Surface-enhanced raman detection method, system, storage medium and electronic device for monitoring dynamic evolution of bacterial drug resistance

Abstract

This invention relates to the field of microbial detection and spectral analysis technology, specifically to a surface-enhanced Raman spectroscopy (SERS) detection method, system, storage medium, and electronic device for monitoring the dynamic evolution of bacterial drug resistance. This invention deeply couples the continuous drift of SERS spectral characteristics with the evolutionary generations of bacteria. By acquiring spectral signals from different generations under antibiotic pressure and combining them with the unsupervised t-SNE algorithm, it can automatically classify the early, middle, and late evolutionary stages of bacteria. This overcomes the limitations of traditional detection methods that rely on endpoint determination, enabling continuous and dynamic tracking of the microbial evolutionary process.

Description

Technical Field

[0001] This invention relates to the field of microbial detection and spectral analysis technology, specifically to a surface-enhanced Raman detection method, system, storage medium, and electronic device for monitoring the dynamic evolution of bacterial drug resistance. Background Technology

[0002] Antibiotic resistance (AMR) has become a serious public health threat worldwide. Gram-negative bacteria, especially Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii, have shown the ability to rapidly acquire resistance in both medical settings and the environment. Long-term or repeated use of antibiotics exerts continuous selective pressure on bacteria, prompting them to gradually evolve resistance through multiple levels of mechanisms, including phenotypic adaptation, metabolic reprogramming, and gene mutation. Traditional methods for detecting resistance mainly include: (1) Phenotypic susceptibility testing (AST), such as microbroth dilution method, disk diffusion method, etc. Although these methods are clinical standard, they usually take 16 to 24 hours or even longer to obtain results. In addition, AST can only obtain the final resistance phenotype and cannot reveal the intermediate stage changes in the process of bacterial resistance formation.

[0003] (2) Molecular detection methods, such as qPCR or nucleic acid amplification-based drug resistance gene detection. These methods rely on known drug resistance gene targets and cannot detect unknown or unfixed mutations, nor can they reflect phenotypic changes that may exist in bacteria in the early stages of drug resistance formation but have not yet manifested as gene changes.

[0004] (3) Whole-genome sequencing (WGS). WGS can identify drug resistance genes on SNPs, indels, resistance islands, integrons, and plasmids, and is a commonly used tool for studying drug resistance mechanisms. However, WGS can usually only detect stable genetic changes and is difficult to capture early phenotypic adaptations such as reversible metabolic adaptations, membrane flux regulation, and changes in outer membrane porin expression that occur in bacteria within a short period of time. In addition, WGS is expensive and complex to operate, and is not suitable for high-throughput or real-time monitoring.

[0005] Recent studies have revealed that the formation of bacterial resistance is often not driven by a single factor, but rather involves gradual changes at multiple levels. For example, in the evolution of carbapenem resistance, bacteria may exhibit phenotypic adaptations such as decreased membrane permeability, altered energy metabolism, enhanced efflux pump activity, and metabolic pathway reprogramming before explicit gene mutations appear. These early changes often determine whether bacteria can successfully fixate resistance mutations. However, traditional techniques struggle to dynamically and in real-time capture these non-genetic and genotypic changes.

[0006] Furthermore, multiple studies have shown that antibiotic resistance evolution is not always accompanied by detectable gene mutations. For example, some strains can rapidly acquire temporary tolerance under antibiotic stress by regulating membrane lipid composition, metabolic flux, and energy state, but these changes revert to normal once the selective pressure ceases. Identifying such phenotypic plasticity relies on techniques that can directly characterize bacterial molecular composition or metabolic state, rather than methods based on genetic information.

[0007] With the development of molecular spectroscopy, surface-enhanced Raman spectroscopy (SERS) has been used for bacterial species identification, metabolic state analysis, and drug-resistant bacteria detection due to its advantages such as high sensitivity, strong fingerprint resolution, simple sample preparation, and no need for culture. SERS utilizes the enhanced electromagnetic field generated by metallic nanomaterials to directly reflect the vibrational modes of biomolecules on the bacterial surface, thereby providing a spectral fingerprint that reflects multi-level information such as cell wall structure, cell membrane components, metabolic molecules, nucleic acids, and proteins.

