Preparation method and application of porous PtCu alloy nano material

Abstract

The invention discloses a preparation method and application of a porous PtCu alloy nano material, and relates to the technical field of biology. The preparation method comprises the following steps: synthesizing a porous PtCu nano-particle micelle by adopting an improved soft template guiding method, centrifuging, washing and drying to obtain a porous PtCu nano-material; the porous PtCu alloy nano material is applied to LDI MS metabolite detection, a CAP patient plasma metabolism fingerprint spectrogram is obtained, and machine learning is carried out to construct a model. The porous PtCu alloy nano material is synthesized and applied to a laser desorption ionization mass spectrometry technology to enhance the signal sensitivity of the porous PtCu alloy nano material, the plasma metabolism fingerprint spectrum of a CAP patient is obtained through the platform, and severe CAP diagnosis and metabolic marker screening are carried out.

Description

Technical Field

[0001] The invention relates to the field of biotechnology, and in particular to a preparation method and application of a porous PtCu alloy nanomaterial. Background Art

[0002] Community-acquired pneumonia (CAP) refers to an acute respiratory infection involving the lung parenchyma that is acquired by individuals in the community. Severe CAP is the most lethal form of CAP and is a leading cause of morbidity and mortality worldwide. Timely and accurate assessment of disease severity in patients with CAP is essential to help clinicians make optimal therapeutic interventions. The diagnosis of severe CAP is mainly based on the criteria established by the Infectious Diseases Society of America (IDSA) / American Thoracic Society (ATS), which requires meeting 1 of the 2 major criteria (need for invasive mechanical ventilation or use of vasopressors) and 3 of the 9 minor criteria (confusion, respiratory rate ≥ 30 times / minute, etc.). However, this method evaluates too many variables and is very time-consuming in practical application, especially in emergency situations. Chest X-ray is also commonly used to diagnose severe CAP, but its sensitivity and specificity are relatively low, it usually requires experienced technicians and physicians, is time-consuming, and has varying levels of interpretation. Therefore, the development of rapid and accurate methods for the diagnosis of severe CAP remains an important clinical need.

[0003] Biomarkers are sensitive indicators that characterize disease progression and pathological status and are expected to identify the severity of CAP. Recently, proteins (e.g., C-reactive protein (CRP), procalcitonin (PCT), IL-6 cytokine) and genes (e.g., circRNA, IRF5) have been used to diagnose and evaluate severe CAP. However, these biomarkers have low accuracy (AUC of 0.56-0.77) and exist in various diseases, which makes it difficult to meet clinical needs. Compared with proteins and genes, small molecule metabolites, as downstream of biological systems, are more easily associated with phenotypes. Some metabolomics studies have identified potential biomarkers for CAP, but few studies have focused on the changes in metabolic characteristics of CAP patients with different severities. In addition, the common liquid chromatography / gas chromatography (LC / GC) mass spectrometry (MS) for metabolite detection requires complex sample pretreatment, which is limited in clinical situations. In contrast, laser desorption / ionization (LDI) MS provides a powerful analytical tool that enables rapid, sensitive, and high-throughput metabolite detection with simple sample preparation.

[0004] For LDI MS, the matrix plays a key role in assisting the desorption / ionization of analytes. Conventional organic matrices cannot effectively detect small molecule metabolites because of poor reproducibility due to uneven crystallization and high background signals generated by the fragmentation of organic molecules under laser irradiation. Inorganic matrices, especially metal nanoparticles (NPs) (e.g., Au, Pt, Cu, Pd), have attracted attention due to their high surface area, strong UV absorption, and small background interference. In particular, Pt NPs have shown excellent performance in LDI MS due to their tunable plasmonic properties and low thermal conductivity. Cu has excellent stability and high conductivity, which may facilitate the LDI process. However, most single metal NPs have poor performance in detecting metabolites in clinical samples due to interference from high-abundance proteins and nucleic acids in biofluids. Alloying with Cu can optimize the electronic structure of the Pt-based alloy surface and enhance its activity and stability. Specifically, by utilizing the synergistic effect between the two metals, the bimetallic system can further enhance the photothermal conversion efficiency and improve the stability, thereby facilitating the desorption / ionization process. In addition, the porous structure of NPs provides a large specific surface area for analyte adsorption, abundant active sites for charge transfer to analytes, and can be used to exclude large molecules. Therefore, porous PtCu alloys as a matrix will enhance metabolite detection in complex clinical biological samples.

