CN120177800B - Application of blood biomarker CFP in the preparation of diagnostic reagents for acute pancreatitis - Google Patents

Abstract

The present invention relates to the field of pharmaceutical biotechnology, specifically the use of a blood biomarker, CFP, in the preparation of a diagnostic reagent for acute pancreatitis. This invention demonstrates that CFP in blood can serve as a sensitive, effective, and predictive biomarker for assessing the severity of acute pancreatitis. Its ease of detection and reproducible results offer advantages in clinical application.

Description

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

Technical Field

The invention relates to the technical field of medical biology, in particular to application of a blood biomarker disintoxication (CFP) in preparation of an acute pancreatitis diagnostic reagent.

Background

Acute pancreatitis (acute pancreatitis, AP) is an inflammatory disease that is triggered by abnormal activation of pancreatic enzymes. Complications may include local or systemic infections, systemic inflammatory response syndrome (systemic inflammatory response syndrome, SIRS) and multiple organ failure (multiple organ failure, MOF), with mortality in severe patients up to 20% -30%. Patient disease progression is highly variable, and early (within 48 hours) prediction of disease severity facilitates rapid and effective intervention.

Biomarkers currently in clinical use include C-reactive protein (CRP), procalcitonin (procalcitonin, PCT) and interleukin-6 (interleukin-6, IL-6). Other biochemical and hematological parameters at the study stage, such as lipopolysaccharide binding protein, interleukin-8, neutrophil to lymphocyte ratio (neutrophil to lymphocyte ratio, NLR) and platelet to lymphocyte ratio (platelet to lymphocyte ratio, PLR), have failed to fully meet clinical requirements. Clinical scoring systems such as APACHE II scoring, ranson scoring and BISAP are also limited, and clinical application is limited because the analysis requires more than 48 hours to be admitted and multiple complex indexes are involved. The ideal biomarker has the characteristics of rapid detection, simple and convenient operation, high accessibility, high economy, high sensitivity, high specificity and the like.

Proteomics, as a powerful research method, has been widely used for biomarker screening of AP different classes of biological samples. However, in screening candidate biomarkers derived from the detection results, the nonlinear dynamics inherent in the inflammatory process must be fully considered. This property is mainly due to the multi-level regulatory mechanisms by which pathogens interact with the host immune system (pro-inflammatory and anti-inflammatory response). In this context, the limitations of the conventional method, which focus on first-order statistical information (such as differential protein expression levels), are gradually revealed, because it is difficult to effectively capture dynamic network interaction information. In response to this challenge, the dynamic network biomarker (Dynamic Network Biomarker, DNB) theory proposed by researchers can identify early warning signals of disease stage transitions and sudden exacerbations of complex diseases by mining higher-order statistical information or differential correlations (e.g., correlation coefficients/covariances of proteomes).

Based on earlier studies, we performed proteomic analysis of peripheral blood samples of healthy population and patients with different AP subtypes using DNB method, found that the key upregulation protein CFP of the complement cascade was significantly correlated with disease progression. CFP is mainly derived from neutrophils, and its expression level is not only related to disease severity, but also reflects the change of the number and functional status of the exo Zhou Zhongxing granulocytes, thus revealing the immunosuppression status of the patient. Current immunosuppressive treatment strategies present significant challenges in patient stratification and optimal intervention timing due to the lack of reliable biomarkers. The invention discovers that CFP has dual biomarker potential of patient stratification and immunotherapy guidance, and highlights good clinical application prospect in AP precise management.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides an application of a blood biomarker CFP in preparing an acute pancreatitis diagnostic reagent.

The invention aims at realizing the following technical scheme that the invention provides application of properdin in preparing a reagent for diagnosing acute pancreatitis severity.

Further, the diagnosis is carried out by measuring the concentration of properdin in the blood of a subject, and when the reduction amplitude of properdin concentration in the blood is-40% -50% compared with that of the blood of a normal group (the negative number of the reduction amplitude indicates that the value of properdin concentration in the blood of the subject is increased compared with that of a control group), the acute pancreatitis is judged to be non-severe, and when the reduction amplitude of properdin concentration in the blood is more than 50% compared with that of properdin concentration in the blood of the normal group, the acute pancreatitis is judged to be severe. Wherein the subject is an acute pancreatitis patient.

Further, the normal group refers to a population matching the same ethnic background and part of the demographic characteristics of the subject. Demographic characteristics include, for example, age, gender, residence, etc.

Further, the diagnosis is performed by determining the concentration of properdin in the blood of the subject, and the decrease in the concentration of properdin in the blood relative to the subject's own blood at an early stage of the disease or before the disease is inversely related to the severity of acute pancreatitis. Wherein the subject is an acute pancreatitis patient.

Further, the concentrations of properdin in the blood of the subject and normal group or subject themselves at an early stage of the disease or prior to the disease are determined using the same assay.

Further, the detection method comprises an electrochemiluminescence technology, an enzyme-linked immunosorbent assay (ELISA), a liquid phase chip technology (xMAP), a single molecule immune array technology (SiMoA), a full-automatic capillary digital Western Blot, a proximity extension analysis technology ((Proximity Extension Assay, PEA), immunoelectrophoresis, high performance liquid chromatography tandem mass spectrometry or a microfluidic chip.

Further, the blood properdin includes one or more of plasma properdin, serum properdin and whole blood properdin. The existing research shows that the expression level of properdin molecule in whole blood, serum and plasma has obvious correlation, and the detection value shows equivalent diagnosis efficacy among three sample types after being treated by hematocrit correction and standard centrifugation procedures. The whole blood sample can be selected according to practical situations in view of the fact that the bedside instant detection can be realized, the blood serum sample is prevented from being interfered by an anticoagulant, and the blood plasma sample keeps the conventional detection continuity.

Further, the application of the reagent for diagnosing the severity of the acute pancreatitis comprises the steps of judging the severity of the illness state of the patient with the acute pancreatitis, judging the progress or recovery degree of the illness state of the patient with the acute pancreatitis, and singly or jointly assisting in guiding the formulation of the administration scheme of the acute pancreatitis, judging the rationality of the administration scheme of the acute pancreatitis and judging the effect of the treatment medicine for the acute pancreatitis.

The invention also provides application of the reagent for detecting the concentration of the blood properdin in preparing a product for diagnosing the severity of acute pancreatitis. The reagent comprises a kit.

The invention also provides a detection system for judging the severity of acute pancreatitis, which comprises a biochemical index detection part and a calculation analysis part;

the biochemical index detection part comprises a step of measuring the concentration of blood properdin;

the calculation analysis part comprises classification of biochemical indexes and/or evaluation of treatment effects and/or formulation of treatment schemes;

The classification of the biochemical indexes comprises the steps of dividing the disease condition of the acute pancreatitis patient into a plurality of severe to non-severe acute pancreatitis according to the range of the concentration value of the blood properdin of the acute pancreatitis patient;

The evaluation of the treatment effect comprises the steps of dividing the treatment effect of the acute pancreatitis patient into a plurality of gradients from effective to ineffective according to the change of the concentration value of the blood properdin of the acute pancreatitis patient for a certain time;

The treatment scheme is formulated according to the severity of the illness state of the patient suffering from the acute pancreatitis or according to the treatment effect of the patient suffering from the acute pancreatitis. In particular, the immunotherapy is formulated to include administration time, administration period, administration dosage, and the like.

The beneficial effects of the invention are as follows:

the existing clinical markers for judging the severity of Acute Pancreatitis (AP) have the defects of insufficient sensitivity and specificity, limited response time and the like, and related hematological parameters and the like are difficult to meet the requirements of early prediction of disease progress and guiding accurate treatment, and the application of the existing clinical prediction scoring system is limited due to complex analysis and long time consumption. The invention provides a novel, efficient and sensitive biomarker for realizing early prediction of acute pancreatitis severity and full course period monitoring, and immune therapy guidance and assessment, which has the following characteristics:

(1) Early prediction, high sensitivity and specificity:

Experimental results show that the plasma CFP is used as an independent risk factor of acute pancreatitis, the distinguishing capability (severe and non-severe recombination AUC: 0.923) of the plasma CFP is superior to that of the conventional blood index (such as NLR, PLR, MLR), the sensitivity is 92.6%, and the specificity is 85.4%. Positive Predictive Value (PPV) 0.926, negative Predictive Value (NPV) 0.854. The plasma CFP level changes significantly in the early stages of the disease, enabling early prediction of acute pancreatitis severity.

(2) Guiding immunotherapy:

plasma CFP concentrations not only reflect disease severity, but also are closely related to neutrophil functional status, characterizing the immunosuppressive status of the body. In the process of treating the rana peptide-zymosan (CAE-Zymosan) mouse SAP model by adopting the immunopotentiator GM-CSF, the CFP concentration can guide the selection of a proper intervention window, so that the immune reconstruction can be realized maximally, the risk of excessive stimulation of cytokines can be avoided, the survival rate of the mouse is remarkably improved, the integrity of the intestinal barrier is restored by reducing the intestinal bacterial translocation, and the outstanding effect of CFP in guiding the immune treatment is demonstrated.

(3) Clinical transformation potential:

The method and the marker provided by the invention have the advantages of simplicity and convenience in operation, high economical efficiency, high accessibility and the like, and are easy to popularize and apply in clinic. By dynamically monitoring the plasma CFP level, a new strategy is hopefully provided for early disease assessment, full cycle monitoring and accurate treatment of acute pancreatitis.

Drawings

Figure 1 identifies critical transition states and severity biomarkers in AP progression based on DNB analysis (a) plasma EV proteomic analysis flow for three clinical queues. HC is health control, NSAP is non-severe acute pancreatitis, SAP is severe acute pancreatitis, (B) a DNB algorithm is applied to reconstruct a proteome track to detect a critical disease state, (C) a single sample DNB related protein identification l-DNB calculation flow and (D) a disease state specific network disturbance map. Nodes proteins (red = state specific DNB molecules), edges Pearson Correlation Coefficient (PCC) fluctuations (thickness proportional to absolute value), and (E) DNB score trajectories identify critical transition thresholds, and (F) state hierarchical DNB subnetworks. Node size: edge connectivity change (larger indicates stronger perturbation), edge: red (PCC gain), blue (PCC lost), and (G) DNB subnetwork (SAP vs HC queue) built based on common proteome data.

