treatment plans; The classification of the biochemical indexes includes: classifying the conditions of patients with acute pancreatitis into several levels from severe to non-severe acute pancreatitis according to the range of the concentration value of properdin in the blood of patients with acute pancreatitis; The evaluation of the treatment effects includes: classifying the treatment effects of patients with acute pancreatitis into several gradients from effective to ineffective according to the change of the concentration value of properdin in the blood of patients with acute pancreatitis over a certain period of time; The formulation of the treatment plan includes: formulating a treatment plan according to the severity of the condition of patients with acute pancreatitis; or formulating a treatment plan according to the treatment effects of patients with acute pancreatitis. In particular, formulating a plan for immunotherapy, including administration time, administration duration or administration dose, etc.

[0017] The beneficial effects of the present invention are: Currently, the markers for judging the severity of acute pancreatitis (AP) that are already available clinically and under research have defects such as insufficient sensitivity and specificity and limited response time. Related hematological parameters are also difficult to meet the needs of early predicting the disease progression and guiding precise treatment. The existing clinical prediction scoring systems are limited in their application due to complex analysis and long time consumption. The present invention provides a new, efficient and sensitive biomarker to achieve early prediction and full-course monitoring of the severity of acute pancreatitis, as well as guidance and evaluation of immunotherapy, with the following characteristics: (1) Early prediction, high sensitivity and specificity: The experimental results show that plasma CFP, as an independent risk factor for acute pancreatitis, has better discrimination ability (AUC for severe and non-severe groups: 0.923) than conventional blood indexes (such as NLR, PLR, MLR), with a sensitivity of 92.6% and a specificity of 85.4%. The positive predictive value (PPV) is 0.926, and the negative predictive value (NPV) is 0.854. The plasma CFP level shows significant changes in the early stage of the disease, enabling early prediction of the severity of acute pancreatitis.

[0018] (2) Guidance for immunotherapy: The plasma CFP concentration not only reflects the disease severity but is also closely related to the functional state of neutrophils, depicting the immunosuppressive state of the body. During the treatment of the cerulein-zymosan (CAE-Zymosan) mouse SAP model with the immunopotentiator GM-CSF, the CFP concentration can guide the selection of an appropriate intervention window, which can not only maximize immune reconstitution but also avoid the risk of cytokine overstimulation, thus significantly improving the survival rate of mice and restoring intestinal barrier integrity by reducing intestinal bacterial translocation, demonstrating the prominent role of CFP in guiding immunotherapy.

[0019] (3)Clinical transformation potential: The methods and markers provided by the present invention have the advantages of simple operation, strong economy, high accessibility, etc., and are easy to be popularized and applied in clinics. By dynamically monitoring the plasma CFP level, it is expected to provide new strategies for the early disease assessment, full-cycle monitoring and precision treatment of acute pancreatitis. Brief Description of the Drawings

[0020] Figure 1 Identifying critical transition states and severity biomarkers in AP progression based on DNB analysis; (A) Plasma EV proteomics analysis process for three clinical cohorts. HC: healthy control; NSAP: non-severe acute pancreatitis; SAP: severe acute pancreatitis; (B) Reconstructing proteomic trajectories using the DNB algorithm to detect disease critical states; (C) l-DNB calculation process for single-sample DNB-related protein identification; (D) Disease state-specific network perturbation maps. Nodes: proteins (red = state-specific DNB molecules); Edges: Pearson correlation coefficient (PCC) fluctuations (thickness is proportional to the absolute value); (E) DNB score trajectories identifying critical transition thresholds; (F) State-stratified DNB sub-networks. Node size: change in edge connectivity (the larger, the stronger the perturbation); Edges: red (PCC gain), blue (PCC loss); (G) DNB sub-network constructed based on public proteomic data (SAP vs HC cohort).

[0021] Figure 2 Differential analysis of the DNB algorithm and private proteomic data (A) Proteomic intensity distributions of each study group: HC (healthy control group), NSAP (non-severe acute pancreatitis group), SAP (severe acute pancreatitis group); (B) Dynamic evolution of the DNB network with disease states. Node size corresponds to the DNB score (quantifying the contribution to network instability), and larger nodes represent key transitional biomarkers; (C) Disease state-specific DNB sub-networks. Node size reflects the edge perturbation intensity (amplitude of connectivity change), edge thickness represents the absolute offset Pearson correlation coefficient (|sPCC|), and core DNB hubs are marked in dark brown; (D) Gene ontology (GO) functional enrichment analysis of core DNB proteins differentiating SAP from NSAP; (E) Venn diagram of the overlap of differentially expressed proteins (DEPs) in each group; (F) Volcano plots of differential protein expression: (i) NSAP vs HC, (ii) SAP vs HC. Dashed lines mark the significance thresholds (p value < 0.05 and |logFC| > 1.5); (G) Hierarchical clustering heat map of the expression profiles of cross-group common differentially expressed proteins.

[0022] Figure 3Study on the correlation between CFP and the severity of SAP; (A) Flow chart of patient enrollment and stratification (NT: healthy control group; NSAP: non-severe acute pancreatitis; SAP: severe acute pancreatitis); (B) Comparison of plasma sC5b-9 concentrations in the NT, NSAP, and SAP groups; (C) Comparative analysis of plasma CFP levels in the three cohorts; (D) ROC curve evaluating the predictive efficacy of plasma CFP in differentiating NSAP from SAP; (E) ROC curve evaluating the accuracy of plasma CFP in differentiating NT from patients with pancreatitis (NSAP + SAP); (F) Scatter plot of the correlation between plasma CFP concentration and the time interval from symptom onset to blood sampling; (G) ROC comparison analysis of CFP and traditional severity prediction indicators (NLR / PLR / MLR): CFP (AUC = 0.923), NLR (AUC = 0.752), PLR (AUC = 0.741), MLR (AUC = 0.670). Data are expressed as mean ± standard deviation. Box plots (B, C) show the median, interquartile range (box), and extreme values (whiskers). The dashed line in the ROC curves (D, E, G) represents the null reference line (AUC = 0.5). (H) Comparison of plasma CFP between the NSAP and SAP groups in this cohort among patients with a time interval from sample collection to onset of illness not exceeding 72 hours.

[0023] Figure 4 Regulatory mechanism of complement activation and neutrophil granule release on plasma CFP; (A) Erythrocyte C4d deposition in clinical groups (HC / NSAP / SAP; n = 5 for biological replicates), and 1, 2, 3 marked in the figure are biological replicates, the same below; (B) C4d expression on the surface of neutrophils (n = 5); (C) Surface expression of neutrophil C5a receptor (C5aR) (n = 6); (D) Levels of neutrophil CD64 and FPR1 (n = 3); (E) Western blot analysis of plasma CFP levels in the HC and SAP groups with or without Zymosan treatment (0.05 or 1 mg / ml); (F) Western blot detection of CFP deposition after co-incubation of plasma from the NT and SAP groups with Zymosan particles (0.05 or 1 mg / ml); (G) Immunofluorescence showing the localization of CFP in neutrophil subgranules (scale bar: 5 μm); (H) Expression of neutrophil CD66b (n = 3); (I, J) Baseline CFP levels in freshly isolated neutrophils from each cohort; (K) Temporal expression of CFP in healthy neutrophils under different conditions (serum-free RPMI, 10% FBS, 10% FBS + inflammatory stimulus); (L, M) Regulation of CFP in neutrophils from clinical groups stimulated with TNFα (50 ng / ml) and fMLP (100 nM).

[0024] Figure 5Flow cytometry comparative analysis of surface protein expression and complement deposition on peripheral blood red blood cells and neutrophils. (A) Flow cytometry analysis of the isolation purity of peripheral blood neutrophils (CD11b+CD66b+) and CD235+ red blood cells; (B) Flow cytometry detection of the expression of neutrophil CD64 and FPR1 receptors in different study groups (HC, NSAP, SAP), repeated groups; (C) Expression characteristics of neutrophil surface C5aR receptors in the HC, NSAP, and SAP cohorts, with numbers 1-4 representing the results of different independent sample collection groups; (D) Representative flow cytometry maps of complement deposition and receptor expression on red blood cells and neutrophils in simultaneously processed HC, NSAP, and SAP samples. The accompanying table details the corresponding patient identifiers, isolated neutrophil concentration, and plasma CFP levels.

[0025] Figure 6 Analysis of the correlation between plasma CFP levels and the function of peripheral neutrophils in patients; (A) Yield of isolated whole blood neutrophils in healthy control group (HC), non-severe acute pancreatitis (NSAP), and severe acute pancreatitis (SAP) patients (n = 8); (B) Real-time chemiluminescence kinetic curve of neutrophil reactive oxygen species (ROS) generation (n = 3); (C) Representative images of crystal violet-stained neutrophil adhesion to polycarbonate membranes in Transwell experiments (n = 3), scale bar: 50 μm; (D) Quantitative analysis of neutrophils migrated to the lower chamber (n = 3); (E) Formation of neutrophil extracellular traps (NETs) induced by Escherichia coli stimulation (neutrophils: bacteria = 1:3) in serum-free medium (0% FBS) (n = 3), scale bar: 50 μm; (F) Killing efficiency of neutrophils against Escherichia coli (n = 3); (G) Protein immunoblot analysis of neutrophil signaling pathway activation (phosphorylated AKT, p42 / 44 ERK, p47, p40, p105, p65) and CFP expression (stimulated with 10% FBS ± 100 ng / ml PMA), with total AKT, ERK, and GAPDH as internal references; (H) ELISA detection of plasma TNFα levels after whole blood LPS (100 ng / ml) stimulation (n = 7 samples). Data are expressed as mean ± standard deviation, *p < 0.01, **p < 0.001, ***p < 0.0001.