[0005] Therefore, technicians in this field are committed to developing a porous PtCu alloy matrix with high laser desorption ionization efficiency for extracting plasma metabolic fingerprints of CAP patients, and conducting machine learning to establish models and screen biomarker combinations. Summary of the invention

[0006] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to develop a porous PtCu alloy matrix with high laser desorption ionization efficiency for extracting the plasma metabolic fingerprint of CAP patients, and to conduct machine learning to establish a model and screen a combination of biomarkers.

[0007] To achieve the above object, the present invention provides a method for preparing a porous PtCu alloy nanomaterial, comprising the following steps:

[0008] Step 1, synthesizing porous PtCu nanoparticle micelles using an improved soft template-guided method;

[0009] Step 2: centrifuge, wash and dry the porous PtCu nanoparticle micelles synthesized in step 1 to obtain porous PtCu nanomaterials.

[0010] Furthermore, the improved soft template-guided method in step 1 includes: dissolving polyether F 127 (Pluronic F127) in deionized water; subsequently, adding HCl solution, and then adding chloroplatinic acid (H2PtCl6·6H2O), cupric chloride (CuCl2) and ascorbic acid in sequence to obtain a mixture; stirring the mixture and reacting it in a water bath to obtain PtCu micelles with different particle sizes.

[0011] Furthermore, the water bath temperature was 85° C. and the reaction time was 3 hours.

[0012] Furthermore, the concentration of HCl was 6M, and the added volumes were 0.1mL / 0.2mL / 0.4mL / 0.6mL respectively; the concentration of H2PtCl6·6H2O was 20mM; the concentration of CuCl2 was 40mM; and the concentration of ascorbic acid was 0.1M.

[0013] Furthermore, step 2 also includes: collecting the precipitate by centrifugation at 8000 rpm for 10 minutes; washing the precipitate with ethanol and water continuously and centrifuging three times to obtain porous PtCu nanoparticles; and drying the porous PtCu nanoparticles in a vacuum drying chamber at 50° C. for 5 hours.

[0014] The present invention also discloses a method for preparing a porous PtCu alloy nanomaterial and application of the prepared porous PtCu alloy nanomaterial in LDI MS metabolite detection, comprising the following steps:

[0015] Step 1) using a porous PtCu alloy material-assisted LDI MS platform to extract plasma metabolic fingerprints of CAP patients;

[0016] Step 2) The plasma metabolic fingerprint spectrum of CAP patients extracted in step 1) is subjected to machine learning to construct a model.

[0017] Furthermore, step 1) also includes:

[0018] Step 1.1, pre-treating plasma with an organic solvent to precipitate proteins, and using the supernatant as a treated plasma sample;

[0019] Step 1.2, using deionized water to prepare a 1 mg / mL matrix solution of the porous PtCu alloy material;

[0020] Step 1.3, loading the sample on the 384 Polish target plate, spotting 1 μL of the treated plasma sample obtained in step 1.1, and drying naturally at room temperature to obtain the 384 Polish target plate after sample loading;

[0021] Step 1.4, loading the matrix onto the sample-loaded 384 Polish target plate obtained in step 1.3, dropping 1 μL of the matrix solution onto the dried blood sample, and drying naturally at room temperature to obtain the matrix-loaded 384 Polish target plate;

[0022] Step 1.5: Using a matrix-assisted laser desorption / ionization mass spectrometer, the matrix-loaded 384 Polish target plate obtained in step 1.4 is tested to obtain a metabolic fingerprint of the plasma of CAP patients.

[0023] Furthermore, step 2) also includes:

[0024] Step 2.1, preprocessing the plasma metabolic fingerprint of CAP patients, including data resampling, spectrum smoothing, baseline correction, spectrum peak matching, and missing value filling, to obtain m / z signal data; CAP patients include mild CAP patients and severe CAP patients;

[0025] Step 2.2, using software to perform metabolic fingerprint heat map analysis on the m / z signal data obtained in step 2.1;

[0026] Step 2.3: The metabolic fingerprint map processed in step 2.1 was used to construct a model through lasso regression, ridge regression and neural network algorithms. The machine learning process adopted a 5-fold cross-validation method; the performance of each algorithm was evaluated to determine the optimal model.

[0027] Furthermore, the above application also includes constructing and identifying a combination of metabolic markers based on plasma metabolic fingerprints.

[0028] Furthermore, the metabolic marker combination is lysine, palmitic acid and cholesterol.

