CN113970538B - Pathogen detection method - Google Patents

Abstract

The invention discloses a pathogen detection method, which comprises the steps of preparing SERS sol-gel solution containing metal salt and coating the solution on the surface of a porous structure prepared in advance, and obtaining an initial detection piece after the solution is solidified; reducing the initial detection piece by utilizing borohydride to obtain an intermediate detection piece containing metal nano particles; functionalizing the metal nano particles to obtain a target detection piece with a molecular recognition element; when a pathogen sample to be detected is obtained, the pathogen sample is contacted with a target detection piece, then a laser is irradiated to the contacted target detection piece, and the Raman light of the target detection piece is collected to obtain a first Raman spectrum corresponding to the pathogen sample; and determining the pathogen concentration corresponding to the pathogen sample according to the first Raman spectrum, the preset characteristic peak and the standard curve. The invention can realize rapid and accurate detection on the basis of keeping the integrity of the pathogen, so as to be suitable for determining the type and concentration of the pathogen.

Description

Pathogen detection method

Technical Field

The present invention relates to a pathogen detection method.

Background

With the widespread use of antibiotics, resistant bacteria are widely available worldwide. Since the genes of bacteria can be inherited not only from relatives but also from non-relatives by means of mobile gene vectors, such as plasmids. Thus, once a strain for a certain antibiotic appears, the strain can be widely spread worldwide. Thus, the use of antibiotics should be effectively regulated. The type of antibiotic used and the content of antibiotic should be precisely adjusted in terms of treatment for bacteria. Unsuitable antibiotic types and insufficient antibiotics cannot effectively play a role in bacteriostasis, and excessive antibiotics can promote the accelerated evolution of bacteria and generate drug-resistant bacteria more quickly. Thus, a rapid, reliable and sensitive platform not only enables the most appropriate type and dosage of antibiotics to be given to the infected patient in time.

There are a number of ways to achieve rapid identification of bacterial viruses, such as PCR, sequencing, etc., that are infected by a patient. However, these methods have the possibility of false positives or false negatives because a small amount of fragments are amplified. The most stable way of today is the Kirby-Bauer antibiotic test, which is mainly to use wafers or disks containing antibiotics to test whether a specific bacterium is sensitive to a specific antibiotic. First, a pure culture of bacteria is isolated from a patient. Then, a known number of bacteria were grown overnight on agar (solid growth medium) plates in the presence of a sheet containing a known number of relevant antibiotics. If the bacteria are sensitive to a particular antibiotic in the sheet, a transparent medium region around the sheet where the bacteria cannot grow, known as a zone of inhibition, will occur. The larger the zone of inhibition around the disc containing the antibiotic, the more susceptible the bacteria to the antibiotic in the disc. In the KB assay, the size of the inhibition zone is inversely proportional to the Minimum Inhibitory Concentration (MIC), which refers to the amount of antibiotic required to prevent bacterial growth in overnight culture. MIC (microgram/milliliter) can be calculated from a known standard curve (linear regression) plot based on observed inhibition zone diameters (millimeters). The clinician may use the KB test results to select the appropriate antibiotic against the patient's particular infection. The use of antibiotics specific to the particular bacteria responsible for the infection may avoid the use of broad spectrum antibiotics, which are directed against many types of bacteria. Therefore, clinical application of KB test results can reduce the evolution frequency of antibiotic resistant bacteria.

It should be noted that MIC scores are used to determine the effective dose of antibiotics to which bacterial populations exposed to insufficient concentrations of a particular drug or broad spectrum antibiotics can develop resistance. Thus, MIC scores help to improve the therapeutic effect of patients, preventing the evolution of drug-resistant microbial strains. However, this method requires a long-term culture of the pathogen, and the determination of the proper drug and concentration of the pathogen during the culture process is time consuming, and is also relatively subjective in terms of the subsequent determination of the drug concentration, lacking accurate determination means. Thus, existing detection schemes for pathogens still have certain drawbacks.

Disclosure of Invention

The technical problem to be solved by the invention is that the current pathogen detection method is time-consuming and labor-consuming, and the pathogen detection method is provided for overcoming the defects in the prior art.

In order to solve the technical problems, the technical scheme adopted by the invention is as follows:

a method of pathogen detection, the method comprising:

preparing a SERS sol-gel solution comprising a metal salt, wherein the SERS sol-gel solution comprises a polymer and a silane oxide;

Coating the SERS sol-gel solution on the surface of a porous structure prepared in advance so as to solidify the SERS sol-gel solution to obtain an initial detection piece;

reducing the initial detection piece by utilizing borohydride to obtain an intermediate detection piece containing metal nano particles;

functionalizing the metal nano particles by utilizing a pre-prepared molecular recognition element to obtain a target detection piece;

When a pathogen sample to be tested is obtained, contacting the pathogen sample with the target detection member so that the pathogen sample specifically binds to the molecular recognition element;

irradiating a laser to the contacted target detection piece, and collecting Raman light of the target detection piece to obtain a first Raman spectrum corresponding to the pathogen sample;

And determining the pathogen concentration corresponding to the pathogen sample according to the first Raman spectrum, a preset characteristic peak and a standard curve.

The pathogen detection method, wherein the polymer comprises polyethylene glycol and polydimethylsiloxane.

The pathogen detection method, wherein when the metal element in the metal salt is gold, the polymer comprises polyethylene glycol and the silane oxide comprises TMOS and MTMS.

The pathogen detection method, wherein when the metal element in the metal salt is silver, the polymer comprises polydimethylsiloxane, and the silane oxide comprises TMOS, MTMS, and ODS.

The pathogen detection method, before the SERS sol-gel solution is smeared on the surface of a porous structure prepared in advance to solidify the SERS sol-gel, further comprises:

When the porous structure is made of glass, heating the porous structure in weak base;

Carrying out hydrothermal treatment on the surface of the heated porous structure;

soaking the cleaned porous structure in methanol;

And (3) carrying out hydrothermal treatment on the porous structure soaked in methanol to remove methanol on the surface of the porous structure.

The pathogen detection method, before the SERS sol-gel solution is smeared on the surface of a porous structure prepared in advance to solidify the SERS sol-gel, further comprises:

When the porous structure is made of plastic, oxidizing the porous structure by using strong acid;

and cleaning the surface of the oxidized porous structure.

The pathogen detection method, wherein the metal nanoparticles are fractal aggregates, and the average size of the metal nanoparticles is 10-200 nanometers.

The pathogen detection method, wherein the molecular recognition element comprises a short peptide, an antibody and an aptamer.

The pathogen detection method, wherein when the molecular recognition element is a short peptide, the short peptide is linked to the metal nanoparticle through cysteine.

