Serum metabolite analysis and detection method and application

Abstract

The invention discloses a serum metabolite analysis and detection method and application. Zirconium dioxide hierarchical pore nanospheres modified by gold nanoparticles arranged in an array mode are used as a matrix, a laser ionization desorption-time-of-flight mass spectrometry method is used for extracting a metabolite fingerprint spectrum in serum, feature extraction and normalization processing are carried out on the obtained metabolite fingerprint spectrum, analysis is carried out through a machine learning algorithm, and the metabolite fingerprint spectrum is obtained. The method is used for distinguishing postmenopausal osteoporosis patients from healthy people and monitoring the development process of osteoporosis. The gold nanoparticle modified zirconium dioxide hierarchical pore nanosphere has high specific surface area and strong ultraviolet absorption capability, can promote ionization efficiency, can more efficiently, simply and conveniently analyze serum metabolites, and has good clinical application prospects in diagnosis and monitoring of postmenopausal osteoporosis.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technology, and in particular to an analytical detection method and application of serum metabolites. Background Art

[0002] Metabolomics is an advanced omics technology that can capture phenotypic changes in organisms by studying downstream metabolites of biological pathways. This technology can reveal the complexity of metabolism behind a variety of diseases, deepen the understanding of disease pathogenesis, and help discover potential disease-specific biomarkers. Metabolomics based on biological fluids has important potential in disease diagnosis because it is non-invasive, easy to collect samples, and can reflect real-time physiological status. Among them, serum, as a comprehensive biological fluid, provides metabolic information from multiple organs and tissues, and has therefore become an ideal choice for detecting disease-specific metabolic changes and improving diagnostic accuracy.

[0003] Mass spectrometry (MS) has become an important tool in metabolomics research due to its excellent sensitivity and extensive metabolite identification database. Among the many mass spectrometry techniques, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOFMS) has attracted much attention due to its unique advantage of being able to analyze complex samples on a large scale, with the advantages of rapid detection, low sample consumption, and low pretreatment requirements. However, traditional organic matrices have high background interference in the low mass range (<1000Da), which poses a challenge to metabolite detection. In particular, metal oxides, as emerging matrices, have attracted widespread attention due to their high stability, strong ultraviolet absorption ability, and ability to form a uniform crystal structure with analytes, which can improve ionization efficiency and sensitivity. Combining metal oxides with noble metal nanoparticles can further enhance the ionization efficiency and significantly improve the efficiency of matrix surface desorption and ionization, thereby improving the overall performance of LDI-TOF MS and obtaining information-rich metabolic fingerprints. This combination has been widely used in disease metabolomics analysis based on LDI-TOF MS, and combining it with machine learning algorithms has great potential in early disease diagnosis and progression monitoring. Summary of the invention

[0004] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide an analytical detection method and application of serum metabolites.

[0005] The inventive concept of the present invention is: in order to improve the ability of serum metabolomics analysis, the present invention provides a disease metabolomics analysis method based on LDI-TOF MS, using zirconium dioxide multi-level porous nanospheres modified with gold nanoparticles as a matrix for mass spectrometry detection. This method does not require complex pre-treatment of samples, and the array arrangement can greatly shorten the experimental time, and can more quickly and easily obtain metabolic information in serum, which improves the basis for early detection of postmenopausal osteoporosis and promotes the application of precision medicine driven by body fluid metabolism in actual clinical practice.

[0006] In order to achieve the above object, the present invention provides a method for analyzing and detecting serum metabolites, comprising the following steps:

[0007] Step 1: preparing zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles;

[0008] Step 2: Arrange the gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres obtained in step 1 and the serum in a mixed array, and directly perform LDI-TOF MS analysis after natural drying.

[0009] Preferably, the method for preparing the gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres in step 1 comprises the following steps:

[0010] Step 1.1: dissolving the template, pore swelling agent and regulator in water, adding the zirconium precursor and ligand to react, collecting the product and washing and drying it to obtain the precursor of hierarchical porous zirconium dioxide nanospheres;

[0011] Step 1.2: placing the precursor of the hierarchical pore zirconium dioxide nanospheres obtained in step 1.1 in a muffle furnace and calcining at high temperature to obtain hierarchical pore zirconium dioxide nanospheres;

[0012] Step 1.3: Disperse the hierarchical porous zirconium dioxide nanospheres obtained in step 1.2 in water, add chloroauric acid aqueous solution and heat to boil, add a reducing agent to react, collect the product, wash and dry, and obtain gold nanoparticle-modified hierarchical porous zirconium dioxide nanospheres.

