Controllable preparation method of metal-metal oxide core-shell nanomaterial and application thereof

Abstract

The application discloses a controllable preparation method of metal-metal oxide core-shell nanomaterial and application thereof, and relates to the technical field of serum metabolite detection. The method comprises the following steps: step 1, gold nanoparticles are prepared by using a seed growth synthesis method; step 2, a SiO2 layer and a ZrO2 layer are coated on the gold nanoparticles in sequence to obtain Au@SiO2@ZrO2 core-shell composite material; and step 3, the obtained Au@SiO2@ZrO2 core-shell composite material is calcined at different temperatures respectively to obtain core-shell nanomaterials with different crystal structures. The application designs and synthesizes the composite nanometer core-shell material Au@SiO2@ZrO2 with adjustable parameters, and the composite hybrid nanomaterial has the advantages of single materials, such as more excellent photoelectric performance, and the effect of 1+1 greater than 2 is achieved for mass spectrometric detection, the LDI-MS small molecule detection performance is significantly improved, and the problem that a single-structure nanomatrix material cannot realize high-performance detection of metabolites in complex biological samples as an LDI-MS matrix is solved.

Description

Technical Field

[0001] This invention relates to the field of serum metabolite detection technology, and in particular to a controllable preparation method of metal-metal oxide core-shell nanomaterials and their applications. Background Technology

[0002] In in vitro diagnostics, the detection of biomarkers (such as proteins, nucleic acids, and metabolites) is becoming increasingly crucial due to its non-invasive and convenient nature. Unlike epigenetically regulated genes and post-translational modified proteins, metabolic biomarkers, as the final products of pathways, can more directly reflect the physiological and pathological processes in the human body. Therefore, metabolite biomarkers are gradually becoming powerful tools for in vitro diagnostics. Traditional metabolomics detection tools, such as gas chromatography / liquid chromatography-mass spectrometry (GC / LC-MS), can detect metabolites after sample pretreatment including desalting, protein removal, derivatization, and concentration. However, GC / LC-MS requires a chromatographic column, involves complex sample pretreatment, has a long detection time, and is costly, making it difficult to achieve low-cost, high-throughput detection of serum samples, thus posing a challenge for clinical applications.

[0003] Matrix-assisted laser desorption / ionization mass spectrometry (MALDI-MS) plays an increasingly prominent role in metabolite detection. Unlike traditional GC / LC-MS, MALDI-MS requires little or no sample pretreatment, enabling rapid, sensitive, and high-throughput detection of metabolites with minimal sample consumption. The matrix is ​​a core component of MALDI-MS. However, traditional organic matrices generate strong background signals in the low molecular weight region (m / z < 500), affecting the detection of small molecule metabolites. Although various inorganic nanomaterials (such as carbon-based, silicon-based, and noble metal materials) have been developed for the detection of small molecule metabolites, their application in complex biological samples remains limited due to interference from various biomacromolecules and the influence of varying pH and high salinity. Therefore, there is an urgent need to develop nanomaterials to achieve efficient detection of metabolites in complex biological samples (such as serum).

[0004] Against this backdrop, researchers in this field are dedicated to developing MALDI MS matrices with higher LDI efficiency for the detection of metabolites in complex biological samples. Summary of the Invention

[0005] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a MALDI MS matrix with higher LDI efficiency for the detection of metabolites in complex biological samples.

[0006] To achieve the above objectives, the present invention provides a controllable preparation method for metal-metal oxide core-shell nanomaterials, comprising the following steps:

[0007] Step 1: Prepare gold nanoparticles using a seed growth synthesis method;

[0008] Step 2: Sequentially coat the gold nanoparticles with SiO2 and ZrO2 layers to obtain Au@SiO2@ZrO2 core-shell composite material;

[0009] Step 3: Calcine the obtained Au@SiO2@ZrO2 core-shell composite materials under different temperature conditions to obtain core-shell nanomaterials with different crystal structures.

[0010] In a preferred embodiment of the present invention, step 2, the step of coating the gold nanoparticles with a SiO2 layer, is as follows:

[0011] Step 2.1: Cool the gold nanoparticles to room temperature, mix them with freshly prepared polyvinylpyrrolidone (PVP-10) solution, and let stand overnight.

[0012] Step 2.2: The polyvinylpyrrolidone-stabilized gold nanoparticles are separated by centrifugation, adjusted to the set volume with deionized water, and then ultrasonically dispersed.