[0026] Figure 7Functional and quantitative analysis of human peripheral blood neutrophils and monocytes. (A) Comparison of neutrophil yields per milliliter of whole blood in each group (HC, NSAP, SAP; n = 8 per group); (B) Representative images of crystal violet-stained neutrophils migrated to the lower chamber in the Transwell assay, reflecting chemotactic ability (n = 3 per group); Scale bar: 50 μm; (C) Evaluation of neutrophil extracellular trap (NET) formation after culturing in RPMI 1640 medium containing Escherichia coli stimulation (neutrophil-bacteria ratio 1:3; n = 3 per group) for 4 hours; Scale bar: 100 μm; (D) ELISA quantitative detection of plasma TNFα levels after in vitro whole blood LPS stimulation (100 ng / mL; n = 7 per group); (E) Expression levels of CD11b on the surface of neutrophils in the HC, NSAP, and SAP cohorts. Data are presented as mean ± standard deviation; Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001, p < 0.0001.

[0027] Figure 8 Establishment and phenotypic characterization of the SAP mouse model. (A) Flow chart of the SAP induction experiment; (B) Kaplan-Meier analysis of survival rates during the 12-day observation period; (C) Dynamic changes in body weights of mice in the untreated group (NT), acute pancreatitis (AP), and SAP groups (n = 5 per group); (D) Serum biochemical indices: amylase (AMY), lipase (LIP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatine kinase isoenzyme (CK-MB), lactate dehydrogenase (LDH), urea, and creatinine (Cr) (n = 5 per group); (E) Differential white blood cell count in peripheral blood: total white blood cells (WBC), neutrophils (Neu), and lymphocytes (Lym) (n = 5 per group); (F) Gross morphology of the small intestine and results of bacterial culture of the lavage fluid; (G) Flow cytometry analysis of peritoneal lavage fluid: CD45+ white blood cells, CD11b+F4 / 80+ macrophages, and CD11b+Ly6G+ neutrophils (n = 5 per group); (H) Histopathological evaluation of pancreatic and small intestinal tissues by hematoxylin-eosin (HE) staining, scale bar: 100 μm; (I) Complement activation markers (CFP, C5b-9) on the surface of peripheral blood white blood cells (WBC, Neu) and bone marrow cells. Data are presented as mean ± standard deviation, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

[0028] Figure 9Study on plasma CFP as a biomarker for survival prognosis in the CAE-zymosan model; (A) Body weight changes of mice in the survival group (Su) and non-survival group (N-Su) at 3.5, 5.5, and 6.5 days after modeling; (B) Protein immunoblot analysis of plasma CFP and CFB in the NT, Su, and N-Su groups; (C) Peripheral blood cell counts (WBC, Neu, Lym), small intestine pathological images, and quantification of bacterial load in lavage fluid (n = 3); (D) Serum biochemical parameters (n = 3); (E) Total number of bone marrow cells in bilateral femurs and tibias (n = 3); (F) Number of neutrophils migrated to the lower chamber in the Transwell assay (n = 3); (G) Killing efficiency of neutrophils against Escherichia coli (n = 3); (H) NET formation under serum-free conditions ± Escherichia coli (1:3) stimulation (n = 3), scale bar: 50 μm; (I) Plasma TNFα level after stimulating whole blood with LPS (5 μg / ml) (n = 3); (J) Protein immunoblot of neutrophil signaling pathways (phosphorylated p65, p40, LC3B, cleaved caspase-3) in the Su / N-Su group (10% FBS ± 100 ng / ml PMA), with Actin as the internal reference. Data are expressed as mean ± standard deviation, * p < 0.05, ** p < 0.01, *** p <0.001, **** p < 0.0001.

[0029] Figure 10Comparative analysis of pathological features and neutrophil functions in surviving and non-surviving mice. (A) Flow cytometry quantification of peritoneal leukocyte subsets: total leukocytes (CD45+), macrophages (CD11b+F4 / 80+), and neutrophils (CD11b+Ly6G+; n = 3 per group); gross morphology of the small intestine and colony counts in intestinal lavage fluid; (B) HE-stained sections of key organs (pancreas, small intestine, kidney, liver, lung) (scale bar: 100 μm); (C) total number of nucleated cells in bilateral femurs and tibias (n = 3 per group); (D) Escherichia coli killing ability of neutrophils (n = 3 per group); (E) quantification of neutrophils in the lower chamber by Transwell migration assay (n = 3 per group); (F) neutrophil adhesion stained with crystal violet on the Transwell membrane (scale bar: 50 μm; n = 3 per group); (G) Western blot analysis of neutrophil signaling pathways: expression of CFP, p-ERK, Rip3, GSDMD, CitH3, and Actin proteins in bone marrow neutrophils from surviving (Su) and non-surviving (N-Su) mice (cultured in RPMI 1640 containing 10% FBS ± 100 ng / ml PMA). Data are presented as mean ± standard deviation. Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001.

[0030] Figure 11 Longitudinal analysis of disease progression-related pathological parameters in the CAE-Zymosan model (6 time points), (A) body weight dynamics of the NT, AP, and SAP groups at 12 h, 36 h, 3 days, 5 days, 8 days, and 12 days after model induction (n = 3–5); (B) plasma amylase (AMY) activity at each time point (n = 3–5; unit: U / L); (C) temporal distribution of circulating leukocyte subsets (WBC, Neu, Lym) (n = 3–5; unit: ×10³ cells / mL); (D) Western blot analysis of plasma calprotectin (CFP) levels in the NT, AP, and SAP groups; (E) plasma TNFα levels detected by ELISA after whole blood stimulation with LPS (5 μg / mL). Data are presented as mean ± standard deviation. Statistical significance: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

[0031] Figure 12Optimization of GM-CSF treatment regimen and evaluation of therapeutic effect in the CAE-zymosan model. (A) Body weight dynamics of the non-treated group (NT), SAP group, and GM-CSF treatment groups (initiated at 0 h, 12 h, and 36 h after zymosan administration) (n = 3-5 per group); (B) Kaplan-Meier survival curves of each group; (C) Representative images of the gross morphology of the small intestine; (D) Colony counts in the intestinal lavage fluid; (E) Flow cytometry quantification of peritoneal leukocyte subsets (n = 3-5 per group); (F) Escherichia coli killing efficiency of bone marrow neutrophils (n = 3-5 per group); (G) Body weight dynamics of mice in the NT group, SAP group, and different GM-CSF administration regimens (200 ng / day × 3 days, 100 ng twice daily × 3 days, 100 ng twice daily × 5 days) (n = 3-5 per group); (H) Flow cytometry quantification of peritoneal leukocyte subsets at different GM-CSF doses (n = 3-5 per group); (I) Small intestine morphology and colony counts in the lavage fluid; (J) Escherichia coli killing ability of neutrophils under different GM-CSF regimens (n = 3-5 per group); (K) Total number of nucleated cells in the bilateral femurs and tibias of bone marrow 0.5 h after zymosan challenge (n = 3-5 per group); (L) Plasma TNFα levels detected by ELISA 0.5 h after LPS-stimulated whole blood culture (5 μg / mL) (n = 4 per group); (M) Western blot analysis of plasma CFP protein levels in the NT group, AP group, and SAP group 0.5 h after the last GM-CSF administration. Data are expressed as mean ± standard deviation. Statistical significance: *p < 0.05, **p < 0.01.

[0032] Figure 13Intervention study of GM-CSF-based immunotherapy guided by plasma properdin concentration for SAP mice; (A) Schematic diagram of GM-CSF treatment experimental design; (B) FITC-dextran permeability experiment to evaluate intestinal barrier integrity (n = 5); (C) Kaplan-Meier analysis of survival rate; (D) Body weight dynamics of GM-CSF treatment group and untreated SAP group (n = 5); (E) Gross morphology of small intestine (left) and colony count of lavage fluid smear plate (right); (F) Flow cytometry analysis of leukocyte subsets in peritoneal lavage fluid: CD45+ total leukocytes, CD11b+F4 / 80+ macrophages and CD11b+Ly6G+ neutrophils (n = 3-5); (G) Flow cytometry detection of percentage of peripheral neutrophils in whole blood after erythrocyte sedimentation; (H) Quantitative analysis of neutrophil migration number in the lower chamber of Transwell in migration experiment (n = 3); (I) Representative image of crystal violet staining showing adherent neutrophils on polycarbonate membrane in Transwell experiment (n = 3), scale bar: 50 μm5; (J) Formation of neutrophil extracellular traps (NETs) induced by Escherichia coli (neutrophil to bacteria ratio of 1:3) combined with platelet-activating factor (PAF, 10 μM) in serum-free medium. Scale bar: 100 μm1; (K) Killing efficiency of neutrophils against Escherichia coli. Data are expressed as mean ± standard deviation, p < 0.05, p < 0.01, p < 0.001, **** p < 0.0001.

[0033] Figure 14 Table 1 shows the basic information of patients from EV proteomics.

[0034] Figure 15 Table 2 shows the basic information of patients for CFP concentration detection.

[0035] Figure 16 Table 3 shows the multivariate analysis.

[0036] Figure 17 Table 4 shows the comparison of the effects of CFP and common indicators for predicting the severity of acute pancreatitis.

[0037] Figure 18 Table 5 shows the summary of the follow-up results of the condition of patients after blood collection in different groups. Detailed implementation manners

[0038] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0039] I. Research idea of the present invention (1)Marker screening: Using the DNB analysis method, integrating private and public proteome datasets, constructing a bioinformatics analysis process, and by mining high-order statistical information or differential correlations (such as proteome correlation / covariance), it is possible to more accurately capture the early warning signals of disease progression and deterioration.

[0040] (2)Patient sample verification: In a prospective cohort study, patients with acute pancreatitis were included, and their plasma samples were collected. The plasma CFP level was detected by ELISA. Combining clinical data, the correlation between the plasma CFP level and disease severity and the dynamic function of neutrophils was analyzed to verify the potential of CFP as a biomarker for predicting disease severity.

[0041] (3)Animal model construction and evaluation: A CAE-Zymosan mouse model was constructed to simulate the pathophysiological characteristics of human SAP, including local pancreatic inflammatory response, multiple organ dysfunction syndrome (MODS), and a biphasic disease progression pattern. By monitoring the differences in plasma CFP levels and neutrophil function between surviving and non-surviving mice, the effectiveness of CFP as a biomarker for disease severity was further verified, and its application in guiding immunotherapy regimens was explored.