[0013] Preferably, in step 1.1:

[0014] The template agent is poloxamer, more preferably, the poloxamer is Pluronic P123 and / or Pluronic F127; more preferably, the poloxamer is a mixture of Pluronic P123 and Pluronic F127; the dosage ratio of Pluronic P123 to Pluronic F127 is 0.3-0.4g:0.08-0.10g.

[0015] The pore swelling agent is 1,3,5-trimethylbenzene;

[0016] The regulator is sodium perchlorate and / or acetic acid, and more preferably, the regulator is a mixture of sodium perchlorate and acetic acid.

[0017] The usage ratio of the template agent to water is 0.38-0.5 g:15-20 mL.

[0018] The zirconium precursor is zirconium chloride and / or its hydrate. More preferably, the zirconium precursor is zirconyl chloride octahydrate.

[0019] The ligand is 2-aminoterephthalic acid.

[0020] More preferably, the usage ratio of poloxamer, 1,3,5-trimethylbenzene, sodium perchlorate, acetic acid, zirconium oxychloride octahydrate, and 2-aminoterephthalic acid is 0.38-0.5 g: 10-20 mL: 0.6-0.8 g: 1-3 mL: 0.6-1.0 g: 0.1-0.3 g.

[0021] Preferably, the reaction temperature is 35-50° C., and the reaction time is 10-15 hours.

[0022] Preferably, in step 1.2: the calcination temperature is 400-600° C., and the calcination time is 2-4 hours.

[0023] Preferably, in step 1.3:

[0024] The reducing agent is trisodium citrate dihydrate,

[0025] The ratio of the multi-level porous zirconium dioxide nanospheres to water is 80-120 mg: 80-120 mL;

[0026] The dosage ratio of the multi-level porous zirconium dioxide nanospheres to the chloroauric acid aqueous solution is 80-120 mg: 1.2-1.8 mL;

[0027] The concentration of the chloroauric acid aqueous solution is 10-20 mg / mL;

[0028] The dosage ratio of the multi-level porous zirconium dioxide nanospheres to the reducing agent is 80-120 mg:75-90 mg;

[0029] The reaction temperature is 100°C and the reaction time is 10 to 30 minutes.

[0030] Preferably, the drying conditions in step 1.1 and step 1.3 are both: vacuum drying at 40-60°C.

[0031] Poloxamer is used as a template for synthesizing hierarchical porous zirconium dioxide nanospheres; 1,3,5-trimethylbenzene is used as a pore swelling agent; sodium perchlorate and acetic acid are used as regulators for the reaction of synthesizing hierarchical porous zirconium dioxide nanospheres; deionized water is used as a reaction solvent; zirconium oxychloride octahydrate is used as a zirconium precursor; 2-aminoterephthalic acid is used as a reaction ligand; chloroauric acid is used as a gold source; and trisodium citrate dihydrate is used as a reducing agent for reducing to gold nanoparticles.

[0032] Preferably, in step 2, the sample is diluted 30-70 times with deionized water and added dropwise to a stainless steel target plate containing array points; then the zirconium dioxide multi-level porous nanospheres modified with gold nanoparticles obtained in step 1 are prepared into a solution, added dropwise to the target plate containing the sample arranged in an array and allowed to stand; the sample is the subject's serum.

[0033] Preferably, the specific conditions of the LDI-TOF MS analysis in step 2 are: using a 355nm Nd:YAG laser light source, a laser frequency of 2000Hz, an acceleration voltage of 20kV, wherein the voltage at ion source 1 is 20kV, and the voltage at ion source 2 is 17.5kV; the acquisition mode is a reflection positive ion mode, and the acquisition range m / z is 100-1000Da; each sample is tested 5 times; mass spectrometry data is obtained from flexControl3.4, and the data is exported in flexAnalysis 3.4.

[0034] The present invention also provides the application of the analytical detection method of the serum metabolites in the early diagnosis of postmenopausal osteoporosis and the monitoring of the disease process.