FIG. 2 DNB analysis of the algorithm and differences in proprietary proteomics data (A) proteomic intensity distribution of study groups HC (healthy control), NSAP (non-severe acute pancreatitis group), SAP (severe acute pancreatitis group), and (B) dynamic evolution of DNB network with disease status. The node size corresponds to the DNB score (quantifying the contribution to network destabilization), larger nodes characterize key transition phase biomarkers, and (C) disease state specific DNB subnetworks. The node size reflects the edge disturbance intensity (connectivity variation amplitude), the edge thickness represents an absolute offset Pearson correlation coefficient (| sPCC |), the core DNB hub is marked in dark brown, the Gene Ontology (GO) function enrichment analysis of the core DNB proteins of SAP and NSAP is distinguished, the Venn diagram of the overlapping condition of the Differential Expression Proteins (DEPs) of each group is distinguished, the volcanic diagram of differential protein expression is distinguished, namely (i) NSAP vs HC and (ii) SAP vs HC. The dotted line marks the significance threshold (p-value <0.05 and | logFC | > 1.5), and (G) hierarchical clustering heatmaps of cross-group consensus differential expression protein expression profiles.

FIG. 3 CFP relates to SAP severity study, (A) patient-in and stratification flow chart (NT: healthy control; NSAP: non-severe acute pancreatitis; SAP: severe acute pancreatitis), (B) NT, NSAP and SAP group plasma sC5B-9 concentration comparison, (C) three group plasma CFP level comparison analysis, (D) ROC curve evaluation of predicted efficacy of plasma CFP in distinguishing NSAP from SAP, (E) ROC curve evaluation of accuracy of plasma CFP identification NT from pancreatitis patient (NSAP+SAP), (F) correlation scatter plot of plasma CFP concentration and symptom appearance to blood sampling time interval, (G) ROC comparison analysis of CFP and traditional severity prediction index (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 median lines, quartile range (boxes) and extrema (bar values). The ROC curves (D, E, G) dashed lines represent invalid reference lines (auc=0.5). (H) Plasma CFP of the group of patients with NSAP and SAP in this cohort were compared with each other with sample collection time not exceeding 72 hours from the time interval of onset.

FIG. 4 shows the regulatory mechanisms of complement activation and neutrophil particle release on plasma CFP, (A) Western blot analysis of plasma CFP levels of clinical groups of red blood cells C4D deposition (HC/NSAP/SAP; n=5 is a biological repetition), 1,2,3 noted in the figure are the same as the biological repetition, (B) neutrophil surface C4D expression (n=5), (C) neutrophil C5a receptor (C5 aR) surface expression (n=6), (D) neutrophil CD64 and FPR1 levels (n=3), (E) Western blot analysis of plasma CFP levels of clinical groups of untreated HC and SAP groups of (F) NT group and SAP group plasma were the same as that of Zymosan particles (0.05 or 1 mg/ml), (G) immunofluorescence showed neutrophil subparticle CFP localization (scale: 5 microns), (H) neutrophil CD66B expression (n=3), (I) and (F) isolated plasma CFP at a time sequence of no stimulation of normal CFP (35F) of normal groups of normal blood serum CFP (35) at normal blood serum CFP of 10% when no stimulation of normal groups of normal blood serum CFP (35F) was performed at normal conditions of normal blood serum CFP (35% of normal blood serum CFP) and normal blood serum CFP (35% of normal blood serum CFP).

FIG. 5 flow cytometry comparison analysis of peripheral red blood cells and neutrophil surface protein expression versus complement deposition. The method comprises the steps of (A) flow cytometry analysis of peripheral blood neutrophil separation purity (CD11b+CD6b+) and CD235+ erythrocytes, (B) flow detection of neutrophil CD64 and FPR1 receptor expression of different research groups (HC, NSAP, SAP), repeated groups, (C) HC, NSAP and SAP queue neutrophil surface C5aR receptor expression characteristics, and numbers 1-4 represent the results of different independent sample collection groups, (D) complement deposition and receptor expression representative flow patterns of erythrocytes and neutrophils in HC, NSAP, SAP samples subjected to synchronous treatment. The matching table details the patient identifier, isolated neutrophil concentration, and plasma CFP level.

FIG. 6 analysis of the functional correlation of plasma CFP levels with patient peripheral neutrophils; the method comprises the steps of (A) obtaining (n=8) the isolation yield of whole blood neutrophils from healthy control groups (HC), non-severe acute pancreatitis (NSAP) and Severe Acute Pancreatitis (SAP), (B) the real-time chemiluminescent kinetics curve (n=3) generated by active oxygen (ROS) of neutrophils, (C) representative images (n=3) of crystal violet-stained neutrophil adhesion polycarbonate membranes in a Transwell experiment, scale: 50 μm, (D) quantitative analysis (n=3) of neutrophils migrating to a lower chamber, (E) forming (n=3) extracellular traps (NET) induced by E.coli stimulation (neutrophil scale: bacteria=1:3) in serum-free medium (0% FBS), and (F) the killing efficiency (n=3) of neutrophils on E.coli, (G) the neutrophil signaling pathway (phosphorylated AKT, p42/44 ERK, p47, p40, p105, p 65) and protein expressed by CFP (PD10%) and (PDK 10%) and (GAK) performing total stimulation (GAH) as a total blood sample of 50ml of (GAH) by ELISA, and (total blood serum stimulation (GAH) as a total stimulation sample of 50ml, after detection. Data are expressed as mean ± standard deviation, p < 0.01, p < 0.001, p < 0.0001.

FIG. 7 human peripheral blood neutrophil and monocyte function and quantification. The results of the analysis were (A) a comparison of the neutrophil yields per mL of whole blood for each group (HC, NSAP, SAP; n=8) a representative image of the crystal violet stained neutrophil migration to the lower chamber in a Transwell experiment, reflecting chemotactic capacity (per n=3), a scale of 50 μm, (C) an evaluation of Neutrophil Extracellular Trap (NETs) formation after 4 hours of culture in RPMI 1640 medium containing E.coli stimulation (neutrophil-bacteria ratio 1:3; n=3 per group), a scale of 100 μm, (D) ELISA quantitative detection of plasma TNFα levels after ex vivo whole blood LPS stimulation (100 ng/mL; n=7 per group), and (E) levels of HC, NSAP and SAP queue neutrophil surface CD11B expression. Data are expressed as mean ± standard deviation, statistical significance of p < 0.05, p < 0.01, p < 0.001, p < 0.0001.

FIG. 8 SAP construction of mouse model and phenotypic characterization. A flow chart of SAP induction experiments, (B) Kaplan-Meier analysis of survival rate in a 12-day observation period, (C) dynamic change of body weight of mice in untreated groups (NT), acute Pancreatitis (AP) and SAP groups (n=5/group), (D) serum biochemical indexes such as Amylase (AMY), lipase (LIP), alanine Aminotransferase (ALT), aspartate Aminotransferase (AST), creatine kinase isoenzyme (CK-MB), lactate Dehydrogenase (LDH), urea and creatinine (Cr) (n=5/group), (E) peripheral blood leukocyte classification count such as total number of White Blood Cells (WBC), neutrophils (Neu) and lymphocytes (Lym) (n=5/group), (F) general morphology of small intestine and peritoneal lavage fluid bacterial culture results, (G) lavage fluid flow type cell analysis such as CD45+ white blood cells, CD11b+F4/80+ macrophages and CD11b+Ly6G+ neutral granulocytes (n=5/group), (H) pancreatic sperm and erythroid tissue (WBC) and (WBC) staining scale and (B) and pathological evaluation of peripheral blood leukocyte surface of human body tissue (CFC) and (WBC) and (CFC) peripheral blood leukocyte surface markers such as human body tissue. Data are expressed as mean ± standard deviation, p <0.05, p < 0.01, p < 0.001, p < 0.0001.

FIG. 9 shows the study of plasma CFP as biomarker for survival prognosis in CAE-zymosan model, (A) change in body weight of 3.5, 5.5 and 6.5 days after model formation in surviving (Su) and non-surviving (N-Su) mice, (B) Western blot analysis of plasma CFP and CFB in NT, su and N-Su groups, (C) peripheral blood cell count (WBC, neu, lym), small intestine pathology image and lavage bacterial load quantification (n=3), (D) serum biochemical parameter (n=3), (E) total number of bilateral tibial bone marrow cells (n=3), (F) number of neutrophils migrating to lower chamber in Transwell assay (n=3), (G) killing efficiency of neutrophils on E.coli (n=3), (H) serum-free condition.+ -. E.coli (1:3) stimulated NET formation (n=3), scale: 50 μm, (I) LPS (5. Mu.g/ml) stimulated whole blood level (n=3), (N=3) and (N=3) signal of 5. Mu.g/ml) activated protein to cut off (P) in the same group as 50% protein map (P-C) signal of P type of 50 ml cell in the P. Data are expressed as mean ± standard deviation, p < 0.05, p < 0.01, p < 0.001, p < 0.0001.

Figure 10 comparative analysis of pathological characteristics and neutrophil function of surviving and non-surviving mice. Flow cytometry was used to quantify the sub-populations of celiac leukocytes, total leukocytes (CD45+), macrophages (CD11b+F4/80+) and neutrophils (CD11b+Ly6G+); each group n=3), colony counts of the general morphology of the small intestine and intestinal lavages, (B) HE stained sections (scale: 100 microns) of key organs (pancreas, small intestine, kidney, liver, lung), (C) total number of bilateral femur and tibia bone marrow nucleated cells (each group n=3), (D) neutrophil E-E killing capacity (each group n=3), (E) Transwell migration assay to quantify the number of lower chamber neutrophils (each group n=3), (F) on-Transwell membrane crystal violet stained neutrophil adhesion (scale: 50 microns; each group n=3), (G) Western blot analysis of neutrophil signaling pathways: surviving (Su) and non-surviving (N-Su) mouse bone marrow neutrophil (cultured in RPMI 1640+ -ng/mL containing 10% of RPMI, representing the protein of the group N-representing the cell, and GSTp, and GSP. Data are expressed as mean ± standard deviation. Statistical significance p < 0.05, p <0.01, p < 0.001.