[0042] II. Experimental materials and methods 1. Patient inclusion and exclusion criteria This invention included patients with acute pancreatitis (AP) diagnosed at West China Hospital of Sichuan University from June 1, 2021 to December 31, 2024. The diagnosis was based on the revised Atlanta criteria. The inclusion criteria included: (1) aged 18 - 70 years; (2) meeting the AP diagnostic criteria. The exclusion criteria were: (1) pregnancy; (2) having chronic underlying diseases (such as chronic pancreatitis, hepatitis, nephritis); (3) pancreatic or other malignant tumors; (4) immunodeficiency (such as HIV infection) or autoimmune diseases (such as systemic lupus erythematosus, rheumatoid arthritis); (5) history of immunosuppressive therapy; (6) having sepsis not related to AP; (7) history of AP attack within 3 months before admission; (8) data missing or repeated sampling. The research protocol was approved by the Ethics Committee of West China Hospital (No. 2021 - 675) and followed the Declaration of Helsinki. The follow-up continued until the patient was discharged or died in the hospital. Peripheral blood samples were collected at admission for subsequent analysis.

[0043] 2. Plasma separation and neutrophil purification Peripheral blood was collected in EDTA-2K anticoagulant tubes, and plasma was separated by centrifugation at 1500 g for 10 minutes at 4°C. After the cell pellet was resuspended in physiological saline, human neutrophil isolation kits were used to isolate peripheral blood mononuclear cells (PBMCs) and neutrophils, and residual red blood cells were removed by hypotonic lysis. Flow cytometry (CD66b+CD11b+ labeling) was used to verify the purity of neutrophils. The purified neutrophils were resuspended in RPMI 1640 medium for functional experiments.

[0044] Mouse experiment part: Neutrophils were isolated from the bone marrow of C57BL / 6J mice by Ficoll-PLUS density gradient method, and flow cytometry (Ly6G+ labeling) was used to verify the cell purity.

[0045] 3. ELISA detection Commercial ELISA kits (product number: ab222864, manufacturer: Abcam, Cambridge, UK) were used to detect the concentrations of properdin (CFP) and soluble terminal complement complex (sC5b-9) in plasma, and the operation strictly followed the instructions of the manual.

[0046] 4. Dynamic network biomarker (DNB) analysis (1) Plasma collection and isolation of extracellular vesicles (EVs) by ultracentrifugation Whole blood samples were collected from EDTA anticoagulant vacuum blood collection tubes by venipuncture, and gently mixed 4-5 times to ensure the full effect of the anticoagulant. Plasma separation was performed by two-step centrifugation: the first centrifugation at 4°C (2,500×g, 15 minutes), and the supernatant was taken for the second centrifugation (same parameters) to obtain platelet-poor plasma (PPP). After aliquoting, the PPP samples were stored at -80°C for later use. All operations followed the ethical guidelines of the Declaration of Helsinki.

[0047] EVs were enriched using an optimized ultracentrifugation protocol: 300 μL aliquots of PPP samples were taken, diluted 1:4 with phosphate buffer (PBS), and transferred to polycarbonate ultracentrifugation tubes. The first centrifugation was performed using a TLA-55 rotor (Beckman Coulter Optima XE) (110,000×g, 4°C, 90 minutes). The EV pellet was resuspended in 1.2 mL PBS and ultracentrifuged again (same parameters) to remove soluble protein contamination. The final purified EVs were resuspended in 30 μL PBS and stored at -80°C for subsequent analysis.

[0048] (2) Liquid chromatography-mass spectrometry (LC-MS) analysis of EV surface proteins Plasma EVs proteomics analysis was performed on 88 samples (68 patients and 20 healthy controls) that met the inclusion and exclusion criteria. EVs were lysed using ice-precooled RIPA lysis buffer (containing protease / phosphatase inhibitors), sonicated (30% amplitude, 3 seconds on / 10 seconds off, 5 minutes, ice bath), and then centrifuged (10,000×g, 4°C, 30 minutes). The supernatant was taken to measure the protein concentration by the Bradford method. 100 μg of protein samples were successively reduced (10 mM TCEP, 56°C, 1 hour), alkylated (20 mM iodoacetamide, protected from light at room temperature for 30 minutes), and precipitated with methanol / chloroform / water (4:1:3, v / v). The proteins were digested with sequencing-grade trypsin (enzyme / substrate ratio 1:50, 37°C, 12 hours), and the peptides were desalted using a C18 ZipTip. 5 μg of peptides were taken for LC-MS analysis.

[0049] The desalted peptides were vacuum-dried and re-dissolved in mobile phase A (2% acetonitrile, 0.1% formic acid). Chromatographic separation was performed using a Thermo Scientific Nano EASY-nLC 1200 system coupled with an Orbitrap Exploris 480 mass spectrometer (nanoelectrospray ion source). 1 μL of the sample was enriched on a PepMap trapping column (300 nL / min) and then gradient eluted at a flow rate of 300 nL / min on a C18 reversed-phase analytical column (250 mm×75 μm, 1.9 μm Reprosil-Pur packing material) (mobile phase B: 0.1% formic acid - 80% acetonitrile; 2% - 35% B within 65 minutes). Mass spectrometry parameters: first-level scan (m / z 350 - 1800, resolution 60,000); second-level HCD fragmentation (30% collision energy, resolution 15,000); dynamic exclusion of single-charged / undetermined-charged ions. Data acquisition was controlled by Xcalibur software (v4.3).

[0050] The raw data was processed using MaxQuant (v1.6.17.0): the digestion specificity was set to trypsin (maximum 2 missed cleavage sites), and the mass tolerances for precursor ions and fragment ions were 10 ppm and 0.02 Da, respectively. The fixed modification was cysteine carbamylation, and the variable modifications were methionine oxidation and N-terminal acetylation. Protein identification required ≥1 specific peptide (false discovery rate FDR < 1%). Label-free quantification (LFQ) was performed based on the iBAQ algorithm, and proteins containing ≥2 specific peptides were retained.

[0051] Data preprocessing included: log2 transformation after normalizing the mean of LFQ intensities, median centering between samples to correct for batch effects, and filling in missing values by the predictive mean matching method using the mice package (v3.15.0) in R language.

[0052] (3)Landscape dynamic network biomarker (l-DNB) method In this study, two disease progression states of acute pancreatitis (AP) were identified by constructing a reference network. The edges in the reference network were defined based on the Pearson correlation coefficient (PCC) calculated from protein expression values: (Equation 1) where \(x_i\) and \(y_i\) represent the expression levels of proteins \(x\) and \(y\) in the \(i\)-th reference sample, respectively, and \(\bar{x}\) and \(\bar{y}\) are the average expression levels of proteins \(x\) and \(y\) in all \(n\) reference samples. When a new sample is added, a perturbed network is constructed by recalculating the PCC value that includes the new sample, denoted as \(PCC_{(n + 1)}\). This PCC difference can reflect the perturbation effect of the new sample on the network. Therefore, the single-sample Pearson correlation coefficient (sPCC) of the new sample relative to the reference sample is defined as: (Equation 2) When the sample size is large enough, \(sPCC_n(x,y)\) at the single-sample level follows an approximate normal distribution. In this study, statistical hypothesis testing (Z-test or U-test) was used to evaluate the significance of sPCC, thereby determining whether there is a significant correlation between proteins \(x\) and \(y\) in a specific sample.

[0053] Under the theoretical framework of dynamic network biomarkers (DNB), when the physiological system transitions from a mild state to a critical state (severe state in this study), the protein expression of a specific molecular module usually satisfies the following three statistical conditions: (i) the expression deviation (ED_in) within the module increases significantly, indicating an increase in protein expression fluctuations; (ii) the correlation coefficient (PCC_in) between proteins within the module increases rapidly, reflecting an enhanced correlation between proteins within the module; (iii) the correlation coefficient (PCC_out) between proteins inside and outside the module drops sharply, indicating a weakened association between modules.

[0054] During the calculation process, the target protein and its main neighboring proteins in the sample-specific network (SSN) are regarded as a local module. According to the DNB theory, the local DNB score of protein \(x\) in sample \(E\) is defined as Equation (3): (Equation 3) Among them, sED_in represents the single-sample expression deviation of the DNB module, which is used to evaluate the expression change of protein x in the sample. The DNB score of the sample is obtained by calculating the mean of the local DNB scores of the top K proteins (DNB module) in the SSN. According to the number of proteins in the SSN and the distribution characteristics of the local DNB scores, K = 30 is set (the DNB scores remain stable under different K values). The "state-level DNB score" of the disease is the mean of the DNB scores of all samples in that state. The critical point is determined by the maximum DNB score, and the proteins in the critical point SSN are defined as DNB proteins (DNB module). To ensure the robustness of DNB proteins, only the proteins that rank among the top K in at least 50% of the patient samples in the same disease state are retained as state-specific DNB proteins, and the DNB scores and ΔPCCs in each state are calculated.

[0055] The state-specific DNB score is calculated using the frequency-weighted aggregation method: The landscape DNB score is expressed as , where f_s(x) is the frequency of occurrence of protein x in the disease state: (Equation 4) N_present(x) represents the number of samples containing protein x, and N is the total number of samples in the disease state. The state-specific ΔPCC is calculated as in Equation (5): (Equation 5) where the frequency of the protein pair (x,y) is defined as Equation (6): (Equation 6) N_present(x,y) is the number of samples in which this interaction is detected, and N is the total number of samples in the disease state. This method captures the early signals of critical transitions in biological systems through dynamic network features.

[0056] (4) Data analysis Differential expression analysis of proteomics: The limma package (version 3.58.1) in R language was used to perform differential expression analysis on proteins in the healthy control group (HC), non-severe acute pancreatitis group (NSAP), and severe acute pancreatitis group (SAP) to identify differentially expressed proteins (DEPs). The identification of differentially expressed proteins was based on the criteria of adjusted p-value < 0.05 and |logFC| > 1.5.

[0057] Data visualization: The expression distribution levels were shown by creating violin plots, volcano plots were constructed to highlight significantly differentially expressed proteins (adjusted p-value < 0.001, |logFC| > 1.5), and Venn diagrams were used to depict the overlaps between differentially expressed proteins. A hierarchical clustering heatmap was generated using the pheatmap package (version 1.0.12) to reveal the expression patterns among groups.