The pathogen detection method, wherein after determining the pathogen concentration corresponding to the pathogen sample according to the preset characteristic peak and the standard curve, further comprises:

processing a target detection piece contacting a pathogen sample by using a preset candidate drug;

Irradiating a laser to the processed target detection piece, and collecting Raman light corresponding to the processed target detection piece to obtain a second Raman spectrum;

And determining target drugs and drug concentrations corresponding to the pathogens according to the first Raman spectrum and the second Raman spectrum.

The beneficial effects are that: compared with the prior art, the invention provides a pathogen detection method, which is realized based on SERS, wherein SERS sol-gel solution is prepared firstly, and the solution contains metal salt for forming metal nano particles later, silicane oxide for providing stable awakening for the metal nano particles and polymer for providing safe environment for pathogens. And in the process of reducing the metal salt, the borohydride is adopted for reduction reaction, so that the metal nano particles without antibodies to the pathogen are obtained, and therefore, the pathogen is prevented from being damaged in the detection process, and the accuracy of pathogen detection can be ensured. In addition, molecular recognition elements are adopted on the surfaces of the metal nano particles to functionalize the metal nano particles so as to improve the recognition specificity.

Drawings

Fig. 1 is a flowchart of a pathogen detection method provided by the present invention.

Fig. 2 is a schematic diagram of binding metal nanoparticles to pathogens via peptide fragments in the pathogen detection method provided by the present invention.

Fig. 3 is a raman spectrum obtained by detecting multiple pathogens using a first detecting member in the pathogen detection method provided by the present invention.

Fig. 4 and fig. 5 are raman spectra obtained by detecting a plurality of pathogens using a second detecting member in the pathogen detection method provided by the present invention.

Fig. 6 is a raman spectrum of a control group in a first verification experiment for the specificity of molecular recognition elements in the pathogen detection method provided by the present invention.

Fig. 7 is a raman spectrum of an experimental group in a first verification experiment for the specificity of a molecular recognition element in the pathogen detection method provided by the present invention.

Fig. 8 is a raman spectrum of a control group in a second validation experiment for the specificity of molecular recognition elements in the pathogen detection method provided by the present invention.

Fig. 9 is a raman spectrum of an experimental group in a second validation experiment for the specificity of a molecular recognition element in the pathogen detection method provided by the present invention.

Fig. 10 is a raman spectrum of a third validation experiment for the specificity of molecular recognition elements in the pathogen detection method provided by the present invention.

FIG. 11 is a Raman spectrum obtained by detecting a plurality of ADE series members using a target detection member in the pathogen detection method provided by the present invention.

FIG. 12 is a Raman spectrum of an adenine solution detected by a first detection member and a first detection member in a pathogen detection method according to the present invention.

Fig. 13 is a raman spectrum obtained after detecting an inactivated pathogen using a first detecting member and a second detecting member in the pathogen detecting method provided by the present invention.

FIG. 14 is a Raman spectrum of a pathogen detection method according to the present invention using a first detection member to detect Listeria monocytogenes.

FIG. 15 is a Raman spectrum of a first detecting member used for detecting Salmonella in the pathogen detection method of the present invention.

FIG. 16 shows a Raman spectrum obtained by detecting Salmonella bacteria with different retention times using a first detection member in the pathogen detection method provided by the present invention.

Fig. 17 is a spectrum chart obtained by overlapping the raman spectra in fig. 16 in the pathogen detection method provided by the present invention.

FIG. 18 is a Raman spectrum of a pathogen detection method according to the present invention using a second detection member to detect Listeria monocytogenes having different retention times.

Fig. 19 is a spectrum obtained by overlapping the raman spectra in fig. 18 in the pathogen detection method provided by the present invention.

FIG. 20 is a Raman spectrum obtained by mixing gold colloid prepared by a non-borohydride reduction method with a pathogen and detecting the mixture in the pathogen detection method.

Fig. 21 is a raman spectrum obtained by mixing a silver colloid prepared by a non-borohydride reduction method with a pathogen and detecting the mixture in the pathogen detection method provided by the invention.

Fig. 22 is a raman spectrum of a control group in a first drug test in a pathogen detection method provided by the present invention.

Fig. 23 is a raman spectrum of an experimental group in a first drug test in a pathogen detection method provided by the present invention.

Fig. 24 is a raman spectrum of a control group in a second drug test in the pathogen detection method provided by the present invention.

Fig. 25 is a raman spectrum of an experimental group in a second drug test in a pathogen detection method provided by the present invention.

Fig. 26 is a raman spectrum of a control group in a third drug test in the pathogen detection method provided by the present invention.

Fig. 27 is a raman spectrum of an experimental group in a third drug test in the pathogen detection method provided by the present invention.

FIG. 28 is a spectrum obtained by overlapping the Raman spectra of FIG. 26 in the pathogen detection method according to the present invention.

Fig. 29 is a raman spectrum of a control group in a fourth drug test in the pathogen detection method provided by the present invention.

Fig. 30 is a raman spectrum of a fourth experimental group in a drug test in a pathogen detection method provided by the present invention.

FIG. 31 is a spectrum chart showing the overlapping of Raman spectra of a control group and an experimental group in a fifth drug test in the pathogen detection method according to the present invention.

Detailed Description

The invention provides a pathogen detection method, which aims to make the purposes, technical schemes and effects of the invention clearer and more definite, and the invention is further described in detail below by referring to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.

It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

The invention will be further described by the description of embodiments with reference to the accompanying drawings.

As shown in fig. 1, the present implementation provides a pathogen detection method, which may include the steps of:

S10, preparing a SERS sol-gel solution containing metal salt, wherein the SERS sol-gel solution comprises a polymer and a silane oxide.

Specifically, the embodiment adopts a wet chemical method to encapsulate metal nano particles such as silver, gold and the like in a porous structure, and detects pathogens based on Surface enhanced raman scattering (Surface-ENHANCED RAMAN SCATTERING, abbreviated as SERS).

To achieve SERS detection, the following conditions need to be met:

1) The material produced is a particle much smaller than the laser incident wavelength (rayleigh systems, similar sized surface defects can also be used).

2) The material prepared has suitable optical properties to couple light (extinction).

3) The free electrons available during excitation are limited by the surface mode or particle size of the protons.

4) The molecules have matching optical properties (absorption) and are coupled to the proton field.

Thus, these special conditions limit SERS to metals Ag, au and Cu with diameters of 5 to 200 nanometers. In this embodiment, the noble metal salt is gold salt or silver salt.