[0008] However, current applications of SERS technology in bacterial research are mainly focused on static analysis, such as the identification of pathogens and the differentiation between susceptible and drug-resistant bacteria. A method is lacking that can continuously monitor the process of bacterial resistance formation under antibiotic pressure using SERS. Summary of the Invention

[0009] This invention aims to overcome the shortcomings of existing technologies that cannot dynamically monitor the formation process of bacterial drug resistance. It provides a surface-enhanced Raman spectroscopy (SERS) detection method, system, storage medium, and electronic device for monitoring the dynamic evolution of bacterial drug resistance. The provided SERS detection method, through a specific substrate material, achieves continuous spectral characterization of bacteria from early stress adaptation, mid-stage metabolic remodeling to late-stage drug resistance formation, simultaneously capturing multi-level dynamics such as phenotypic changes, metabolic regulation, and genetic mutations.

[0010] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a surface-enhanced Raman spectroscopy (SMR) detection method for monitoring the dynamic evolution of bacterial resistance, comprising: acquiring SMR spectral signals of bacterial samples from different generations under antibiotic selection pressure; preprocessing the SMR spectral signals and performing dimensionality reduction and clustering analysis using the t-SNE algorithm; identifying the continuous change trend of the SMR spectral signals of the bacterial samples based on the distribution and aggregation of the clustered spectral data points in the low-dimensional space, and distinguishing the resistance evolution generations of the bacterial samples, wherein the resistance evolution generations include early, middle, and late stages.

[0011] When bacteria encounter antibiotic selective pressure, they don't instantly mutate into "drug-resistant bacteria." Instead, they undergo a continuous struggle and evolutionary process: early stress adaptation, where bacteria sense stress and begin to respond, such as altering cell membrane permeability to prevent drug entry; mid-stage metabolic remodeling, where bacteria change their energy metabolism pathways or aggressively express efflux pumps to expel the drug; and late-stage resistance formation, where prolonged selective pressure ultimately leads to irreversible mutations in the bacterial genome, resulting in stable resistance. Therefore, identifying the antibiotic resistance evolutionary generation of bacteria is crucial for providing targeted countermeasures.

[0012] Based on this premise, the inventors have provided a method to solve the problem of dynamically monitoring the formation of bacterial resistance. Specifically, the t-SNE algorithm is used to achieve nonlinear dimensionality reduction, preserving local similarities in SERS spectral data. After entering the complete SERS spectrum into t-SNE, the algorithm automatically clusters similar spectral features together. Because bacterial evolution is continuous and gradual, the changes in spectral signals are also gradual. Therefore, in the reduced low-dimensional space, data points from different generations will naturally form "clusters" with clear trajectories. By identifying the continuous changing trends of the spectral signals brought about by evolution, the inventors can objectively and clearly delineate the boundaries between the early, middle, and late stages in the spatiotemporal dimension, achieving dynamic monitoring with low cost, fast detection speed, and high sensitivity.

[0013] Preferably, the bacteria to be tested is one of Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, or Pseudomonas aeruginosa.

[0014] Preferably, the bacteria are prepared as a bacterial suspension, dropped onto the substrate material for surface-enhanced Raman detection, and surface-enhanced Raman spectral signals are obtained; And / or, the detection parameters for acquiring the surface-enhanced Raman spectroscopy signal are: laser wavelength 785 nm, spectral range 600~1800 cm⁻¹. -1 The resolution is no less than 3.5 cm. -1 Laser power 10~30 mW, cumulative scans 3~5 times; And / or, the preprocessing includes cosmic ray removal, flat baseline correction, and normalization performed sequentially; And / or, the parameters of the t-SNE algorithm are: perplexity = 30, learning rate = 200, n_iter = 2000, random_state = 42; And / or, the continuous variation trend refers to the continuous variation trend of the characteristic peaks in the surface-enhanced Raman spectroscopy signal; the characteristic peaks include the 720 cm⁻¹ peak. -1 1090 cm -1 1130 cm -1 ; And / or, the early stage refers to the phenotypic adaptation period under initial antibiotic stress, corresponding to generations 0-6; And / or, the intermediate period is the membrane remodeling and metabolic regulation period, corresponding to generations 7 to 10; And / or, the late stage refers to the period of genetic mutation fixation and stable drug resistance formation, corresponding to generations 11 to 14.