[0058] Functional enrichment analysis: The clusterProfiler package (version 4.10.1) was used for functional enrichment analysis, and biological processes and pathways enriched in differentially expressed proteins were determined through GO and KEGG pathway analyses (p < 0.05, q < 0.2).

[0059] Analysis environment: All analyses and visualizations were performed in R language (version 4.3.3).

[0060] 5. Basic experimental part (1) Isolation of white blood cells (WBC) - Erythrocyte sedimentation method Fresh anticoagulated whole blood was mixed with erythrocyte sedimentation solution at a ratio of 1:1 and then diluted with 0.9% saline. The mixture was allowed to stand at 20 ± 2 °C for 30 minutes to promote erythrocyte sedimentation. After phase separation, the upper layer of liquid rich in white blood cells was carefully aspirated. Residual erythrocytes were lysed using hypotonic ammonium chloride-potassium chloride buffer. The isolated white blood cell fraction was immunostained with fluorescein-labeled anti-mouse antibodies (against CD45-APC, Ly6G-FITC, CD11b-PB, and CFP) for subsequent phenotypic analysis.

[0061] (2) Determination of lipopolysaccharide (LPS)-induced TNFα production Fresh anticoagulated whole blood was aliquoted into sterile polypropylene tubes and stimulated with ultrapure LPS (100 ng / mL for human samples, 5 μg / mL for mouse samples) at 37 °C for 4 hours in a humidified incubator with 5% CO2. After stimulation, the samples were centrifuged at 3,000 g for 10 minutes at 4 °C to pellet the cell components. The resulting plasma supernatants were collected, aliquoted, and stored at -80 °C until cytokine quantification.

[0062] (3) Enzyme-linked immunosorbent assay (ELISA) Detection of human peripheral blood TNFα using a high - adsorbing 96 - well plate: Coat the anti - human TNFα capture antibody (diluted 1:1000) overnight at 4°C. Block with 5% bovine serum albumin (BSA) for 1 hour at room temperature. Add the calibrator (recombinant human TNFα) and the test samples in sequence and incubate for 2 hours, then incubate with the biotinylated detection antibody (diluted 1:1000) and the streptavidin - horseradish peroxidase (HRP) conjugate (diluted 1:5000) for 1 hour each in sequence. Wash 4 times and 7 times respectively with PBS (PBST) containing 0.05% Tween - 20 after each step. Develop color with 3,3',5,5' - tetramethylbenzidine (TMB), terminate the reaction with 1 M H2SO4, and detect the absorbance at 450 nm (reference wavelength 570 nm) using an enzyme - linked immunosorbent assay reader.

[0063] (4) Flow cytometry and cell sorting Human peripheral blood neutrophils were incubated on ice for 30 minutes and labeled with CD66b - FITC, CD11b - PB, C5aR - PE, FPR1 - APC, and CD64 - PE / Cyanine7 fluorescent antibodies. C4d detection used an indirect labeling method: After incubation with the primary antibody (anti - C4d, diluted 1:1500) for 1 hour, the secondary antibody (diluted 1:500) was labeled for 30 minutes. Mouse peritoneal lavage cells were synchronously labeled with CD45 - APC, Ly6G - FITC, CD11b - PB, and F4 / 80 - PE / Cyanine7. All samples were detected by an Agilent flow cytometer and analyzed using FlowJov10.6 software.

[0064] Cell sorting was performed using human peripheral blood cells after removing red blood cells labeled with CD66b - FITC, CD3 - APC, CD14 - PE / Cyanine7, and CD11b - PB, adjusted to 5×10 6 –1×10 7 cells / mL and sorted by a BD FACS Aria SORP system.

[0065] (5) Complement activation and CFP / C5b - 9 deposition detection Zymosan (1 mg / mL) was heat - activated by boiling for 30 minutes. 40% (v / v) plasma was diluted in gelatin - barbital buffer containing Ca²⁺ / Mg²⁺ (GVB++), and incubated with zymosan at a final concentration of 0.05 or 1 mg / mL at 37°C for 2 hours. CFP deposition was quantified by Western blotting and flow cytometry, and the C5b - 9 complex was detected synchronously.

[0066] (6) Subcellular co - localization analysis Peripheral blood neutrophils were attached to cover slips by low-speed centrifugation, fixed with 4% paraformaldehyde for 15 minutes, and blocked with 10% goat serum for 1 hour (at room temperature). Incubate with anti-CFP, LTF, MMP9, and MPO primary antibodies (diluted 1:100) overnight at 4°C, and then incubate with AF488 / AF594-conjugated secondary antibodies (diluted 1:500) for 1 hour. After DAPI counterstaining, observe using an Olympus IXplore live cell imaging system.

[0067] (7)Western blot analysis Extract tissue / cell proteins using pre-chilled RIPA lysis buffer containing protease / phosphatase inhibitors, and determine protein concentration by the BCA method. After electrophoresis on 12.5% SDS-PAGE, transfer to a nitrocellulose membrane, block with 5% skim milk-TBST for 1 hour (at room temperature), and then incubate with primary antibodies overnight at 4°C, including: anti-β-actin (1:5000), anti-Gapdh (1:5000), anti-AKT (1:5000), anti-phosphorylated AKT (1:2500), anti-phosphorylated p40phox (Thr154) (1:1000), anti-phosphorylated p47phox (Ser345) (1:1000), anti-ERK (1:5000), anti-phosphorylated p44 / 42 MAPK (Erk1 / 2) (Thr202 / Tyr204) (1:1000), anti-caspase-3 (1:1000), anti-p62 (1:1000), anti-LC3B (1:1000), anti-CFP (1:500), anti-phosphorylated NF-κB p105 (Ser932) (1:1000), anti-phosphorylated NF-κB p65 (Ser468) (1:1000), anti-DSDMD (1:1000), anti-CitH3 (1:1000). After washing with TBST, incubate with HRP-labeled secondary antibodies (1:5000, 1 hour at room temperature), develop using chemiluminescence (ECL, Abbkine), and collect signals using a Touch Imager system. Plasma samples were diluted (human: 10-fold; mouse: 5-fold) and then denatured.

[0068] (8)Neutrophil CFP release kinetics Freshly isolated human neutrophils were cultured in RPMI 1640 containing 10% fetal bovine serum (FBS), serum-free RPMI 1640, and 10% FBS-RPMI medium containing lipopolysaccharide (LPS, 100 ng / mL), fMLP (100 nM), PMA (100 ng / mL), TNFα (50 ng / mL), C5a (20 nM), or GM-CSF (20 ng / mL). Collect cells at 0.5, 1, and 2 hours respectively, and analyze the dynamic expression of CFP by Western blot.

[0069] 6. Neutrophil function assay (1)Reactive oxygen species (ROS) detection Neutrophils (1×10 5 cells / well) were suspended in phenol red-free and serum-free RPMI 1640 medium and added to a 96-well white microplate. After pre-equilibrating at 37 °C for 15 minutes, phorbol 12-myristate 13-acetate (PMA, 100 ng / mL) and luminol (500 μM) were added. The control group contained only luminol medium. A BioTek Synergy H1 microplate reader was used to record the chemiluminescence signal in real time, once every 2 minutes for 90 minutes.

[0070] (2)Neutrophil extracellular trap (NET) formation analysis Human neutrophils (2.5×10 5 cells / well) were seeded in a 48-well plate and stimulated with PMA (100 ng / mL) or Escherichia coli (MOI 3:1) for 3 hours at 37 °C and 5% CO2. The negative control was unstimulated cells. Cells were fixed with 4% paraformaldehyde for 1 hour, permeabilized with 0.1% Triton X-100, and stained with SYTOX Green nucleic acid dye (100 nM) for 30 minutes in the dark. After washing 3 times with PBS, NET structures were observed using an Olympus IX73 fluorescence microscope.

[0071] (3)Neutrophil migration assay Human peripheral blood neutrophils (at a concentration of 1×10^6 cells / mL, suspended in RPMI 1640 medium containing 0.1% BSA) were seeded onto the upper chamber of a 3-μm pore size Transwell insert (Corning #353096), and 500 μL of chemotactic factor medium (RPMI 1640 / 0.1% BSA + 20 nM N-formylmethionyl-leucyl-phenylalanine (fMLP)) was added to the lower chamber. After incubation at 37 °C for 3 hours, cells that migrated to the lower surface of the membrane were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet for 10 minutes. After mounting, images were taken using a bright-field microscope (Olympus IX73). Cells that completely migrated to the lower chamber were collected by centrifugation (300 g, 5 minutes) and quantified using a hemocytometer. In the mouse neutrophil experiment, the fMLP concentration was adjusted to 50 nM and the incubation time was extended to 4 hours.

[0072] (4)Bacterial killing assay E. coli was cultured in a shaker at 37 °C (200 rpm) until the stationary phase. The cells were collected by centrifugation (10,000 g, 3 minutes), washed once with sterile saline, and adjusted to a concentration of 1×10^9 CFU / mL. The bacterial suspension was incubated with 20% (v / v) autologous serum at 37 °C for 30 minutes for opsonization. Neutrophils (0.8×10^6 cells / well) and opsonized bacteria were co-cultured at an effector-to-target ratio of 5:1 in RPMI-1640 for 90 minutes (37 °C), with the opsonized bacterial suspension without neutrophils as the control. 10 μL of the sample was added to 10 mM NaOH (pH 10.0) to lyse the cells, serially diluted, and inoculated onto LB agar plates, and the colony-forming units (CFU) were counted after incubation at 37 °C for 18 hours. The formula for calculating the bactericidal efficiency is as follows: Bactericidal activity (%) = (CFU in the experimental group / CFU in the control group) × 100% 7. Construction and characterization of the CAE-zymosan mouse model (1) Optimized modeling method Male C57BL / 6 mice (6 - 8 weeks old, weighing 22 ± 2 g, Beijing Huafukang Biotechnology Co., Ltd.) were used and housed in a specific pathogen-free (SPF) environment. All procedures were carried out in accordance with the regulations of the Experimental Animal Ethics Committee of Sichuan University (approval number #20190509023). The mice were randomly grouped by ear tag stratification randomization method, and the experimenters and evaluators were all blinded.