[0029] In the preferred embodiment 1 of the present invention, the preparation process of the porous PtCu alloy nanomaterial is described in detail;

[0030] In another preferred embodiment 2 of the present invention, the LDIMS platform assisted by porous PtCu alloy material is described in detail for the plasma metabolic fingerprint extraction process of CAP patients;

[0031] In another preferred embodiment 3 of the present invention, a machine learning process based on plasma metabolic fingerprint spectra of patients with mild and severe CAP is described in detail;

[0032] In another preferred embodiment 4 of the present invention, the process of constructing a combination of metabolic markers based on plasma metabolic fingerprints is described in detail.

[0033] The beneficial technical effects of the present invention are as follows:

[0034] 1. Detection performance advantages

[0035] The present invention designs porous PtCu alloy nanomaterials with high specific surface area and stable porous state, combines the advantages of each component, exhibits better photoelectric performance and improved surface melting structure, and enhances metabolite desorption / ionization efficiency. This innovation improves the detection capability of mass spectrometry technology in clinical applications and realizes efficient detection of low-concentration metabolites in complex biological samples.

[0036] The prepared porous PtCu alloy nanomaterial has a high specific surface area and a stable porous state, integrating the advantages of each component, showing better photoelectric performance and improved surface melting structure, enhancing the metabolite desorption / ionization efficiency, and significantly improving the LDI MS metabolite detection performance (compared with a single component, the performance is improved by 143 times).

[0037] The porous PtCu alloy-assisted laser desorption ionization mass spectrometry (LDI MS) technology itself has the characteristics of high throughput, rapid detection, and simple sample pretreatment. It can complete the analysis of a large number of samples in a short time, meet the needs of large-scale clinical screening, and can significantly improve detection efficiency and reduce costs.

[0038] 2. Outstanding clinical diagnostic value

[0039] The AUC of the metabolite fingerprint for diagnosing severe CAP in the present invention is as high as 0.832, and the AUC of the constructed metabolite marker combination is 0.846, with a diagnostic performance superior to that of traditional clinical biomarkers (such as CRP, PCT, etc., AUC=0.560-0.770).

[0040] 3. Controllable synthesis of nanomaterials

[0041] The porous PtCu alloy nanomaterials are synthesized by a simple one-pot hydrothermal synthesis process, which has good controllability and can be stably manufactured in laboratory and production environments. The cost of the synthesis process is controllable and suitable for large-scale production.

[0042] 4. Industrialization prospects

[0043] The technology of the present invention not only provides an advanced means for the clinical diagnosis of severe CAP, but also has broad application prospects. With the increasing demand for precision medicine and early disease detection, this technology will significantly improve the efficiency and accuracy of severe CAP diagnosis after combining with the existing diagnostic platform, and has good industrialization potential.

[0044] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a scanning and transmission electron microscopic image of a porous PtCu alloy material of a preferred embodiment 1 of the present invention;

[0046] Figure 2 It is a representative metabolic fingerprint spectrum of mild and severe CAP patients in a preferred embodiment 2 of the present invention;

[0047] Figure 3 It is a heat map corresponding to 181 m / z signals after preprocessing of plasma metabolic fingerprint spectra of 69 CAP patients in a preferred embodiment 3 of the present invention;

[0048] Figure 4 is a ROC curve for diagnosing severe CAP using metabolic fingerprints according to a preferred embodiment 3 of the present invention;

[0049] Figure 5 The ROC curve for diagnosing severe CAP constructed based on a plasma metabolite marker group and a single metabolite marker according to a preferred embodiment 4 of the present invention. DETAILED DESCRIPTION

[0050] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0051] Example 1 Preparation of porous PtCu alloy nanomaterials

[0052] Preparation of porous PtCu alloy nanomaterials for use as matrix materials for laser desorption ionization mass spectrometry:

[0053] Step 1: Synthesis of porous PtCu nanoparticles. Porous PtCu alloy was synthesized by an improved soft template-guided method. Specifically, 300 mg of Pluronic F127 was dissolved in 3.6 mL of deionized water. Subsequently, 0.1 mL / 0.2 mL / 0.4 mL / 0.6 mL of HCl solution (6 M) (named PtCu-1 / 2 / 3 / 4, respectively) was added, followed by 12.8 mL of H2PtCl6·6H2O (20 mM), CuCl2 (40 mM) and ascorbic acid (AA, 0.1 M) were added in sequence. The mixture was stirred and reacted in a water bath at 85 ° C for 3 hours to obtain PtCu micelles with different particle sizes. The scanning and transmission electron microscopy images of the porous PtCu alloy materials are shown in Figure 2. Figure 1 As shown, part a is PtCu-1, part b is PtCu-2, part c is PtCu-3, and part d is PtCu-4; the scale bar is 50 nm.