[0015] The collected surface-enhanced Raman spectroscopy signals can simultaneously reflect changes in cell wall peptidoglycan structure, membrane lipid composition and membrane flux, nucleic acid / protein metabolic levels, and energy metabolism-related molecular changes. This information can be used to determine the evolution of bacterial drug resistance generations.

[0016] Preferably, the OD of the bacterial suspension 600 It ranges from 0.1 to 0.5.

[0017] Preferably, the substrate material used to obtain the surface-enhanced Raman spectroscopy signal is a polyvinylidene fluoride electrospun film with silver nanoparticles deposited in situ on its surface after alkaline etching. Preferably, the in-situ deposition is performed in 1 to 3 cycles. More preferably, the in-situ deposition is performed in 2 cycles.

[0018] Preferably, the method for preparing the substrate material used to obtain the surface-enhanced Raman spectroscopy signal includes: performing alkaline etching on an electrospun film containing polyvinylidene fluoride and hydroxylamine hydrochloride, followed by cyclic deposition in a growth solution containing a silver source and a reducing agent, so that silver nanoparticles grow in situ on the surface of the electrospun film to obtain the substrate material.

[0019] This invention uses electrospinning to prepare the substrate film. The advantages of electrospinned film are that it has a uniform pore structure and high mechanical strength, which can provide a high specific surface area reaction interface for subsequent silver ion etching and deposition.

[0020] Alkali-etched polyvinylidene fluoride (PVDF) electrospun films can expose or generate reaction sites rich in hydroxyl groups and amides, enabling the film surface to interact with silver seeds and promoting the adhesion and growth of silver particles. Controlling the number of deposition cycles during cyclic deposition can adjust the Ag hotspot density, nanometer gap size, and SERS enhancement factor stability, thereby obtaining substrate materials with high batch-to-batch repeatability and stable enhancement effects.

[0021] Preferably, the electrospinning parameters during the preparation of the electrospinning membrane are: applied voltage 15~20 kV, spinning solution injection rate 1~2 mL / h, collector rotation speed 200~400 r / min, and distance from the nozzle to the collector 12~18 cm; And / or, the alkaline solution is sodium hydroxide; And / or, the etching process takes at least 30 minutes; And / or, the number of cyclic depositions is 1 to 3; And / or, the silver source is silver nitrate; And / or, the reducing agent is ascorbic acid.

[0022] Preferably, the cyclic deposition is performed twice.

[0023] This invention provides a bacterial resistance evolution monitoring system to be used in conjunction with a surface-enhanced Raman spectroscopy method for monitoring the dynamic evolution of bacterial resistance, comprising: The acquisition module is used to acquire surface-enhanced Raman spectral signals of bacterial samples from different generations under antibiotic selective pressure. The preprocessing module is used to preprocess the surface-enhanced Raman spectral signal obtained by the acquisition module; The analysis module incorporates t-SNE for dimensionality reduction and clustering analysis; The identification module identifies the distribution and aggregation of spectral data points in a low-dimensional space after clustering, and identifies the continuous change trend of the enhanced Raman spectral signal on the surface of the bacteria to be tested, distinguishing the drug resistance evolution generation of the bacteria to be tested, which includes early, middle and late stages.

[0024] The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a surface-enhanced Raman detection method for monitoring the dynamic evolution of bacterial resistance.

[0025] The present invention provides an electronic device, characterized in that it comprises: a memory for storing a computer program; and a processor for executing the computer program to implement a surface-enhanced Raman detection method for monitoring the dynamic evolution of bacterial resistance.

[0026] Therefore, the present invention has the following beneficial effects: (1) This invention deeply couples the continuous drift of SERS spectral features with the evolutionary generations of bacteria. By acquiring the spectral signals of different generations under antibiotic pressure and combining them with the unsupervised algorithm t-SNE, it can automatically divide the bacteria into three evolutionary stages: early, middle and late. This breaks through the limitations of traditional detection endpoint determination and realizes continuous and dynamic tracking of the microbial evolution process.

[0027] (2) The SERS fingerprint spectrum obtained by this invention can directly reflect the overall metabolic and expression levels of bacterial surface biomolecules (such as peptidoglycan, lipids, nucleic acids and proteins). Therefore, this method can capture early phenotypic and metabolic changes in bacteria before irreversible gene mutations occur, providing an irreplaceable tool for early clinical intervention and in-depth research on drug resistance mechanisms.