[0073] After the mice were fasted for 12 hours, they were intraperitoneally injected with caerulein (CAE; 100 μg / kg) 9 times at 1-hour intervals, followed by a single intraperitoneal injection of zymosan (1 g / kg). After the modeling, the mice were allowed free access to food and water. Pancreatic tissue and plasma samples were collected at different time points after zymosan injection for histopathological evaluation and biochemical index detection, respectively.

[0074] (2) Survival analysis experiment Three groups of models (n = 10 / group) were established based on the optimized parameters: negative control (NT): treated with saline, acute pancreatitis (AP): treated with only CAE, severe acute pancreatitis (SAP): treated with CAE + zymosan. The survival rate and clinical scores (activity, posture, piloerection response) were continuously monitored for 12 days, and recorded twice a day.

[0075] (3) Hematological analysis EDTA-K2 anticoagulated whole blood was collected by retro-orbital venous plexus puncture, and routine blood tests were completed using a Mindray BC-5120 automated hematology analyzer.

[0076] (4) Plasma biochemical analysis EDTA-K2 anticoagulated blood was centrifuged at 3,000×g (4°C, 20 minutes) to obtain plasma. Detection was performed according to the standard procedure of Roche Cobas c311 biochemical analyzer: Samples were diluted 1:10 for amylase (AMY) and lipase (LIP); undiluted samples were used for alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatine kinase isoenzyme (CK-MB), lactate dehydrogenase (LDH), blood urea nitrogen (BUN), and creatinine (Cr).

[0077] (5) Peritoneal lavage and analysis Under sterile conditions, 5 mL of PBS was injected into the abdominal cavity, gently mixed, and the lavage fluid was recovered. Bacterial quantification: The lavage fluid was serially diluted and inoculated onto LB agar, and colonies were counted after incubation at 37°C for 18 hours.

[0078] Immunophenotype analysis: Lavage cells were labeled with CD45-APC, Ly6G-FITC, CD11b-PB, and F4 / 80-PE-Cy7 fluorescent antibodies, and leukocyte subsets were detected by flow cytometry.

[0079] (6) Histopathological evaluation Liver, lung, kidney, pancreas, and small intestine tissues were fixed in 10% neutral buffered formalin for 24 hours, embedded in paraffin, and sectioned (5 μm). Sections stained with hematoxylin and eosin (H&E) were digitized using a 3DHISTECH Pannoramic MIDI whole-slide scanner (20× objective).

[0080] (7) Intestinal barrier function detection Twelve hours after GM-CSF administration, FITC-dextran (40 kDa, 0.5 mg / g body weight) was administered by oral gavage. Serum was collected 4 hours later, and the concentration of FITC-dextran in serum was measured by fluorescence (excitation wavelength 485 nm, emission wavelength 528 nm) and quantified using a standard curve.

[0081] 8. Comparative analysis of surviving and dead individuals SAP model mice (n = 20) were monitored for endpoint indicators four times a day: hypothermia, bradykinesia, prone position, and respiratory distress. Mice meeting the euthanasia criteria were sacrificed by overdose anesthesia with isoflurane and tissues were processed immediately. Individuals from the same litter that survived and received the same CAE-zymosan treatment were used as controls. The detection indicators were consistent with those of the longitudinal study. All data were collected by blinded researchers using pre-calibrated instruments, and statistical randomness was ensured by random cage assignment.

[0082] 9. Optimization of GM-CSF treatment regimen guided by plasma properdin concentration The CAE - zymosan model longitudinally monitored plasma CFP concentration and TNFα after whole - blood LPS stimulation. Time - series analysis was performed at 6 time points: 12 hours, 36 hours, 3 days, 5 days, 8 days, and 12 days after modeling (n = 30 / time point, 10 in each group: NT group, AP group, SAP group). End - point sample collection indicators included: systemic indicators: body weight, complete blood count (CBC), plasma amylase (AMY), TNFα (ELISA method); semi - quantitative detection of plasma CFP (Western blot method).

[0083] According to the changes in plasma CFP concentration and TNFα production after whole - blood LPS stimulation detected in the above - mentioned study, the optimal dosing regimen of granulocyte - macrophage colony - stimulating factor (GM - CSF) was determined using a single - factor screening method (n = 5 in each group): dosing timing: 0 hours, 12 hours, 36 hours after zymosan - induced SAP; dose regimen: 200 μg once a day × 3 days, 100 μg twice a day × 3 days, 100 μg twice a day × 5 days.

[0084] The efficacy was evaluated through mortality, bone marrow neutrophil function detection, and intestinal barrier integrity indicators. Finally, the optimal regimen (initiated 36 hours after SAP induction, 100 μg subcutaneous injection twice a day for 5 days) was selected for subsequent research, and untreated SAP mice were used as positive controls.

[0085] 10. Data Analysis Clinical data were analyzed using R software (v4.3.1): categorical variables were expressed as frequency (percentage), and chi - square test or Fisher's exact test was used; normally distributed continuous variables were expressed as mean ± standard deviation, and t - test was used; non - parametric data were analyzed using the Mann - Whitney test. Variables with p < 0.05 in univariate analysis were included in multivariate logistic regression, and the optimal cut - off value of the receiver operating characteristic (ROC) curve was determined by the Youden index.

[0086] Experimental data were analyzed using GraphPad Prism (v10.0): results were presented as mean ± standard deviation. Inter - group comparisons were performed using unpaired t - test, one - way ANOVA with Bonferroni correction, or two - way ANOVA with Tukey's post - hoc test. Significance was defined as two - tailed p < 0.05.

[0087] Experimental Example 1 Landscape Dynamic Network Biomarker (l - DNB) Method for Identifying Key States of Acute Pancreatitis and Identifying Predictive Markers of Disease Severity This invention was applied to cohort samples (demographic and clinical characteristics are detailed in Figure 14Plasma extracellular vesicles (EVs) were isolated and analyzed. Plasma protein mass spectrometry data were cited from the supplementary materials of the reference (DIA-Based Proteomic Analysis of Plasma Protein Profiles in Patients with Severe Acute Pancreatitis), and the original data were included in the l-DNB analysis of the present invention after the same preprocessing process. This literature contains proteomic data of 3 healthy volunteers (control group) and 10 SAP patients (experimental group).

[0088] The present invention uses the dynamic network biomarker (DNB) method to identify the critical state before the deterioration of AP by analyzing the molecular correlation pattern and the topological structure of the interaction network, and constructs a multi-molecular cooperative network for warning disease progression.

[0089] Specifically, the present invention uses the landscape DNB (l-DNB) method to construct a sample-specific molecular network through three stages: Reference network construction: Based on the protein expression data of 15 healthy samples and the high-confidence protein interaction information in the STRING database, a stable reference network is established; Sample-specific network generation: According to the DNB theory, calculate the Pearson correlation coefficient (PCC) of the protein expression values at three stages of each patient sample, and construct a sample-specific network (SSN) through statistical perturbation analysis with the control sample. Critical state identification: Calculate the molecular local DNB score step by step to form a landscape map representing the overall state of the network. To determine the core DNB proteins and regulatory network modules, further compare the changes in the SSN before and after estimating the critical point through differential network analysis, and use a voting mechanism to screen key nodes: Only the proteins that are hub nodes in more than half of the SSNs are retained as core proteins for subsequent functional verification. This method integrates the expression characteristics at the single-sample level and the prior interaction information, significantly improving the detection sensitivity of the disease critical state at the network level. The network-based key node detection (l-DNB) method is a model-free analysis framework, which does not require a large sample size required for machine learning and avoids the overfitting problem of traditional methods. This framework identifies key biomarkers in pathological progression by systematically evaluating three DNB criteria (based on the dynamic characteristics of the critical transition state of biological systems) for each sample.

[0090] Specifically, the proteomic profiles of plasma extracellular vesicles of 88 subjects were analyzed, and finally 228 proteins with stable expression among samples were screened out ( Figure 1 A). The l-DNB method was used to identify the proteomic dynamic critical state before disease deterioration ( Figure 1 B), and the flow chart shows a three-step analysis strategy for identifying DNB core proteins from single-sample data ( Figure 1C). There was no significant difference in protein expression intensity between groups. The network topology constructed based on the DNB theory and l-DNB method showed that ( Figure 1 D): The network of the healthy control group (HC) presented a localized fluctuation pattern, maintaining homeostatic regulation; the network of mild acute pancreatitis (NSAP) had the most significant fluctuations, characterizing the maximum network instability in the critical transition state; the network of severe acute pancreatitis (SAP) showed the coexistence of amplified fluctuations and topological reconstruction, with the core DNB proteins forming stable hub structures, suggesting the pathological strengthening of the network architecture through a compensatory dysregulation mechanism. DNB score analysis indicated that the NSAP stage was the key window period for disease intervention ( Figure 1 E), confirming that l-DNB could effectively identify the critical point before the deterioration of acute pancreatitis (AP).

[0091] Furthermore, novel biomarkers were screened from the DNB core proteins based on the change in Pearson correlation coefficient (∆PCC) of the sample-specific network (SSN). The types of network edges included: (i) edges with increased correlation (PCC_(n + 1)> PCC_n); (ii) edges with decreased correlation (PCC_(n + 1)< PCC_n); (iii) edges with unchanged correlation (PCC_(n + 1)≈ PCC_n). Compared with the reference network, the regulatory networks at each disease stage had significant changes ( Figure 1 F). Proteins such as APOH, APOA1, CFP, PLG, and CLU showed characteristics of edges with increased or decreased correlation in the disease state. Among them, CFP showed edges with decreased correlation in both the NSAP and SAP groups, indicating a significant reduction in its interaction partners, and this trend intensified with disease progression. Validation in public datasets showed that ( Figure 1 G), the change in the node degree of CFP in the sample network of SAP patients was the most significant, and the change amplitude of its PCC value was the largest, further corroborating the instability of the CFP-centered regulatory network in disease progression and suggesting its key role in the occurrence of SAP. In addition, CFP showed the highest local DNB score in the SAP state, characterizing its transformation into a dynamic network hub, manifested as enhanced fluctuations and selective loss of connections, reflecting the reprogramming characteristics of the regulatory network ( Figure 2 A - C).