[0013] Step 2.3: Under vigorous stirring, add a mixture of anhydrous ethanol and concentrated ammonia solution to the dispersion, then add anhydrous ethanol solution of TEOS dropwise, and stir vigorously overnight at room temperature.

[0014] Step 2.4: Centrifuge the solution and wash it with water to obtain Au@SiO2 solid.

[0015] In a preferred embodiment of the present invention, step 2, the step of coating the ZrO2 layer, is as follows:

[0016] Step 2.5: Disperse Au@SiO2 solid in the mixed solution and stir until homogeneous to obtain a mixture;

[0017] Step 2.6: Inject zirconium butoxide containing ethanol into the mixture, stir evenly at room temperature, centrifuge to separate the precipitate, wash with ethanol, and redisperse in water to obtain Au@SiO2@ZrO2 core-shell composite material.

[0018] Preferably, in step 2.5, the mixed solution is a mixture of hydroxypropyl cellulose, ethanol and deionized water, and for every 100 mg of hydroxypropyl cellulose, 20 ml of ethanol and 0.1 ml of deionized water are used.

[0019] In another preferred embodiment of the present invention, in step 3, the Au@SiO2@ZrO2 core-shell composite material is calcined at 600°C, 900°C and 1100°C respectively.

[0020] This invention also discloses the application of the metal-metal oxide core-shell nanomaterials prepared by the above method in detecting the metabolic fingerprint of pancreatic cancer serum, comprising the following steps:

[0021] After diluting the serum sample with deionized water, the blood sample was dropped onto a 384 Polish target plate of LDI MS. After it was allowed to dry naturally at room temperature, an aqueous solution of Au@SiO2@ZrO2 core-shell nanomaterials dissolved in deionized water was dropped onto the dried blood sample. After drying at room temperature, the sample was loaded for detection to obtain the serum metabolic fingerprint of pancreatic cancer patients.

[0022] In a preferred embodiment of the present invention, the pancreatic cancer serum metabolic fingerprint is used for early diagnosis and / or prognostic prediction of pancreatic cancer.

[0023] In a preferred embodiment of the present invention, the method for early diagnosis includes:

[0024] The obtained serum metabolic fingerprints from pancreatic cancer patients were preprocessed to obtain m / z signals;

[0025] The collected serum sample data were divided into training and test sets. The LASSO algorithm was optimized and trained on the training set using 20 repeated 5-fold cross-validation to obtain the performance of the LASSO algorithm on the training set.

[0026] The trained model is used to make predictions on the test set to obtain the performance of the LASSO algorithm on the test set.

[0027] Based on LASSO scores, serum metabolic markers for the early diagnosis of pancreatic cancer were selected, including glycine (Gly), valine (Val), glutamate (Glu), glucose (Glc), and palmitic acid (Paa).

[0028] The present invention also provides a serum metabolic marker for the early diagnosis of pancreatic cancer, wherein the serum metabolic marker includes glycine (Gly), valine (Val), glutamate (Glu), glucose (Glc), and palmitic acid (Paa).

[0029] In another preferred embodiment of the present invention, the method for prognosis prediction includes:

[0030] The serum metabolic fingerprints of patients with pancreatic cancer prognosis were preprocessed to obtain m / z signals;

[0031] The collected serum sample data were divided into training and test sets. First, univariate Cox regression analysis was performed on the training set samples to identify metabolite characteristics related to overall survival of pancreatic cancer patients. Then, multivariate Cox regression analysis was used to select metabolites that were independently related to overall survival and to construct a metabolic prognostic prediction scoring system (MPP).

[0032] Using the MPP scoring system, data were divided into high-scoring and low-scoring groups based on the median score, and the results were validated using a test set to construct a prognostic prediction method for pancreatic cancer based on metabolic characteristics.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] (1) This invention designs and synthesizes a composite nano-core-shell material Au@SiO2@ZrO2 with adjustable parameters. The composite hybrid nanomaterial integrates the advantages of each component single material, such as better photoelectric properties, and achieves a 1+1 greater than 2 effect in improving mass spectrometry detection. It significantly improves the small molecule detection performance of LDI-MS and solves the problem that single-structure nano matrix materials cannot achieve high-performance detection of metabolites in complex biological samples when used as LDI-MS matrix.

