CN116399864A - Bacterial drug resistance detection method based on gram staining and machine vision - Google Patents
Bacterial drug resistance detection method based on gram staining and machine vision Download PDFInfo
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Abstract
The invention provides a bacterial drug resistance detection method based on gram staining and machine vision, which comprises the following steps: step S1, a cell wall synthesis inhibitor acts on gram-positive bacteria, and after shaking, mixing and incubation treatment, centrifugal operation is carried out to obtain a mixture; step S2, adding a proper amount of sterile water into the mixture, transferring the mixture onto a glass slide for drying treatment, and then carrying out gram staining to obtain a first bacterial film sample; step S3, sequentially dripping a crystal violet solution, flushing, a gram iodine solution, flushing, an ethanol water solution, flushing, a safranin staining solution and flushing to the first bacterial film sample to obtain a final sample; and S4, observing the final sample to obtain a plurality of dyed pictures, extracting gray features of each dyed picture to obtain feature values, and analyzing each feature value to obtain a bacterial drug resistance detection result. The invention has the beneficial effect that the rapid detection of drug resistance of gram-positive bacteria can be realized.
Description
Technical Field
The invention relates to the technical field of bacterial drug resistance detection, in particular to a bacterial drug resistance detection method based on gram staining and machine vision.
Background
Infections caused by gram-positive bacteria are a terrible clinical threat, both morbidity and mortality are high, and mortality from drug-resistant bacterial infections is higher than from sensitive bacterial infections, and the current criteria for clinically detecting bacterial resistance are phenotypic methods of Antibiotic Susceptibility Testing (AST), such as paper spread testing and broth dilution testing, which require bacterial culture, typically taking 2-3 days, and genotyping methods based on DNA, gene amplification or sequencing, which may present false positives in addition to the cost of equipment and reagents far exceeding that of traditional AST, and in addition to other techniques approved by the U.S. Food and Drug Administration (FDA), such as MALDI-TOF, multiplex Automated Digital Microscopy (MADM) and accelerated Pheno systems based on fluorescence in situ hybridization, which, although mostly have high sensitivity, have complex sample pretreatment steps and require expensive and complex instrumentation.
Disclosure of Invention
The invention aims to solve the problems that: the bacterial drug resistance detection method based on gram staining and machine vision can effectively shorten the detection time of gram positive bacteria, reduce the cost of equipment and reagents, shorten the types of medicines and pretreatment steps, omit expensive and complex instruments, and simultaneously ensure the accuracy of detection results.
In order to solve the problems, the invention provides a bacterial drug resistance detection method based on gram staining and machine vision, which comprises the following steps:
step S1, a cell wall synthesis inhibitor acts on gram-positive bacteria, and after shaking, mixing and incubation treatment, centrifugal operation is carried out to obtain a mixture;
step S2, adding a proper amount of sterile water into the mixture, transferring the mixture onto a glass slide for drying treatment, and then carrying out gram staining to obtain a corresponding first bacterial film sample;
step S3, sequentially performing a crystal violet solution dropwise adding operation, a flushing operation, a gram iodine solution dropwise adding operation, a flushing operation, an ethanol water solution dropwise adding operation, a flushing operation, a safranin staining solution dropwise adding operation and a flushing operation on the first bacterial film sample to obtain a final sample;
and S4, carrying out microscopic observation on the final sample to obtain a plurality of dyed pictures, respectively carrying out gray feature extraction on each dyed picture by adopting a machine vision algorithm to obtain corresponding feature values, and then analyzing each feature value to obtain a corresponding bacterial drug resistance detection result.
Preferably, in the step S2, the mixture added with the sterile water is transferred onto the slide and subjected to a drying treatment at an ambient temperature of 40 ℃.