[0035] Preferably, the application is based on a serum metabolite fingerprint obtained by LDI-TOF MS analysis of batch samples and scores the machine learning algorithm based on the fingerprint.

[0036] Preferably, the specific conditions for obtaining the serum metabolite fingerprint are: normalizing the mass spectrum peak intensity of the mass spectrum obtained by LDI-TOFMS analysis of the batch samples with the total ion current, taking the average value of the mass spectrum of each sample, and listing the mass spectrum peak intensity of each sample to obtain the metabolite fingerprint; the batch samples include a training set and a validation set;

[0037] And / or, the machine learning algorithm steps include: inputting training set sample data, training the model, evaluating the model, inputting test set sample data for blind testing, and evaluating the test set blind testing results.

[0038] The present invention also provides a postmenopausal osteoporosis diagnosis and monitoring system, which uses a laser desorption ionization time-of-flight mass spectrometer to analyze metabolites in serum, and uses zirconium dioxide multi-level porous nanospheres modified with gold nanoparticles as matrix-assisted desorption ionization;

[0039] The laser desorption ionization time-of-flight mass spectrometer is used to obtain an LDI-TOF MS mass spectrum of metabolites in a serum sample of a subject, and to determine whether the subject suffers from postmenopausal osteoporosis based on the score of the mass spectrum in a machine learning algorithm.

[0040] The present invention utilizes zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles as matrix-assisted desorption ionization, and due to its strong ultraviolet absorption ability, large specific surface area and surface roughness, it can improve the charge transfer ability in LDI-TOF MS analysis, enhance the desorption ionization efficiency, and obtain a high-quality serum metabolic fingerprint map. With the help of a machine learning algorithm, early diagnosis of postmenopausal osteoporosis and tracking of the development process can be achieved, which shows that the method of the present invention is helpful for large-scale clinical screening of various diseases based on metabolic analysis.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) The zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles have a large specific surface area, providing a large number of adsorption sites for the adsorption of metabolites. The modification of gold nanoparticles can significantly enhance the surface charge transfer ability, improve the analysis efficiency of LDI-TOF MS, and obtain high-quality serum metabolic fingerprints;

[0043] (2) The method of the present invention uses LDI-TOF MS to perform metabolite analysis on samples arranged in an array, and does not require cumbersome sample pretreatment processes. Therefore, the method of the present invention can obtain serum metabolic information more simply and quickly;

[0044] (3) The method of the present invention uses a machine learning algorithm to identify the differences in serum metabolism between postmenopausal osteoporosis patients and healthy people. It can distinguish serum samples from patients and healthy people with high accuracy and sensitivity, and successfully achieves rapid in vitro diagnosis of postmenopausal osteoporosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a transmission electron microscope photo of the zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles in Example 1;

[0046] Figure 2 This is the pore size distribution diagram of the zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles in Example 1;

[0047] Figure 3 The X-ray powder diffraction spectrum of the zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles in Example 1;

[0048] Figure 4The UV-visible absorption spectrum of the zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles in Example 1;

[0049] Figure 5 The serum metabolic fingerprint mass spectrum obtained for the array-arranged gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres of Example 2;

[0050] Figure 6 is the score scatter plot of all serum samples in Example 3;

[0051] Figure 7 is the receiver characteristic curve based on the training set and the validation set of Example 3;

[0052] Figure 8 is a score scatter plot of serum samples based on important features of Example 4;

[0053] Fig. 9 The receiver characteristic curves of the training set and the validation set based on the important features of Example 4; DETAILED DESCRIPTION

[0054] In order to make the present invention more clearly understood, preferred embodiments are described in detail below with reference to the accompanying drawings.

[0055] A method for analyzing and detecting serum metabolites comprises the following steps:

[0056] Step 1: preparing zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles;

[0057] Step 1.1: Dissolve the template, pore swelling agent and regulator in water, add the zirconium precursor and ligand to react, collect the product, wash it, and vacuum dry it at 40-60° C. to obtain a precursor of hierarchical porous zirconium dioxide nanospheres;

[0058] The templates were Pluronic P123 and Pluronic F127;

[0059] The pore swelling agent is 1,3,5-trimethylbenzene;

[0060] The conditioning agent is a mixture of sodium perchlorate and acetic acid;

[0061] The ratio of the template to water is 0.38-0.5 g: 15-20 mL;