A SERS sol-gel solution is prepared and a sol-gel with SERE activity is prepared from a multicomponent system comprising at least a silane oxide and a noble metal salt. In this embodiment, the silane oxide includes pure solutions of silane oxides such as tetramethyl orthosilicate (TETRAMETHYL ORTHOSILICATE, TMOS), methyltrimethoxysilane (Methyltrimethoxysilane, MTMS), or octadecyltrimethoxysilane (Trimethoxyoctadecylsilane, ODS), or combinations thereof, and the noble metal salts include metal salts such as gold chloride, silver nitrate precursors, and the like. In order to enhance the robustness, stability and repeatability of subsequent test pieces, polymers such as polyethylene glycol (Polyethylene glycol, PEG) and Polydimethylsiloxane (PDMS) were used to improve the sol-gel formulation and performance. The polymer also provides a stable platform for pathogens, and the characteristics of polyethylene glycol and polydimethylsiloxane are adopted so that the pathogens cannot generate non-antigenicity, non-immunogenicity and protein rejection, and the activity and the integrity of the pathogens are not affected.

In the first SERS sol-gel solution prepared in this embodiment, the noble metal salt is silver salt, and the noble metal salt solution is composed of silver nitrate, methanol and ammonium hydroxide, which are mixed to form a more stable silver-diamine precursor complex ([ Ag (NH 3) 2] (OH) or [ Ag (NH 3) 2] +) solution. SERS sol-gel solutions also include a variety of silane oxides. In this example, 1.2 volumes of a large silver nitrate solution and twice the volume of methanol were mixed, wherein the components of the silver nitrate solution were silver nitrate including 0.1 to 1.0mol/L and 28% NH3OH (aq), and the volume ratio of silver nitrate to NH3OH was 1:3. the volume of the mixed solution is mixed with the solution of the silanization oxide. In this example, the silane oxide solution is composed of TMOS, MTMS and ODS, TMOS: MTMS: the volume ratio of ODS is 1:5:1.

And adding PDMS into the mixed solution of the noble metal salt solution and the silane oxide, wherein the volume of the mixed solution of PDMS is 1 volume, and mixing for 1-5 minutes to obtain the SERS sol-gel solution.

Thus, finally, the volume ratio of silver salt solution, silane oxide and polymer is 10:17.5:1.

Taking a noble metal salt solution as a solution A, a silane oxide as a solution B and a polymer as a solution C, the components and volume ratios of the first implementation mode can be as follows:

Solution A= [ (1 mol/L) AgNO3 (aq) + (14.5 mol/L) NH3OH (aq) ] +CH3OH (volume ratio 1:0.2:2)

Solution B=TMOS/MTMS/ODS (1:5:1 v/v/v).

Solution c=pdms

Solution A and solution B and solution C are mixed in a ratio of 10:17.5:1.

The AgNO3 concentration of a volume may be 0.1mol/L, 0.5mol/L, 1.0mol/L, and other concentrations between 0.1mol/L and 1.0mol/L, preferably 1.0mol/L.

In the second SERS sol-gel solution prepared in this embodiment, the noble metal salt is gold (Au), the noble metal salt solution is a salt solution prepared by mixing HAuCl4 with HNO3, the silane oxide is a mixture of TMOS and MTMS, and the polymer is PEG. The volume ratio of the three is that the noble metal salt solution: a silane oxide: polymer = 10:10:1. mixing the three to obtain the SERS sol-gel solution.

Taking noble metal salt solution as solution A, silane oxide as solution B and polymer as solution C, the components and volume ratio of the second implementation mode can be as follows:

solution A= (0.25 mol/L) HAuCl4.3H2O (aq) + (70%) HNO3 (aq) (volume ratio 1:0.25)

Solution b=tmos/MTMS (pure, 5:1 v/v).

Solution c=peg (pure)

Solution A and solution B and solution C are mixed in a ratio of 10:10:1.

It is noted that in this example, the polymer used for the silver salt solution was PDMS and the PEG was used for the gold salt, and this choice was not randomly chosen but determined by the nature of the two. SERS can subsequently bind pathogens to metal nanoparticles as an extraction force that can exhibit varying degrees of chemical polarity, thus selectively focusing single or multiple target chemicals on metal sites in a porous structure. Among the previously listed silanoxides, TMOS is very polar, whereas MTMS and ODS have non-polarity, polymers PEG and PDMS improve sol-gel performance by affecting the degree of polarity, PEG making it more polar, and PDMS making it less polar and more hydrophobic. Gold is not suitable for making it more hydrophobic in chemical composition, so the polymer used is PEG. Thus, the polymer modified sol-gel works in conjunction with the electronic properties of gold and silver nanoparticles to become more efficient in selectively extracting and pre-concentrating the target pathogens at the metal site, thereby increasing sensitivity. The polymer also helps to strengthen the sol-gel, making it stronger and more stable, thereby greatly improving shelf life and repeatability, the addition of the sol-gel of the polymer does not compromise pathogen integrity, and improving detection accuracy.

And S20, coating the SERS sol-gel solution on the surface of a porous structure prepared in advance, so that the SERS sol-gel solution is solidified, and an initial detection piece is obtained.

Specifically, the SERS sol-gel solution is then applied to the surface of the porous structure prepared in advance. In this embodiment, the porous structure used is a well that is optically transparent, such as a well of a common 6-well plate, 24-well plate, 96-well plate. The SERS sol-gel solution is smeared on the surface of the porous structure, i.e. on the bottom of the wells. In this embodiment, in order to reduce the cost and increase the testing rate, the sample loading well of the 96-well plate is mainly used as a porous structure for applying SERS sol-gel solution.

The SERS sol-gel solution is applied to the surface of the porous structure to allow it to cure at ambient temperature to form a gel matrix, preferably for at least 24 hours, preferably 3 days. The SERS sol-gel solution can be solidified in a room temperature (20-25 ℃) environment, and components in the SERS sol-gel solution can be prevented from being decomposed under the action of light in a matte environment, so that the stability of a structure is improved. The element obtained after curing becomes the initial test piece. Since the silane oxide forms porous spaces after curing, many tiny holes exist on the surface of the porous structure, so that the metal nano particles formed later are fixed in the holes of the initial detection piece.

Further, the surface of the porous structure needs to be suitably pretreated to increase the adsorption force between the coating layer formed after the subsequent solidification and the porous structure. Since the SERS sol-gel solution employed in this example contains a large amount of silane oxide, the purpose of the pretreatment of the porous structure is to generate-OH groups on its surface.

Because the existing 96-well plate is mainly divided into two types of materials, one is made of glass and the other is made of plastic, such as PS (Polystyrene ) materials, different processing modes are performed for the two materials.