Preferably, the step S3 includes:
step S31, a proper amount of crystal violet solution is dripped into the first bacterial film sample and acts for 1 minute, and then deionized water is adopted to rinse the glass slide to obtain a second bacterial film sample;
step S32, a proper amount of gram iodine solution is dripped into the second bacterial film sample and acts for 1 minute, and then the deionized water is adopted to flush the glass slide to obtain a third bacterial film sample;
step S33, a proper amount of ethanol water solution is dripped into the third bacterial film sample for washing, and then the deionized water is adopted for washing the glass slide to obtain a fourth bacterial film sample;
and step S34, a proper amount of safranin staining solution is dripped into the fourth bacterial film sample and acts for 2 minutes, then the deionized water is adopted to flush the glass slide, and moisture absorption treatment is carried out to obtain the final sample.
Preferably, in the step S33, the third bacterial film sample is washed three times continuously by dropping the ethanol aqueous solution.
Preferably, the concentration of the aqueous ethanol solution is 95%.
Preferably, the step S4 includes:
s41, carrying out microscopic observation on the final sample to obtain a plurality of dyeing pictures;
step S42, respectively preprocessing each dyed picture and extracting gray features to obtain corresponding gray values, carrying out normalization processing on each dyed picture by a gray stretching method to obtain normalized images, then extracting the normalized images by a gray segmentation method to obtain bacterial areas, taking the normalized images containing the bacterial areas as final detection images and taking the gray values corresponding to the final detection images as the feature values;
and S43, visualizing the characteristic value by adopting an unsupervised analysis method, and analyzing the characteristic value by utilizing a pre-constructed supervision model to obtain a corresponding bacterial drug resistance detection result.
Preferably, in the step S1, the concentration of the cell wall synthesis inhibitor is 256. Mu.g/mL, and the concentration of the gram-positive bacteria is 10 7 CFU/mL。
Preferably, in the step S4, the final sample is observed by an optical microscope oil microscope at a magnification of 1000 times to obtain a plurality of stained pictures.
The invention has the following beneficial effects:
1) The preparation method only needs to prepare cell wall synthesis inhibitor, gram positive bacteria, sterile water, crystal violet solution, gram iodine solution, ethanol water solution, safranin staining solution and deionized water in advance, so that the types of medicines and pretreatment steps are greatly reduced, and the cost of reagents is reduced;
2) According to the invention, gray scale feature extraction is carried out on the dyed picture by utilizing a machine vision algorithm, and the feature value is directly analyzed to obtain a bacterial drug resistance detection result, so that the detection time of gram-positive bacteria can be effectively shortened, and meanwhile, the accuracy of the detection result is ensured;
3) The invention only needs to use the glass slide and the optical microscope for detection, can effectively reduce the cost of equipment and omits expensive and complex instruments.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
FIG. 2 is a flowchart showing the step S3 of the present invention;
FIG. 3 is a flowchart showing the step S4 of the present invention;
FIG. 4 is a microscopic image of the methicillin-resistant Staphylococcus aureus MRSA strain No.21B06749 of the present invention after gram staining;
FIG. 5 is a microscope image of the methicillin-resistant Staphylococcus aureus MRSA strain No.21R05333 of the present invention after gram staining;
FIG. 6 is a microscope image of methicillin-sensitive Staphylococcus aureus MSSA strain No.21B09710 after gram staining in accordance with the present invention;
FIG. 7 is a microscope image of methicillin-sensitive Staphylococcus aureus MSSA strain No.21W02902 after gram staining in accordance with the present invention;
fig. 8 (a) is a three-dimensional graph of the characteristic values of MRSA bacteria and MSSA bacteria of the present invention, and (b) is a violin graph of the characteristic values of MRSA bacteria and MSSA bacteria;
FIG. 9 (a) shows the PCA score of the dataset of the present invention, (b) shows the principal component analysis of MRSA bacteria and MSSA bacteria, and (c) shows the t-SNE analysis of MRSA bacteria and MSSA bacteria;
fig. 10 (a) shows the subject working characteristics obtained by analyzing MSSA strain and MRSA strain by LDA model, and (b) shows the subject working characteristics obtained by screening MSSA strain and MRSA strain by ANN model.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In a preferred embodiment of the present invention, based on the above-mentioned problems of the prior art, there is now provided a bacterial resistance detection method based on gram staining and machine vision, as shown in fig. 1, comprising the steps of:
step S1, a cell wall synthesis inhibitor acts on gram-positive bacteria, and after shaking, mixing and incubation treatment, centrifugal operation is carried out to obtain a mixture;
step S2, adding a proper amount of sterile water into the mixture, transferring the mixture onto a glass slide for drying treatment, and then carrying out gram staining to obtain a corresponding first bacterial film sample;
step S3, sequentially performing a crystal violet solution dropwise adding operation, a flushing operation, a gram iodine solution dropwise adding operation, a flushing operation, an ethanol water solution dropwise adding operation, a flushing operation, a safranin staining solution dropwise adding operation and a flushing operation on the first bacterial film sample to obtain a final sample;
and S4, carrying out microscopic observation on the final sample to obtain a plurality of dyed pictures, respectively carrying out gray feature extraction on each dyed picture by adopting a machine vision algorithm to obtain corresponding feature values, and then analyzing each feature value to obtain a corresponding bacterial drug resistance detection result.