[0062] The zirconium precursor is zirconyl chloride octahydrate;

[0063] The ligand is 2-aminoterephthalic acid;

[0064] The amount ratio of Pluronic P123, Pluronic F127, 1,3,5-trimethylbenzene, sodium perchlorate, acetic acid, zirconium chloride octahydrate, and 2-aminoterephthalic acid is 0.3-0.4 g: 0.08-0.10 g: 10-20 mL: 0.6-0.8 g: 1-3 mL: 0.6-1.0 g: 0.1-0.3 g;

[0065] The reaction temperature is 35-50°C and the reaction time is 10-15 hours;

[0066] Step 1.2: The precursor of the hierarchical pore zirconium dioxide nanospheres obtained in step 1.1 is placed in a muffle furnace and calcined at a high temperature of 400 to 600° C. for 2 to 4 hours to obtain hierarchical pore zirconium dioxide nanospheres;

[0067] Step 1.3: Disperse the hierarchical pore zirconium dioxide nanospheres obtained in step 1.2 in water, add an aqueous solution of chloroauric acid and heat to boil, add a reducing agent to react, collect the product, wash it, and vacuum dry it at 40-60° C. to obtain hierarchical pore zirconium dioxide nanospheres modified with gold nanoparticles;

[0068] The reducing agent is trisodium citrate dihydrate,

[0069] The ratio of the multi-level porous zirconium dioxide nanospheres to water is 80-120 mg: 80-120 mL;

[0070] The dosage ratio of the multi-level porous zirconium dioxide nanospheres to the chloroauric acid aqueous solution is 80-120 mg: 1.2-1.8 mL;

[0071] The concentration of the chloroauric acid aqueous solution is 10-20 mg / mL;

[0072] The dosage ratio of the multi-level porous zirconium dioxide nanospheres to the reducing agent is 80-120 mg:75-90 mg;

[0073] The reaction temperature is 100°C and the reaction time is 10 to 30 minutes;

[0074] Step 2: Arrange the gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres obtained in step 1 and the serum in a mixed array, and directly perform LDI-TOF MS analysis after natural drying.

[0075] Poloxamer is a PEO-PPO-PEO nonionic triblock copolymer composed of polyoxyethylene (PEO) and polyoxypropylene (PPO), with trade names of Pluronic, Lutrol F (BASF, Germany) and Poloxamer (INCI). The poloxamer series includes more than 50 polymers with different PPO and PEO block ratios, with molecular weights ranging from 1600 to 14600 Da, and PPO percentages ranging from 15% to higher ratios. These different block ratios make poloxamer exhibit different properties in drug carriers, surfactants and biomedical applications.

[0076] Pluronic P123, chemical structure: polyoxyethylene chain 20-polyoxypropylene chain 70-polyoxyethylene chain 20, molecular weight: 5.8KD, has good biocompatibility and biodegradability.

[0077] Pluronic F127, also known as Poloxamer 407, is an amphiphilic triblock polymer with a chemical structure of polyoxyethylene chain 106-polyoxypropylene chain 70-polyoxyethylene chain 106, a molecular weight of 12.6KD, and good biocompatibility and biodegradability.

[0078] Pluronic is used as a template to form micelles to guide the assembly of zirconium precursors and generate ordered hierarchical pore zirconium dioxide nanospheres. After calcination, the organic template is removed to retain a stable porous structure.

[0079] 1,3,5-Trimethylbenzene is used as a pore swelling agent and embedded in the hydrophobic core of poloxamer to expand the volume of micelles, adjust the pore size, optimize the specific surface area and pore volume of the material, and enhance the adsorption performance, which is beneficial to the subsequent loading of gold nanoparticles and the analysis of serum metabolites.

[0080] Sodium perchlorate and acetic acid are used as regulators to control the pH value and ionic strength of the solution. Sodium perchlorate stabilizes the micelle structure and promotes the uniform deposition of inorganic substances; acetic acid buffers the pH to prevent the precursor from hydrolyzing too quickly, ensuring that the morphology and size of the nanospheres are controllable. Water is used as a solvent to provide a uniform reaction medium, dissolve each reagent, and ensure sufficient mixing.