When the porous structure is made of glass, the surface of the glass structure is subjected to hydrothermal surface treatment, so that the concentration and distribution of silanol groups are standardized. The glass structure is first heated in a weak alkaline environment. For example, the weak base is 10 mg/ml KOH, the heating temperature is 100 degrees Celsius, and the heating time is preferably 1 hour. This process enables on the one hand cleaning of the glass structure surface and on the other hand the creation of a layer of homogeneous-OH pendant groups as a sol-gel fixation substrate. Then immersing the porous structure in deionized water for cleaning, and heating in water at 100 ℃ for 1 hour in order to improve the cleaning speed and the cleaning force. Then immersed in methanol and heated at 60℃for 1 hour. Since methanol is volatile, the porous structure is finally immersed in water and heated at 100 ℃ for 1 hour. These hydrothermal pretreatments will ensure that the highly immobilized sol-gel coating has a uniform hydrophilic surface. These treated porous structures were used as carriers for carrying SERS gel-sol solutions.

When the porous structure is made of plastic, such as polypropylene or polystyrene, the surface of the porous structure can be derivatized with-OH groups to ensure bonding with silanol groups of the sol-gel, thereby immobilizing the SERS substrate.

1) The porous structure is oxidized with a strong acid, for example, by washing with dilute chromic acid or dichromic acid for 5 minutes, followed by washing the pores with sulfuric acid for 5 minutes.

2) The surface of the oxidized porous structure is washed, for example, with deionized water for 5 minutes.

The first step is to oxidize the pituitary methyl groups on polypropylene with strong acid to form acidic functional groups. This renders the surface hydrophilic and the water rinse can clean and form the desired-OH derivatized surface to immobilize the SERS gel-sol solution.

In this example, a 96-well plate was used, and after pretreatment of the 96-well plate, 50uL of SERS sol-gel solution was first added dropwise to each well in the 96-well plate, and then sealed and gelled, allowing it to cure at room temperature for a period of time. The curing time is at least 24 hours.

S30, performing reduction treatment on the initial detection piece by utilizing borohydride to obtain an intermediate detection piece containing metal nano particles.

Specifically, the noble metal salt is then reduced with borohydride to produce active metal nanoparticles embedded in the porous structure and gel matrix. The metal nano particles are fractal aggregates with the average size of 10-20 nm.

For example, pre-chilled borohydride is used to reduce metal ions in the initial detection piece to produce metal nanoparticles. The sodium borohydride used in this example was sodium borohydride, and the reduction time was 1 to 4 minutes.

To avoid the influence of residual sodium borohydride on the detection of the initial detection piece, the initial detection piece is washed after reduction. The initial test piece is washed with ionized water, then the alkali remained in the initial test piece is neutralized by acid, and finally the initial test piece is washed with water to neutralize and remove the remained sodium borohydride (alkaline) and other chemical species/ions. The specific washing process can be 1-2 min water washing, 1-4 min nitric acid (70% HNO 3) washing and 1-2 min water washing.

The concentration of the sodium borohydride solution for reducing the metal ions is 0.01mol/L to 0.03mol/L, preferably 0.0132 mol/L (1 mg/mL), and the time is 1 to 4min. Typically, two volumes of sodium borohydride are used per volume of SERS sol-gel solution.

After the washing, the plate is stored in a dark place, and in this embodiment, the sample-adding hole is resealed by using a matting agent.

Further, it was found through experiments that the repeated steps of reduction and washing with water increased the sensitivity to gold, thereby increasing the aggregation of gold particles, affecting the size of the metal nanoparticles. For example, after sodium borohydride reduction and washing, the sodium borohydride reduction and washing process is repeated for the initial test piece.

In addition, the reduced metal particles may be subjected to acid washing, and the acidic environment may affect the aggregation of the metal particles, thereby affecting their fractal size.

Once cured, these initial test pieces may be used. They can be sealed and stored up to 90 days when needed. They have good sensitivity and reproducibility. Note that the individual components and proportions of components in the SERS sol-gel formulation are adjusted to selectively extract the desired target analyte or pathogen onto the metal particles where they interact with the appropriate laser-generated surface plasmon field, resulting in a SERS enhancement effect.

After sol gel curing, 50-100uL of dilute sodium borohydride (0.01-0.03 mol/L) was added to each 96 well to reduce the metal particles in the metal salt solution, resulting in metal nanoparticles having a size of 10-200 nm. And then using ultrapure water to clear redundant sodium borohydride in each hole, neutralizing the residual alkali solution in the holes by using dilute nitric acid, and finally performing secondary cleaning by using water to obtain the SERS substrate containing the metal nano particles.

And S40, functionalizing the metal nano particles by utilizing a pre-prepared molecular recognition element to obtain the target detection piece.

Specifically, although the size of the substance contacted with the metal nano particles can be realized by adjusting the component proportion in the SERS gel-sol solution, the screening effect is achieved. However, since many pathogens are not very different in size, the screening effect is limited. To increase the specificity of the detection member in this embodiment, the embodiment employs a molecule having a capability of specifically binding to the metal nanoparticle. These purposeful molecules are also referred to as molecular recognition elements (Molecular Recognition Element, MRE). These molecular recognition elements can bind to certain characteristic molecules on pathogens to achieve specific recognition effects. Molecular recognition elements include short peptides, antibodies, and aptamers.

After the initial detection piece is obtained, the metal nano particles in the initial detection piece are functionalized by utilizing the prepared molecular recognition elements, wherein the functionalization refers to the connection of the molecular recognition elements and the metal nano particles so as to endow the metal nano particles with the function of specifically binding pathogens.

And (3) smearing the MRE on the surface of the initial detection piece, and culturing for a period of time at room temperature, so that the MRE permeates into a gel matrix and the surface of the metal nano-particles is functionalized, thus obtaining the target detection piece.

In this example, a concentration of MRE (0.5-5 mg/mL) was placed on the surface of the initial test piece and allowed to incubate at 22-30℃for 30 minutes to 24 hours. Followed by washing with water or buffer. The amount of MRE is preferably such that it covers the metal nanoparticle surface completely (monolayer coverage of Langmuir-Blodget isotherm).

Through testing, the target detection piece obtained after the metal nano particles are functionalized by adopting MRE can be stabilized at 6 months.

In a first functionalized embodiment of this example, an antibody is used as the MRE.

Antibodies, also called immunoglobulins (Ig), are large Y-shaped proteins produced primarily by plasma cells and used by the immune system to bind pathogens, such as pathogenic bacteria and viruses, which may be gram-negative or gram-positive, and viruses, which may be DNA or RNA viruses. When the Fc end of the antibody binds to the metal nanoparticle in SERS, the antigen binding site of the antibody will be exposed, allowing it to bind to pathogens in the surrounding solution and act to capture the virus. These antibodies, developed in advance for different pathogens, can be produced in large quantities from bovine or porcine or mouse sources. For a particular pathogen, its specificity is typically 95-98%.