FIG. 11 longitudinal analysis of pathological parameters related to disease progression in CAE-Zymosan model (6 time points), (A) Western blot analysis of NT, AP and SAP group weight dynamics (n=3-5) 12 hours, 36 hours, 3 days, 5 days, 8 days and 12 days after induction of model, (B) plasma Amylase (AMY) activity (n=3-5; unit: U/L) at each time point, (C) time distribution (n=3-5; unit: x 103 cells/mL) of circulating leukocyte subpopulation (WBC, neu, lym), (D) Western blot analysis of NT, AP and SAP group plasma Calprotectin (CFP) levels, (E) ELISA assay of plasma TNFα levels after whole blood LPS stimulation (5 μg/mL). Data are expressed as mean ± standard deviation. Statistical significance p < 0.05, p < 0.01, p < 0.001, p < 0.0001.

FIG. 12 CAE optimization of GM-CSF treatment protocol and efficacy assessment in zymosan model. (a) non-treatment (NT), SAP and GM-CSF treatment (0 hours, 12 hours, 36 hours after zymosan administration) groups (n=3-5 per group), weight dynamics (n=3-5 per group) (B) groups Kaplan-Meier viability curves, (C) representative images of the large body forms of the small intestine, (D) colony counts of intestinal lavage fluid, (E) flow cytometry to quantify the small intestinal cell subpopulations (n=3-5 per group), (F) killing efficiency of bone marrow neutrophil escherichia coli (n=3-5 per group), (G) NT groups, SAP groups and different GM-CSF administration regimens (200 ng/day x3 days, 100 x5 days) two times daily, 100 ng) mouse dynamics (n=3-5 per group), (H) fluid quantification of small intestinal cell subpopulations (n=3-5) at different GM-CSF doses, (J) fluid flow cytometry quantification (n=3-5) nuclear granulocyte subpopulations (n=3-5) and (G) nuclear granulocyte killing fluid (GM-5) at different levels (n=3-5) and (n=3-5) total number of colony counts (p) at 0.4 mg/5G) after challenge of the time of the total number of the low-CSF (K-5) groups (0.0.g) and (n=4G) after the low-5G-CSF administration of the yeast (n=0.3 days) Western blot analysis of plasma CFP protein level of AP group and SAP group. Data are expressed as mean ± standard deviation. Statistical significance p < 0.05, p < 0.01.

FIG. 13 shows the schematic diagram of the design of a GM-CSF therapeutic experiment for the intervention study of SAP mice under guidance of plasma properdin concentration, (A) the schematic diagram of the design of a GM-CSF therapeutic experiment, (B) FITC-dextran permeation experiment to evaluate the integrity of intestinal barriers (n=5), (C) the analysis of survival rate Kaplan-Meier, (D) the quantitative analysis of the body weight dynamics (n=5) of GM-CSF therapeutic group and untreated SAP group, (E) the colony count (right) of small intestine gross morphology (left) and lavage fluid coated plates, (F) the flow analysis of peritoneal lavage fluid leukocyte subpopulations (CD45+ total leukocytes, CD11b+F4/80+ macrophages and CD11b+Ly6G+ neutrophil (n=3-5), (G) the flow detection of the percentage of whole blood peripheral neutrophil after sedimentation of erythrocytes, (H) the quantitative analysis of the migration quantity (n=3) of the transfer cells in a transfer chamber, and (I) the quantitative analysis of the number of crystal violet dye on a polycarbonate membrane in a transfer experiment, (F) the flow analysis of the representative image of adhesion neutrophil cells (n=3) in a transfer chamber, and (50 mu M) of bacteria in a blood cell culture medium (pH) of bacteria strain (pH) and (pH) of bacteria (E) and (pH) of bacteria). The ratio is 100 mu m1, and the killing efficiency of the neutral granulocyte to the escherichia coli. Data are expressed as mean ± standard deviation, p <0.05, p <0.01, p < 0.001,/p < 0.0001.

FIG. 14 is basic information for patients of the proteomic origin of Table 1 EV.

Fig. 15 is patient base information for CFP concentration detection of table 2.

FIG. 16 is a multifactor analysis of Table 3.

FIG. 17 is a comparison of the effects of Table 4 CFP on the predictive common indicators of acute pancreatitis severity.

FIG. 18 is a summary of the condition tracking results after blood collection for different groups of patients in Table 5.

Detailed Description

The technical solution of the present invention will be described in further detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.

1. The invention is studied

(1) Screening a marker:

By adopting a DNB analysis method, integrating private and public proteome data sets, constructing a bioinformatics analysis flow, and by mining high-order statistical information or differential relevance (such as proteome relevance/covariance), early warning signals of disease condition transformation and deterioration can be captured more accurately.

(2) Patient sample verification:

in a prospective cohort study, acute pancreatitis patients were enrolled, plasma samples thereof were collected, and plasma CFP levels were detected by ELISA. In combination with clinical data, the correlation of plasma CFP levels with disease severity, neutrophil functional dynamics was analyzed, verifying the potential of CFP as a biomarker for disease severity prognosis.

(3) Animal model construction and evaluation:

A CAE-Zymosan mouse model was constructed to simulate human SAP pathophysiological features including local pancreatic inflammatory response, multiple Organ Dysfunction Syndrome (MODS) and biphasic disease progression patterns. The effectiveness of CFP as a biomarker for disease severity was further validated by monitoring differences in plasma CFP levels and neutrophil function in surviving and non-surviving mice and explored its use in the guidance of immunotherapeutic regimens.

2. Experimental materials and methods

1. Patient inclusion and exclusion criteria

The invention is incorporated into patients with Acute Pancreatitis (AP) diagnosed at the department of western medicine, university of si, between 1, 6, 2021 and 31, 12, 2024. Diagnosis is based on the revised atlanta standard. Inclusion criteria included (1) age 18-70 years and (2) compliance with AP diagnostic criteria. The exclusion criteria were (1) pregnancy, (2) incorporation of chronic underlying diseases (e.g. chronic pancreatitis, hepatitis, nephritis), (3) pancreas or other malignancy, (4) immunodeficiency (e.g. HIV infection) or autoimmune diseases (e.g. systemic lupus erythematosus, rheumatoid arthritis), (5) history of immunosuppressive therapy, (6) incorporation of non-AP-related sepsis, (7) history of AP onset within 3 months prior to admission, and (8) data loss or repeated sampling. The study protocol was approved by the national ethics Committee of China Western medicine (No. 2021-675), following the declaration of Helsinki. Follow-up continues until the patient is discharged or die. Peripheral blood samples were collected at admission for subsequent analysis.

2. Plasma separation and neutrophil purification

Peripheral blood was collected in EDTA-2K anticoagulation tubes and plasma was isolated by centrifugation at 1500 g for 10 min at 4 ℃. After the cell pellet is resuspended in physiological saline, peripheral Blood Mononuclear Cells (PBMCs) and neutrophils are separated by using a human neutrophil separation kit, and residual erythrocytes are removed by a hypotonic lysis method. Flow cytometry (cd66 b+cd11b+ markers) confirmed neutrophil purity. Purified neutrophils were resuspended in RPMI 1640 medium for functional experiments.

The experimental part of mice uses Ficoll-PLUS density gradient method to separate neutrophils from C57BL/6J mouse bone marrow, and flow cytometry (Ly6G+ labeling) to verify cell purity.

3. ELISA detection

The concentrations of properdin (CFP) and soluble final complement complex (sC 5 b-9) in plasma were measured using a commercial ELISA kit (cat# ab222864, manufacturer: abcam, cambridge, UK) and the procedure was followed exactly as described.

4. Dynamic Network Biomarker (DNB) analysis

(1) Separation of Extracellular Vesicles (EVs) by plasma collection and ultracentrifugation

And (3) collecting an EDTA anticoagulation vacuum blood collection tube whole blood sample through venipuncture, and gently mixing for 4-5 times to ensure the sufficient effect of the anticoagulant. The plasma was separated by a two-step centrifugation method, wherein the first centrifugation (2,500×g,15 minutes) was performed at 4℃and the supernatant was subjected to a second centrifugation (same parameters) to obtain Platelet Poor Plasma (PPP). And freezing the PPP sample at-80 ℃ for standby after sub-packaging. All operations follow the ethical specifications of the declaration of helsinki.

EVs enrichment was performed using an optimized ultracentrifugation protocol by taking 300. Mu.L PPP aliquots, diluting with Phosphate Buffered Saline (PBS) at 1:4, and transferring to polycarbonate ultracentrifuge tubes. First centrifugation (110,000Xg, 4 ℃ C., 90 minutes) was performed using a TLA-55 rotor (Beckman Coulter Optima XE). EV pellet was resuspended with 1.2 mL PBS and then ultracentrifuged again (same parameters) to remove soluble protein contamination. The final purified EVs were resuspended in 30. Mu.L PBS, -80℃for storage for subsequent analysis.

(2) Analysis of EVs surface proteins by liquid chromatography-mass spectrometry (LC-MS)

Plasma EVs proteomic analysis was performed on 88 samples meeting the inclusion criteria (68 patients, 20 healthy controls). EVs lysis was performed using ice-pre-chilled RIPA lysate (with protease/phosphatase inhibitors), sonicated (30% amplitude, 3 seconds on/10 seconds off, 5 minutes, ice bath) and centrifuged (10,000Xg, 4 ℃ C., 30 minutes). The protein concentration was determined by the Bradford method from the supernatant. 100. Mu.g of protein samples were sequentially subjected to reduction (10 mM TCEP,56 ℃ C., 1 hour), alkylation (20 mM iodoacetamide, 30 minutes at room temperature protected from light) and methanol/chloroform/water (4:1:3, v/v) precipitation. Proteins were digested with sequencing grade trypsin (enzyme/substrate ratio 1:50,37 ℃ C., 12 hours), peptide fragments were desalted by C18 ZipTip and 5. Mu.g of peptide fragments were taken for LC-MS analysis.