[0092] GO enrichment analysis was performed on the DNB core proteins of SAP vs NSAP in plasma vesicle mass spectrometry data ( Figure 2 D), and it was found that they were significantly enriched in pathways closely related to the severity of AP, including the complement pathway (C3, C5, C9, CFP), protease inhibition system (SERPINA1, SERPINC1, SERPIND1), oxidative stress (CAT), coagulation cascade (F2, FGA, FGG), fibrinolysis system (PLG), and inflammation - metabolism interaction pathway (TTR).

[0093] Compared with traditional differential expression analysis methods, the method of the present invention successfully detects CFP molecules that are related to the severity of AP but have subtle expression fluctuations (usually <70% variation), and such molecules are easily overlooked by traditional analysis paradigms. To identify differential proteins between groups, the present invention adopts a conventional differential analysis method ( Figure 2 E-G). In the comparison between the healthy control group (HC) and the non-severe acute pancreatitis (NSAP) group, and between the HC and the severe acute pancreatitis (SAP) group (adjusted p-value < 0.05 and |logFC| > 1.5), dozens of differentially expressed molecules were found in total. Among them, the most significantly differentially expressed ones are acute-phase response proteins such as serum amyloid A (SAA), C-reactive protein (CRP), and lipopolysaccharide-binding protein (LBP16). However, no significant difference in protein expression was detected between the severe and mild groups (adjusted p-value < 0.05 and |logFC| > 1.5). Conventional analysis failed to identify complement factor properdin (CFP) as a differential molecule due to two factors: the limited range of CFP expression fluctuations: the maximum variation amplitude in each study group did not exceed 70%; the biphasic dynamic characteristics: as a component of the complement system, CFP presents a dynamic pattern of first increasing and then gradually decreasing during disease progression. This result is consistent with the literature reports, indicating that conventional differential analysis methods are not sensitive enough to biomarkers such as CFP with complex dynamic characteristics and are difficult to distinguish the severity of the disease.

[0094] Experimental Example 2 Plasma CFP concentration can stratify acute pancreatitis patients with different conditions The present invention included 142 AP patients. After screening by exclusion criteria, 122 patients were finally included in the study, including 41 SAP patients and 81 non-severe acute pancreatitis (NSAP) patients ( Figure 3 A). There were no significant differences in the baseline data between the two groups ( Figure 15 ).

[0095] Univariate analysis showed that there were significant differences in lymphocyte count, albumin, blood glucose, CRP, CFP, IL-6, PCT, neutrophil / lymphocyte ratio (NLR), and platelet / lymphocyte ratio (PLR) between the NSAP and SAP groups, and they were included in the multivariate analysis ( Figure 16 ). As Figure 16 shown, CFP is the only independent risk factor with statistical significance (p < 0.05). Further detection of the plasma sC5b-9 level in each group found that the expression in the SAP group was the lowest but without statistical significance ( Figure 3 B).

[0096] Since CFP is not a routine clinical test index, the present invention determines its normal reference value range through 44 healthy controls (HC). The plasma CFP concentrations of the three groups were: HC group (23.16±5.324) μg / mL, NSAP group (19.11±6.305) μg / mL, SAP group (9.908±3.311) μg / mL, and the differences between groups were significant (p<0.0001) ( Figure 3 C).

[0097] When CFP was analyzed as a continuous variable, its concentration showed a significant gradient change among the three groups. The ROC curves of the NSAP and SAP groups ( Figure 3 D) showed that when the cut-off value was 11.565 μg / mL, the area under the curve (AUC) reached 0.923, the sensitivity was 0.926, the specificity was 0.854, the positive predictive value (PPV) was 0.926, and the negative predictive value (NPV) was 0.854. The ROC curves of the HC group and the disease group (SAP+NSAP) ( Figure 3 E) showed that when the cut-off value was 18.055 μg / mL, the AUC was 0.811, the sensitivity was 0.639, the specificity was 0.886, the PPV was 0.940, and the NPV was 0.470, indicating that CFP has a better predictive efficacy for differentiating NSAP and SAP.

[0098] Aiming at the heterogeneity of the time window from the onset of the disease to blood sampling in patients, the present invention analyzed the time correlation between the disease progression and the plasma CFP level. The scatter plot analysis ( Figure 3 F) showed that the plasma CFP concentration was significantly negatively correlated with the time after the onset of the disease, showing a progressive depletion characteristic with the prolongation of the disease course. The comparative analysis with classical indexes such as NLR, PLR and monocyte / lymphocyte ratio (MLR) further confirmed the prognostic judgment advantage of CFP ( Figure 3 G and Figure 17 ). In the present invention, the plasma CFP level of the healthy control group was 23.16±5.32 μg / mL, which was significantly higher than the literature reports: Stover et al. reported that the healthy population was 18.4±5.51 μg / mL (range 7.6-34.10 μg / mL), and the study of Southeast Asian populations showed 8.6 (8.0-9.3) μg / mL. These differences are due to race-related biological variations and technical differences in detection methods, suggesting that demographic characteristics and methodological biases need to be systematically controlled in multi-center biomarker research and the establishment of population-specific reference ranges. Since there is a clinical need for a biomarker for early prediction of AP, the patient groups in the above NSAP and SAP groups with a blood sampling time within 72 hours after the onset of the disease were analyzed and compared ( Figure 3In group (H), the results showed that the plasma CFP in the NSAP group was still significantly higher than that in the SAP group (p < 0.01), suggesting that plasma CFP can also meet the requirements and is worthy of verification in a larger cohort.

[0099] In addition, the disease progression of the studied patient cohort was also tracked after blood sampling, and the ICU admission rate, operation rate, length of hospital stay, and occurrence of complications of the patients were recorded. The results showed that the prognosis of the NSAP group was significantly better than that of the SAP group. This study suggests that the plasma CFP concentration not only reflects the condition of the patients at the time of sampling, but also has an obvious predictive effect on the prognosis of the patients ( Figure 18 ).

[0100] In summary, plasma CFP is a severity-related dynamic biomarker, and its predictive efficacy provides a clinical practical tool for early risk stratification of AP. The specific mechanism of action will be further elucidated in subsequent studies.

[0101] Experimental Example 3 The regulation of plasma CFP concentration mainly depends on the complement activation pathway The mechanism of plasma CFP depletion involves three reasons: (i) excessive consumption mediated by complement cascade activation; (ii) synthetic defects caused by abnormal neutrophil function; (iii) blocked CFP mobilization due to disorder of the granule exocytosis mechanism. The present invention conducts mechanism analysis around the above hypotheses.

[0102] The degree of complement activation was evaluated by detecting the deposition level of C4d on the surface of peripheral blood cells by flow cytometry. The results are shown in Figure 4 and Figure 5 . Peripheral blood neutrophils were purified by density gradient centrifugation, and the purity was greater than 95% ( Figure 5 A). In addition, peripheral blood red blood cells (CD235+) were also stained for analysis ( Figure 5 A). Activation of the classical / lectin pathway can cause C4 to be cleaved into C4d, and its covalent binding property makes it a persistent marker of complement activation. C4d on the surface of red blood cells was detected only in some samples, and the expression level in the SAP group was significantly higher than that in other groups ( Figure 4 A). C4d on the surface of neutrophils could be detected in all samples, and the expression level in the SAP group was significantly higher than that in the control group ( Figure 4 B). The deposition level of neutrophil C4d was significantly negatively correlated with the plasma CFP concentration ( Figure 5 D). The expression of C5aR was not significantly correlated with the disease severity ( Figure 4 C and Figure 5 C). In addition, the up-regulation of the expression of CD64 (FcγRI) on the surface of neutrophils and the down-regulation of the expression of FPR1 (fMLP receptor) in the SAP group ( Figure 4 D and Figure 5B), combined with the results of blood routine tests (elevated neutrophils), indicating that severe patients are in an infectious state ( Figure 5 D), it is speculated that pathogen infection drives abnormal complement activation and accelerates the consumption of CFP.

[0103] In vitro complement activation experiments using zymosan, a component of the cell wall of Saccharomyces cerevisiae, found that the concentration of CFP decreased significantly after plasma was co-incubated with zymosan ( Figure 4 E), confirming that CFP is consumed during complement activation. Confocal microscopy showed that compared with the healthy control group, the deposition ability of CFP on the surface of zymosan in the plasma of SAP patients was significantly reduced ( Figure 4 F), suggesting that its complement activation ability is weakened and its pathogen clearance function is impaired.

[0104] Quantitative analysis of neutrophils showed that the neutrophil count in the peripheral blood of the disease group was significantly higher than that of the healthy control group ( Figure 5 D), although the SAP group showed a higher level of neutrophilia ( Figure 5 D), its plasma CFP was still continuously lowly expressed, ruling out the possibility that insufficient neutrophils led to CFP deficiency. Through laser confocal localization, it was found that CFP in human primary neutrophils co-localized with the specific granule marker lactoferrin (LTF), but not with the gelatinase granule marker MMP-9 and the azurophilic granule marker MPO ( Figure 4 G), confirming that it is stored in specific granules. The expression of CD66b (specific granule marker) on the surface of neutrophils in the SAP group was significantly higher than that in the NSAP and healthy groups ( Figure 4 H and Figure 5 D), suggesting enhanced exocytosis of specific granules. Cross-cohort analysis of CFP expression in neutrophils ( Figure 4 I, J) showed heterogeneity among patients: some cases showed high CFP expression (due to inflammatory stimulation), while others showed CFP depletion (related to continuous release). In vitro stimulation experiments confirmed that various inflammatory mediators can induce CFP release ( Figure 4 K, L, M), and no defect in CFP secretion function was found in severe patients.

[0105] The above results confirmed that the abnormal activation of the complement system in SAP patients is the core mechanism driving the accelerated consumption of plasma CFP, providing a reasonable explanation for its decreased concentration.

[0106] Experimental Example 4 Study on the correlation between plasma CFP concentration and peripheral neutrophil function After clarifying the association between peripheral neutrophil count and plasma CFP concentration, the present invention further explores the functional interaction mechanism between the two. The circulating neutrophil count shows different distributions in SAP patients: 62% show neutrophilia, and 38% have paradoxical neutropenia ( Figure 6 A and Figure 7 A). To reduce the potential interference of experimental variation on the assessment of neutrophil function, the present invention synchronously collects samples from healthy control group (HC), non-severe acute pancreatitis (NSAP) and severe acute pancreatitis (SAP) patients (completed within 40 minutes) for experiments.