[0028] (3) In this invention, the bacterial sample to be tested only needs to be simply centrifuged and washed, and then directly dropped onto the substrate material and allowed to air dry before spectral acquisition. The entire process does not require long-term culture and amplification, nor does it require the use of chemical fixatives or fluorescent dyes, which greatly shortens the sample preparation and detection cycle, while preserving the original molecular composition and phenotypic characteristics of the bacteria to the greatest extent.

[0029] (4) The present invention uses the electrospun film after alkali etching as the base film to prepare the base material, which exposes more reaction sites on the film surface, resulting in high density and uniform distribution of silver particles deposited on the film in situ, and obtaining a base material that can reflect a more complete SERS spectral signal.

[0030] (5) This invention introduces the t-SNE unsupervised dimensionality reduction algorithm, which enables the machine to autonomously find the intrinsic distribution patterns of high-dimensional spectral data in low-dimensional space. Without the need to predefine complex biological labels, the algorithm can automatically complete the grouping based on the continuous changing trend of key Raman characteristic peak signals, realizing the complete objectification, automation and accurate decoding of the drug resistance evolution stage. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the preparation process of PVDF@Ag substrate material. In the diagram, 1, 2, and 3 represent the number of silver deposition steps.

[0032] Figure 2 The results are TEM images of different substrate materials. In the images, A is the PVDF film, B is the PVDF film after etching, and C, D, and E are PVDF@Ag substrate materials after 1, 2, and 3 silver depositions, respectively.

[0033] Figure 3 The SERS spectra of the PVDF@Ag substrate material after 1, 2, and 3 silver depositions in Example 1 are shown.

[0034] Figure 4 This is a schematic diagram of a surface-enhanced Raman spectroscopy method for monitoring the dynamic evolution of bacterial resistance.

[0035] Figure 5 Evolutionary diagram of imipenem-induced Klebsiella pneumoniae.

[0036] Figure 6 This is a clustering spectrum of bacteria at different evolutionary stages.

[0037] Figure 7 This is a spectrum of resistance sharing across species.

[0038] Figure 8 A comparison chart of PCA clustering and t-SNE clustering.

[0039] Figure 9 The effect of NaOH etching on PVDF@Ag substrate material is shown. In the figure, A is the SEM image of the material without etching and after two silver depositions, and B is the SERS spectrum of the material without etching.

[0040] Figure 10 This is a hardware structure diagram of a computer terminal.

[0041] Figure 11 This is a schematic diagram of an electronic device.

[0042] Figure 12 This is a schematic diagram of the system. Detailed Implementation

[0043] The present invention will be further described below with reference to specific embodiments. Those skilled in the art will be able to implement the present invention based on these descriptions. Furthermore, the embodiments of the present invention described below are generally only some, not all, of the embodiments of the present invention. Therefore, all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0044] Example 1: PVDF@Ag substrate material according to Figure 1 The diagram shown completes the following operations: First, N,N-dimethylformamide (DMF) and acetone were mixed at a mass ratio of 7:3 as a solvent. Then, 12 wt% of polyvinylidene fluoride (PVDF, average Mw≈534,000) and 2 wt% of hydroxylamine hydrochloride (HHA) were added. The mixture was then magnetically stirred in a water bath at 60°C for 6 hours to prepare a uniform and transparent electrospinning solution.

[0045] Subsequently, electrospinning was performed at 25℃ and 40-50% relative humidity (electrospinning conditions: applied voltage 18 kV, liquid feed rate 1 mL / h, collecting roller speed 300 r / min, receiving distance 15 cm), resulting in PVDF nanofiber membranes with fiber diameters of 150-300 nm and membrane thicknesses of 50-200 μm. Figure 2 A), and then placed in an 80℃ vacuum drying oven to dry for 12 hours.

[0046] Next, the dried PVDF nanofiber membrane was cut into strips of 1 cm × 2 cm and etched in a 1 mol / L NaOH solution at room temperature for 30 minutes to remove weakly polar areas on the surface and expose hydrophilic groups to enhance adhesion. After etching, the membrane was rinsed three times with deionized water and allowed to air dry to obtain the etched PVDF membrane. Figure 2 B).