The desalted peptide was dried in vacuo and redissolved in mobile phase A (2% acetonitrile, 0.1% formic acid). Chromatographic separation employed Thermo Scientific Nano EASY-nLC 1200 system coupled with Orbitrap Exploris 480 mass spectrometer (nano-spray ion source). 1. mu.L of the sample was concentrated by PepMap trap column (300 nL/min) and eluted in a gradient at 300 nL/min (mobile phase B:0.1% formic acid-80% acetonitrile; 2% -35% B in 65 min) on a C18 reverse phase analytical column (250 mm X75 μm,1.9 μm Repro sil-Pur packing). Mass spectral parameters were primary scan (m/z 350-1800, resolution 60,000), secondary HCD fragmentation (30% collision energy, resolution 15,000), dynamic exclusion of singly/multiply charged ions. Data acquisition was controlled by Xcalibur software (v 4.3).

Raw data were treated with MaxQuant (v1.6.17.0) with cleavage specificity set to trypsin (maximum 2 leaky cleavage sites) with mass tolerances of precursor and fragment ions of 10 ppm and 0.02 Da, respectively. The fixed modification is cysteine carbamylation, and the variable modification is methionine oxidation and N-terminal acetylation. Protein identification requires more than or equal to 1 specific peptide segment (error discovery rate FDR < 1%). And carrying out label-free quantification (LFQ) based on iBAQ algorithm, and reserving protein containing more than or equal to 2 specific peptide fragments.

The data preprocessing comprises log2 conversion after LFQ intensity mean normalization, center correction of batch effect among samples, and filling of missing values by an R language mice package (v3.15.0) prediction mean matching method.

(3) Landscape dynamic network biomarker (l-DNB) method

The present study identified two disease progression states of Acute Pancreatitis (AP) by constructing a reference network. The Pearson Correlation Coefficient (PCC) calculated based on protein expression values is defined with reference to edges in the network:

(1)

Where x_i and y_i represent the expression levels of proteins x and y in the ith reference sample, respectively, and x ̄ and ȳ 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 values containing the new sample, denoted PCC_ (n+1). This PCC difference may reflect the perturbing effect of the new sample on the network, thus defining a single sample pearson correlation coefficient (sPCC) for the new sample relative to the reference sample:

(2)

When the sample size is large enough, sPCC _n (x, y) of the single sample level obeys an approximately normal distribution. The study uses a statistical hypothesis test (Z test or U test) to evaluate sPCC for significance to determine whether protein x and y have significant correlation in a particular sample.

In the framework of Dynamic Network Biomarker (DNB) theory, when a physiological system is switched from a light state to a critical state (referred to as a critical state in the study), protein expression of a specific molecular module generally meets three statistical conditions, namely (i) a significant increase in intra-module protein expression deviation (ED_in) and indicates increased protein expression fluctuation, (ii) a rapid increase in intra-module protein correlation coefficient (PCC_in) and reflects increased intra-module protein correlation, and (iii) a rapid decrease in intra-module and intra-module protein correlation coefficient (PCC_out) and shows reduced inter-module correlation.

In the calculation process, the target proteins in the sample-specific network (SSN) and their main neighboring proteins are considered as local modules. According to the DNB theory, the local DNB score for protein x in sample E is defined as formula (3):

(3)

Wherein sED _in represents a single sample expression bias of the DNB module, for assessing changes in protein x expression in a sample. The DNB score of the sample was obtained by calculating the local DNB score mean for the top K protein (DNB module) in SSN. K=30 was set according to the number of proteins in SSN and local DNB score distribution profile (DNB score remained stable at different K values). The "status-level DNB score" of a disease is the average of all sample DNB scores in that status, the critical point is determined by the maximum DNB score, and the proteins in the critical point SSN are defined as DNB proteins (DNB modules). To ensure the robustness of the DNB protein, only the protein at the top K of the homeotropic column in at least 50% of patient samples in the same disease state was retained as a state-specific DNB protein and the DNB score and Δpcc in each state was calculated.

The state-specific DNB scores were calculated using a 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:

(4)

N_present (x) represents the number of samples containing protein x, and N is the total number of samples of the disease state. The state-specific Δpcc calculation is as in formula (5):

(5)

Wherein the frequency of the protein pair (x, y) is defined by formula (6):

(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. The method captures early signals of critical transitions of biological systems through dynamic network features.

(4) Data analysis

Differential expression analysis of proteomics proteins in Healthy Control (HC), non-severe acute pancreatitis group (NSAP) and severe acute pancreatitis group (SAP) were subjected to differential expression analysis using limma package in R language (version 3.58.1) to identify Differentially Expressed Proteins (DEPs). The identification of differentially expressed proteins was based on criteria of adjusted p-values <0.05 and | logFC | > 1.5.

Visualization of data by creating violin plots to show the level of expression profile, constructing volcanic plots to highlight significantly differentially expressed proteins (adjusted p values <0.001, | logFC | > 1.5), and using venturi plots to depict the overlap between differentially expressed proteins. Hierarchical clustered heatmaps are generated using pheatmap packages (version 1.0.12) to reveal expression patterns between groups.

Functional enrichment analysis was performed using the clusterProfiler package (version 4.10.1) and the biological processes and pathways enriched in differentially expressed proteins were determined by GO and KEGG pathway analysis (p <0.05, q < 0.2).

Analysis environment all analyses and visualizations were done in the R language (version 4.3.3).

5. Basic experimental part

(1) White Blood Cell (WBC) separation-erythrocyte sedimentation method

Fresh anticoagulated whole blood is mixed with erythrocyte sedimentation liquid in a ratio of 1:1, and then diluted with 0.9% physiological saline. The mixture was allowed to stand at 20±2 ℃ for 30 minutes to promote erythrocyte sedimentation. After phase separation, the upper leukocyte-rich liquid was carefully aspirated. Residual erythrocytes were lysed using hypotonic ammonium chloride-potassium chloride buffer. The isolated leukocyte fractions were immunostained with fluorescein-labeled anti-mouse antibodies (directed against CD45-APC, ly6G-FITC, CD11b-PB and CFP) for subsequent phenotypic analysis.

(2) Lipopolysaccharide (LPS) -induced TNFa production assay

Fresh anticoagulated whole blood was dispensed into sterile polypropylene tubes and stimulated with ultrapure LPS (human sample 100 ng/mL, mouse sample 5. Mu.g/mL) at 37℃for 4 hours in a humidified incubator containing 5% CO ₂. Following stimulation, the cell components were pelleted by centrifugation at 3,000 g for 10 minutes at 4 ℃. The resulting plasma supernatant was collected, split-packed and stored at-80 ℃ until cytokine quantification.

(3) ELISA (ELISA)

Human peripheral blood TNFα assay was performed using a high-adsorption 96-well plate, coated with anti-human TNFα capture antibody (1:1000 dilution) overnight at 4 ℃. Blocking with 5% Bovine Serum Albumin (BSA) was performed for 1 hour at room temperature. A calibrator (recombinant human tnfα) was added sequentially and incubated with the sample to be tested for 2 hours, followed by 1 hour incubation with each of biotinylated detection antibody (1:1000 dilution), streptavidin-horseradish peroxidase (HRP) conjugate (1:5000 dilution) sequentially. After each step, the cells were washed 4 and 7 times with PBS (PBST) containing 0.05% Tween-20, respectively. The reaction was stopped by 3,3', 5' -Tetramethylbenzidine (TMB) development and the absorbance at 450nm (reference wavelength 570 nm) was measured using a microplate reader.

(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. The C4d assay employs an indirect labeling method, in which after incubation of primary antibody (anti-C4 d,1:1500 dilution) for 1 hour, secondary antibody (1:500 dilution) is labeled for 30 minutes. The synchronous markers of the celiac lavage cells of the mice, CD45-APC, ly6G-FITC, CD11b-PB and F4/80-PE/Cyanine7. All samples were examined by Agilent flow cytometer and analyzed by FlowJo v10.6 software.

Cell sorting was performed by removing human peripheral blood cells from CD66b-FITC, CD3-APC, CD14-PE/Cyanine7 and CD11b-PB labeled erythrocytes, adjusting to 5X 10 ⁶–1×10⁷ cells/mL, and sorting with BD FACS Aria SORP system.

(5) Complement activation and CFP/C5b-9 deposition assays

Zymosan (1 mg/mL) was heat activated by boiling for 30 minutes. 40% (v/v) plasma was diluted in Ca2+/Mg2+ -containing gelatin barbital buffer (GVB++), and incubated with zymosan at final concentration 0.05 or 1Mg/mL for 2 hours at 37 ℃. CFP deposition was quantified by western immunoblotting and flow cytometry, and C5b-9 complex was detected simultaneously.

(6) Subcellular co-localization analysis

Peripheral blood neutrophils were attached to a cover slip by low-speed centrifugation, 4% paraformaldehyde was fixed for 15 minutes, and 10% goat serum was blocked for 1 hour (room temperature). anti-CFP, LTF, MMP and MPO primary antibody (1:100 dilution) were incubated overnight at 4 ℃ followed by incubation with AF488/AF594 conjugated secondary antibody (1:500 dilution) for 1 hour. After DAPI counterstain, observations were made using Olympus IXplore live cell imaging system.

(7) Western blot analysis

Tissue/cell proteins were extracted using pre-chilled RIPA lysate containing protease/phosphatase inhibitors and protein concentration was determined by BCA method. Transfer to nitrocellulose membrane after electrophoresis by 12.5% SDS-PAGE, 5% nonfat milk powder-TBST blocked for 1 hour (room temperature) and then incubated overnight with primary antibody at 4℃including anti- β -actin (1:5000), anti-Gapdh (1:5000), anti-AKT (1:5000), anti-phosphorylated AKT (1:2500), anti-phosphorylated p40phox (Thr 154) (1:1000), anti-phosphorylated p47phox (Ser 345) (1:1000), anti-ERK (1:5000), anti-phosphorylated p44/42 MAPK (Erk 1/2) (Thr 202/Tyr 204) (1:1000), anti-caspase-3 (1:1000), anti-p 62 (1:1000), anti-LC 3B (1:1000), anti-CFP (1:500), anti-phosphorylated NF- κ B p105 (Ser) (1:1000), anti-phosphorylated NF- κ B p65 (Ser) (1:468 1000), anti-DSDMD (1: citH 3). After TBST washing, incubation with HRP-labeled secondary antibody (1:5000, 1 hour at room temperature), development by chemiluminescence (ECL, abbkine) and signal acquisition by the Touch Imager system. Plasma samples were denatured after dilution (10 fold human; 5 fold murine).