Thiol groups may be provided at the ends of the antibody, which can immobilize the antibody on gold or silver nanoparticles and orient the antibody perpendicular to the metal surface, thus allowing complete monolayer surface coverage. Metal nanoparticles that were successfully functionalized by thiols will have a unique peak in the raman spectrum around 650cm ^-1.

Functionalization of gold or silver metal nanoparticle surfaces can also be achieved without terminal thiol modification, and antibodies non-covalently bind to the metal surface without the additional modification described above. Successful functionalization will have peaks between 1200 and 1600cm ^-1 in the raman spectrum due to antibody protein pattern generation and will produce a weak background signal in the raman spectrum.

In the second functionalization approach of this example, a short peptide is used as the MRE.

The surface of pathogens generally has specific peptide fragments against which short peptides with specific binding functions can be designed. The sequence of the short peptide can be determined by the biological platform for a large phage library. Short peptides bind to peptide fragments on the surface of pathogens via the N-terminus, and their binding specificity and sensitivity are more stable and sensitive than antibodies. Once the sequence of the peptide is known, the short peptide can be obtained by chemical synthesis. In addition, the C-terminal of the synthesized short peptide is modified with glycine spacers and cysteine residues to covalently bond the short peptide to the metal nanoparticle. For example, a peptide designed against the pathogen Listeria Monocytogenes (LM) is WVIPPWPQIK-C.

After the metal nano-particles are functionalized by the polypeptide, whether the functionalization is finished or not can be detected through a special single peak after the functionalization in the Raman spectrum, and the peak is changed into a detection peak in the embodiment. Since cysteine was used as the linking metal nanoparticle and the short peptide in this example, the detection peak was generated at the position of 630cm ^-1 in this example.

S50, when a pathogen sample to be detected is obtained, the pathogen sample is contacted with the target detection piece, so that the pathogen sample is specifically combined with the molecular recognition element.

Specifically, when a pathogen sample to be measured is obtained, the prepared pathogen sample is smeared on the surface of the target detection member, and the pathogen sample is brought into contact with the metal nanoparticles in the target detection member. In this embodiment, after the pathogen sample is placed on the surface of the target detection member, the pathogen sample is incubated at room temperature for 1-2 minutes, and under the action of gravity, the pathogen sample penetrates through the target detection member and contacts the metal nanoparticles.

Since the MRE is capable of specific recognition of the pathogen in the pathogen sample, while the MRE is bound to the metal nanoparticles, which are immobilized in the holes formed by the solidification of the SERS sol-gel solution, the MRE will bind specifically to the pathogen during contact with the metal nanoparticles, thereby stabilizing the pathogen around the metal nanoparticles. As shown in fig. 2, the left is the surface of the metal nanoparticle that is not bound to the pathogen, and the right is the surface of the metal nanoparticle that is bound to the pathogen.

Pathogen samples are applied to the wells (10-60 uL), allowed to incubate (1-5 min, preferably 2 min), and then washed with ultrapure water or buffer (60-100 uL). The incubation time is not too long nor too short, which may result in insufficient pathogen binding to the MRE.

When used to detect related pathogens, such as gram positive and methicillin resistant staphylococcus aureus (METHICILLIN RESISTANT Staphylococcus Aureus, MRSA), or gram negative and drug resistant salmonella or campylobacter, MREs in SERS substrates can specifically bind to them within 10 minutes (no false positive or false negative), identifying and quantifying the presence of viable pathogens with the required sensitivity.

Further, when the pathogen sample is pure, the test is performed after incubation, but in practical application, the pathogen sample is often mixed with a liquid, for example, a body fluid, so when the pathogen sample is mixed with a liquid, the target detection member is then washed with a buffer to remove the substances not specifically bound to the MRE.

S60, irradiating a laser to the contacted target detection piece, and collecting the Raman light of the target detection piece to obtain a first Raman spectrum corresponding to the pathogen sample.

Specifically, a laser is then irradiated onto the target detection member after contact with the pathogen sample. The wavelength, accumulation time and the like of the laser can be adjusted according to requirements, the laser parameters adopted in the embodiment are 785nm wavelength laser, the laser power is 75mW, and the accumulation time is 30s.

Under the action of the laser, the pathogen emits raman light, which is scattered based on SERS. Means for collecting raman light are prepared in advance, and during scattering, i.e. 30s as mentioned before, the raman light is collected, so that a first raman spectrum corresponding to the pathogen sample is obtained.

The above-described object detection piece prepared using silver as a metal element is referred to as a first detection piece, and the object detection piece prepared using gold as a metal element is referred to as a second detection piece.

To verify whether the object detecting member provided in this embodiment can perform the function of detecting pathogens, the first detecting member is used to detect existing pathogens to obtain a raman spectrum as shown in fig. 3, where the raman spectrum has an abscissa of raman shift (RAMAN SHIFT/cm "1) and an ordinate of relative intensity (a.u./cps).

In FIG. 3, pathogens used for detection include (A) Listeria monocytogenes (L. Monocytogenes, LM), (B) Salmonella (S. Tyrpi, ST), (C) Escherichia coli O157:H7 (ECO 157:H 7), (D) Escherichia coli K12 (E.coli K12, ECK 12), (E) Escherichia coli (E.coli, EC), and (F) methicillin-resistant Staphylococcus aureus (MRSA, SA). The concentration of the pathogen used in this test was 10 ⁶ CFU/mL and the volume was 40ul.

Meanwhile, a second detection member is also used for detecting a plurality of pathogens, the concentration of the strain in a pathogen sample is 10-6 CFU/mL, the volume is 40ul, raman spectra shown in fig. 4 and 5 are obtained, the pathogen types adopted comprise (A) gram-positive single-cell Listeria (L.monocytogenies, LM), (B) gram-negative Salmonella (Salmonella typhi, S.typhi, ST), (C) gram-positive methicillin-resistant staphylococcus aureus (gram-positivemethicillinresistant Staphylococcus aureus, MRSA, SA), (D) gram-positive streptococcus mutans (Streptococcus mutans, ST), (E) gram-negative escherichia coli (ESCHERICHIA COLI E.coli, EC), (F) gram-negative jejuni (C.juni, CJ), (G) gram-negative Yersinia causing pestis, and (H) gram-negative Yersinia causing gram-negative bacteria (YERSINIA PESTIS, Y.jejuns, YPS) causing pestis, and (F) gram-negative soil-type F causing gram-negative bacteria (34. F).

The specific binding of MRE to a specific pathogen is important in order to ensure the effectiveness of subsequent detection. Therefore, after the MRE is combined with the pathogen, substances which are not combined with the MRE in the pathogen sample are required to be cleaned, so that nonspecific signals in subsequent spectrums are eliminated, and the signals in the obtained Raman spectrums are all caused by the pathogen. Although in theory MREs are able to bind specifically to pathogens, since this example modifies the environment of the MRE, in such an environment the specificity of MRE for pathogen binding needs to be verified.