[0035] (2) By adjusting the particle size of gold nanoparticles and the crystal phase of zirconium dioxide shell, efficient photothermal conversion and charge separation can be achieved, which is conducive to the synergistic regulation of laser desorption and ionization processes of Au@SiO2@ZrO2. The temperature increases by 5 times, the photoelectric response increases by 2 times, and the detection performance is improved by up to 1000 times.

[0036] (3) Based on the LDI MS platform of Au@SiO2@ZrO2 nanomatrix, metabolites were detected. Based on the metabolites, high-performance diagnosis of diseases was achieved, and metabolic markers with stable diagnostic performance were screened out. The accuracy rate of early diagnosis of pancreatic cancer was as high as 92%, which solved the problem that existing metabolic markers could not achieve stable diagnostic performance.

[0037] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0038] Figure 1 This is a transmission electron microscopy image of Au@SiO2@ZrO2 provided in a preferred embodiment of the present invention;

[0039] Figure 2 This is a scanning electron micrograph of Au@SiO2@ZrO2 provided in a preferred embodiment of the present invention;

[0040] Figure 3 This is a raw mass spectrum of glucose detection using Au@SiO2@ZrO2 provided in a preferred embodiment of the present invention;

[0041] Figure 4 This is a performance graph of metabolic markers provided in a preferred embodiment of the present invention for the diagnosis of early pancreatic cancer;

[0042] Figure 5 This is a flowchart of a preferred embodiment of the present invention for constructing a prognostic prediction model for pancreatic cancer based on serum metabolic markers;

[0043] Figure 6 This is a preferred embodiment of the present invention, which provides the predictive performance of a pancreatic cancer prognostic prediction model constructed based on serum metabolic markers. Detailed Implementation

[0044] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0045] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0046] Example 1: A controllable preparation method of Au@SiO2@ZrO2 nanoparticles, comprising the following steps:

[0047] The core gold nanoparticles were synthesized using a seed growth method. This method allows for precise control of the particle size by manipulating the reaction generation. Specifically, in a 250 ml three-necked round-bottom flask, 150 ml of a 2.2 mM sodium citrate solution in Milli-Q water was heated for 15 minutes. A condenser was used to prevent solvent evaporation. Once boiling began, 1 ml of a 25 mM HAuCl4 solution was added. The solution color changed from yellow to bluish-gray and then to pale pink within 10 minutes. The resulting particles were approximately 15 nanometers in size, coated with negatively charged citrate ions, allowing them to suspend well in H2O. These served as seeds for subsequent reactions. The solution was then immediately cooled in the same container until it reached 90°C. Then, 1 ml of a 60 mM sodium citrate solution and 1 ml of a 25 mM HAuCl4 solution were added sequentially (approximately 2 minutes apart). By repeating this process (adding 1 ml of 60 mM sodium citrate and 1 ml of 25 mM HAuCl4 sequentially), the size of the gold nanoparticles gradually increased, eventually reaching approximately 85 nanometers. The concentration of each generation of nanoparticles was roughly the same as the concentration of the original seed particles.

[0048] The Au@SiO2@ZrO2 core-shell assembly was performed using a layer-by-layer method. First, a SiO2 layer was coated. Specifically, the obtained gold nanoparticles were cooled to room temperature and mixed with a freshly prepared solution of polyvinylpyrrolidone (PVP-10) at a concentration of 12.8 mg / ml (0.325 ml), and allowed to stand overnight. The PVP-stabilized gold nanoparticles were then centrifuged and their volume adjusted to 6 ml with deionized water. Next, the solution was ultrasonically dispersed, and under vigorous magnetic stirring, a mixture of anhydrous ethanol (18.90 ml) and concentrated ammonia (0.3 ml) was added to the dispersion. Then, a solution of TEOS (1.19 ml) in anhydrous ethanol (12.80 ml) was added dropwise, and the mixture was vigorously stirred overnight at room temperature. Subsequently, the solution was centrifuged, and the Au@SiO2 solid was washed several times with water.

[0049] Next, a ZrO2 layer was coated, and Au@SiO2 was redispersed in ethanol (28 ml). The above Au@SiO2 solution was then dispersed in a solution containing hydroxypropyl cellulose (HPC, 100 mg), ethanol (20 ml), and deionized water (0.1 ml). After stirring for 30 minutes, zirconium butoxide (ZBOT, 250 ml) containing ethanol (4.5 ml) was injected into the mixture. The mixture was then stirred at 600 rpm for 20 hours at room temperature. The precipitate was separated by centrifugation, washed with ethanol, and redispersed in water (20 ml) to obtain the Au@SiO2@ZrO2 core-shell composite material.