(8) Neutrophil CFP release kinetics

Freshly isolated human neutrophils were cultured in 10% FBS-RPMI medium containing 10% Fetal Bovine Serum (FBS) RPMI 1640, serum-free RPMI 1640 and lipopolysaccharide (LPS, 100 ng/mL), fMLP (100 nM), PMA (100 ng/mL), TNF alpha (50 ng/mL), C5a (20 nM) or GM-CSF (20 ng/mL), respectively. Cells were collected at 0.5, 1 and 2 hours, respectively, and CFP expression dynamics were analyzed by western blot.

6. Neutrophil function assay

(1) Reactive Oxygen Species (ROS) detection

Neutrophils (1×10 ⁵ cells/well) were suspended in phenol red-free, serum-free RPMI 1640 medium and added to 96-well white microplates. After pre-equilibration for 15 min at 37 ℃, phorbol ester (PMA, 100 ng/mL) and luminol (500 μm) were added. The control group contained only luminol medium. Chemiluminescent signals were recorded in real time using a BioTek Synergy H1 microplate detector, every 2 minutes for 90 minutes.

(2) Neutrophil Extracellular Trap (NET) formation assay

Human neutrophils (2.5X10 ⁵ cells/well) were seeded in 48 well plates and stimulated with PMA (100 ng/mL) or E.coli (MOI 3:1) at 37℃under 5% CO ₂ for 3 hours. Negative control was unstimulated cells. 4% paraformaldehyde was fixed for 1 hour, 0.1% Triton X-100 permeabilized, and SYTOX Green nucleic acid dye (100 nM) stained for 30 minutes in the dark. After 3 washes in PBS, NET structure was observed using Olympus IX73 fluorescence microscope.

(3) Neutrophil migration assay

Human peripheral blood neutrophils (concentration 1X10≡6/mL, suspension in RPMI 1640 medium containing 0.1% BSA) were inoculated into a Transwell cell (Corning # 353096) with a 3 μm pore size, and 500. Mu.L of chemokine medium (RPMI 1640/0.1% BSA+20 nM N-formylmethylsulfonyl-leucyl-phenylalanine (fMLP)) was added to the lower cell. After incubation at 37 ℃ for 3 hours, cells migrating to the lower surface of the membrane were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet for 10 minutes, and imaged by bright field microscopy (Olympus IX 73) after sealing. The cells that were completely migrated in the lower chamber were collected by centrifugation (300 g,5 min) and quantified by a blood cell counting plate. In the mouse neutrophil experiments, fMLP concentration was adjusted to 50 nM and incubation time extended to 4 hours.

(4) Bacterial killing experiments

Coli was cultured on a 37℃shaker (200 rpm) to stationary phase, centrifuged (10,000 g,3 min) to collect the cells, which were washed once with sterile physiological saline to adjust the concentration to 1X 10≡9 CFU/mL. The bacterial suspension was conditioned by incubation with 20% (volume ratio) autologous serum at 37 ℃ for 30 minutes. Neutrophils (0.8X10A/well) were co-cultured with conditioned bacteria in RPMI-1640 for 90 min (37 ℃) at 5:1 effector-target ratio, with conditioned bacterial suspension without neutrophils as control. 10. Mu.L of the sample was added to 10 mM NaOH (pH 10.0) to lyse the cells, and the cells were inoculated on LB agar plates after gradient dilution, and cultured at 37℃for 18 hours to obtain Colony Forming Units (CFU). The calculation formula of the sterilization efficiency is as follows:

Bactericidal activity (%) = (experimental group CFU/control group CFU) ×100%

7. Construction and characterization of CAE-zymosan mouse model

(1) Optimized molding method

Male C57BL/6 mice (body weight 22.+ -.2 g, fukang Biotechnology Co., beijing) 6-8 weeks old were used for breeding in a pathogen free (SPF) environment. All procedures followed the university of Sichuan laboratory animal ethical committee Specification (lot # 20190509023). Grouping according to an ear mark layering random method, and designing by blind methods by experiment execution and evaluation personnel.

After the mice had fasted for 12 hours, 9 intraperitoneal injections of ranpirin (CAE; 100. Mu.g/kg) were made at 1 hour intervals, followed by a single intraperitoneal injection of zymosan (1 g/kg). After the modeling is finished, the free diet of the mice is recovered. Pancreatic tissue and plasma samples were collected at different time points after zymosan injection, and histopathological evaluation and biochemical index detection were performed, respectively.

(2) Survival analysis experiment

Three sets of models (n=10/set) were established based on optimized parameters, negative control (NT), normal saline treatment, acute Pancreatitis (AP), CAE-only treatment, severe Acute Pancreatitis (SAP), cae+zymosan treatment. Survival and clinical scores (activity, posture, hair-setting response) were continuously monitored for 12 days and recorded 2 times daily.

(3) Hematology analysis

EDTA-K2 anticoagulated whole blood is collected through retroorbital venipuncture, and blood routine detection is completed by using a Micrui BC-5120 full-automatic blood analyzer.

(4) Biochemical analysis of plasma

EDTA-K2 anticoagulants were centrifuged at 3,000Xg (4 ℃ C., 20 min) to obtain plasma. The diluted samples of Amylase (AMY) and Lipase (LIP) and the stock samples of glutamic pyruvic transaminase (ALT), glutamic oxaloacetic transaminase (AST), creatine kinase isoenzyme (CK-MB), lactic Dehydrogenase (LDH), urea nitrogen (BUN) and creatinine (Cr) are detected according to the standard flow of the Roche Cobas 311 biochemical analyzer of 1:10.

(5) Peritoneal lavage and analysis

Injecting 5mL PBS to abdominal cavity under aseptic condition, and recovering lavage liquid after gentle mixing. Bacteria quantification, the lavage fluid is inoculated on LB agar after gradient dilution, and colonies are counted after culturing for 18 hours at 37 ℃.

Immunophenotyping lavage cells were labeled with CD45-APC, ly6G-FITC, CD11b-PB, F4/80-PE-Cy7 fluorescent antibodies, and flow cytometry was performed to detect leukocyte subsets.

(6) Histopathological evaluation

Liver, lung, kidney, pancreas and small intestine tissues were fixed with 10% neutral buffered formalin for 24 hours, paraffin embedded and sectioned (5 μm). Hematoxylin-eosin (H & E) stained sections were digitized (20 x objective) using a 3DHISTECH Pannoramic MIDI full slide scanner.

(7) Intestinal barrier function detection

FITC-dextran (40 kDa,0.5 mg/g body weight) was administered orally by gavage 12 hours after GM-CSF administration. Serum was collected after 4 hours, and the serum FITC-dextran concentration was measured by fluorescence (excitation wavelength 485 nm, emission wavelength 528 nm) and quantified on a standard curve.

8. Comparative analysis of surviving and dead individuals

Endpoint index monitoring was performed four times daily on SAP model mice (n=20) for hypothermia, bradykinesia, prone state, and respiratory distress. Mice meeting euthanasia criteria were sacrificed by isoflurane overdose and tissues were immediately treated with littermates surviving and individuals receiving the same CAE-zymosan treatment as controls. The detection index is consistent with the longitudinal study, all data are collected by blind researchers using a pre-calibration instrument, and statistical randomness is ensured through cage-bit random grouping.

9. Optimization of GM-CSF treatment regimen under guidance of plasma properdin concentration

The CAE-zymosan model longitudinally monitors plasma CFP concentrations and tnfα following whole blood LPS stimulation. Timing analysis was performed at 6 time points (n=30/time point, 10 per group: NT, AP, SAP) 12 hours, 36 hours, 3 days, 5 days, 8 days, and 12 days after modeling. The end point sample collection indexes comprise systemic indexes such as body weight, blood convention (CBC), plasma Amylase (AMY) and TNF alpha (ELISA method), and plasma CFP semi-quantitative detection (Western blot method).

Based on the plasma CFP concentrations detected in the above study and the changes in tnfα production following whole blood LPS stimulation, an optimal dosing regimen of granulocyte-macrophage colony-stimulating factor (GM-CSF) was determined using a single factor screening method (n=5 per group) with dosing timing of 0 hours, 12 hours, 36 hours after zymosan induction of SAP, dosing regimen of 200 μg once daily for 3 days, 100 μg twice daily for 5 days.

Efficacy was assessed by mortality, bone marrow neutrophil function detection, and intestinal barrier integrity index, and the optimal regimen (36 hours after SAP induction, 100 μg subcutaneous injection twice daily for 5 days) was finally selected for subsequent study, untreated SAP mice served as positive control.

10. Data analysis

Clinical data are analyzed by adopting R software (v4.3.1), classification variables are expressed in terms of frequency (percentage), chi-square test or Fisher accurate test is adopted, normal distribution continuous variables are expressed in terms of mean value +/-standard deviation, t test is adopted, and non-parameter data are subjected to Mann-Whitney test. The variable p <0.05 in the univariate analysis was included in a multi-factor logistic regression and the optimal cut-off of the subject's working characteristics (ROC) curve was determined by the Youden index.

Experimental data were analyzed using GRAPHPAD PRISM (v 10.0) and results are presented as mean ± standard deviation. The group comparisons used either unpaired t-test, bonferroni corrected one-way anova or Tukey post-test two-way anova. Significance was defined as double tail p <0.05.

Experimental example 1 identification of landscape dynamic network biomarker (l-DNB) method for identifying acute pancreatitis Key status and disease severity predictive markers

The present invention isolated and analyzed plasma Extracellular Vesicles (EVs) from pooled samples (demographics and clinical features detailed in fig. 14). Plasma protein mass spectrometry data were taken from the supplement material of reference （DIA-Based Proteomic Analysis of Plasma Protein Profiles in Patients with Severe Acute Pancreatitis）, whose raw data were subjected to the same pretreatment protocol and incorporated into the l-DNB analysis of the present invention. The literature contains proteomic data of 3 healthy volunteers (control group) and 10 SAP patients (experimental group).

The invention adopts a Dynamic Network Biomarker (DNB) method, identifies the critical state before AP deterioration by analyzing the molecular correlation mode and the interactive network topology structure, and constructs a multi-molecular cooperative network for early warning the disease progress.