[0047] Finally, a silver growth solution containing 0.1 M silver nitrate, 0.1 M ascorbic acid, 5 wt% PVP, and acetonitrile / ethanol mixed solvent and deionized water was prepared. The silver growth solution was stirred for 10 minutes in an ice bath at 10°C to homogenize it. The etched PVDF film was then immersed in the silver growth solution and gently magnetically stirred for 60 minutes to allow silver ions to be reduced and deposited in situ. After removal, the film was washed and dried sequentially. This "deposition-washing-drying" process was repeated 1 to 3 times (to obtain PVDF@Ag substrate materials with low, high, and aggregation hotspots, respectively). In this example, two cyclic depositions were performed to obtain a silver-gray PVDF@Ag substrate material with uniformly distributed 20-60 nm silver nanoparticles on its surface. Figure 2 D).

[0048] The obtained PVDF@Ag substrate material and the substrate materials at each stage were subjected to TEM detection, and the results are as follows: Figure 2 As shown in the figure, observations reveal that with increasing deposition cycles, the number of particles adhering to the surface of the PVDF nanofiber membrane increases. Figure 2 C Figure 2 D、 Figure 2 E corresponds to PVDF@Ag substrate materials with low, high, and clustered hotspots, respectively. Figure 3 The SERS spectra of low-, high-, and aggregated PVDF@Ag substrate materials during monitoring in Example 2 show that the PVDF@Ag substrate material exhibits the most complete representation of the weak characteristic peaks in the SERS spectrum with 2 cycles of deposition.

[0049] Example 2: Surface-enhanced Raman Detection Method for Monitoring the Dynamic Evolution of Bacterial Resistance according to Figure 4 The diagram shown completes the following operations: First, clinically isolated drug-sensitive Klebsiella pneumoniae progenitor strains were obtained and cultured overnight at 37°C on LB agar plates. A single colony was picked from the LB plate and inoculated into 10 mL of LB liquid medium, then cultured overnight with shaking at 37°C and 180 rpm to obtain a stable growth culture. Subsequently, 0.5 mL of the overnight culture was inoculated into 50 mL of fresh LB liquid medium at a 1:100 volume ratio, and imipenem was added for drug induction culture. During induction, a gradually increasing IPM concentration gradient was used for continuous subculturing. The specific concentrations were: 0 μg / mL, 0.0625 μg / mL, 0.125 μg / mL, 0.25 μg / mL, 0.5 μg / mL, 1 μg / mL, 2 μg / mL, 4 μg / mL, 4 μg / mL, 8 μg / mL, 8 μg / mL, 16 μg / mL, 16 μg / mL, 32 μg / mL, and 32 μg / mL, corresponding to induction times from day 0 to day 14 (generations 0-14). A drug exposure cycle was performed every 24 hours. Culture conditions were 37 ℃ with shaking at 180 rpm for 24 hours. The evolution of Klebsiella pneumoniae induced by imipenem during culture is shown in [link to relevant documentation]. Figure 5 After each cycle of drug induction, the culture was streaked onto LB agar and incubated overnight at 37 °C. Once single colonies formed on the plates, healthy single colonies were selected for amplification and culture, and these were used as the evolutionary strains obtained under that round of drug stress.

[0050] Then, 10 mL of bacterial culture was centrifuged at 8000 rpm and washed twice with deionized water, resuspended, and adjusted to OD. 600 After adjusting the concentration to 0.1~0.5, centrifuge again and resuspend the precipitate in 100 μL of deionized water to complete sample preparation and obtain bacterial suspension.

[0051] Next, 10 μL of the bacterial suspension was directly added to the central region of the high-hotspot PVDF@Ag substrate material prepared in Example 1 after two cycles, and allowed to air dry for 10 minutes. No fixation or staining was required throughout the process to preserve the original molecular structure of the bacteria.

[0052] After drying, the samples were analyzed using a portable Raman spectrometer (laser wavelength 785 nm, power 30 mW) at a wavelength of 600–1800 cm⁻¹. -1 SERS spectra were acquired within the specified spectral range, with a resolution of 3.5 cm⁻¹. -1 Each point was scanned three times, and at least 20 spectra were collected for each sample for statistical analysis. After obtaining the raw spectra, cosmic ray removal, baseline correction, and normalization (to bring the intensity into the range of 0 to 1) were performed as preprocessing steps.