[0081] As a zirconium source, octahydrate zirconyl chloride hydrolyzes to form zirconium oxygen clusters, which interact with organic templates and gradually form precursors with specific structures. The calcination process decomposes the organic template and ligands to obtain well-crystallized multi-level porous zirconium dioxide nanospheres. 2-Aminoterephthalic acid is used as a ligand to form coordination bonds with zirconium ions, regulate the polymerization and growth of the precursor, and control the size and morphology of the nanospheres; at the same time, nitrogen-containing functional groups are introduced to increase the surface activity of the material, which is beneficial to the loading of gold nanoparticles and the interaction with serum metabolites.

[0082] Chloroauric acid is used as a gold source to provide gold ions, which are introduced into the surface or pores of zirconium dioxide nanospheres. Under the reduction of trisodium citrate dihydrate, gold ions are reduced to gold nanoparticles and evenly distributed on the nanospheres. Citrate ions act as stabilizers to prevent gold nanoparticles from aggregating, enhance the hydrophilicity of the material, and promote their dispersion in the aqueous phase. The surface plasmon resonance effect of gold nanoparticles can enhance the electromagnetic field and improve the ionization efficiency of laser ionization desorption mass spectrometry.

[0083] In summary, the raw materials work synergistically to construct gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres with excellent functionality, which have high specific surface area, strong ultraviolet absorption ability and good surface activity, providing an ideal platform for efficient and simple analysis of serum metabolites.

[0084] Embodiment 1:

[0085] Preparation of gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres:

[0086] (1) 0.36 g of Pluronic P123 and 0.09 g of Pluronic F127 were dissolved in 18 mL of deionized water. After being fully dissolved by ultrasonication, 2.4 mL of glacial acetic acid, 0.7 mg of sodium perchlorate and 18 mL of 1,3,5-trimethylbenzene were added. After being mixed evenly, 0.95 g of zirconium chloride octahydrate and 0.3 g of 2-aminoterephthalic acid were added. The mixture was reacted at 40° C. for 12 h. After the reaction was completed, the product was washed with dimethylformamide and ethanol and dried in vacuum at 50° C.;

[0087] (2) placing the product obtained in step (1) in a muffle furnace and calcining at 400° C. for 2 h to obtain zirconium dioxide hierarchical porous nanospheres;

[0088] (3) Disperse the zirconium dioxide multi-level porous nanospheres obtained in step (2) in 100 mL of deionized water, add 1.5 mL of a 20 mg / mL aqueous solution of chloroauric acid, heat and stir, add 79.9 mg of trisodium citrate dihydrate after boiling, stop heating after reacting for 15 minutes, wash the product with ethanol and water after cooling to room temperature, and vacuum dry at 50° C. to obtain zirconium dioxide multi-level porous nanospheres modified with gold nanoparticles.

[0089] The transmission electron microscopy image of the gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres prepared as above is shown in FIG. Figure 1 As shown; the pore size distribution of gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres is shown Figure 2 As shown; the X-ray powder diffraction pattern of the zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles is shown Figure 3 As shown; the UV-visible absorption spectrum of the zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles is shown Figure 4 shown.

[0090] Analysis results: From Figure 1 The obtained gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres present obvious porous sphere structure with a diameter of about 100 nanometers. Figure 2 The porous structure of the zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles can be obtained, which has microporous and mesoporous structures, and the pore sizes are mainly around 1.6 nanometers, 2.9 nanometers and 20 nanometers; Figure 3 The obtained gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres have excellent crystallinity; Figure 4 The obtained zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles have excellent absorption intensity at the wavelength of the laser light source.

[0091] Embodiment 2:

[0092] The gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres obtained in Example 1 were used as a matrix and mixed with serum to form an array for the detection of metabolites in the serum of 100 postmenopausal osteoporosis patients, 57 postmenopausal osteopenia patients and 43 healthy subjects.

[0093] (1) The sera of 100 postmenopausal osteoporosis patients, 57 postmenopausal osteopenia patients, and 43 healthy subjects were randomly divided into training and validation sets in a ratio of 4:1, corresponding to 160 and 40 samples, respectively.

[0094] (2) The serum diluted 50 times was dripped onto a stainless steel target plate with 16×24 array points. After drying, the gold nanoparticle-modified zirconium dioxide multi-level porous nanospheres obtained in Example 1 were dissolved in deionized water and dripped onto the stainless steel target plate with 16×24 array points to obtain array-arranged gold nanoparticle-modified zirconium dioxide multi-level porous nanospheres, which were then dried naturally.