The method comprises the steps of constructing a sample specific molecular network through three stages by using a landscape DNB (l-DNB) method, constructing a reference network, constructing a stable reference network based on protein expression data of 15 healthy samples and high-confidence protein interaction information of a STRING database, generating a sample specific network, calculating Pearson Correlation Coefficient (PCC) of three-stage protein expression values of each patient sample according to DNB theory, constructing critical state identification of the Sample Specific Network (SSN) through statistical disturbance analysis with a control sample, and calculating molecular local DNB scores step by step to form a landscape map for representing the overall state of the network. In order to determine the core DNB protein and the regulation network module, the change of SSN before and after estimating the critical point is further compared through the analysis of a difference network, and a voting mechanism is adopted to screen key nodes, wherein only proteins which are all hinge nodes in more than half SSNs are reserved as the core protein for subsequent function verification. The method integrates the expression characteristics of the single sample level and priori interaction information, and remarkably improves the detection sensitivity of a network layer to critical states of diseases. The network-based key node detection (l-DNB) method is a model-free analysis framework, does not need large sample size required by machine learning, and avoids the problem of over-fitting of the traditional method. The framework identifies key biomarkers in pathology progression by systematically evaluating 3 DNB criteria (based on the dynamics of the critical transition state of the biological system) for each sample.

Specifically, the proteomic profile of plasma extracellular vesicles from 88 subjects was analyzed, and finally 228 proteins stably expressed between samples were screened (fig. 1A). The dynamic critical state of the proteome before disease progression was identified using the l-DNB method (FIG. 1B), and a flow chart shows a three-step analysis strategy for identifying DNB core proteins from single sample data (FIG. 1C). There was no significant difference in protein expression intensity between groups. The network topology structure constructed based on DNB theory and l-DNB method is shown in figure 1D, wherein a health control group (HC) network presents a localized fluctuation mode to maintain steady state regulation, a light acute pancreatitis (NSAP) network fluctuates most remarkably to represent the maximum network instability in a critical transition state, a Severe Acute Pancreatitis (SAP) network presents the characteristic of coexistence of fluctuation amplification and topology reconstruction, and a core DNB protein forms a stable hub structure to prompt realization of pathological network architecture reinforcement through a decompensation mechanism. DNB scoring analysis showed that NSAP phase was the critical window period for disease intervention (FIG. 1E), confirming that l-DNB was effective in identifying the pre-malignant critical point of Acute Pancreatitis (AP).

The novel biomarker in DNB core protein was further screened based on the pearson correlation coefficient variability (fatpcc) of the Sample Specific Network (SSN). The network edge type comprises (i) a correlation obtaining edge (PCC_ (n+1) > PCC_n), (ii) a correlation losing edge (PCC_ (n+1) < PCC_n), and (iii) a correlation unchanged edge (PCC_ (n+1) ≡PCC_n). The regulatory network was significantly altered at each stage of disease compared to the reference network (fig. 1F). Proteins such as APOH, APOA1, CFP, PLG, CLU exhibit correlation acquisition or loss of edge characteristics in disease states. Wherein CFP showed a missing edge of correlation at both NSAP and SAP groups, indicating that its interaction partners were significantly reduced and this trend was exacerbated as the disease progressed. Public dataset verification showed (fig. 1G) that CFP nodes in SAP patient sample networks varied most significantly, and that PCC values varied most widely, further corroborating instability of CFP-centric regulatory networks in disease progression, suggesting a key role in SAP development. In addition, CFP exhibited the highest local DNB score at the SAP state, characterizing its transition to the dynamic network hub, manifested as increased volatility and loss of selective connectivity, reflecting the reprogramming characteristics of the regulatory network (fig. 2A-C).

GO enrichment analysis of SAP VS NSAP DNB core protein in plasma vesicle mass spectrometry data (fig. 2D) found that it was significantly enriched in pathways closely related to AP severity, including complement pathway (C3, C5, C9, CFP), protease inhibition system (SERPINA 1, SERPINC1, SERPIND 1), oxidative stress (CAT), coagulation cascade (F2, FGA, FGG), fibrinolytic system (PLG), and inflammatory-metabolic interaction pathway (TTR).

In contrast to traditional differential expression analysis methods, the present method successfully detects CFP molecules that correlate with AP severity but have subtle expression fluctuations (typically <70% variation), which are easily ignored by traditional analysis paradigms. To identify the differential proteins between groups, the present invention employs conventional differential analysis methods (FIGS. 2E-G). In the comparison of the healthy control group (HC) with the non-severe acute pancreatitis (NSAP) group, HC with the Severe Acute Pancreatitis (SAP) group (corrected p-value <0.05 and | logFC | > 1.5), several tens of differentially expressed molecules were found in total, of which the most significant differences in expression were acute phase reactive proteins such as Serum Amyloid A (SAA), C-reactive protein (CRP), lipopolysaccharide binding protein (LBP 16). However, no significant protein expression differences were detected between the severe and mild groups (corrected p-value <0.05 and | logFC | > 1.5). Conventional analysis fails to identify Complement Factor Properdin (CFP) as a differential molecule, and results from two factors, CFP expression fluctuation range is limited, the maximum variation amplitude in each study group is not more than 70%, and biphasic dynamic characteristics, CFP as a complement system component shows a dynamic mode of ascending and then gradually descending in disease progression. The results are consistent with literature reports, which show that the conventional differential analysis method has insufficient sensitivity to CFP (circulating fluid bed) biomarkers with complex dynamic characteristics, and the severity of diseases is difficult to distinguish.

Experimental example 2 plasma CFP concentration stratification of patients with acute pancreatitis of different conditions

The invention included 142 AP patients and a final 122 patients group after standard screening exclusion, 41 patients with SAP and 81 patients with non-severe acute pancreatitis (non-severe acute pancreatitis, NSAP) (fig. 3A). There were no significant differences between the two baseline data (fig. 15).

Single factor 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 NSAP and SAP groups, incorporating a multi-factor analysis (fig. 16). As shown in fig. 16, CFP is the only statistically significant (p < 0.05) independent risk factor. Further examination of the plasma sC5B-9 levels in each group revealed that the SAP group was expressed minimally but without statistical differences (FIG. 3B).

Because of the non-clinical routine detection index of CFP, the invention determines the normal reference value range through 44 Healthy Controls (HC). The three groups of plasma CFP concentrations were HC (23.16+ -5.324) μg/mL, NSAP (19.11+ -6.305) μg/mL, SAP (9.908 + -3.311) μg/mL, respectively, with significant differences between groups (p < 0.0001) (FIG. 3C).

CFP, when analyzed as a continuous variable, exhibited a significant gradient in concentration between the three groups. The ROC curve (FIG. 3D) of NSAP and SAP set shows that the area under the curve (AUC) reaches 0.923, sensitivity 0.926, specificity 0.854, positive Predictive Value (PPV) 0.926, negative Predictive Value (NPV) 0.854 at a cutoff of 11.565 μg/mL. The ROC curves (FIG. 3E) for HC and disease groups (SAP+NSAP) show an AUC of 0.811, sensitivity of 0.639, and specificity of 0.886,PPV 0.940,NPV 0.470 at a cutoff of 18.055 μg/mL, indicating that CFP has better predictive performance for differentiating NSAP from SAP.

The present invention analyzes the time dependence of disease progression on plasma CFP levels for heterogeneity of patient morbidity to blood sampling time windows. Scatter plot analysis (fig. 3F) showed that plasma CFP concentrations were significantly inversely correlated with time after onset, exhibiting progressive depletion characteristics with prolonged disease course. The prognostic advantage of CFP was further confirmed by comparative analysis with classical indicators such as NLR, PLR and monocyte/lymphocyte ratio (MLR) (fig. 3G and 17). The plasma CFP level of the healthy control group is 23.16+/-5.32 mug/mL, which is obviously higher than that of the healthy group reported by literature report Stover and the like, namely 18.4+/-5.51 mug/mL (range 7.6-34.10 mug/mL), and the study of the southeast Asian group is 8.6 (8.0-9.3) mug/mL. These differences stem from ethnicity-related biological variations and technical differences in detection methods, suggesting that demographic characteristics and methodological bias need to be systematically controlled when multi-center biomarker studies and establishing crowd-specific reference ranges. Because of the clinical need for the early AP prognosis markers, analysis compared the patient population with no more than 72 hours of time to onset in both NSAP and SAP groups (fig. 3H), suggesting that plasma CFP is still significantly higher in NSAP group than in SAP group (p < 0.01), suggesting that plasma CFP can also meet demand, worth validating in larger cohorts.

In addition, the patient queue in the study was also followed for disease progression after blood sampling, and the patient ICU occupancy rate, surgery rate, hospitalization duration, occurrence of complications and the like were recorded, which indicated that the prognosis of NSAP group was significantly better than that of SAP group. This study suggests that plasma CFP concentrations not only reflect the condition of the patient at the time of sampling, but also have a significant predictive effect on the prognosis of the patient (fig. 18).

In summary, plasma CFP is a dynamic biomarker of severity dependence, its predictive efficacy provides a clinically practical tool for early risk stratification of AP, and specific mechanisms of action will be elucidated in depth in subsequent studies.

Experimental example 3 plasma CFP concentration modulation depends primarily on the complement activation pathway

The mechanism of plasma CFP depletion involves three causes, (i) excessive consumption mediated by complement cascade activation, (ii) synthesis defects caused by neutrophil dysfunction, and (iii) blocked CFP mobilization due to a disturbed granule exocytosis mechanism. The present invention is resolved around the hypothesis unfolding mechanism described above.

The extent of complement activation was assessed by flow cytometry to detect the level of peripheral blood cell surface C4d deposition, the results of which are shown in figures 4 and 5. Peripheral blood neutrophils were purified by density gradient centrifugation with a purity of greater than 95% (fig. 5A). In addition, peripheral red blood cells (cd235+) were also stained for analysis (fig. 5A). Classical/lectin pathway activation can cause cleavage of C4 to C4d, the covalently bound nature of which makes it a persistent marker of complement activation. The erythrocyte surface C4d was detected only in part of the samples, with SAP group expression levels significantly higher than other groups (fig. 4A). Neutrophil surface C4d was detectable in all samples and SAP group expression levels were significantly higher than control (fig. 4B). Neutrophil C4D deposition levels were significantly inversely correlated with plasma CFP concentrations (fig. 5D). While C5aR expression had no significant correlation with disease severity (fig. 4C and 5C). In addition, up-regulation of neutrophil surface CD64 (fcyri) expression and down-regulation of FPR1 (fMLP receptor) expression in the SAP group (fig. 4D and 5B), combined with blood routine testing results (neutrophil elevation) suggested that severe patients had an infectious status (fig. 5D), presumably pathogen infection driven complement aberrant activation and accelerated CFP consumption.