[0053] Finally, the t-SNE unsupervised learning algorithm (parameters: perplexity=30, learning rate=200, n_iter=2000, random_state=42) is used to perform dimensionality reduction and cluster analysis on the preprocessed spectrum. The algorithm will perform dimensionality reduction and cluster analysis based on the bacterial Raman characteristic peaks (720, 1090, 1130 cm⁻¹). -1 The continuously changing trend automatically divides the spectrum across generations into categories such as Figure 6 The three evolutionary stages are: the early stage (generations 0-6), the mid stage (generations 7-10), and the late stage (generations 11-14).

[0054] Example 3 This embodiment is basically the same as Embodiment 2, except that: clinically isolated imipenem-resistant Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii were obtained and continuously passaged, and corresponding tests were performed. The results are as follows. Figure 7 As shown.

[0055] Comparative Example 1: Different Models This comparative example is basically the same as Example 2, except that PCA clustering is used instead of the t-SNE unsupervised learning algorithm (t-SNE clustering). The result is as follows: Figure 8 As shown, observation reveals that although PCA also has the effect of clustering and partitioning, its boundaries are unclear during the partitioning process, and the partitions are mixed together.

[0056] Comparative Example 2: Substrate Material Method A: The substrate material was synthesized according to the method in Example 1. The "deposition-washing-drying" process was repeated twice. PVDF was replaced with PVA or PAN to obtain PVA@Ag substrate material and PAN@Ag substrate material.

[0057] The resulting PVA@Ag substrate material exhibits extremely poor SERS monitoring signal stability during SERS detection in Example 2 because the PVA membrane automatically dissolves upon contact with the aqueous solution.

[0058] The obtained PAN@Ag substrate material has poor tension after PAN spinning, and the membrane structure is easily damaged during post-modification, which also affects SERS detection.

[0059] Method B: The substrate material was synthesized according to the method of Example 1, but without alkaline etching. Silver ions were directly deposited in situ on the surface of the PVDF nanofiber membrane through in-situ reduction deposition. The "deposition-washing-drying" process was repeated twice. The resulting PVDF@Ag substrate material is as follows: Figure 9 As shown. Comparison Figure 9 A and Figure 2D. Without alkali etching, Ag cannot be uniformly deposited on the PVDF nanofiber film due to the presence of CF bonds on the PVDF nanofiber film; this affects the presentation of the SERS spectrum, resulting in indistinct or even shifted characteristic peaks. Figure 9 B).

[0060] Example 4: Computer-readable storage medium The method provided in this embodiment 2 can be executed in a mobile terminal, computer terminal or similar computing device. Figure 10 A hardware block diagram of a computer terminal (or mobile device) for implementing a surface-enhanced Raman detection method for monitoring the dynamic evolution of bacterial resistance is shown. Figure 10 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 10 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 10 The more or fewer components shown, or having the same Figure 10 The different configurations shown.

[0061] Example 5 Electronic Device Figure 11 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 11 As shown, the electronic device may include: one or more ( Figure 11 (Only one is shown) processor 202, memory 204, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0062] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the surface-enhanced Raman detection method and apparatus for monitoring the dynamic evolution of bacterial drug resistance in Example 4. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned surface-enhanced Raman detection method for monitoring the dynamic evolution of bacterial drug resistance. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0063] Example 6: Bacterial Resistance Evolution Monitoring System A bacterial resistance evolution monitoring system 300, such as Figure 12 As shown, it includes: The acquisition module 302 is used to acquire the surface-enhanced Raman spectral signals of the bacterial samples under different evolutionary generations under antibiotic selective pressure; Preprocessing module 304 is used to preprocess the surface-enhanced Raman spectral signal obtained by the acquisition module; Analysis module 306 incorporates t-SNE for dimensionality reduction and cluster analysis; The identification module 308 identifies the continuous change trend of the enhanced Raman spectral signal on the surface of the bacteria under test based on the distribution and aggregation of the clustered spectral data points in the low-dimensional space, and distinguishes the drug resistance evolution generation of the bacteria under test, which includes early, middle and late stages.