[0054] Step 2: Centrifugation, washing and drying of materials. The product was collected by centrifugation at 8000 rpm for 10 minutes. The remaining Pluronic F127 template was removed by washing and centrifugation three times with ethanol and water. Finally, the porous PtCu nanoparticles were dried in a vacuum drying chamber at 50 °C for 5 hours.

[0055] Example 2: Porous PtCu alloy-assisted LDI MS platform for plasma metabolic fingerprint extraction in CAP patients

[0056] The specific steps are:

[0057] Step 1: Pre-treat plasma with an organic solvent to precipitate proteins. Specifically, 20 μL of plasma sample was mixed with 20 μL of organic solvent (methanol / acetonitrile = 1 / 1, v / v), stirred for 10 minutes, then centrifuged at 12,000 rpm for 10 minutes, and the supernatant was extracted for further experiments.

[0058] Step 2: Prepare the porous PtCu alloy material into a 1 mg / mL matrix solution with deionized water.

[0059] Step 3: Load the sample on the 384 Polish target plate, spot 1 μL of each treated plasma sample and dry it naturally at room temperature.

[0060] Step 4: Load the matrix on the 384 Polish target plate, drop 1 μL of the matrix solution on the dried blood sample, and dry it naturally at room temperature.

[0061] Step 5: Use matrix-assisted laser desorption ionization mass spectrometry to collect plasma metabolic fingerprints of CAP patients.

[0062] Step 6: Obtain the plasma metabolic fingerprint spectra of CAP patients for subsequent analysis. Representative metabolic fingerprint spectra of mild and severe CAP patients are shown in Figure 2 shown.

[0063] Example 3 Machine learning based on plasma metabolic fingerprints of patients with mild and severe CAP

[0064] The specific steps are:

[0065] Step 1: Preprocess the plasma metabolic fingerprint of CAP patients, including data resampling, spectral smoothing, baseline correction, spectral peak matching, and missing value filling to obtain m / z signals.

[0066] Step 2: Using the “Heatmap” package in Python (version 3.7), the plasma samples of 39 patients with mild CAP and 30 patients with severe CAP were analyzed by metabolic fingerprint heat map. The heat maps corresponding to 181 m / z signals of the plasma metabolic fingerprint spectra of 69 patients with CAP were preprocessed. Figure 3 shown.

[0067] Step 3: Construct a diagnostic model for severe CAP. The collected plasma sample data (39 mild plasma samples and 30 severe plasma samples) were divided into a discovery set and a validation set. Three commonly used algorithms in Orange software (3.37.0) were used to construct a diagnostic model for severe CAP, including lasso regression (LR), ridge regression (RR), and neural network (NN). The machine learning process used a 5-fold cross-validation method. The performance of each algorithm was evaluated to determine the optimal model, and the ROC curve for diagnosing severe CAP using metabolic fingerprint mapping was constructed. Figure 4 shown.

[0068] Example 4 Construction of metabolic marker combination based on plasma metabolic fingerprint

[0069] The specific steps are:

[0070] Step 1: m / z features were screened based on two conditions: (1) two-sided t-test p value < 0.05; (2) average intensity > 200.

[0071] Step 2: Determine the precise molecular weight of the feature using Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS). Sample loading was performed on the mass spectrometry target plate using step 2, the biomarkers were accurately measured using Fourier transform ion cyclotron resonance mass spectrometry, and the precise molecular weight of the feature was confirmed using Bruker Compass data analysis software (version 5.0).

[0072] Step 3: The accurate molecular weights from FTICR MS were compared with the Human Metabolome Database (HMDB) to identify the metabolite markers with an m / z mass tolerance of 5 ppm.

[0073] Step 4: Validation of identified metabolite markers using LC-MS. Pooled plasma samples were prepared by combining samples from randomly selected 1 / 3 participants. 100 μL of pooled plasma was mixed with 400 μL of organic solvent (methanol / ACN, v / v=1:1), incubated at -20°C for 2 hours, and then centrifuged at 12,000 rpm for 20 minutes. The resulting supernatant was transferred to a clean EP tube and dried using a vacuum concentrator. The dried extract was resuspended in 100 μL of solvent (methanol / water, v / v=3:7), vortexed for 3 minutes, and centrifuged at 12,000 rpm for 20 minutes. The supernatant was transferred to a liquid mass spectrometry injection vial. LC-MS analysis was performed using an ACQUITY Premier UPLC system coupled to a SELECT SERIES Cyclic IMS (waters, American) mass spectrometer. The raw LC-MS data were analyzed using TraceFinder software to validate potential metabolic biomarkers, see Table 1.