In vitro complement activation experiments with zymosan (zymosan), a cell wall component of Saccharomyces cerevisiae, found a significant decrease in CFP concentration after co-incubation of plasma with zymosan (FIG. 4E), confirming that CFP was consumed during complement activation. Confocal microscopy imaging showed a significant decrease in CFP deposition on zymosan surfaces in SAP patient plasma compared to healthy control (fig. 4F), suggesting that complement activation is reduced and pathogen clearance is impaired.

Neutrophil quantitative analysis showed that peripheral blood neutrophil counts were significantly higher in the disease group than in the healthy control group (fig. 5D), although SAP group exhibited higher levels of neutrophilia (fig. 5D), plasma CFP remained low, eliminating the possibility of insufficient neutrophil numbers leading to CFP deficiency. CFP was found to co-localize with the specific granule marker Lactoferrin (LTF) in human primary neutrophils by confocal laser localization, but not with gelatinase granule marker MMP-9 and the azurophilic granule marker MPO (fig. 4G), confirming its storage in specific granules. The SAP group was significantly more expressing neutrophil surface CD66b (specific granule marker) than NSAP and healthy group (fig. 4H and 5D), suggesting enhanced specific granule exocytosis. Analysis of CFP expression across the cohort neutrophils (fig. 4I, J) showed heterogeneity among patients, with some cases exhibiting high CFP expression (from inflammatory stimuli) and others exhibiting CFP depletion (associated with sustained release). In vitro stimulation experiments demonstrated that various inflammatory mediators induced CFP release (fig. 4K, L, M), and that severe patients did not find defects in CFP secretion function.

The above results demonstrate that abnormal activation of the complement system in SAP patients is a central mechanism driving accelerated consumption of plasma CFP, providing a reasonable explanation for its concentration reduction.

Experimental example 4 correlation study of plasma CFP concentration and Ex Zhou Zhongxing granulocyte function

After defining the association of the extracellular Zhou Zhongxing granulocyte number with the plasma CFP concentration, the present invention further explores the functional interaction mechanism of both. Circulating neutrophil counts exhibited a different distribution in SAP patients, 62% exhibited neutrophilia and 38% exhibited contradictory neutropenia (fig. 6A and 7A). To reduce potential interference of experimental variation on neutrophil function assessment, the invention synchronously collects samples (completed within 40 minutes) of healthy control group (HC), non-severe acute pancreatitis (NSAP) and Severe Acute Pancreatitis (SAP) patients for experiment.

Functional profiling showed that Reactive Oxygen Species (ROS) production of neutrophils was significantly impaired in SAP patients (fig. 6B). Rapid ROS production is the core basis for neutrophil immune function, and its functional defects suggest that neutrophil overall function is impaired.

Transwell migration experiments demonstrated that SAP neutrophils had migration barriers, polycarbonate membrane cell retention was increased (FIG. 6C), and chemotactic efficiency was significantly reduced (FIGS. 6D and 7B). Migration defects are positively correlated with insufficient ROS production, while over-activation of adhesion is correlated with upregulation of surface markers such as selectins, integrin beta 2, and cell adhesion molecules. Adhesion experiments show that the neutrophil adhesion capacity of SAP patients is enhanced, but migration activity is significantly reduced. Adhesion is mediated by selectins, β2 integrins (β2 integrins) and Cell Adhesion Molecules (CAMs), whose dynamic changes reflect the state of cell activation. Previous studies have shown that upregulation of surface integrin expression promotes neutrophil-endothelial cell adhesion enhancement. This high adhesion state impedes the extravasation of neutrophils into the lung tissue and alveoli, thereby impairing the bacterial clearance of the pulmonary infection. Thus, the increased adhesion of SAP neutrophils is an important mechanism for the impaired host anti-infective defenses.

Neutrophil extracellular trap (nes) formation was assessed quantitatively by Sytox Green fluorescence (fig. 6E and fig. 7C). Although the mouse model demonstrates that inflammatory mediators drive NETosis exacerbate pancreatitis, SAP neutrophils exhibit the paradox phenomenon of reduced generation of nes. This phenomenon is caused by two interdependent mechanisms, (i) enhanced bacterial clearance activity of SAP neutrophils (phagocytosis index: 4.7.+ -. 1.2 vs. HC 2.1.+ -. 0.6) retention of elastase and myeloperoxidase in the phagolysosome, blocking nuclear translocation required for chromatin depolymerization, and (ii) impaired NADPH oxidase activity limiting the ROS-dependent activation of peptide acyl arginine deiminase 4 (PAD 4), a requirement for histone citrullination. The neutrophil bactericidal capacity of SAP patients was significantly impaired (fig. 6F) and strongly correlated with defects in ROS production. SAP neutrophils had reduced overall bactericidal capacity, but their bactericidal activity in plasma was abnormally increased (data not shown). This contradiction suggests that the bactericidal effect of SAP plasma is primarily dependent on the Membrane Attack Complex (MAC) produced during complement activation, which is an increase in C4d deposition on peripheral blood cells of SAP patients, indicating a significant enhancement in complement cascade activity.

Signal pathway analysis (FIG. 6G) showed that the P47, P40 and ERK phosphorylation levels of SAP group neutrophils were reduced, while AKT phosphorylation was enhanced. The reduced p47phox/p40phox phosphorylation suggests a disturbance in NADPH oxidase complex assembly, directly explaining the ROS deficiency observed in functional experiments. ERK dephosphorylation reflects the interruption of the MAPK-ROS positive feedback loop, AKT overactivation being compensatory activation of the PI3K pathway. Elevated p105 phosphorylation suggests that TLR 4-driven classical NF- κb signaling is associated with circulating pathogen-associated molecular patterns (e.g., LPS) caused by bacterial translocation in SAP patients.

LPS stimulated whole blood leukocyte TNF alpha production experiments showed that peripheral monocytes of SAP patients exhibited an immunosuppressive phenotype, manifested by reduced TNF alpha secretion (FIGS. 6H and 7D). Notably, some SAP patients exhibit conflicting high activation characteristics of neutrophils, with this heterogeneity reflecting phase fluctuations in disease progression—progressive functional depletion following early neutrophil overactivation. Neutrophil dysfunction was mediated by multiple immunosuppressive pressures (C5 a, LPS, cytokines and immune metabolism) together, but the total expression of neutrophil surface CD11b was not significantly different (fig. 7E).

The invention provides a new strategy for clinical evaluation of disease severity, which can correct individual baseline differences by dynamically monitoring plasma CFP concentration, realize longitudinal tracking of immune dysfunction progress.

Experimental example 5 SAP establishment of mouse model and pathological characteristics

In order to more widely explore the clinical application potential of CFP, the invention carries out the research of a mouse acute pancreatitis model. The invention adopts a modeling strategy of synchronously activating complement cascade and macrophage phagocytosis by intraperitoneal injection of zymosan (zymosan). Based on zymosan's biodegradation properties, systemic inflammatory response and Multiple Organ Dysfunction Syndrome (MODS) can be permanently induced. The CAE-Zymosan mixed model accurately simulates the SIRS-MODS double pathological process characteristic of clinical SAP through the combination of ranpirin (caerulein) induction of pancreatic interstitial edema and zymosan-mediated disease deterioration.

The CAE-Zymosan modeling protocol (FIG. 8A) reproduces the pattern of clinical characteristic biphasic mortality of SAP (FIG. 8B) with acute inflammatory phase (0-72 hours) exhibiting rapid death (20-40%), and subacute organ failure phase (72 hours-10 days) exhibiting progressive death (15-30%). Maximum weight loss reached 20% of baseline, with the lowest value seen 3-5 days after molding (fig. 8C).

The results of the model 12 hours SAP mice pathology showed that biochemical tests showed 3-5 fold higher levels of Amylase (AMY) and Lipase (LIP) in SAP model mice than in AP control, liver injury markers (ALT, AST) and renal function indicators (urea, creatinine) were significantly increased (FIG. 8D), hematological indicators showed significant leukopenia with decreased neutrophil/lymphocyte counts (FIG. 8E), intestinal pathology assessment showed significant shortening of small intestine length (18.6+ -1.2 vs 27.4+ -1.5 cm), suggesting severe intestinal inflammation, and significant increases in peritoneal lavage colony forming units, confirming bacterial translocation (FIG. F, G).

Pancreatic histopathological evaluation showed typical SAP characteristics of extensive acinar necrosis (43±5% of total tissue area), interstitial edema, and dense inflammatory infiltrates (fig. 8H). Flow cytometric analysis showed up-regulation of terminal complement component expression, 4.1-fold increase in circulating neutrophil surface membrane attack complex (MAC/C5 b-9) density compared to control, 2.9-fold increase in bone marrow cells (fig. 8I).

The CAE-Zymosan model effectively reproduces the core pathophysiological characteristics of human SAP, namely the local pancreatic inflammatory response, MODS and biphasic disease progress modes, and provides a reliable platform for subsequent mechanism research and therapeutic intervention.

Experimental example 6 CAE-Zymosan model for differentiating plasma CFP concentrations in surviving and non-surviving mice

To elucidate the key knowledge blank of the differences in extracellular Zhou Zhongxing granulocyte function between clinical survivors and non-survivors, the present invention reveals the functional heterogeneity of neutrophils in mice with different survival outcomes using the CAE-Zymosan model. CAE-Zymosan induced non-surviving mice exhibited a significantly different pathophysiological phenotype than surviving mice in that after zymosan administration, non-surviving mice exhibited sustained weight loss and bradykinesia, and pre-moribund characteristics that were typical of being distinguishable from surviving mice including hypothermia, bradykinesia, prone state, and respiratory distress.

The experimental results showed that the growth curve showed no sign of recovery in non-surviving mice (fig. 9A) and that there was a significant difference in plasma CFP concentrations in both groups of mice (fig. 9B). Non-surviving mice exhibited reduced intestinal length, bacterial translocation, and massive recruitment of inflammatory cells (macrophages and neutrophils) in peritoneal lavage fluid (fig. 9C and 10A).