[0095] (3) Mass spectrometry analysis: The stainless steel target plate in step (2) was directly subjected to LDI-TOF MS analysis using a Bruker ultrafleXtreme mass spectrometer with a 355 nm Nd:YAG laser source, a laser frequency of 2000 Hz, an acceleration voltage of 20 kV, ion source 1 voltage of 20 kV, and ion source 2 voltage of 17.5 kV; the acquisition mode was reflectron cation mode, and the acquisition range m / z was 100-1000 Da; each sample was tested 5 times; mass spectrometry data were obtained from flexControl 3.4 and exported in flexAnalysis 3.4. The mass spectrum is shown in Figure 2. Figure 5 shown.

[0096] Analysis results: From Figure 5 It can be seen that with the help of zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles, rich metabolite information in serum can be obtained, and the metabolite fingerprints of disease and health are significantly distinguished, so it has the potential to be used for diagnosis and monitoring of disease progression.

[0097] Embodiment 3:

[0098] The serum metabolic fingerprint obtained in Example 2 was used for the diagnosis and tracking of postmenopausal osteoporosis using a deep learning algorithm.

[0099] (1) Use the MALDIquant package in R software to process the raw mass spectrometry data and export the mass spectrometry peak intensity table.

[0100] (2) The grouping method in Example 2 divides the mass spectrum peak intensity table into a training set and a validation set, and uses the training set data to train the deep learning model.

[0101] (3) The trained deep learning model is used to predict the validation set data. The model outputs the scores of all samples. The score scatter plot of all samples is as follows: Figure 6 shown.

[0102] (4) The samples were divided into osteoporosis group and non-osteoporosis group. The probability scores of the training set and validation set samples were used to draw the receiver characteristic curve, and the area under the curve (AUC) was calculated. The receiver characteristic curve is shown in Figure 7 shown.

[0103] Analysis results: From Figure 6 It can be seen that the machine learning algorithm can distinguish disease samples from healthy samples very well. Figure 7 It can be seen that the probability score output by the machine learning algorithm can effectively distinguish osteoporosis from non-osteoporosis, and has great potential in the diagnosis and monitoring of postmenopausal osteoporosis.

[0104] Embodiment 4:

[0105] The metabolic fingerprint of the serum obtained in Example 2 was used to extract a potential biomarker panel using a feature selection algorithm, and the biomarker panel was combined with a deep learning algorithm for the diagnosis and monitoring of postmenopausal osteoporosis.

[0106] (1) Use the MALDIquant package in R software to process the raw mass spectrometry data and export the mass spectrometry peak intensity table.

[0107] (2) The mass spectrum peak intensity table is divided into a training set and a validation set according to the grouping method in Example 2, and the training set data is used for feature selection.

[0108] (3) The trained deep learning model is used to predict the validation set data. The model outputs the scores of all samples. The score scatter plot of all samples is as follows: Figure 8 shown.

[0109] (4) The samples were divided into osteoporosis group and non-osteoporosis group. The probability scores of the training set and validation set samples were used to draw the receiver characteristic curve, and the area under the curve (AUC) was calculated. The receiver characteristic curve is shown in Fig. 9 shown.

[0110] Analysis results: From Figure 8 It can be seen that the deep learning algorithm based on important features can distinguish disease samples from healthy samples very well. Fig. 9 It can be seen that the probability score output by the deep learning algorithm based on important features can effectively distinguish osteoporosis from non-osteoporosis, and has great potential in the diagnosis and monitoring of postmenopausal osteoporosis.

[0111] Embodiment 5:

[0112] The only change from Example 1 is that the mixed poloxamer triblock polymer composed of 0.36 g of Pluronic P123 and 0.09 g of Pluronic F127 used in Example 1 is replaced by 0.45 g of Pluronic P123.

[0113] Embodiment 6:

[0114] The only change from Example 1 is that the mixed poloxamer triblock polymer composed of 0.36 g of Pluronic P123 and 0.09 g of Pluronic F127 used in Example 1 is replaced by 0.45 g of Pluronic F127.