In particular, in this example, considering that gram staining is an important microorganism classification tool, bacteria can be classified into gram-positive bacteria (purple) and gram-negative bacteria (pink) according to chemical and structural composition of cell walls, antibiotics of cell wall synthesis inhibitors such as penicillin, oxacillin, vancomycin and the like are added to bacteria and bacteria growth cultures, synthesis of cell wall peptidoglycan can be effectively inhibited, integrity of cell walls of gram-positive bacteria is damaged in the presence of antibiotics, and complex of crystal violet and iodine is not preserved in the decoloring process, so that it is gram-negative.
Preferably, a Machine Vision (MV) method belongs to a sub-field of artificial intelligence, and has achieved a great leap in recent years, and the machine vision method plays an important role in biomedical image analysis as well.
Preferably, the cell wall integrity of the gram-positive bacteria is destroyed and the color of the gram-negative bacteria appears under the action of the cell wall synthesis inhibitor, however, the gram-positive drug-resistant bacteria are relatively stable and still appear as the color of the gram-positive bacteria, and the change of the color can be detected by a machine vision method, so that the sensitive bacteria and the drug-resistant bacteria of the gram-positive bacteria can be directly classified by monitoring the change of the color of the staining.
Preferably, taking two strains of methicillin-resistant staphylococcus aureus MRSA and methicillin-sensitive staphylococcus aureus MSSA as examples, taking MRSA strain No.21B06749 for gram staining, and a microscopic image as shown in FIG. 4, wherein (a) represents staining results of three parallel experiments without treatment with oxacillin sodium, and (b), (c) and (d) represent staining results of three parallel experiments with oxacillin sodium, and the scale is 10 μm; gram staining was performed on MRSA strain No.21R05333, and a microscopic image is shown in FIG. 5, wherein (a) represents staining results of a treatment without using oxacillin sodium, and (b), (c) and (d) represent staining results of three parallel experiments with oxacillin sodium, with a scale of 10 μm; gram staining was performed on MSSA strain No.21B09710, and a microscopic image is shown in fig. 6, wherein (a) represents staining results of three parallel experiments without treatment with oxacillin sodium, and (b), (c) and (d) represent staining results of three parallel experiments with oxacillin sodium, with a scale of 10 μm; MSSA strain No.21W02902 was subjected to gram staining, and the microscopic image is shown in FIG. 7, wherein (a) represents the staining result of the treatment without using oxacillin sodium, and (b), (c) and (d) represent the staining result of three parallel experiments with oxacillin sodium, with a scale of 10. Mu.m.
In a preferred embodiment of the present invention, in step S2, the mixture with sterile water is transferred to a glass slide and dried at an ambient temperature of 40 ℃.
In a preferred embodiment of the present invention, as shown in fig. 2, step S3 includes:
step S31, a proper amount of crystal violet solution is dripped into the first bacterial film sample and acts for 1 minute, and then deionized water is adopted to rinse the glass slide to obtain a second bacterial film sample;
step S32, a proper amount of gram iodine solution is dripped into the second bacterial film sample and acts for 1 minute, and then deionized water is adopted to rinse the glass slide to obtain a third bacterial film sample;
step S33, a proper amount of ethanol aqueous solution is dripped into the third bacterial film sample for washing, and then deionized water is adopted for washing the glass slide to obtain a fourth bacterial film sample;
step S34, a proper amount of safranin staining solution is dripped into the fourth bacterial film sample and acts for 2 minutes, then deionized water is adopted to wash the glass slide, and moisture absorption treatment is carried out to obtain a final sample.