Claims

1. A surface-enhanced Raman spectroscopy method for monitoring the dynamic evolution of bacterial drug resistance, characterized in that, include: Surface-enhanced Raman spectral signals of bacterial samples from different generations under antibiotic selective pressure were obtained; After preprocessing the surface-enhanced Raman spectroscopy signal, dimensionality reduction and clustering analysis are performed using the t-SNE algorithm; Based on the distribution and aggregation of spectral data points in low-dimensional space after clustering, and by identifying the continuous change trend of the surface-enhanced Raman spectral signal of the bacteria under test, the drug resistance evolution generation of the bacteria under test can be distinguished, including early, middle and late drug resistance evolution generations.

2. The surface-enhanced Raman spectroscopy method as described in claim 1, characterized in that, The bacteria to be tested are one of Klebsiella pneumoniae, Escherichia coli, or Acinetobacter baumannii.

3. The surface-enhanced Raman detection method as described in claim 1, characterized in that, Bacteria were prepared as a bacterial suspension, dropped onto a substrate material, and surface-enhanced Raman detection was performed to obtain surface-enhanced Raman spectral signals. And / or, the detection parameters for acquiring the surface-enhanced Raman spectroscopy signal are: laser wavelength 785 nm, spectral range 600~1800 cm⁻¹. -1 The resolution is no less than 3.5 cm. -1 Laser power 10~30 mW, cumulative scans 3~5 times; And / or, the preprocessing includes cosmic ray removal, flat baseline correction, and normalization performed sequentially; And / or, the parameters of the t-SNE algorithm are: perplexity = 30, learning rate = 200, n_iter = 2000, random_state = 42; And / or, the continuous variation trend refers to the continuous variation trend of the characteristic peaks in the surface-enhanced Raman spectroscopy signal; the characteristic peaks include the 720 cm⁻¹ peak. -1 1090 cm -1 1130 cm -1 ; And / or, the early stage refers to the phenotypic adaptation period under initial antibiotic stress, corresponding to generations 0-6; And / or, the intermediate period is the membrane remodeling and metabolic regulation period, corresponding to generations 7 to 10; And / or, the late stage refers to the period of genetic mutation fixation and stable drug resistance formation, corresponding to generations 11 to 14.

4. The surface-enhanced Raman spectroscopy method as described in claim 3, characterized in that, The OD of the bacterial suspension 600 It ranges from 0.1 to 0.

5.

5. The surface-enhanced Raman spectroscopy method as described in claim 1 or 3, characterized in that, The substrate material used to obtain the surface-enhanced Raman spectroscopy signal was a polyvinylidene fluoride electrospun film with silver nanoparticles deposited in situ on the surface after alkaline etching.

6. The surface-enhanced Raman detection method as described in claim 1 or 3, characterized in that, The method for preparing the substrate material used to obtain surface-enhanced Raman spectroscopy signals includes: etching an electrospun film containing polyvinylidene fluoride and hydroxylamine hydrochloride with an alkaline solution, followed by cyclic deposition in a growth solution containing a silver source and a reducing agent, so that silver nanoparticles grow in situ on the surface of the electrospun film to obtain the substrate material.

7. The surface-enhanced Raman detection method as described in claim 6, characterized in that, The electrospinning parameters for preparing the electrospinned membrane are: applied voltage 15~20 kV, spinning solution injection rate 1~2 mL / h, collector rotation speed 200~400 r / min, and distance from the nozzle to the collector 12~18 cm. And / or, the alkaline solution is sodium hydroxide; And / or, the etching process takes at least 30 minutes; And / or, the number of cyclic depositions is 1 to 3; And / or, the silver source is silver nitrate; And / or, the reducing agent is ascorbic acid.

8. A bacterial resistance evolution monitoring system, characterized in that, To be used in conjunction with the surface-enhanced Raman detection method as described in any one of claims 1 to 7, comprising: The acquisition module is used to acquire surface-enhanced Raman spectral signals of bacterial samples from different generations under antibiotic selective pressure. The preprocessing module is used to preprocess the surface-enhanced Raman spectral signal obtained by the acquisition module; The analysis module incorporates t-SNE for dimensionality reduction and clustering analysis; The identification module identifies the distribution and aggregation of spectral data points in a low-dimensional space after clustering, and identifies the continuous change trend of the enhanced Raman spectral signal on the surface of the bacteria to be tested, distinguishing the drug resistance evolution generation of the bacteria to be tested, which includes early, middle and late stages.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the surface-enhanced Raman detection method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the surface-enhanced Raman detection method as described in any one of claims 1 to 7.

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