[0115] The formation of hierarchical porous nanospheres is mainly based on the anisotropic nucleation of spherical cones, which have non-centrosymmetric, radially arranged pore structures, and gradually grow from anisotropic spherical nanocones to isotropic nanospheres through the continuous increase of radius and solid angle. The pore structure can be affected by the stacking parameter (P) of the surfactant, which is mainly determined by the hydrophobic / hydrophilic ratio, that is, V H / V L Value, where V H and V L are the volume fractions of the hydrophobic part polyoxypropylene (PPO) and the hydrophilic part polyoxyethylene (PEO), respectively. The partially closed conical layered mesoporous structure formed at a higher P value plays a key role in regulating the assembly mode of the anisotropic growth of hierarchical porous nanostructures. Compared with Pluronic P123, Pluronic F127 has the same hydrophobic part, but its hydrophilic chain is longer, which can better stabilize the structure. By finely adjusting the mass ratio of Pluronic P123 and Pluronic F127 in the reaction system, the stacking parameters of the surfactant can be precisely controlled.

[0116] The zirconium dioxide multi-level porous nanospheres obtained in Example 5 are of an unstable macroporous structure; and the zirconium dioxide multi-level porous nanospheres obtained in Example 6 are of an octahedral structure.

[0117] Absorbance test, wavelength range: 190-800nm, wavelength scanning interval: 2nm.

[0118] Absorbance test table

[0119] wavelength Example 1 Example 5 Example 6 <![CDATA[ZrO2]]> 500nm 0.192 0.143 0.131 0.068 354nm 0.368 0.295 0.264 0.235

[0120] The present invention analyzes the absorbance data of Example 1, Example 5 and Example 6 and finds that the introduction of gold nanoparticles and the selection of different template agents have a significant effect on the light absorption performance of zirconium dioxide multi-level hole nanospheres. In Example 1, the synergistic effect of Pluronic P123 and F127 forms a more stable and regular multi-level hole structure, P123 provides a good skeleton structure, and the long hydrophilic chain of F127 further enhances the stability and specific surface area of ​​the hole structure, thereby increasing the loading amount of gold nanoparticles, making the light absorption performance significantly better than the other two groups at wavelengths of 500nm and 354nm. Example 5 uses only P123 to form a macroporous structure, but due to the lack of the stabilizing effect of F127, the structure is relatively unstable, the distribution of gold nanoparticles is uneven, and the light absorption efficiency is reduced. In Example 6, F127 is used alone to form a regular octahedral hole structure, but the pore size is small, which limits the loading of gold nanoparticles, resulting in a further reduction in light absorption performance. In contrast, the unmodified ZrO2 has the weakest light absorption performance.

[0121] The above embodiments are only preferred embodiments of the present invention and are not any formal or substantial limitations of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the present invention, and these improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. A method for analyzing and detecting serum metabolites, characterized in that: The steps include: Step 1: preparing zirconium dioxide hierarchical porous nanospheres modified with gold nanoparticles; Step 2: Arrange the gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres obtained in step 1 and the serum in a mixed array, and directly perform LDI-TOF MS analysis after natural drying.

2. The method for analyzing and detecting serum metabolites according to claim 1, characterized in that: The method for preparing the gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres in step 1 comprises the following steps: Step 1.1: dissolving the template, pore swelling agent and regulator in water, adding the zirconium precursor and ligand to react, collecting the product and washing and drying it to obtain the precursor of hierarchical porous zirconium dioxide nanospheres; Step 1.2: placing the precursor of the hierarchical pore zirconium dioxide nanospheres obtained in step 1.1 in a muffle furnace and calcining at high temperature to obtain hierarchical pore zirconium dioxide nanospheres; Step 1.3: Disperse the hierarchical porous zirconium dioxide nanospheres obtained in step 1.2 in water, add chloroauric acid aqueous solution and heat to boil, add a reducing agent to react, collect the product, wash and dry, and obtain gold nanoparticle-modified hierarchical porous zirconium dioxide nanospheres.

3. The method for analyzing and detecting serum metabolites according to claim 2, characterized in that: In step 1.1: The template agent is poloxamer; the pore swelling agent is 1,3,5-trimethylbenzene; the regulator is sodium perchlorate and / or acetic acid; The ratio of the template to water is 0.38-0.5 g: 15-20 mL; The zirconium precursor is zirconyl chloride octahydrate; The ligand is 2-aminoterephthalic acid.