In a preferred embodiment of the present invention, in step S33, the third bacterial film sample is washed three times continuously by dripping an aqueous ethanol solution.
In a preferred embodiment of the invention, the concentration of the aqueous ethanol solution is 95%.
In a preferred embodiment of the present invention, as shown in fig. 3, step S4 includes:
step S41, carrying out microscopic observation on a final sample to obtain a plurality of dyeing pictures;
step S42, respectively preprocessing each dyed picture and extracting gray features to obtain corresponding gray values, carrying out normalization processing on each dyed picture by a gray stretching method to obtain normalized images, then extracting the normalized images by a gray segmentation method to obtain bacterial areas, taking the normalized images containing the bacterial areas as final detection images and taking the gray values corresponding to the final detection images as feature values;
and S43, visualizing the characteristic value by adopting an unsupervised analysis method, and analyzing the characteristic value by utilizing a pre-constructed supervision model to obtain a corresponding bacterial drug resistance detection result.
Specifically, in the present embodiment, the machine vision analysis of the dyed picture includes: in the process of preprocessing and gray feature extraction, all the dyed pictures are normalized through gray (0-255) stretching to eliminate calculation errors caused by different brightness of each dyed picture, a gray segmentation method is adopted to extract bacterial areas (ROI) in the dyed pictures, the ROI is reduced to obtain normalized images, the simplified color images are converted into red, green and blue channel images, average gray values (R_mean, G_mean and B_mean) of single channel images are calculated respectively and are used as feature values for further analysis, the model is constructed and optimized, feature data are visualized through unsupervised analysis (principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE)), and then bacteria are classified through a Linear (LDA) and nonlinear classification model (ANN) construction model.
Preferably, taking MRSA bacteria and MSSA bacteria as examples, a three-dimensional graph of characteristic values of MRSA bacteria and MSSA bacteria is shown in fig. 8 (a), and a violin graph of characteristic values of MRSA bacteria and MSSA bacteria is shown in fig. 8 (b).
Preferably, the collected dye pictures are classified by two different constructed supervision models (LDA model and ANN model), model training, verification and optimization are performed by 3 times of cross verification, the dye pictures in the data set are randomly divided into three groups, two groups are used for training, the rest group is used for verification, and the circulation is performed three times until all data participate in training and testing, and in the cross verification process, a subject working characteristic curve (ROC), an Area Under Curve (AUC) and an accuracy index are calculated to optimize and verify the model, and parameters are adjusted for multiple times until the optimal performance of the model is achieved.
Preferably, the PCA scoring graph of the dataset is shown in FIG. 9 (a), the MRSA bacteria and MSSA bacteria principal component analysis graph is shown in FIG. 9 (b), and the MRSA bacteria and MSSA bacteria t-SNE analysis graph is shown in FIG. 9 (c).
Preferably, the subject working characteristics obtained by analyzing the MSSA strain and the MRSA strain by the LDA model are shown in (a) of fig. 10, and the subject working characteristics obtained by screening the MSS strain a and the MRSA strain by the ANN model are shown in (b) of fig. 10.
In a preferred embodiment of the present invention, in step S1, the concentration of the cell wall synthesis inhibitor is 256. Mu.g/mL, and the concentration of the gram-positive bacteria is 10 7 CFU/mL。
In the preferred embodiment of the present invention, in step S4, the final sample is observed by an optical microscope oil lens at 1000 times to obtain a plurality of stained pictures.