[0050] Finally, the obtained Au@SiO2@ZrO2 core-shell composite materials were calcined at 600, 900 and 1100 degrees Celsius to obtain core-shell nanomaterials with different crystal structures.

[0051] in, Figure 1 This is a transmission electron microscopy image of Au@SiO2@ZrO2 prepared in this invention; Figure 2 The image shows a scanning electron microscope image of Au@SiO2@ZrO2 provided by this invention.

[0052] This invention can control the physical parameters of core-shell nanomaterials (gold core particle size, silica layer thickness, and zirconium dioxide layer thickness) by adjusting the growth generation of gold nanoparticles and the amounts of precursors TEOS, ZBOT, HPC, deionized water, and concentrated ammonia added.

[0053] The crystal phase structure of the zirconium dioxide layer can be controlled by adjusting the calcination temperature.

[0054] Example 2: LDI MS platform based on Au@SiO2@ZrO2 core-shell composite material for the detection of metabolic fingerprints in pancreatic cancer serum.

[0055] The serum sample was diluted 10 times with deionized water. One microliter of the diluted blood sample was dropped onto a 384 Polish target plate of LDI MS and allowed to air dry at room temperature. One microliter of a 1 mg / mL solution of Au@SiO2@ZrO2 core-shell material dissolved in deionized water was then dropped onto the dried blood sample and allowed to air dry at room temperature. The sample was then analyzed to obtain the serum metabolic fingerprint of pancreatic cancer patients.

[0056] Example 3: Construction of early diagnostic biomarkers based on serum metabolic fingerprints of pancreatic cancer

[0057] The obtained serum metabolic fingerprints of pancreatic cancer patients were preprocessed, including data resampling, spectral smoothing, baseline correction, peak alignment, and missing value imputation, to obtain the m / z signal. The collected serum samples (300 healthy controls and 311 pancreatic cancer patients) were divided into training and test sets. Twenty repeated 5-fold cross-validation was used to optimize the parameters and train the LASSO algorithm on the training set, obtaining its performance on the training set. The trained model was then used to predict on the test set, yielding its performance on the test set.

[0058] Furthermore, serum metabolic markers for early diagnosis of pancreatic cancer were screened based on LASSO scores, including glycine (Gly), valine (Val), glutamate (Glu), glucose (Glc), and palmitic acid (Paa), such as... Figure 3 The image shows the raw mass spectrum of glucose detected by Au@SiO2@ZrO2.

[0059] Figure 4 This is a graph showing the performance of metabolic biomarkers in the early diagnosis of pancreatic cancer.

[0060] The aforementioned biomarkers can achieve efficient early diagnosis of pancreatic cancer and hold promise for clinical application. This addresses the problems of low throughput, complex operation, high cost, and slow analysis speed associated with traditional pancreatic cancer detection techniques.

[0061] Example 4: Constructing a prognostic prediction model based on serum metabolic fingerprinting of pancreatic cancer

[0062] In addition, the applicant has developed a metabolite prognostic prediction (MPP) scoring system for predicting the prognosis of pancreatic cancer, the process of which is as follows: Figure 5As shown, enrolled patients were randomly assigned to the discovery group (training set) or the validation group (test set) at a 2:1 ratio. The serum metabolic fingerprints of pancreatic cancer patients obtained from these fingerprints were preprocessed, including data resampling, spectral smoothing, baseline correction, peak alignment, and missing value imputation, to obtain the m / z signal for further analysis. In the discovery cohort (training set), univariate Cox regression analysis was first performed to identify metabolite characteristics associated with overall survival in pancreatic cancer patients. Subsequently, multivariate Cox regression analysis was used to further select metabolites independently associated with overall survival, and these were used to construct the MPP scoring system. Using the MPP scoring system, data were divided into high-score and low-score groups based on the median score, and the results were validated using the test set, constructing a prognostic prediction method for pancreatic cancer based on metabolic characteristics. This identified a set of three metabolite characteristics associated with pancreatic cancer (PC) prognosis.

[0063] The predictive performance of the pancreatic cancer prognostic prediction model constructed based on this serum metabolic marker, such as... Figure 6 As shown. By Figure 6 It can be seen that the prediction model has high specificity and sensitivity.