In the first validation experiment of this example, MRE was a short peptide designed for s.tyri, and the experiment was divided into a control group and an experimental group. FIG. 6 is a Raman spectrum corresponding to a control group, which first acquires a Raman spectrum (A) without any component added to a test well, then adds S.tyri (10-4 CFU/ml) (B) to the test well, finally washes a target detection component after target contact, and acquires a corresponding Raman spectrum (C) after washing. FIG. 7 is a chart of the Raman spectrum corresponding to SERS corresponding to the experimental group, wherein the experimental group firstly obtains the Raman spectrum (A) when no component is added in the test hole, then LM (10-4 CFU/ml) is added in the test hole, finally the test hole is cleaned, and the corresponding Raman spectrum (C) is obtained after the cleaning. By comparing FIG. 6 with FIG. 7, it was found that the peak on the Raman spectrum was only a fingerprint of thiol-linked Cys-peptide at 630cm ^-1 without any addition of any component, and that ADE characteristic peaks could be effectively detected after pathogen addition. However, after washing, only pathogen samples with pathogen S.tyri survived, whereas non-specifically bound LM was cleared and only the peak at 630cm ^-1 was detected.

In the second validation experiment of this example, MRE was designed for the pathogen LM, and the experimental protocol was similar to that of the first validation experiment, and was also divided into control and experimental groups. FIG. 8 is a Raman spectrum image of a control group, (A) a Raman spectrum obtained when no component is added to the target detection unit, (B) LM (10≡4CFU/ml) is added to the target detection unit, (C) the target detection unit after contact with a pathogen is washed, and a corresponding Raman spectrum is obtained after washing. FIG. 9 shows the Raman spectra of the experimental group, (A) the Raman spectrum obtained when no component was added to the target detection module, (B) S.tyri (10-4 CFU/ml) was added to the target detection module, (C) the target detection module after the pathogen contact was washed, and the corresponding Raman spectrum was obtained after washing. By comparing FIG. 8 with FIG. 9, it was found that the peak in the Raman spectrum was only a fingerprint of thiol-linked Cys-peptide at 630cm ^-1 without any addition of any component, and that the characteristic peak could be detected efficiently after the addition of the pathogen. However, after washing, only pathogen samples with pathogen LM survived, whereas non-specifically bound s.tyri was cleared and only the Cys-peptide fingerprint was detected.

In the third verification experiment of this embodiment, MRE in the target detection member is designed for a short peptide of pathogen S.tyrphi, the pathogen concentration is 10≡4CFU/mL, the target detection member is the first detection member of this embodiment, and the candidate drug is ciprofloxacin. In fig. 10, (a) is a raman spectrum corresponding to a target detecting member to which no pathogen was added, and (B) is a raman spectrum corresponding to a target detecting member to which a pathogen was added and washed. In addition, the raman spectrum corresponding to the LM-added cleaning is compared with the target detection piece, and after comparison, the characteristic peak of the raman spectrum corresponding to the S.typhi-added cleaning target detection component is found, while the characteristic peak of the raman spectrum corresponding to the LM-added cleaning target detection component is not found. This is the same as the results of the first two validation experiments. It is further demonstrated that the MREs in the subject assay provided in this example are capable of specifically binding to pathogens.

The above verification experiments all demonstrate that although the environment of the MRE is specific in this example, its ability to specifically bind to pathogens remains, the MRE of this example being able to bind specific pathogens to metal nanoparticles.

And S70, determining the pathogen concentration corresponding to the pathogen sample according to the first Raman spectrum, a preset characteristic peak and a standard curve corresponding to the pathogen.

Specifically, in general, the greater the number of pathogens, the higher the corresponding signal intensity, and thus, for a pathogen, the standard curve corresponding to that pathogen can be prepared by the signal intensity of the characteristic peak corresponding to the pathogen at different concentration gradients.

After the first Raman spectrum is obtained, determining signal peaks for comparison in the first Raman spectrum according to the shape and the existence area of the preset characteristic peaks. And then calculating the concentration of the pathogen corresponding to the pathogen according to the signal intensity of the signal peak and a standard curve corresponding to the pathogen in advance.

As shown in fig. 3-5, in raman spectra corresponding to all pathogens, a distinct single peak appears at 743cm ^-1, while a distinct double peak appears at the 1339cm ^-1 region. Although both unimodal and bimodal represent pathogen binding to metal nanoparticles and thus can be used as characteristic peaks in this example, unimodal is more aggregated than bimodal, and the specificity between unimodal and bimodal is not defined, and therefore this example also involves several experiments to determine characteristic peaks.

First, the shape and position of this single peak were compared with fingerprint peaks in the database, and the peak appearing at 743cm ^-1 was determined to be a biological feature of the adenine (Adenine, ADE) series. Adenine is a nucleotide molecule composed of adenine, ribose or deoxyribose, and several phosphates, and forms in organisms are classified into adenosine monophosphate (Adenosine monophosphate, AMP), adenosine diphosphate (Adenosine' -diphosphate, ADP) and adenosine triphosphate (Adenosine Triphosphate, ATP).

Since the adenine series includes a plurality of different chemical components, it is necessary to detect whether the target detection member is capable of forming a peak at the same position for the different chemical components, i.e., a single peak at 743cm ^-1 will include all members of the adenine series.

To verify this, the present example uses a first assay to detect members of different adenine families at the same concentration (100 ppb), as shown in FIG. 11, (A) ADP, (B) adenosine (Adenosine, A), (C) AMP, (D) ADP and (E) ATP all appear as distinct single peaks at 743cm ^-1. Thus, in the detection of pathogens, the ADE series of fingerprint bees of their raman spectra have positional singleness.

Because the components of the first detection part and the second detection part are different, whether the ADE series fingerprint peaks in the Raman spectra corresponding to the two parts are identical or not needs to be further verified. As shown in FIG. 12, the adenine solution having the same concentration (100 ppb) was detected by using the first target detecting element and the second detecting element, (A) a Raman spectrum obtained by the first detecting element, and (B) a Raman spectrum obtained by the second detecting element. By comparison, it can be found that the positions of the peaks detected based on the two detection pieces are the same. The concentration of adenine detected was subsequently lowered, and the target test piece was found to be the lowest capable of recognizing a concentration of 50ng/mL (50 ppb).

Since the adenine series is closely related to life activities, but since the target detection member of the present scheme needs to detect the content of the living pathogen for the subsequent test of the type and concentration of the drug against the pathogen, the adenine series can only be used as a characteristic peak of the living pathogen for further verification.