Embodiment one:
effect of oxacillin sodium with MSSA and MRSA:
the conditioned OD was obtained by using LB medium 600 Diluting bacterial liquid of MSSA and MRSA with value of 0.7-0.8 to 10 7 CFU·mL -1 Dissolving 1mg of oxacillin sodium in 1mL of sterile water to prepare 1000 mug.mL -1 The prepared benzoicillin sodium solution is diluted to 512 mug.mL step by using sterile water -1 500 mu L of benzoicillin sodium solution is taken and added with 500 mu L of 10 7 CFU·mL -1 After incubation in a thermostatic mixer at 37℃for 30min at 1250rpm, centrifugation at 10000pm for 2min, washing with 100. Mu.L of sterile water for 2 times, adding 20. Mu.L of sterile water to the pellet, mixing, transferring to a slide, drying at 40℃and performing the corresponding detection test.
Although the present disclosure is described above, the scope of protection of the present disclosure is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the scope of the invention.
Claims (8)
1. A bacterial resistance detection method based on gram staining and machine vision, comprising the steps of:
step S1, a cell wall synthesis inhibitor acts on gram-positive bacteria, and after shaking, mixing and incubation treatment, centrifugal operation is carried out to obtain a mixture;
step S2, adding a proper amount of sterile water into the mixture, transferring the mixture onto a glass slide for drying treatment, and then carrying out gram staining to obtain a corresponding first bacterial film sample;
step S3, sequentially performing a crystal violet solution dropwise adding operation, a flushing operation, a gram iodine solution dropwise adding operation, a flushing operation, an ethanol water solution dropwise adding operation, a flushing operation, a safranin staining solution dropwise adding operation and a flushing operation on the first bacterial film sample to obtain a final sample;
and S4, carrying out microscopic observation on the final sample to obtain a plurality of dyed pictures, respectively carrying out gray feature extraction on each dyed picture by adopting a machine vision algorithm to obtain corresponding feature values, and then analyzing each feature value to obtain a corresponding bacterial drug resistance detection result.
2. The method according to claim 1, wherein in the step S2, the mixture added with the sterile water is transferred onto the slide and dried at an ambient temperature of 40 ℃.
3. The method for detecting bacterial resistance according to claim 1, wherein the step S3 comprises:
step S31, a proper amount of crystal violet solution is dripped into the first bacterial film sample and acts for 1 minute, and then deionized water is adopted to rinse the glass slide to obtain a second bacterial film sample;
step S32, a proper amount of gram iodine solution is dripped into the second bacterial film sample and acts for 1 minute, and then the deionized water is adopted to flush the glass slide to obtain a third bacterial film sample;
step S33, a proper amount of ethanol water solution is dripped into the third bacterial film sample for washing, and then the deionized water is adopted for washing the glass slide to obtain a fourth bacterial film sample;
and step S34, a proper amount of safranin staining solution is dripped into the fourth bacterial film sample and acts for 2 minutes, then the deionized water is adopted to flush the glass slide, and moisture absorption treatment is carried out to obtain the final sample.
4. The method according to claim 3, wherein in the step S33, the third bacterial film sample is washed three times continuously by dropping the aqueous ethanol solution.
5. The method for detecting bacterial resistance according to claim 3, wherein the concentration of the aqueous ethanol solution is 95%.
6. The method for detecting bacterial resistance according to claim 1, wherein the step S4 comprises:
s41, carrying out microscopic observation on the final sample to obtain a plurality of dyeing pictures;
step S42, respectively preprocessing each dyed picture and extracting gray features to obtain corresponding gray values, carrying out normalization processing on each dyed picture by a gray stretching method to obtain normalized images, then extracting the normalized images by a gray segmentation method to obtain bacterial areas, taking the normalized images containing the bacterial areas as final detection images and taking the gray values corresponding to the final detection images as the feature values;
and S43, visualizing the characteristic value by adopting an unsupervised analysis method, and analyzing the characteristic value by utilizing a pre-constructed supervision model to obtain a corresponding bacterial drug resistance detection result.
7. The method for detecting bacterial resistance according to claim 1, wherein in the step S1, the concentration of the cell wall synthesis inhibitor is 256. Mu.g/mL, and the concentration of the gram-positive bacteria is 10 7 CFU/mL。
8. The method according to claim 1, wherein in the step S4, the final sample is observed by an optical microscope at a magnification of 1000 times to obtain a plurality of the stained pictures.
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