4. The method for analyzing and detecting serum metabolites according to claim 2, characterized in that: In step 1.3: The reducing agent is trisodium citrate dihydrate, The ratio of the multi-level porous zirconium dioxide nanospheres to water is 80-120 mg: 80-120 mL; The dosage ratio of the multi-level porous zirconium dioxide nanospheres to the chloroauric acid aqueous solution is 80-120 mg: 1.2-1.8 mL; The concentration of the chloroauric acid aqueous solution is 10-20 mg / mL; The dosage ratio of the multi-level porous zirconium dioxide nanospheres to the reducing agent is 80-120 mg:75-90 mg; The reaction temperature is 100°C and the reaction time is 10 to 30 minutes.

5. The method for analyzing and detecting serum metabolites according to claim 1, characterized in that: In step 2, the sample is diluted 30-70 times with deionized water and added dropwise to a stainless steel target plate containing array points; then the zirconium dioxide multi-level porous nanospheres modified with gold nanoparticles obtained in step 1 are prepared into a solution, added dropwise to the target plate containing the sample arranged in an array and allowed to stand; the sample is the subject's serum.

6. The method for analyzing and detecting serum metabolites according to claim 1, characterized in that: The specific conditions of the LDI-TOF MS analysis in step 2 are as follows: using a 355nm Nd:YAG laser light source, a laser frequency of 2000Hz, an acceleration voltage of 20kV, wherein the voltage at ion source 1 is 20kV, and the voltage at ion source 2 is 17.5kV; the acquisition mode is a reflection positive ion mode, and the acquisition range m / z is 100-1000Da; each sample is tested 5 times; mass spectrometry data is obtained from flexControl 3.4, and the data is exported in flexAnalysis 3.

4.

7. Use of the analytical detection method for serum metabolites according to any one of claims 1 to 6 in the early diagnosis of postmenopausal osteoporosis and monitoring of disease progression.

8. The use of the analytical detection method for serum metabolites according to claim 7 in the early diagnosis of postmenopausal osteoporosis and disease progression monitoring, characterized in that: The application is based on the serum metabolite fingerprint obtained by LDI-TOF MS analysis of batch samples and scores the machine learning algorithm based on the fingerprint.

9. The use of the analytical detection method for serum metabolites according to claim 8 in the early diagnosis of postmenopausal osteoporosis and disease progression monitoring, characterized in that: The specific conditions for obtaining the serum metabolite fingerprint are as follows: the mass spectra obtained by LDI-TOF MS analysis of the batch samples are normalized by the total ion current to obtain the mass spectrum peak intensity, the mass spectrum of each sample is averaged, and the mass spectrum peak intensity of each sample is tabulated to obtain the metabolite fingerprint; the batch samples include a training set and a validation set; And / or, the machine learning algorithm steps include: inputting training set sample data, training the model, evaluating the model, inputting test set sample data for blind testing, and evaluating the test set blind testing results.

10. A postmenopausal osteoporosis diagnosis and monitoring system, using laser desorption ionization time-of-flight mass spectrometry to analyze metabolites in serum, using gold nanoparticle-modified zirconium dioxide multi-level porous nanospheres as matrix-assisted desorption ionization; The laser desorption ionization time-of-flight mass spectrometer is used to obtain an LDI-TOFMS mass spectrum of metabolites in a serum sample of a subject, and to determine whether the subject suffers from postmenopausal osteoporosis based on a score of the mass spectrum in a machine learning algorithm; The gold nanoparticle-modified zirconium dioxide hierarchical porous nanospheres are prepared by the following method: Step 1.1: dissolving the template, pore swelling agent and regulator in water, adding the zirconium precursor and ligand to react, collecting the product and washing and drying it to obtain the precursor of hierarchical porous zirconium dioxide nanospheres; Step 1.2: placing the precursor of the hierarchical pore zirconium dioxide nanospheres obtained in step 1.1 in a muffle furnace and calcining at high temperature to obtain hierarchical pore zirconium dioxide nanospheres; Step 1.3: Disperse the hierarchical porous zirconium dioxide nanospheres obtained in step 1.2 in water, add chloroauric acid aqueous solution and heat to boil, add a reducing agent to react, collect the product, wash and dry, and obtain gold nanoparticle-modified hierarchical porous zirconium dioxide nanospheres.

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