The detection conditions are the same as before, but the pathogen sample used for detection is a pathogen killed by heat or ultraviolet light. At the same time as the target detection member was used for detection, the same pathogen sample was cultured on the medium, and no clone was grown, confirming that the pathogen sample used for detection had been completely killed. As shown in fig. 13, (a) a spectrum after detection by the first detecting member of this embodiment, and (B) a spectrum after detection by the second detecting member of this embodiment. In the figure, no distinct peak appears at the position 743cm ^-1. Therefore, with the SERS detector of this embodiment, the ADE fingerprint of a dead pathogen cannot be detected, whereas the ADE fingerprint of a surviving pathogen can be detected.

Because of the significance, stability, and specificity of ADE fingerprint bees for living pathogens in the multiple assays of this example, this example uses ADE fingerprint peaks appearing in the 743cm ^-1 region of the raman spectrum as characteristic peaks indicative of pathogens.

For the above-mentioned pathogens, in order to verify the stability of the detection result of the SERS detector of this embodiment, repeated experiments of multiple wells were also performed in this embodiment.

As shown in FIG. 14, the pathogen used for detection was gram positive bacterium LM (pathogen sample concentration 10≡4 CFU/mL) and the first detection member was used for detection, and (A) to (E) were repeated tests for the same pathogen sample. As can be seen from the figure, although there is a certain difference in raman spectra corresponding to each target detection component, the peaks at 743cm ^-1 are almost identical, thus indicating high reproducibility between target detection component individuals.

As shown in FIG. 15, the pathogen used for detection was gram-negative bacteria S.typhi (pathogen sample concentration 10. Sup. 4 CFU/mL), the first detection member was used for detection, and (A) to (E) were repeated tests for the same pathogen sample. As in the previous experiment, although there was some difference in raman spectra obtained for each well, the peak at 743 cm ^-1 was almost identical, further demonstrating high reproducibility between target test piece individuals.

In addition, experiments for detecting other pathogens are not repeated herein, and after multiple tests, when the concentration of pathogen samples is 10≡3CFU/mL or even lower, the SERS detection part of the embodiment can effectively detect fingerprint peaks at the position 743cm ^-1.

Further, in order to verify the stability of the target detection member in this embodiment, the pathogen used is s.typhi, the target detection member is the first detection member, and raman spectra corresponding to different residence times of the pathogen in the same target detection member are compared. As shown in fig. 16, (a) t=0, (B) t=1 hour, (C) t=2 hours, and (D) t=3 hours. In the single plot, the peaks at 743cm-1 for A-D are almost identical. Overlapping A to D results in FIG. 17.

Meanwhile, the pathogen is LM, the target detection part is a second detection part, and Raman spectra corresponding to different residence time of the pathogen in the same target detection part are compared. As shown in fig. 18, (a) t=0, and (B) t=3 hours. In the single plot, the peaks at 743cm-1 for A-B are almost identical. Overlapping A-B results in FIG. 19.

From FIGS. 16-19, it can be seen that the peaks at 743cm ^-1 of the Raman spectrum obtained from pathogen detection at different residence times are almost identical and no signal degradation occurs. Thus, the subject detection member provided in this embodiment does not affect the activity of the pathogen when in contact with a living pathogen.

In contrast, currently, citrate or non-borohydride is often used as a reducing agent for preparing metal nanoparticles, and the metal nanoparticles produced in this way have antibacterial activity. As shown in fig. 20, gold colloid was prepared by a non-borohydride reduction method, and the gold colloid and pathogen (concentration 10+7cfu/mL) were mixed and detected, wherein (a) t=0, (B) t=15 minutes, and (C) =30 minutes. From this graph, it was found that at 15 minutes, the characteristic peak signal was significantly degraded, and at 30 minutes, the characteristic peak signal was hardly detected. Meanwhile, as shown in fig. 21, the silver colloid is prepared by adopting a non-borohydride reduction method in this embodiment, and the silver colloid and the pathogen (with the concentration of 10≡7cfu/mL) are mixed and detected, wherein (a) t=0, (B) t=15 minutes, and (C) t=30 minutes. Similar to FIG. 20, at 15 minutes, significant degradation of the characteristic peak signal occurred, and at 30 minutes, the peak signal of 743cm ^-1 was almost undetectable.

According to the embodiment, on one hand, borohydride is used as a reducing agent, so that the antibacterial activity of the metal nano particles is reduced, on the other hand, a polymer is added in the SERS sol-gel solution, the survival time of pathogens is prolonged, and the stability of a finally obtained target detection piece is enhanced. Thus, the subject detection member of the present embodiments enables quantitative and reliable investigation of viable pathogens.

After verifying that the target detecting member of the present embodiment can be used for detection of pathogens, it is also tested whether it can effectively determine the drug that kills the pathogen and the drug concentration.

First, a target detection member contacting a pathogen sample is treated with a predetermined drug candidate. The type and concentration of drug candidates available for the pathogen are determined in advance based on the type of pathogen. For example, pathogen A, drug B is the type of drug candidate identified, and drug B is employed in the treatment of pathogen A at concentrations B1, B2. The different types and concentrations of candidate drug types are then applied to the pathogen-bound target test element.

And then irradiating a laser to the processed target detection piece, and collecting the Raman light corresponding to the processed target detection piece to obtain a second Raman spectrum. The second raman spectrum is the raman spectrum obtained by detecting the pathogen after the drug treatment.

And finally, determining the target drug and the drug concentration corresponding to the pathogen according to the first Raman spectrum and the second Raman spectrum. Since the raman spectrum obtained by the target detecting member can accurately test the activity of the pathogen, when there is no characteristic peak or the characteristic peak is extremely weak in the raman spectrum corresponding to a candidate drug of a certain concentration and type, the candidate drug can be used as the target drug, and the concentration can be used as the drug concentration of the target drug.

The present example performs a number of tests to verify whether the pathogen associated with the target test element will be killed by the drug.

The target detection member used for the first drug test in this embodiment is a first detection member, the pathogen is EC O157:H7, the concentration is 10≡4CFU/mL, and the candidate drug is ciprofloxacin. In this test, a control group and an experimental group are set, wherein the control group is not added with a candidate drug, but the experimental group is added with a candidate drug, and the test is performed after different times of adding the candidate drug, fig. 22 is the control group, fig. 23 is the experimental group, (a) t=0 hours, (B) t=1 hours, (C) t=2 hours, (D) t=3 hours, (E) t=4 hours, (F) t=5 hours, and t=0 hours is the raman spectrum corresponding to the time when the candidate drug is just added. The characteristic peaks of the control group remained unchanged at different time points, while the experimental group showed significantly attenuation. The comparison of the two proves that the target detection part of the embodiment has no antipathogen effect, has higher stability, and the activity of the pathogen, the sensitivity to the medicament and the integrity are well maintained, so that the reliable medicament test can be performed.