[0107] Functional characteristic analysis shows that the production of reactive oxygen species (ROS) by neutrophils in SAP patients is significantly impaired ( Figure 6 B). The rapid generation of ROS is the core basis of neutrophil immune function, and its functional defect indicates impaired overall neutrophil function.

[0108] The Transwell migration experiment confirms the migration barrier of SAP neutrophils: the cell retention rate on the polycarbonate membrane increases ( Figure 6 C), and the chemotaxis efficiency is significantly reduced ( Figure 6 D and Figure 7 B). The migration defect is positively correlated with insufficient ROS production, while over-activated adhesion is related to the up-regulation of surface markers such as selectins, integrin β2 and cell adhesion molecules. The adhesion experiment shows that the adhesion ability of neutrophils in SAP patients is enhanced, but the migration activity is significantly reduced. Adhesion is mediated by selectins, β2 integrins and cell adhesion molecules (CAMs), and its dynamic change reflects the cell activation state. Previous studies have shown that the up-regulation of surface integrin expression can promote the enhancement of neutrophil-endothelial cell adhesion. This high adhesion state hinders the extravasation of neutrophils into the lung tissue and alveoli, thus weakening the ability to clear bacteria in lung infections. Therefore, the hyperadhesion of SAP neutrophils is an important mechanism for the impairment of host anti-infection defense function.

[0109] The formation of neutrophil extracellular traps (NETs) is evaluated by Sytox Green fluorescence quantification ( Figure 6 E and Figure 7C). Although mouse models have confirmed that the driving of NETosis by inflammatory mediators can exacerbate pancreatitis, there is a paradoxical phenomenon of reduced NETs formation in neutrophils of SAP. This phenomenon is caused by two interdependent mechanisms: (i) the enhanced bacterial clearance activity of SAP neutrophils (phagocytosis index: 4.7 ± 1.2 vs. HC 2.1 ± 0.6) retains elastase and myeloperoxidase in the phagolysosome, blocking the nuclear translocation required for chromatin decondensation; (ii) the impaired activity of NADPH oxidase limits the ROS-dependent activation of peptidylarginine deiminase 4 (PAD4), a prerequisite for histone citrullination. The bactericidal ability of neutrophils in SAP patients is significantly impaired ( Figure 6 F), and there is a strong correlation with the defect in ROS generation. The overall bactericidal ability of SAP neutrophils is weakened, but the bactericidal activity in their plasma is anomalously enhanced (data not shown). This contradictory phenomenon suggests that the bactericidal effect of SAP plasma mainly depends on the membrane attack complex (MAC) generated during complement activation: the increased deposition of C4d on peripheral blood cells of SAP patients indicates a significant enhancement of the complement cascade activity.

[0110] Signal pathway analysis ( Figure 6 G) shows that the phosphorylation levels of p47, p40 and ERK in neutrophils of the SAP group are decreased, while the phosphorylation of AKT is enhanced. The weakened phosphorylation of p47phox / p40phox indicates a disorder in the assembly of the NADPH oxidase complex, directly explaining the ROS defect observed in the functional experiments. The dephosphorylation of ERK reflects the interruption of the MAPK-ROS positive feedback loop, and the overactivation of AKT is a compensatory activation of the PI3K pathway. The increased phosphorylation of p105 indicates the activation of the classical NF-κB signal driven by TLR4, which is related to the circulating pathogen-associated molecular patterns (such as LPS) caused by bacterial translocation in SAP patients.

[0111] The experiment on the generation of TNFα by LPS-stimulated whole blood leukocytes shows that peripheral monocytes of SAP patients present an immunosuppressive phenotype, manifested as weakened TNFα secretion ( Figure 6 H and Figure 7 D). It is worth noting that neutrophils of some SAP patients present paradoxical hyperactivation characteristics, and this heterogeneity reflects the temporal fluctuations of disease progression - the neutrophils are overactivated in the early stage and then gradually exhausted. Neutrophil dysfunction is jointly mediated by multiple immunosuppressive stresses (C5a, LPS, cytokines and immunometabolism), but there is no significant difference in the total expression level of CD11b on the surface of neutrophils ( Figure 7 E).

[0112] The present invention proposes that by dynamically monitoring the plasma CFP concentration, individual baseline differences can be corrected to achieve longitudinal tracking of the progression of immunodysfunction, providing a new strategy for clinical assessment of disease severity.

[0113] Experimental Example 5 Establishment and Pathological Characteristics of SAP Mouse Model In order to more extensively explore the clinical application potential of CFP, the present invention conducted research on a mouse model of acute pancreatitis. The present invention adopted a modeling strategy of simultaneously activating the complement cascade and macrophage phagocytosis by intraperitoneal injection of zymosan. Based on the biorecalcitrant properties of zymosan, it can persistently induce systemic inflammatory response and multiple organ dysfunction syndrome (MODS). The CAE-Zymosan hybrid model precisely simulates the characteristic SIRS-MODS dual pathological process of clinical SAP by inducing pancreatic interstitial edema with caerulein and mediating the deterioration of the condition with zymosan.

[0114] CAE-Zymosan Modeling Scheme ( Figure 8 A) Can reproduce the characteristic biphasic mortality pattern of clinical SAP ( Figure 8 B): Rapid death (20 - 40%) occurs during the acute inflammatory phase (0 - 72 hours), and progressive death (15 - 30%) occurs during the subacute organ failure phase (72 hours - 10 days). The maximum weight loss reaches 20% of the baseline, and the lowest value is seen 3 - 5 days after modeling ( Figure 8 C).

[0115] The pathological results of SAP mice 12 hours after modeling showed that biochemical tests showed that compared with the AP control group, the levels of amylase (AMY) and lipase (LIP) in SAP model mice increased by 3 - 5 times, and the liver injury markers (ALT, AST) and renal function indicators (urea, creatinine) were significantly increased ( Figure 8 D); Hematological indicators showed significant leukopenia accompanied by a decrease in neutrophil / lymphocyte counts ( Figure 8 E); Intestinal pathological evaluation showed that the small intestine length was significantly shortened (18.6 ± 1.2 vs 27.4 ± 1.5 cm), indicating severe intestinal inflammation; The colony-forming units in the peritoneal lavage fluid were significantly increased, confirming bacterial translocation ( Figure 8 F, G).

[0116] Histopathological evaluation of pancreatic tissue showed typical SAP characteristics: extensive acinar necrosis (accounting for 43 ± 5% of the total tissue area), interstitial edema, and dense inflammatory infiltration ( Figure 8 H). Flow cytometry analysis showed upregulation of the expression of terminal complement components, and the density of membrane attack complex (MAC / C5b-9) on the surface of circulating neutrophils increased by 4.1 times compared with the control group, and that of bone marrow cells increased by 2.9 times ( Figure 8 I).

[0117] The CAE-Zymosan model effectively reproduces the core pathophysiological features of human SAP, including local pancreatic inflammatory response, MODS, and the biphasic disease progression pattern, providing a reliable platform for subsequent mechanism research and therapeutic intervention.

[0118] Experimental Example 6 Plasma CFP concentration for differentiating surviving and non-surviving mice in the CAE-Zymosan model To clarify the key knowledge gap regarding the functional differences of peripheral neutrophils between clinical survivors and non-survivors, the present invention uses the CAE-Zymosan model to reveal the functional heterogeneity of neutrophils in mice with different survival outcomes. Compared with surviving mice, non-surviving mice induced by CAE-Zymosan exhibit significantly different pathophysiological phenotypes: after administration of zymosan, non-surviving mice show continuous weight loss and motor retardation, and typical characteristics before death that can distinguish them from surviving mice include hypothermia, motor retardation, prone position, and respiratory distress.

[0119] The experimental results show that the growth curve indicates that non-surviving mice do not show signs of recovery ( Figure 9 A); there are significant differences in the plasma CFP concentration between the two groups of mice ( Figure 9 B). The non-surviving group of mice shows a shortened small intestine length, bacterial translocation, and massive recruitment of inflammatory cells (macrophages and neutrophils) in the peritoneal lavage fluid ( Figure 9 C and Figure 10 A).

[0120] Blood biochemical tests show that the amylase levels in both groups of mice return to normal, but the lipase level in the death group of mice is significantly increased ( Figure 9 D). Comparison between the surviving group and the group sacrificed at 12 hours shows a significant improvement in their physiological functions, while the main organ functions of the death group of mice continue to be damaged, especially the pancreas, manifested as acinar cell vacuolization / necrosis and inflammatory cell infiltration ( Figure 10 B).

[0121] Further functional analysis of mouse bone marrow neutrophils reveals that: the total number of bone marrow cells in the surviving group is within the normal range, while that in the death group only reaches one-half or lower of the normal value ( Figure 9 E and Figure 10 C). The neutrophil function of 83% of the death group of mice is significantly reduced, manifested as a decrease in the number of migrating cells, a decline in bactericidal ability, and a reduction in NETs formation ( Figure 9 F-H and Figure 10(D-F). Functional similarities were only observed in a certain pair of mice during the 3.5-day observation period. The survival difference suggests that the death group remained in the acute inflammatory phase, and its death pathological mechanism is related to the systemic inflammatory response syndrome (SIRS). It is worth noting that only 33% of the mice in the death group showed more severe peripheral monocyte immunosuppression compared with the survival group, indicating that the state of monocyte immunosuppression cannot fully reflect the overall function of the mice ( Figure 9 I).

[0122] Studies on the signaling pathways of bone marrow neutrophils in the two groups showed that the most significantly different pathways were the NADPH oxidase signaling, apoptosis, autophagy, and NF-κB signaling ( Figure 9 J and Figure 10 G).

[0123] The number of bone marrow cells in the mice of the death group decreased significantly, accompanied by impaired neutrophil function, and this phenomenon was significantly positively correlated with the decrease in circulating CFP levels. This pathophysiological connection highly coincides with the clinical observations of patients with severe acute pancreatitis described above, suggesting the existence of a conservative mechanism in the progression of critical illness across species. It is worth noting that in a mouse population with the same genetic background, there is a high degree of heterogeneity in the progression of acute pancreatitis, manifested as significant differences in mortality among individuals, indicating that the disease process is regulated by non-genetic factors (such as the microenvironment or immune status).