[0074] Table 1 Information related to metabolic biomarkers in the diagnostic marker panel for severe CAP

[0075]

[0076]

[0077] The constructed metabolite marker panel has better diagnostic performance than single metabolites and reported protein and gene markers. The ROC curves for diagnosing severe CAP based on the plasma metabolite marker panel and single metabolite marker are shown in Figure 2. Figure 5 shown.

[0078] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for preparing a porous PtCu alloy nanomaterial, characterized in that: The method comprises the following steps: Step 1, synthesizing porous PtCu nanoparticle micelles using an improved soft template-guided method; Step 2: centrifuge, wash and dry the porous PtCu nanoparticle micelles synthesized in step 1 to obtain the porous PtCu nanomaterial.

2. The method according to claim 1, characterized in that The improved soft template guiding method in step 1 comprises: dissolving polyether F 127 in deionized water; subsequently, adding hydrogen chloride solution, and then sequentially adding chloroplatinic acid, cupric chloride and ascorbic acid to obtain a mixture; stirring the mixture and reacting it in a water bath to obtain PtCu micelles with different particle sizes.

3. The method according to claim 2, characterized in that The water bath temperature is 85° C., and the reaction time is 3 hours.

4. The method according to claim 2, characterized in that The concentration of HCl is 6M, and the added volumes are 0.1mL / 0.2mL / 0.4mL / 0.6mL respectively; the concentration of H2PtCl6·6H2O is 20mM; the concentration of CuCl2 is 40mM; and the concentration of ascorbic acid is 0.1M.

5. The method according to claim 1, characterized in that The step 2 further comprises: collecting the precipitate by centrifugation at a speed of 8000 rpm for 10 minutes; washing the precipitate with ethanol and water continuously and centrifuging three times to obtain porous PtCu nanoparticles; and drying the porous PtCu nanoparticles in a vacuum drying chamber at 50° C. for 5 hours.

6. Application of the porous PtCu alloy nanomaterial prepared by the preparation method according to any one of claims 1 to 5 in LDI MS metabolite detection, characterized in that: The application comprises the following steps: Step 1) using a porous PtCu alloy material-assisted LDI MS platform to extract plasma metabolic fingerprints of CAP patients; Step 2) The plasma metabolic fingerprint spectrum of CAP patients extracted in step 1) is subjected to machine learning to construct a model.

7. The use according to claim 6, characterized in that The step 1) further comprises: Step 1.1, pre-treating plasma with an organic solvent to precipitate proteins, and using the supernatant as a treated plasma sample; Step 1.2, using deionized water to prepare a 1 mg / mL matrix solution of the porous PtCu alloy material; Step 1.3, loading the sample on the 384 Polish target plate, spotting 1 μL of the treated plasma sample obtained in step 1.1, and drying naturally at room temperature to obtain the 384 Polish target plate after sample loading; Step 1.4, loading the matrix onto the sample-loaded 384 Polish target plate obtained in step 1.3, dropping 1 μL of the matrix solution onto the dried blood sample, and drying naturally at room temperature to obtain the matrix-loaded 384 Polish target plate; Step 1.5: Using a matrix-assisted laser desorption ionization mass spectrometer, the matrix-loaded 384 Polish target plate obtained in step 1.4 is detected to obtain a metabolic fingerprint of the plasma of CAP patients.

8. The use according to claim 5, characterized in that The step 2) further comprises: Step 2.1, preprocessing the plasma metabolic fingerprint of the CAP patient, including data resampling, spectrum smoothing, baseline correction, spectrum peak matching, and missing value filling, to obtain m / z signal data as the processed metabolic fingerprint; the CAP patient includes a mild CAP patient and a severe CAP patient; Step 2.2, using software to perform metabolic fingerprint heat map analysis on the m / z signal data obtained in step 2.1; Step 2.3, constructing a model using the processed metabolic fingerprint obtained in step 2.1 through lasso regression, ridge regression and neural network algorithms, wherein the machine learning process adopts a 5-fold cross-validation method; evaluating the performance of each algorithm to determine the optimal model.

9. The use according to claim 8, characterized in that The application also includes identifying a metabolic marker combination based on plasma metabolic fingerprint construction.

10. The use according to claim 9, characterized in that The metabolic marker combination is lysine, palmitic acid and cholesterol.

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