Blood biochemical tests showed that amylase levels restored to normal in both groups, but lipase levels increased significantly in the dead group (fig. 9D). Comparison of the surviving group with the 12-hour sacrificed group showed a significant improvement in physiological function, while the main organ function of the dead group mice was continuously impaired, especially pancreas, showing acinar cell cavitation/necrosis and inflammatory cell infiltration (fig. 10B).

Further functional analysis of mouse bone marrow neutrophils found that the total number of bone marrow cells in the surviving group was in the normal range, while the dead group was only one-half or less of the normal value (FIGS. 9E and 10C). The 83% dead mice exhibited significantly reduced neutrophil function, manifested by reduced numbers of migrating cells, reduced bactericidal capacity, and reduced formation of NETs (FIGS. 9F-H and 10D-F). Functional similarity was observed only in a certain pair of mice during the 3.5 day observation period. The survival differences suggest that the death group persists in the acute inflammatory phase, and that its death pathology is associated with Systemic Inflammatory Response Syndrome (SIRS). Notably, only 33% of the dead mice exhibited more severe peripheral monocyte immunosuppression than the surviving mice, suggesting that the monocyte immunosuppression status did not fully reflect the overall function of the mice (fig. 9I).

Two sets of bone marrow neutrophil signaling pathway studies showed that the most distinct pathways were NADPH oxidase signaling, apoptosis, autophagy, and NF- κb signaling (fig. 9J and 10G).

The number of bone marrow cells in the dead group of mice was significantly reduced with impaired neutrophil function, a phenomenon that was significantly positively correlated with reduced circulating CFP levels. This pathophysiological link is highly consistent with the clinical observations of severe acute pancreatitis patients described above, suggesting a conserved mechanism in the progression of cross-species crisis. Notably, in a population of mice with the same genetic background, acute pancreatitis progression is highly heterogeneous, manifested by significant inter-individual mortality differences, suggesting that disease progression is regulated by non-genetic factors (such as microenvironment or immune status).

Experimental example 7 plasma properdin concentration directed GM-CSF based immunotherapy for intervention study in SAP mice

GM-CSF (granulocyte-macrophage colony stimulating factor) is a pleiotropic cytokine expressed by hematopoietic cells and can regulate myeloid progenitor cell differentiation, neutrophil survival and effector function. Dog model researches show that GM-CSF reduces bacterial translocation by enhancing neutrophil activity, and provides basis for application of GM-CSF in acute pancreatitis. Human clinical trials have further demonstrated that GM-CSF alone or in combination with interferon gamma (IFN- γ) can reverse monocyte dysfunction and enhance LPS-induced tnfα production, exhibiting therapeutic potential.

The above experimental results show that both neutrophils and monocytes of clinical SAP patients have significant inhibitory dysfunction, and that non-surviving mice of SAP model also have a similar phenotype. GM-CSF is a potential therapeutic candidate due to its dual function regulatory capability on both cell types. To achieve accurate medical strategies, the system of the invention evaluates circulating CFP levels in combination with whole blood to stimulate tnfα production as a biomarker value to guide GM-CSF dosing, aims to determine optimal treatment thresholds, can achieve immune reconstitution and can avoid the risk of cytokine overstimulation.

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

SAP group mice showed sustained weight loss (FIG. 11A), transient hyperamylase reached peak at 12h, recovery at 72h (FIG. 11B), 62% decrease in circulating neutrophils in acute phase (12-36 h) (0.9+ -0.2 vs 2.4+ -0.3X103/. Mu.L), 47% decrease in lymphocytes (2.1+ -0.4 vs 4.0+ -0.5X103/. Mu.L), supposedly recruited inflammatory cells activated in SAP pathology to the foci, and gradual reconstitution of hematopoietic function in SAP group in recovery phase (. Gtoreq.3d).

Plasma CFP levels in SAP mice tended to decrease from 12h to 5d after induction, rebound in late (8-12 d) CFP concentrations occurred, individual heterogeneity was present (FIG. 11C), and CFB levels remained relatively stable throughout the experiment. The mouse CFP source is different from the human, and in addition to neutrophils, liver, lymph and spleen also contribute significantly, and this multi-tissue synthesis property explains that SAP-AP differences are weaker than human pathological manifestations.

LPS-stimulated tnfα production experiments showed that peripheral monocyte function was maintained for 36h, followed by (to 5D) sustained inhibition (fig. 11D, suggesting that there is a temporal differentiation of monocytes from neutrophil function impairment, reflecting the differential regulatory mechanisms of both in SAP progression.

The results of the above longitudinal studies suggest that mice are in critical illness from 0-36 hours post-molding, characterized by plasma CFP concentrations and/or monocyte function inhibition. Based on this, three initial dosing time points of 0 hours, 12 hours and 36 hours were screened. Treatment protocol optimization was then performed to determine two key variables, (1) treatment initiation time window, (2) subcutaneous GM-CSF dosing. The results show that 36 hours of initiation of treatment resulted in optimal efficacy, in particular high survival (100%), rapid recovery of body weight and small intestine length (2.5 days), improved intestinal barrier function (FITC-dextran leakage experiments) and inhibition of peritoneal bacterial translocation (fig. 12A-E and fig. 13B). In contrast, the 0 hour treatment group had lower survival and sustained weight loss, and the 12 hour treatment group exhibited intermediate efficacy. The bactericidal activity was ranked as 36 hours > 12 hours >0 hours (fig. 12F). Immune cell function was complete (plasma CFP was normal, bone marrow cells were abundant, neutrophil function was not impaired) 0 hours after modeling (fig. 12K-M), plasma CFP was decreased, bone marrow cells were decreased, suggesting that neutrophil function may be impaired, but monocyte activity (tnfα continuous secretion) was still retained 12 hours after modeling. This transient immunosuppressive state, characterized by neutrophil dysfunction but retention of monocyte activity, exacerbates secondary inflammation induced following GM-CSF administration. Treatment of GM-CSF is based on real-time immune monitoring, avoiding dosing during the monocyte hyperresponse phase, to reduce the risk of pro-inflammatory events. 36 hours after modeling, SAP mice exhibited a synergistic immunosuppressive phenotype, with further reduction in plasma CFP levels, continued progression of bone marrow depletion, and significant impairment of monocyte function (reduced tnfα secretion). The maximum therapeutic effect achieved by administering GM-CSF at this stage is achieved by simultaneous repair of myeloid lineage (neutrophil and monocyte) function. In conclusion, combining plasma CFP levels with LPS-stimulated whole blood tnfα responses optimizes the therapeutic window, providing a theoretical framework for accurate immune intervention of SAP.

Three GM-CSF dosing regimens were further tested, 200 ng single daily doses (3 day course), 100 ng double daily doses (3 day course) and 100 ng double daily doses (5 day course). Although weight loss occurred early in the 5 day course, it was associated with significantly reduced bacterial translocation and enhanced bactericidal activity (fig. 12G-J). The optimal protocol was finally determined to be 36 hours post-molding to initiate treatment with a dose of 100 ng twice daily for 5 days (fig. 13A).

In formal treatment experiments, the most significant effect of the GM-CSF group was an increase in mouse survival (100% vs. 40%) (fig. 13C). However, no significant differences were detected in body weight, bone marrow neutrophil function, and cell signaling pathways (fig. 13D-K), which resulted from the untreated group of surviving mice had recovered by themselves.

Intestinal tract is a key hub for SAP progression, pancreatic injury exacerbates intestinal barrier destruction, driving bacterial translocation and systemic inflammatory cascade. This study found that GM-CSF restores intestinal barrier integrity by enhancing neutrophil mediated bactericidal activity, suggesting that intestinal-derived bacterial dissemination is the core mechanism of SAP intestinal damage. GM-CSF has dual regulatory effects on immunosuppressive neutrophils and monocytes, and its efficacy depends on precise timing of administration. In summary, preclinical validation studies demonstrate the practical value of plasma CFP quantitative analysis in guiding immunotherapeutic protocols, creating a transformation research framework for CFP as biomarker-driven neutrophil/monocyte targeted intervention strategy.

The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.

Claims (7)

1. Application of a reagent for detecting blood properdin concentration in the preparation of a product for diagnosing the severity of acute pancreatitis. 2. Use of the reagent for detecting blood properdin concentration according to claim 1 in the preparation of a product for diagnosing the severity of acute pancreatitis, characterized in that the diagnosis is performed by measuring the properdin concentration in the subject's blood. When the properdin concentration in the blood decreases by -40% to 50% relative to the properdin concentration in the normal group, it is judged as non-severe acute pancreatitis; when the properdin concentration in the blood decreases by more than 50% relative to the properdin concentration in the normal group, it is judged as severe acute pancreatitis. 3. Use of the reagent for detecting blood properdin concentration according to claim 2 in the preparation of a product for diagnosing the severity of acute pancreatitis, characterized in that the normal group refers to a group of people with the same ethnic background and matching some demographic characteristics as the subject. 4. Use of the reagent for detecting blood properdin concentration according to claim 2 in the preparation of a product for diagnosing the severity of acute pancreatitis, characterized in that the properdin concentration in the blood of the subject and the normal control group is measured using the same detection method. 5. Use of the reagent for detecting blood properdin concentration according to claim 4 in the preparation of a product for diagnosing the severity of acute pancreatitis, characterized in that the detection method comprises electrochemiluminescence technology, enzyme-linked immunosorbent assay, liquid phase chip technology, single molecule immunoassay technology, fully automated capillary digital Western Blot, proximity extension analysis technology, immunoelectrophoresis, high performance liquid chromatography tandem mass spectrometry or microfluidic chip. 6. Use of the reagent for detecting blood properdin concentration according to claim 2 in the preparation of a product for diagnosing the severity of acute pancreatitis, characterized in that the type of properdin in the blood includes one or more of plasma properdin, serum properdin and whole blood properdin. 7. Use of the reagent for detecting blood properdin concentration according to claim 1 in the preparation of a product for diagnosing the severity of acute pancreatitis, characterized in that the use of the product for diagnosing the severity of acute pancreatitis includes use alone or in combination to assist in guiding the formulation of a medication regimen for acute pancreatitis.