The pathogen adopted in the second drug test is MRSA, the test mode is similar to that of the first drug test, the target detection part is the first detection part, the candidate drug adopted is vancomycin, the test is carried out after the candidate drug is added into a test hole for different time, fig. 24 is a raman spectrum corresponding to a control group with t=4 hours, fig. 25 is an experimental group, (a) t=0 hours, (B) t=1 hour, (C) t=2 hours, (D) t=3 hours, and (E) t=4 hours, and t=0 hours is the raman spectrum corresponding to the time when the candidate drug is just added. As can be seen from fig. 24 and 25, the ADE peak of the pathogen is lower and lower, that is to say the pathogen activity is lower and lower, with the effect of vancomycin over time.

The pathogen used in the third drug test of this example was s.typi (concentration 10≡4cfu/mL), the SERS detector was the first detector in this example, the drug candidate used was ciprofloxacin, fig. 26 is a raman spectrum after the test for the control group, to which no drug candidate was added in the test wells of the control group, where (a) t=0 hours, (B) t=1 hour, (C) t=2 hours, and (D) t=3 hours. Fig. 27 is a raman spectrum after testing against the experimental group, in which (a) t=0 hours, (B) t=1 hour, (C) t=2 hours, and (D) t=3 hours, were added to the test wells of the experimental group. Fig. 28 is a spectral image obtained by overlapping all raman spectra of the control group. In the control group, the signal intensity of the ADE characteristic peak hardly changed with the lapse of time, whereas in the experimental group, the signal of the characteristic peak became lower.

The pathogen used in the fourth drug test of this example was LM (at a concentration of 10≡4CFU/mL), the target test piece was the first test piece in this example, and the drug candidate was ampicillin for the treatment of mononucleosis. Fig. 29 is a raman spectrum of a control group to which a drug candidate was not added, and includes (a) t=0 hours and (B) t=120 minutes to obtain raman spectra obtained by obtaining corresponding raman light, respectively. Fig. 30 shows raman spectra corresponding to the experimental group, and raman spectra obtained by adding the candidate drug (a) t=0 hours, (B) t=30 minutes, (C) t=60 minutes, (D) t=90 minutes, and (E) t=120 minutes, respectively, were obtained. It is clear that after two hours of drug candidate addition, no characteristic peaks could be seen in the raman spectra, but the pathogen activity remained unchanged in the wells where no drug was added.

The pathogen adopted in the fifth drug test of this embodiment is MRSA, the pathogen concentration is 10≡4CFU/mL, the target detection part is the first detection part in this embodiment, and the candidate drugs adopted are vancomycin and methicillin. FIG. 31 is a Raman spectrum of multiple groups, (A) a control group without drug; (B) an experimental group after the addition of methicillin Lin Sixiao; (C) groups four hours after vancomycin addition. Since the pathogen adopted in the test has drug resistance to methicillin, the pathogen is killed in the raman spectrum corresponding to the test group to which methicillin is added, that is, the characteristic peak is not obviously reduced, but the characteristic peak is obviously reduced in the raman spectrum corresponding to the test group to which vancomycin is added, which is consistent with the result of fig. 31. This test further demonstrates that the subject detection member provided by the present embodiments can be effectively used to quickly determine the type of drug and its concentration for a particular pathogen.

Claims (5)

1. A method of pathogen detection for non-disease diagnosis or treatment purposes, the method comprising:

preparing a SERS sol-gel solution comprising a metal salt, wherein the SERS sol-gel solution comprises a polymer and a silane oxide;

Coating the SERS sol-gel solution on the surface of a porous structure prepared in advance so as to solidify the SERS sol-gel solution to obtain an initial detection piece;

reducing the initial detection piece by utilizing borohydride to obtain an intermediate detection piece containing metal nano particles;

functionalizing the metal nano particles by using a pre-prepared molecular recognition element to obtain a target detection piece;

When a pathogen sample to be tested is obtained, contacting the pathogen sample with the target detection member so that the pathogen sample specifically binds to the molecular recognition element;

irradiating a laser to the contacted target detection piece, and collecting Raman light of the target detection piece to obtain a first Raman spectrum corresponding to the pathogen sample;

Determining pathogen concentration corresponding to the pathogen sample according to the first Raman spectrum, a preset characteristic peak and a standard curve;

the polymer comprises polyethylene glycol and polydimethylsiloxane;

when the metal element in the metal salt is gold, the metal salt solution is a salt solution mixed by tetrachloroauric acid and nitric acid, the polymer comprises polyethylene glycol, the silane oxide comprises TMOS and MTMS, and the metal salt solution comprises: the silane oxide: the volume ratio of the polymer = 10:10:1, a step of;

When the metal element in the metal salt is silver, the metal salt solution is composed of silver nitrate, methanol and ammonium hydroxide, the polymer comprises polydimethylsiloxane, the silane oxide comprises TMOS, MTMS and ODS, and the volume ratio of the metal salt solution to the silane oxide to the polymer is 10:17.5:1, a step of;

Before the SERS sol-gel solution is smeared on the surface of the porous structure prepared in advance so as to solidify the SERS sol-gel, the method further comprises:

When the porous structure is made of glass, heating the porous structure in weak base at 100 ℃ for 1 hour;

carrying out hydrothermal treatment at 100 ℃ for 1 hour on the surface of the heated porous structure;

soaking the cleaned porous structure in methanol at 60 ℃ for 1 hour;

Performing hydrothermal treatment at 100 ℃ for 1 hour on the porous structure after soaking methanol to remove methanol on the surface of the porous structure;

When the porous structure is made of plastic, oxidizing the porous structure by using strong acid;

and cleaning the surface of the oxidized porous structure.

2. The method of claim 1, wherein the metal nanoparticles are fractal aggregates, and wherein the metal nanoparticles have an average size of 10 nm to 200 nm.

3. The method of claim 1, wherein the molecular recognition element comprises a short peptide, an antibody, and an aptamer.

4. A method according to claim 3, wherein when the molecular recognition element is a short peptide, the short peptide is linked to the metal nanoparticle by a cysteine.

5. The method of claim 1, wherein after determining the pathogen concentration corresponding to the pathogen sample according to the first raman spectrum, a preset characteristic peak, and a standard curve, further comprising:

processing a target detection piece contacting a pathogen sample by using a preset candidate drug;

Irradiating a laser to the processed target detection piece, and collecting Raman light corresponding to the processed target detection piece to obtain a second Raman spectrum;

And determining target drugs and drug concentrations corresponding to the pathogens according to the first Raman spectrum and the second Raman spectrum.