[0124] Experimental Example 7 Intervention study on GM-CSF-based immunotherapy guided by plasma properdin concentration for SAP mice GM-CSF (granulocyte-macrophage colony-stimulating factor) is a pleiotropic cytokine expressed by hematopoietic cells, which can regulate the differentiation of myeloid progenitor cells, neutrophil survival, and effector functions. Studies on canine models have shown that GM-CSF reduces bacterial translocation by enhancing neutrophil activity, providing a basis for its application in acute pancreatitis. Further human clinical trials have confirmed that GM-CSF alone or in combination with interferon γ (IFN-γ) can reverse monocyte dysfunction and enhance LPS-induced TNFα production, demonstrating its therapeutic potential.

[0125] The above experimental results show that both neutrophils and monocytes in clinical SAP patients have significant inhibitory dysfunction, and non-surviving mice in the SAP model also have similar phenotypes. Due to its dual functional regulation ability on these two types of cells, GM-CSF has become a potential therapeutic candidate molecule. To achieve a precision medicine strategy, the present invention systematically evaluates the value of circulating CFP levels combined with whole blood-stimulated TNFα production as biomarkers for guiding GM-CSF administration, aiming to determine the optimal treatment threshold that can achieve immune reconstruction and avoid the risk of cytokine overstimulation.

[0126] Combined with the pathological cycle of the CAE-Zymosan mouse model, several time points (12 h, 36 h, 3 d, 5 d, 8 d, 12 d) were selected for longitudinal analysis to select the appropriate timing for GM-CSF intervention.

[0127] Mice in the SAP group showed continuous weight loss ( Figure 11 A); transient hyperamylasemia peaked at 12 h and recovered at 72 h ( Figure 11 B); during the acute phase (12 - 36 h), circulating neutrophils decreased by 62% (0.9 ± 0.2 vs 2.4 ± 0.3 × 10³ / μL) and lymphocytes decreased by 47% (2.1 ± 0.4 vs 4.0 ± 0.5 × 10³ / μL). It is speculated that inflammatory cells activated under SAP pathological conditions are recruited to the lesion site; during the recovery phase (≥3 d), the hematopoietic function of the SAP group was gradually restored.

[0128] The plasma CFP level in SAP mice showed a downward trend from 12 h to 5 d after induction, and the CFP concentration rebounded in the late stage (8 - 12 d), with individual heterogeneity ( Figure 11 C); the CFB level remained relatively stable throughout the experiment. The source of CFP in mice is different from that in humans. In addition to neutrophils, the liver, lymph, and spleen also contribute significantly. This multi-tissue synthesis characteristic explains that the difference between SAP-AP in mice is weaker than the pathological manifestation in humans.

[0129] The experiment of LPS-stimulated TNFα production showed that the function of peripheral monocytes was maintained until 36 h and then continuously inhibited (up to 5 d) ( Figure 11 D, indicating that there is a temporal difference in the functional damage of monocytes and neutrophils, reflecting the differential regulatory mechanisms of the two in the SAP process.

[0130] The above longitudinal study results suggest that mice are in a critically ill condition within 0 - 36 hours after modeling, characterized by a decrease in plasma CFP concentration and / or inhibition of monocyte function. Based on this, three initial administration time points of 0 hour, 12 hours, and 36 hours were screened. Subsequently, the treatment plan was optimized to determine two key variables: (1) the treatment start time window; (2) the subcutaneous GM-CSF administration dose. The results showed that starting treatment at 36 hours could obtain the optimal efficacy, specifically manifested as high survival rate (100%), rapid recovery of body weight and small intestine length (2.5 days), improvement of intestinal barrier function (FITC-dextran leakage experiment), and inhibition of abdominal bacterial translocation ( Figure 12 A - E and Figure 13 B). In contrast, the survival rate of the 0-hour treatment group was lower and there was continuous weight loss, and the 12-hour treatment group showed intermediate efficacy. The bactericidal activity ranking was 36-hour group > 12-hour group > 0-hour group ( Figure 12F). At 0 hours after modeling, immune cell functions were intact (normal plasma CFP, sufficient bone marrow cells, and undamaged neutrophil functions) ( Figure 12 K-M); at 12 hours after modeling: plasma CFP decreased and bone marrow cells decreased, suggesting that neutrophil functions might be damaged, but monocyte activity (continuous TNFα secretion) was still retained. This transient immunosuppressive state characterized by neutrophil dysfunction but retained monocyte activity exacerbated the secondary inflammation induced after GM-CSF administration. The treatment with GM-CSF needs to be based on real-time immune monitoring to avoid administration during the high monocyte response period to reduce the risk of pro-inflammation. At 36 hours after modeling, SAP mice presented a synergistic immunosuppressive phenotype, with further decreased plasma CFP levels, continuous progression of bone marrow depletion, and significant damage to monocyte functions (decreased TNFα secretion). Administration of GM-CSF at this stage could achieve the maximum therapeutic effect, and its mechanism was realized by synchronously repairing the functions of the myeloid cell lineage (neutrophils and monocytes). In summary, the treatment window was optimized by combining plasma CFP levels with the whole blood TNFα response stimulated by LPS, providing a theoretical framework for precise immune intervention in SAP.

[0131] Three GM-CSF administration regimens were further tested: single daily administration of 200 ng (3-day course), twice-daily administration of 100 ng (3-day course), and twice-daily administration of 100 ng (5-day course). Although there was a weight loss at the beginning of the 5-day course, it was associated with a significantly reduced bacterial translocation rate and enhanced bactericidal activity ( Figure 12 G-J). The optimized regimen was finally determined to start treatment at 36 hours after modeling and continue administration at a dose of 100 ng twice a day for 5 days ( Figure 13 A).

[0132] In the formal treatment experiment, the most significant effect in the GM-CSF group was the improvement in the survival rate of mice (100% vs. 40%) ( Figure 13 C). However, no significant differences were detected in terms of body weight, bone marrow neutrophil functions, and cell signaling pathways ( Figure 13 D-K), which was due to the self-recovery of the surviving mice in the untreated group.

[0133] The intestine is a key hub in the progression of SAP. Pancreatic injury exacerbates intestinal barrier disruption, driving bacterial translocation and systemic inflammatory cascades. This study found that GM-CSF repairs intestinal barrier integrity by enhancing neutrophil-mediated bactericidal activity, suggesting that intestinal-derived bacterial dissemination is the core mechanism of intestinal injury in SAP. GM-CSF has dual regulatory effects on immunosuppressed neutrophils and monocytes, and its efficacy depends on precise dosing timing. In summary, preclinical validation studies confirmed the practical value of plasma CFP quantitative analysis in guiding immunotherapy regimens, establishing a translational research framework for CFP as a biomarker-driven neutrophil / monocyte-targeted intervention strategy.

[0134] The above are only the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the concept described herein through the above teachings or the techniques or knowledge in related fields. Any changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. Application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis.

2. The application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis according to claim 1, characterized in that The diagnosis is carried out by measuring the concentration of properdin in the blood of the subject. When the reduction range of the properdin concentration in the blood relative to the properdin concentration in the blood of the normal group is -40% to 50%, it is judged as non-severe acute pancreatitis; when the reduction range of the properdin concentration in the blood relative to the properdin concentration in the blood of the normal group is more than 50%, it is judged as severe acute pancreatitis.

3. The application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis according to claim 2, characterized in that The normal group refers to a group with the same racial background as the subject and matching some demographic characteristics.

4. The application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis according to claim 1, characterized in that The diagnosis is carried out by measuring the concentration of properdin in the blood of the subject. The reduction range of the properdin concentration in the blood relative to the properdin concentration in the subject's own blood in the early stage of the disease or before getting sick is negatively correlated with the severity of acute pancreatitis.

5. The application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis according to claim 2 or 4, characterized in that The properdin concentration in the blood of the subject and the normal group or the subject's own blood in the early stage of the disease or before getting sick is measured by the same detection method.

6. The application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis according to claim 5, characterized in that The detection methods include electrochemiluminescence technology, enzyme-linked immunosorbent assay, liquid-phase chip technology, single-molecule immune array technology, fully automatic capillary digital Western Blot, proximity extension assay technology, immunoelectrophoresis, high-performance liquid chromatography tandem mass spectrometry or microfluidic chip.

7. The application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis according to claim 2 or 4, characterized in that The types of properdin in the blood include one or more of plasma properdin, serum properdin and whole blood properdin.

8. The application of properdin in the preparation of a reagent for diagnosing the severity of acute pancreatitis according to claim 1, characterized in that The uses of the reagent for diagnosing the severity of acute pancreatitis include judging the severity of the condition of acute pancreatitis patients, judging the progress or recovery degree of the condition of acute pancreatitis patients, being used alone or in combination to assist in guiding the formulation of the drug administration plan for acute pancreatitis, judging the rationality of the drug administration plan for acute pancreatitis, and judging the effect of the treatment drugs for acute pancreatitis.

9. Application of a reagent for detecting the concentration of properdin in blood in the preparation of a product for diagnosing the severity of acute pancreatitis.

10. A detection system for judging the severity of acute pancreatitis, characterized in that The detection system includes a biochemical index detection part and a calculation and analysis part; The biochemical index detection part includes measuring the concentration of properdin in the blood; The calculation and analysis part includes the classification of biochemical indexes and / or the evaluation of treatment effects and / or the formulation of treatment plans; The classification of the biochemical indexes includes: dividing the condition of acute pancreatitis patients into several levels from severe to non-severe acute pancreatitis according to the range of the properdin concentration value in the blood of acute pancreatitis patients; The evaluation of treatment effects includes: dividing the treatment effects of acute pancreatitis patients into several gradients from effective to ineffective according to the change of the properdin concentration value in the blood of acute pancreatitis patients within a certain period of time; The formulation of treatment plans includes: formulating a treatment plan according to the severity of the condition of acute pancreatitis patients; or formulating a treatment plan according to the treatment effects of acute pancreatitis patients.

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