[0064] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. The application of metal-metal oxide core-shell nanomaterials in the preparation of reagents for detecting serum metabolic fingerprints of pancreatic cancer, characterized in that, Includes the following steps: After diluting the serum sample with deionized water, the blood sample was dropped onto the LDIMS 384Polish target plate and allowed to dry naturally at room temperature. Then, an aqueous solution of Au@SiO2@ZrO2 core-shell nanomaterials dissolved in deionized water was dropped onto the dried blood sample, dried at room temperature, and then sampled for detection to obtain the serum metabolic fingerprint of pancreatic cancer patients. The controllable preparation method of the metal-metal oxide core-shell nanomaterial includes the following steps: Step 1: Prepare gold nanoparticles using a seed growth synthesis method; Step 2: Sequentially coat the gold nanoparticles with SiO2 and ZrO2 layers to obtain Au@SiO2@ZrO2 core-shell composite material; Step 3: Calcine the obtained Au@SiO2@ZrO2 core-shell composite materials under different temperature conditions to obtain core-shell nanomaterials with different crystal structures.

2. The application as described in claim 1, characterized in that, In step 2, the step of coating the gold nanoparticles with a SiO2 layer is as follows: Step 2.1: Cool the gold nanoparticles to room temperature, mix them with freshly prepared polyvinylpyrrolidone (PVP-10) solution, and let stand overnight. Step 2.2: The polyvinylpyrrolidone-stabilized gold nanoparticles are separated by centrifugation, adjusted to the set volume with deionized water, and then ultrasonically dispersed. Step 2.3: Under vigorous stirring, add a mixture of anhydrous ethanol and concentrated ammonia solution to the dispersion, then add anhydrous ethanol solution of tetraethyl orthosilicate (TEOS) dropwise, and stir vigorously overnight at room temperature. Step 2.4: Centrifuge the solution and wash it with water to obtain Au@SiO2 solid.

3. The application as described in claim 2, characterized in that, In step 2, the step of coating the ZrO2 layer is as follows: Step 2.5: Disperse Au@SiO2 solid in the mixed solution and stir until homogeneous to obtain a mixture; Step 2.6: Inject zirconium butoxide containing ethanol into the mixture, stir evenly at room temperature, centrifuge to separate the precipitate, wash with ethanol, and redisperse in water to obtain Au@SiO2@ZrO2 core-shell composite material.

4. The application as described in claim 3, characterized in that, In step 2.5, the mixed solution is a mixture of hydroxypropyl cellulose, ethanol and deionized water, and for every 100 mg of hydroxypropyl cellulose, 20 ml of ethanol and 0.1 ml of deionized water are used.

5. The application as described in claim 3, characterized in that, In step 3, the Au@SiO2@ZrO2 core-shell composite material was calcined at 600℃, 900℃ and 1100℃, respectively.

6. The application according to claim 1, characterized in that, The pancreatic cancer serum metabolic fingerprint is used for the early diagnosis and / or prognostic prediction of pancreatic cancer.

7. The application according to claim 6, characterized in that, Methods used for early diagnosis include: The obtained serum metabolic fingerprints from pancreatic cancer patients were preprocessed to obtain m / z signals; The collected serum sample data were divided into training and test sets. The LASSO algorithm was optimized for parameters and trained on the training set using 20 repeated 5-fold cross-validation to obtain the performance of the LASSO algorithm on the training set. The trained model is used to make predictions on the test set to obtain the performance of the LASSO algorithm on the test set. Based on LASSO scores, serum metabolic markers for the early diagnosis of pancreatic cancer were selected, including glycine (Gly), valine (Val), glutamate (Glu), glucose (Glc), and palmitic acid (Paa).

8. The application according to claim 6, characterized in that, Methods used for prognostic prediction include: The serum metabolic fingerprints of patients with pancreatic cancer prognosis were preprocessed to obtain m / z signals; The collected serum sample data were divided into training and test sets. First, univariate Cox regression analysis was performed on the training set samples to identify metabolite characteristics related to overall survival of pancreatic cancer patients. Then, multivariate Cox regression analysis was used to select metabolites that were independently related to overall survival and to construct a metabolic prognostic prediction scoring system (MPP). Using the MPP scoring system, data were divided into high-scoring and low-scoring groups based on the median score, and the results were validated using a test set to construct a prognostic prediction method for pancreatic cancer based on metabolic characteristics.

Images